fix: pipeline MACD 参数统一为标准 12/26/9（与 web/交易所一致）

Co-authored-by: Cursor <cursoragent@cursor.com>

.gitignore

@@ -37,3 +37,10 @@ feature_meta
 .DS_Store
 .DS_Store
 .DS_Store
+.DS_Store
+data_provider/._config.json
+.gstack/
+
+# ESS gate / engineering-loop working dirs（归档进 docs/runs/）
+.gates/
+loop/

AGENTS.md

@@ -0,0 +1,33 @@
+# chan — Agent Entry
+
+本仓受 ESS 约束。不要一上来扫全库或加载全部 governance。
+
+## Boot
+
+1. `docs/PROJECT_PROFILE.md`
+2. `docs/PROJECT_RULES.md`
+3. `docs/STATE/CURRENT.md` + `docs/AGENT_MEMORY.md`
+4. 有进行中任务再读 `docs/TASKS/` / 对应 ECR / HANDOFF
+5. 角色文件：ESS 根目录 `agents/{ARCHITECT|ENGINEER|REVIEWER|RELEASE_MANAGER}.md`
+
+## Roles（选一）
+
+| 意图 | 角色 |
+|------|------|
+| 规格 / 架构 / ECR | ARCHITECT |
+| 实现 / 修 bug | ENGINEER |
+| 审阅 | REVIEWER |
+| 发版 / tag | RELEASE_MANAGER |
+
+## Never
+
+- 无 ECR 改 `config/` / `strategies/` 交易逻辑
+- 无 ADR 改缠论算法语义
+- 无 ECR 删减 `/api/analyze` 字段
+- 把聊天记录当成完成；阶段结束须落盘 `docs/`
+
+## Pointers
+
+- TRACEABILITY: `docs/TRACEABILITY.md`
+- CHANGELOG: `docs/CHANGELOG/CHANGELOG.md`
+- 人类向导：`CLAUDE.md`

CLAUDE.md

@@ -0,0 +1,116 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+缠论 (Chan Theory) technical analysis system for Freqtrade. Implements Chan Zhong Shui Chan's theory for crypto/stock trading, including fractal (分型), stroke (笔), segment (线段), pivot/center (中枢), and buy/sell point (买卖点) detection.
+
+## Governance
+
+- Agent 入口：`AGENTS.md`（boot 顺序）· `docs/PROJECT_PROFILE.md` · `docs/AGENT_MEMORY.md` · `docs/STATE/CURRENT.md`
+- ESS 文档：`docs/ECR/`、`docs/ENGINEERING_SPEC/`、`docs/TRACEABILITY.md`、`docs/CHANGELOG/`
+- **正式引擎包**：`chanlun/`；strategies / web 已用 `from chanlun import ...`
+- 根目录 `Chan*.py` / `TF_DF.py` 仍为 **兼容 shim**（旧脚本可用）
+- 变更分级：无 ECR 不改 strategies/config；无 ADR 不改缠论算法语义
+
+## Core Architecture
+
+### Chan Theory Engine (`chanlun/`)
+
+```text
+chanlun/
+  core/          # KLU KLC BI SBI SEG ZS BIZS BSP Enum CTime
+  pipeline/      # orchestrator(ChanLun) + timeframe(TF_DF) + builders/
+  indicators/    # ChanMACD*
+  analysis/      # Zone Classifier Pivot Heng PY Find_Trend ...
+```
+
+Data processing pipeline (each step feeds the next):
+
+1. **`chanlun.core.ChanKLU`** — Raw K-line unit with TA indicators and pattern recognition
+2. **`chanlun.core.ChanKLC`** — Combined K-line: inclusion + fractal; `.next`/`.pre` linked list
+3. **`chanlun.core.ChanBI`** — Stroke (笔)
+4. **`chanlun.core.ChanSBI`** — Special stroke → SEG
+5. **`chanlun.core.ChanSEG`** — Segment (线段)
+6. **`chanlun.core.ChanZS`** / **`ChanBIZS`** — Centers (中枢)
+7. **`chanlun.core.ChanBSP`** — Buy/Sell points
+8. **`chanlun.pipeline.orchestrator.ChanLun`** — Orchestrator
+9. **`chanlun.pipeline.timeframe.TF_DF`** — Timeframe facade；实现拆在 `pipeline/builders/`
+
+### Services
+
+- **外部 DATA_SERVICE** — 行情服务（env: `DATA_SERVICE_URL`）；本仓库可不含 data_provider 源码
+- **`web/`** — Flask UI：`create_app()` + `api/` blueprints + `services/`；前端 `static/js/app/`。默认端口见 `web/config.py`（`FLASK_PORT`，常见 8128）
+- **`strategies/`** — Freqtrade strategies（本 ECR 不改）
+- **`config/`** — Freqtrade configs（本 ECR 不改）
+
+### Data Flow
+
+```
+Exchange / DATA_SERVICE → Freqtrade Strategy / web → ChanLun → TF_DF
+  → KLU → KLC → BI → SBI → SEG → ZS → BSP
+```
+
+## Common Commands
+
+### Freqtrade Trading
+
+```bash
+# Live trade
+freqtrade trade -c ./user_data/Chan/config/<config>.json --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies
+
+# Backtest
+freqtrade backtesting -c ./user_data/Chan/config/<config>.json --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies --timerange=20251008-
+
+# Download data
+freqtrade download-data -c ./user_data/Chan/config/<config>.json -t 1m 1h 1d --pairs BTC/USDT:USDT --timerange=20240101-
+
+# Hyperopt
+freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/<config>.json -e 200 --timerange=20250201-20250901
+
+# Plot
+freqtrade plot-dataframe --strategy <StrategyName> --datadir user_data/data/binance -c ./user_data/Chan/config/<config>.json --timerange=20250721-
+```
+
+### Data Provider
+
+```bash
+# Docker
+cd data_provider && docker compose up -d
+
+# Direct
+cd data_provider && python main.py
+
+# With custom config
+CONFIG_PATH=./config.json python main.py
+```
+
+### Web UI
+
+```bash
+cd web && python app.py
+# or via gunicorn:
+gunicorn -w 4 -b 0.0.0.0:8123 app:app
+
+# Deploy scripts:
+cd web && ./deploy.sh          # standard
+cd web && ./deploy_venv.sh     # Ubuntu 22.04+ (venv)
+```
+
+### Docker (Freqtrade)
+
+```bash
+sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/<config>.json --strategy <StrategyName> --strategy-path ./user_data/Chan/strategies --timerange=20250721-
+```
+
+## Key Conventions
+
+- All Chan theory classes are prefixed with `Chan` (e.g., `ChanBI`, `ChanZS`)
+- Strategies import `ChanLun` and add `sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))` to import from parent
+- MACD params: `MACD(26, 52, 9)` by default (slow period 52 instead of standard 26)
+- Enums in `ChanEnum.py` use `auto()` values
+- `ChanKLC` is a linked-list style data structure with `.next`/`.pre` pointers
+- The `TF_DF` class is the primary data container per timeframe
+- K-line direction uses `Chan_KLINE_DIR` (UP/DOWN/COMBINE/INCLUDED)
+- All text comments/commits are in Chinese

ChanBI.py

@@ -1,132 +1,2 @@
-from decimal import Decimal
-import ChanKLC
-from ChanEnum import Chan_BI_DIR
-class ChanBI():
-	def __init__(self, klc: ChanKLC, index, ddir=Chan_BI_DIR.UP):
-		self.start_klc = klc
-		self.end_klc = klc
-		self.next = None
-		self.pre = None
-		self.dir = ddir
-		self.index = index
-		self.is_sure = False
-		self.high = klc.high
-		self.low = klc.low
-		self.sure_time = None
-		self.klc_list = []
-		self.klc_list.append(klc)
-		self.end_time = klc.end_time
-		self.start_time = klc.start_time
-		self.macd_hist = 0
-		self.macd_div = 0
-		self.seg = None
-		self.height = 0
-		self.width = 0
-		self.slop = 0
-		self.fib_list = []
-		self.seg_index = 0
-		self.bi_zs = None
-		self.seg_zs = None
-	def set_bi_zs(self, bi_zs):
-		for klc in self.klc_list:
-			klc.set_bi_zs(bi_zs)
-	def set_seg(self, seg):
-		self.seg = seg
-		self.seg_index = len(seg.bi_list)-1
-	def set_macdhist(self, macd_hist):
-		self.macd_hist = macd_hist
-	def set_macd_div(self, macd_div):
-		self.macd_div = macd_div
-	def cal_macd_div(self):
-		self.macd_div = 0.0
-		if self.pre and self.pre.pre:
-			if self.pre.pre.macd_hist == 0:
-				self.macd_div = 0.0
-			else:
-				self.macd_div = self.macd_hist / self.pre.pre.macd_hist
-			#print(self.start_time, self.end_time, self.macd_hist, self.pre.pre.macd_hist, self.macd_div)
-	def cal_macdhist(self):
-		self.macd_hist = 0
-		for klc in self.klc_list:
-			for klu in klc.klu_list:
-				if self.dir == Chan_BI_DIR.UP and klu.macdhist > 0: 
-					self.macd_hist += klu.macdhist
-				if self.dir == Chan_BI_DIR.DOWN and klu.macdhist < 0:
-					self.macd_hist -= klu.macdhist
-	def check_bi_zs_overlap(self):
-		if self.next and self.next.next:
-			if self.dir == Chan_BI_DIR.UP:
-				return self.low < self.next.next.high
-			else:
-				return self.high > self.next.next.low
-		else:
-			return False
-	def check_overlap(self):
-		if self.next and self.next.next and self.next.next.is_sure:
-			if self.dir == Chan_BI_DIR.UP:
-				return self.high > self.next.low and self.high < self.next.next.high
-			else:
-				return self.high > self.next.high and self.low > self.next.next.low
-		else:
-			return False
-	def set_end_klc(self, klc, sure_klc):
-		if self.dir == Chan_BI_DIR.UP and klc.high > self.high:
-			self.high = klc.high
-		if self.dir == Chan_BI_DIR.DOWN and klc.low < self.low:
-			self.low = klc.low
-		self.end_klc = klc
-		self.set_is_sure(True, sure_klc.end_time)
-		self.end_time = klc.end_time
-		self.cal_properties()
-		#print(self.start_time, klc.fx, "This bi is ended", len(self.klc_list), klc.index - self.start_klc.index)
-	def cal_properties(self):
-		if self.is_sure:
-			self.height = float(format(self.high - self.low, ".2f"))
-			self.width = self.end_klc.index - self.start_klc.index
-			self.slop = float(format(self.height / self.width, ".2f"))
-			fib_list = [0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0]
-			for fib in fib_list:
-				self.fib_list.append(float(format(self.height * fib + self.low, ".2f")))
-		#print(self.end_time, self.height, self.width, self.slop, self.fib_list)
-	def set_is_sure(self, is_sure, time):
-		self.is_sure = is_sure
-		self.sure_time = time
-	def set_start_klc(self, klc, ddir):
-		self.start_klc = klc
-		self.klc_list = []
-		self.klc_list.append(klc)
-		self.high = klc.high
-		self.low = klc.low
-		self.dir = ddir
-	def set_pre(self, bi):
-		self.pre = bi
-	def set_next(self, bi):
-		self.next = bi
-	def add_klc(self, klc):
-		added = False
-		if len(self.klc_list) > 0:
-			for index in range(0, len(self.klc_list)):
-				if self.klc_list[index].index == klc.index:
-					added = True
-					break
-		if not added:
-			self.klc_list.append(klc)
-			#print(self.start_time, klc.start_time)
-		#print(klc.end_time, klc.index)
-		self.end_klc = klc
-		self.end_time = klc.klu_list[-1].time
-		self.cal_macdhist()
-		self.cal_macd_div()
-	def append_klc_list(self, klc_list):
-		self.klc_list.append(klc_list)
-	def get_decimal(self, value):
-		return Decimal("{:.2f}".format(value))
-	def update_bi(self, klc):
-		self.end_klc = None
-		if self.dir == Chan_BI_DIR.UP and klc.high > self.high:
-			self.high = klc.high
-		if self.dir == Chan_BI_DIR.DOWN and klc.low < self.low:
-			self.low = klc.low
-		self.is_sure = False
-		self.sure_time = None
-		#print(self.start_time, klc.start_time, klc.fx, "This bi is extended")
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanBI import *  # noqa: F403

ChanBIZS.py

@@ -1,161 +1,2 @@
-from ChanEnum import Chan_ZS_DIR, Chan_ZS_TYPE, Chan_BI_DIR
-import ChanBI
-# 中枢
-class ChanBIZS():
-    def __init__(self, start_bi: ChanBI, index, ddir: Chan_ZS_DIR):
-        self.start_klc = start_bi.start_klc
-        self.start_time = self.start_klc.start_time
-        self.end_time = None
-        self.index = index
-        self.start_bi = start_bi
-        self.bi_list = []
-        self.bi_list.append(start_bi)
-        self.end_bi = None
-        self.bi_out = None
-        self.is_sure = False
-        self.zg = 0
-        self.zd = 0
-        self.gg = 0
-        self.dd = 0
-        self.dir = ddir
-        self.sure_time = None
-        self.end_klc = None
-        self.zs_type = Chan_ZS_TYPE.NORMAL
-        start_bi.set_bi_zs(self)
-    def set_end_bi(self, end_bi, sure_time):
-        self.end_bi = end_bi
-        self.set_end_time(end_bi.end_klc.end_time)
-        self.is_sure = True
-        self.sure_time = sure_time
-        end_bi.set_bi_zs(self)
-        #print(self.start_time, self.is_sure, len(self.bi_list), self.dir, self.zs_type)
-    def set_end_time(self, end_time):
-        self.end_time = end_time
-    def set_zg(self, zg):
-        self.zg = zg
-    def set_zd(self, zd):
-        self.zd = zd
-    def set_gg(self, gg):
-        self.gg = gg
-    def set_dd(self, dd):
-        self.dd = dd
-    def add_bi(self, bi: ChanBI):
-        if bi:
-            self.bi_list.append(bi)
-            if bi.high > self.gg:
-                self.gg = bi.high
-            if bi.low < self.dd:
-                self.dd = bi.low
-            bi.set_bi_zs(self)
-            self.classify_zs()
-    def set_pre(self, pre):
-        self.pre = pre
-    def set_next(self, next):
-        self.next = next
-    def classify_zs(self):
-        """
-        根据中枢内笔的高低点变化趋势，对中枢进行分类
-        
-        分类逻辑：
-        - 取中枢内向上笔的高点（peaks）和向下笔的低点（valleys）
-        - 比较前半段和后半段的均值，判断高点和低点的整体趋势
-        
-        分类结果：
-        - RISING    上升中枢：高点抬高 + 低点抬高 → 多方占优，可能向上突破
-        - FALLING   下行中枢：高点降低 + 低点降低 → 空方占优，可能向下突破
-        - CONVERGING 收敛中枢：高点降低 + 低点抬高 → 区间收窄，即将选择方向
-        - DIVERGING  扩散中枢：高点抬高 + 低点降低 → 波动加剧，市场不稳定
-        - NORMAL    常规中枢：无明显趋势 → 多空均衡，区间震荡
-        """
-        if len(self.bi_list) < 3:
-            self.zs_type = Chan_ZS_TYPE.NORMAL
-            return
-
-        # 提取向上笔的高点（peaks）和向下笔的低点（valleys）
-        peaks = [bi.high for bi in self.bi_list if bi.dir == Chan_BI_DIR.UP]
-        valleys = [bi.low for bi in self.bi_list if bi.dir == Chan_BI_DIR.DOWN]
-
-        high_trend = self._calc_trend(peaks)
-        low_trend = self._calc_trend(valleys)
-
-        if high_trend > 0 and low_trend > 0:
-            self.zs_type = Chan_ZS_TYPE.RISING
-        elif high_trend < 0 and low_trend < 0:
-            self.zs_type = Chan_ZS_TYPE.FALLING
-        elif high_trend < 0 and low_trend > 0:
-            self.zs_type = Chan_ZS_TYPE.CONVERGING
-        elif high_trend > 0 and low_trend < 0:
-            self.zs_type = Chan_ZS_TYPE.DIVERGING
-        else:
-            self.zs_type = Chan_ZS_TYPE.NORMAL
-
-    def _calc_trend(self, values):
-        """
-        计算序列的趋势方向
-        将序列分为前后两半，比较均值：
-        - 后半均值 > 前半均值 → 返回 1（上升趋势）
-        - 后半均值 < 前半均值 → 返回 -1（下降趋势）
-        - 相等或数据不足 → 返回 0（无趋势）
-        
-        使用均值比较而非首尾比较，可以过滤单笔异常波动带来的误判
-        """
-        if len(values) < 2:
-            return 0
-        mid = len(values) // 2
-        first_half = values[:mid] if mid > 0 else values[:1]
-        second_half = values[mid:]
-        avg_first = sum(first_half) / len(first_half)
-        avg_second = sum(second_half) / len(second_half)
-        # 使用中枢区间的一定比例作为阈值，避免微小波动误判
-        threshold = abs(avg_first) * 0.005 if avg_first != 0 else 0
-        if avg_second - avg_first > threshold:
-            return 1
-        elif avg_first - avg_second > threshold:
-            return -1
-        else:
-            return 0
-
-    def is_weakening(self):
-        """
-        判断中枢是否在衰弱（即将反向突破的信号）
-        
-        衰弱条件：
-        1. 中枢内笔数 >= 5（有足够的数据判断）
-        2. 最后一笔的MACD面积相比同方向前一笔出现背驰（macd_div < 1）
-        3. 中枢类型为收敛型或常规型
-        
-        返回: True表示中枢力量衰弱，可能反向
-        """
-        if len(self.bi_list) < 5:
-            return False
-        last_bi = self.bi_list[-1]
-        # 最后一笔与同方向前一笔比较MACD面积是否背驰
-        if last_bi.macd_div > 0 and last_bi.macd_div < 1.0:
-            return True
-        return False
-
-    def get_zs_strength(self):
-        """
-        计算中枢强度，用于辅助判断中枢延续还是反向
-        
-        返回字典包含：
-        - type: 中枢类型 (Chan_ZS_TYPE)
-        - bi_count: 中枢内笔数
-        - range_ratio: 中枢区间占比 = (zg - zd) / (gg - dd)，越小说明中枢越紧密
-        - last_bi_div: 最后一笔的MACD背驰比率
-        - is_weakening: 是否衰弱
-        - is_extending: 是否在延伸（笔数 >= 9 可能升级）
-        """
-        total_range = self.gg - self.dd if self.gg != self.dd else 1
-        zs_range = self.zg - self.zd if self.zg != self.zd else 0
-        range_ratio = zs_range / total_range if total_range > 0 else 0
-        last_bi_div = self.bi_list[-1].macd_div if len(self.bi_list) > 0 else 0
-
-        return {
-            'type': self.zs_type,
-            'bi_count': len(self.bi_list),
-            'range_ratio': round(range_ratio, 4),
-            'last_bi_div': round(last_bi_div, 4),
-            'is_weakening': self.is_weakening(),
-            'is_extending': len(self.bi_list) >= 9,  # 9段可能升级
-        }
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanBIZS import *  # noqa: F403

ChanBSP.py

@@ -1,24 +1,2 @@
-import ChanBI
-from ChanEnum import Chan_BSP_TYPE, Chan_BSP_DIR
-
-class ChanBSP():
-    def __init__(self, bi: ChanBI, index, type: Chan_BSP_TYPE, ddir: Chan_BSP_DIR, sure_time, zs_count, zs, seg):
-        self.bi = bi
-        self.klc = bi.end_klc
-        self.index = index
-        self.type = type
-        self.start_time = self.klc.start_time
-        self.end_time = self.klc.end_time
-        if sure_time:
-            self.is_sure = True
-            self.sure_time = sure_time
-        else:
-            self.is_sure = False
-            self.sure_time = None
-        self.dir = ddir
-        self.zs_count = zs_count
-        self.zs = zs
-        self.seg = bi.seg
-    def set_sure_time(self, sure_time):
-        self.is_sure = True
-        self.sure_time = sure_time
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanBSP import *  # noqa: F403

ChanCTime.py

@@ -1,44 +1,2 @@
-from datetime import datetime
-
-
-class ChanCTime:
-    def __init__(self, year, month, day, hour, minute, second=0, auto=True):
-        self.year = year
-        self.month = month
-        self.day = day
-        self.hour = hour
-        self.minute = minute
-        self.second = second
-        self.auto = auto  # 自适应对天的理解
-        self.set_timestamp()  # set self.ts
-
-    def __str__(self):
-        if self.hour == 0 and self.minute == 0:
-            return f"{self.year:04}/{self.month:02}/{self.day:02}"
-        else:
-            return f"{self.year:04}/{self.month:02}/{self.day:02} {self.hour:02}:{self.minute:02}"
-
-    def to_str(self):
-        if self.hour == 0 and self.minute == 0:
-            return f"{self.year:04}/{self.month:02}/{self.day:02}"
-        else:
-            return f"{self.year:04}/{self.month:02}/{self.day:02} {self.hour:02}:{self.minute:02}"
-
-    def toDateStr(self, splt=''):
-        return f"{self.year:04}{splt}{self.month:02}{splt}{self.day:02}"
-
-    def toDate(self):
-        return ChanCTime(self.year, self.month, self.day, 0, 0, auto=False)
-
-    def set_timestamp(self):
-        if self.hour == 0 and self.minute == 0 and self.auto:
-            date = datetime(self.year, self.month, self.day, 23, 59, self.second)
-        else:
-            date = datetime(self.year, self.month, self.day, self.hour, self.minute, self.second)
-        self.ts = date.timestamp()
-
-    def __gt__(self, t2):
-        return self.ts > t2.ts
-
-    def __ge__(self, t2):
-        return self.ts >= t2.ts
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanCTime import *  # noqa: F403

ChanEnum.py

@@ -1,368 +1,2 @@
-from enum import Enum, auto
-from typing import Literal
-
-
-class Chan_DATA_SRC(Enum):
-    BAO_STOCK = auto()
-    CCXT = auto()
-    CSV = auto()
-
-class Chan_ZS_DIR(Enum):
-    UP = auto()
-    DOWN = auto()
-
-class Chan_ZS_TYPE(Enum):
-    """中枢类型分类"""
-    NORMAL = auto()       # 常规中枢：高低点无明显趋势，区间震荡
-    RISING = auto()       # 上升中枢：高点抬高，低点也抬高，重心上移
-    FALLING = auto()      # 下行中枢：高点降低，低点也降低，重心下移
-    CONVERGING = auto()   # 收敛中枢：高点降低，低点抬高，区间收窄（三角收敛）
-    DIVERGING = auto()    # 扩散中枢：高点抬高，低点降低，区间扩大（喇叭口）
-class Chan_K_DIR(Enum):
-    BULL = auto()
-    BEAR = auto()
-    CROSS = auto()
-
-class Chan_EMA_POS(Enum):
-    """K线与任意EMA的位置关系（与趋势方向无关的客观分类，支持threshold容差）"""
-    ABOVE = auto()              # 完全在EMA上方（远离）：low > ema + threshold
-    NEAR_ABOVE = auto()         # 在EMA上方但接近：ema < low <= ema + threshold
-    CROSS_CLOSE_ABOVE = auto()  # 跨越EMA，收盘在上方：close > ema, low <= ema（含threshold范围内触碰）
-    ON_EMA = auto()             # 收盘价在EMA附近：abs(close - ema) <= threshold
-    CROSS_CLOSE_BELOW = auto()  # 跨越EMA，收盘在下方：close < ema, high >= ema（含threshold范围内触碰）
-    NEAR_BELOW = auto()         # 在EMA下方但接近：ema - threshold <= high < ema
-    BELOW = auto()              # 完全在EMA下方（远离）：high < ema - threshold
-    UNKNOWN = auto()            # 未知（EMA值无效）
-
-class Chan_EMA_SEMANTIC(Enum):
-    """K线与EMA结合趋势方向的语义状态（用于交易判断）"""
-    STRONG_TREND = auto()       # 7: 顺势K线完全在EMA趋势侧（强势，远未及EMA）
-    TREND_SIDE = auto()         # 6: 完全在EMA趋势侧（正常趋势运行）
-    RECOVER = auto()            # 5: 逆势后穿越EMA回到趋势侧（收复EMA，趋势恢复）
-    TOUCH_FAIL = auto()         # 4: 逆势触碰EMA但未穿越（反弹/反抽力度不足）
-    DEEP_COUNTER = auto()       # 3: 完全在EMA逆势侧（深度回调/反抽）
-    BREAK = auto()              # 2: 穿越EMA，收盘在逆势侧（支撑/压力失败）
-    TOUCH_HOLD = auto()         # 1: 触碰EMA，收盘守住趋势侧（支撑/压力有效）
-    WEAK_COUNTER = auto()       # 8: 逆势K线完全在EMA逆势侧（弱势，远未到EMA）
-    APPROACHING = auto()        # 9: K线接近EMA但未触碰（即将测试支撑/压力）
-    NEUTRAL = auto()            # 0: 盘整/无法判断
-class Chan_KL_TYPE(Enum):
-    K_1S = auto()
-    K_1M = auto()
-    K_DAY = auto()
-    K_WEEK = auto()
-    K_MON = auto()
-    K_YEAR = auto()
-    K_5M = auto()
-    K_15M = auto()
-    K_30M = auto()
-    K_60M = auto()
-    K_1H = auto()
-    K_2H = auto()
-    K_4H = auto()
-    K_6H = auto()
-    K_8H = auto()
-    K_12H = auto()
-    K_1D = auto()
-    K_3D = auto()
-    K_3M = auto()
-    K_QUARTER = auto()
-
-
-class Chan_KLINE_DIR(Enum):
-    UP = auto()
-    DOWN = auto()
-    COMBINE = auto()
-    INCLUDED = auto()
-class Chan_KLU_TYPE(Enum):
-    BigBull = auto()
-    MiddleBull = auto()
-    SmallBull = auto()
-    BigBear = auto()
-    MiddleBear = auto()
-    SmallBear = auto()
-    Cross = auto()
-    
-class Chan_KLU_PATTERN(Enum):
-    # 单根K线形态
-    HAMMER = auto()              # 锤子线
-    INVERTED_HAMMER = auto()     # 倒锤子线
-    SHOOTING_STAR = auto()       # 射击之星
-    HANGING_MAN = auto()         # 上吊线
-    DOJI = auto()                # 十字星
-    LONG_LEGGED_DOJI = auto()    # 长腿十字星
-    GRAVESTONE_DOJI = auto()     # 墓碑十字星
-    DRAGONFLY_DOJI = auto()      # 蜻蜓十字星
-    MARUBOZU = auto()            # 光头光脚
-    SPINNING_TOP = auto()        # 纺锤线
-    
-    # 双根K线形态
-    BULLISH_ENGULFING = auto()   # 看涨吞没
-    BEARISH_ENGULFING = auto()   # 看跌吞没
-    PIERCING_LINE = auto()       # 刺透形态
-    DARK_CLOUD_COVER = auto()    # 乌云盖顶
-    TWEEZER_TOP = auto()         # 镊子顶
-    TWEEZER_BOTTOM = auto()      # 镊子底
-    HARAMI = auto()              # 孕线
-    BULLISH_HARAMI = auto()      # 看涨孕线
-    BEARISH_HARAMI = auto()      # 看跌孕线
-    
-    # 三根K线形态
-    MORNING_STAR = auto()        # 早晨之星
-    EVENING_STAR = auto()        # 黄昏之星
-    THREE_WHITE_SOLDIERS = auto() # 红三兵
-    THREE_BLACK_CROWS = auto()   # 三只乌鸦
-    THREE_INNER_UP = auto()      # 上升三法
-    THREE_INNER_DOWN = auto()    # 下降三法
-    ABANDONED_BABY = auto()      # 弃婴形态
-    
-    # 多根K线形态
-    DOUBLE_TOP = auto()          # 双顶
-    DOUBLE_BOTTOM = auto()       # 双底
-    TRIPLE_TOP = auto()          # 三顶
-    TRIPLE_BOTTOM = auto()       # 三底
-    HEAD_AND_SHOULDERS = auto()  # 头肩顶
-    INVERSE_HEAD_SHOULDERS = auto() # 头肩底
-    ROUNDING_BOTTOM = auto()     # 圆弧底
-    ROUNDING_TOP = auto()        # 圆弧顶
-    
-    # 缺口形态
-    BREAKAWAY_GAP = auto()       # 突破缺口
-    RUNAWAY_GAP = auto()         # 持续缺口
-    EXHAUSTION_GAP = auto()      # 衰竭缺口
-    
-    # 特殊形态
-    ISLAND_REVERSAL = auto()     # 岛形反转
-    KEY_REVERSAL = auto()        # 关键反转
-    INSIDE_BAR = auto()          # 内包线
-    OUTSIDE_BAR = auto()         # 外包线
-    
-    # 趋势形态
-    HIGHER_HIGH = auto()         # 更高高点
-    HIGHER_LOW = auto()          # 更高低点
-    LOWER_HIGH = auto()          # 更低高点
-    LOWER_LOW = auto()           # 更低低点
-    
-    # 支撑阻力形态
-    SUPPORT_BOUNCE = auto()      # 支撑反弹
-    RESISTANCE_REJECTION = auto() # 阻力拒绝
-    BREAKOUT = auto()            # 突破
-    BREAKDOWN = auto()           # 跌破
-    
-    # 成交量相关形态
-    VOLUME_SPIKE = auto()        # 成交量激增
-    VOLUME_DECLINE = auto()      # 成交量萎缩
-    
-    # 未知/无形态
-    UNKNOWN = auto()             # 未知形态
-
-
-class Chan_FX_TYPE(Enum):
-    BOTTOM = auto()
-    TOP = auto()
-    UNKNOWN = auto()
-    UP = auto()
-    DOWN = auto()
-    TT = auto()
-    BB = auto()
-    PTOP = auto()
-    PBOTTOM = auto()
-class Chan_FX(Enum):
-    CONTINUATION = auto()
-    REVERSAL = auto()
-    UNKNOWN = auto()
-class Chan_PRICE_TREND(Enum):
-    UP = auto()
-    DOWN = auto()
-    FLAT = auto()
-    UNKNOWN = auto()
-class Chan_KLC_FX(Enum):
-    TOP0 = auto()
-    TOP1 = auto()
-    TOP2 = auto()
-    TOP3 = auto()
-    TOP4 = auto()
-    TOP5 = auto()
-    TOP6 = auto()
-    TOP7 = auto()
-    TOP8 = auto()
-    BOTTOM0 = auto()
-    BOTTOM1 = auto()
-    BOTTOM2 = auto()
-    BOTTOM3 = auto()
-    BOTTOM4 = auto()
-    BOTTOM5 = auto()
-    BOTTOM6 = auto()
-    BOTTOM7 = auto()
-    BOTTOM8 = auto()
-    UNKNOWN = auto()
-# 统一的MACD状态枚举，包含所有可能的状态
-class Chan_MACD_STATE(Enum):
-    """MACD状态枚举 - 包含所有可能的状态"""
-    # 穿越状态
-    CROSS0_UP = auto()      # 穿零轴后快速向上，能量柱呈现一根比一根长的排列方式
-    CROSS0_DOWN = auto()    # 穿零轴后快速向下，能量柱呈现一根比一根短的排列方式
-    
-    CROSS_OS = auto()       # 穿零轴后缠绕/粘合，黄白线沿着能量柱运行，黄白线在运行的过程中没有释放出反向能量柱
-    CROSS_REV = auto()      # 穿零轴后倒挂，MACD黄白线在穿零轴的时候与零轴的距离比较近，同时黄白线沿着能量柱运行，在运行的过程中，能量柱衰减导致它跟黄白线之间形成夹角空位，同时黄白线产生交叉并释放反向能量柱。
-    
-    # 趋势状态
-    NEAR0  = auto()
-    NEAR0_52 = auto()          # 价格在EMA52附近/价格接触EMA52并马上离开，需要观察离开强度
-    NEAR0_DIFF = auto()        # MACD白线接近零轴，价格未到EMA52
-    NEAR0_PERFECT = auto()     # MACD白线接近零轴和价格接触或短暂击穿EMA52，而MACD黄线不穿零轴，完美形态
-    NEAR0_24 = auto()          # MACD黄白线接近零轴和价格在EMA24附近
-    # 位置状态
-    HIGH = auto()           # 高位：MACD黄白线离开能量柱到高点，能量柱最大开始减弱
-    HIGH_EMPTY = auto()     # 高位空：MACD黄白线处于高位，能量柱衰减，与黄白线形成空间夹角
-    RETURN_ZERO = auto()    # 归零轴：能量柱呈现一根比一根短的排列方式
-    RZ_UP = auto()             # 归零轴后的零轴上涨
-    RZ_DOWN = auto()           # 归零轴后的零轴下跌
-    UP = auto()         # 穿零轴后向上
-    DOWN = auto()       # 穿零轴后向下
-    PEAK = auto()       # 峰值：MACD白线处于高位
-    # 基础状态
-    UNKNOWN = auto()        # 未知
-    START = auto()          # 开始
-class Chan_MACDSEG_DIR(Enum):
-    ABOVE = auto()
-    UNDER = auto()
-class Chan_MACDUNITTF_TYPE(Enum):
-    START = auto()
-    CROSS0 = auto()
-    NEAR0 = auto()
-class Chan_MACDUNITTF_JUMP(Enum):
-    CONTUNE = auto()
-    DISCRETE = auto()
-class Chan_MACDUNITTF_DIV(Enum):
-    CONTUNE = auto()
-    DISCRETE = auto()
-    UNDIV = auto()
-class Chan_MACDHISTSET_DIR(Enum):
-    ABOVE = auto()
-    UNDER = auto()
-class Chan_MACDUNITTF_DIR(Enum):
-    ABOVE = auto()
-    UNDER = auto()
-class Chan_MACDHIST_STATE(Enum):
-    UP = auto()
-    DOWN = auto()
-    PEAK = auto()
-    UNKNOWN = auto()
-    
-class Chan_BI_DIR(Enum):
-    UP = auto()
-    DOWN = auto()
-
-class Chan_SEG_DIR(Enum):
-    UP = auto()
-    DOWN = auto()
-
-class Chan_BI_TYPE(Enum):
-    UNKNOWN = auto()
-    STRICT = auto()
-    SUB_VALUE = auto()  # 次高低点成笔
-    TIAOKONG_THRED = auto()
-    DAHENG = auto()
-    TUIBI = auto()
-    UNSTRICT = auto()
-    TIAOKONG_VALUE = auto()
-
-
-Chan_BSP_MAIN_TYPE = Literal['1', '2', '3']
-
-class Chan_BSP_DIR(Enum):
-    BUY = auto()
-    SELL = auto()
-class Chan_BSP_TYPE(Enum):
-    B1 = auto()
-    B2 = auto()
-    B3 = auto()
-    S1 = auto()
-    S2 = auto()
-    S3 = auto()
-    NONE = auto()
-"""
-class Chan_BSP_TYPE(Enum):
-    T1 = '1'
-    T1P = '1p'
-    T2 = '2'
-    T2S = '2s'
-    T3A = '3a'  # 中枢在1类后面
-    T3B = '3b'  # 中枢在1类前面
-    T3 = '3'
-    T3E ='3e' # T3退出点
-    QJT = 'qjt' # 区间套突破
-    QJT1 = 'qjt1' # 区间套一类买点
-    QJT2 = 'qjt2' # 区间套一类卖点 
-    QJT3 = 'qjt3' # 区间套三类买点
-    def main_type(self) -> Chan_BSP_MAIN_TYPE:
-        return self.value[0]  # type: ignore
-
-"""
-class Chan_AUTYPE(Enum):
-    QFQ = auto()
-    HFQ = auto()
-    NONE = auto()
-
-
-class Chan_TREND_TYPE(Enum):
-    MEAN = "mean"
-    MAX = "max"
-    MIN = "min"
-
-
-class Chan_TREND_LINE_SIDE(Enum):
-    INSIDE = auto()
-    OUTSIDE = auto()
-
-
-class Chan_LEFT_SEG_METHOD(Enum):
-    ALL = auto()
-    PEAK = auto()
-
-
-class Chan_FX_CHECK_METHOD(Enum):
-    STRICT = auto()
-    LOSS = auto()
-    HALF = auto()
-    TOTALLY = auto()
-
-
-class Chan_SEG_TYPE(Enum):
-    BI = auto()
-    SEG = auto()
-
-
-class Chan_MACD_ALGO(Enum):
-    AREA = auto()
-    PEAK = auto()
-    FULL_AREA = auto()
-    DIFF = auto()
-    SLOPE = auto()
-    AMP = auto()
-    VOLUMN = auto()
-    AMOUNT = auto()
-    VOLUMN_AVG = auto()
-    AMOUNT_AVG = auto()
-    TURNRATE_AVG = auto()
-    RSI = auto()
-
-
-class Chan_DATA_FIELD:
-    FIELD_TIME = "time_key"
-    FIELD_OPEN = "open"
-    FIELD_HIGH = "high"
-    FIELD_LOW = "low"
-    FIELD_CLOSE = "close"
-    FIELD_VOLUME = "volume"  # 成交量
-    FIELD_TURNOVER = "turnover"  # 成交额
-    FIELD_TURNRATE = "turnover_rate"  # 换手率
-
-class Chan_KLC_STATE:
-    """笔当下状态（缠论笔定理）。任意时刻必属其一。"""
-    S10 = "(1, 0)"   # 顶分型构造中 (1,0)
-    S_10 = "(-1, 0)"   # 底分型构造中 (-1,0)
-    S11 = "(1,1)"  # 向上笔延续中 
-    S_11 = "(-1,1)" # 向下笔延续中
-    UNKNOWN = "Unknown" # 初始状态
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanEnum import *  # noqa: F403

ChanHeng.py

@@ -1,414 +1,2 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-"""
-使用 ccxt 获取币安交易所所有 `*/USDT` 交易对最新 100 根 1 小时 K 线数据，并筛选出长期横盘的币种。
-
-横盘判定基于以下三项指标（均可通过命令行参数调整）：
-1. 价格振幅占均价的比例（默认 ≤ 5%）
-2. 收盘价线性回归斜率占均价的比例（默认 ≤ 0.05%）
-3. 收盘价标准差占均价的比例（默认 ≤ 1.5%）
-
-满足以上全部条件的交易对会被视为长期横盘。
-"""
-
-import argparse
-import csv
-import logging
-import math
-import statistics
-import sys
-import time
-from dataclasses import dataclass
-from typing import Iterable, List, Optional, Sequence
-
-import ccxt
-
-# python ChanHeng.py --range-threshold 5 --slope-threshold 5 --std-threshold 0.015
-
-DEFAULT_LIMIT = 100
-DEFAULT_TIMEFRAME = "1h"
-STABLECOINS = {
-    "USDT",
-    "USDC",
-    "BUSD",
-    "TUSD",
-    "USDP",
-    "DAI",
-    "FDUSD",
-    "SUSD",
-    "UST",
-    "USTC",
-    "EUR",
-    "TRY",
-    "BFUSD",
-    "USDE",
-    "XUSD",
-    "USD1",
-    "XUSD"
-}
-
-
-@dataclass
-class SidewaysMetrics:
-    symbol: str
-    price_range_pct: float
-    slope_pct: float
-    std_pct: float
-    mean_close: float
-    last_close: float
-    data_points: int
-
-
-def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="筛选币安长期横盘币种（默认 500 根 1 小时 K 线）"
-    )
-    parser.add_argument(
-        "--timeframe",
-        default=DEFAULT_TIMEFRAME,
-        help="K 线周期（默认：1h）",
-    )
-    parser.add_argument(
-        "--limit",
-        type=int,
-        default=DEFAULT_LIMIT,
-        help="每个交易对获取的 K 线数量（默认：500）",
-    )
-    parser.add_argument(
-        "--range-threshold",
-        type=float,
-        default=0.05,
-        help="最大价格振幅占均价比例阈值（默认：0.05，表示 5%%）",
-    )
-    parser.add_argument(
-        "--slope-threshold",
-        type=float,
-        default=0.0005,
-        help="线性回归斜率占均价比例阈值（默认：0.0005，约 0.05%%）",
-    )
-    parser.add_argument(
-        "--std-threshold",
-        type=float,
-        default=0.015,
-        help="标准差占均价比例阈值（默认：0.015，表示 1.5%%）",
-    )
-    parser.add_argument(
-        "--quote",
-        action="append",
-        default=[],
-        help="只保留指定计价货币的交易对，可重复指定（示例：--quote USDT --quote FDUSD）",
-    )
-    parser.add_argument(
-        "--symbol",
-        action="append",
-        default=[],
-        help="仅检测指定交易对，可重复（不指定则遍历所有符合条件的现货交易对）",
-    )
-    parser.add_argument(
-        "--max-symbols",
-        type=int,
-        default=None,
-        help="限制最多检测的交易对数量（用于调试）",
-    )
-    parser.add_argument(
-        "--sleep",
-        type=float,
-        default=0.35,
-        help="请求失败后的基础重试等待秒数（默认：0.35）",
-    )
-    parser.add_argument(
-        "--retries",
-        type=int,
-        default=3,
-        help="单个交易对请求失败后的最大重试次数（默认：3）",
-    )
-    parser.add_argument(
-        "--include-inactive",
-        action="store_true",
-        help="包含已下架/不可交易的交易对（默认不包含）",
-    )
-    parser.add_argument(
-        "--export",
-        type=str,
-        default=None,
-        help="将筛选结果导出为 CSV 文件的路径",
-    )
-    parser.add_argument(
-        "--verbose",
-        action="store_true",
-        help="输出更详细的日志信息",
-    )
-    return parser.parse_args(argv)
-
-
-def setup_logging(verbose: bool) -> None:
-    level = logging.DEBUG if verbose else logging.INFO
-    logging.basicConfig(
-        level=level,
-        format="%(asctime)s [%(levelname)s] %(message)s",
-        datefmt="%Y-%m-%d %H:%M:%S",
-    )
-
-
-def create_exchange() -> ccxt.binance:
-    exchange = ccxt.binance({"enableRateLimit": True})
-    exchange.options["defaultType"] = "spot"
-    return exchange
-
-
-def iter_target_symbols(
-    exchange: ccxt.binance,
-    quotes: Sequence[str],
-    includes: Sequence[str],
-    include_inactive: bool,
-) -> List[str]:
-    markets = exchange.load_markets()
-    filtered = []
-
-    quote_set = {quote.upper() for quote in quotes}
-    include_set = {sym.upper() for sym in includes}
-
-    for symbol, meta in markets.items():
-        if not meta.get("spot", False):
-            continue
-        if not include_inactive and meta.get("active") is False:
-            continue
-
-        normalized_symbol = symbol.upper()
-
-        if include_set and normalized_symbol not in include_set:
-            continue
-
-        parts = symbol.split("/")
-        if len(parts) != 2:
-            continue
-
-        base_asset, quote_asset = parts[0].upper(), parts[1].upper()
-
-        target_quote = quote_set or {"USDT"}
-        if quote_asset not in target_quote:
-            continue
-
-        if base_asset in STABLECOINS:
-            continue
-
-        filtered.append(symbol)
-
-    filtered.sort()
-    logging.info(
-        "已筛选 %s 个目标交易对（quote 过滤：%s，专门列表：%s）",
-        len(filtered),
-        ",".join(sorted(quote_set or {"USDT"})),
-        ",".join(sorted(include_set)) or "无",
-    )
-    return filtered
-
-
-def fetch_ohlcv_with_retry(
-    exchange: ccxt.binance,
-    symbol: str,
-    timeframe: str,
-    limit: int,
-    retries: int,
-    base_sleep: float,
-) -> List[List[float]]:
-    attempt = 0
-    while True:
-        try:
-            return exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
-        except ccxt.RateLimitExceeded as exc:
-            wait_time = max(exchange.rateLimit / 1000.0 if exchange.rateLimit else 0, base_sleep)
-            logging.debug("触发限频，等待 %.2f 秒后重试 %s：%s", wait_time, symbol, exc)
-            time.sleep(wait_time)
-        except (ccxt.NetworkError, ccxt.ExchangeError) as exc:
-            attempt += 1
-            if attempt > retries:
-                logging.warning("多次获取失败，跳过 %s：%s", symbol, exc)
-                return []
-            wait_time = base_sleep * attempt
-            logging.debug("请求失败，等待 %.2f 秒后重试 %s（第 %d 次）：%s", wait_time, symbol, attempt, exc)
-            time.sleep(wait_time)
-
-
-def linear_regression_slope(values: Sequence[float]) -> float:
-    n = len(values)
-    if n < 2:
-        return 0.0
-    mean_x = (n - 1) / 2.0
-    mean_y = sum(values) / n
-    numerator = 0.0
-    denominator = 0.0
-    for idx, value in enumerate(values):
-        dx = idx - mean_x
-        numerator += dx * (value - mean_y)
-        denominator += dx * dx
-
-    if denominator == 0:
-        return 0.0
-    return numerator / denominator
-
-
-def compute_sideways_metrics(closes: Sequence[float], symbol: str) -> Optional[SidewaysMetrics]:
-    if not closes:
-        return None
-
-    mean_close = sum(closes) / len(closes)
-    if math.isclose(mean_close, 0.0):
-        return None
-
-    max_close = max(closes)
-    min_close = min(closes)
-    price_range_pct = (max_close - min_close) / mean_close
-
-    slope = linear_regression_slope(closes)
-    slope_pct = slope / mean_close
-
-    std_dev = statistics.pstdev(closes) if len(closes) > 1 else 0.0
-    std_pct = std_dev / mean_close
-
-    return SidewaysMetrics(
-        symbol=symbol,
-        price_range_pct=price_range_pct,
-        slope_pct=slope_pct,
-        std_pct=std_pct,
-        mean_close=mean_close,
-        last_close=closes[-1],
-        data_points=len(closes),
-    )
-
-
-def is_sideways(metrics: SidewaysMetrics, range_threshold: float, slope_threshold: float, std_threshold: float) -> bool:
-    return (
-        metrics.price_range_pct <= range_threshold
-        and abs(metrics.slope_pct) <= slope_threshold
-        and metrics.std_pct <= std_threshold
-    )
-
-
-def export_results(path: str, results: Sequence[SidewaysMetrics]) -> None:
-    fieldnames = [
-        "symbol",
-        "price_range_pct",
-        "slope_pct",
-        "std_pct",
-        "mean_close",
-        "last_close",
-        "data_points",
-    ]
-    with open(path, "w", newline="", encoding="utf-8") as fp:
-        writer = csv.DictWriter(fp, fieldnames=fieldnames)
-        writer.writeheader()
-        for item in results:
-            writer.writerow(
-                {
-                    "symbol": item.symbol,
-                    "price_range_pct": f"{item.price_range_pct:.6f}",
-                    "slope_pct": f"{item.slope_pct:.6f}",
-                    "std_pct": f"{item.std_pct:.6f}",
-                    "mean_close": f"{item.mean_close:.8f}",
-                    "last_close": f"{item.last_close:.8f}",
-                    "data_points": item.data_points,
-                }
-            )
-    logging.info("结果已导出至 %s", path)
-
-
-def run(argv: Optional[Sequence[str]] = None) -> int:
-    args = parse_args(argv)
-    if not args.quote:
-        args.quote = ["USDT"]
-    setup_logging(args.verbose)
-
-    exchange = create_exchange()
-    symbols = iter_target_symbols(
-        exchange=exchange,
-        quotes=args.quote,
-        includes=args.symbol,
-        include_inactive=args.include_inactive,
-    )
-
-    if args.max_symbols is not None:
-        symbols = symbols[: args.max_symbols]
-        logging.info("出于调试目的，仅检测前 %d 个交易对。", len(symbols))
-
-    if not symbols:
-        logging.error("未找到任何满足条件的交易对，请检查过滤条件。")
-        return 1
-
-    sideways_results: List[SidewaysMetrics] = []
-    total = len(symbols)
-
-    for idx, symbol in enumerate(symbols, start=1):
-        logging.info("(%d/%d) 正在获取 %s 的 %s K 线（limit=%d）", idx, total, symbol, args.timeframe, args.limit)
-        ohlcv = fetch_ohlcv_with_retry(
-            exchange=exchange,
-            symbol=symbol,
-            timeframe=args.timeframe,
-            limit=args.limit,
-            retries=args.retries,
-            base_sleep=args.sleep,
-        )
-
-        if len(ohlcv) < max(100, args.limit // 2):
-            logging.debug("交易对 %s 返回数据不足（%d 根），跳过。", symbol, len(ohlcv))
-            continue
-
-        closes = [entry[4] for entry in ohlcv if entry[4] is not None]
-        metrics = compute_sideways_metrics(closes, symbol)
-        if not metrics:
-            continue
-
-        if is_sideways(metrics, args.range_threshold, args.slope_threshold, args.std_threshold):
-            sideways_results.append(metrics)
-            logging.info(
-                "识别为横盘：%s | 振幅 %.2f%% | 斜率 %.4f%% | 标准差 %.2f%%",
-                symbol,
-                metrics.price_range_pct * 100,
-                metrics.slope_pct * 100,
-                metrics.std_pct * 100,
-            )
-        else:
-            logging.debug(
-                "未满足条件：%s | 振幅 %.2f%% | 斜率 %.4f%% | 标准差 %.2f%%",
-                symbol,
-                metrics.price_range_pct * 100,
-                metrics.slope_pct * 100,
-                metrics.std_pct * 100,
-            )
-
-    if not sideways_results:
-        logging.warning("未检测到满足定义的长期横盘交易对。")
-        return 0
-
-    sideways_results.sort(key=lambda item: (item.price_range_pct, abs(item.slope_pct), item.std_pct))
-    print("=" * 88)
-    print(
-        f"共识别 {len(sideways_results)} 个长期横盘交易对（阈值：振幅≤{args.range_threshold:.2%}，"
-        f"斜率≤{args.slope_threshold:.2%}，标准差≤{args.std_threshold:.2%}）"
-    )
-    print("=" * 88)
-    header = f"{'Symbol':15s} {'Range%':>10s} {'Slope%':>10s} {'STD%':>10s} {'Mean':>14s} {'Last':>14s} {'Count':>6s}"
-    print(header)
-    print("-" * len(header))
-    for item in sideways_results:
-        print(
-            f"{item.symbol:15s}"
-            f" {item.price_range_pct * 100:10.4f}"
-            f" {item.slope_pct * 100:10.4f}"
-            f" {item.std_pct * 100:10.4f}"
-            f" {item.mean_close:14.8f}"
-            f" {item.last_close:14.8f}"
-            f" {item.data_points:6d}"
-        )
-
-    if args.export:
-        export_results(args.export, sideways_results)
-
-    logging.info("任务完成。")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(run())
-
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.ChanHeng import *  # noqa: F403

ChanKLC.py

@@ -1,620 +1,2 @@
-import copy
-from typing import Dict, Optional
-
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_KLC_FX
-from ChanEnum import Chan_K_DIR, Chan_MACD_STATE, Chan_PRICE_TREND, Chan_EMA_POS
-from ChanEnum import Chan_EMA_SEMANTIC, Chan_BSP_TYPE, Chan_KLC_STATE, Chan_FX
-import ChanKLU
-import ChanCTime
-import Chan_FX_Box
-# 根据结合律合并K线后的K线
-class ChanKLC():
-	def __init__(self, klu: ChanKLU, index, ddir=Chan_KLINE_DIR.UP):
-		self.start_time = klu.time
-		self.end_time = None
-		self.high = klu.high
-		self.low = klu.low
-		self.dir = ddir
-		self.index = index
-		self.klu_list = []
-		self.add_klu(klu)
-		self.fx = Chan_FX_TYPE.UNKNOWN
-		self.next = None
-		self.pre = None
-		self.start_klu = klu
-		self.end_klu = None
-		self.state = "00"
-		self.klc_state = Chan_KLC_STATE.UNKNOWN
-		self.open = klu.open
-		self.close = klu.close
-		self.volume = klu.volume
-		self.bi = None
-		self.distance = 0
-		self.klc_fx_type = Chan_KLC_FX.UNKNOWN
-		self.rsi = klu.rsi
-		self.volume_ratio = klu.volume_ratio
-		self.macdhist = klu.macdhist
-		self.body = klu.body
-		self.upper_shadow = klu.upper_shadow
-		self.lower_shadow = klu.lower_shadow
-		self.body_ratio = klu.body_ratio
-		self.upper_shadow_ratio = klu.upper_shadow_ratio
-		self.lower_shadow_ratio = klu.lower_shadow_ratio
-		self.candle_dir = klu.candle_dir
-		self.range = klu.range
-		self.bb_out = True
-		self.macd = klu.macd
-		self.signal = klu.signal
-		self.state = Chan_MACD_STATE.UNKNOWN
-		self.continue_div = False
-		self.separate_div = False
-		self.ema24 = klu.ema24
-		self.ema26 = klu.ema26
-		self.ema52 = klu.ema52
-		self.ema104 = klu.ema104
-		self.ema156 = klu.ema156
-		self.ema208 = klu.ema208
-		self.ema13 = klu.ema13
-		self.ema7 = klu.ema7
-		self.trend = Chan_PRICE_TREND.UNKNOWN
-		self.exception = klu.exception
-		self.klc_dir = Chan_KLINE_DIR.UP if klu.close > klu.open else Chan_KLINE_DIR.DOWN
-		self.ema_dir = klu.ema_dir
-		self.bsp = False
-		self.bsp_type = Chan_BSP_TYPE.NONE
-		# EMA状态字典：key为EMA名称，value为 {'pos': Chan_EMA_POS, 'semantic': Chan_EMA_SEMANTIC}
-		self.ema_status = {}
-		# 向后兼容：保留 ema52_status 和 ema52_pos
-		self.ema52_status = 0
-		self.ema52_pos = Chan_EMA_POS.UNKNOWN
-		self.bb2633upper = klu.bb2633upper
-		self.bb2633lower = klu.bb2633lower
-		self.bb2633middle = klu.bb2633middle
-		self.ema5 = klu.ema5
-		self.ma5 = klu.ma5
-		self.fx_box = None
-		self.in_fx = False
-		self.fx_confirmed = False
-		self.ema52_dis = klu.high - klu.ema52 if klu.close > klu.ema52 else klu.ema52 - klu.low
-		self.ema26_dis = klu.high - klu.ema26 if klu.close > klu.ema26 else klu.ema26 - klu.low
-		self.macd_signal_dis = abs(klu.macd - klu.signal)
-		self.ema52_ema26_dis = abs(klu.ema52 - klu.ema26)
-		self.fx_type = Chan_FX.UNKNOWN
-		self.bi_zs = None
-		self.seg_zs = None
-		self.last_bi_zs = None
-	# ==================== EMA 通用计算方法 ====================
-
-	@staticmethod
-	def cal_ema_pos(high, low, close, ema_value, threshold=0):
-		"""
-		计算K线与任意EMA的客观位置关系（与趋势方向无关，支持threshold容差）
-		
-		参数:
-		  high, low, close: K线的高低收盘价
-		  ema_value: EMA的值
-		  threshold: 容差值（绝对值），在此范围内视为"接近/触碰"
-		             例如 BTC 价格 $100,000 时 threshold=100 表示差100点视为触碰
-		返回:
-		  Chan_EMA_POS 枚举值
-		
-		判断逻辑（以threshold=100, ema=97000为例）：
-		  ema_zone = [96900, 97100]  （EMA上下各扩展threshold）
-		  
-		  ABOVE:             low > 97100     K线完全在zone上方（远离EMA）
-		  NEAR_ABOVE:        97000 < low <= 97100  K线在上方但下影线进入zone（接近EMA）
-		  CROSS_CLOSE_ABOVE: close > 97000, low <= 97000  K线穿越EMA，收盘在上方
-		  ON_EMA:            abs(close - 97000) <= 100  收盘价在zone内
-		  CROSS_CLOSE_BELOW: close < 97000, high >= 97000  K线穿越EMA，收盘在下方
-		  NEAR_BELOW:        96900 <= high < 97000  K线在下方但上影线进入zone（接近EMA）
-		  BELOW:             high < 96900     K线完全在zone下方（远离EMA）
-		"""
-		if ema_value is None or ema_value == 0:
-			return Chan_EMA_POS.UNKNOWN
-
-		ema_upper = ema_value + threshold  # EMA zone 上界
-		ema_lower = ema_value - threshold  # EMA zone 下界
-
-		# 1. 收盘价在EMA附近（zone内）
-		if threshold > 0 and abs(close - ema_value) <= threshold:
-			# 收盘价在zone内，但还需要看是否有实际穿越
-			if low <= ema_value and close >= ema_value:
-				return Chan_EMA_POS.CROSS_CLOSE_ABOVE  # 实际穿越了精确EMA线
-			elif high >= ema_value and close <= ema_value:
-				return Chan_EMA_POS.CROSS_CLOSE_BELOW
-			return Chan_EMA_POS.ON_EMA
-
-		# 2. K线实际穿越了精确的EMA线
-		if close > ema_value and low <= ema_value:
-			return Chan_EMA_POS.CROSS_CLOSE_ABOVE
-		if close < ema_value and high >= ema_value:
-			return Chan_EMA_POS.CROSS_CLOSE_BELOW
-		if close == ema_value:
-			return Chan_EMA_POS.ON_EMA
-
-		# 3. 没有实际穿越，检查是否"接近"（在threshold zone内）
-		if close > ema_value:
-			# K线在EMA上方
-			if threshold > 0 and low <= ema_upper:
-				return Chan_EMA_POS.NEAR_ABOVE   # 下影线进入zone，接近但未触碰
-			return Chan_EMA_POS.ABOVE             # 远离EMA
-		else:
-			# K线在EMA下方
-			if threshold > 0 and high >= ema_lower:
-				return Chan_EMA_POS.NEAR_BELOW   # 上影线进入zone，接近但未触碰
-			return Chan_EMA_POS.BELOW             # 远离EMA
-
-	@staticmethod
-	def cal_ema_semantic(ema_pos, kline_dir, ema_dir):
-		"""
-		根据客观位置 + K线方向 + 趋势方向，计算语义状态
-		
-		参数:
-		  ema_pos: Chan_EMA_POS 客观位置
-		  kline_dir: Chan_KLINE_DIR K线方向 (UP/DOWN/COMBINE/INCLUDED)
-		  ema_dir: int 趋势方向 (1=多头, -1=空头, 0=盘整)
-		返回:
-		  Chan_EMA_SEMANTIC 枚举值
-		
-		语义含义（以多头为例，空头完全对称）：
-		  TOUCH_HOLD:   触碰EMA，收盘守住趋势侧（支撑/压力有效）
-		  BREAK:        穿越EMA，收盘在逆势侧（支撑/压力失败）
-		  DEEP_COUNTER: 完全在EMA逆势侧（深度回调/反抽）
-		  TOUCH_FAIL:   逆势触碰EMA但未穿越（反弹/反抽力度不足）
-		  RECOVER:      逆势后穿越EMA回到趋势侧（收复EMA）
-		  TREND_SIDE:   完全在EMA趋势侧（正常运行）
-		  STRONG_TREND: 顺势K线完全在EMA趋势侧（强势，远未及EMA）
-		  WEAK_COUNTER: 逆势K线完全在EMA逆势侧（弱势，远未到EMA）
-		"""
-		if ema_pos == Chan_EMA_POS.UNKNOWN:
-			return Chan_EMA_SEMANTIC.NEUTRAL
-
-		# 统一处理：将多头/盘整和空头映射到同一套逻辑
-		# is_bull=True 时，"趋势侧"=上方，"逆势侧"=下方
-		# is_bull=False时，"趋势侧"=下方，"逆势侧"=上方
-		is_bull = ema_dir >= 0  # 多头和盘整都按多头逻辑处理
-
-		# K线是否是顺势方向（多头下UP为顺势，空头下DOWN为顺势）
-		is_trend_kline = (kline_dir == Chan_KLINE_DIR.UP) if is_bull else (kline_dir == Chan_KLINE_DIR.DOWN)
-		is_counter_kline = (kline_dir == Chan_KLINE_DIR.DOWN) if is_bull else (kline_dir == Chan_KLINE_DIR.UP)
-
-		# 位置映射：多头下 ABOVE=趋势侧, BELOW=逆势侧; 空头反过来
-		trend_side = Chan_EMA_POS.ABOVE if is_bull else Chan_EMA_POS.BELOW
-		counter_side = Chan_EMA_POS.BELOW if is_bull else Chan_EMA_POS.ABOVE
-		near_trend = Chan_EMA_POS.NEAR_ABOVE if is_bull else Chan_EMA_POS.NEAR_BELOW
-		near_counter = Chan_EMA_POS.NEAR_BELOW if is_bull else Chan_EMA_POS.NEAR_ABOVE
-		cross_to_trend = Chan_EMA_POS.CROSS_CLOSE_ABOVE if is_bull else Chan_EMA_POS.CROSS_CLOSE_BELOW
-		cross_to_counter = Chan_EMA_POS.CROSS_CLOSE_BELOW if is_bull else Chan_EMA_POS.CROSS_CLOSE_ABOVE
-
-		# COMBINE / INCLUDED 方向：只看位置，不区分强弱
-		if not is_trend_kline and not is_counter_kline:
-			if ema_pos == trend_side:
-				return Chan_EMA_SEMANTIC.TREND_SIDE
-			elif ema_pos in (near_trend, cross_to_trend, Chan_EMA_POS.ON_EMA):
-				return Chan_EMA_SEMANTIC.APPROACHING
-			elif ema_pos in (near_counter, cross_to_counter):
-				return Chan_EMA_SEMANTIC.APPROACHING
-			elif ema_pos == counter_side:
-				return Chan_EMA_SEMANTIC.DEEP_COUNTER
-			return Chan_EMA_SEMANTIC.NEUTRAL
-
-		# 逆势K线（多头下的下跌K线 / 空头下的上涨K线）
-		if is_counter_kline:
-			if ema_pos == trend_side:
-				return Chan_EMA_SEMANTIC.STRONG_TREND   # 逆势K线仍在趋势侧（回调很浅）
-			elif ema_pos == near_trend:
-				return Chan_EMA_SEMANTIC.APPROACHING     # 接近EMA，即将测试支撑/压力
-			elif ema_pos == cross_to_trend:
-				return Chan_EMA_SEMANTIC.TOUCH_HOLD      # 触碰EMA后守住趋势侧
-			elif ema_pos == Chan_EMA_POS.ON_EMA:
-				return Chan_EMA_SEMANTIC.TOUCH_HOLD      # 收盘在EMA附近，视为守住
-			elif ema_pos == cross_to_counter:
-				return Chan_EMA_SEMANTIC.BREAK            # 穿越EMA到逆势侧
-			elif ema_pos == near_counter:
-				return Chan_EMA_SEMANTIC.BREAK            # 接近EMA但收盘在逆势侧，也视为击穿
-			elif ema_pos == counter_side:
-				return Chan_EMA_SEMANTIC.DEEP_COUNTER     # 完全在逆势侧
-
-		# 顺势K线（多头下的上涨K线 / 空头下的下跌K线）
-		if is_trend_kline:
-			if ema_pos == counter_side:
-				return Chan_EMA_SEMANTIC.WEAK_COUNTER     # 顺势K线却在逆势侧（弱势）
-			elif ema_pos == near_counter:
-				return Chan_EMA_SEMANTIC.APPROACHING      # 从逆势侧接近EMA
-			elif ema_pos == cross_to_counter:
-				return Chan_EMA_SEMANTIC.TOUCH_FAIL       # 触碰EMA但未穿越回趋势侧
-			elif ema_pos == Chan_EMA_POS.ON_EMA:
-				return Chan_EMA_SEMANTIC.TOUCH_FAIL       # 收盘在EMA附近，未确认突破
-			elif ema_pos == cross_to_trend:
-				return Chan_EMA_SEMANTIC.RECOVER           # 从逆势侧穿越回趋势侧
-			elif ema_pos == near_trend:
-				return Chan_EMA_SEMANTIC.RECOVER           # 接近趋势侧（刚收复EMA附近）
-			elif ema_pos == trend_side:
-				return Chan_EMA_SEMANTIC.TREND_SIDE        # 完全在趋势侧（正常）
-
-		return Chan_EMA_SEMANTIC.NEUTRAL
-
-	@staticmethod
-	def semantic_to_int(semantic):
-		"""将 Chan_EMA_SEMANTIC 枚举转换为整数，兼容旧的 ema52_status 数值"""
-		mapping = {
-			Chan_EMA_SEMANTIC.TOUCH_HOLD: 1,
-			Chan_EMA_SEMANTIC.BREAK: 2,
-			Chan_EMA_SEMANTIC.DEEP_COUNTER: 3,
-			Chan_EMA_SEMANTIC.TOUCH_FAIL: 4,
-			Chan_EMA_SEMANTIC.RECOVER: 5,
-			Chan_EMA_SEMANTIC.TREND_SIDE: 6,
-			Chan_EMA_SEMANTIC.STRONG_TREND: 7,
-			Chan_EMA_SEMANTIC.WEAK_COUNTER: 8,
-			Chan_EMA_SEMANTIC.APPROACHING: 9,
-			Chan_EMA_SEMANTIC.NEUTRAL: 0,
-		}
-		return mapping.get(semantic, 0)
-
-	# threshold_pct: 阈值百分比，用于自动计算绝对阈值
-	# 例如 0.001 表示 EMA 值的 0.1%，BTC $100,000 时 threshold = $100
-	threshold_pct = 0.001
-	def set_bsp_type(self, bsp_type):
-		if bsp_type and bsp_type != Chan_BSP_TYPE.NONE:
-			self.bsp_type = bsp_type
-			self.bsp = True
-	def cal_all_ema_status(self):
-		"""
-		统一计算所有EMA与K线的位置关系和语义状态
-		
-		threshold 自动按 EMA 值的百分比计算（cls.threshold_pct，默认0.1%）
-		  - BTC $100,000 时：threshold ≈ $100
-		  - ETH $3,000 时：threshold ≈ $3
-		  - SOL $200 时：threshold ≈ $0.2
-		
-		结果存储在 self.ema_status 字典中，格式：
-		  {
-		    'ema24':  {'pos': Chan_EMA_POS, 'semantic': Chan_EMA_SEMANTIC, 'value': float, 'threshold': float},
-		    'ema52':  {...},
-		    ...
-		  }
-		
-		同时保持向后兼容：self.ema52_pos 和 self.ema52_status
-		"""
-		ema_configs = {
-			'ema24':  self.ema24,
-			'ema52':  self.ema52,
-			'ema104': self.ema104,
-			'ema156': self.ema156,
-			'ema208': self.ema208,
-		}
-		self.ema_status = {}
-		for name, value in ema_configs.items():
-			# 按 EMA 值的百分比自动计算阈值
-			threshold = abs(value) * self.threshold_pct if value and self.threshold_pct > 0 else 0
-			pos = ChanKLC.cal_ema_pos(self.high, self.low, self.close, value, threshold)
-			semantic = ChanKLC.cal_ema_semantic(pos, self.dir, self.ema_dir)
-			self.ema_status[name] = {
-				'pos': pos,
-				'semantic': semantic,
-				'value': value,
-				'threshold': threshold,
-			}
-		# 向后兼容
-		self.ema52_pos = self.ema_status['ema52']['pos']
-		self.ema52_status = ChanKLC.semantic_to_int(self.ema_status['ema52']['semantic'])
-	def get_ema_pos(self, ema_name):
-		"""获取指定EMA的客观位置，如 klc.get_ema_pos('ema24')"""
-		if ema_name in self.ema_status:
-			return self.ema_status[ema_name]['pos']
-		return Chan_EMA_POS.UNKNOWN
-	def check_ema_pos(self):
-		if len(self.ema_status) > 0:
-			for ema_name, pos in self.ema_status.items():
-				#print(self.end_time, ema_name, pos['pos'])
-				if ((self.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc_fx_type == Chan_KLC_FX.TOP2) and pos['pos'] == Chan_EMA_POS.CROSS_CLOSE_BELOW) or ((self.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc_fx_type == Chan_KLC_FX.BOTTOM2) and pos['pos'] == Chan_EMA_POS.CROSS_CLOSE_ABOVE):
-					#print("---------------------")
-					return ema_name
-		return None
-	def get_ema_semantic(self, ema_name):
-		"""获取指定EMA的语义状态，如 klc.get_ema_semantic('ema52')"""
-		if ema_name in self.ema_status:
-			return self.ema_status[ema_name]['semantic']
-		return Chan_EMA_SEMANTIC.NEUTRAL
-	def set_trend(self, trend):
-		self.trend = trend
-	def to_string(self):
-		out = ""
-		start = self.start_time if self.start_time is not None else ""
-		end = self.end_time if self.end_time is not None else ""
-		price_diff = getattr(self, 'price_diff', None)
-		out += str(start) + " " + str(end) + " " + str(self.close) + " " + str(self.ema24) + " " + str(self.ema52) + " " + str(self.trend) + " " + str(self.close - self.ema52)
-		return out
-	def set_bi_zs(self, bi_zs):
-		if bi_zs:
-			self.bi_zs = bi_zs
-	def set_klc_fx_type(self, klc_fx_type):
-		#print(self.start_time, klc_fx_type, self.get_feature_data()['klu_macd'], self.get_feature_data()['klu_macdhist'], self.get_feature_data()['klu_rsi'])
-		self.klc_fx_type = klc_fx_type
-		#self.cal_fx()
-		ema_name = self.check_ema_pos()
-		hist_div = abs(self.macdhist - self.next.macdhist)
-		#print(self.end_time, self.dir, abs(self.macdhist), hist_div)
-		#if ema_name:
-			#print(self.end_time, ema_name, self.ema_status[ema_name]['semantic'], hist_div)
-		#self.cal_bb_out()
-		#print(self.pre.start_time, self.next.end_time, self.klc_fx_type)
-		if klc_fx_type == Chan_KLC_FX.TOP1 or klc_fx_type == Chan_KLC_FX.TOP2 or klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc_fx_type == Chan_KLC_FX.BOTTOM2:
-			self.cal_fx_box()
-			self.cal_fx_type()
-	def cal_fx_type(self):
-		if self.fx == Chan_FX_TYPE.TOP and self.next:
-			if self.ema52_dis > self.ema26_dis:
-				if self.pre.macd < self.macd and self.macd < self.next.macd:
-					self.fx_type = Chan_FX.CONTINUATION
-				else:
-					self.fx_type = Chan_FX.REVERSAL
-		elif self.fx == Chan_FX_TYPE.BOTTOM and self.next:
-			if self.ema52_dis < self.ema26_dis:
-				if self.pre.macd > self.macd and self.macd > self.next.macd:
-					self.fx_type = Chan_FX.CONTINUATION
-				else:
-					self.fx_type = Chan_FX.REVERSAL
-		#if self.fx_type != Chan_FX.UNKNOWN and self.fx_type != Chan_FX.CONTINUATION:
-			#print(self.end_time, self.fx_type)
-	def cal_fx_box(self):
-		# 每次重算前先清空，避免旧box残留
-		self.fx_box = None
-		start_time = None
-		end_time = None
-		high = 0
-		low = 0
-		display = False
-		if self.pre and self.next and self.next.end_time:
-			self.next.in_fx = True
-			if self.fx == Chan_FX_TYPE.TOP:
-				start_time = self.pre.end_time
-				end_time = self.next.end_time
-				high = self.high
-				low = self.pre.low if self.pre.low < self.next.low else self.next.low
-				if self.next.close < self.pre.low:
-					display = True
-			elif self.fx == Chan_FX_TYPE.BOTTOM:
-				start_time = self.pre.end_time
-				end_time = self.next.end_time
-				high = self.pre.high if self.pre.high > self.next.high else self.next.high
-				low = self.low
-				if self.next.close > self.pre.high:
-					display = True
-		if high > 0 and self.next.end_time and display:
-			#print(start_time, end_time, high, low)
-			# Chan_FX_BOX 这里导入的是模块，类名在模块内部为 Chan_FX_Box
-			self.fx_confirmed = True
-			self.fx_box = Chan_FX_Box.Chan_FX_Box(start_time, end_time, high, low)
-	def check_fx_confirmed(self, last_top, last_bottom):
-		if last_top and last_bottom and False:
-			if last_top.index > last_bottom.index:
-				if self.in_fx == False and last_top.fx_confirmed == False:
-					pre = last_top.pre
-					if pre.low > self.close:
-						last_top.fx_confirmed = True
-						if last_top.fx_box:
-							last_top.fx_box.end_time = self.end_time
-							#print(self.end_time, "fx_confirmed top")
-						else:
-							high = last_top.high
-							low = self.low
-							last_top.fx_box = Chan_FX_Box.Chan_FX_Box(last_top.pre.start_time, self.end_time, high, low)
-							#print(self.end_time, "fx_confirmed new box top")
-			elif self.in_fx == False and last_bottom.fx_confirmed == False:
-				pre = last_bottom.pre
-				if pre.high < self.close:
-					last_bottom.fx_confirmed = True
-					if last_bottom.fx_box:
-						last_bottom.fx_box.end_time = self.end_time
-						#print(self.end_time, "fx_confirmed bottom")
-					else:
-						high = self.high
-						low = last_bottom.low
-						last_bottom.fx_box = Chan_FX_Box.Chan_FX_Box(last_bottom.pre.start_time, self.end_time, high, low)
-						#print(self.end_time, "fx_confirmed new box bottom")
-	def add_klu(self, klu):
-		self.klu_list.append(klu)
-	def check_klc_state(self, last_fx_klc):
-		if last_fx_klc and last_fx_klc.fx == Chan_FX_TYPE.TOP:
-			if self.high > last_fx_klc.high:
-				self.klc_state = Chan_KLC_STATE.S11
-			else:
-				self.klc_state = Chan_KLC_STATE.S_11
-		elif last_fx_klc and last_fx_klc.fx == Chan_FX_TYPE.BOTTOM:
-			if self.low < last_fx_klc.low:
-				self.klc_state = Chan_KLC_STATE.S_11
-			else:
-				self.klc_state = Chan_KLC_STATE.S11
-		if self.pre and self.pre.fx == Chan_FX_TYPE.TOP:
-			self.klc_state = Chan_KLC_STATE.S10
-		elif self.pre and self.pre.fx == Chan_FX_TYPE.BOTTOM:
-			self.klc_state = Chan_KLC_STATE.S_10
-		#print(self.end_time, self.klc_state)
-	def set_end_klu(self, klu):
-		self.end_klu = klu
-		self.end_time = klu.time
-		self.close = klu.close
-		for klu in self.klu_list:
-			if klu.exception:
-				self.exception = True
-				print(klu.time, "exception")
-			if klu.separate_div > 0:
-				self.separate_div = True
-			if klu.continue_div:
-				self.continue_div = klu.continue_div
-			if klu.macd_state != Chan_MACD_STATE.UNKNOWN:
-				self.state = klu.macd_state
-			klu.set_klc(self)
-		self.klc_dir = Chan_KLINE_DIR.UP if self.close > self.open else Chan_KLINE_DIR.DOWN
-		self.cal_indicators()
-		self.cal_all_ema_status()
-		if self.open > self.high:
-			self.open = self.high
-		if self.close > self.high:
-			self.close = self.high
-		if self.close < self.low:
-			self.close = self.low
-		if self.open < self.low:
-			self.open = self.low
-		#print(self.end_time, self.open, self.close, self.high, self.low)
-		#print(klu.time, klu.open, klu.close, klu.high, klu.low)
-	def cal_fx(self):
-		if  self.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc_fx_type == Chan_KLC_FX.TOP2:
-			#print(self.end_time, self.fx, self.macd, self.macdhist, len(self.klu_list))
-			if self.state == Chan_MACD_STATE.HIGH_EMPTY and self.macd > 0:
-				#print(self.end_time, self.state, self.macd, self.klc_fx_type)
-				self.klc_fx_type = Chan_KLC_FX.TOP6
-			if self.separate_div or self.continue_div:
-				self.klc_fx_type = Chan_KLC_FX.TOP7
-			if self.signal > 0 and self.macd > self.signal:
-				self.klc_fx_type = Chan_KLC_FX.TOP8
-		else:
-			if self.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc_fx_type == Chan_KLC_FX.BOTTOM2:
-				if self.macdhist > 0 and self.macd < 0:
-					self.klc_fx_type = Chan_KLC_FX.BOTTOM5
-					return
-				if self.state == Chan_MACD_STATE.HIGH_EMPTY and self.macd < 0:
-					self.klc_fx_type = Chan_KLC_FX.BOTTOM6
-					#print(self.end_time, self.state, self.macd, self.klc_fx_type)
-				if self.separate_div or self.continue_div:
-					self.klc_fx_type = Chan_KLC_FX.BOTTOM7
-				if self.signal < 0 and self.macd < self.signal:
-					self.klc_fx_type = Chan_KLC_FX.BOTTOM8
-	def cal_bb_out(self):
-		for klu in self.klu_list:
-			if self.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc_fx_type == Chan_KLC_FX.TOP2:
-				#print(self.start_time, self.klc_fx_type, klu.high, klu.bb52upper, self.macd, self.next.macd, klu.time)
-				if self.high >= klu.bb52upper and klu.bb52upper > 0 and self.next and self.high > self.next.high:
-					self.klc_fx_type = Chan_KLC_FX.TOP4
-					print(self.end_time, self.klc_fx_type)
-			if self.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc_fx_type == Chan_KLC_FX.BOTTOM2:
-				#print(self.start_time, self.klc_fx_type, klu.low, klu.bb52lower, self.macd, self.next.macd, klu.time)
-				if self.low <= klu.bb52lower and klu.bb52lower > 0 and self.next and self.low < self.next.low:
-					self.klc_fx_type = Chan_KLC_FX.BOTTOM4
-					print(self.end_time, self.klc_fx_type)
-	def cal_indicators(self):
-		for index in range(1, len(self.klu_list)):
-			self.volume += self.klu_list[index].volume
-			self.rsi += self.klu_list[index].rsi
-			self.volume_ratio += self.klu_list[index].volume_ratio
-			self.macdhist += self.klu_list[index].macdhist
-			self.ema26 += self.klu_list[index].ema26
-			self.ema24 += self.klu_list[index].ema24
-			self.ema52 += self.klu_list[index].ema52
-			self.ema104 += self.klu_list[index].ema104
-			self.ema156 += self.klu_list[index].ema156
-			self.ema208 += self.klu_list[index].ema208
-			self.ema13 += self.klu_list[index].ema13
-			self.ema7 += self.klu_list[index].ema7
-			self.bb2633upper += self.klu_list[index].bb2633upper
-			self.bb2633lower += self.klu_list[index].bb2633lower
-			self.bb2633middle += self.klu_list[index].bb2633middle
-			self.ma5 += self.klu_list[index].ma5
-			self.ema5 += self.klu_list[index].ema5
-			if self.ema_dir != self.klu_list[index].ema_dir:
-				self.ema_dir = 0
-		n = len(self.klu_list)
-		self.rsi = self.rsi / n
-		self.volume_ratio = self.volume_ratio / n
-		self.volume = self.volume / n
-		self.macdhist = self.macdhist / n
-		self.ema26 = self.ema26 / n
-		self.ema24 = self.ema24 / n
-		self.ema52 = self.ema52 / n
-		self.ema104 = self.ema104 / n
-		self.ema156 = self.ema156 / n
-		self.ema208 = self.ema208 / n
-		self.ema13 = self.ema13 / n
-		self.ema7 = self.ema7 / n
-		self.ma5 = self.ma5 / n
-		self.ema5 = self.ema5 / n
-		self.bb2633upper = self.bb2633upper / n
-		self.bb2633lower = self.bb2633lower / n
-		self.bb2633middle = self.bb2633middle / n
-		if len(self.klu_list) > 0:
-			self.macd = self.klu_list[-1].macd
-			self.signal = self.klu_list[-1].signal
-		self.body = abs(self.close - self.open)
-		self.upper_shadow = self.high - max(self.close, self.open)
-		self.lower_shadow = min(self.close, self.open) - self.low
-		self.body_ratio = self.body / self.open
-		self.upper_shadow_ratio = self.upper_shadow / self.open
-		self.lower_shadow_ratio = self.lower_shadow / self.open
-		self.candle_dir = Chan_K_DIR.CROSS if self.close == self.open else Chan_K_DIR.BULL if self.close > self.open else Chan_K_DIR.BEAR
-		self.range = self.high - self.low
-	def set_next(self, klc):
-		self.next = klc
-	def set_pre(self, klc):
-		self.pre = klc
-	def set_state(self, state):
-		self.state = state
-	def check_klu_included(self, klu):
-		if self.high >= klu.high:
-			# high大于，low小于，左包含
-			if self.low <= klu.low:
-				self.add_klu(klu=klu)
-				# gn>gn-1
-				if self.dir == Chan_KLINE_DIR.UP:
-					# UP -> max(dn)
-					self.low = klu.low
-				else:
-					# DOWN -> min(gn)
-					self.high = klu.high
-				#self.print(klu, "Z")
-				return True
-				# high大于，low大于，不包含
-			else:
-				# if self.low > klu.low
-				# high相等，右包含
-				if self.high == klu.high:
-					self.add_klu(klu=klu)
-					# UP -> max(gn)
-					if self.dir == Chan_KLINE_DIR.UP:
-						self.high = klu.high
-					else:
-						# DOWN -> min(dn)
-						self.low = klu.low
-					return True
-				else:
-					return False
-		else:
-			# high小于，low大于，右包含
-			if self.low >= klu.low:
-				self.add_klu(klu=klu)
-				# gn>gn-1
-				if self.dir == Chan_KLINE_DIR.UP:
-					# UP -> max(gn)
-					self.high = klu.high
-				else:
-					# DOWN -> min(dn)
-					self.low = klu.low
-				#self.print(klu, "Y")
-				return True
-			else:
-				# high小于，low小于，不包含
-				return False
-	def set_fx(self, fx: Chan_FX_TYPE):
-		self.fx = fx
-	def cal_invisible(self):
-		if self.fx == Chan_FX_TYPE.TOP:
-			if self.macdhist < 0 and self.macd > 0:
-				self.klc_fx_type = Chan_KLC_FX.TOP5
-		else:
-			if self.fx == Chan_FX_TYPE.BOTTOM:
-				if self.macdhist > 0 and self.macd < 0:
-					self.klc_fx_type = Chan_KLC_FX.BOTTOM5
-	def set_pre_fx(self):
-		if self.pre and self.pre.pre:
-			self.pre.fx = self.check_fx(self.pre.pre, self.pre)
-	def check_fx(self, k1, k2):
-		if k2.high > k1.high and k2.high > self.high:
-			return Chan_FX_TYPE.TOP
-		elif k2.low < k1.low and k2.low < self.low:
-			return Chan_FX_TYPE.BOTTOM
-		else:
-			return Chan_FX_TYPE.UNKNOWN
-	def set_bi(self, bi):
-		self.bi = bi
-		self.distance = self.index - bi.start_klc.index
-		#print(self.start_time, self.distance, bi.index, bi.dir)
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanKLC import *  # noqa: F403

ChanKLU.py

@@ -1,400 +1,2 @@
-from ChanEnum import Chan_FX_TYPE, Chan_KLU_TYPE, Chan_K_DIR, Chan_MACD_STATE, Chan_MACDHIST_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN, Chan_KLC_FX
-class ChanKLU:
-	def __init__(self, time, open, high, low, close, volume):
-		# _time, _close, _open, _high, _low, _extra_info={}
-		self.kl_type = None
-		self.time = time
-		self.close = close
-		self.open = open
-		self.high = high
-		self.low = low
-		self.volume = volume
-		self.idx = 0
-		self.index = 0
-		self.macd = 0
-		self.signal = 0
-		self.macdhist = 0
-		self.klc = None
-		self.rsi = 0
-		self.volume_ratio = 0
-		self.bb52upper = 0
-		self.bb52lower = 0
-		# === 新增：K线类型 ===
-		self.kline_type = None  # K线类型：大阳线、大阴线、小阳线、小阴线
-		self.pattern = Chan_KLU_PATTERN.UNKNOWN
-  
-		# === 新增：实时分型相关属性 ===
-		self.pre = None  # 前一根K线
-		self.next = None  # 后一根K线  
-		self.fx_type = Chan_FX_TYPE.UNKNOWN      # 分型类型：0=无分型，1=顶分型，-1=底分型
-		self.fx_strength = 0  # 分型强度：0-100
-		self.fx_confirmed = False  # 分型是否确认
-		self.klu_type = None
-		self.range = self.high - self.low
-		self.body = abs(self.close - self.open)
-		self.upper_shadow = self.high - max(self.close, self.open)
-		self.lower_shadow = min(self.close, self.open) - self.low
-		self.body_ratio = self.body / self.range if self.range != 0 else 0
-		self.upper_shadow_ratio = self.upper_shadow / self.body if self.body != 0 else float('inf')
-		self.lower_shadow_ratio = self.lower_shadow / self.body if self.body != 0 else float('inf')
-		self.exception = False
-		#self.cal_exception()
-		self.candle_dir = Chan_K_DIR.CROSS if self.close == self.open else Chan_K_DIR.BULL if self.close > self.open else Chan_K_DIR.BEAR
-		
-		self.continue_div = 0
-		self.separate_div = 0
-		self.near0_return = 0
-		self.ema52 = 0
-		self.ema24 = 0
-		self.ema26 = 0
-		self.ema104 = 0
-		self.ema156 = 0
-		self.ema208 = 0
-		self.macd_slop = 0
-		self.signal_slop = 0
-		self.hist_slop = 0
-		self.hist_state = Chan_MACDHIST_STATE.UNKNOWN
-		self.macd_state = Chan_MACD_STATE.UNKNOWN
-		self.macd_hist_gap = 0
-		self.trend = Chan_PRICE_TREND.UNKNOWN
-		self.seg_histset_index = 0
-		# === 归零轴细化与模式/背离 ===
-		self.zero_axis = False                 # 是否归零轴（穿越或接近）
-		self.zero_axis_state = "none"         # {none,crossing,near}
-		self.zero_axis_side = 0               # 1:above, -1:under, 0:none
-		self.zero_axis_score = 0              # 0-100 综合评分
-		self.mode1_touch_ema52 = False        # 单边后触碰EMA52
-		self.mode2_fast_to_zero = False       # 快线向零收敛
-		self.mode3_double_tf = False          # 双周期归零（近似占位，由上层填充高周期确认）
-		self.mode3_dir = "none"               # {long_strong_rebound, short_strong_rebound, none}
-		self.mode4_touch52_no_zero = False    # 先触碰EMA52但黄白线未归零
-		self.div_type = "none"                # {bearish, bullish, hidden_bearish, hidden_bullish, none}
-		self.div_score = 0.0                  # 背离强度(0-100)
-		self.ema_dir = 0
-		self.get_ema_dir()
-		self.bb2633upper = 0
-		self.bb2633lower = 0
-		self.bb2633middle = 0
-		self.ma5 = 0
-		self.ema5 = 0
-		#print(self.open, self.close, self.high, self.low, self.candle_dir, self.strength)
-	def set_macd_state(self, state):
-		self.macd_state = state
-	def set_pattern(self, pattern):
-		self.pattern = pattern
-	def set_seg_histset_index(self, seg_histset_index):
-		self.seg_histset_index = seg_histset_index
-		#print(self.time, self.seg_histset_index)
-	def to_string(self):
-		return f"{self.time} {self.candle_dir} {self.pattern}"
-	def cal_exception(self):
-		if self.upper_shadow_ratio > 5 or self.lower_shadow_ratio > 5:
-			self.exception = True
-			#print(self.time, self.upper_shadow_ratio, self.lower_shadow_ratio, self.body, self.lower_shadow, self.upper_shadow, self.high, self.low, self.close, self.open)
-		#self.exception = False
-	def set_trend(self, trend):
-		self.trend = trend
-	def set_separate_div(self, separate_div):
-		self.separate_div = separate_div
-		bb2633_status = self.check_bb2633()
-		if self.klc and self.klc.pre and self.klc.next:
-			fx = self.check_fx_dir(self.klc.pre, self.klc.next)
-			if fx == Chan_FX_TYPE.TOP:
-				if self.macdhist > 0:
-					self.separate_div = separate_div
-				else:
-					self.separate_div = 0
-			elif fx == Chan_FX_TYPE.BOTTOM:
-				if self.macdhist < 0:
-					self.separate_div = separate_div
-				else:
-					self.separate_div = 0
-		if bb2633_status == 0:
-			self.separate_div = 0
-	def check_bb2633(self, threadhold=300):
-		#print(self.time, self.high, self.bb2633upper, self.low, self.bb2633lower)
-		if abs(self.high - self.bb2633upper) < threadhold:
-			#print(self.time, self.high, self.bb2633upper)
-			return 1
-		if abs(self.low - self.bb2633lower) < threadhold:
-			#print(self.time, self.low, self.bb2633lower)
-			return -1
-		return 0
-	def check_fx_dir(self, pre, next):
-		fx = Chan_FX_TYPE.UNKNOWN
-		if pre.klc_fx_type == Chan_KLC_FX.TOP1 or pre.klc_fx_type == Chan_KLC_FX.TOP2 or next.klc_fx_type == Chan_KLC_FX.TOP1 or next.klc_fx_type == Chan_KLC_FX.TOP2 or self.klc.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc.klc_fx_type == Chan_KLC_FX.TOP2:
-			fx = Chan_FX_TYPE.TOP
-		elif pre.klc_fx_type == Chan_KLC_FX.BOTTOM1 or pre.klc_fx_type == Chan_KLC_FX.BOTTOM2 or next.klc_fx_type == Chan_KLC_FX.BOTTOM1 or next.klc_fx_type == Chan_KLC_FX.BOTTOM2 or self.klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc.klc_fx_type == Chan_KLC_FX.BOTTOM2:
-			fx = Chan_FX_TYPE.BOTTOM
-		return fx
-	def set_next(self, next):
-		self.next = next
-		#if self.fx_type != Chan_FX_TYPE.UNKNOWN and self.fx_strength > 1:
-			#print(self.index, self.time, self.fx_type, self.fx_confirmed, self.fx_strength)
-	def set_pre(self, pre):
-		self.pre = pre
-	def set_klc(self, klc):
-		self.klc = klc
-	def set_histset(self, histset):
-		"""设置HistSet关联"""
-		self.histset = histset
-	
-	def set_seg(self, seg):
-		"""设置Seg关联"""
-		self.seg = seg
-		
-	def set_unittf(self, unittf):
-		"""设置UnitTF关联"""
-		self.unittf = unittf
-	def set_idx(self, idx):
-		self.idx = idx
-		self.index = idx
-	def check_price_ema156(self):
-		if self.check_indicators():
-			if self.close > self.ema156:
-				return 1
-			elif self.close < self.ema156:
-				return -1
-			else:
-				return 0
-		else:
-			return 0
-	def get_ema_dir(self):
-		if self.check_indicators():
-			if self.ema24 > self.ema52 and self.ema52 > self.ema104 and self.ema104 > self.ema156:
-				self.ema_dir = 1
-			elif self.ema24 < self.ema52 and self.ema52 < self.ema104 and self.ema104 < self.ema156:
-				self.ema_dir = -1
-			else:
-				self.ema_dir = 0
-	def check_indicators(self):
-		if self.ema156 == 0:
-			return False
-		else:
-			return True
-	def set_indicators(self, item):
-		self.macd = float(item['macd']) if 'macd' in item and item['macd'] else 0
-		self.signal = float(item['macdsignal']) if 'macdsignal' in item and item['macdsignal'] else 0
-		self.macdhist = float(item['macdhist']) if 'macdhist' in item and item['macdhist'] else 0
-		self.ema26 = float(item['ema26']) if 'ema26' in item and item['ema26'] else 0
-		self.ema52 = float(item['ema52']) if 'ema52' in item and item['ema52'] else 0
-		self.ema24 = float(item['ema24']) if 'ema24' in item and item['ema24'] else 0
-		self.ema104 = float(item['ema104']) if 'ema104' in item and item['ema104'] else 0
-		self.ema156 = float(item['ema156']) if 'ema156' in item and item['ema156'] else 0
-		self.ema208 = float(item['ema208']) if 'ema208' in item and item['ema208'] else 0
-		self.ema13 = float(item['ema13']) if 'ema13' in item and item['ema13'] else 0
-		self.ema7 = float(item['ema7']) if 'ema7' in item and item['ema7'] else 0
-		self.rsi = float(item['rsi']) if 'rsi' in item and item['rsi'] else 0
-		self.volume_ratio = float(item['volume_ratio']) if 'volume_ratio' in item and item['volume_ratio'] else 0
-		self.bb52upper = float(item['bb52upper']) if 'bb52upper' in item and item['bb52upper'] else 0
-		self.bb52lower = float(item['bb52lower']) if 'bb52lower' in item and item['bb52lower'] else 0
-		self.bb2633upper = float(item['bb2633upper']) if 'bb2633upper' in item and item['bb2633upper'] else 0
-		self.bb2633lower = float(item['bb2633lower']) if 'bb2633lower' in item and item['bb2633lower'] else 0
-		self.bb2633middle = float(item['bb2633middle']) if 'bb2633middle' in item and item['bb2633middle'] else 0
-		self.ma5 = float(item['ma5']) if 'ma5' in item and item['ma5'] else 0
-		self.ema5 = float(item['ema5']) if 'ema5' in item and item['ema5'] else 0
-	def cal_macd_state(self):
-		# 按定义精简实现：优先级 CROSS0 > 位置(HIGH/HE/RETURN_ZERO) > NEAR0 > UNKNOWN
-		# 首条或缺前一根
-		if not hasattr(self, 'pre') or self.pre is None:
-			self.macd_state = Chan_MACD_STATE.START
-			return self.macd_state
-
-		# 基本校验
-		if (self.macd == 0 and self.signal == 0 and self.macdhist == 0) or self.ema52 == 0:
-			self.macd_state = Chan_MACD_STATE.UNKNOWN
-			return self.macd_state
-		# 归零轴判断
-		if self.signal > 0:
-			if self.macd < self.signal:
-				if 0 < self.low - self.ema52 < 100:
-					self.near0_return = 0
-				elif self.close > self.ema52 and self.low < self.ema52 and self.open > self.ema52:
-					self.near0_return = 0
-				elif self.close < self.ema52 and self.open > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-				elif self.close < self.ema52 and self.open < self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-				elif self.close > self.ema52 and self.open > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-			elif self.close > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-				self.near0_return = 0
-		else:
-			if self.macd > self.signal:
-				if 0 < self.ema52 - self.high < 100:
-					self.near0_return = 0
-				elif self.close < self.ema52 and self.high > self.ema52 and self.open < self.ema52:
-					self.near0_return = 0
-				elif self.close < self.ema52 and self.open > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-				elif self.close > self.ema52 and self.open < self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-				elif self.close > self.ema52 and self.high > self.ema52 and self.open >= self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-				elif self.close > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
-					self.near0_return = 0
-		# 向上穿越EMA52 7
-		if self.close > self.ema52 and self.open < self.ema52:
-			self.near0_return = 0
-		# 向下穿越EMA52 8
-		elif self.close < self.ema52 and self.open > self.ema52:
-			self.near0_return = 0
-		if self.pre.near0_return == 7:
-			# 向上穿越后的一根价格再EMA52上方 9
-			if self.low > self.ema52 and self.close > self.open:
-				self.near0_return = 9
-		if self.pre.near0_return == 8:
-			# 向下穿越后的一根价格再EMA52下方 10
-			if self.high < self.ema52 and self.close < self.open:
-				self.near0_return = 10
-		# CROSS0 仅以 Signal 穿越零轴判定
-		if self.pre.signal >= 0 and self.signal < 0:
-			self.macd_state = Chan_MACD_STATE.CROSS0_DOWN
-			return self.macd_state
-		if self.pre.signal <= 0 and self.signal > 0:
-			self.macd_state = Chan_MACD_STATE.CROSS0_UP
-			return self.macd_state
-		# 穿零轴后的形态：缠绕/倒挂（基于前一状态为CROSS0_*）
-		if self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP or self.pre.macd_state == Chan_MACD_STATE.CROSS0_DOWN:
-			direction = 1 if self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP else -1
-			hist_same_dir = (self.macdhist * direction) > 0
-			hist_decreasing = abs(self.macdhist) < abs(self.pre.macdhist)
-			lines_tight = abs(self.macd - self.signal) <= 12
-			# 倒挂：能量柱衰减且黄白线相对方向不利/出现反向能量释放
-			if hist_decreasing and (((self.macd - self.signal) * direction) < 0 or not hist_same_dir):
-				self.macd_state = Chan_MACD_STATE.CROSS_REV
-				return self.macd_state
-			# 缠绕/粘合：紧贴能量柱运行，无反向能量释放
-			if lines_tight and hist_same_dir:
-				self.macd_state = Chan_MACD_STATE.CROSS_OS
-				return self.macd_state
-		# 趋近零轴：细化 NEAR0_* 判定
-		NEAR0_EPS = 15
-		lines_near_zero = abs(self.macd) <= NEAR0_EPS or abs(self.signal) <= NEAR0_EPS
-		touch_52 = (self.ema52 != 0) and ((abs(self.close - self.ema52) <= NEAR0_EPS) or (self.low <= self.ema52 <= self.high))
-		touch_24 = (self.ema24 != 0) and ((abs(self.close - self.ema24) <= NEAR0_EPS) or (self.low <= self.ema24 <= self.high))
-		# 完美形态：白线接近零轴 + 价格触碰/轻破EMA52 + 黄线不穿零轴
-		if abs(self.macd) <= NEAR0_EPS and touch_52 and (not (self.pre.signal >= 0 and self.signal < 0)) and (not (self.pre.signal <= 0 and self.signal > 0)):
-			self.macd_state = Chan_MACD_STATE.NEAR0_PERFECT
-			#self.near0_return = 1
-			return self.macd_state
-		# EMA24 附近
-		if lines_near_zero and touch_24:
-			self.macd_state = Chan_MACD_STATE.NEAR0_24
-			#self.near0_return = 2
-			return self.macd_state
-		# EMA52 附近
-		if lines_near_zero and touch_52:
-			self.macd_state = Chan_MACD_STATE.NEAR0_52
-			#self.near0_return = 3
-			return self.macd_state
-		# 白线接近零轴但价格未至EMA52
-		if abs(self.macd) <= NEAR0_EPS and not touch_52:
-			self.macd_state = Chan_MACD_STATE.NEAR0_DIFF
-			#self.near0_return = 4
-			return self.macd_state
-		# 一般近零轴
-		if lines_near_zero or touch_52:
-			self.macd_state = Chan_MACD_STATE.NEAR0
-			#self.near0_return = 5
-			return self.macd_state
-		
-		# 穿零轴后离开零轴
-		if self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP and ((self.macd >= self.pre.macd and self.signal >= self.pre.signal) or (abs(self.macdhist) >= abs(self.pre.macdhist))):
-			self.macd_state = Chan_MACD_STATE.UP
-			return self.macd_state
-		if self.pre.macd_state == Chan_MACD_STATE.CROSS0_DOWN and ((self.macd <= self.pre.macd and self.signal <= self.pre.signal) or (abs(self.macdhist) >= abs(self.pre.macdhist))):
-			self.macd_state = Chan_MACD_STATE.DOWN
-			return self.macd_state
-		if self.pre.macd_state == Chan_MACD_STATE.UP and self.macd > self.pre.macd and self.signal > self.pre.signal:
-			self.macd_state = Chan_MACD_STATE.UP
-			return self.macd_state
-		if self.pre.macd_state == Chan_MACD_STATE.DOWN and self.macd < self.pre.macd and self.signal < self.pre.signal:
-			self.macd_state = Chan_MACD_STATE.DOWN
-			return self.macd_state
-		# 趋势兜底：强势同步上行/下行直接进入 UP/DOWN
-		if self.macd > 0 and self.signal > 0 and (self.macd >= self.pre.macd and self.signal >= self.pre.signal):
-			self.macd_state = Chan_MACD_STATE.UP
-			return self.macd_state
-		if self.macd < 0 and self.signal < 0 and (self.macd <= self.pre.macd and self.signal <= self.pre.signal):
-			self.macd_state = Chan_MACD_STATE.DOWN
-			return self.macd_state
-		# 峰值：白线高位出现局部顶
-		if hasattr(self.pre, 'pre') and self.pre and self.pre.pre and self.macd > 0:
-			if self.pre.macd > self.pre.pre.macd and self.pre.macd > self.macd:
-				self.macd_state = Chan_MACD_STATE.PEAK
-				return self.macd_state
-		# 高位状态的位置状态， 高位，高位空，归零轴
-		if (self.pre.macd_state == Chan_MACD_STATE.UP or self.pre.macd_state == Chan_MACD_STATE.HIGH or self.pre.macd_state == Chan_MACD_STATE.RZ_UP or self.pre.macd_state == Chan_MACD_STATE.PEAK or self.pre.macd_state == Chan_MACD_STATE.HIGH_EMPTY) and self.macd > 0:
-			# 高位空（正区间）：能量柱衰减且黄白线间距较大
-			if abs(self.pre.macdhist) > 0 and abs(self.macdhist) < abs(self.pre.macdhist) and abs(self.macd - self.signal) > 5:
-				self.macd_state = Chan_MACD_STATE.HIGH_EMPTY
-				return self.macd_state
-			if abs(self.pre.macd - self.macd) < 10:
-				self.macd_state = Chan_MACD_STATE.HIGH
-				return self.macd_state
-			else:
-				if self.macd > self.pre.macd and self.signal > self.pre.signal:
-					self.macd_state = Chan_MACD_STATE.UP
-					return self.macd_state
-				elif self.macd < self.pre.macd and self.signal < self.pre.signal:
-					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
-					return self.macd_state
-		if (self.pre.macd_state == Chan_MACD_STATE.DOWN or self.pre.macd_state == Chan_MACD_STATE.HIGH or self.pre.macd_state == Chan_MACD_STATE.RZ_DOWN or self.pre.macd_state == Chan_MACD_STATE.PEAK or self.pre.macd_state == Chan_MACD_STATE.HIGH_EMPTY) and self.macd < 0:
-			# 高位空（负区间）：能量柱衰减且黄白线间距较大
-			if abs(self.pre.macdhist) > 0 and abs(self.macdhist) < abs(self.pre.macdhist) and abs(self.macd - self.signal) > 5:
-				self.macd_state = Chan_MACD_STATE.HIGH_EMPTY
-				return self.macd_state
-			if abs(self.pre.macd - self.macd) < 10:
-				self.macd_state = Chan_MACD_STATE.HIGH
-				return self.macd_state
-			else:
-				if self.macd > self.pre.macd and self.signal > self.pre.signal:
-					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
-					return self.macd_state
-				elif self.macd < self.pre.macd and self.signal < self.pre.signal:
-					self.macd_state = Chan_MACD_STATE.DOWN
-					return self.macd_state
-		
-		# 离开0轴开始上涨或者下跌阶段，高位之前的
-		if self.macd > 0 and self.pre:
-			if (self.pre.macd_state == Chan_MACD_STATE.NEAR0 or self.pre.macd_state == Chan_MACD_STATE.RZ_UP or self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP) and (self.signal > self.pre.signal or self.close > self.ema52):
-				self.macd_state = Chan_MACD_STATE.RZ_UP
-				return self.macd_state
-		elif self.macd < 0 and self.pre:
-			if (self.pre.macd_state == Chan_MACD_STATE.NEAR0 or self.pre.macd_state == Chan_MACD_STATE.RZ_DOWN or self.pre.macd_state == Chan_MACD_STATE.CROSS0_DOWN) and (self.signal < self.pre.signal or self.close < self.ema52):
-				self.macd_state = Chan_MACD_STATE.RZ_DOWN
-				return self.macd_state
-		# 归零轴走势
-		if self.pre.macd_state == Chan_MACD_STATE.RETURN_ZERO:
-			if self.macd > 0:
-				if self.pre.macd > self.macd or abs(self.macdhist) <= abs(self.pre.macdhist) or self.signal <= self.pre.signal:
-					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
-					return self.macd_state
-			else:
-				if self.pre.macd < self.macd or abs(self.macdhist) <= abs(self.pre.macdhist) or self.signal >= self.pre.signal:
-					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
-					return self.macd_state
-		# 从 NEAR0 收敛到零轴的归零轴承接（正负两侧）
-		if self.pre.macd_state == Chan_MACD_STATE.NEAR0:
-			# 正区间朝零轴收敛
-			if self.macd > 0 and self.pre.macd > 0 and self.macd <= self.pre.macd and self.signal <= self.pre.signal:
-				self.macd_state = Chan_MACD_STATE.RETURN_ZERO
-				return self.macd_state
-			# 负区间朝零轴收敛
-			if self.macd < 0 and self.pre.macd < 0 and self.macd >= self.pre.macd and self.signal >= self.pre.signal:
-				self.macd_state = Chan_MACD_STATE.RETURN_ZERO
-				return self.macd_state
-		# 其余情况
-		if self.pre.macd_state == Chan_MACD_STATE.UNKNOWN:
-			if self.macd > 0 and self.close > self.ema52 and self.pre.pre and (self.pre.pre.macd_state == Chan_MACD_STATE.UP or self.pre.pre.macd_state == Chan_MACD_STATE.RZ_UP):
-				self.macd_state = self.pre.pre.macd_state
-				return self.macd_state
-			elif self.macd < 0 and self.close < self.ema52 and self.pre.pre and (self.pre.pre.macd_state == Chan_MACD_STATE.DOWN or self.pre.pre.macd_state == Chan_MACD_STATE.RZ_DOWN):
-				self.macd_state = self.pre.pre.macd_state
-				return self.macd_state
-			else:
-				self.macd_state = Chan_MACD_STATE.UNKNOWN
-				return self.macd_state
-		return self.macd_state
-	
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanKLU import *  # noqa: F403

ChanLun.py

@@ -1,174 +1,3 @@
-import warnings
-
-# 抑制 Docker 内 technical.util 的 fillna/ffill/bfill 的 pandas FutureWarning（pandas 2.x 弃用 object 静默 downcast）
-warnings.filterwarnings(
-    "ignore",
-    category=FutureWarning,
-    message=".*Downcasting object dtype arrays on \\.fillna.*",
-)
-
-from datetime import timedelta
-from pandas import DataFrame
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX, Chan_MACD_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN
-from ChanKLU import ChanKLU
-from ChanKLC import ChanKLC
-from ChanBI import ChanBI
-from ChanSBI import ChanSBI
-from ChanSEG import ChanSEG
-from ChanZS import ChanZS
-from ChanBSP import ChanBSP
-import talib.abstract as ta
-import pandas as pd
-from technical.util import resample_to_interval
-from decimal import Decimal
-import numpy as np
-from ChanMACD import ChanMACD
-from TF_DF import TF_DF
-
-class ChanLun():
-	def __init__(self):
-		self.time2m = 2
-		self.time3m = 3
-		self.time5m = 5
-		self.time10m = 10
-		self.time20m = 20
-		self.time_m_intervals = [2, 3, 5, 10, 20]
-		self.time_m_symbols = ['2m', '3m', '5m', '10m', '20m']
-		self.time30m = 30
-		self.time45m = 45
-		self.time_m15_intervals = [30, 45]
-		self.time_m15_symbols = ['30m', '45m']
-		self.time2h = 2*60
-		self.time4h = 4*60
-		self.time6h = 6*60
-		self.time8h = 8*60
-		self.time12h = 12*60
-		self.time16h = 16*60
-		self.time_h_intervals = [2*60, 4*60, 6*60, 8*60, 12*60, 16*60]
-		self.time_h_symbols = ['2h', '4h', '6h', '8h', '12h', '16h']
-		self.time2d = 2*24*60
-		self.time3d = 3*24*60
-		self.time_d_intervals = [2*24*60, 3*24*60]
-		self.time_d_symbols	 = ['2d', '3d']
-		self.time1w = 7*24*60
-		self.time2w = 14*24*60
-		self.time_w_intervals = [14*24*60]
-		self.time_w_symbols = ['2w']
-		self.time2M = 2*30*24*60
-		self.time3M = 3*30*24*60
-		self.time6M = 6*30*24*60
-		self.time1y = 12*30*24*60
-		self.time_M_intervals = [2*30*24*60, 3*30*24*60, 6*30*24*60, 12*30*24*60]
-		self.time_M_symbols	 = ['2M', '3M', '6M', '1y']
-		self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
-		self.tf_df_dict = {}
-		self.ema_symbols = ['5m', '15m', '30m', '45m', '1h', '2h', '4h', '8h', '12h', '1d', '2d', '3d']
-		self.tf_df = TF_DF()
-	def init_data(self, dataframe, intervals, timeframes):
-		for index in range(0, len(intervals)):
-			timeframe = timeframes[index]
-			interval = intervals[index]
-			self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
-	def init_dataframes(self, dataframe_m=None, dataframe_15m=None, dataframe_h=None, dataframe_d=None, dataframe_w=None, dataframe_M=None):
-		self.tf_df_dict = {}
-		if dataframe_m is not None:
-			self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
-			self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
-		if dataframe_15m is not None:
-			self.tf_df_dict['15m'] = TF_DF(dataframe_15m, 1, '15m')
-			self.init_data(dataframe_15m, self.time_m15_intervals, self.time_m15_symbols)
-		if dataframe_h is not None:
-			self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h')
-			self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols)
-		if dataframe_d is not None:
-			self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
-			self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
-		if dataframe_w is not None and False:
-			self.tf_df_dict['1w'] = TF_DF(dataframe_w, 1, '1w')
-			self.init_data(dataframe_w, self.time_w_intervals, self.time_w_symbols)
-		if dataframe_M is not None and False:
-			self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
-			self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
-	def get_ema52_dict(self):
-		if len(self.tf_df_dict) > 0:
-			return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols}
-		return None
-	def get_ema24_dict(self):
-		if len(self.tf_df_dict) > 0:
-			return {key: self.tf_df_dict[key].get_ema24() for key in self.ema_symbols}
-		return None
-	def get_current_klc_dict(self):
-		if len(self.tf_df_dict) > 0:
-			return {key: self.tf_df_dict[key].get_current_klc() for key in self.ema_symbols}
-		return None
-	def get_tf_df_by_timeframe(self, timeframe):
-		if timeframe in self.tf_df_dict:
-			return self.tf_df_dict[timeframe]
-		return None
-	def check_price_ema52(self, price):
-		key_list = []
-		if len(self.tf_df_dict) > 0:
-			ema52_dict = self.get_ema52_dict()
-			for key in self.ema_symbols:
-				if ema52_dict[key] is not None:
-					if abs(price - ema52_dict[key]) < 100:
-						key_list.append(key)
-		return key_list
-	def get_ema_bsp(self, long_tf='1h', short_tf='15m'):
-		if long_tf in self.tf_df_dict and short_tf in self.tf_df_dict:
-			long_df = self.tf_df_dict[long_tf]
-			short_df = self.tf_df_dict[short_tf]
-			return long_df.get_ema_bsp(short_df)
-		return None
-
-
- 
- 
- 
- 
- 	# TF_DF methods ------------------------------------------
-	def get_ema_state(self, dataframe):
-		return self.tf_df.get_ema_state(dataframe)
-	def get_klu_state(self, dataframe):
-		return self.tf_df.get_klu_state(dataframe)
-	def check_fx(self, klc):
-		return self.tf_df.check_fx(klc)
-	def add_indicators1(self, df):
-		return self.tf_df.add_indicators(df)
-	def get_bi_list(self, dataframe):
-		return self.tf_df.get_bi_list(dataframe)
-	def get_kl_data(self, dataframe:DataFrame):
-		return self.tf_df.cal_kl_data(dataframe)
-	def cal_volume_ratio(self, dataframe, window=10):
-		return self.tf_df.cal_volume_ratio(dataframe, window)
-	def calculate_seg_zs(self, bi_list, seg_list):
-		return self.get_seg_zs_list(bi_list, seg_list)
-	def get_seg_list(self, bi_list):
-		return self.tf_df.get_seg_list(bi_list)
-	def cal_trend(self, klc_list):
-		return self.tf_df.cal_trend(klc_list)
-	def check_top_fx(self, last_bottom, klc):
-		return self.tf_df.check_top_fx(last_bottom, klc)
-	def check_bottom_fx(self, last_top, klc):
-		return self.tf_df.check_bottom_fx(last_top, klc)
-	def cal_bi_list(self, klc_list):
-		return self.tf_df.cal_bi_list(klc_list)
-	def find_first_bsp(self, bi_list, bi_zs_list):
-		return self.tf_df.find_first_bsp(bi_list, bi_zs_list)
-	def find_second_bsp(self, bi_list, first_bsp_list):
-		return self.tf_df.find_second_bsp(bi_list, first_bsp_list)
-	def find_all_bsp(self, bi_list, bi_zs_list):
-		return self.tf_df.find_all_bsp(bi_list, bi_zs_list)
-	def get_zs_list(self, bi_list, seg_list):
-		return self.tf_df.get_zs_list(bi_list, seg_list)
-	def cal_bi_zs(self, seg_list):
-		return self.tf_df.cal_bi_zs(seg_list)
-	def cal_bi_zs_list(self, bi_list):
-		#return self.tf_df.cal_bi_zs(bi_list)
-		return self.tf_df.cal_bi_zs_list(bi_list)
-	def get_decimal(self, value):
-		return Decimal("{:.2f}".format(value))
-	def get_klc_list(self, klu_list):
-		return self.tf_df.get_klc_list(klu_list)
-	def get_klu_list(self, dataframe):
-		return self.tf_df.cal_klu_pattern(self.get_kl_data(dataframe))
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.pipeline.orchestrator import ChanLun  # noqa: F401
+from chanlun.pipeline.timeframe import TF_DF  # noqa: F401

ChanLun_Classifier.py

@@ -1,529 +1,2 @@
-import sys 
-import os
-#sys.setrecursionlimit(1000000) #例如这里设置为一百万
-#sys.path.append(os.path.abspath("/freqtrade/user_data/Chan"))
-sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
-import numpy as np
-from datetime import timedelta
-from pandas import DataFrame
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX
-from ChanKLU import ChanKLU
-from ChanKLC import ChanKLC
-from ChanBI import ChanBI
-from ChanSBI import ChanSBI
-from ChanSEG import ChanSEG
-from ChanZS import ChanZS
-from ChanBSP import ChanBSP
-import talib.abstract as ta
-import pandas as pd
-import matplotlib.pyplot as plt
-from matplotlib.dates import DateFormatter, date2num
-import matplotlib.patches as patches
-from technical.util import resample_to_interval
-from decimal import Decimal
-from ChanLun import ChanLun
-import xgboost as xgb
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report
-
-class ChanLunClassifier:
-    def __init__(self, dataframe: DataFrame):
-        self.dataframe = dataframe
-        self.model = None
-    chan = ChanLun()
-
-    def train_model(self, dataframe=None, data_file_path=None, model_file_path='chan_xgb_model.json', use_cv=False, custom_params=None, model_name=None):
-        """
-        使用dataframe前80%的数据训练XGBoost模型
-        :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
-        :param data_file_path: 特征数据保存路径，可选
-        :param model_file_path: 模型保存路径
-        :param use_cv: 是否使用交叉验证寻找最佳参数
-        :param custom_params: 自定义模型参数
-        :return: 训练好的模型
-        """
-        if dataframe is None:
-            dataframe = self.dataframe
-        
-        # 分割数据集，前80%用于训练
-        train_size = int(len(dataframe) * 0.8)
-        train_df = dataframe.iloc[:train_size].copy()
-        
-        # 获取训练集特征和标签
-        save_csv = True if data_file_path else False
-        X_train, y_train = self.get_feature_data(train_df, save_csv=save_csv, csv_path=data_file_path if data_file_path else 'feature_data.csv')
-        
-        if len(X_train) == 0:
-            print("没有提取到足够的特征数据进行训练")
-            return None
-            
-        # 保存特征数据的步骤已经移到get_feature_data方法中处理
-        # 以下是原有代码
-        #{'eta': 0.03, 'max_depth': 4, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0.1, 'min_child_weight': 3, 'alpha': 1, 'lambda': 3},    
-        # 默认XGBoost参数
-        default_params = {
-            'objective': 'binary:logistic',
-            'max_depth': 8,
-            'eta': 0.01,
-            'subsample': 0.8,
-            'colsample_bytree': 0.8,
-            'eval_metric': 'auc',
-            'gamma': 0.0,
-            'min_child_weight': 1,
-            'alpha': 0, # L1正则化
-            'lambda': 0.5, # L2正则化
-            'scale_pos_weight': 1
-        }
-        
-        # 使用自定义参数覆盖默认参数
-        if custom_params:
-            for key, value in custom_params.items():
-                default_params[key] = value
-        
-        params = default_params
-        dtrain = xgb.DMatrix(X_train, label=y_train)
-        
-        # 如果使用交叉验证寻找最佳参数
-        if use_cv:
-            from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
-            from sklearn.metrics import make_scorer, accuracy_score, f1_score
-            import numpy as np
-            
-            # 转换为sklearn兼容格式
-            xgb_model = xgb.XGBClassifier(
-                objective=params['objective'],
-                max_depth=params['max_depth'],
-                learning_rate=params['eta'],
-                subsample=params['subsample'],
-                colsample_bytree=params['colsample_bytree'],
-                gamma=params['gamma'],
-                min_child_weight=params['min_child_weight'],
-                reg_alpha=params['alpha'],
-                reg_lambda=params['lambda'],
-                scale_pos_weight=params['scale_pos_weight'],
-                use_label_encoder=False,
-                eval_metric='auc'
-            )
-            
-            # 参数网格
-            param_grid = {
-                'max_depth': [3, 5, 7, 9],
-                'learning_rate': [0.01, 0.05, 0.1, 0.2],
-                'subsample': [0.6, 0.8, 1.0],
-                'colsample_bytree': [0.6, 0.8, 1.0],
-                'min_child_weight': [1, 3, 5],
-                'gamma': [0, 0.1, 0.2],
-                'n_estimators': [50, 100, 200]
-            }
-            
-            # 使用随机搜索寻找最佳参数（比网格搜索快）
-            random_search = RandomizedSearchCV(
-                estimator=xgb_model,
-                param_distributions=param_grid,
-                n_iter=10,  # 随机尝试的参数组合数
-                scoring=make_scorer(f1_score),
-                cv=5,
-                verbose=1,
-                n_jobs=-1,
-                random_state=42
-            )
-            
-            print("进行交叉验证参数搜索...")
-            random_search.fit(X_train, y_train)
-            
-            # 获取最佳参数
-            best_params = random_search.best_params_
-            print(f"最佳参数: {best_params}")
-            
-            # 使用最佳参数更新模型参数
-            params['max_depth'] = best_params['max_depth']
-            params['eta'] = best_params['learning_rate']
-            params['subsample'] = best_params['subsample']
-            params['colsample_bytree'] = best_params['colsample_bytree']
-            params['min_child_weight'] = best_params['min_child_weight']
-            params['gamma'] = best_params['gamma']
-            num_round = best_params['n_estimators']
-
-            # 使用最佳参数训练最终模型
-            self.model = xgb.train(params, dtrain, num_round)
-        else:
-            # 标准训练（不使用交叉验证）
-            # 使用早停机制避免过拟合
-            # 分割训练集为训练和验证
-            eval_size = int(len(X_train) * 0.2)
-            X_eval = X_train[-eval_size:]
-            y_eval = y_train[-eval_size:]
-            X_train_part = X_train[:-eval_size]
-            y_train_part = y_train[:-eval_size]
-            
-            dtrain_part = xgb.DMatrix(X_train_part, label=y_train_part)
-            deval = xgb.DMatrix(X_eval, label=y_eval)
-            
-            # 评估列表
-            evallist = [(dtrain_part, 'train'), (deval, 'eval')]
-            
-            # 训练模型，使用早停
-            num_round = 1000  # 设置较大的轮数，让早停机制决定何时停止
-            self.model = xgb.train(
-                params, 
-                dtrain_part, 
-                num_round, 
-                evallist, 
-                early_stopping_rounds=50, # 50轮内评估指标无改善则停止
-                verbose_eval=True
-            )
-            
-            # 使用全部训练数据重新训练最终模型，使用最佳轮数
-            # best_rounds = self.model.best_ntree_limit
-            # 兼容新版本的XGBoost
-            if hasattr(self.model, 'best_ntree_limit'):
-                best_rounds = self.model.best_ntree_limit
-            elif hasattr(self.model, 'best_iteration'):
-                best_rounds = self.model.best_iteration
-            elif hasattr(self.model, 'best_ntree_idx'):
-                best_rounds = self.model.best_ntree_idx
-            else:
-                # 如果都不存在，使用默认值
-                best_rounds = num_round
-            print(f"最佳轮数: {best_rounds}")
-            
-            # 使用全部训练数据和最佳轮数训练最终模型
-            self.model = xgb.train(params, dtrain, best_rounds)
-        
-        # 保存模型
-        if model_file_path:
-            self.model.save_model(model_name + model_file_path)
-            
-        # 特征重要性分析
-        if hasattr(self.model, 'get_score'):
-            importance = self.model.get_score(importance_type='gain')
-            print("\n特征重要性 (gain):")
-            for key, value in sorted(importance.items(), key=lambda x: x[1], reverse=True):
-                print(f"{key}: {value}")
-            
-        return self.model
-    def load_model(self, model_name=None, model_file_path='chan_xgb_model.json'):
-        if model_name:
-            self.model = xgb.Booster()
-            self.model.load_model(model_name + model_file_path)
-        else:
-            self.model = xgb.Booster()
-            self.model.load_model(model_file_path)
-    def find_best_params(self, dataframe=None, save_csv=False, csv_path_prefix='param_', model_name=None):
-        """
-        寻找最佳参数组合
-        :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
-        :param save_csv: 是否保存特征数据到CSV文件
-        :param csv_path_prefix: CSV文件保存路径前缀，会自动添加参数信息
-        :return: 最佳参数
-        """
-        # 不同参数组合
-        param_combinations = [
-            # 低学习率，深树
-            {'eta': 0.01, 'max_depth': 8, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'min_child_weight': 1},
-            # 中等学习率，中等树深度
-            {'eta': 0.05, 'max_depth': 5, 'subsample': 0.7, 'colsample_bytree': 0.7, 'gamma': 0.1, 'min_child_weight': 3},
-            # 高学习率，浅树
-            {'eta': 0.1, 'max_depth': 3, 'subsample': 0.6, 'colsample_bytree': 0.6, 'gamma': 0.2, 'min_child_weight': 5},
-            # 正则化较强 best here
-            {'eta': 0.03, 'max_depth': 4, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0.1, 'min_child_weight': 3, 'alpha': 1, 'lambda': 3},
-            # 正则化较弱
-            {'eta': 0.08, 'max_depth': 6, 'subsample': 0.9, 'colsample_bytree': 0.9, 'gamma': 0, 'min_child_weight': 1, 'alpha': 0, 'lambda': 0.5},
-        ]
-        
-        best_score = 0
-        best_params = None
-        best_model = None
-        
-        for i, params in enumerate(param_combinations):
-            print(f"\n尝试参数组合: {params}")
-            # 生成CSV文件名，包含一些参数信息
-            param_info = f"eta{params['eta']}_depth{params['max_depth']}"
-            train_csv_path = f"{csv_path_prefix}train_{param_info}.csv" if save_csv else None
-            
-            model = self.train_model(dataframe=dataframe, data_file_path=train_csv_path, custom_params=params, model_name=model_name)
-            
-            # 分割数据集，后20%用于测试
-            if dataframe is None:
-                dataframe = self.dataframe
-            
-            train_size = int(len(dataframe) * 0.8)
-            test_df = dataframe.iloc[train_size:].copy()
-            
-            # 获取测试集特征和标签
-            test_csv_path = f"{csv_path_prefix}test_{param_info}.csv" if save_csv else None
-            X_test, y_test = self.get_validate_feature_data(test_df, save_csv=save_csv, csv_path=test_csv_path)
-            
-            if len(X_test) == 0:
-                print("没有提取到足够的测试特征数据")
-                continue
-            
-            # 预测
-            dtest = xgb.DMatrix(X_test)
-            y_pred_prob = model.predict(dtest)
-            y_pred = [1 if p > 0.5 else 0 for p in y_pred_prob]
-            
-            # 计算F1分数
-            f1 = f1_score(y_test, y_pred, zero_division=0)
-            print(f"F1分数: {f1:.4f}")
-            
-            if f1 > best_score:
-                best_score = f1
-                best_params = params
-                best_model = model
-                
-        print(f"\n最佳参数组合 (F1={best_score:.4f}):")
-        print(best_params)
-        self.model = best_model
-        
-        return best_params
-    
-    def get_feature_data(self, dataframe, save_csv=False, csv_path='feature_data.csv'):
-        """
-        从dataframe提取特征数据
-        :param dataframe: 输入的DataFrame
-        :param save_csv: 是否保存特征数据到CSV文件
-        :param csv_path: CSV文件保存路径
-        :return: 特征矩阵X和标签y
-        """
-        # 使用ChanLun获取bi_list
-        klc_list = self.chan.get_klc_list(dataframe)
-        bi_list = self.chan.cal_bi_list(klc_list)
-        # 筛选方向为UP的bi的起始klc
-        feature_data = []
-        labels = []
-        feature_keys = []  # 用于保存特征名称
-        
-        bi_index = 1
-        sample_list = []
-        for klc in klc_list:
-            if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
-                sample_list.append(klc)
-        klc_count = 0
-        print('Processing data...')
-        for klc in sample_list:
-            if bi_index >= len(bi_list):
-                bi_index = len(bi_list) - 1
-            #bi = bi_list[bi_index]
-            #if klc.end_klu and bi.end_klc and klc.start_klu.index >= bi.start_klc.start_klu.index and klc.end_klu.index <= bi.end_klc.end_klu.index:
-                #klc.set_bi(bi)
-            
-            # 提取特征
-            features = klc.get_feature_data()
-            
-            # 保存第一个样本的特征名称，用于CSV列名
-            if len(feature_keys) == 0:
-                feature_keys = list(features.keys())
-            # 将特征转换为模型可用的格式
-            feature_vec = []
-            for key, value in features.items():
-                if isinstance(value, (int, float)):
-                    feature_vec.append(value)
-                else:
-                    feature_vec.append(0)
-            
-            # 判断这个bi是否赚钱（这里简单定义为：如果bi的结束价格高于起始价格，则标记为1，否则为0）
-            # 这个标签定义可以根据实际需求修改
-            matched = False
-            for bi in bi_list:
-                if bi.end_klc and bi.end_klc.index == klc.index:
-                    #print(bi.start_time, bi.start_klc.start_time, bi.dir)
-                    label = 1
-                    matched = True
-                    break
-            if not matched:
-                label = 0
-            
-            feature_data.append(feature_vec)
-            labels.append(label)
-            klc_count += 1
-            percent = klc_count/len(sample_list)*100
-            if percent % 10 == 0:
-                print('Data processed:', percent, '%')
-        for index, key in enumerate(feature_keys):
-            print(index, key, feature_data[0][index])
-        # 如果需要保存到CSV
-        if save_csv:
-            # 创建DataFrame保存特征数据
-            # 只保留数值型特征
-            numeric_feature_keys = [key for i, key in enumerate(feature_keys) 
-                                   if i < len(feature_data[0]) if isinstance(feature_data[0][i], (int, float))]
-            
-            # 创建特征数据的DataFrame
-            df_features = pd.DataFrame(feature_data, columns=numeric_feature_keys)
-            # 添加标签列
-            df_features['label'] = labels
-            # 添加时间信息便于分析
-            if len(sample_list) > 0:
-                times = [klc.start_time for klc in sample_list]
-                df_features['time'] = times
-            
-            # 保存到CSV
-            df_features.to_csv(csv_path, index=False)
-            print(f"特征数据已保存到 {csv_path}")
-            
-        # 在return前添加
-        positive_count = np.sum(labels)
-        print(f"正样本数量: {positive_count}, 负样本数量: {len(labels) - positive_count}")
-        print("Trainning data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
-        return np.array(feature_data), np.array(labels)
-    def get_validate_feature_data(self, dataframe, save_csv=False, csv_path='validate_feature_data.csv'):
-        """
-        从dataframe提取特征数据
-        :param dataframe: 输入的DataFrame
-        :param save_csv: 是否保存特征数据到CSV文件
-        :param csv_path: CSV文件保存路径
-        :return: 特征矩阵X和标签y
-        """
-        # 使用ChanLun获取bi_list
-        klc_list = self.chan.get_klc_list(dataframe)
-        bi_list = self.chan.cal_bi_list(klc_list)
-        seg_list = self.chan.get_seg_list(bi_list)
-        # 筛选方向为UP的bi的起始klc
-        feature_data = []
-        labels = []
-        feature_keys = []  # 用于保存特征名称
-        
-        bi_index = 1
-        sample_list = []
-        for klc in klc_list:
-            if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
-                sample_list.append(klc)
-        for klc in sample_list:
-            if bi_index >= len(bi_list):
-                bi_index = len(bi_list) - 1
-            bi = bi_list[bi_index]
-            # 提取特征
-            features = klc.get_feature_data()
-            
-            # 保存第一个样本的特征名称，用于CSV列名
-            if len(feature_keys) == 0:
-                feature_keys = list(features.keys())
-            
-            # 将特征转换为模型可用的格式
-            feature_vec = []
-            # 与get_feature_data保持一致，只使用相同的特征集
-            for key, value in features.items():
-                if isinstance(value, (int, float)):
-                    feature_vec.append(value)
-                else:
-                    feature_vec.append(0)
-            seg = seg_list[bi_index]
-            matched = False
-            for bi in bi_list:
-                if bi.end_klc and bi.end_klc.index == klc.index:
-                    label = 1
-                    matched = True
-                    break
-            if not matched:
-                label = 0
-            
-            feature_data.append(feature_vec)
-            labels.append(label)
-            
-        # 如果需要保存到CSV
-        if save_csv:
-            # 创建DataFrame保存特征数据
-            # 只保留数值型特征
-            numeric_feature_keys = [key for i, key in enumerate(feature_keys) 
-                                  if i < len(feature_data[0]) if isinstance(feature_data[0][i], (int, float))]
-            
-            # 创建特征数据的DataFrame
-            df_features = pd.DataFrame(feature_data, columns=numeric_feature_keys)
-            # 添加标签列
-            df_features['label'] = labels
-            # 添加时间信息便于分析
-            if len(sample_list) > 0:
-                times = [klc.start_time for klc in sample_list]
-                df_features['time'] = times
-            
-            # 保存到CSV
-            df_features.to_csv(csv_path, index=False)
-            print(f"验证特征数据已保存到 {csv_path}")
-            
-        print("Validating data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
-        return np.array(feature_data), np.array(labels)
-    def validate_model(self, dataframe=None, save_csv=False, csv_path='validate_feature_data.csv'):
-        """
-        使用dataframe后20%的数据验证模型
-        :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
-        :param save_csv: 是否保存特征数据到CSV文件
-        :param csv_path: CSV文件保存路径
-        :return: 验证结果
-        """
-        if self.model is None:
-            print("模型尚未训练，请先调用train_model方法")
-            return None
-            
-        if dataframe is None:
-            dataframe = self.dataframe
-        
-        # 分割数据集，后20%用于测试
-        train_size = int(len(dataframe) * 0.8)
-        test_df = dataframe.iloc[train_size:].copy()
-        
-        # 获取测试集特征和标签
-        X_test, y_test = self.get_validate_feature_data(test_df, save_csv=save_csv, csv_path=csv_path)
-        
-        if len(X_test) == 0:
-            print("没有提取到足够的测试特征数据")
-            return None
-        
-        # 预测
-        dtest = xgb.DMatrix(X_test)
-        y_pred_prob = self.model.predict(dtest)
-        y_pred = [1 if p > 0.5 else 0 for p in y_pred_prob]
-        
-        # 计算评估指标
-        accuracy = accuracy_score(y_test, y_pred)
-        precision = precision_score(y_test, y_pred, zero_division=0)
-        recall = recall_score(y_test, y_pred, zero_division=0)
-        f1 = f1_score(y_test, y_pred, zero_division=0)
-        
-        # 打印评估报告
-        print("模型评估结果:")
-        print(f"准确率: {accuracy:.4f}")
-        print(f"精确率: {precision:.4f}")
-        print(f"召回率: {recall:.4f}")
-        print(f"F1分数: {f1:.4f}")
-        print("\n分类报告:")
-        print(classification_report(y_test, y_pred, zero_division=0))
-        
-        return {
-            'accuracy': accuracy,
-            'precision': precision,
-            'recall': recall,
-            'f1': f1,
-            'y_test': y_test,
-            'y_pred': y_pred,
-            'y_pred_prob': y_pred_prob
-        }
-    
-    def predict(self, klc):
-        """
-        使用训练好的模型预测单个KLC
-        :param klc: 需要预测的ChanKLC对象
-        :return: 预测结果（概率值）
-        """
-        if self.model is None:
-            print("模型尚未训练，请先调用train_model方法")
-            return None
-            
-        # 提取特征
-        features = klc.get_feature_data()
-        feature_vec = []
-        # 与get_feature_data保持一致，只使用相同的特征集
-        for key, value in features.items():
-            if isinstance(value, (int, float)):
-                feature_vec.append(value)
-            else:
-                feature_vec.append(0)
-                
-        # 转换为模型输入格式
-        dtest = xgb.DMatrix(np.array([feature_vec]))
-        
-        # 预测
-        return self.get_decimal(self.model.predict(dtest)[0])
-    
-    def get_decimal(self, value):
-        return Decimal("{:.4f}".format(value))
-    
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.ChanLun_Classifier import *  # noqa: F403

ChanMACD.py

@@ -1,274 +1,2 @@
-from ChanKLU import ChanKLU
-from ChanEnum import Chan_MACD_STATE, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIR, Chan_MACDUNITTF_TYPE
-from ChanMACDSeg import ChanMACDSeg
-from ChanMACDUnitTF import ChanMACDUnitTF
-from ChanMACDHistSet import ChanMACDHistSet
-
-class ChanMACD():
-	def __init__(self, klu_list: list[ChanKLU]):
-		self.klu_list = klu_list
-		self.seg_list = []
-		self.unittf_list = []
-		self.histset_list = []
-		# 状态标记列表
-		self.high_position_list = []      # 高位列表
-		self.high_empty_list = []         # 高位空列表
-		self.return_zero_list = []        # 归零轴列表
-		self.cross0_up_list = []          # 向上穿越零轴列表
-		self.cross0_down_list = []        # 向下穿越零轴列表
-		# 计算段 / UnitTF / HistSet 及状态标记
-		self.cal_macd_state()
-		self.get_klu_sd_list()
-	def get_klu_sd(self):
-		if self.klu_list:
-			sd = self.klu_list[-1].separate_div
-			if sd > 1:
-				print(self.klu_list[-1].time, sd)
-				return True
-		return False
-	def get_klu_sd_list(self):
-		sd_list = []
-		if self.klu_list:
-			for klu in self.klu_list:
-				hist = klu.macdhist
-				singal = False
-				if klu.pre and klu.next:
-					if klu.signal > 0:
-						signal = klu.pre.signal > klu.signal and klu.next.signal < klu.signal
-					else:
-						signal = klu.pre.signal < klu.signal and klu.next.signal > klu.signal
-				sd = klu.separate_div
-				if sd > 1 and ((hist > 0 and hist < 200) or (hist < 0 and hist > -200)):
-					sd_list.append(klu.time)
-					#print(klu.time, sd)
-		return sd_list
-	def cal_macd_state(self):
-		last_seg = None
-		last_unittf = None
-		last_histset = None
-		last_klu = None
-		for klu in self.klu_list:
-			# initialise first histset
-			if klu.macd == 0 and klu.signal == 0 and klu.macdhist == 0:
-				continue
-			if last_histset is None:
-				if klu.macdhist > 0:
-					last_histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.ABOVE)
-					self.histset_list.append(last_histset)
-				else:
-					last_histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.UNDER)
-					self.histset_list.append(last_histset)
-			else:
-				# initialise first seg and unittf
-				if last_seg is None:
-					# create histset afterwards
-					if last_histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
-						if klu.macdhist > 0:
-							last_histset.add_klu(klu)
-						else:
-							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.UNDER)
-							self.histset_list.append(histset)
-							last_histset.set_next(histset)
-							histset.set_pre(last_histset)
-							last_histset.set_end_klu(last_klu)
-							last_histset = histset
-					else:
-						if klu.macdhist < 0:
-							last_histset.add_klu(klu)
-						else:
-							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE)
-							self.histset_list.append(histset)
-							last_histset.set_next(histset)
-							histset.set_pre(last_histset)
-							last_histset.set_end_klu(last_klu)
-							last_histset = histset
-					if last_klu.signal >= 0 and klu.signal < 0:
-						last_unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, None, Chan_MACDUNITTF_DIR.UNDER, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
-						self.unittf_list.append(last_unittf)
-						last_seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, None, Chan_MACDSEG_DIR.UNDER, last_unittf)
-						self.seg_list.append(last_seg)
-					elif last_klu.signal <= 0 and klu.signal > 0:
-						last_unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, None, Chan_MACDUNITTF_DIR.ABOVE, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
-						self.unittf_list.append(last_unittf)
-						last_seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, None, Chan_MACDSEG_DIR.ABOVE, last_unittf)
-						self.seg_list.append(last_seg)
-				# after the first seg and unittf
-				else:
-					# create histset afterwards
-					if last_histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
-						if klu.macdhist > 0:
-							last_histset.add_klu(klu)
-						else:
-							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.UNDER)
-							self.histset_list.append(histset)
-							last_histset.set_next(histset)
-							histset.set_pre(last_histset)
-							last_histset.set_end_klu(last_klu)
-							last_histset = histset
-							if last_unittf:
-								last_unittf.add_histset(last_histset)
-					else:
-						if klu.macdhist < 0:
-							last_histset.add_klu(klu)
-						else:
-							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE)
-							self.histset_list.append(histset)
-							last_histset.set_next(histset)
-							histset.set_pre(last_histset)
-							last_histset.set_end_klu(last_klu)
-							last_histset = histset
-							if last_unittf:
-								last_unittf.add_histset(last_histset)
-					if last_klu.signal >= 0 and klu.signal < 0:
-						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.CROSS0)
-						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, Chan_MACDUNITTF_DIR.UNDER, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
-						self.unittf_list.append(unittf)
-						last_unittf.set_next(unittf)
-						last_seg.set_end_klu(last_klu)
-						seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, last_seg, Chan_MACDSEG_DIR.UNDER, unittf)
-						self.seg_list.append(seg)
-						last_seg.set_next(seg)
-						last_seg = seg
-						last_unittf = unittf
-					elif last_klu.signal <= 0 and klu.signal > 0:
-						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.CROSS0)
-						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, Chan_MACDUNITTF_DIR.ABOVE, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
-						self.unittf_list.append(unittf)
-						last_unittf.set_next(unittf)
-						last_seg.set_end_klu(last_klu)
-						seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, last_seg, Chan_MACDSEG_DIR.ABOVE, unittf)
-						self.seg_list.append(seg)
-						last_seg.set_next(seg)
-						last_seg = seg
-						last_unittf = unittf
-					elif last_unittf.is_end and last_klu.macd < klu.macd and klu.macd > klu.signal:
-						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, Chan_MACDUNITTF_DIR.ABOVE, Chan_MACDUNITTF_TYPE.NEAR0, last_histset)
-						self.unittf_list.append(unittf)
-						last_unittf.set_next(unittf)
-						last_seg.add_unittf(unittf)
-						last_unittf = unittf
-						last_seg.add_klu(klu)
-					else:
-						if not last_unittf.is_end:
-							last_unittf.add_klu(klu)
-						last_seg.add_klu(klu)
-			last_klu = klu
-			klu.cal_macd_state()
-			#print(klu.time, klu.macd_state, klu.continue_div, klu.separate_div, klu.macd, klu.signal, klu.macdhist, klu.ema24, klu.ema52, klu.close)  
-		return self.klu_list         
-	def cal_macd(self):
-		last_seg = None
-		last_unittf = None
-		last_histset = None
-		histset = None
-		last_klu = None
-		for klu in self.klu_list:          
-			klu.cal_macd_state()
-			print(klu.time, klu.macd_state)
-			# 1) 只有当 MACD 已可用(非 UNKNOWN)时，才开始初始化段/单元
-			if last_seg is None:
-				if klu.macd_state != Chan_MACD_STATE.UNKNOWN:
-					# 初始化首个直方图集合（根据当前柱体正负）
-					if klu.macdhist >= 0:
-						histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.ABOVE)
-					else:
-						histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.UNDER)
-					self.histset_list.append(histset)
-					last_histset = histset
-					
-					# 初始化首段
-					seg_dir = Chan_MACDSEG_DIR.ABOVE if klu.signal >= 0 else Chan_MACDSEG_DIR.UNDER
-					seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, None, seg_dir, last_unittf)
-					self.seg_list.append(seg)
-					last_seg = seg
-
-					# 初始化首个UnitTF
-					unittf_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
-					unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, None, unittf_dir, Chan_MACDUNITTF_TYPE.START, histset)
-					self.unittf_list.append(unittf)
-					last_unittf = unittf
-					last_seg.add_unittf(unittf)
-				# 未就绪则继续等下一根；已就绪亦已完成首个结构初始化，继续下一根
-				last_klu = klu
-				continue
-			 # 3) 直方图集合（基于当前 unittf）
-			if klu.macdhist >= 0:
-				if last_histset and last_histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
-					last_histset.add_klu(klu)
-				else:
-					# 结束旧 histset（以前一根结束更合理）
-					if last_histset and last_klu:
-						last_histset.set_end_klu(last_klu)
-					histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE if klu.macdhist >= 0 else Chan_MACDHISTSET_DIR.UNDER)
-					self.histset_list.append(histset)
-					if last_histset:
-						last_histset.set_next(histset)
-					last_histset = histset
-					if last_unittf:
-						last_unittf.add_histset(histset)
-			else:
-				if last_histset and last_histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER:
-					last_histset.add_klu(klu)
-				else:
-					# 结束旧 histset（以前一根结束更合理）
-					if last_histset and last_klu:
-						last_histset.set_end_klu(last_klu)
-					histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE if klu.macdhist >= 0 else Chan_MACDHISTSET_DIR.UNDER)
-					self.histset_list.append(histset)
-					if last_histset:
-						last_histset.set_next(histset)
-					last_histset = histset
-					if last_unittf:
-						last_unittf.add_histset(histset)
-			# 2) 过零切段（使用KLU中的穿越状态）
-			if (klu.macd_state == Chan_MACD_STATE.CROSS0_UP or 
-				klu.macd_state == Chan_MACD_STATE.CROSS0_DOWN):
-				# 结束旧 unittf
-				last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.CROSS0)
-				# 新的单位时间周期
-				new_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
-				unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, new_dir, Chan_MACDUNITTF_TYPE.CROSS0, histset)
-				self.unittf_list.append(unittf)
-				last_unittf.set_next(unittf)
-				last_unittf = unittf
-				# 收尾旧段
-				last_seg.set_end_klu(last_klu)
-				# 新段方向取反
-				new_dir = Chan_MACDSEG_DIR.UNDER if last_seg.seg_dir == Chan_MACDSEG_DIR.ABOVE else Chan_MACDSEG_DIR.ABOVE
-				seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, last_seg, new_dir, last_unittf)
-				self.seg_list.append(seg)
-				last_seg.set_next(seg)
-				last_seg = seg
-				last_seg.add_unittf(unittf)
-			else:
-				# 4) UnitTF 状态机：用黄线Signal的归零轴
-				if last_klu.macd_state == Chan_MACD_STATE.NEAR0 and last_unittf.div_count > 1:
-					#print(klu.time, klu.macd_state)
-					if klu.macd_state == Chan_MACD_STATE.RZ_UP:
-						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.NEAR0)
-						new_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
-						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, new_dir, Chan_MACDUNITTF_TYPE.NEAR0, histset)
-						self.unittf_list.append(unittf)
-						last_unittf.set_next(unittf)
-						last_unittf = unittf
-						last_seg.add_unittf(unittf)
-					elif klu.macd_state == Chan_MACD_STATE.RZ_DOWN:
-						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.NEAR0)
-						new_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
-						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, new_dir, Chan_MACDUNITTF_TYPE.NEAR0, histset)
-						self.unittf_list.append(unittf)
-						last_unittf.set_next(unittf)
-						last_unittf = unittf
-						last_seg.add_unittf(unittf)
-					else:
-						last_unittf.add_klu(klu)
-						last_seg.add_klu(klu)
-				else:
-					last_unittf.add_klu(klu)
-					last_seg.add_klu(klu)
-
-			last_klu = klu
-		last_histset.set_end_klu(last_klu)
-		last_unittf.set_end_klu(last_klu, None)
-		last_seg.set_end_klu(last_klu)
-		return self.klu_list
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.indicators.ChanMACD import *  # noqa: F403

ChanMACDHistSet.py

@@ -1,117 +1,2 @@
-from ChanEnum import Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACD_STATE
-
-class ChanMACDHistSet():
-    def __init__(self, index, start_time, start_klu, pre_histset, dir):
-        self.index = index
-        self.start_time = start_time
-        self.end_time = None
-        self.klu_list = []
-        self.klu_list.append(start_klu)
-        self.histset_dir = dir
-        self.next = None
-        self.pre = pre_histset
-        self.peak_klu = None
-        self.area = start_klu.macdhist
-        self.unittf_div = Chan_MACDUNITTF_DIV.UNDIV
-        self.middle_klu = None
-        self.div_count = 0
-        self.last_klu = start_klu
-        self.start_klu = start_klu
-        self.peak_div_list = []
-        self.middle_area = 0
-        self.total_macdhist = 0
-    def set_next(self, next_histset):
-        self.next = next_histset
-    def set_pre(self, pre_histset):
-        self.pre = pre_histset
-    def set_middle_klu(self, middle_klu):
-        self.middle_klu = middle_klu
-        #self.middle_area = abs(middle_klu.macdhist)
-        #self.middle_klu = None
-    def set_unittf_div(self, unittf_div):
-        self.unittf_div = unittf_div
-    def add_klu(self, klu):
-        klu.set_histset(self)
-        self.klu_list.append(klu)
-        self.area += abs(klu.macdhist)
-        if self.middle_klu:
-            self.middle_area += abs(klu.macdhist)
-        if self.middle_klu and self.middle_klu.index + 1 == klu.index:
-            self.low_klu = None
-            self.peak_klu = None
-            self.div_count = 0
-            self.peak_div_list = []
-        else:
-            if self.last_klu:
-                self.cal_macdhist_klu(klu)
-        self.last_klu = klu
-    def cal_macdhist_klu(self, klu):
-        if self.middle_klu:
-            if klu.index >= self.middle_klu.index + 2:
-                if klu.pre.pre:
-                    if abs(klu.pre.macdhist) > abs(klu.pre.pre.macdhist) and abs(klu.pre.macdhist) > abs(klu.macdhist):
-                        if self.peak_klu:
-                            if abs(klu.pre.macdhist) > abs(self.peak_klu.macdhist):
-                                self.peak_klu = klu.pre
-                                #self.div_count = 0
-                                #self.peak_div_list = []
-                            else:
-                                if klu.pre.macd * klu.pre.macdhist > 0:
-                                    self.peak_div_list.append(klu.pre)
-                                    self.div_count += 1
-                                    klu.pre.continue_div = True
-                        else:
-                            self.peak_klu = klu.pre
-        else:
-            if len(self.klu_list) >= 3:
-                if klu.pre.pre:
-                    if abs(klu.pre.macdhist) > abs(klu.pre.pre.macdhist) and abs(klu.pre.macdhist) > abs(klu.macdhist):
-                        if self.peak_klu:
-                            if abs(klu.pre.macdhist) > abs(self.peak_klu.macdhist):
-                                self.peak_klu = klu.pre
-                                #self.div_count = 0
-                                #self.peak_div_list = []
-                            else:
-                                if klu.pre.macd * klu.pre.macdhist > 0 and klu.pre.signal * klu.pre.macdhist > 0:
-                                    self.peak_div_list.append(klu.pre)
-                                    self.div_count += 1
-                                    klu.pre.continue_div = True
-                        else:
-                            self.peak_klu = klu.pre
-    def set_end_klu(self, end_klu):
-        self.end_klu = end_klu
-        self.end_time = end_klu.time
-        #if len(self.peak_div_list) > 0:
-            #klu = self.peak_div_list[-1]
-            #if klu.macd * klu.macdhist > 0:
-                #end_klu.continue_div = True
-                #print(end_klu.time, "Continue Div")
-        if self.start_klu.index == end_klu.index:
-            self.peak_klu = self.start_klu
-        if self.start_klu.index + 1 == end_klu.index:
-            if abs(self.start_klu.macdhist) > abs(end_klu.macdhist):
-                self.peak_klu = self.start_klu
-            else:
-                self.peak_klu = end_klu
-        if len(self.klu_list) >= 3 and self.peak_klu == None:
-            self.peak_klu = self.klu_list[0]
-            for klu in self.klu_list:
-                if abs(klu.macdhist) > abs(self.peak_klu.macdhist):
-                    self.peak_klu = klu
-        peak_str = ""
-        state_str = ""
-        for peak_div in self.peak_div_list:
-            peak_str += f"{peak_div.time}, "
-            state_str += f"{peak_div.macd_state}, "
-        total_macdhist = 0
-        first_klu = self.klu_list[0]
-        last_klu = self.klu_list[-1]
-        if (first_klu.macd > 0 and last_klu.macd > 0 and first_klu.macdhist > 0) or (first_klu.macd < 0 and last_klu.macd < 0 and first_klu.macdhist < 0):
-            for klu in self.klu_list:
-                self.total_macdhist += klu.macdhist
-            if abs(self.total_macdhist) < 150:
-                #print(self.end_time, "Total MACDHist: ", self.total_macdhist)
-                last_klu.separate_div = 99999
-        #if self.peak_klu and len(self.peak_div_list) > 0:
-            #print("Continue Div: ",self.start_time, "Peak:", self.peak_klu.time, "Div: ", peak_str, state_str)
-        
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.indicators.ChanMACDHistSet import *  # noqa: F403

ChanMACDSeg.py

@@ -1,49 +1,2 @@
-from ChanEnum import Chan_MACDSEG_DIR
-
-
-class ChanMACDSeg():
-    def __init__(self, index, start_time, start_klu, pre_seg, seg_dir, start_unittf):
-        self.index = index
-        self.start_time = start_time
-        self.end_time = None
-        self.start_klu = start_klu
-        self.end_klu = None
-        self.klu_list = []
-        self.klu_list.append(start_klu)
-        self.unittf_list = []
-        self.seg_dir = seg_dir
-        self.pre = pre_seg
-        self.next = None
-        self.high_klu = start_klu
-        self.low_klu = start_klu
-        self.ref_klu = None
-        self.unittf_list.append(start_unittf)
-    def set_next(self, next_seg):
-        self.next = next_seg
-    def set_pre(self, pre_seg):
-        self.pre = pre_seg
-    def add_klu(self, klu):
-        if klu:
-            self.klu_list.append(klu)
-            klu.set_seg(self)
-            if self.seg_dir == Chan_MACDSEG_DIR.ABOVE:
-                if klu.macdhist > self.high_klu.macdhist:
-                    self.high_klu = klu
-                else:
-                    if klu.macdhist < self.low_klu.macdhist:
-                        self.low_klu = klu
-            else:
-                if klu.macdhist < self.high_klu.macdhist:
-                    self.high_klu = klu
-                else:
-                    if klu.macdhist > self.low_klu.macdhist:
-                        self.low_klu = klu
-            if self.high_klu.index != self.start_klu.index:
-                self.ref_klu = self.high_klu
-    def add_unittf(self, unittf):
-        self.unittf_list.append(unittf)
-        unittf.set_next(self)
-    def set_end_klu(self, end_klu):
-        self.add_klu(end_klu)
-        self.end_klu = end_klu
-        self.end_time = end_klu.time
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.indicators.ChanMACDSeg import *  # noqa: F403

ChanMACDUnitTF.py

@@ -1,141 +1,2 @@
-from ChanEnum import Chan_MACD_STATE, Chan_MACDUNITTF_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACDUNITTF_TYPE
-
-
-class ChanMACDUnitTF():
-    def __init__(self, index, start_time, start_klu, pre_unittf, unittf_dir, start_type, start_histset):
-        self.index = index
-        self.start_time = start_time
-        self.end_time = None
-        self.start_klu = start_klu
-        self.end_klu = None
-        self.klu_list = []
-        self.klu_list.append(start_klu)
-        self.histset_list = []
-        self.histset_list.append(start_histset)
-        self.div_count = 0
-        start_histset.set_middle_klu(start_klu)
-        self.next = None
-        self.pre = pre_unittf
-        self.unittf_dir = unittf_dir
-        self.start_type = start_type
-        self.end_type = None
-        self.peak_klu = None
-        self.div_type = Chan_MACDUNITTF_DIV.UNDIV
-        self.div_peak_list = []
-        self.is_end = False
-    def set_next(self, next_unittf):
-        self.next = next_unittf
-    def set_pre(self, pre_unittf):
-        self.pre = pre_unittf
-    def add_histset(self, histset):
-        self.histset_list.append(histset)
-    def add_klu(self, klu):
-        self.klu_list.append(klu)
-        self.cal_peak_div()
-        self.cal_macd_state()
-    def cal_peak_div(self):
-        self.div_count = 0
-        self.div_peak_list = []
-        self.peak_klu = None
-        if len(self.histset_list) == 0:
-            return
-        if len(self.histset_list) == 1:
-            self.div_type = self.histset_list[0].unittf_div
-            self.peak_klu = self.histset_list[0].peak_klu
-        else:
-            
-            for index in range(0, len(self.histset_list)):
-                histset = self.histset_list[index]
-                if self.same_dir(histset):
-                    #if histset.peak_klu:
-                        #print("Unittf: ", self.start_klu.time, len(self.histset_list), histset.peak_klu.time)
-                    if self.peak_klu:
-                        if histset.peak_klu:
-                            if abs(histset.peak_klu.macdhist) >= abs(self.peak_klu.macdhist):
-                                self.peak_klu = histset.peak_klu
-                                self.div_type = Chan_MACDUNITTF_DIV.UNDIV
-                                self.div_count = 0
-                            else:
-                                self.div_type = Chan_MACDUNITTF_DIV.DISCRETE
-                                self.div_count += 1
-                                self.div_peak_list.append(histset.peak_klu)
-                                #print("Unittf: ", self.start_klu.time)
-                                if histset.peak_klu.macd > 0 and histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
-                                    histset.peak_klu.set_separate_div(self.div_count)
-                                elif histset.peak_klu.macd < 0 and histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER:
-                                    histset.peak_klu.set_separate_div(self.div_count)
-                    else:
-                        if histset.peak_klu:
-                            self.peak_klu = histset.peak_klu
-    def cal_macd_state(self):
-        if len(self.klu_list) > 0:
-            last_klu = self.klu_list[0]
-            macd_peak_klu = None
-            signal_peak_klu = None
-            for index in range(1, len(self.klu_list)):
-                klu = self.klu_list[index]
-                if klu.macd == 0 and klu.signal == 0 and klu.macdhist == 0:
-                    continue
-                if self.start_type == Chan_MACDUNITTF_TYPE.CROSS0 or self.start_type == Chan_MACDUNITTF_TYPE.NEAR0:
-                    if self.unittf_dir == Chan_MACDUNITTF_DIR.ABOVE:
-                        if macd_peak_klu is None:
-                            if last_klu.macd < klu.macd:
-                                if last_klu.signal < last_klu.macdhist:
-                                    last_klu.set_macd_state(Chan_MACD_STATE.UP)
-                                else:
-                                    last_klu.set_macd_state(Chan_MACD_STATE.HIGH)
-                            else:
-                                macd_peak_klu = last_klu
-                                last_klu.set_macd_state(Chan_MACD_STATE.PEAK)
-                        elif klu.macd > macd_peak_klu.macd:
-                            macd_peak_klu = None
-                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH)
-                        elif last_klu.signal < klu.signal:
-                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH_EMPTY)
-                        elif signal_peak_klu is None:
-                            signal_peak_klu = last_klu
-                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH_EMPTY)
-                        elif klu.signal > signal_peak_klu.signal:
-                            signal_peak_klu = None
-                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH)
-                        elif last_klu.macd < last_klu.signal:
-                            last_klu.set_macd_state(Chan_MACD_STATE.RETURN_ZERO)
-                        if self.return_zero(last_klu, klu):
-                            self.end_type = Chan_MACDUNITTF_TYPE.NEAR0
-                            self.is_end = True
-                            self.end_klu = klu
-                            self.end_time = klu.time
-                            klu.set_macd_state(Chan_MACD_STATE.NEAR0)
-                            #print(self.index, last_klu.time, last_klu.macd, last_klu.signal, last_klu.macd_state, klu.macd_state)
-                            break
-                        #print(self.index, last_klu.time, last_klu.macd, last_klu.signal, last_klu.macd_state)
-                last_klu = klu
-                
-    def return_zero(self, last_klu, klu):
-        return_zero = False
-        if last_klu.close < last_klu.ema52 and klu.close > klu.ema52:
-            return_zero = True
-        return_zero = False
-        return return_zero
-    def same_dir(self, histset):
-        if self.unittf_dir == Chan_MACDUNITTF_DIR.ABOVE:
-            return histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE
-        else:
-            return histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER
-    def set_end_klu(self, end_klu, end_type):
-        self.end_type = end_type
-        self.end_klu = end_klu
-        self.end_time = end_klu.time
-        self.is_end = True
-        self.cal_macd_state()
-        div_time = ""
-        for div in self.div_peak_list:
-            div_time += f"{div.time}, "
-        histset_time = ""
-        for histset in self.histset_list:
-            histset_time += f"{histset.start_time}, "
-        
-        #if self.peak_klu and self.div_type == Chan_MACDUNITTF_DIV.DISCRETE:
-            #print("Cross Div: ", self.start_klu.time, self.peak_klu.time, self.div_count, self.div_type, self.unittf_dir, div_time, len(self.histset_list))
-        
-        
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.indicators.ChanMACDUnitTF import *  # noqa: F403

ChanPY.py

@@ -1,402 +1,2 @@
-import sys 
-import os
-#sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/chan.py"))
-sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
-from Chan import CChan
-from BuySellPoint.BS_Point import CBS_Point
-from ChanConfig import CChanConfig
-from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, DATA_FIELD, BSP_TYPE, FX_TYPE, BI_DIR, KLINE_DIR, SEG_DIR
-from KLine.KLine_Unit import CKLine_Unit
-from Common.CTime import CTime
-from Common.func_util import kltype_lt_day, str2float
-from Bi.Bi import CBi
-from typing import Dict, List
-from functools import reduce
-from pandas import DataFrame
-from datetime import datetime, timedelta, timezone
-
-
-def GetColumnNameFromFieldList(fileds: str):
-	_dict = {
-		"time": DATA_FIELD.FIELD_TIME,
-		"open": DATA_FIELD.FIELD_OPEN,
-		"high": DATA_FIELD.FIELD_HIGH,
-		"low": DATA_FIELD.FIELD_LOW,
-		"close": DATA_FIELD.FIELD_CLOSE,
-		"volume": DATA_FIELD.FIELD_VOLUME
-	}
-	return [_dict[x] for x in fileds.split(",")]
-class ChanPY():
-	k_type = KL_TYPE.K_5M
-	config = CChanConfig({
-		"bi_strict": True,
-		"bi_algo": "normal",
-		"trigger_step": True,
-		"skip_step": 0,
-		"divergence_rate": float("inf"),
-		"bsp2_follow_1": False,
-		"bsp3_follow_1": False,
-		"min_zs_cnt": 1,
-		"bs1_peak": False,
-		"macd_algo": "peak",
-		"bs_type": '1,2,3a,1p,2s,3b',
-		"print_warning": True,
-		"zs_algo": "normal",
-	})
-	chan = CChan(
-		code="BTC/USDT:USDT",
-		data_src=DATA_SRC.CCXT,
-		lv_list=[k_type],
-		config=config,
-		autype=AUTYPE.QFQ,
-	)
-	klu_list = []
-	bsps = []
-	chanIn = True
-	#def __init__(self, dataframe):
-		#self.klu_list = self.get_kl_data(dataframe)
-		#for klu in self.klu_list:
-			#self.chan.trigger_load({self.k_type: [klu]})
-	def add_klu(self, klu):
-		if klu:
-			self.chan.trigger_load({self.k_type: [klu]})
-			self.klu_list.append(klu)
-	def add_klu_from_dataframe(self, dataframe):
-		if len(dataframe) > len(self.klu_list) and len(dataframe) - len(self.klu_list) == 1:
-			klu = self.get_last_klu(dataframe)
-			self.chan.trigger_load({self.k_type: [klu]})
-			self.klu_list.append(klu)
-	def parse_time_column(self, inp):
-		if len(inp) == 10:
-			year = int(inp[:4])
-			month = int(inp[5:7])
-			day = int(inp[8:10])
-			hour = minute = 0
-		elif len(inp) == 17:
-			year = int(inp[:4])
-			month = int(inp[4:6])
-			day = int(inp[6:8])
-			hour = int(inp[8:10])
-			minute = int(inp[10:12])
-		elif len(inp) == 19:
-			year = int(inp[:4])
-			month = int(inp[5:7])
-			day = int(inp[8:10])
-			hour = int(inp[11:13])
-			minute = int(inp[14:16])
-		else:
-			raise Exception(f"unknown time column from TradingView:{inp}")
-		return CTime(year, month, day, hour, minute, auto=not kltype_lt_day(self.k_type))
-
-	def create_item_dict(self, data, column_name):
-		for i in range(len(data)):
-			data[i] = self.parse_time_column(data[i]) if i == 0 else str2float(data[i])
-		return dict(zip(column_name, data))
-	def get_last_klu(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
-		item = dataframe.iloc[-1]
-		date = item['date']
-		o = item['open']
-		h = item['high']
-		l = item['low']
-		c = item['close']
-		v = item['volume']
-		#time_obj = date.fromtimestamp(date)
-		time_str = date.strftime('%Y-%m-%d %H:%M:%S')
-		item_data = [
-			time_str,
-			o,
-			h,
-			l,
-			c,
-			v
-		]
-		klu =  CKLine_Unit(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)), autofix=True)
-		klu.set_idx(len(dataframe)-1)
-		return klu
-	def get_kl_data(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
-		klu_list = []
-		for i in range(0, len(dataframe)):
-			item = dataframe.iloc[i]
-			date = item['date']
-			o = item['open']
-			h = item['high']
-			l = item['low']
-			c = item['close']
-			v = item['volume']
-			#time_obj = date.fromtimestamp(date)
-			time_str = date.strftime('%Y-%m-%d %H:%M:%S')
-			item_data = [
-				time_str,
-				o,
-				h,
-				l,
-				c,
-				v
-			]
-			klu =  CKLine_Unit(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)), autofix=True)
-			klu.set_idx(i)
-			klu_list.append(klu)
-		return klu_list
-	def get_bsp_type(self, bsp_type, is_buy):
-		if is_buy:
-			if bsp_type == BSP_TYPE.T1:
-				return 1
-			if bsp_type == BSP_TYPE.T1P:
-				return 2
-			if bsp_type == BSP_TYPE.T2:
-				return 3
-			if bsp_type == BSP_TYPE.T2S:
-				return 4
-			if bsp_type == BSP_TYPE.T3A:
-				return 5
-			if bsp_type == BSP_TYPE.T3B:
-				return 6
-		else:
-			if bsp_type == BSP_TYPE.T1:
-				return -1
-			if bsp_type == BSP_TYPE.T1P:
-				return -2
-			if bsp_type == BSP_TYPE.T2:
-				return -3
-			if bsp_type == BSP_TYPE.T2S:
-				return -4
-			if bsp_type == BSP_TYPE.T3A:
-				return -5
-			if bsp_type == BSP_TYPE.T3B:
-				return -6
-	def get_bsps(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
-		bsps = []
-		updown = []
-		bi_sure = []
-		if self.chanIn:
-			kl_data = self.get_kl_data(dataframe)
-			bsp_list = []
-			bsp_list_pre_len = 0
-			last_bsp_value = 0
-			last_updown = -1
-			bi_list_pre_len = 0
-			pre_bi = None
-			zs_list_pre_len = 0
-			pre_zs = None
-			for klu in kl_data:  # 获取单根K线
-				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
-				self.last_kline = klu
-				bsp_list = self.chan.get_bsp()
-				kl_datas = self.chan.kl_datas[self.k_type]
-				bi_list = kl_datas.bi_list
-				lst = kl_datas.lst
-				if len(bsp_list) > 0:
-					last_bsp = bsp_list[-1]
-					#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, lst[-2].fx, bi_list[-1].dir, bi_list[-1].is_sure,klu.close)
-					if bsp_list_pre_len > len(bsp_list):
-						if abs(last_bsp_value) == 1 or abs(last_bsp_value) == 2:
-							bsps.append(1)
-							#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, 98)
-						else:
-							bsps.append(99)
-					else:
-						if bsp_list_pre_len == len(bsp_list):
-							if klu.idx == last_bsp.klu.idx:
-								last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
-								bsps.append(last_bsp_value)
-								#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value)
-							else:
-								bsps.append(0)
-						else:
-							last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
-							bsps.append(last_bsp_value)
-							#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value)
-				else:
-					bsps.append(0)
-				bsp_list_pre_len = len(bsp_list)
-				
-				#Check zs -----------------------------------
-				zs_list = kl_datas.zs_list
-				if len(zs_list) > 0:
-					zs = zs_list[-1]
-					#if zs_list_pre_len > len(zs_list):
-						#print("No zs", zs.begin.time)
-					#if len(zs_list) > zs_list_pre_len:
-						#print(zs.begin.time, zs.end.time, zs.end.idx, zs.high, zs.low, zs.peak_high, zs.peak_low)
-					zs_list_pre_len = len(zs_list)
-					pre_zs = zs
-
-				#Check Bi -----------------------------------
-				if len(bi_list) > 0:
-					last_bi = bi_list[-1]
-					if len(bi_list) == 1:
-						if last_bi.dir == BI_DIR.UP:
-							updown.append(1)
-							last_updown = 1
-						else:
-							updown.append(-1)
-							last_updown = -1
-					else:
-						if last_updown == 1:
-							if last_bi.dir == BI_DIR.UP:
-								updown.append(0)
-							else:
-								updown.append(-1)
-								last_updown = -1
-						else:
-							if last_bi.dir == BI_DIR.DOWN:
-								updown.append(0)
-							else:
-								updown.append(1)
-								last_updown = 1
-				else:
-					updown.append(0)
-				bi_list = kl_datas.bi_list
-				if len(bi_list) > 0:
-					last_bi = bi_list[-1]
-					#if bi_list_pre_len > len(bi_list):
-						#print("Bi ", klu.time, pre_bi.idx, pre_bi.is_sure, bi_list[-1].idx, bi_list[-1].is_sure)
-					if last_bi.is_sure:
-						bi_sure.append(1)
-						#print(klu.time, last_bi.is_sure)
-					else:
-						bi_sure.append(0)
-					pre_bi = bi_list[-1]
-					bi_list_pre_len = len(bi_list)
-				else:
-					bi_sure.append(0)
-				#if bsps[-1] != 0 or updown[-1] != 0:
-					#print(klu.time, bsps[-1], updown[-1], bi_list[-1].is_sure)
-			self.chanIn = False
-		else:
-			klu = self.get_last_klu(dataframe)
-			if self.last_kline.time < klu.time:
-				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
-				self.last_kline = klu
-		for index in range(0, len(bsps)):
-			if not (abs(bsps[index]) == 1 or abs(bsps[index]) == 2):
-				bsps[index] = 0
-			else:
-				if bsps[index] == 2:
-					bsps[index] = 1
-				else:
-					if bsps[index] == -2:
-						bsps[index] = -1
-					else:
-						bsps[index] = 0
-		#print(bsps)
-		#print(updown)
-		kl_datas = self.chan.kl_datas[self.k_type]
-		#for zs in kl_datas.zs_list:
-			#print(zs.begin.time, zs.end.time)
-		return bsps, updown, bi_sure
-	
-	def get_bsp_state1(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
-		bsps = []
-		if self.chanIn:
-			kl_data = self.get_kl_data(dataframe)
-			self.chan.trigger_load({self.k_type: kl_data})
-			bsp_list = self.chan.get_bsp()
-			bsp_index = 0
-			for klu in kl_data:
-				if bsp_index >= len(bsp_list):
-					bsp_index = len(bsp_list) - 1
-				bsp = bsp_list[bsp_index]
-				if klu.idx == bsp.klu.idx:
-					bsp_type = self.get_bsp_type(bsp.type[0], bsp.is_buy)
-					if abs(bsp_type) == 1 or abs(bsp_type) == 10:
-						bsps.append(1)
-					else:
-						bsps.append(0)
-					bsp_index = bsp_index + 1
-				else:
-					bsps.append(0)
-			self.chanIn = False
-		else:
-			klu = CKLine_Unit(self.create_item_dict(self.get_last_item_data(dataframe), GetColumnNameFromFieldList(fields)), autofix=True)
-			if self.last_kline.time < klu.time:
-				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
-				self.last_kline = klu
-		return bsps
-	def get_bsp_state(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
-		if self.chanIn:
-			kl_data = self.get_kl_data(dataframe)
-			bsp_list = []
-			bsp_list_pre_len = 0
-			last_bsp_value = 0
-			last_bsp_index = 0
-			for klu in kl_data:  # 获取单根K线
-				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
-				self.last_kline = klu
-				bsp_list = self.chan.get_bsp()
-				kl_datas = self.chan.kl_datas[self.k_type]
-				bi_list = kl_datas.bi_list
-				lst = kl_datas.lst
-				if len(bsp_list) > 0:
-					last_bsp = bsp_list[-1]
-					#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, lst[-2].fx, bi_list[-1].dir, bi_list[-1].is_sure,klu.close)
-					if bsp_list_pre_len > len(bsp_list):
-						if abs(last_bsp_value) == 1:
-							self.bsps.append(1)
-							#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, 98)
-						else:
-							self.bsps.append(99)
-					else:
-						if bsp_list_pre_len == len(bsp_list):
-							if klu.idx == last_bsp.klu.idx:
-								if  last_bsp.klu.idx - last_bsp_index > 3:
-									last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
-									self.bsps.append(last_bsp_value)
-								else:
-									self.bsps.append(0)
-								last_bsp_index = last_bsp.klu.idx
-									#if abs(last_bsp_value) == 1 or abs(last_bsp_value) == 2:
-										#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, "Knonw")
-							else:
-								self.bsps.append(0)
-						else:
-							if klu.idx == last_bsp.klu.idx:
-								if  last_bsp.klu.idx - last_bsp_index > 3:
-									last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
-									self.bsps.append(last_bsp_value)
-								else:
-									self.bsps.append(0)
-								last_bsp_index = last_bsp.klu.idx
-									#if abs(last_bsp_value) == 1 or abs(last_bsp_value) == 2:
-										#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, "Knonw")
-							else:
-								self.bsps.append(0)
-				else:
-					self.bsps.append(0)
-				bsp_list_pre_len = len(bsp_list)
-			self.chanIn = False
-		else:
-			klu = self.get_last_klu(dataframe)
-			if self.last_kline.time < klu.time:
-				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
-				self.last_kline = klu
-				bsp_list = self.chan.get_bsp()
-				last_bsp = bsp_list[-1]
-				if last_bsp.klu.idx == klu.idx:
-					self.bsps.append(self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy))
-				else:
-					self.bsps.append(0)
-		for index in range(0, len(self.bsps)):
-			if not (abs(self.bsps[index]) == 1 or abs(self.bsps[index]) == 2):
-				self.bsps[index] = 0
-			else:
-				if self.bsps[index] == 2:
-					self.bsps[index] = 10
-				else:
-					if self.bsps[index] == -2:
-						self.bsps[index] = -10
-					else:
-						if self.bsps[index] == 1:
-							self.bsps[index] = 1
-						else:
-							if self.bsps[index] == -1:
-								self.bsps[index] = -1
-							else:
-								self.bsps[index] = 0
-		return self.bsps
-
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.ChanPY import *  # noqa: F403

ChanPivotClassifier.py

@@ -0,0 +1,2 @@
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.ChanPivotClassifier import *  # noqa: F403

ChanPivotMonitor.py

@@ -0,0 +1,2 @@
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.ChanPivotMonitor import *  # noqa: F403

ChanSBI.py

@@ -1,91 +1,2 @@
-import copy
-from typing import Dict, Optional
-
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR
-import ChanKLU
-from ChanBI import ChanBI
-
-class ChanSBI():
-    def __init__(self, start_bi: ChanBI, index, dir=Chan_BI_DIR.UP):
-        self.start_bi = start_bi
-        self.end_bi = None
-        self.index = index
-        self.dir = dir
-        self.high = start_bi.high
-        self.low = start_bi.low
-        self.pre = None
-        self.next = None
-        self.fx = Chan_FX_TYPE.UNKNOWN
-        self.bi_list = []
-        self.bi_list.append(start_bi)
-        self.has_fx_gap = False
-    def set_fx(self, fx):
-        self.fx = fx
-    def set_end_bi(self, bi):
-        self.end_bi = bi
-    def set_pre(self, sbi):
-        self.pre = sbi
-    def set_next(self, sbi):
-        self.next = sbi
-    def add_bi(self, bi):
-        self.bi_list.append(bi)
-    def check_fx(self):
-        if self.pre and self.next:
-            #print(self.pre.start_bi.start_time, self.start_bi.start_time, self.end_bi.end_time, self.next.start_bi.start_time, self.pre.high, self.high, self.next.high, self.pre.low, self.low, self.next.low, self.dir)
-            if self.high > self.pre.high and self.high > self.next.high:
-                self.fx = Chan_FX_TYPE.TOP
-                #print(self.start_bi.start_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.fx)
-                if self.low > self.pre.high:
-                    self.has_fx_gap = True
-                #print(self.start_bi.start_time, self.end_bi.end_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.dir, self.has_fx_gap, self.fx)
-                return Chan_FX_TYPE.TOP
-            else:
-                if self.low < self.pre.low and self.low < self.next.low:
-                    self.fx = Chan_FX_TYPE.BOTTOM
-                    #print(self.start_bi.start_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.fx)
-                    if self.high < self.pre.low:
-                        self.has_fx_gap = True
-                    #print(self.start_bi.start_time, self.end_bi.end_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.dir, self.has_fx_gap, self.fx)
-                    return Chan_FX_TYPE.BOTTOM
-        return Chan_FX_TYPE.UNKNOWN
-    def check_seg_bi_broken(self):
-        broken = False
-        if self.fx == Chan_FX_TYPE.TOP:
-            if self.next.low < self.pre.high:
-                broken = True
-        elif self.fx == Chan_FX_TYPE.BOTTOM:
-                if self.next.high > self.pre.low:
-                    broken = True
-        return broken
-    def check_bi_included(self, bi):
-        included = False
-        if self.high > bi.high:
-            # high大于，low小于，左包含
-            if self.low < bi.low:
-                included = True
-                # high大于，low大于，不包含
-            else:
-                # if self.low > bi.low
-                # high相等，右包含
-                included = False
-        else:
-            included = False
-            # high小于，low大于，右包含
-            #if self.low > bi.low:
-                #included = True
-        if included:
-            if self.pre:
-                if self.high > self.pre.high and self.low < self.pre.low:
-                    included = True
-        if included:
-            self.add_bi(bi)
-            # gn>gn-1
-            if self.dir == Chan_BI_DIR.DOWN:
-                # UP -> max(dn)
-                self.low = bi.low
-            else:
-                # DOWN -> min(gn)
-                self.high = bi.high
-            #self.print(bi, "Z")
-        #print(self.start_bi.start_time, bi.start_time, included)
-        return included
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanSBI import *  # noqa: F403

ChanSEG.py

@@ -1,191 +1,2 @@
-import copy
-from typing import Dict, Optional
-
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_SEG_DIR, Chan_BI_DIR, Chan_ZS_DIR
-import ChanCTime
-from ChanBI import ChanBI
-from ChanBIZS import ChanBIZS
-class ChanSEG():
-    def __init__(self, start_bi: ChanBI, index, ddir=Chan_SEG_DIR.UP, pre_end_bi: ChanBI = None):
-        self.start_bi = start_bi
-        self.start_time = start_bi.start_time
-        self.end_time = None
-        self.end_bi = None
-        self.dir = ddir
-        self.low = 0
-        self.high = 0
-        if self.dir == Chan_SEG_DIR.UP and start_bi:
-            self.low = start_bi.low
-        else:
-            if start_bi:
-                self.high = start_bi.high
-        self.index = index
-        self.pre = None
-        self.next = None
-        self.bi_list = []
-        self.bi_list.append(start_bi)
-        self.is_sure = False
-        self.sure_time = None
-        self.macd_hist = 0
-        self.macd_div = 0
-        self.start_bi.set_seg(self)
-        self.pre_end_bi = pre_end_bi
-        if self.pre_end_bi:
-            self.ini_seg()
-    def ini_seg(self):
-        next_bi = self.start_bi.next
-        for index in range(self.start_bi.index+1, self.pre_end_bi.index):
-            if next_bi:
-                self.bi_list.append(next_bi)
-                next_bi.set_seg(self)
-                next_bi = next_bi.next
-    def set_macdhist(self, macd_hist):
-        self.macd_hist = macd_hist
-    def set_macd_div(self, macd_div):
-        self.macd_div = macd_div
-    def set_end_bi(self, bi: ChanBI, sure_bi: ChanBI):
-        self.end_bi = bi
-        if bi and bi.is_sure:
-            if self.dir == Chan_SEG_DIR.UP:
-                self.high = bi.high
-            else:
-                self.low = bi.low
-            self.is_sure = True
-            self.end_time = bi.end_klc.end_time
-        if sure_bi.is_sure:
-            self.sure_time = sure_bi.sure_time
-        self.format_bi_list()
-    def pre_set_end_bi(self, bi: ChanBI):
-        self.end_bi = bi
-        if bi and bi.is_sure:
-            if self.dir == Chan_SEG_DIR.UP:
-                self.high = bi.high
-            else:
-                self.low = bi.low
-            self.end_time = bi.end_klc.end_time
-        self.format_bi_list()
-    def set_pre(self, seg):
-        self.pre = seg
-    def set_next(self, seg):
-        self.next = seg
-    def set_sure(self, sure_bi):
-        if sure_bi.is_sure:
-            self.sure_time = sure_bi.sure_time
-        self.is_sure = True
-        self.format_bi_list()
-    def format_bi_list(self):
-        self.bi_list = []
-        self.bi_list.append(self.start_bi)
-        if self.end_bi:
-            next_bi = self.start_bi.next
-            for i in range(self.start_bi.index, self.end_bi.index):
-                if next_bi:
-                    self.bi_list.append(next_bi)
-                    next_bi.set_seg(self)
-                    next_bi = next_bi.next
-    def add_bi(self, bi: ChanBI):
-        if len(self.bi_list) > 0:
-            self.bi_list.append(bi)
-            bi.set_seg(self)
-            self.end_time = bi.end_time
-            self.end_bi = bi
-    def cal_bi_zs(self):
-        zs_list = []
-        if len(self.bi_list) > 3:
-            last_zs = None
-            if self.dir == Chan_SEG_DIR.UP:
-                for index in range(1, len(self.bi_list)):
-                    bi = self.bi_list[index]
-                    if bi.next == None or bi.next.next == None:
-                        continue
-                    bi2 = bi.next
-                    bi3 = bi.next.next
-                    if len(zs_list) == 0 or (last_zs and last_zs.is_sure):
-                        if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.DOWN:
-                            zg = min(bi.high, bi2.high, bi3.high)
-                            zd = max(bi.low, bi2.low, bi3.low)
-                            gg = max(bi.high, bi2.high, bi3.high)
-                            dd = min(bi.low, bi2.low, bi3.low)
-                            zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.UP)
-                            zs.set_zg(zg)
-                            zs.set_zd(zd)
-                            zs.set_gg(gg)
-                            zs.set_dd(dd)
-                            zs.add_bi(bi2)
-                            zs.add_bi(bi3)
-                            zs_list.append(zs)
-                            last_zs = zs
-                    else:
-                        if bi.index > last_zs.bi_list[-1].index and bi.dir == Chan_BI_DIR.DOWN and bi.is_sure:
-                            if bi.low > last_zs.zg or bi.high < last_zs.zd:
-                                last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
-                                if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.DOWN:
-                                    zg = min(bi.high, bi2.high, bi3.high)
-                                    zd = max(bi.low, bi2.low, bi3.low)
-                                    gg = max(bi.high, bi2.high, bi3.high)
-                                    dd = min(bi.low, bi2.low, bi3.low)
-                                    zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.UP)
-                                    zs.set_zg(zg)
-                                    zs.set_zd(zd)
-                                    zs.set_gg(gg)
-                                    zs.set_dd(dd)
-                                    zs.add_bi(bi2)
-                                    zs.add_bi(bi3)
-                                    zs_list.append(zs)
-                                    last_zs = zs
-                            else:
-                                last_zs.add_bi(bi.pre)
-                                last_zs.add_bi(bi)
-                    if index == len(self.bi_list) - 1 and last_zs and not last_zs.is_sure:
-                        #print(bi.start_time, "BI", last_zs.is_sure)
-                        last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
-            else:
-                for index in range(1, len(self.bi_list)):
-                    bi = self.bi_list[index]
-                    if bi.next == None or bi.next.next == None:
-                        continue
-                    bi2 = bi.next
-                    bi3 = bi.next.next
-                    if len(zs_list) == 0 or (last_zs and last_zs.is_sure):
-                        if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.UP:
-                            zg = min(bi.high, bi2.high, bi3.high)
-                            zd = max(bi.low, bi2.low, bi3.low)
-                            gg = max(bi.high, bi2.high, bi3.high)
-                            dd = min(bi.low, bi2.low, bi3.low)
-                            zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.DOWN)
-                            zs.set_zg(zg)
-                            zs.set_zd(zd)
-                            zs.set_gg(gg)
-                            zs.set_dd(dd)
-                            zs.add_bi(bi2)
-                            zs.add_bi(bi3)
-                            zs_list.append(zs)
-                            last_zs = zs
-                    else:
-                        if bi.index > last_zs.bi_list[-1].index and bi.dir == Chan_BI_DIR.UP and bi.is_sure:
-                            if bi.low > last_zs.zg or bi.high < last_zs.zd:
-                                last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
-                                if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.UP:
-                                    zg = min(bi.high, bi2.high, bi3.high)
-                                    zd = max(bi.low, bi2.low, bi3.low)
-                                    gg = max(bi.high, bi2.high, bi3.high)
-                                    dd = min(bi.low, bi2.low, bi3.low)
-                                    zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.DOWN)
-                                    zs.set_zg(zg)
-                                    zs.set_zd(zd)
-                                    zs.set_gg(gg)
-                                    zs.set_dd(dd)
-                                    zs.add_bi(bi2)
-                                    zs.add_bi(bi3)
-                                    zs_list.append(zs)
-                                    last_zs = zs
-                            else:
-                                last_zs.add_bi(bi.pre)
-                                last_zs.add_bi(bi)
-                    if index == len(self.bi_list) - 1 and last_zs and not last_zs.is_sure:
-                        #print(bi.start_time, "BI", last_zs.is_sure)
-                        last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
-                        
-        #print(self.start_time, len(zs_list))
-        #print(self.bi_list[-1].end_time, "end_bi")
-        return zs_list
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanSEG import *  # noqa: F403

ChanZS.py

@@ -1,108 +1,2 @@
-from typing import Dict, Optional
-
-import ChanKLC, ChanSEG
-import ChanCTime
-from ChanEnum import Chan_ZS_DIR
-# 中枢
-class ChanZS():
-    def __init__(self, start_seg: ChanSEG, index, ddir: Chan_ZS_DIR):
-        self.start_klc = start_seg.start_bi.start_klc
-        self.start_time = self.start_klc.start_time
-        self.end_time = None
-        self.index = index
-        self.next = None
-        self.pre = None
-        self.start_seg = start_seg
-        self.seg_list = []
-        self.seg_list.append(start_seg)
-        self.end_seg = None
-        self.last_bi_in = None
-        self.bi_out = None
-        self.is_sure = False
-        self.zg = 0
-        self.zd = 0
-        self.gg = 0
-        self.dd = 0
-        self.dir = ddir
-        self.sure_time = None
-        self.end_klc = None
-        self.bi_out_count = 0
-        self.bi_out_list = []
-        self.bi_out_seg_list = []
-        self.bi_out_seg = None
-        self.is_extended = False
-    def set_last_bi_in(self, last_bi_in):
-        self.last_bi_in = last_bi_in
-    def set_bi_out(self, bi_out, bi_out_seg):
-        if bi_out:
-            #print(bi_out.start_klc.start_time, bi_out.sure_time, bi_out.dir, bi_out_seg.dir, len(self.bi_out_list))
-            if len(self.bi_out_list) > 0:
-                last_bi = self.bi_out_list[-1]
-                if last_bi.index != bi_out.index:
-                    self.bi_out_list.append(bi_out)
-                    self.bi_out_seg_list.append(bi_out_seg)
-            else:
-                self.bi_out_list.append(bi_out)
-                self.bi_out_seg_list.append(bi_out_seg)
-        self.bi_out = bi_out
-        self.bi_out_seg = bi_out_seg
-    def set_end_klc(self, end_klc, sure_time, bi_out_count, seg):
-        self.end_klc = end_klc
-        self.set_end_time(end_klc.end_time)
-        self.is_sure = True
-        self.sure_time = sure_time
-        self.bi_out_count = bi_out_count
-        self.end_seg = seg
-    def set_end_seg(self, end_seg):
-        self.end_seg = end_seg
-    def set_pre(self, pre):
-        self.pre = pre
-    def set_next(self, next):
-        self.next = next
-    def set_end_time(self, end_time):
-        self.end_time = end_time
-    def add_klc(self, klc):
-        self.klc_list.append(klc)
-    def add_seg(self, seg):
-        self.seg_list.append(seg)
-        self.end_time = seg.end_time
-        self.end_seg = seg
-    def set_zg(self, zg):
-        self.zg = zg
-    def set_zd(self, zd):
-        self.zd = zd
-    def set_gg(self, gg):
-        self.gg = gg
-    def set_dd(self, dd):
-        self.dd = dd
-    def extend_zs(self, seg_list):
-        self.is_sure = False
-        self.end_seg = None
-        self.end_klc = None
-        self.sure_time = None
-        for seg in seg_list:
-            if seg.end_bi.high > self.gg:
-                self.set_gg(seg.end_bi.high)
-            if seg.end_bi.low < self.dd:
-                self.set_dd(seg.end_bi.low)
-            self.seg_list.append(seg)
-        self.is_extended = True
-        #print(self.start_time, "extend zs", seg_list[-1].end_time)
-
-# 大级别中枢：由多个区间重叠（扩张）的笔/线段中枢合并而成，用于显示更大级别的震荡区间
-class ChanZS_Big():
-    def __init__(self, zs_list):
-        assert len(zs_list) >= 1
-        self.zs_list = list(zs_list)
-        first = self.zs_list[0]
-        last = self.zs_list[-1]
-        self.start_time = first.start_time
-        self.end_time = last.end_time if last.end_time else None
-        self.start_klc = first.start_klc
-        self.end_klc = last.end_klc
-        # 大级别区间取并集：包住所有子中枢
-        self.zd = min(zs.zd for zs in self.zs_list)
-        self.zg = max(zs.zg for zs in self.zs_list)
-        self.dd = min(zs.dd for zs in self.zs_list)
-        self.gg = max(zs.gg for zs in self.zs_list)
-        self.index = 0  # 由外部设置
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.ChanZS import *  # noqa: F403

ChanZone.py

@@ -0,0 +1,2 @@
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.ChanZone import *  # noqa: F403

Chan_FX_Box.py

@@ -1,7 +1,2 @@
-class Chan_FX_Box():
-	def __init__(self, start_time, end_time, high, low):
-		self.start_time = start_time
-		self.end_time = end_time
-		self.high = high
-		self.low = low
-	
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.core.Chan_FX_Box import *  # noqa: F403

Find_Trend.py

@@ -1,448 +1,2 @@
-import ccxt
-import pandas as pd
-import numpy as np
-import mplfinance as mpf
-from talib import MACD, SMA
-from datetime import datetime, timedelta
-import logging
-import datetime as dt
-
-# Configure logging
-logging.basicConfig(
-    filename='chanlun_trading.log',
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s'
-)
-
-# Configuration (user to modify)
-BINANCE_API_KEY = 'your_api_key'  # Replace with your Binance API key
-BINANCE_API_SECRET = 'your_api_secret'  # Replace with your Binance API secret
-SIMULATION_MODE = True  # Set to False for live trading
-
-# 1. Fetch K-line data from Binance (multi-timeframe support)
-def fetch_binance_data(symbol='BTC/USDT', timeframe='5m', limit=500):
-    try:
-        exchange = ccxt.binance({
-            'apiKey': BINANCE_API_KEY if not SIMULATION_MODE else '',
-            'secret': BINANCE_API_SECRET if not SIMULATION_MODE else '',
-            'enableRateLimit': True,
-            'options': {'defaultType': 'spot'}
-        })
-        since = exchange.parse8601((datetime.now(dt.UTC) - timedelta(days=7)).isoformat())
-        ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since, limit)
-        df = pd.DataFrame(ohlcv, columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])
-        df['Date'] = pd.to_datetime(df['Date'], unit='ms')
-        df.set_index('Date', inplace=True)
-        logging.info(f"Fetched {len(df)} K-lines for {symbol} ({timeframe})")
-        return df
-    except Exception as e:
-        logging.error(f"Failed to fetch data: {e}")
-        raise
-
-# 2. K-line merging (vectorized)
-def merge_kline(df):
-    try:
-        df = df.copy()
-        merged_data = []
-        trend = np.sign(df['Close'].diff().shift(-1))  # 1: up, -1: down, 0: neutral
-        
-        # Detect inclusion
-        is_included = ((df['High'].shift(-1) <= df['High']) & (df['Low'].shift(-1) >= df['Low'])) | \
-                      ((df['High'].shift(-1) >= df['High']) & (df['Low'].shift(-1) <= df['Low']))
-        
-        i = 0
-        while i < len(df) - 1:
-            if is_included.iloc[i]:
-                current_k = df.iloc[i]
-                next_k = df.iloc[i + 1]
-                high = max(current_k['High'], next_k['High'])
-                low = min(current_k['Low'], next_k['Low'])
-                open_price = current_k['Open']
-                close_price = next_k['Close'] if trend.iloc[i] >= 0 else next_k['Close']
-                volume = current_k['Volume'] + next_k['Volume']
-                
-                merged_data.append({
-                    'Date': next_k.name,
-                    'Open': open_price,
-                    'High': high,
-                    'Low': low,
-                    'Close': close_price,
-                    'Volume': volume
-                })
-                i += 2
-            else:
-                current_k = df.iloc[i]
-                merged_data.append({
-                    'Date': current_k.name,
-                    'Open': current_k['Open'],
-                    'High': current_k['High'],
-                    'Low': current_k['Low'],
-                    'Close': current_k['Close'],
-                    'Volume': current_k['Volume']
-                })
-                i += 1
-        
-        if i == len(df) - 1:
-            last_k = df.iloc[i]
-            merged_data.append({
-                'Date': last_k.name,
-                'Open': last_k['Open'],
-                'High': last_k['High'],
-                'Low': last_k['Low'],
-                'Close': last_k['Close'],
-                'Volume': last_k['Volume']
-            })
-        
-        merged_df = pd.DataFrame(merged_data)
-        merged_df['Date'] = pd.to_datetime(merged_df['Date'])
-        merged_df.set_index('Date', inplace=True)
-        logging.info(f"Merged K-lines: {len(df)} -> {len(merged_df)}")
-        return merged_df
-    except Exception as e:
-        logging.error(f"K-line merging failed: {e}")
-        raise
-
-# 3. Detect fractals (vectorized)
-def detect_fractals(df):
-    try:
-        df = df.copy()
-        df['is_top'] = (df['High'] > df['High'].shift(1)) & (df['High'] > df['High'].shift(-1)) & \
-                       (df['High'] > df['High'].shift(2)) & (df['High'] > df['High'].shift(-2))
-        df['is_bottom'] = (df['Low'] < df['Low'].shift(1)) & (df['Low'] < df['Low'].shift(-1)) & \
-                          (df['Low'] < df['Low'].shift(2)) & (df['Low'] < df['Low'].shift(-2))
-        df['is_top'] = df['is_top'].fillna(False)
-        df['is_bottom'] = df['is_bottom'].fillna(False)
-        logging.info(f"Detected {df['is_top'].sum()} top fractals and {df['is_bottom'].sum()} bottom fractals")
-        return df
-    except Exception as e:
-        logging.error(f"Fractal detection failed: {e}")
-        raise
-
-# 4. Detect strokes
-def detect_strokes(df):
-    try:
-        strokes = []
-        last_fractal = None
-        last_price = None
-        last_index = None
-        
-        for i in range(len(df)):
-            if df['is_top'].iloc[i] or df['is_bottom'].iloc[i]:
-                current_fractal = 'top' if df['is_top'].iloc[i] else 'bottom'
-                current_price = df['High'].iloc[i] if current_fractal == 'top' else df['Low'].iloc[i]
-                
-                if last_fractal is None:
-                    last_fractal = current_fractal
-                    last_price = current_price
-                    last_index = df.index[i]
-                    continue
-                
-                if (last_fractal == 'top' and current_fractal == 'bottom' and current_price < last_price) or \
-                   (last_fractal == 'bottom' and current_fractal == 'top' and current_price > last_price):
-                    strokes.append({
-                        'start_time': last_index,
-                        'end_time': df.index[i],
-                        'start_price': last_price,
-                        'end_price': current_price,
-                        'type': 'down' if current_fractal == 'bottom' else 'up',
-                        'volume': df['Volume'].loc[last_index:df.index[i]].sum()
-                    })
-                
-                last_fractal = current_fractal
-                last_price = current_price
-                last_index = df.index[i]
-        
-        logging.info(f"Detected {len(strokes)} strokes")
-        return strokes
-    except Exception as e:
-        logging.error(f"Stroke detection failed: {e}")
-        raise
-
-# 5. Detect segments
-def detect_segments(strokes):
-    try:
-        segments = []
-        if len(strokes) < 3:
-            return segments
-        
-        i = 0
-        while i < len(strokes) - 2:
-            stroke1, stroke2, stroke3 = strokes[i], strokes[i+1], strokes[i+2]
-            
-            if stroke1['type'] == 'up' and stroke2['type'] == 'down' and stroke3['type'] == 'up':
-                if stroke3['end_price'] > stroke1['end_price']:
-                    segments.append({
-                        'start_time': stroke1['start_time'],
-                        'end_time': stroke3['end_time'],
-                        'start_price': stroke1['start_price'],
-                        'end_price': stroke3['end_price'],
-                        'type': 'up'
-                    })
-                    i += 3
-                else:
-                    i += 1
-            elif stroke1['type'] == 'down' and stroke2['type'] == 'up' and stroke3['type'] == 'down':
-                if stroke3['end_price'] < stroke1['end_price']:
-                    segments.append({
-                        'start_time': stroke1['start_time'],
-                        'end_time': stroke3['end_time'],
-                        'start_price': stroke1['start_price'],
-                        'end_price': stroke3['end_price'],
-                        'type': 'down'
-                    })
-                    i += 3
-                else:
-                    i += 1
-            else:
-                i += 1
-        
-        logging.info(f"Detected {len(segments)} segments")
-        return segments
-    except Exception as e:
-        logging.error(f"Segment detection failed: {e}")
-        raise
-
-# 6. Detect pivots (midlines)
-def detect_pivots(strokes):
-    try:
-        pivots = []
-        if len(strokes) < 3:
-            return pivots
-        
-        for i in range(len(strokes) - 2):
-            s1, s2, s3 = strokes[i:i+3]
-            high = min(s1['start_price'], s1['end_price'], s2['start_price'], s2['end_price'], 
-                       s3['start_price'], s3['end_price'])
-            low = max(s1['start_price'], s1['end_price'], s2['start_price'], s2['end_price'], 
-                      s3['start_price'], s3['end_price'])
-            
-            if high > low:
-                pivots.append({
-                    'start_time': s1['start_time'],
-                    'end_time': s3['end_time'],
-                    'high': high,
-                    'low': low
-                })
-        
-        logging.info(f"Detected {len(pivots)} pivots")
-        return pivots
-    except Exception as e:
-        logging.error(f"Pivot detection failed: {e}")
-        raise
-
-# 7. Analyze higher timeframe (30m)
-def analyze_higher_timeframe(df_30m):
-    try:
-        df_30m = detect_fractals(df_30m)
-        strokes_30m = detect_strokes(df_30m)
-        
-        if not strokes_30m:
-            return 'neutral'
-        
-        last_stroke = strokes_30m[-1]
-        logging.info(f"30m trend: {last_stroke['type']}")
-        return last_stroke['type']
-    except Exception as e:
-        logging.error(f"Higher timeframe analysis failed: {e}")
-        raise
-
-# 8. Back-divergence detection (enhanced)
-def detect_back_divergence(df, strokes, higher_trend):
-    try:
-        macd, signal, hist = MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
-        sma20 = SMA(df['Close'], timeperiod=20)
-        df['macd'] = macd
-        df['hist'] = hist
-        df['sma20'] = sma20
-        df['buy_signal'] = False
-        df['sell_signal'] = False
-        
-        stroke_metrics = []
-        for stroke in strokes:
-            start_idx = df.index.get_loc(stroke['start_time'])
-            end_idx = df.index.get_loc(stroke['end_time'])
-            hist_segment = df['hist'].iloc[start_idx:end_idx+1]
-            price_change = abs(stroke['end_price'] - stroke['start_price'])
-            hist_area = sum(abs(h) for h in hist_segment if not np.isnan(h))
-            volume = stroke['volume']
-            stroke_metrics.append({
-                'start_time': stroke['start_time'],
-                'end_time': stroke['end_time'],
-                'type': stroke['type'],
-                'price_change': price_change,
-                'hist_area': hist_area,
-                'volume': volume
-            })
-        
-        for i in range(2, len(stroke_metrics)):
-            current_stroke = stroke_metrics[i]
-            prev_stroke = stroke_metrics[i-2]
-            
-            if current_stroke['type'] != prev_stroke['type']:
-                continue
-            
-            current_end_idx = df.index.get_loc(current_stroke['end_time'])
-            
-            # Uptrend back-divergence (sell signal)
-            if current_stroke['type'] == 'up':
-                price_increase = df['High'].loc[current_stroke['end_time']] > df['High'].loc[prev_stroke['end_time']]
-                hist_decrease = current_stroke['hist_area'] < prev_stroke['hist_area']
-                volume_decrease = current_stroke['volume'] < prev_stroke['volume']
-                is_top_fractal = df['is_top'].loc[current_stroke['end_time']]
-                hist_positive = df['hist'].iloc[current_end_idx] > 0 or \
-                               (df['hist'].iloc[current_end_idx] < 0 and df['hist'].iloc[current_end_idx-1] > 0)
-                sma_trend = df['Close'].iloc[current_end_idx] > df['sma20'].iloc[current_end_idx]
-                trend_match = higher_trend in ['up', 'neutral']
-                
-                if price_increase and hist_decrease and volume_decrease and is_top_fractal and \
-                   hist_positive and sma_trend and trend_match:
-                    df.loc[df.index[current_end_idx], 'sell_signal'] = True
-            
-            # Downtrend back-divergence (buy signal)
-            elif current_stroke['type'] == 'down':
-                price_decrease = df['Low'].loc[current_stroke['end_time']] < df['Low'].loc[prev_stroke['end_time']]
-                hist_decrease = current_stroke['hist_area'] < prev_stroke['hist_area']
-                volume_decrease = current_stroke['volume'] < prev_stroke['volume']
-                is_bottom_fractal = df['is_bottom'].loc[current_stroke['end_time']]
-                hist_negative = df['hist'].iloc[current_end_idx] < 0 or \
-                               (df['hist'].iloc[current_end_idx] > 0 and df['hist'].iloc[current_end_idx-1] < 0)
-                sma_trend = df['Close'].iloc[current_end_idx] < df['sma20'].iloc[current_end_idx]
-                trend_match = higher_trend in ['down', 'neutral']
-                
-                if price_decrease and hist_decrease and volume_decrease and is_bottom_fractal and \
-                   hist_negative and sma_trend and trend_match:
-                    df.loc[df.index[current_end_idx], 'buy_signal'] = True
-        
-        logging.info(f"Detected {df['buy_signal'].sum()} buy signals and {df['sell_signal'].sum()} sell signals")
-        return df
-    except Exception as e:
-        logging.error(f"Back-divergence detection failed: {e}")
-        raise
-
-# 9. Execute trade
-def execute_trade(exchange, symbol, signal, amount=0.001):
-    try:
-        if SIMULATION_MODE:
-            msg = f"[SIMULATION] {'Buy' if signal == 'buy' else 'Sell'} {amount} {symbol} at {datetime.now(dt.UTC)}"
-            print(msg)
-            logging.info(msg)
-            return
-        
-        if signal == 'buy':
-            order = exchange.create_market_buy_order(symbol, amount)
-            msg = f"Buy order executed: {order}"
-            print(msg)
-            logging.info(msg)
-        elif signal == 'sell':
-            order = exchange.create_market_sell_order(symbol, amount)
-            msg = f"Sell order executed: {order}"
-            print(msg)
-            logging.info(msg)
-    except Exception as e:
-        msg = f"Trade execution failed: {e}"
-        print(msg)
-        logging.error(msg)
-
-# 10. Plot chart
-def plot_chart(df, strokes, segments, pivots):
-    try:
-        # Initialize additional plots
-        apds = []
-        alines = []  # For line segments
-        
-        # Plot strokes as line segments
-        for stroke in strokes:
-            alines.append([(stroke['start_time'], stroke['start_price']),
-                           (stroke['end_time'], stroke['end_price'])])
-        
-        # Plot segments as line segments
-        for segment in segments:
-            alines.append([(segment['start_time'], segment['start_price']),
-                           (segment['end_time'], segment['end_price'])])
-        
-        # Plot pivots as horizontal lines
-        for pivot in pivots:
-            alines.append([(pivot['start_time'], pivot['high']),
-                           (pivot['end_time'], pivot['high'])])
-            alines.append([(pivot['start_time'], pivot['low']),
-                           (pivot['end_time'], pivot['low'])])
-        
-        # Add alines to plot (single color for simplicity, can customize)
-        if alines:
-            apds.append(mpf.make_addplot(
-                None,  # No y-data needed for alines
-                alines=alines,
-                type='line',
-                color=['blue' if i < len(strokes) else 'purple' if i < len(strokes) + len(segments) else 'orange'
-                       for i in range(len(alines))],
-                linestyle=['--' if i < len(strokes) else '-' if i < len(strokes) + len(segments) else ':'
-                           for i in range(len(alines))]
-            ))
-        
-        # Plot buy/sell signals
-        buy_signals = df[df['buy_signal']]['Close']
-        sell_signals = df[df['sell_signal']]['Close']
-        apds.append(mpf.make_addplot(buy_signals, type='scatter', markersize=100, marker='^', color='green'))
-        apds.append(mpf.make_addplot(sell_signals, type='scatter', markersize=100, marker='v', color='red'))
-        
-        # Plot K-line chart
-        mpf.plot(df, type='candle', addplot=apds, title='Chanlun Advanced Analysis', style='yahoo')
-        logging.info("Chart plotted successfully")
-    except Exception as e:
-        logging.error(f"Chart plotting failed: {e}")
-        raise
-
-# 11. Main function
-def main():
-    try:
-        # Initialize exchange
-        exchange = ccxt.binance({
-            'apiKey': BINANCE_API_KEY if not SIMULATION_MODE else '',
-            'secret': BINANCE_API_SECRET if not SIMULATION_MODE else '',
-            'enableRateLimit': True,
-            'options': {'defaultType': 'spot'}
-        })
-        
-        # Fetch data
-        df_5m = fetch_binance_data(symbol='BTC/USDT', timeframe='5m', limit=500)
-        df_30m = fetch_binance_data(symbol='BTC/USDT', timeframe='30m', limit=200)
-        
-        # Merge 5m K-lines
-        df_5m = merge_kline(df_5m)
-        
-        # Detect fractals, strokes, segments, pivots
-        df_5m = detect_fractals(df_5m)
-        strokes = detect_strokes(df_5m)
-        segments = detect_segments(strokes)
-        pivots = detect_pivots(strokes)
-        
-        # Analyze 30m trend
-        higher_trend = analyze_higher_timeframe(df_30m)
-        print(f"30m Trend: {higher_trend}")
-        
-        # Detect back-divergence
-        df_5m = detect_back_divergence(df_5m, strokes, higher_trend)
-        
-        # Plot chart
-        plot_chart(df_5m, strokes, segments, pivots)
-        
-        # Output and execute trades
-        print("Buy Signals:")
-        buy_signals = df_5m[df_5m['buy_signal']][['Close']]
-        print(buy_signals)
-        for idx, row in buy_signals.iterrows():
-            execute_trade(exchange, 'BTC/USDT', 'buy', amount=0.001)
-        
-        print("Sell Signals:")
-        sell_signals = df_5m[df_5m['sell_signal']][['Close']]
-        print(sell_signals)
-        for idx, row in sell_signals.iterrows():
-            execute_trade(exchange, 'BTC/USDT', 'sell', amount=0.001)
-        
-        logging.info("Main function completed successfully")
-    except Exception as e:
-        logging.error(f"Main function failed: {e}")
-        raise
-
-if __name__ == "__main__":
-    main()
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.analysis.Find_Trend import *  # noqa: F403

chanlun/__init__.py

@@ -0,0 +1,11 @@
+"""缠论引擎正式包。
+
+推荐::
+    from chanlun import ChanLun, TF_DF
+    from chanlun.core.ChanEnum import Chan_BI_DIR
+"""
+
+from chanlun.pipeline.orchestrator import ChanLun
+from chanlun.pipeline.timeframe import TF_DF
+
+__all__ = ["ChanLun", "TF_DF"]

chanlun/analysis/ChanHeng.py

@@ -0,0 +1,414 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+"""
+使用 ccxt 获取币安交易所所有 `*/USDT` 交易对最新 100 根 1 小时 K 线数据，并筛选出长期横盘的币种。
+
+横盘判定基于以下三项指标（均可通过命令行参数调整）：
+1. 价格振幅占均价的比例（默认 ≤ 5%）
+2. 收盘价线性回归斜率占均价的比例（默认 ≤ 0.05%）
+3. 收盘价标准差占均价的比例（默认 ≤ 1.5%）
+
+满足以上全部条件的交易对会被视为长期横盘。
+"""
+
+import argparse
+import csv
+import logging
+import math
+import statistics
+import sys
+import time
+from dataclasses import dataclass
+from typing import Iterable, List, Optional, Sequence
+
+import ccxt
+
+# python ChanHeng.py --range-threshold 5 --slope-threshold 5 --std-threshold 0.015
+
+DEFAULT_LIMIT = 100
+DEFAULT_TIMEFRAME = "1h"
+STABLECOINS = {
+    "USDT",
+    "USDC",
+    "BUSD",
+    "TUSD",
+    "USDP",
+    "DAI",
+    "FDUSD",
+    "SUSD",
+    "UST",
+    "USTC",
+    "EUR",
+    "TRY",
+    "BFUSD",
+    "USDE",
+    "XUSD",
+    "USD1",
+    "XUSD"
+}
+
+
+@dataclass
+class SidewaysMetrics:
+    symbol: str
+    price_range_pct: float
+    slope_pct: float
+    std_pct: float
+    mean_close: float
+    last_close: float
+    data_points: int
+
+
+def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="筛选币安长期横盘币种（默认 500 根 1 小时 K 线）"
+    )
+    parser.add_argument(
+        "--timeframe",
+        default=DEFAULT_TIMEFRAME,
+        help="K 线周期（默认：1h）",
+    )
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=DEFAULT_LIMIT,
+        help="每个交易对获取的 K 线数量（默认：500）",
+    )
+    parser.add_argument(
+        "--range-threshold",
+        type=float,
+        default=0.05,
+        help="最大价格振幅占均价比例阈值（默认：0.05，表示 5%%）",
+    )
+    parser.add_argument(
+        "--slope-threshold",
+        type=float,
+        default=0.0005,
+        help="线性回归斜率占均价比例阈值（默认：0.0005，约 0.05%%）",
+    )
+    parser.add_argument(
+        "--std-threshold",
+        type=float,
+        default=0.015,
+        help="标准差占均价比例阈值（默认：0.015，表示 1.5%%）",
+    )
+    parser.add_argument(
+        "--quote",
+        action="append",
+        default=[],
+        help="只保留指定计价货币的交易对，可重复指定（示例：--quote USDT --quote FDUSD）",
+    )
+    parser.add_argument(
+        "--symbol",
+        action="append",
+        default=[],
+        help="仅检测指定交易对，可重复（不指定则遍历所有符合条件的现货交易对）",
+    )
+    parser.add_argument(
+        "--max-symbols",
+        type=int,
+        default=None,
+        help="限制最多检测的交易对数量（用于调试）",
+    )
+    parser.add_argument(
+        "--sleep",
+        type=float,
+        default=0.35,
+        help="请求失败后的基础重试等待秒数（默认：0.35）",
+    )
+    parser.add_argument(
+        "--retries",
+        type=int,
+        default=3,
+        help="单个交易对请求失败后的最大重试次数（默认：3）",
+    )
+    parser.add_argument(
+        "--include-inactive",
+        action="store_true",
+        help="包含已下架/不可交易的交易对（默认不包含）",
+    )
+    parser.add_argument(
+        "--export",
+        type=str,
+        default=None,
+        help="将筛选结果导出为 CSV 文件的路径",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="输出更详细的日志信息",
+    )
+    return parser.parse_args(argv)
+
+
+def setup_logging(verbose: bool) -> None:
+    level = logging.DEBUG if verbose else logging.INFO
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s [%(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+
+def create_exchange() -> ccxt.binance:
+    exchange = ccxt.binance({"enableRateLimit": True})
+    exchange.options["defaultType"] = "spot"
+    return exchange
+
+
+def iter_target_symbols(
+    exchange: ccxt.binance,
+    quotes: Sequence[str],
+    includes: Sequence[str],
+    include_inactive: bool,
+) -> List[str]:
+    markets = exchange.load_markets()
+    filtered = []
+
+    quote_set = {quote.upper() for quote in quotes}
+    include_set = {sym.upper() for sym in includes}
+
+    for symbol, meta in markets.items():
+        if not meta.get("spot", False):
+            continue
+        if not include_inactive and meta.get("active") is False:
+            continue
+
+        normalized_symbol = symbol.upper()
+
+        if include_set and normalized_symbol not in include_set:
+            continue
+
+        parts = symbol.split("/")
+        if len(parts) != 2:
+            continue
+
+        base_asset, quote_asset = parts[0].upper(), parts[1].upper()
+
+        target_quote = quote_set or {"USDT"}
+        if quote_asset not in target_quote:
+            continue
+
+        if base_asset in STABLECOINS:
+            continue
+
+        filtered.append(symbol)
+
+    filtered.sort()
+    logging.info(
+        "已筛选 %s 个目标交易对（quote 过滤：%s，专门列表：%s）",
+        len(filtered),
+        ",".join(sorted(quote_set or {"USDT"})),
+        ",".join(sorted(include_set)) or "无",
+    )
+    return filtered
+
+
+def fetch_ohlcv_with_retry(
+    exchange: ccxt.binance,
+    symbol: str,
+    timeframe: str,
+    limit: int,
+    retries: int,
+    base_sleep: float,
+) -> List[List[float]]:
+    attempt = 0
+    while True:
+        try:
+            return exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
+        except ccxt.RateLimitExceeded as exc:
+            wait_time = max(exchange.rateLimit / 1000.0 if exchange.rateLimit else 0, base_sleep)
+            logging.debug("触发限频，等待 %.2f 秒后重试 %s：%s", wait_time, symbol, exc)
+            time.sleep(wait_time)
+        except (ccxt.NetworkError, ccxt.ExchangeError) as exc:
+            attempt += 1
+            if attempt > retries:
+                logging.warning("多次获取失败，跳过 %s：%s", symbol, exc)
+                return []
+            wait_time = base_sleep * attempt
+            logging.debug("请求失败，等待 %.2f 秒后重试 %s（第 %d 次）：%s", wait_time, symbol, attempt, exc)
+            time.sleep(wait_time)
+
+
+def linear_regression_slope(values: Sequence[float]) -> float:
+    n = len(values)
+    if n < 2:
+        return 0.0
+    mean_x = (n - 1) / 2.0
+    mean_y = sum(values) / n
+    numerator = 0.0
+    denominator = 0.0
+    for idx, value in enumerate(values):
+        dx = idx - mean_x
+        numerator += dx * (value - mean_y)
+        denominator += dx * dx
+
+    if denominator == 0:
+        return 0.0
+    return numerator / denominator
+
+
+def compute_sideways_metrics(closes: Sequence[float], symbol: str) -> Optional[SidewaysMetrics]:
+    if not closes:
+        return None
+
+    mean_close = sum(closes) / len(closes)
+    if math.isclose(mean_close, 0.0):
+        return None
+
+    max_close = max(closes)
+    min_close = min(closes)
+    price_range_pct = (max_close - min_close) / mean_close
+
+    slope = linear_regression_slope(closes)
+    slope_pct = slope / mean_close
+
+    std_dev = statistics.pstdev(closes) if len(closes) > 1 else 0.0
+    std_pct = std_dev / mean_close
+
+    return SidewaysMetrics(
+        symbol=symbol,
+        price_range_pct=price_range_pct,
+        slope_pct=slope_pct,
+        std_pct=std_pct,
+        mean_close=mean_close,
+        last_close=closes[-1],
+        data_points=len(closes),
+    )
+
+
+def is_sideways(metrics: SidewaysMetrics, range_threshold: float, slope_threshold: float, std_threshold: float) -> bool:
+    return (
+        metrics.price_range_pct <= range_threshold
+        and abs(metrics.slope_pct) <= slope_threshold
+        and metrics.std_pct <= std_threshold
+    )
+
+
+def export_results(path: str, results: Sequence[SidewaysMetrics]) -> None:
+    fieldnames = [
+        "symbol",
+        "price_range_pct",
+        "slope_pct",
+        "std_pct",
+        "mean_close",
+        "last_close",
+        "data_points",
+    ]
+    with open(path, "w", newline="", encoding="utf-8") as fp:
+        writer = csv.DictWriter(fp, fieldnames=fieldnames)
+        writer.writeheader()
+        for item in results:
+            writer.writerow(
+                {
+                    "symbol": item.symbol,
+                    "price_range_pct": f"{item.price_range_pct:.6f}",
+                    "slope_pct": f"{item.slope_pct:.6f}",
+                    "std_pct": f"{item.std_pct:.6f}",
+                    "mean_close": f"{item.mean_close:.8f}",
+                    "last_close": f"{item.last_close:.8f}",
+                    "data_points": item.data_points,
+                }
+            )
+    logging.info("结果已导出至 %s", path)
+
+
+def run(argv: Optional[Sequence[str]] = None) -> int:
+    args = parse_args(argv)
+    if not args.quote:
+        args.quote = ["USDT"]
+    setup_logging(args.verbose)
+
+    exchange = create_exchange()
+    symbols = iter_target_symbols(
+        exchange=exchange,
+        quotes=args.quote,
+        includes=args.symbol,
+        include_inactive=args.include_inactive,
+    )
+
+    if args.max_symbols is not None:
+        symbols = symbols[: args.max_symbols]
+        logging.info("出于调试目的，仅检测前 %d 个交易对。", len(symbols))
+
+    if not symbols:
+        logging.error("未找到任何满足条件的交易对，请检查过滤条件。")
+        return 1
+
+    sideways_results: List[SidewaysMetrics] = []
+    total = len(symbols)
+
+    for idx, symbol in enumerate(symbols, start=1):
+        logging.info("(%d/%d) 正在获取 %s 的 %s K 线（limit=%d）", idx, total, symbol, args.timeframe, args.limit)
+        ohlcv = fetch_ohlcv_with_retry(
+            exchange=exchange,
+            symbol=symbol,
+            timeframe=args.timeframe,
+            limit=args.limit,
+            retries=args.retries,
+            base_sleep=args.sleep,
+        )
+
+        if len(ohlcv) < max(100, args.limit // 2):
+            logging.debug("交易对 %s 返回数据不足（%d 根），跳过。", symbol, len(ohlcv))
+            continue
+
+        closes = [entry[4] for entry in ohlcv if entry[4] is not None]
+        metrics = compute_sideways_metrics(closes, symbol)
+        if not metrics:
+            continue
+
+        if is_sideways(metrics, args.range_threshold, args.slope_threshold, args.std_threshold):
+            sideways_results.append(metrics)
+            logging.info(
+                "识别为横盘：%s | 振幅 %.2f%% | 斜率 %.4f%% | 标准差 %.2f%%",
+                symbol,
+                metrics.price_range_pct * 100,
+                metrics.slope_pct * 100,
+                metrics.std_pct * 100,
+            )
+        else:
+            logging.debug(
+                "未满足条件：%s | 振幅 %.2f%% | 斜率 %.4f%% | 标准差 %.2f%%",
+                symbol,
+                metrics.price_range_pct * 100,
+                metrics.slope_pct * 100,
+                metrics.std_pct * 100,
+            )
+
+    if not sideways_results:
+        logging.warning("未检测到满足定义的长期横盘交易对。")
+        return 0
+
+    sideways_results.sort(key=lambda item: (item.price_range_pct, abs(item.slope_pct), item.std_pct))
+    print("=" * 88)
+    print(
+        f"共识别 {len(sideways_results)} 个长期横盘交易对（阈值：振幅≤{args.range_threshold:.2%}，"
+        f"斜率≤{args.slope_threshold:.2%}，标准差≤{args.std_threshold:.2%}）"
+    )
+    print("=" * 88)
+    header = f"{'Symbol':15s} {'Range%':>10s} {'Slope%':>10s} {'STD%':>10s} {'Mean':>14s} {'Last':>14s} {'Count':>6s}"
+    print(header)
+    print("-" * len(header))
+    for item in sideways_results:
+        print(
+            f"{item.symbol:15s}"
+            f" {item.price_range_pct * 100:10.4f}"
+            f" {item.slope_pct * 100:10.4f}"
+            f" {item.std_pct * 100:10.4f}"
+            f" {item.mean_close:14.8f}"
+            f" {item.last_close:14.8f}"
+            f" {item.data_points:6d}"
+        )
+
+    if args.export:
+        export_results(args.export, sideways_results)
+
+    logging.info("任务完成。")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(run())
+

chanlun/analysis/ChanLun_Classifier.py

@@ -0,0 +1,529 @@
+import sys 
+import os
+#sys.setrecursionlimit(1000000) #例如这里设置为一百万
+#sys.path.append(os.path.abspath("/freqtrade/user_data/Chan"))
+sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
+import numpy as np
+from datetime import timedelta
+from pandas import DataFrame
+from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS
+from chanlun.core.ChanBSP import ChanBSP
+import talib.abstract as ta
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.dates import DateFormatter, date2num
+import matplotlib.patches as patches
+from technical.util import resample_to_interval
+from decimal import Decimal
+from chanlun.pipeline.orchestrator import ChanLun
+import xgboost as xgb
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report
+
+class ChanLunClassifier:
+    def __init__(self, dataframe: DataFrame):
+        self.dataframe = dataframe
+        self.model = None
+    chan = ChanLun()
+
+    def train_model(self, dataframe=None, data_file_path=None, model_file_path='chan_xgb_model.json', use_cv=False, custom_params=None, model_name=None):
+        """
+        使用dataframe前80%的数据训练XGBoost模型
+        :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
+        :param data_file_path: 特征数据保存路径，可选
+        :param model_file_path: 模型保存路径
+        :param use_cv: 是否使用交叉验证寻找最佳参数
+        :param custom_params: 自定义模型参数
+        :return: 训练好的模型
+        """
+        if dataframe is None:
+            dataframe = self.dataframe
+        
+        # 分割数据集，前80%用于训练
+        train_size = int(len(dataframe) * 0.8)
+        train_df = dataframe.iloc[:train_size].copy()
+        
+        # 获取训练集特征和标签
+        save_csv = True if data_file_path else False
+        X_train, y_train = self.get_feature_data(train_df, save_csv=save_csv, csv_path=data_file_path if data_file_path else 'feature_data.csv')
+        
+        if len(X_train) == 0:
+            print("没有提取到足够的特征数据进行训练")
+            return None
+            
+        # 保存特征数据的步骤已经移到get_feature_data方法中处理
+        # 以下是原有代码
+        #{'eta': 0.03, 'max_depth': 4, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0.1, 'min_child_weight': 3, 'alpha': 1, 'lambda': 3},    
+        # 默认XGBoost参数
+        default_params = {
+            'objective': 'binary:logistic',
+            'max_depth': 8,
+            'eta': 0.01,
+            'subsample': 0.8,
+            'colsample_bytree': 0.8,
+            'eval_metric': 'auc',
+            'gamma': 0.0,
+            'min_child_weight': 1,
+            'alpha': 0, # L1正则化
+            'lambda': 0.5, # L2正则化
+            'scale_pos_weight': 1
+        }
+        
+        # 使用自定义参数覆盖默认参数
+        if custom_params:
+            for key, value in custom_params.items():
+                default_params[key] = value
+        
+        params = default_params
+        dtrain = xgb.DMatrix(X_train, label=y_train)
+        
+        # 如果使用交叉验证寻找最佳参数
+        if use_cv:
+            from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
+            from sklearn.metrics import make_scorer, accuracy_score, f1_score
+            import numpy as np
+            
+            # 转换为sklearn兼容格式
+            xgb_model = xgb.XGBClassifier(
+                objective=params['objective'],
+                max_depth=params['max_depth'],
+                learning_rate=params['eta'],
+                subsample=params['subsample'],
+                colsample_bytree=params['colsample_bytree'],
+                gamma=params['gamma'],
+                min_child_weight=params['min_child_weight'],
+                reg_alpha=params['alpha'],
+                reg_lambda=params['lambda'],
+                scale_pos_weight=params['scale_pos_weight'],
+                use_label_encoder=False,
+                eval_metric='auc'
+            )
+            
+            # 参数网格
+            param_grid = {
+                'max_depth': [3, 5, 7, 9],
+                'learning_rate': [0.01, 0.05, 0.1, 0.2],
+                'subsample': [0.6, 0.8, 1.0],
+                'colsample_bytree': [0.6, 0.8, 1.0],
+                'min_child_weight': [1, 3, 5],
+                'gamma': [0, 0.1, 0.2],
+                'n_estimators': [50, 100, 200]
+            }
+            
+            # 使用随机搜索寻找最佳参数（比网格搜索快）
+            random_search = RandomizedSearchCV(
+                estimator=xgb_model,
+                param_distributions=param_grid,
+                n_iter=10,  # 随机尝试的参数组合数
+                scoring=make_scorer(f1_score),
+                cv=5,
+                verbose=1,
+                n_jobs=-1,
+                random_state=42
+            )
+            
+            print("进行交叉验证参数搜索...")
+            random_search.fit(X_train, y_train)
+            
+            # 获取最佳参数
+            best_params = random_search.best_params_
+            print(f"最佳参数: {best_params}")
+            
+            # 使用最佳参数更新模型参数
+            params['max_depth'] = best_params['max_depth']
+            params['eta'] = best_params['learning_rate']
+            params['subsample'] = best_params['subsample']
+            params['colsample_bytree'] = best_params['colsample_bytree']
+            params['min_child_weight'] = best_params['min_child_weight']
+            params['gamma'] = best_params['gamma']
+            num_round = best_params['n_estimators']
+
+            # 使用最佳参数训练最终模型
+            self.model = xgb.train(params, dtrain, num_round)
+        else:
+            # 标准训练（不使用交叉验证）
+            # 使用早停机制避免过拟合
+            # 分割训练集为训练和验证
+            eval_size = int(len(X_train) * 0.2)
+            X_eval = X_train[-eval_size:]
+            y_eval = y_train[-eval_size:]
+            X_train_part = X_train[:-eval_size]
+            y_train_part = y_train[:-eval_size]
+            
+            dtrain_part = xgb.DMatrix(X_train_part, label=y_train_part)
+            deval = xgb.DMatrix(X_eval, label=y_eval)
+            
+            # 评估列表
+            evallist = [(dtrain_part, 'train'), (deval, 'eval')]
+            
+            # 训练模型，使用早停
+            num_round = 1000  # 设置较大的轮数，让早停机制决定何时停止
+            self.model = xgb.train(
+                params, 
+                dtrain_part, 
+                num_round, 
+                evallist, 
+                early_stopping_rounds=50, # 50轮内评估指标无改善则停止
+                verbose_eval=True
+            )
+            
+            # 使用全部训练数据重新训练最终模型，使用最佳轮数
+            # best_rounds = self.model.best_ntree_limit
+            # 兼容新版本的XGBoost
+            if hasattr(self.model, 'best_ntree_limit'):
+                best_rounds = self.model.best_ntree_limit
+            elif hasattr(self.model, 'best_iteration'):
+                best_rounds = self.model.best_iteration
+            elif hasattr(self.model, 'best_ntree_idx'):
+                best_rounds = self.model.best_ntree_idx
+            else:
+                # 如果都不存在，使用默认值
+                best_rounds = num_round
+            print(f"最佳轮数: {best_rounds}")
+            
+            # 使用全部训练数据和最佳轮数训练最终模型
+            self.model = xgb.train(params, dtrain, best_rounds)
+        
+        # 保存模型
+        if model_file_path:
+            self.model.save_model(model_name + model_file_path)
+            
+        # 特征重要性分析
+        if hasattr(self.model, 'get_score'):
+            importance = self.model.get_score(importance_type='gain')
+            print("\n特征重要性 (gain):")
+            for key, value in sorted(importance.items(), key=lambda x: x[1], reverse=True):
+                print(f"{key}: {value}")
+            
+        return self.model
+    def load_model(self, model_name=None, model_file_path='chan_xgb_model.json'):
+        if model_name:
+            self.model = xgb.Booster()
+            self.model.load_model(model_name + model_file_path)
+        else:
+            self.model = xgb.Booster()
+            self.model.load_model(model_file_path)
+    def find_best_params(self, dataframe=None, save_csv=False, csv_path_prefix='param_', model_name=None):
+        """
+        寻找最佳参数组合
+        :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path_prefix: CSV文件保存路径前缀，会自动添加参数信息
+        :return: 最佳参数
+        """
+        # 不同参数组合
+        param_combinations = [
+            # 低学习率，深树
+            {'eta': 0.01, 'max_depth': 8, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'min_child_weight': 1},
+            # 中等学习率，中等树深度
+            {'eta': 0.05, 'max_depth': 5, 'subsample': 0.7, 'colsample_bytree': 0.7, 'gamma': 0.1, 'min_child_weight': 3},
+            # 高学习率，浅树
+            {'eta': 0.1, 'max_depth': 3, 'subsample': 0.6, 'colsample_bytree': 0.6, 'gamma': 0.2, 'min_child_weight': 5},
+            # 正则化较强 best here
+            {'eta': 0.03, 'max_depth': 4, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0.1, 'min_child_weight': 3, 'alpha': 1, 'lambda': 3},
+            # 正则化较弱
+            {'eta': 0.08, 'max_depth': 6, 'subsample': 0.9, 'colsample_bytree': 0.9, 'gamma': 0, 'min_child_weight': 1, 'alpha': 0, 'lambda': 0.5},
+        ]
+        
+        best_score = 0
+        best_params = None
+        best_model = None
+        
+        for i, params in enumerate(param_combinations):
+            print(f"\n尝试参数组合: {params}")
+            # 生成CSV文件名，包含一些参数信息
+            param_info = f"eta{params['eta']}_depth{params['max_depth']}"
+            train_csv_path = f"{csv_path_prefix}train_{param_info}.csv" if save_csv else None
+            
+            model = self.train_model(dataframe=dataframe, data_file_path=train_csv_path, custom_params=params, model_name=model_name)
+            
+            # 分割数据集，后20%用于测试
+            if dataframe is None:
+                dataframe = self.dataframe
+            
+            train_size = int(len(dataframe) * 0.8)
+            test_df = dataframe.iloc[train_size:].copy()
+            
+            # 获取测试集特征和标签
+            test_csv_path = f"{csv_path_prefix}test_{param_info}.csv" if save_csv else None
+            X_test, y_test = self.get_validate_feature_data(test_df, save_csv=save_csv, csv_path=test_csv_path)
+            
+            if len(X_test) == 0:
+                print("没有提取到足够的测试特征数据")
+                continue
+            
+            # 预测
+            dtest = xgb.DMatrix(X_test)
+            y_pred_prob = model.predict(dtest)
+            y_pred = [1 if p > 0.5 else 0 for p in y_pred_prob]
+            
+            # 计算F1分数
+            f1 = f1_score(y_test, y_pred, zero_division=0)
+            print(f"F1分数: {f1:.4f}")
+            
+            if f1 > best_score:
+                best_score = f1
+                best_params = params
+                best_model = model
+                
+        print(f"\n最佳参数组合 (F1={best_score:.4f}):")
+        print(best_params)
+        self.model = best_model
+        
+        return best_params
+    
+    def get_feature_data(self, dataframe, save_csv=False, csv_path='feature_data.csv'):
+        """
+        从dataframe提取特征数据
+        :param dataframe: 输入的DataFrame
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path: CSV文件保存路径
+        :return: 特征矩阵X和标签y
+        """
+        # 使用ChanLun获取bi_list
+        klc_list = self.chan.get_klc_list(dataframe)
+        bi_list = self.chan.cal_bi_list(klc_list)
+        # 筛选方向为UP的bi的起始klc
+        feature_data = []
+        labels = []
+        feature_keys = []  # 用于保存特征名称
+        
+        bi_index = 1
+        sample_list = []
+        for klc in klc_list:
+            if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
+                sample_list.append(klc)
+        klc_count = 0
+        print('Processing data...')
+        for klc in sample_list:
+            if bi_index >= len(bi_list):
+                bi_index = len(bi_list) - 1
+            #bi = bi_list[bi_index]
+            #if klc.end_klu and bi.end_klc and klc.start_klu.index >= bi.start_klc.start_klu.index and klc.end_klu.index <= bi.end_klc.end_klu.index:
+                #klc.set_bi(bi)
+            
+            # 提取特征
+            features = klc.get_feature_data()
+            
+            # 保存第一个样本的特征名称，用于CSV列名
+            if len(feature_keys) == 0:
+                feature_keys = list(features.keys())
+            # 将特征转换为模型可用的格式
+            feature_vec = []
+            for key, value in features.items():
+                if isinstance(value, (int, float)):
+                    feature_vec.append(value)
+                else:
+                    feature_vec.append(0)
+            
+            # 判断这个bi是否赚钱（这里简单定义为：如果bi的结束价格高于起始价格，则标记为1，否则为0）
+            # 这个标签定义可以根据实际需求修改
+            matched = False
+            for bi in bi_list:
+                if bi.end_klc and bi.end_klc.index == klc.index:
+                    #print(bi.start_time, bi.start_klc.start_time, bi.dir)
+                    label = 1
+                    matched = True
+                    break
+            if not matched:
+                label = 0
+            
+            feature_data.append(feature_vec)
+            labels.append(label)
+            klc_count += 1
+            percent = klc_count/len(sample_list)*100
+            if percent % 10 == 0:
+                print('Data processed:', percent, '%')
+        for index, key in enumerate(feature_keys):
+            print(index, key, feature_data[0][index])
+        # 如果需要保存到CSV
+        if save_csv:
+            # 创建DataFrame保存特征数据
+            # 只保留数值型特征
+            numeric_feature_keys = [key for i, key in enumerate(feature_keys) 
+                                   if i < len(feature_data[0]) if isinstance(feature_data[0][i], (int, float))]
+            
+            # 创建特征数据的DataFrame
+            df_features = pd.DataFrame(feature_data, columns=numeric_feature_keys)
+            # 添加标签列
+            df_features['label'] = labels
+            # 添加时间信息便于分析
+            if len(sample_list) > 0:
+                times = [klc.start_time for klc in sample_list]
+                df_features['time'] = times
+            
+            # 保存到CSV
+            df_features.to_csv(csv_path, index=False)
+            print(f"特征数据已保存到 {csv_path}")
+            
+        # 在return前添加
+        positive_count = np.sum(labels)
+        print(f"正样本数量: {positive_count}, 负样本数量: {len(labels) - positive_count}")
+        print("Trainning data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
+        return np.array(feature_data), np.array(labels)
+    def get_validate_feature_data(self, dataframe, save_csv=False, csv_path='validate_feature_data.csv'):
+        """
+        从dataframe提取特征数据
+        :param dataframe: 输入的DataFrame
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path: CSV文件保存路径
+        :return: 特征矩阵X和标签y
+        """
+        # 使用ChanLun获取bi_list
+        klc_list = self.chan.get_klc_list(dataframe)
+        bi_list = self.chan.cal_bi_list(klc_list)
+        seg_list = self.chan.get_seg_list(bi_list)
+        # 筛选方向为UP的bi的起始klc
+        feature_data = []
+        labels = []
+        feature_keys = []  # 用于保存特征名称
+        
+        bi_index = 1
+        sample_list = []
+        for klc in klc_list:
+            if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
+                sample_list.append(klc)
+        for klc in sample_list:
+            if bi_index >= len(bi_list):
+                bi_index = len(bi_list) - 1
+            bi = bi_list[bi_index]
+            # 提取特征
+            features = klc.get_feature_data()
+            
+            # 保存第一个样本的特征名称，用于CSV列名
+            if len(feature_keys) == 0:
+                feature_keys = list(features.keys())
+            
+            # 将特征转换为模型可用的格式
+            feature_vec = []
+            # 与get_feature_data保持一致，只使用相同的特征集
+            for key, value in features.items():
+                if isinstance(value, (int, float)):
+                    feature_vec.append(value)
+                else:
+                    feature_vec.append(0)
+            seg = seg_list[bi_index]
+            matched = False
+            for bi in bi_list:
+                if bi.end_klc and bi.end_klc.index == klc.index:
+                    label = 1
+                    matched = True
+                    break
+            if not matched:
+                label = 0
+            
+            feature_data.append(feature_vec)
+            labels.append(label)
+            
+        # 如果需要保存到CSV
+        if save_csv:
+            # 创建DataFrame保存特征数据
+            # 只保留数值型特征
+            numeric_feature_keys = [key for i, key in enumerate(feature_keys) 
+                                  if i < len(feature_data[0]) if isinstance(feature_data[0][i], (int, float))]
+            
+            # 创建特征数据的DataFrame
+            df_features = pd.DataFrame(feature_data, columns=numeric_feature_keys)
+            # 添加标签列
+            df_features['label'] = labels
+            # 添加时间信息便于分析
+            if len(sample_list) > 0:
+                times = [klc.start_time for klc in sample_list]
+                df_features['time'] = times
+            
+            # 保存到CSV
+            df_features.to_csv(csv_path, index=False)
+            print(f"验证特征数据已保存到 {csv_path}")
+            
+        print("Validating data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
+        return np.array(feature_data), np.array(labels)
+    def validate_model(self, dataframe=None, save_csv=False, csv_path='validate_feature_data.csv'):
+        """
+        使用dataframe后20%的数据验证模型
+        :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
+        :param save_csv: 是否保存特征数据到CSV文件
+        :param csv_path: CSV文件保存路径
+        :return: 验证结果
+        """
+        if self.model is None:
+            print("模型尚未训练，请先调用train_model方法")
+            return None
+            
+        if dataframe is None:
+            dataframe = self.dataframe
+        
+        # 分割数据集，后20%用于测试
+        train_size = int(len(dataframe) * 0.8)
+        test_df = dataframe.iloc[train_size:].copy()
+        
+        # 获取测试集特征和标签
+        X_test, y_test = self.get_validate_feature_data(test_df, save_csv=save_csv, csv_path=csv_path)
+        
+        if len(X_test) == 0:
+            print("没有提取到足够的测试特征数据")
+            return None
+        
+        # 预测
+        dtest = xgb.DMatrix(X_test)
+        y_pred_prob = self.model.predict(dtest)
+        y_pred = [1 if p > 0.5 else 0 for p in y_pred_prob]
+        
+        # 计算评估指标
+        accuracy = accuracy_score(y_test, y_pred)
+        precision = precision_score(y_test, y_pred, zero_division=0)
+        recall = recall_score(y_test, y_pred, zero_division=0)
+        f1 = f1_score(y_test, y_pred, zero_division=0)
+        
+        # 打印评估报告
+        print("模型评估结果:")
+        print(f"准确率: {accuracy:.4f}")
+        print(f"精确率: {precision:.4f}")
+        print(f"召回率: {recall:.4f}")
+        print(f"F1分数: {f1:.4f}")
+        print("\n分类报告:")
+        print(classification_report(y_test, y_pred, zero_division=0))
+        
+        return {
+            'accuracy': accuracy,
+            'precision': precision,
+            'recall': recall,
+            'f1': f1,
+            'y_test': y_test,
+            'y_pred': y_pred,
+            'y_pred_prob': y_pred_prob
+        }
+    
+    def predict(self, klc):
+        """
+        使用训练好的模型预测单个KLC
+        :param klc: 需要预测的ChanKLC对象
+        :return: 预测结果（概率值）
+        """
+        if self.model is None:
+            print("模型尚未训练，请先调用train_model方法")
+            return None
+            
+        # 提取特征
+        features = klc.get_feature_data()
+        feature_vec = []
+        # 与get_feature_data保持一致，只使用相同的特征集
+        for key, value in features.items():
+            if isinstance(value, (int, float)):
+                feature_vec.append(value)
+            else:
+                feature_vec.append(0)
+                
+        # 转换为模型输入格式
+        dtest = xgb.DMatrix(np.array([feature_vec]))
+        
+        # 预测
+        return self.get_decimal(self.model.predict(dtest)[0])
+    
+    def get_decimal(self, value):
+        return Decimal("{:.4f}".format(value))
+    

chanlun/analysis/ChanPY.py

@@ -0,0 +1,402 @@
+import sys 
+import os
+#sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/chan.py"))
+sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
+from Chan import CChan
+from BuySellPoint.BS_Point import CBS_Point
+from ChanConfig import CChanConfig
+from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, DATA_FIELD, BSP_TYPE, FX_TYPE, BI_DIR, KLINE_DIR, SEG_DIR
+from KLine.KLine_Unit import CKLine_Unit
+from Common.CTime import CTime
+from Common.func_util import kltype_lt_day, str2float
+from Bi.Bi import CBi
+from typing import Dict, List
+from functools import reduce
+from pandas import DataFrame
+from datetime import datetime, timedelta, timezone
+
+
+def GetColumnNameFromFieldList(fileds: str):
+	_dict = {
+		"time": DATA_FIELD.FIELD_TIME,
+		"open": DATA_FIELD.FIELD_OPEN,
+		"high": DATA_FIELD.FIELD_HIGH,
+		"low": DATA_FIELD.FIELD_LOW,
+		"close": DATA_FIELD.FIELD_CLOSE,
+		"volume": DATA_FIELD.FIELD_VOLUME
+	}
+	return [_dict[x] for x in fileds.split(",")]
+class ChanPY():
+	k_type = KL_TYPE.K_5M
+	config = CChanConfig({
+		"bi_strict": True,
+		"bi_algo": "normal",
+		"trigger_step": True,
+		"skip_step": 0,
+		"divergence_rate": float("inf"),
+		"bsp2_follow_1": False,
+		"bsp3_follow_1": False,
+		"min_zs_cnt": 1,
+		"bs1_peak": False,
+		"macd_algo": "peak",
+		"bs_type": '1,2,3a,1p,2s,3b',
+		"print_warning": True,
+		"zs_algo": "normal",
+	})
+	chan = CChan(
+		code="BTC/USDT:USDT",
+		data_src=DATA_SRC.CCXT,
+		lv_list=[k_type],
+		config=config,
+		autype=AUTYPE.QFQ,
+	)
+	klu_list = []
+	bsps = []
+	chanIn = True
+	#def __init__(self, dataframe):
+		#self.klu_list = self.get_kl_data(dataframe)
+		#for klu in self.klu_list:
+			#self.chan.trigger_load({self.k_type: [klu]})
+	def add_klu(self, klu):
+		if klu:
+			self.chan.trigger_load({self.k_type: [klu]})
+			self.klu_list.append(klu)
+	def add_klu_from_dataframe(self, dataframe):
+		if len(dataframe) > len(self.klu_list) and len(dataframe) - len(self.klu_list) == 1:
+			klu = self.get_last_klu(dataframe)
+			self.chan.trigger_load({self.k_type: [klu]})
+			self.klu_list.append(klu)
+	def parse_time_column(self, inp):
+		if len(inp) == 10:
+			year = int(inp[:4])
+			month = int(inp[5:7])
+			day = int(inp[8:10])
+			hour = minute = 0
+		elif len(inp) == 17:
+			year = int(inp[:4])
+			month = int(inp[4:6])
+			day = int(inp[6:8])
+			hour = int(inp[8:10])
+			minute = int(inp[10:12])
+		elif len(inp) == 19:
+			year = int(inp[:4])
+			month = int(inp[5:7])
+			day = int(inp[8:10])
+			hour = int(inp[11:13])
+			minute = int(inp[14:16])
+		else:
+			raise Exception(f"unknown time column from TradingView:{inp}")
+		return CTime(year, month, day, hour, minute, auto=not kltype_lt_day(self.k_type))
+
+	def create_item_dict(self, data, column_name):
+		for i in range(len(data)):
+			data[i] = self.parse_time_column(data[i]) if i == 0 else str2float(data[i])
+		return dict(zip(column_name, data))
+	def get_last_klu(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		item = dataframe.iloc[-1]
+		date = item['date']
+		o = item['open']
+		h = item['high']
+		l = item['low']
+		c = item['close']
+		v = item['volume']
+		#time_obj = date.fromtimestamp(date)
+		time_str = date.strftime('%Y-%m-%d %H:%M:%S')
+		item_data = [
+			time_str,
+			o,
+			h,
+			l,
+			c,
+			v
+		]
+		klu =  CKLine_Unit(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)), autofix=True)
+		klu.set_idx(len(dataframe)-1)
+		return klu
+	def get_kl_data(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		klu_list = []
+		for i in range(0, len(dataframe)):
+			item = dataframe.iloc[i]
+			date = item['date']
+			o = item['open']
+			h = item['high']
+			l = item['low']
+			c = item['close']
+			v = item['volume']
+			#time_obj = date.fromtimestamp(date)
+			time_str = date.strftime('%Y-%m-%d %H:%M:%S')
+			item_data = [
+				time_str,
+				o,
+				h,
+				l,
+				c,
+				v
+			]
+			klu =  CKLine_Unit(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)), autofix=True)
+			klu.set_idx(i)
+			klu_list.append(klu)
+		return klu_list
+	def get_bsp_type(self, bsp_type, is_buy):
+		if is_buy:
+			if bsp_type == BSP_TYPE.T1:
+				return 1
+			if bsp_type == BSP_TYPE.T1P:
+				return 2
+			if bsp_type == BSP_TYPE.T2:
+				return 3
+			if bsp_type == BSP_TYPE.T2S:
+				return 4
+			if bsp_type == BSP_TYPE.T3A:
+				return 5
+			if bsp_type == BSP_TYPE.T3B:
+				return 6
+		else:
+			if bsp_type == BSP_TYPE.T1:
+				return -1
+			if bsp_type == BSP_TYPE.T1P:
+				return -2
+			if bsp_type == BSP_TYPE.T2:
+				return -3
+			if bsp_type == BSP_TYPE.T2S:
+				return -4
+			if bsp_type == BSP_TYPE.T3A:
+				return -5
+			if bsp_type == BSP_TYPE.T3B:
+				return -6
+	def get_bsps(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		bsps = []
+		updown = []
+		bi_sure = []
+		if self.chanIn:
+			kl_data = self.get_kl_data(dataframe)
+			bsp_list = []
+			bsp_list_pre_len = 0
+			last_bsp_value = 0
+			last_updown = -1
+			bi_list_pre_len = 0
+			pre_bi = None
+			zs_list_pre_len = 0
+			pre_zs = None
+			for klu in kl_data:  # 获取单根K线
+				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
+				self.last_kline = klu
+				bsp_list = self.chan.get_bsp()
+				kl_datas = self.chan.kl_datas[self.k_type]
+				bi_list = kl_datas.bi_list
+				lst = kl_datas.lst
+				if len(bsp_list) > 0:
+					last_bsp = bsp_list[-1]
+					#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, lst[-2].fx, bi_list[-1].dir, bi_list[-1].is_sure,klu.close)
+					if bsp_list_pre_len > len(bsp_list):
+						if abs(last_bsp_value) == 1 or abs(last_bsp_value) == 2:
+							bsps.append(1)
+							#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, 98)
+						else:
+							bsps.append(99)
+					else:
+						if bsp_list_pre_len == len(bsp_list):
+							if klu.idx == last_bsp.klu.idx:
+								last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
+								bsps.append(last_bsp_value)
+								#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value)
+							else:
+								bsps.append(0)
+						else:
+							last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
+							bsps.append(last_bsp_value)
+							#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value)
+				else:
+					bsps.append(0)
+				bsp_list_pre_len = len(bsp_list)
+				
+				#Check zs -----------------------------------
+				zs_list = kl_datas.zs_list
+				if len(zs_list) > 0:
+					zs = zs_list[-1]
+					#if zs_list_pre_len > len(zs_list):
+						#print("No zs", zs.begin.time)
+					#if len(zs_list) > zs_list_pre_len:
+						#print(zs.begin.time, zs.end.time, zs.end.idx, zs.high, zs.low, zs.peak_high, zs.peak_low)
+					zs_list_pre_len = len(zs_list)
+					pre_zs = zs
+
+				#Check Bi -----------------------------------
+				if len(bi_list) > 0:
+					last_bi = bi_list[-1]
+					if len(bi_list) == 1:
+						if last_bi.dir == BI_DIR.UP:
+							updown.append(1)
+							last_updown = 1
+						else:
+							updown.append(-1)
+							last_updown = -1
+					else:
+						if last_updown == 1:
+							if last_bi.dir == BI_DIR.UP:
+								updown.append(0)
+							else:
+								updown.append(-1)
+								last_updown = -1
+						else:
+							if last_bi.dir == BI_DIR.DOWN:
+								updown.append(0)
+							else:
+								updown.append(1)
+								last_updown = 1
+				else:
+					updown.append(0)
+				bi_list = kl_datas.bi_list
+				if len(bi_list) > 0:
+					last_bi = bi_list[-1]
+					#if bi_list_pre_len > len(bi_list):
+						#print("Bi ", klu.time, pre_bi.idx, pre_bi.is_sure, bi_list[-1].idx, bi_list[-1].is_sure)
+					if last_bi.is_sure:
+						bi_sure.append(1)
+						#print(klu.time, last_bi.is_sure)
+					else:
+						bi_sure.append(0)
+					pre_bi = bi_list[-1]
+					bi_list_pre_len = len(bi_list)
+				else:
+					bi_sure.append(0)
+				#if bsps[-1] != 0 or updown[-1] != 0:
+					#print(klu.time, bsps[-1], updown[-1], bi_list[-1].is_sure)
+			self.chanIn = False
+		else:
+			klu = self.get_last_klu(dataframe)
+			if self.last_kline.time < klu.time:
+				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
+				self.last_kline = klu
+		for index in range(0, len(bsps)):
+			if not (abs(bsps[index]) == 1 or abs(bsps[index]) == 2):
+				bsps[index] = 0
+			else:
+				if bsps[index] == 2:
+					bsps[index] = 1
+				else:
+					if bsps[index] == -2:
+						bsps[index] = -1
+					else:
+						bsps[index] = 0
+		#print(bsps)
+		#print(updown)
+		kl_datas = self.chan.kl_datas[self.k_type]
+		#for zs in kl_datas.zs_list:
+			#print(zs.begin.time, zs.end.time)
+		return bsps, updown, bi_sure
+	
+	def get_bsp_state1(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		bsps = []
+		if self.chanIn:
+			kl_data = self.get_kl_data(dataframe)
+			self.chan.trigger_load({self.k_type: kl_data})
+			bsp_list = self.chan.get_bsp()
+			bsp_index = 0
+			for klu in kl_data:
+				if bsp_index >= len(bsp_list):
+					bsp_index = len(bsp_list) - 1
+				bsp = bsp_list[bsp_index]
+				if klu.idx == bsp.klu.idx:
+					bsp_type = self.get_bsp_type(bsp.type[0], bsp.is_buy)
+					if abs(bsp_type) == 1 or abs(bsp_type) == 10:
+						bsps.append(1)
+					else:
+						bsps.append(0)
+					bsp_index = bsp_index + 1
+				else:
+					bsps.append(0)
+			self.chanIn = False
+		else:
+			klu = CKLine_Unit(self.create_item_dict(self.get_last_item_data(dataframe), GetColumnNameFromFieldList(fields)), autofix=True)
+			if self.last_kline.time < klu.time:
+				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
+				self.last_kline = klu
+		return bsps
+	def get_bsp_state(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		if self.chanIn:
+			kl_data = self.get_kl_data(dataframe)
+			bsp_list = []
+			bsp_list_pre_len = 0
+			last_bsp_value = 0
+			last_bsp_index = 0
+			for klu in kl_data:  # 获取单根K线
+				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
+				self.last_kline = klu
+				bsp_list = self.chan.get_bsp()
+				kl_datas = self.chan.kl_datas[self.k_type]
+				bi_list = kl_datas.bi_list
+				lst = kl_datas.lst
+				if len(bsp_list) > 0:
+					last_bsp = bsp_list[-1]
+					#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, lst[-2].fx, bi_list[-1].dir, bi_list[-1].is_sure,klu.close)
+					if bsp_list_pre_len > len(bsp_list):
+						if abs(last_bsp_value) == 1:
+							self.bsps.append(1)
+							#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, 98)
+						else:
+							self.bsps.append(99)
+					else:
+						if bsp_list_pre_len == len(bsp_list):
+							if klu.idx == last_bsp.klu.idx:
+								if  last_bsp.klu.idx - last_bsp_index > 3:
+									last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
+									self.bsps.append(last_bsp_value)
+								else:
+									self.bsps.append(0)
+								last_bsp_index = last_bsp.klu.idx
+									#if abs(last_bsp_value) == 1 or abs(last_bsp_value) == 2:
+										#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, "Knonw")
+							else:
+								self.bsps.append(0)
+						else:
+							if klu.idx == last_bsp.klu.idx:
+								if  last_bsp.klu.idx - last_bsp_index > 3:
+									last_bsp_value = self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy)
+									self.bsps.append(last_bsp_value)
+								else:
+									self.bsps.append(0)
+								last_bsp_index = last_bsp.klu.idx
+									#if abs(last_bsp_value) == 1 or abs(last_bsp_value) == 2:
+										#print(klu.time, klu.idx, last_bsp.klu.time, last_bsp.klu.idx, last_bsp_value, "Knonw")
+							else:
+								self.bsps.append(0)
+				else:
+					self.bsps.append(0)
+				bsp_list_pre_len = len(bsp_list)
+			self.chanIn = False
+		else:
+			klu = self.get_last_klu(dataframe)
+			if self.last_kline.time < klu.time:
+				self.chan.trigger_load({self.k_type: [klu]})  # 喂给CChan新增k线
+				self.last_kline = klu
+				bsp_list = self.chan.get_bsp()
+				last_bsp = bsp_list[-1]
+				if last_bsp.klu.idx == klu.idx:
+					self.bsps.append(self.get_bsp_type(last_bsp.type[0], last_bsp.is_buy))
+				else:
+					self.bsps.append(0)
+		for index in range(0, len(self.bsps)):
+			if not (abs(self.bsps[index]) == 1 or abs(self.bsps[index]) == 2):
+				self.bsps[index] = 0
+			else:
+				if self.bsps[index] == 2:
+					self.bsps[index] = 10
+				else:
+					if self.bsps[index] == -2:
+						self.bsps[index] = -10
+					else:
+						if self.bsps[index] == 1:
+							self.bsps[index] = 1
+						else:
+							if self.bsps[index] == -1:
+								self.bsps[index] = -1
+							else:
+								self.bsps[index] = 0
+		return self.bsps
+

chanlun/analysis/ChanPivotClassifier.py

@@ -0,0 +1,292 @@
+"""
+中枢结构特征提取 + 标签化
+Market Structure Dataset Builder — Phase 1
+
+定位: 训练数据集构建工具，不是交易信号生成器。
+Feature 描述中枢内部结构，Label 记录中枢后实际演化。
+"""
+
+import math
+import json
+from typing import Optional
+from chanlun.core.ChanEnum import Chan_BI_DIR
+
+
+class ChanPivotClassifier:
+    """
+    中枢结构特征提取 + 标签化
+    输入: bi_zs_list (list[ChanBIZS])
+    输出: 结构化数据集 (list[dict])
+    """
+
+    DATASET_VERSION = "pivot_v1"
+    FEATURE_SCHEMA = ["duration_norm", "contraction", "shift_norm"]
+    LABEL_SCHEMA = {"name": "break_direction", "values": ["up", "down", "none"]}
+
+    def __init__(self, bi_zs_list: list, symbol: str = "", timeframe: str = ""):
+        self.bi_zs_list = bi_zs_list
+        self.symbol = symbol
+        self.timeframe = timeframe
+
+    # ------------------------------------------------------------------
+    # Feature extraction
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def calc_duration(zs) -> int:
+        """持续时间: 第一笔首K → 最后一笔末K 的 index 差"""
+        bi_list = zs.bi_list
+        start_idx = bi_list[0].start_klc.index
+        end_idx = bi_list[-1].end_klc.index
+        return end_idx - start_idx
+
+    @staticmethod
+    def calc_contraction(zs) -> float:
+        """收敛率: 后窗口振幅均值 / 前窗口振幅均值"""
+        bi_list = zs.bi_list
+        if len(bi_list) < 4:
+            return 1.0
+
+        n = min(3, len(bi_list) // 2)
+        first_ranges = [bi.high - bi.low for bi in bi_list[:n]]
+        last_ranges = [bi.high - bi.low for bi in bi_list[-n:]]
+
+        first_mean = sum(first_ranges) / len(first_ranges)
+        last_mean = sum(last_ranges) / len(last_ranges)
+
+        if first_mean == 0:
+            return 1.0
+        return last_mean / first_mean
+
+    @staticmethod
+    def calc_shift(zs) -> tuple[float, float]:
+        """重心漂移: 前后半段重心均值差 (原始值, 归一化值)"""
+        bi_list = zs.bi_list
+        mid = len(bi_list) // 2
+
+        first_centers = [(bi.high + bi.low) / 2 for bi in bi_list[:mid]]
+        last_centers = [(bi.high + bi.low) / 2 for bi in bi_list[mid:]]
+
+        shift_raw = (
+            sum(last_centers) / len(last_centers)
+            - sum(first_centers) / len(first_centers)
+        )
+
+        zs_height = zs.zg - zs.zd
+        if zs_height == 0:
+            shift_norm = 0.0
+        else:
+            shift_norm = shift_raw / zs_height
+
+        return shift_raw, shift_norm
+
+    @staticmethod
+    def compute_duration_norm(duration_raw: int, historical_durations: list) -> float:
+        """用历史窗口均值归一化 duration"""
+        if not historical_durations:
+            return 1.0
+        avg = sum(historical_durations) / len(historical_durations)
+        if avg == 0:
+            return 1.0
+        return duration_raw / avg
+
+    @staticmethod
+    def compute_features(zs, historical_durations: Optional[list] = None):
+        """计算单个中枢的全部结构特征（实时友好）"""
+        duration_raw = ChanPivotClassifier.calc_duration(zs)
+        contraction = ChanPivotClassifier.calc_contraction(zs)
+        shift_raw, shift_norm = ChanPivotClassifier.calc_shift(zs)
+
+        if historical_durations is not None and len(historical_durations) > 0:
+            duration_norm = ChanPivotClassifier.compute_duration_norm(
+                duration_raw, historical_durations
+            )
+        else:
+            duration_norm = 1.0
+
+        return {
+            "duration_raw": duration_raw,
+            "duration_norm": round(duration_norm, 4),
+            "contraction": round(contraction, 4),
+            "shift_raw": round(shift_raw, 6),
+            "shift_norm": round(shift_norm, 4),
+            "zs_height": round(zs.zg - zs.zd, 6),
+        }
+
+    # ------------------------------------------------------------------
+    # Label computation
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _clamp(x: float, lo: float = 0.0, hi: float = 1.0) -> float:
+        return max(lo, min(hi, x))
+
+    def _compute_label(self, zs, contraction: float, shift_norm: float) -> dict:
+        """计算标签: up / down / none + 连续置信度"""
+        bi_out = zs.bi_out
+
+        if bi_out is None:
+            return {
+                "label": "none",
+                "label_confidence": 0.0,
+                "label_detail": {
+                    "bi_out_dir": "none",
+                    "score_breakout": 0.0,
+                    "score_shift": 0.0,
+                    "score_contraction": 0.0,
+                },
+            }
+
+        zs_height = zs.zg - zs.zd
+        if zs_height == 0:
+            zs_height = 1e-8
+
+        # ---- 向上突破分数 ----
+        if bi_out.dir == Chan_BI_DIR.UP:
+            raw_breakout = (bi_out.high - zs.gg) / zs_height
+            score_breakout_up = self._clamp(raw_breakout)
+            score_shift_up = math.tanh(self._clamp(shift_norm, -3.0, 3.0))
+            score_contraction_up = max(0.0, 1.0 - contraction)
+        else:
+            score_breakout_up = 0.0
+            score_shift_up = 0.0
+            score_contraction_up = 0.0
+
+        up_score = (
+            score_breakout_up * 0.5
+            + score_shift_up * 0.3
+            + score_contraction_up * 0.2
+        )
+
+        # ---- 向下突破分数 ----
+        if bi_out.dir == Chan_BI_DIR.DOWN:
+            raw_breakout = (zs.dd - bi_out.low) / zs_height
+            score_breakout_down = self._clamp(raw_breakout)
+            score_shift_down = math.tanh(self._clamp(-shift_norm, -3.0, 3.0))
+            score_contraction_down = max(0.0, 1.0 - contraction)
+        else:
+            score_breakout_down = 0.0
+            score_shift_down = 0.0
+            score_contraction_down = 0.0
+
+        down_score = (
+            score_breakout_down * 0.5
+            + score_shift_down * 0.3
+            + score_contraction_down * 0.2
+        )
+
+        # ---- 判定 ----
+        threshold = 0.15
+
+        if up_score > down_score and up_score > threshold:
+            label = "up"
+            confidence = up_score
+            detail = {
+                "bi_out_dir": "up",
+                "score_breakout": round(score_breakout_up, 4),
+                "score_shift": round(score_shift_up, 4),
+                "score_contraction": round(score_contraction_up, 4),
+            }
+        elif down_score > up_score and down_score > threshold:
+            label = "down"
+            confidence = down_score
+            detail = {
+                "bi_out_dir": "down",
+                "score_breakout": round(score_breakout_down, 4),
+                "score_shift": round(score_shift_down, 4),
+                "score_contraction": round(score_contraction_down, 4),
+            }
+        else:
+            label = "none"
+            confidence = max(up_score, down_score)
+            bi_dir = "up" if bi_out.dir == Chan_BI_DIR.UP else "down"
+            detail = {
+                "bi_out_dir": bi_dir,
+                "score_breakout": round(max(score_breakout_up, score_breakout_down), 4),
+                "score_shift": round(max(score_shift_up, score_shift_down), 4),
+                "score_contraction": round(max(score_contraction_up, score_contraction_down), 4),
+            }
+
+        return {
+            "label": label,
+            "label_confidence": round(confidence, 4),
+            "label_detail": detail,
+        }
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def extract(self) -> list[dict]:
+        """主入口：对每个中枢提取 3 特征 + 1 标签"""
+
+        # 第一遍：计算原始值
+        raw = []
+        for i, zs in enumerate(self.bi_zs_list):
+            if not zs.is_sure or len(zs.bi_list) < 3:
+                continue
+
+            duration_raw = ChanPivotClassifier.calc_duration(zs)
+            contraction = ChanPivotClassifier.calc_contraction(zs)
+            shift_raw, shift_norm = ChanPivotClassifier.calc_shift(zs)
+
+            raw.append({
+                "zs": zs,
+                "zs_index": i,
+                "duration_raw": duration_raw,
+                "contraction": contraction,
+                "shift_raw": shift_raw,
+                "shift_norm": shift_norm,
+                "zs_height": zs.zg - zs.zd,
+            })
+
+        # 第二遍：组装输出 + 计算 label
+        result = []
+        for r in raw:
+            zs = r["zs"]
+            historical = [x["duration_raw"] for x in raw]
+            duration_norm = ChanPivotClassifier.compute_duration_norm(
+                r["duration_raw"], historical
+            )
+            label_info = self._compute_label(zs, r["contraction"], r["shift_norm"])
+
+            # 时间处理
+            start_time = None
+            end_time = None
+            if hasattr(zs, "start_time") and zs.start_time is not None:
+                start_time = str(zs.start_time)
+            if hasattr(zs, "end_time") and zs.end_time is not None:
+                end_time = str(zs.end_time)
+
+            result.append({
+                "dataset_version": self.DATASET_VERSION,
+                "feature_schema": self.FEATURE_SCHEMA,
+                "label_schema": self.LABEL_SCHEMA,
+
+                "symbol": self.symbol,
+                "timeframe": self.timeframe,
+                "zs_index": r["zs_index"],
+                "zs_start_time": start_time,
+                "zs_end_time": end_time,
+
+                "duration_norm": round(duration_norm, 4),
+                "contraction": round(r["contraction"], 4),
+                "shift_norm": round(r["shift_norm"], 4),
+
+                "label": label_info["label"],
+                "label_confidence": label_info["label_confidence"],
+                "label_detail": label_info["label_detail"],
+
+                "duration_raw": r["duration_raw"],
+                "shift_raw": round(r["shift_raw"], 6),
+                "zs_height": round(r["zs_height"], 6),
+            })
+
+        return result
+
+    def export_json(self, path: str):
+        """导出为 JSON 文件"""
+        data = self.extract()
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2, ensure_ascii=False, default=str)
+        return len(data)

chanlun/analysis/ChanPivotMonitor.py

@@ -0,0 +1,145 @@
+"""
+实时中枢特征跟踪器
+Real-time Pivot Feature Tracker
+
+定位: 观察者 — 不修改管线，只观察 bi_zs_list 中当前中枢的特征变化。
+每次管线重算后调用 update()，检测 bi_count 是否增长，若增长则重新计算
+shift / contraction / duration。
+"""
+
+from collections import deque
+from typing import Optional
+from chanlun.analysis.ChanPivotClassifier import ChanPivotClassifier
+
+
+class ChanPivotMonitor:
+    """
+    实时追踪当前中枢的结构特征。
+
+    update() 每次管线重算后调用，对比 bi_count 判断是否有新笔加入中枢。
+    若 bi_count 增长则重新计算 3 个结构特征并返回最新值。
+    """
+
+    def __init__(self, window_size: int = 10):
+        self._window_size = window_size
+        self._duration_history: deque[int] = deque(maxlen=window_size)
+        self._current_zs_id: Optional[tuple] = None
+        self._current_bi_count: int = 0
+        self._current_is_sure: bool = False
+        self._current_state: Optional[dict] = None
+        self._duration_added_for_zs: set = set()  # 已加入窗口的中枢 ID（上限 200）
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def update(self, bi_zs_list: list) -> Optional[dict]:
+        """
+        主入口：检测当前中枢特征变化。
+
+        参数:
+            bi_zs_list: 当前管线产出的笔中枢列表
+
+        返回:
+            特征 dict（有变化时），无变化返回 None
+        """
+        if not bi_zs_list:
+            self._current_zs_id = None
+            self._current_bi_count = 0
+            self._current_is_sure = False
+            self._current_state = None
+            return None
+
+        zs = self._find_current_zs(bi_zs_list)
+        if zs is None:
+            return None
+
+        zs_id = self._make_zs_id(zs)
+        bi_count = len(zs.bi_list)
+        is_sure = zs.is_sure
+
+        # 无变化 → 跳过
+        if (zs_id == self._current_zs_id
+                and bi_count == self._current_bi_count
+                and is_sure == self._current_is_sure):
+            return None
+
+        # 中枢切换 → 将旧中枢 duration 加入窗口
+        if zs_id != self._current_zs_id:
+            self._maybe_add_to_history()
+
+        self._current_zs_id = zs_id
+        self._current_bi_count = bi_count
+        self._current_is_sure = is_sure
+
+        features = ChanPivotClassifier.compute_features(
+            zs, list(self._duration_history)
+        )
+
+        self._current_state = {
+            "zs_id": zs_id,
+            "zs_index": zs.index,
+            "zs_dir": str(zs.dir),
+            "bi_count": bi_count,
+            "is_sure": zs.is_sure,
+            "zg": round(zs.zg, 6),
+            "zd": round(zs.zd, 6),
+            "gg": round(zs.gg, 6),
+            "dd": round(zs.dd, 6),
+            **features,
+            "start_time": str(t) if (t := getattr(zs, "start_time", None)) else None,
+        }
+
+        # 中枢刚变为已确认时，将其 duration 加入滚动窗口
+        if is_sure and zs_id not in self._duration_added_for_zs:
+            self._add_duration(features["duration_raw"])
+            self._duration_added_for_zs.add(zs_id)
+
+        return self._current_state
+
+    def get_current(self) -> Optional[dict]:
+        """返回当前中枢的最新特征"""
+        return self._current_state
+
+    def get_duration_history(self) -> list[int]:
+        """返回用于归一化的 duration 滚动窗口"""
+        return list(self._duration_history)
+
+    # ------------------------------------------------------------------
+    # Internal
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _make_zs_id(zs) -> tuple:
+        """生成中枢的稳定标识（基于首笔首K线时间戳，不随 DataFrame 窗口偏移而变化）"""
+        bi0 = zs.bi_list[0]
+        return (bi0.start_klc.start_time,)
+
+    @staticmethod
+    def _find_current_zs(bi_zs_list: list):
+        """
+        找到当前活跃中枢：
+        优先取最后一个 is_sure=False（形成中）的中枢，
+        没有则取最后一个 is_sure=True 的中枢。
+        """
+        forming = None
+        last_sure = None
+        for zs in bi_zs_list:
+            if len(zs.bi_list) < 3:
+                continue
+            if not zs.is_sure:
+                forming = zs
+            else:
+                last_sure = zs
+        return forming if forming is not None else last_sure
+
+    def _add_duration(self, duration_raw: int):
+        """将已确认中枢的 duration 加入滚动窗口"""
+        self._duration_history.append(duration_raw)
+
+    def _maybe_add_to_history(self):
+        """旧中枢切换前，若已确认且未记录过，则将其 duration 加入窗口"""
+        if (self._current_state and self._current_state["is_sure"]
+                and self._current_zs_id not in self._duration_added_for_zs):
+            self._add_duration(self._current_state["duration_raw"])
+            self._duration_added_for_zs.add(self._current_zs_id)

chanlun/analysis/ChanZone.py

@@ -0,0 +1,566 @@
+"""
+结构价值区 (Structure Zone) 系统
+
+将多时间周期的 Chan 中枢边界 (ZD/ZG/GG/DD) 和 EMA52 统一表示为带强度评分的价值区对象。
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Dict, Optional, Any
+from datetime import datetime
+
+
+# ============================================================
+# Dataclasses
+# ============================================================
+
+@dataclass
+class RawZonePoint:
+    """内部中间结构：从 Chan 中枢提取的单个价格点"""
+    price: float
+    timeframe: str            # '5m', '1h', '4h' 等
+    structure_type: str       # 'bi_zhongshu' | 'xd_zhongshu' | 'ema52'
+    boundary_type: str        # 'ZD' | 'ZG' | 'GG' | 'DD' | 'EMA52'
+    source_zs_id: int         # 来源 ZS 在列表中的 index（调试用）
+    is_sure: bool             # 来源 ZS 是否已完成
+    candle_time: Optional[str] = None  # 来源 ZS 的 end_time（用于 recency 计算）
+
+
+@dataclass
+class StructureZone:
+    """统一的价值区对象"""
+    id: int
+    lower: float
+    upper: float
+    center: float                        # (lower + upper) / 2
+    width_pct: float                     # (upper - lower) / center * 100
+    zone_type: str                       # 'support' | 'resistance' | 'neutral'
+    timeframes: List[str]                # 参与形成此区间的时间周期
+    structure_types: List[str]           # 参与形成的结构类型
+    boundary_types: List[str]            # 参与形成的边界类型
+    overlap_count: int                   # 聚类中的原始点数
+    touch_count: int                     # MVP: 等于 overlap_count
+    recency_score: float                 # 0.0 - 1.0, 1.0 = 最近
+    ema52_distance_pct: float            # 到最近 EMA52 的距离百分比
+    ema52_aligned: bool                  # 是否有 EMA52 落在区间内
+    strength_score: float                # 0-100 综合评分
+    confidence: float                    # 0.0 - 1.0
+    first_seen: Optional[str]            # 最早的 candle_time
+    last_seen: Optional[str]             # 最晚的 candle_time
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class StructureZoneConfig:
+    """StructureZone 提取与评分配置"""
+    cluster_radius_pct: float = 0.5         # 价格聚类半径（百分比）
+    min_overlap_for_zone: int = 2           # 最少重叠点数才能形成区间
+    max_zones: int = 20                     # 返回的最大区间数
+    recency_halflife_bars: int = 50         # recency 衰减半衰期（K线数）
+    zone_timeframes: List[str] = field(default_factory=lambda: ['4h', '1h', '30m', '15m', '5m'])
+    kl_lines_per_tf: int = 500              # 每个时间周期使用最近多少根K线
+    structure_weights: Dict[str, float] = field(default_factory=lambda: {
+        'bi_zhongshu':   1.0,   # 笔中枢 — 最直接的价格行为
+        'xd_zhongshu':   0.8,   # 线段中枢 — 较高级别但粒度较粗
+        'ema52':         0.4,   # EMA — 趋势参考，弱于结构
+    })
+
+
+# ============================================================
+# Extraction
+# ============================================================
+
+def extract_raw_points_from_tf_df(
+    tf_df_dict: Dict[str, Any],
+    ema_symbols: List[str],
+    config: StructureZoneConfig,
+) -> List[RawZonePoint]:
+    """
+    从 ChanLun.tf_df_dict 中提取所有原始价格点。
+    仅处理 config.zone_timeframes 中存在的时间周期。
+    """
+    points: List[RawZonePoint] = []
+
+    for tf_name in config.zone_timeframes:
+        if tf_name not in tf_df_dict:
+            continue
+
+        tf_df = tf_df_dict[tf_name]
+
+        # 1. 笔中枢 (ChanBIZS)
+        try:
+            if hasattr(tf_df, 'seg_list') and tf_df.seg_list:
+                bi_zs_result = tf_df.cal_bi_zs(tf_df.seg_list)
+                if bi_zs_result:
+                    _extract_from_zs_objects(
+                        points, tf_name, 'bi_zhongshu', bi_zs_result, config.kl_lines_per_tf
+                    )
+        except Exception:
+            pass
+
+        # 2. 线段中枢 (ChanZS)
+        try:
+            zs_list = getattr(tf_df, 'zs_list', None)
+            if zs_list:
+                _extract_from_zs_objects(
+                    points, tf_name, 'xd_zhongshu', zs_list, config.kl_lines_per_tf
+                )
+        except Exception:
+            pass
+
+    # 3. EMA52 值
+    for tf_name in config.zone_timeframes:
+        if tf_name in tf_df_dict:
+            try:
+                ema_val = tf_df_dict[tf_name].get_ema52()
+                if ema_val is not None and ema_val > 0:
+                    points.append(RawZonePoint(
+                        price=float(ema_val),
+                        timeframe=tf_name,
+                        structure_type='ema52',
+                        boundary_type='EMA52',
+                        source_zs_id=-1,
+                        is_sure=True,
+                        candle_time=None,
+                    ))
+            except Exception:
+                pass
+
+    return points
+
+
+def _extract_from_zs_objects(
+    points: List[RawZonePoint],
+    tf_name: str,
+    structure_type: str,
+    zs_list,
+    kl_limit: int,
+):
+    """从 ZS 链表中提取 ZD/ZG/GG/DD 点"""
+    count = 0
+    node = zs_list
+    while hasattr(node, 'next'):
+        node = node.next
+    # 从链表头开始遍历
+    head = zs_list
+    # 收集所有节点
+    all_nodes = []
+    cur = head
+    while cur is not None and hasattr(cur, 'next'):
+        all_nodes.append(cur)
+        cur = cur.next
+    # 只取最近 kl_limit 根K线内的 ZS
+    all_nodes = all_nodes[-kl_limit:] if len(all_nodes) > kl_limit else all_nodes
+
+    for idx, zs in enumerate(all_nodes):
+        if not getattr(zs, 'is_sure', False):
+            continue
+        try:
+            zg = float(zs.zg)
+            zd = float(zs.zd)
+            gg = float(zs.gg) if getattr(zs, 'gg', 0) else zg
+            dd = float(zs.dd) if getattr(zs, 'dd', 0) else zd
+            end_time = str(zs.end_time) if hasattr(zs, 'end_time') and zs.end_time else None
+        except (ValueError, TypeError, AttributeError):
+            continue
+
+        if zg <= 0 or zd <= 0:
+            continue
+
+        zs_id = getattr(zs, 'index', idx)
+        points.append(RawZonePoint(price=zg, timeframe=tf_name, structure_type=structure_type,
+                                   boundary_type='ZG', source_zs_id=zs_id, is_sure=True,
+                                   candle_time=end_time))
+        points.append(RawZonePoint(price=zd, timeframe=tf_name, structure_type=structure_type,
+                                   boundary_type='ZD', source_zs_id=zs_id, is_sure=True,
+                                   candle_time=end_time))
+        points.append(RawZonePoint(price=gg, timeframe=tf_name, structure_type=structure_type,
+                                   boundary_type='GG', source_zs_id=zs_id, is_sure=True,
+                                   candle_time=end_time))
+        points.append(RawZonePoint(price=dd, timeframe=tf_name, structure_type=structure_type,
+                                   boundary_type='DD', source_zs_id=zs_id, is_sure=True,
+                                   candle_time=end_time))
+
+
+def extract_raw_points_from_serialized(
+    analyses: Dict[str, Dict],
+    ema52_dict: Dict[str, Optional[float]],
+    config: StructureZoneConfig,
+) -> List[RawZonePoint]:
+    """
+    从已序列化的分析结果中提取价格点（用于 web API，避免重复计算）。
+    analyses: {'5m': {'zs_list': [...], 'bi_zs_list': [...]}, '15m': {...}, ...}
+    ema52_dict: {'5m': 123.45, '15m': None, ...}
+    """
+    points: List[RawZonePoint] = []
+
+    for tf_name in config.zone_timeframes:
+        if tf_name not in analyses:
+            continue
+
+        analysis = analyses[tf_name]
+
+        # 笔中枢
+        bi_zs_items = analysis.get('bi_zs_list', [])
+        for idx, zs in enumerate(bi_zs_items):
+            if not zs.get('is_sure', False):
+                continue
+            try:
+                zg = float(zs['zg']); zd = float(zs['zd'])
+                gg = float(zs.get('gg', zg)); dd = float(zs.get('dd', zd))
+                end_time = zs.get('end_time')
+            except (ValueError, KeyError):
+                continue
+            if zg <= 0 or zd <= 0:
+                continue
+            points.append(RawZonePoint(price=zg, timeframe=tf_name, structure_type='bi_zhongshu',
+                                       boundary_type='ZG', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+            points.append(RawZonePoint(price=zd, timeframe=tf_name, structure_type='bi_zhongshu',
+                                       boundary_type='ZD', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+            points.append(RawZonePoint(price=gg, timeframe=tf_name, structure_type='bi_zhongshu',
+                                       boundary_type='GG', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+            points.append(RawZonePoint(price=dd, timeframe=tf_name, structure_type='bi_zhongshu',
+                                       boundary_type='DD', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+
+        # 线段中枢
+        zs_items = analysis.get('zs_list', [])
+        for idx, zs in enumerate(zs_items):
+            if not zs.get('is_sure', False):
+                continue
+            try:
+                zg = float(zs['zg']); zd = float(zs['zd'])
+                gg = float(zs.get('gg', zg)); dd = float(zs.get('dd', zd))
+                end_time = zs.get('end_time')
+            except (ValueError, KeyError):
+                continue
+            if zg <= 0 or zd <= 0:
+                continue
+            points.append(RawZonePoint(price=zg, timeframe=tf_name, structure_type='xd_zhongshu',
+                                       boundary_type='ZG', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+            points.append(RawZonePoint(price=zd, timeframe=tf_name, structure_type='xd_zhongshu',
+                                       boundary_type='ZD', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+            points.append(RawZonePoint(price=gg, timeframe=tf_name, structure_type='xd_zhongshu',
+                                       boundary_type='GG', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+            points.append(RawZonePoint(price=dd, timeframe=tf_name, structure_type='xd_zhongshu',
+                                       boundary_type='DD', source_zs_id=idx, is_sure=True,
+                                       candle_time=str(end_time) if end_time else None))
+
+    # EMA52
+    for tf_name in config.zone_timeframes:
+        ema_val = ema52_dict.get(tf_name)
+        if ema_val is not None and ema_val > 0:
+            points.append(RawZonePoint(
+                price=float(ema_val),
+                timeframe=tf_name,
+                structure_type='ema52',
+                boundary_type='EMA52',
+                source_zs_id=-1,
+                is_sure=True,
+                candle_time=None,
+            ))
+
+    return points
+
+
+# ============================================================
+# Clustering
+# ============================================================
+
+def cluster_raw_points(
+    points: List[RawZonePoint],
+    config: StructureZoneConfig,
+) -> List[List[RawZonePoint]]:
+    """
+    贪心单通聚类：将价格相近的 RawZonePoint 归为一组。
+    仅在 1D 价格轴上操作，O(n log n)。
+    """
+    if not points:
+        return []
+
+    sorted_points = sorted(points, key=lambda p: p.price)
+    clusters: List[List[RawZonePoint]] = []
+
+    for p in sorted_points:
+        placed = False
+        for cluster in reversed(clusters):
+            # 检查是否可以放入当前聚类（与聚类均价比较）
+            avg_price = sum(pt.price for pt in cluster) / len(cluster)
+            if abs(p.price - avg_price) / avg_price * 100 <= config.cluster_radius_pct:
+                cluster.append(p)
+                placed = True
+                break
+        if not placed:
+            clusters.append([p])
+
+    # 过滤点数不足的聚类
+    return [c for c in clusters if len(c) >= config.min_overlap_for_zone]
+
+
+# ============================================================
+# Scoring & Building
+# ============================================================
+
+def build_structure_zones(
+    clusters: List[List[RawZonePoint]],
+    current_price: float,
+    ema52_values: Dict[str, Optional[float]],
+    latest_candle_time: Optional[str],
+    config: StructureZoneConfig,
+) -> List[StructureZone]:
+    """
+    从聚类构建 StructureZone 列表，计算所有字段和评分。
+    """
+    zones: List[StructureZone] = []
+
+    # 收集所有 EMA52 值
+    ema_prices = [v for v in ema52_values.values() if v is not None and v > 0]
+
+    for zone_id, cluster in enumerate(clusters):
+        prices = [p.price for p in cluster]
+        lower = min(prices)
+        upper = max(prices)
+        center = (lower + upper) / 2
+        width_pct = (upper - lower) / center * 100 if center > 0 else 0.0
+
+        # 区间类型
+        if upper < current_price:
+            zone_type = 'support'      # 区间在当前价格下方 → 支撑
+        elif lower > current_price:
+            zone_type = 'resistance'   # 区间在当前价格上方 → 阻力
+        else:
+            zone_type = 'neutral'      # 区间跨越当前价格
+
+        timeframes = sorted(set(p.timeframe for p in cluster))
+        structure_types = sorted(set(p.structure_type for p in cluster))
+        boundary_types = sorted(set(p.boundary_type for p in cluster))
+        overlap_count = len(cluster)
+
+        # Recency
+        times = [p.candle_time for p in cluster if p.candle_time]
+        first_seen = min(times) if times else None
+        last_seen = max(times) if times else None
+        recency_score = _calc_recency(last_seen, latest_candle_time, config.recency_halflife_bars)
+
+        # EMA52 alignment
+        ema52_distance_pct = 999.0
+        ema52_aligned = False
+        if ema_prices:
+            distances = [abs(center - ep) / ep * 100 for ep in ema_prices]
+            ema52_distance_pct = round(min(distances), 2)
+        ema52_aligned = any(lower <= ep <= upper for ep in ema_prices)
+
+        # Strength score
+        strength_score = _calc_strength(cluster, config, recency_score, ema52_aligned, ema52_distance_pct, width_pct)
+
+        # Confidence
+        confidence = _calc_confidence(overlap_count, len(timeframes), cluster)
+
+        zones.append(StructureZone(
+            id=zone_id + 1,
+            lower=round(lower, 2),
+            upper=round(upper, 2),
+            center=round(center, 2),
+            width_pct=round(width_pct, 2),
+            zone_type=zone_type,
+            timeframes=timeframes,
+            structure_types=structure_types,
+            boundary_types=boundary_types,
+            overlap_count=overlap_count,
+            touch_count=overlap_count,  # MVP: 等于 overlap_count
+            recency_score=round(recency_score, 3),
+            ema52_distance_pct=ema52_distance_pct,
+            ema52_aligned=ema52_aligned,
+            strength_score=round(strength_score, 1),
+            confidence=round(confidence, 2),
+            first_seen=first_seen,
+            last_seen=last_seen,
+        ))
+
+    # 按强度降序排列
+    zones.sort(key=lambda z: z.strength_score, reverse=True)
+
+    # 截断
+    if config.max_zones > 0 and len(zones) > config.max_zones:
+        zones = zones[:config.max_zones]
+
+    return zones
+
+
+def _calc_recency(
+    last_seen: Optional[str],
+    latest_time: Optional[str],
+    halflife_bars: int,
+) -> float:
+    """计算 recency 分数：越近越高"""
+    if not last_seen or not latest_time:
+        return 0.5
+
+    try:
+        # 尝试解析 ISO 格式时间
+        from dateutil import parser
+        t_last = parser.parse(last_seen)
+        t_latest = parser.parse(latest_time)
+        offset_seconds = (t_latest - t_last).total_seconds()
+        if offset_seconds < 0:
+            return 1.0
+        # 假设每根K线平均 5 分钟
+        bar_seconds = 300
+        offset_bars = offset_seconds / bar_seconds
+        # 指数衰减: 2 ^ (-offset / halflife)
+        score = 2.0 ** (-offset_bars / halflife_bars)
+        return float(score)
+    except Exception:
+        return 0.5
+
+
+def _calc_strength(
+    cluster: List[RawZonePoint],
+    config: StructureZoneConfig,
+    recency_score: float,
+    ema52_aligned: bool,
+    ema52_distance_pct: float,
+    width_pct: float,
+) -> float:
+    """计算综合强度评分 (0-100)"""
+
+    # 组件 1: 结构类型多样性 (0-40)
+    structure_type_counts: Dict[str, int] = {}
+    for p in cluster:
+        structure_type_counts[p.structure_type] = structure_type_counts.get(p.structure_type, 0) + 1
+    total = sum(structure_type_counts.values())
+    structure_score = 0.0
+    for st, count in structure_type_counts.items():
+        weight = config.structure_weights.get(st, 0.5)
+        structure_score += weight * count
+    structure_score = min(structure_score / max(1, total), 1.0)
+    c1 = structure_score * 40
+
+    # 组件 2: 多周期确认 (0-25)
+    tf_set = set(p.timeframe for p in cluster)
+    tf_diversity = len(tf_set)
+    c2 = min(tf_diversity / 5, 1.0) * 25
+
+    # 组件 3: 区间紧密度 (0-15) — 越窄越强
+    tightness = max(0.0, 1.0 - (width_pct / 3.0))
+    c3 = tightness * 15
+
+    # 组件 4: Recency (0-10)
+    c4 = recency_score * 10
+
+    # 组件 5: EMA52 共振 (0-10)
+    if ema52_aligned:
+        ema_proximity = max(0.0, 1.0 - (ema52_distance_pct / 2.0))
+        c5 = ema_proximity * 10
+    else:
+        c5 = 0.0
+
+    return c1 + c2 + c3 + c4 + c5
+
+
+def _calc_confidence(
+    overlap_count: int,
+    tf_count: int,
+    cluster: List[RawZonePoint],
+) -> float:
+    """计算置信度 (0-1)"""
+    base = min(overlap_count / 6.0, 0.85)
+    # 多周期加分
+    tf_bonus = min(tf_count / 5.0, 0.1)
+    # 是否所有点都来自 sure 的 ZS
+    all_sure = all(p.is_sure for p in cluster)
+    sure_bonus = 0.05 if all_sure else 0.0
+    return min(base + tf_bonus + sure_bonus, 1.0)
+
+
+# ============================================================
+# Top-level pipeline
+# ============================================================
+
+def analyze_structure_zones(
+    tf_df_dict: Dict[str, Any],
+    ema_symbols: List[str],
+    current_price: Optional[float] = None,
+    config: Optional[StructureZoneConfig] = None,
+) -> List[StructureZone]:
+    """
+    一站式分析：提取 → 聚类 → 评分 → 返回排序后的 StructureZone 列表。
+    """
+    if config is None:
+        config = StructureZoneConfig()
+
+    # 提取
+    raw_points = extract_raw_points_from_tf_df(tf_df_dict, ema_symbols, config)
+
+    if not raw_points:
+        return []
+
+    # 获取当前价格
+    if current_price is None:
+        for tf_name in config.zone_timeframes:
+            if tf_name in tf_df_dict:
+                try:
+                    ema_val = tf_df_dict[tf_name].get_ema52()
+                    if ema_val and ema_val > 0:
+                        current_price = float(ema_val)
+                        break
+                except Exception:
+                    pass
+        if current_price is None:
+            current_price = 0.0
+
+    # EMA52 值
+    ema52_values = {}
+    for tf_name in config.zone_timeframes:
+        if tf_name in tf_df_dict:
+            try:
+                ema52_values[tf_name] = tf_df_dict[tf_name].get_ema52()
+            except Exception:
+                ema52_values[tf_name] = None
+
+    # 最晚时间
+    latest_time = None
+    times = [p.candle_time for p in raw_points if p.candle_time]
+    if times:
+        latest_time = max(times)
+
+    # 聚类
+    clusters = cluster_raw_points(raw_points, config)
+
+    # 构建 & 评分
+    return build_structure_zones(clusters, current_price, ema52_values, latest_time, config)
+
+
+def analyze_structure_zones_from_serialized(
+    analyses: Dict[str, Dict],
+    ema52_dict: Dict[str, Optional[float]],
+    current_price: float,
+    config: Optional[StructureZoneConfig] = None,
+) -> List[StructureZone]:
+    """
+    从已序列化的分析结果构建 StructureZone（用于 web API）。
+    """
+    if config is None:
+        config = StructureZoneConfig()
+
+    raw_points = extract_raw_points_from_serialized(analyses, ema52_dict, config)
+
+    if not raw_points:
+        return []
+
+    # 最晚时间
+    latest_time = None
+    times = [p.candle_time for p in raw_points if p.candle_time]
+    if times:
+        latest_time = max(times)
+
+    # EMA52 值（用于 alignment 检测）
+    ema_values = {tf: v for tf, v in ema52_dict.items() if v is not None and v > 0}
+
+    clusters = cluster_raw_points(raw_points, config)
+    return build_structure_zones(clusters, current_price, ema_values, latest_time, config)

chanlun/analysis/Find_Trend.py

@@ -0,0 +1,448 @@
+import ccxt
+import pandas as pd
+import numpy as np
+import mplfinance as mpf
+from talib import MACD, SMA
+from datetime import datetime, timedelta
+import logging
+import datetime as dt
+
+# Configure logging
+logging.basicConfig(
+    filename='chanlun_trading.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+# Configuration (user to modify)
+BINANCE_API_KEY = 'your_api_key'  # Replace with your Binance API key
+BINANCE_API_SECRET = 'your_api_secret'  # Replace with your Binance API secret
+SIMULATION_MODE = True  # Set to False for live trading
+
+# 1. Fetch K-line data from Binance (multi-timeframe support)
+def fetch_binance_data(symbol='BTC/USDT', timeframe='5m', limit=500):
+    try:
+        exchange = ccxt.binance({
+            'apiKey': BINANCE_API_KEY if not SIMULATION_MODE else '',
+            'secret': BINANCE_API_SECRET if not SIMULATION_MODE else '',
+            'enableRateLimit': True,
+            'options': {'defaultType': 'spot'}
+        })
+        since = exchange.parse8601((datetime.now(dt.UTC) - timedelta(days=7)).isoformat())
+        ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since, limit)
+        df = pd.DataFrame(ohlcv, columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])
+        df['Date'] = pd.to_datetime(df['Date'], unit='ms')
+        df.set_index('Date', inplace=True)
+        logging.info(f"Fetched {len(df)} K-lines for {symbol} ({timeframe})")
+        return df
+    except Exception as e:
+        logging.error(f"Failed to fetch data: {e}")
+        raise
+
+# 2. K-line merging (vectorized)
+def merge_kline(df):
+    try:
+        df = df.copy()
+        merged_data = []
+        trend = np.sign(df['Close'].diff().shift(-1))  # 1: up, -1: down, 0: neutral
+        
+        # Detect inclusion
+        is_included = ((df['High'].shift(-1) <= df['High']) & (df['Low'].shift(-1) >= df['Low'])) | \
+                      ((df['High'].shift(-1) >= df['High']) & (df['Low'].shift(-1) <= df['Low']))
+        
+        i = 0
+        while i < len(df) - 1:
+            if is_included.iloc[i]:
+                current_k = df.iloc[i]
+                next_k = df.iloc[i + 1]
+                high = max(current_k['High'], next_k['High'])
+                low = min(current_k['Low'], next_k['Low'])
+                open_price = current_k['Open']
+                close_price = next_k['Close'] if trend.iloc[i] >= 0 else next_k['Close']
+                volume = current_k['Volume'] + next_k['Volume']
+                
+                merged_data.append({
+                    'Date': next_k.name,
+                    'Open': open_price,
+                    'High': high,
+                    'Low': low,
+                    'Close': close_price,
+                    'Volume': volume
+                })
+                i += 2
+            else:
+                current_k = df.iloc[i]
+                merged_data.append({
+                    'Date': current_k.name,
+                    'Open': current_k['Open'],
+                    'High': current_k['High'],
+                    'Low': current_k['Low'],
+                    'Close': current_k['Close'],
+                    'Volume': current_k['Volume']
+                })
+                i += 1
+        
+        if i == len(df) - 1:
+            last_k = df.iloc[i]
+            merged_data.append({
+                'Date': last_k.name,
+                'Open': last_k['Open'],
+                'High': last_k['High'],
+                'Low': last_k['Low'],
+                'Close': last_k['Close'],
+                'Volume': last_k['Volume']
+            })
+        
+        merged_df = pd.DataFrame(merged_data)
+        merged_df['Date'] = pd.to_datetime(merged_df['Date'])
+        merged_df.set_index('Date', inplace=True)
+        logging.info(f"Merged K-lines: {len(df)} -> {len(merged_df)}")
+        return merged_df
+    except Exception as e:
+        logging.error(f"K-line merging failed: {e}")
+        raise
+
+# 3. Detect fractals (vectorized)
+def detect_fractals(df):
+    try:
+        df = df.copy()
+        df['is_top'] = (df['High'] > df['High'].shift(1)) & (df['High'] > df['High'].shift(-1)) & \
+                       (df['High'] > df['High'].shift(2)) & (df['High'] > df['High'].shift(-2))
+        df['is_bottom'] = (df['Low'] < df['Low'].shift(1)) & (df['Low'] < df['Low'].shift(-1)) & \
+                          (df['Low'] < df['Low'].shift(2)) & (df['Low'] < df['Low'].shift(-2))
+        df['is_top'] = df['is_top'].fillna(False)
+        df['is_bottom'] = df['is_bottom'].fillna(False)
+        logging.info(f"Detected {df['is_top'].sum()} top fractals and {df['is_bottom'].sum()} bottom fractals")
+        return df
+    except Exception as e:
+        logging.error(f"Fractal detection failed: {e}")
+        raise
+
+# 4. Detect strokes
+def detect_strokes(df):
+    try:
+        strokes = []
+        last_fractal = None
+        last_price = None
+        last_index = None
+        
+        for i in range(len(df)):
+            if df['is_top'].iloc[i] or df['is_bottom'].iloc[i]:
+                current_fractal = 'top' if df['is_top'].iloc[i] else 'bottom'
+                current_price = df['High'].iloc[i] if current_fractal == 'top' else df['Low'].iloc[i]
+                
+                if last_fractal is None:
+                    last_fractal = current_fractal
+                    last_price = current_price
+                    last_index = df.index[i]
+                    continue
+                
+                if (last_fractal == 'top' and current_fractal == 'bottom' and current_price < last_price) or \
+                   (last_fractal == 'bottom' and current_fractal == 'top' and current_price > last_price):
+                    strokes.append({
+                        'start_time': last_index,
+                        'end_time': df.index[i],
+                        'start_price': last_price,
+                        'end_price': current_price,
+                        'type': 'down' if current_fractal == 'bottom' else 'up',
+                        'volume': df['Volume'].loc[last_index:df.index[i]].sum()
+                    })
+                
+                last_fractal = current_fractal
+                last_price = current_price
+                last_index = df.index[i]
+        
+        logging.info(f"Detected {len(strokes)} strokes")
+        return strokes
+    except Exception as e:
+        logging.error(f"Stroke detection failed: {e}")
+        raise
+
+# 5. Detect segments
+def detect_segments(strokes):
+    try:
+        segments = []
+        if len(strokes) < 3:
+            return segments
+        
+        i = 0
+        while i < len(strokes) - 2:
+            stroke1, stroke2, stroke3 = strokes[i], strokes[i+1], strokes[i+2]
+            
+            if stroke1['type'] == 'up' and stroke2['type'] == 'down' and stroke3['type'] == 'up':
+                if stroke3['end_price'] > stroke1['end_price']:
+                    segments.append({
+                        'start_time': stroke1['start_time'],
+                        'end_time': stroke3['end_time'],
+                        'start_price': stroke1['start_price'],
+                        'end_price': stroke3['end_price'],
+                        'type': 'up'
+                    })
+                    i += 3
+                else:
+                    i += 1
+            elif stroke1['type'] == 'down' and stroke2['type'] == 'up' and stroke3['type'] == 'down':
+                if stroke3['end_price'] < stroke1['end_price']:
+                    segments.append({
+                        'start_time': stroke1['start_time'],
+                        'end_time': stroke3['end_time'],
+                        'start_price': stroke1['start_price'],
+                        'end_price': stroke3['end_price'],
+                        'type': 'down'
+                    })
+                    i += 3
+                else:
+                    i += 1
+            else:
+                i += 1
+        
+        logging.info(f"Detected {len(segments)} segments")
+        return segments
+    except Exception as e:
+        logging.error(f"Segment detection failed: {e}")
+        raise
+
+# 6. Detect pivots (midlines)
+def detect_pivots(strokes):
+    try:
+        pivots = []
+        if len(strokes) < 3:
+            return pivots
+        
+        for i in range(len(strokes) - 2):
+            s1, s2, s3 = strokes[i:i+3]
+            high = min(s1['start_price'], s1['end_price'], s2['start_price'], s2['end_price'], 
+                       s3['start_price'], s3['end_price'])
+            low = max(s1['start_price'], s1['end_price'], s2['start_price'], s2['end_price'], 
+                      s3['start_price'], s3['end_price'])
+            
+            if high > low:
+                pivots.append({
+                    'start_time': s1['start_time'],
+                    'end_time': s3['end_time'],
+                    'high': high,
+                    'low': low
+                })
+        
+        logging.info(f"Detected {len(pivots)} pivots")
+        return pivots
+    except Exception as e:
+        logging.error(f"Pivot detection failed: {e}")
+        raise
+
+# 7. Analyze higher timeframe (30m)
+def analyze_higher_timeframe(df_30m):
+    try:
+        df_30m = detect_fractals(df_30m)
+        strokes_30m = detect_strokes(df_30m)
+        
+        if not strokes_30m:
+            return 'neutral'
+        
+        last_stroke = strokes_30m[-1]
+        logging.info(f"30m trend: {last_stroke['type']}")
+        return last_stroke['type']
+    except Exception as e:
+        logging.error(f"Higher timeframe analysis failed: {e}")
+        raise
+
+# 8. Back-divergence detection (enhanced)
+def detect_back_divergence(df, strokes, higher_trend):
+    try:
+        macd, signal, hist = MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        sma20 = SMA(df['Close'], timeperiod=20)
+        df['macd'] = macd
+        df['hist'] = hist
+        df['sma20'] = sma20
+        df['buy_signal'] = False
+        df['sell_signal'] = False
+        
+        stroke_metrics = []
+        for stroke in strokes:
+            start_idx = df.index.get_loc(stroke['start_time'])
+            end_idx = df.index.get_loc(stroke['end_time'])
+            hist_segment = df['hist'].iloc[start_idx:end_idx+1]
+            price_change = abs(stroke['end_price'] - stroke['start_price'])
+            hist_area = sum(abs(h) for h in hist_segment if not np.isnan(h))
+            volume = stroke['volume']
+            stroke_metrics.append({
+                'start_time': stroke['start_time'],
+                'end_time': stroke['end_time'],
+                'type': stroke['type'],
+                'price_change': price_change,
+                'hist_area': hist_area,
+                'volume': volume
+            })
+        
+        for i in range(2, len(stroke_metrics)):
+            current_stroke = stroke_metrics[i]
+            prev_stroke = stroke_metrics[i-2]
+            
+            if current_stroke['type'] != prev_stroke['type']:
+                continue
+            
+            current_end_idx = df.index.get_loc(current_stroke['end_time'])
+            
+            # Uptrend back-divergence (sell signal)
+            if current_stroke['type'] == 'up':
+                price_increase = df['High'].loc[current_stroke['end_time']] > df['High'].loc[prev_stroke['end_time']]
+                hist_decrease = current_stroke['hist_area'] < prev_stroke['hist_area']
+                volume_decrease = current_stroke['volume'] < prev_stroke['volume']
+                is_top_fractal = df['is_top'].loc[current_stroke['end_time']]
+                hist_positive = df['hist'].iloc[current_end_idx] > 0 or \
+                               (df['hist'].iloc[current_end_idx] < 0 and df['hist'].iloc[current_end_idx-1] > 0)
+                sma_trend = df['Close'].iloc[current_end_idx] > df['sma20'].iloc[current_end_idx]
+                trend_match = higher_trend in ['up', 'neutral']
+                
+                if price_increase and hist_decrease and volume_decrease and is_top_fractal and \
+                   hist_positive and sma_trend and trend_match:
+                    df.loc[df.index[current_end_idx], 'sell_signal'] = True
+            
+            # Downtrend back-divergence (buy signal)
+            elif current_stroke['type'] == 'down':
+                price_decrease = df['Low'].loc[current_stroke['end_time']] < df['Low'].loc[prev_stroke['end_time']]
+                hist_decrease = current_stroke['hist_area'] < prev_stroke['hist_area']
+                volume_decrease = current_stroke['volume'] < prev_stroke['volume']
+                is_bottom_fractal = df['is_bottom'].loc[current_stroke['end_time']]
+                hist_negative = df['hist'].iloc[current_end_idx] < 0 or \
+                               (df['hist'].iloc[current_end_idx] > 0 and df['hist'].iloc[current_end_idx-1] < 0)
+                sma_trend = df['Close'].iloc[current_end_idx] < df['sma20'].iloc[current_end_idx]
+                trend_match = higher_trend in ['down', 'neutral']
+                
+                if price_decrease and hist_decrease and volume_decrease and is_bottom_fractal and \
+                   hist_negative and sma_trend and trend_match:
+                    df.loc[df.index[current_end_idx], 'buy_signal'] = True
+        
+        logging.info(f"Detected {df['buy_signal'].sum()} buy signals and {df['sell_signal'].sum()} sell signals")
+        return df
+    except Exception as e:
+        logging.error(f"Back-divergence detection failed: {e}")
+        raise
+
+# 9. Execute trade
+def execute_trade(exchange, symbol, signal, amount=0.001):
+    try:
+        if SIMULATION_MODE:
+            msg = f"[SIMULATION] {'Buy' if signal == 'buy' else 'Sell'} {amount} {symbol} at {datetime.now(dt.UTC)}"
+            print(msg)
+            logging.info(msg)
+            return
+        
+        if signal == 'buy':
+            order = exchange.create_market_buy_order(symbol, amount)
+            msg = f"Buy order executed: {order}"
+            print(msg)
+            logging.info(msg)
+        elif signal == 'sell':
+            order = exchange.create_market_sell_order(symbol, amount)
+            msg = f"Sell order executed: {order}"
+            print(msg)
+            logging.info(msg)
+    except Exception as e:
+        msg = f"Trade execution failed: {e}"
+        print(msg)
+        logging.error(msg)
+
+# 10. Plot chart
+def plot_chart(df, strokes, segments, pivots):
+    try:
+        # Initialize additional plots
+        apds = []
+        alines = []  # For line segments
+        
+        # Plot strokes as line segments
+        for stroke in strokes:
+            alines.append([(stroke['start_time'], stroke['start_price']),
+                           (stroke['end_time'], stroke['end_price'])])
+        
+        # Plot segments as line segments
+        for segment in segments:
+            alines.append([(segment['start_time'], segment['start_price']),
+                           (segment['end_time'], segment['end_price'])])
+        
+        # Plot pivots as horizontal lines
+        for pivot in pivots:
+            alines.append([(pivot['start_time'], pivot['high']),
+                           (pivot['end_time'], pivot['high'])])
+            alines.append([(pivot['start_time'], pivot['low']),
+                           (pivot['end_time'], pivot['low'])])
+        
+        # Add alines to plot (single color for simplicity, can customize)
+        if alines:
+            apds.append(mpf.make_addplot(
+                None,  # No y-data needed for alines
+                alines=alines,
+                type='line',
+                color=['blue' if i < len(strokes) else 'purple' if i < len(strokes) + len(segments) else 'orange'
+                       for i in range(len(alines))],
+                linestyle=['--' if i < len(strokes) else '-' if i < len(strokes) + len(segments) else ':'
+                           for i in range(len(alines))]
+            ))
+        
+        # Plot buy/sell signals
+        buy_signals = df[df['buy_signal']]['Close']
+        sell_signals = df[df['sell_signal']]['Close']
+        apds.append(mpf.make_addplot(buy_signals, type='scatter', markersize=100, marker='^', color='green'))
+        apds.append(mpf.make_addplot(sell_signals, type='scatter', markersize=100, marker='v', color='red'))
+        
+        # Plot K-line chart
+        mpf.plot(df, type='candle', addplot=apds, title='Chanlun Advanced Analysis', style='yahoo')
+        logging.info("Chart plotted successfully")
+    except Exception as e:
+        logging.error(f"Chart plotting failed: {e}")
+        raise
+
+# 11. Main function
+def main():
+    try:
+        # Initialize exchange
+        exchange = ccxt.binance({
+            'apiKey': BINANCE_API_KEY if not SIMULATION_MODE else '',
+            'secret': BINANCE_API_SECRET if not SIMULATION_MODE else '',
+            'enableRateLimit': True,
+            'options': {'defaultType': 'spot'}
+        })
+        
+        # Fetch data
+        df_5m = fetch_binance_data(symbol='BTC/USDT', timeframe='5m', limit=500)
+        df_30m = fetch_binance_data(symbol='BTC/USDT', timeframe='30m', limit=200)
+        
+        # Merge 5m K-lines
+        df_5m = merge_kline(df_5m)
+        
+        # Detect fractals, strokes, segments, pivots
+        df_5m = detect_fractals(df_5m)
+        strokes = detect_strokes(df_5m)
+        segments = detect_segments(strokes)
+        pivots = detect_pivots(strokes)
+        
+        # Analyze 30m trend
+        higher_trend = analyze_higher_timeframe(df_30m)
+        print(f"30m Trend: {higher_trend}")
+        
+        # Detect back-divergence
+        df_5m = detect_back_divergence(df_5m, strokes, higher_trend)
+        
+        # Plot chart
+        plot_chart(df_5m, strokes, segments, pivots)
+        
+        # Output and execute trades
+        print("Buy Signals:")
+        buy_signals = df_5m[df_5m['buy_signal']][['Close']]
+        print(buy_signals)
+        for idx, row in buy_signals.iterrows():
+            execute_trade(exchange, 'BTC/USDT', 'buy', amount=0.001)
+        
+        print("Sell Signals:")
+        sell_signals = df_5m[df_5m['sell_signal']][['Close']]
+        print(sell_signals)
+        for idx, row in sell_signals.iterrows():
+            execute_trade(exchange, 'BTC/USDT', 'sell', amount=0.001)
+        
+        logging.info("Main function completed successfully")
+    except Exception as e:
+        logging.error(f"Main function failed: {e}")
+        raise
+
+if __name__ == "__main__":
+    main()

chanlun/analysis/fx_strength_config.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+分型强度检测配置文件
+用于调整分型强度计算的各项参数和权重
+"""
+
+class FxStrengthConfig:
+    """分型强度检测配置类"""
+    
+    def __init__(self):
+        # ===== 权重配置 (总分100分) =====
+        self.price_difference_weight = 40  # 价格差异强度权重
+        self.breakthrough_weight = 20      # 突破历史点位权重  
+        self.volume_weight = 15           # 成交量确认权重
+        self.rsi_divergence_weight = 15   # RSI背离权重
+        self.macd_divergence_weight = 10  # MACD背离权重
+        
+        # ===== 价格差异参数 =====
+        self.price_diff_multiplier = 1000  # 价格差异放大倍数
+        self.max_price_score = 20          # 价格差异最高得分
+        
+        # ===== 突破检测参数 =====
+        self.breakthrough_lookback = 10    # 回看K线数量
+        self.breakthrough_multiplier = 500 # 突破幅度放大倍数
+        self.max_breakthrough_score = 20   # 突破最高得分
+        
+        # ===== 成交量参数 =====
+        self.volume_lookback = 5           # 计算平均成交量的回看期数
+        self.volume_multiplier = 10        # 成交量放大倍数
+        self.max_volume_score = 15         # 成交量最高得分
+        self.min_volume_ratio = 1.0        # 最小成交量比率
+        
+        # ===== RSI背离参数 =====
+        self.rsi_divergence_divisor = 2    # RSI背离除数
+        self.max_rsi_score = 15            # RSI最高得分
+        
+        # ===== MACD背离参数 =====
+        self.macd_divergence_multiplier = 100  # MACD背离放大倍数
+        self.max_macd_score = 10               # MACD最高得分
+        
+        # ===== 强度等级阈值 =====
+        self.extreme_threshold = 80   # 极强分型阈值
+        self.strong_threshold = 60    # 强分型阈值
+        self.medium_threshold = 40    # 中等分型阈值
+        self.weak_threshold = 20      # 弱分型阈值
+        
+        # ===== 其他参数 =====
+        self.min_strength = 0         # 最小强度分数
+        self.max_strength = 100       # 最大强度分数
+        
+    def get_strength_level_name(self, strength):
+        """根据强度分数获取等级名称"""
+        if strength >= self.extreme_threshold:
+            return "极强"
+        elif strength >= self.strong_threshold:
+            return "强"
+        elif strength >= self.medium_threshold:
+            return "中等"
+        elif strength >= self.weak_threshold:
+            return "弱"
+        else:
+            return "极弱"
+    
+    def is_strong_fractal(self, strength, custom_threshold=None):
+        """判断是否为强分型"""
+        threshold = custom_threshold if custom_threshold is not None else self.strong_threshold
+        return strength >= threshold
+    
+    def validate_config(self):
+        """验证配置参数的合理性"""
+        total_weight = (self.price_difference_weight + 
+                       self.breakthrough_weight + 
+                       self.volume_weight + 
+                       self.rsi_divergence_weight + 
+                       self.macd_divergence_weight)
+        
+        if total_weight != 100:
+            print(f"警告: 权重总和为{total_weight}，不等于100")
+            
+        if not (0 <= self.extreme_threshold <= 100):
+            print(f"警告: 极强阈值{self.extreme_threshold}不在合理范围内")
+            
+        if not (self.weak_threshold < self.medium_threshold < 
+                self.strong_threshold < self.extreme_threshold):
+            print("警告: 强度阈值设置不合理")
+            
+        return True
+    
+    def print_config(self):
+        """打印当前配置"""
+        print("=== 分型强度检测配置 ===")
+        print(f"价格差异权重: {self.price_difference_weight}分")
+        print(f"突破点位权重: {self.breakthrough_weight}分")
+        print(f"成交量权重: {self.volume_weight}分")
+        print(f"RSI背离权重: {self.rsi_divergence_weight}分")
+        print(f"MACD背离权重: {self.macd_divergence_weight}分")
+        print()
+        print("=== 强度等级阈值 ===")
+        print(f"极强: >={self.extreme_threshold}分")
+        print(f"强: {self.strong_threshold}-{self.extreme_threshold-1}分")
+        print(f"中等: {self.medium_threshold}-{self.strong_threshold-1}分")
+        print(f"弱: {self.weak_threshold}-{self.medium_threshold-1}分")
+        print(f"极弱: <{self.weak_threshold}分")
+
+
+# 默认配置实例
+DEFAULT_CONFIG = FxStrengthConfig()
+
+# 保守配置 (更严格的分型识别)
+CONSERVATIVE_CONFIG = FxStrengthConfig()
+CONSERVATIVE_CONFIG.price_difference_weight = 50
+CONSERVATIVE_CONFIG.breakthrough_weight = 25
+CONSERVATIVE_CONFIG.volume_weight = 15
+CONSERVATIVE_CONFIG.rsi_divergence_weight = 10
+CONSERVATIVE_CONFIG.macd_divergence_weight = 0
+CONSERVATIVE_CONFIG.strong_threshold = 70
+CONSERVATIVE_CONFIG.extreme_threshold = 85
+
+# 激进配置 (更宽松的分型识别)
+AGGRESSIVE_CONFIG = FxStrengthConfig()
+AGGRESSIVE_CONFIG.price_difference_weight = 30
+AGGRESSIVE_CONFIG.breakthrough_weight = 15
+AGGRESSIVE_CONFIG.volume_weight = 20
+AGGRESSIVE_CONFIG.rsi_divergence_weight = 20
+AGGRESSIVE_CONFIG.macd_divergence_weight = 15
+AGGRESSIVE_CONFIG.strong_threshold = 50
+AGGRESSIVE_CONFIG.extreme_threshold = 70
+
+# 技术指标重点配置 (重视技术指标背离)
+TECHNICAL_CONFIG = FxStrengthConfig()
+TECHNICAL_CONFIG.price_difference_weight = 25
+TECHNICAL_CONFIG.breakthrough_weight = 15
+TECHNICAL_CONFIG.volume_weight = 10
+TECHNICAL_CONFIG.rsi_divergence_weight = 25
+TECHNICAL_CONFIG.macd_divergence_weight = 25
+
+
+if __name__ == "__main__":
+    print("=== 分型强度配置演示 ===\n")
+    
+    configs = {
+        "默认配置": DEFAULT_CONFIG,
+        "保守配置": CONSERVATIVE_CONFIG,  
+        "激进配置": AGGRESSIVE_CONFIG,
+        "技术指标配置": TECHNICAL_CONFIG
+    }
+    
+    for name, config in configs.items():
+        print(f"=== {name} ===")
+        config.print_config()
+        config.validate_config()
+        print() 

chanlun/analysis/wyckoff/__init__.py

@@ -0,0 +1,7 @@
+"""威科夫分析（启发式）：交易区间 / 阶段 / 事件 / Volume Profile / Live。"""
+from __future__ import annotations
+
+from .engine import analyze_wyckoff
+from .live import execution_signal_from_wyckoff
+
+__all__ = ["analyze_wyckoff", "execution_signal_from_wyckoff"]

chanlun/analysis/wyckoff/engine.py

@@ -0,0 +1,196 @@
+"""威科夫分析入口：Cycle → Phase → Event → VP + Live（MULTI-CYCLE / LIVE-STRUCTURE）。
+
+range.py 只产 TradingRange；Confirmed 走 events.py；Live 走 live.py。
+cycles[0]=ACTIVE；禁止 cycles[-1] 取 active。
+Execution 只消费 Confirmed（见 live.execution_signal_from_wyckoff）。
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+
+from .events import build_phases, detect_bias_and_events
+from .live import analyze_live_structure
+from .range import detect_trading_ranges
+from .volume_profile import compute_volume_profile
+
+
+def _fmt_time(v) -> Optional[str]:
+	if v is None:
+		return None
+	if hasattr(v, "isoformat"):
+		try:
+			return v.isoformat()
+		except Exception:
+			pass
+	return str(v)
+
+
+def _empty(vp_bins: int) -> Dict[str, Any]:
+	return {
+		"cycles": [],
+		"trading_range": None,
+		"bias": "unknown",
+		"phases": [],
+		"events": [],
+		"volume_profile": {"bins": [], "poc": None, "vah": None, "val": None, "bin_count": vp_bins},
+		"volume_confirm": {"avg_volume": 0.0, "event_checks": {}},
+		"live": None,
+	}
+
+
+def _confidence_for_confirmed(
+	tr: Dict[str, Any],
+	phases: List[Dict[str, Any]],
+	events: List[Dict[str, Any]],
+) -> Dict[str, float]:
+	range_c = float(tr.get("range_confidence") or 0.5)
+	labels = {p.get("phase") for p in phases}
+	phase_c = 0.35
+	if "A" in labels and "B" in labels:
+		phase_c += 0.15
+	if "C" in labels:
+		phase_c += 0.2
+	if "D" in labels or "E" in labels:
+		phase_c += 0.15
+	phase_c = min(0.95, phase_c)
+	types = {e.get("type") for e in events}
+	event_c = 0.25
+	for t in ("Spring", "UTAD", "SOS", "SOW", "LPS", "LPSY"):
+		if t in types:
+			event_c += 0.12
+	event_c = min(0.95, event_c)
+	overall = 0.4 * range_c + 0.3 * phase_c + 0.3 * event_c
+	return {
+		"range": round(range_c, 3),
+		"phase": round(phase_c, 3),
+		"event": round(event_c, 3),
+		"overall": round(overall, 3),
+	}
+
+
+def _build_cycle(
+	work: pd.DataFrame,
+	tr: Dict[str, Any],
+	cycle_id: int,
+	vp_bins: int,
+) -> Dict[str, Any]:
+	bias, events, volume_confirm = detect_bias_and_events(work, tr)
+	phases = build_phases(work, tr, bias, events)
+	vp = compute_volume_profile(
+		work,
+		int(tr["abs_start_idx"]),
+		int(tr["abs_end_idx"]),
+		bin_count=vp_bins,
+	)
+	for ev in events:
+		ev["time"] = _fmt_time(ev.get("time"))
+	for ph in phases:
+		ph["start_time"] = _fmt_time(ph.get("start_time"))
+		ph["end_time"] = _fmt_time(ph.get("end_time"))
+
+	is_active = cycle_id == 0
+	trading_range = {
+		"start_time": _fmt_time(tr.get("start_time")),
+		"end_time": _fmt_time(tr.get("end_time")),
+		"high": float(tr["high"]),
+		"low": float(tr["low"]),
+		"mid": float(tr["mid"]),
+		"active": bool(is_active),
+		"bars": int(tr.get("bars", 0)),
+	}
+	conf = _confidence_for_confirmed(tr, phases, events)
+
+	# Live 层：仅 ACTIVE 周期做推演；历史周期归档为 COMPLETED
+	if is_active:
+		live = analyze_live_structure(
+			work, tr, confirmed_events=events, confirmed_phases=phases, bias=bias,
+		)
+		lifecycle = live.get("lifecycle") or "FORMING"
+	else:
+		live = None
+		lifecycle = "COMPLETED"
+
+	return {
+		"id": int(cycle_id),
+		"role": "latest" if is_active else "historical",
+		# MULTI-CYCLE：时间线角色
+		"status": "ACTIVE" if is_active else "HISTORICAL",
+		# LIVE-STRUCTURE：生命周期
+		"lifecycle": lifecycle,
+		"direction": "latest" if is_active else "historical",
+		"period": {
+			"start_time": _fmt_time(tr.get("start_time")),
+			"end_time": _fmt_time(tr.get("end_time")),
+			"bars": int(tr.get("bars", 0)),
+		},
+		"confidence": conf,
+		"trading_range": trading_range,
+		"bias": bias,
+		# 兼容旧读法：顶层 phases/events = confirmed
+		"phases": phases,
+		"events": events,
+		"confirmed": {
+			"phases": phases,
+			"events": events,
+			"volume_confirm": volume_confirm,
+		},
+		"live": live,
+		"volume_profile": vp,
+		"volume_confirm": volume_confirm,
+	}
+
+
+def analyze_wyckoff(
+	df: pd.DataFrame,
+	lookback: int = 120,
+	vp_bins: int = 50,
+	min_bars: int = 24,
+	atr_mult: float = 1.2,
+	range_start_time=None,
+	prefer_start_time=None,
+	max_cycles: int = 8,
+) -> Dict[str, Any]:
+	"""
+	多周期威科夫分析。
+	cycles[0] = ACTIVE；顶层 phases/events 只镜像 Confirmed。
+	顶层 live 镜像 cycles[0].live。
+	"""
+	empty = _empty(vp_bins)
+	if df is None or len(df) < 30:
+		return empty
+	if not all(c in df.columns for c in ("open", "high", "low", "close")):
+		return empty
+	work = df.copy()
+	if "volume" not in work.columns:
+		work["volume"] = 1.0
+
+	trs = detect_trading_ranges(
+		work,
+		lookback=lookback,
+		min_bars=max(8, int(min_bars)),
+		atr_mult=atr_mult,
+		max_cycles=max(1, min(8, int(max_cycles))),
+		prefer_start_time=prefer_start_time,
+		range_start_time=range_start_time,
+	)
+	if not trs:
+		return empty
+
+	cycles: List[Dict[str, Any]] = []
+	for i, tr in enumerate(trs):
+		cycles.append(_build_cycle(work, tr, cycle_id=i, vp_bins=vp_bins))
+
+	active = cycles[0]
+	return {
+		"cycles": cycles,
+		"trading_range": active["trading_range"],
+		"bias": active["bias"],
+		"phases": active["confirmed"]["phases"],
+		"events": active["confirmed"]["events"],
+		"volume_profile": active["volume_profile"],
+		"volume_confirm": active["volume_confirm"],
+		"live": active.get("live"),
+		"lifecycle": active.get("lifecycle"),
+	}

chanlun/analysis/wyckoff/events.py

@@ -0,0 +1,369 @@
+"""威科夫阶段与事件（启发式）。"""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+
+def _bar_time(df: pd.DataFrame, i: int):
+	row = df.iloc[i]
+	if "date" in df.columns and pd.notna(row["date"]):
+		return row["date"]
+	if "timestamp" in df.columns:
+		return row["timestamp"]
+	return i
+
+
+def _avg_vol(df: pd.DataFrame, i: int, win: int = 20) -> float:
+	a = max(0, i - win + 1)
+	v = df["volume"].astype(float).iloc[a : i + 1]
+	m = float(v.mean()) if len(v) else 0.0
+	return m if m > 0 else 1.0
+
+
+def detect_bias_and_events(
+	df: pd.DataFrame,
+	tr: Dict[str, Any],
+) -> Tuple[str, List[Dict[str, Any]], Dict[str, Any]]:
+	"""
+	返回 bias、events、volume_confirm。
+
+	Spring/UTAD 相对「结构高低」判定：取区间内次低/次高（剔除单根极值），
+	避免箱体把假破低点吃进 lo 后永远刺不破、从而无 C 阶段。
+	"""
+	hi = float(tr["high"])
+	lo = float(tr["low"])
+	mid = float(tr["mid"])
+	tol = float(tr.get("tol") or (hi - lo) * 0.05)
+	s = int(tr["abs_start_idx"])
+	e = int(tr["abs_end_idx"])
+	events: List[Dict[str, Any]] = []
+
+	# 结构边界：用次低/次高作假破参照（至少 8 根才启用）
+	seg = df.iloc[s : e + 1]
+	event_lo, event_hi = lo, hi
+	if len(seg) >= 8:
+		lows = seg["low"].astype(float)
+		highs = seg["high"].astype(float)
+		# nsmallest(2) 的较大者 = 次低；nlargest(2) 的较小者 = 次高
+		event_lo = float(lows.nsmallest(min(2, len(lows))).iloc[-1])
+		event_hi = float(highs.nlargest(min(2, len(highs))).iloc[-1])
+		# 勿比公布箱沿更「松」：结构带应在箱内
+		event_lo = max(event_lo, lo)
+		event_hi = min(event_hi, hi)
+		# 若次低仍等于极值（多根同价），略抬参照便于识别收回
+		if abs(event_lo - lo) < 1e-12:
+			event_lo = lo + max(tol * 0.35, (hi - lo) * 0.02)
+		if abs(event_hi - hi) < 1e-12:
+			event_hi = hi - max(tol * 0.35, (hi - lo) * 0.02)
+
+	# 扫描区间内及之后（含 tail_reserve）
+	scan_end = int(tr.get("abs_scan_end_idx", min(len(df) - 1, e + 15)))
+	scan_end = min(len(df) - 1, max(scan_end, e))
+	spring = None
+	utad = None
+	sos = None
+	sod = None  # sign of weakness / distribution breakdown
+	lps = None
+	lpsy = None
+
+	for i in range(s + 2, scan_end + 1):
+		row = df.iloc[i]
+		low = float(row["low"])
+		high = float(row["high"])
+		close = float(row["close"])
+		vol = float(row["volume"]) if "volume" in df.columns else 0.0
+		avg_v = _avg_vol(df, i)
+		ratio = vol / avg_v if avg_v else 0.0
+
+		# Spring: pierce below structural support then close back
+		if spring is None and low < event_lo - tol * 0.35 and close >= event_lo - tol * 0.35:
+			vol_ok = ratio <= 1.35 or (i + 1 <= scan_end and float(df.iloc[min(i + 1, scan_end)]["volume"]) / avg_v < 1.2)
+			spring = {
+				"type": "Spring",
+				"time": _bar_time(df, i),
+				"price": low,
+				"note": "假破下沿后收回",
+				"volume_ratio": round(ratio, 3),
+				"volume_ok": bool(vol_ok),
+				"idx": i,
+			}
+
+		# UTAD: pierce above structural resistance then close back
+		if utad is None and high > event_hi + tol * 0.35 and close <= event_hi + tol * 0.35:
+			vol_ok = ratio >= 0.8
+			utad = {
+				"type": "UTAD",
+				"time": _bar_time(df, i),
+				"price": high,
+				"note": "假破上沿后跌回",
+				"volume_ratio": round(ratio, 3),
+				"volume_ok": bool(vol_ok),
+				"idx": i,
+			}
+
+		# SOS: close above high with volume
+		if sos is None and close > hi + tol * 0.15:
+			vol_ok = ratio >= 1.15
+			sos = {
+				"type": "SOS",
+				"time": _bar_time(df, i),
+				"price": close,
+				"note": "放量上破交易区间",
+				"volume_ratio": round(ratio, 3),
+				"volume_ok": bool(vol_ok),
+				"idx": i,
+			}
+
+		# SOW / breakdown
+		if sod is None and close < lo - tol * 0.15:
+			vol_ok = ratio >= 1.15
+			sod = {
+				"type": "SOW",
+				"time": _bar_time(df, i),
+				"price": close,
+				"note": "放量下破交易区间",
+				"volume_ratio": round(ratio, 3),
+				"volume_ok": bool(vol_ok),
+				"idx": i,
+			}
+
+	# LPS after SOS: pullback that holds above mid/high-band with lighter volume
+	if sos is not None:
+		si = int(sos["idx"])
+		for i in range(si + 1, min(len(df), si + 25)):
+			row = df.iloc[i]
+			low = float(row["low"])
+			close = float(row["close"])
+			vol = float(row["volume"]) if "volume" in df.columns else 0.0
+			avg_v = _avg_vol(df, i)
+			ratio = vol / avg_v if avg_v else 0.0
+			if low >= mid - tol and close >= hi - tol * 2:
+				vol_ok = ratio <= 1.05
+				lps = {
+					"type": "LPS",
+					"time": _bar_time(df, i),
+					"price": low,
+					"note": "突破后缩量回踩不破",
+					"volume_ratio": round(ratio, 3),
+					"volume_ok": bool(vol_ok),
+					"idx": i,
+				}
+				break
+
+	if sod is not None:
+		si = int(sod["idx"])
+		for i in range(si + 1, min(len(df), si + 25)):
+			row = df.iloc[i]
+			high = float(row["high"])
+			close = float(row["close"])
+			vol = float(row["volume"]) if "volume" in df.columns else 0.0
+			avg_v = _avg_vol(df, i)
+			ratio = vol / avg_v if avg_v else 0.0
+			if high <= mid + tol and close <= lo + tol * 2:
+				vol_ok = ratio <= 1.05
+				lpsy = {
+					"type": "LPSY",
+					"time": _bar_time(df, i),
+					"price": high,
+					"note": "下跌突破后缩量反抽不过",
+					"volume_ratio": round(ratio, 3),
+					"volume_ok": bool(vol_ok),
+					"idx": i,
+				}
+				break
+
+	# 冲突清理：已判定吸筹且有 SOS 时，丢弃更早的 UTAD（避免阶段/图面误导）
+	# 派发且有 SOW 时，丢弃更晚才合理的 Spring 假信号同理在偏置后再滤
+	keep = []
+	for ev in (spring, sos, lps, utad, sod, lpsy):
+		if not ev:
+			continue
+		keep.append(ev)
+
+	# bias（先算）
+	last_c = float(df["close"].iloc[-1])
+	bias = "unknown"
+	if sos and (not sod or int(sos.get("idx", 0)) >= int(sod.get("idx", 0))):
+		bias = "accumulation"
+	elif sod and (not sos or int(sod.get("idx", 0)) > int(sos.get("idx", 0))):
+		bias = "distribution"
+	elif spring and not utad:
+		bias = "accumulation"
+	elif utad and not spring:
+		bias = "distribution"
+	elif last_c >= mid:
+		bias = "accumulation"
+	else:
+		bias = "distribution"
+
+	filtered = []
+	for ev in keep:
+		if bias == "accumulation" and ev["type"] == "UTAD" and sos and int(ev["idx"]) <= int(sos["idx"]):
+			continue
+		if bias == "distribution" and ev["type"] == "Spring" and sod and int(ev["idx"]) <= int(sod["idx"]):
+			continue
+		filtered.append(ev)
+
+	events = [{k: v for k, v in ev.items() if k != "idx"} for ev in filtered]
+
+	avg_volume = float(df["volume"].astype(float).iloc[max(0, e - 20) : e + 1].mean()) if "volume" in df.columns else 0.0
+	volume_confirm = {
+		"avg_volume": avg_volume,
+		"event_checks": {ev["type"]: {"volume_ok": ev.get("volume_ok"), "volume_ratio": ev.get("volume_ratio")} for ev in events},
+	}
+	return bias, events, volume_confirm
+
+
+def build_phases(
+	df: pd.DataFrame,
+	tr: Dict[str, Any],
+	bias: str,
+	events: List[Dict[str, Any]],
+	min_bars: int = 3,
+) -> List[Dict[str, Any]]:
+	"""
+	按威科夫事件锚点切分 A–E（启发式）。
+
+	吸筹：A停止 → B筑底 → C测试(Spring) → D拉升(SOS…LPS) → E离开
+	派发：A停止 → B筑顶 → C测试(UTAD) → D派发(SOW…LPSY) → E离开
+
+	无 Spring/UTAD 时：若已有 SOS/SOW，用突破前末次沿带测试补 C；仍无则省略 C。
+	"""
+	s = int(tr["abs_start_idx"])
+	e = int(tr["abs_end_idx"])
+	hi = float(tr["high"])
+	lo = float(tr["low"])
+	n_last = len(df) - 1
+	min_span = max(2, min_bars - 1)
+	range_len = max(1, e - s)
+
+	def _match_idx(t) -> Optional[int]:
+		if t is None:
+			return None
+		lo = max(0, s - 2)
+		hi = min(len(df), e + 40)
+		for i in range(lo, hi):
+			if _bar_time(df, i) == t:
+				return i
+		try:
+			tt = pd.Timestamp(t)
+			sample = None
+			if "date" in df.columns and len(df):
+				sample = df["date"].iloc[min(s, n_last)]
+			if sample is not None and getattr(sample, "tzinfo", None) is not None and tt.tzinfo is None:
+				tt = tt.tz_localize(sample.tzinfo)
+			for i in range(lo, hi):
+				bt = _bar_time(df, i)
+				try:
+					if abs((pd.Timestamp(bt) - tt).total_seconds()) <= 1:
+						return i
+				except Exception:
+					continue
+		except Exception:
+			pass
+		return None
+
+	event_idx: Dict[str, int] = {}
+	for ev in events:
+		idx = _match_idx(ev.get("time"))
+		if idx is not None:
+			event_idx[str(ev.get("type"))] = idx
+
+	accum = bias != "distribution"
+	if accum:
+		c_ev = event_idx.get("Spring")
+		d_ev = event_idx.get("SOS")
+		d_tail = event_idx.get("LPS") or d_ev
+	else:
+		c_ev = event_idx.get("UTAD")
+		d_ev = event_idx.get("SOW")
+		d_tail = event_idx.get("LPSY") or d_ev
+
+	# 有 D 无明确测试事件时：用突破前最后一次触及下/上沿作为 C（次级测试）
+	if c_ev is None and d_ev is not None:
+		band = lo + (hi - lo) * 0.28 if accum else hi - (hi - lo) * 0.28
+		for i in range(int(d_ev) - 1, s + 1, -1):
+			row = df.iloc[i]
+			if accum and float(row["low"]) <= band:
+				c_ev = i
+				break
+			if not accum and float(row["high"]) >= band:
+				c_ev = i
+				break
+
+	def _lab(phase: str) -> str:
+		if accum:
+			m = {"A": "A停止下跌", "B": "B筑底", "C": "C测试", "D": "D拉升", "E": "E离开"}
+		else:
+			m = {"A": "A停止上涨", "B": "B筑顶", "C": "C测试", "D": "D派发", "E": "E离开"}
+		return m.get(phase, phase)
+
+	a_end = s + max(min_bars, range_len // 5)
+
+	c_start = c_end = None
+	if c_ev is not None:
+		c_start = max(s, int(c_ev) - 1)
+		c_end = min(n_last, int(c_ev) + 1)
+
+	if d_ev is not None:
+		d_start = int(d_ev)
+		d_end = min(n_last, max(int(d_tail or d_ev), d_start) + max(min_bars, range_len // 8))
+		if d_tail is not None:
+			d_end = max(d_end, min(n_last, int(d_tail) + 1))
+	else:
+		d_start = d_end = None
+
+	if c_start is not None:
+		b_end = max(a_end + 1, c_start)
+	elif d_start is not None:
+		b_end = max(a_end + 1, d_start)
+	else:
+		b_end = max(a_end + 1, e)
+
+	if d_end is not None:
+		e_start = min(n_last, d_end)
+		e_end = n_last
+	else:
+		e_start = e_end = None
+
+	raw = [("A", s, a_end), ("B", a_end, b_end)]
+	if c_start is not None and c_end is not None:
+		raw.append(("C", c_start, c_end))
+	if d_start is not None and d_end is not None:
+		raw.append(("D", d_start, d_end))
+	if e_start is not None and e_end is not None and e_end > e_start:
+		raw.append(("E", e_start, e_end))
+
+	phases: List[Dict[str, Any]] = []
+	cursor = s
+	for phase, _a, _b in raw:
+		if cursor >= n_last:
+			break
+		a = max(int(_a), cursor)
+		b = int(max(int(_b), a))
+		need = 1 if phase == "C" else min_span
+		if b < a + need:
+			b = min(n_last, a + need)
+		b = int(np.clip(b, a, n_last))
+		if b < a:
+			continue
+		if phases and phases[-1].get("_a") == a and phases[-1].get("_b") == b:
+			continue
+		phases.append(
+			{
+				"phase": phase,
+				"label": _lab(phase),
+				"start_time": _bar_time(df, a),
+				"end_time": _bar_time(df, b),
+				"_a": a,
+				"_b": b,
+			}
+		)
+		cursor = b
+	for p in phases:
+		p.pop("_a", None)
+		p.pop("_b", None)
+	return phases

chanlun/analysis/wyckoff/live.py

@@ -0,0 +1,258 @@
+"""威科夫 Live / Developing 层（WYCKOFF-LIVE-STRUCTURE-001）。
+
+独立于 Confirmed Engine：不修改 events 确认条件，不写入 confirmed.events。
+Execution 不得消费本模块输出。
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Set
+
+import numpy as np
+import pandas as pd
+
+
+def _avg_vol(df: pd.DataFrame, i: int, win: int = 20) -> float:
+	a = max(0, i - win + 1)
+	v = df["volume"].astype(float).iloc[a : i + 1]
+	m = float(v.mean()) if len(v) else 0.0
+	return m if m > 0 else 1.0
+
+
+def _empty_live() -> Dict[str, Any]:
+	return {
+		"lifecycle": "UNKNOWN",
+		"range_formation": None,
+		"phase_candidate": None,
+		"event_candidates": [],
+		"next_expected": None,
+		"confidence": {
+			"cycle": 0.0,
+			"phase": 0.0,
+			"event": 0.0,
+			"structure": 0.0,
+			"volume": 0.0,
+			"overall": 0.0,
+		},
+		"note": "",
+	}
+
+
+def analyze_live_structure(
+	df: pd.DataFrame,
+	tr: Optional[Dict[str, Any]],
+	confirmed_events: Optional[List[Dict[str, Any]]] = None,
+	confirmed_phases: Optional[List[Dict[str, Any]]] = None,
+	bias: str = "unknown",
+) -> Dict[str, Any]:
+	"""
+	基于当前 TradingRange 与已确认事件，推演 Live candidates。
+	confirmed_* 只读，用于避免重复提示已确认事件，不修改之。
+	"""
+	out = _empty_live()
+	if df is None or len(df) < 20 or tr is None:
+		out["note"] = "insufficient structure"
+		return out
+
+	confirmed_events = confirmed_events or []
+	confirmed_phases = confirmed_phases or []
+	confirmed_types: Set[str] = {str(e.get("type")) for e in confirmed_events if e.get("type")}
+
+	s = int(tr["abs_start_idx"])
+	e = int(tr["abs_end_idx"])
+	scan_end = int(tr.get("abs_scan_end_idx", len(df) - 1))
+	scan_end = min(len(df) - 1, max(scan_end, e))
+	hi = float(tr["high"])
+	lo = float(tr["low"])
+	mid = float(tr["mid"])
+	tol = float(tr.get("tol") or (hi - lo) * 0.05)
+	atr = float(tr.get("atr") or max((hi - lo) * 0.2, 1e-9))
+
+	seg = df.iloc[s : e + 1]
+	if len(seg) < 8:
+		out["note"] = "range too short"
+		return out
+
+	# —— Range Formation（横盘 / 波动收敛）——
+	closes = seg["close"].astype(float)
+	highs = seg["high"].astype(float)
+	lows = seg["low"].astype(float)
+	vols = seg["volume"].astype(float) if "volume" in seg.columns else pd.Series([1.0] * len(seg))
+	half = max(4, len(seg) // 2)
+	vol_early = float(np.std(closes.iloc[:half])) if half > 1 else 0.0
+	vol_late = float(np.std(closes.iloc[-half:])) if half > 1 else 0.0
+	width = hi - lo
+	width_atr = width / atr if atr > 0 else 99.0
+	converging = vol_early > 1e-12 and vol_late < vol_early * 0.85
+	range_ok = 1.2 <= width_atr <= 10.0 and len(seg) >= 16
+	structure_score = 0.35
+	if range_ok:
+		structure_score += 0.25
+	if converging:
+		structure_score += 0.2
+	if width_atr <= 6.0:
+		structure_score += 0.1
+	structure_score = float(min(0.95, structure_score))
+
+	out["range_formation"] = {
+		"potential_trading_range": bool(range_ok),
+		"converging": bool(converging),
+		"width_atr": round(width_atr, 3),
+		"bars": int(len(seg)),
+	}
+
+	# —— 最近 K 形态（Phase C / Event candidates）——
+	i = scan_end
+	row = df.iloc[i]
+	o = float(row["open"])
+	h = float(row["high"])
+	l = float(row["low"])
+	c = float(row["close"])
+	rng = max(h - l, 1e-9)
+	lower_wick = min(o, c) - l
+	upper_wick = h - max(o, c)
+	avg_v = _avg_vol(df, i)
+	vol = float(row["volume"]) if "volume" in df.columns else avg_v
+	vol_ratio = vol / avg_v if avg_v else 1.0
+	volume_score = float(np.clip(1.1 - abs(vol_ratio - 1.0) * 0.35, 0.2, 0.95))
+
+	phase_candidate = None
+	phase_conf = 0.0
+	# Phase C：测低 + 下影 + 缩量（吸筹语境）
+	near_lo = l <= lo + tol * 1.2
+	test_low = l < mid and lower_wick >= rng * 0.35
+	vol_contract = vol_ratio <= 1.05
+	if bias != "distribution" and near_lo and test_low and vol_contract:
+		phase_candidate = "C"
+		phase_conf = 0.55 + (0.1 if lower_wick >= rng * 0.5 else 0) + (0.08 if vol_ratio < 0.9 else 0)
+	# Phase D 候选：价格在箱上半、有上破意图但未确认 SOS
+	elif c >= mid and (h >= hi - tol or c > hi - tol * 0.5):
+		phase_candidate = "D"
+		phase_conf = 0.5 + (0.1 if c > mid else 0)
+	elif c < mid and (l <= lo + tol):
+		phase_candidate = "B"
+		phase_conf = 0.45
+
+	# 已有 confirmed phase 时，candidate 取「下一阶段」提示，不覆盖事实
+	confirmed_phase_set = {str(p.get("phase")) for p in confirmed_phases}
+	if "E" in confirmed_phase_set:
+		phase_candidate = phase_candidate or "E"
+		phase_conf = max(phase_conf, 0.7)
+	elif "D" in confirmed_phase_set and phase_candidate is None:
+		phase_candidate = "D"
+		phase_conf = max(phase_conf, 0.65)
+
+	out["phase_candidate"] = phase_candidate
+	phase_conf = float(min(0.92, phase_conf))
+
+	# —— Event candidates（仅 Spring / SOS / LPS / UTAD）——
+	candidates: List[Dict[str, Any]] = []
+
+	def _add(typ: str, conf: float, note: str) -> None:
+		if typ in confirmed_types:
+			return  # 已确认则不再作为 candidate
+		candidates.append(
+			{
+				"type": typ,
+				"confidence": round(float(min(0.9, conf)), 3),
+				"confirmed": False,
+				"note": note,
+			}
+		)
+
+	# Spring candidate：刺破或贴近下沿，收盘收回，但未达 Confirmed 规则（或不在 confirmed）
+	pierce_lo = l < lo - tol * 0.15
+	close_back = c >= lo - tol * 0.5
+	if pierce_lo and close_back:
+		_add("Spring", 0.5 + (0.12 if vol_ratio <= 1.2 else 0) + (0.08 if close_back else 0), "假破下沿收回（未确认）")
+	elif l <= lo + tol * 0.35 and close_back and lower_wick >= rng * 0.4:
+		_add("Spring", 0.45 + (0.1 if vol_contract else 0), "测下沿长下影（未确认）")
+
+	# UTAD candidate
+	pierce_hi = h > hi + tol * 0.15
+	close_back_dn = c <= hi + tol * 0.5
+	if pierce_hi and close_back_dn:
+		_add("UTAD", 0.5 + (0.1 if vol_ratio >= 0.9 else 0), "假破上沿跌回（未确认）")
+
+	# SOS candidate：接近/轻破上沿，量能一般，未确认
+	if c > hi - tol * 0.4 or h >= hi:
+		sos_conf = 0.48 + (0.12 if c > hi else 0) + (0.1 if vol_ratio >= 1.05 else 0)
+		_add("SOS", sos_conf, "上破/逼近箱顶（未确认）")
+
+	# LPS candidate：站上 mid/上沿带后回踩
+	if c >= mid and l >= mid - tol * 1.5 and l > lo + (hi - lo) * 0.25:
+		_add("LPS", 0.46 + (0.1 if vol_ratio <= 1.0 else 0), "箱内上沿带回踩（未确认）")
+
+	candidates.sort(key=lambda x: x["confidence"], reverse=True)
+	out["event_candidates"] = candidates[:4]
+
+	event_score = float(candidates[0]["confidence"]) if candidates else 0.25
+
+	# next_expected（简规则）
+	next_exp = None
+	if "Spring" in confirmed_types and "SOS" not in confirmed_types:
+		next_exp = "SOS"
+	elif "SOS" in confirmed_types and "LPS" not in confirmed_types:
+		next_exp = "LPS"
+	elif "UTAD" in confirmed_types and "SOW" not in confirmed_types:
+		next_exp = "SOW"
+	elif any(c["type"] == "Spring" for c in candidates):
+		next_exp = "Test"
+	elif any(c["type"] == "SOS" for c in candidates):
+		next_exp = "LPS"
+	out["next_expected"] = next_exp
+
+	# —— lifecycle ——
+	key_confirmed = confirmed_types & {"Spring", "SOS", "UTAD", "SOW", "LPS", "LPSY"}
+	if key_confirmed:
+		lifecycle = "CONFIRMED"
+	elif range_ok or phase_candidate or candidates:
+		lifecycle = "FORMING"
+	else:
+		lifecycle = "UNKNOWN"
+	out["lifecycle"] = lifecycle
+
+	cycle_c = structure_score
+	overall = 0.35 * cycle_c + 0.25 * phase_conf + 0.25 * event_score + 0.15 * volume_score
+	out["confidence"] = {
+		"cycle": round(cycle_c, 3),
+		"phase": round(phase_conf, 3),
+		"event": round(event_score, 3),
+		"structure": round(structure_score, 3),
+		"volume": round(volume_score, 3),
+		"overall": round(float(overall), 3),
+	}
+	parts = []
+	if out["range_formation"]["potential_trading_range"]:
+		parts.append("Potential TR")
+	if phase_candidate:
+		parts.append(f"Phase {phase_candidate} candidate")
+	if candidates:
+		parts.append(f"{candidates[0]['type']} candidate")
+	out["note"] = "; ".join(parts) if parts else "observing"
+	return out
+
+
+def execution_signal_from_wyckoff(payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+	"""
+	Execution 边界：只允许 Confirmed。
+	返回 source='confirmed' 的信号描述；Live-only 时返回 None。
+	"""
+	if not payload:
+		return None
+	cycles = payload.get("cycles") or []
+	active = cycles[0] if cycles else None
+	events = []
+	if active and isinstance(active.get("confirmed"), dict):
+		events = list(active["confirmed"].get("events") or [])
+	if not events:
+		# 兼容旧顶层 events（均为 confirmed 镜像）
+		events = list(payload.get("events") or [])
+	if not events:
+		return None
+	last = events[-1]
+	return {
+		"source": "confirmed",
+		"type": last.get("type"),
+		"time": last.get("time"),
+		"lifecycle": (active or {}).get("lifecycle") or "CONFIRMED",
+	}

chanlun/analysis/wyckoff/range.py

@@ -0,0 +1,442 @@
+"""交易区间检测：仅负责 TradingRange（起止/高低/结构分）。
+
+WYCKOFF-MULTI-CYCLE-001：Phase/Event/VP 不得进入本模块。
+过滤顺序固定：detect → quality → trend → overlap(<0.2) → accept → mask。
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+MAX_CYCLES = 8
+OVERLAP_RATIO_MAX = 0.2
+
+
+def _atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
+	high = df["high"].astype(float)
+	low = df["low"].astype(float)
+	close = df["close"].astype(float)
+	prev_close = close.shift(1)
+	tr = pd.concat(
+		[
+			(high - low).abs(),
+			(high - prev_close).abs(),
+			(low - prev_close).abs(),
+		],
+		axis=1,
+	).max(axis=1)
+	return tr.rolling(period, min_periods=max(3, period // 2)).mean()
+
+
+def _robust_width(seg: pd.DataFrame) -> float:
+	"""用 90/10 分位估宽，避免单根影线把长窗卡死。"""
+	h = seg["high"].astype(float)
+	l = seg["low"].astype(float)
+	if len(seg) < 6:
+		return float(h.max() - l.min())
+	return float(np.nanpercentile(h, 90) - np.nanpercentile(l, 10))
+
+
+def _score_segment(
+	length: int,
+	near_hi: int,
+	near_lo: int,
+	inside: float,
+	width: float,
+	atr: float,
+) -> float:
+	"""结构质量分（非 Phase/Event）。"""
+	touch = min(near_hi, 6) + min(near_lo, 6)
+	width_pen = (width / atr) if atr > 0 else width
+	return float(touch) * 4.0 + float(inside) * 25.0 - width_pen * 3.0 + min(length / 40.0, 2.0)
+
+
+def _time_col(df: pd.DataFrame) -> Optional[str]:
+	if "date" in df.columns:
+		return "date"
+	if "timestamp" in df.columns:
+		return "timestamp"
+	return None
+
+
+def _bar_index_at_or_after(work: pd.DataFrame, ts: Any) -> Optional[int]:
+	col = _time_col(work)
+	if col is None or ts is None:
+		return None
+	try:
+		target = pd.Timestamp(ts)
+	except Exception:
+		return None
+	series = pd.to_datetime(work[col], utc=True, errors="coerce")
+	if target.tzinfo is None:
+		target = target.tz_localize("UTC")
+	else:
+		target = target.tz_convert("UTC")
+	if series.isna().all():
+		return None
+	ge = series >= target
+	if ge.any():
+		return int(np.flatnonzero(ge.to_numpy())[0])
+	return 0
+
+
+def _pack_range(
+	work: pd.DataFrame,
+	df: pd.DataFrame,
+	start_i: int,
+	end_i: int,
+	hi: float,
+	lo: float,
+	tol: float,
+	last_atr: float,
+	score: float,
+	n: int,
+	window_offset: int = 0,
+) -> Dict[str, Any]:
+	"""组装 TradingRange（仅结构字段）。"""
+	mid = (hi + lo) / 2.0
+	last_c = float(work["close"].iloc[min(end_i, len(work) - 1)])
+	price_in_box = (lo - tol * 1.5) <= last_c <= (hi + tol * 1.5)
+	bars = int(end_i - start_i + 1)
+	# 结构置信：归一化 score（启发式）
+	range_conf = float(np.clip(score / 55.0, 0.05, 0.99))
+	best = {
+		"start_idx": int(start_i),
+		"end_idx": int(end_i),
+		"high": float(hi),
+		"low": float(lo),
+		"mid": float(mid),
+		"active": bool(price_in_box),
+		"atr": float(last_atr),
+		"tol": float(tol),
+		"bars": bars,
+		"score": float(score),
+		"quality": float(score),
+		"range_confidence": range_conf,
+	}
+
+	def _ts(row) -> Any:
+		col = _time_col(work)
+		if col and pd.notna(row[col]):
+			return row[col]
+		return None
+
+	best["start_time"] = _ts(work.iloc[best["start_idx"]])
+	best["end_time"] = _ts(work.iloc[best["end_idx"]])
+	# window_offset：slice 相对父 DataFrame 的起点；勿用 len(df)-len(work)
+	offset = int(window_offset)
+	best["abs_start_idx"] = offset + best["start_idx"]
+	best["abs_end_idx"] = offset + best["end_idx"]
+	best["abs_scan_end_idx"] = offset + n - 1
+	return best
+
+
+def _overlap_ratio(a0: int, a1: int, b0: int, b1: int) -> float:
+	"""两闭区间重叠长度 / 较短区间长度。"""
+	lo = max(a0, b0)
+	hi = min(a1, b1)
+	if hi < lo:
+		return 0.0
+	overlap = hi - lo + 1
+	shorter = min(a1 - a0 + 1, b1 - b0 + 1)
+	if shorter <= 0:
+		return 0.0
+	return float(overlap) / float(shorter)
+
+
+def _passes_quality(tr: Dict[str, Any], min_bars: int) -> bool:
+	if tr is None:
+		return False
+	if int(tr.get("bars") or 0) < max(8, min_bars // 2):
+		return False
+	if float(tr.get("score") or 0) < 12.0:
+		return False
+	hi = float(tr["high"])
+	lo = float(tr["low"])
+	atr = float(tr.get("atr") or 0) or 1.0
+	if (hi - lo) / atr > 12.0:
+		return False
+	return True
+
+
+def _passes_trend_filter(work: pd.DataFrame, tr: Dict[str, Any]) -> bool:
+	"""趋势污染：定向位移过大则非震荡箱。"""
+	s = int(tr["start_idx"])
+	e = int(tr["end_idx"])
+	seg = work.iloc[s : e + 1]
+	if len(seg) < 8:
+		return False
+	c0 = float(seg["close"].iloc[0])
+	c1 = float(seg["close"].iloc[-1])
+	atr = float(tr.get("atr") or 0) or 1.0
+	drift = abs(c1 - c0) / atr
+	# 相对箱宽：漂移占箱宽过大 → 趋势
+	width = max(float(tr["high"]) - float(tr["low"]), atr)
+	drift_frac = abs(c1 - c0) / width
+	if drift > 6.0 and drift_frac > 0.55:
+		return False
+	return True
+
+
+def _detect_in_window(
+	df: pd.DataFrame,
+	win_start: int,
+	win_end: int,
+	min_bars: int = 24,
+	atr_mult: float = 1.2,
+	tail_reserve: int = 12,
+	prefer_start_time: Any = None,
+	range_start_time: Any = None,
+) -> Optional[Dict[str, Any]]:
+	"""
+	在 df[win_start:win_end+1] 内检测单个 TradingRange。
+	只返回箱体结构，不含 Phase/Event/VP。
+	"""
+	if df is None or win_end < win_start:
+		return None
+	slice_df = df.iloc[win_start : win_end + 1].reset_index(drop=True)
+	lookback = len(slice_df)
+	if lookback < min_bars + 5:
+		return None
+
+	work = slice_df
+	n = len(work)
+	reserve = min(tail_reserve, max(0, n - min_bars - 2))
+	core_end = n - reserve if reserve > 0 else n
+	core = work.iloc[:core_end]
+	if len(core) < min_bars:
+		core = work
+		core_end = n
+		reserve = 0
+
+	atr = _atr(work)
+	last_atr = float(atr.iloc[core_end - 1]) if atr.notna().iloc[:core_end].any() else float(
+		(core["high"] - core["low"]).mean()
+	)
+	if not np.isfinite(last_atr) or last_atr <= 0:
+		last_atr = float(core["close"].iloc[-1]) * 0.01
+
+	eff_atr_mult = float(atr_mult)
+	if lookback >= 280:
+		eff_atr_mult = atr_mult * 1.7
+	elif lookback >= 160:
+		eff_atr_mult = atr_mult * 1.3
+	width_factor = 3.8 + min(2.2, max(0.0, (lookback - 80) / 100.0))
+	max_width = last_atr * eff_atr_mult * width_factor
+	tol = last_atr * eff_atr_mult * 0.35
+
+	prefer_i = None
+	if prefer_start_time is not None:
+		prefer_i = _bar_index_at_or_after(work, prefer_start_time)
+
+	if range_start_time is not None:
+		start_i = _bar_index_at_or_after(work, range_start_time)
+		if start_i is not None and start_i <= core_end - 8:
+			seg = work.iloc[start_i:core_end]
+			hi = float(seg["high"].max())
+			lo = float(seg["low"].min())
+			rw = _robust_width(seg)
+			if 0 < rw <= max_width * 1.15:
+				near_hi = int((seg["high"] >= hi - tol).sum())
+				near_lo = int((seg["low"] <= lo + tol).sum())
+				inside = float(((seg["close"] >= lo - tol) & (seg["close"] <= hi + tol)).mean())
+				if near_hi >= 2 and near_lo >= 2 and inside >= 0.70:
+					score = _score_segment(len(seg), near_hi, near_lo, inside, rw, last_atr)
+					return _pack_range(
+						work, df, start_i, core_end - 1, hi, lo, tol, last_atr, score, n,
+						window_offset=win_start,
+					)
+
+	eff_min_bars = max(8, int(min_bars))
+	cn = len(core)
+	max_bars = min(cn, max(eff_min_bars * 2, min(96, max(eff_min_bars + 8, int(cn * 0.5)))))
+	cands: List[Tuple[float, int, int, int, float, float, float]] = []
+
+	def _try_seg(start_i: int, end_i: int, prefer_boost: float = 0.0) -> None:
+		if end_i - start_i + 1 < eff_min_bars:
+			return
+		if start_i < 0 or end_i >= cn or start_i > end_i:
+			return
+		seg = work.iloc[start_i : end_i + 1]
+		hi = float(seg["high"].max())
+		lo = float(seg["low"].min())
+		rw = _robust_width(seg)
+		if rw <= 0 or rw > max_width:
+			return
+		raw_w = hi - lo
+		if raw_w > max_width * 1.35:
+			return
+		near_hi = int((seg["high"] >= hi - tol).sum())
+		near_lo = int((seg["low"] <= lo + tol).sum())
+		if near_hi < 2 or near_lo < 2:
+			return
+		inside = float(((seg["close"] >= lo - tol) & (seg["close"] <= hi + tol)).mean())
+		if inside < 0.72:
+			return
+		length = end_i - start_i + 1
+		score = _score_segment(length, near_hi, near_lo, inside, rw, last_atr) + prefer_boost
+		cands.append((score, length, start_i, end_i, hi, lo, rw))
+
+	for length in range(min(cn, max_bars), eff_min_bars - 1, -4):
+		start_i = cn - length
+		boost = 0.0
+		if prefer_i is not None:
+			dist = abs(start_i - int(prefer_i))
+			if dist <= 6:
+				boost = 10.0
+			elif dist <= 14:
+				boost = 4.0
+			elif start_i > int(prefer_i) + 16:
+				boost = -10.0
+		_try_seg(start_i, cn - 1, boost)
+
+	if prefer_i is not None:
+		pi = int(prefer_i)
+		if 0 <= pi < cn:
+			align_max = min(cn, max(max_bars, int(cn * 0.65)))
+			alen = cn - pi
+			if eff_min_bars <= alen <= align_max:
+				_try_seg(pi, cn - 1, prefer_boost=18.0)
+			elif alen > align_max:
+				start_i = max(0, cn - align_max)
+				if start_i > pi:
+					start_i = pi
+					end_i = min(cn - 1, pi + align_max - 1)
+				else:
+					end_i = cn - 1
+				_try_seg(start_i, end_i, prefer_boost=12.0)
+
+	if not cands:
+		return None
+
+	cands.sort(key=lambda x: x[0], reverse=True)
+	best_score = cands[0][0]
+	band = max(4.0, abs(best_score) * 0.10)
+	near = [c for c in cands if c[0] >= best_score - band]
+	chosen = max(near, key=lambda x: (x[1], x[0]))
+	score, _length, start_i, end_i, hi, lo, _rw = chosen
+	return _pack_range(work, df, start_i, end_i, hi, lo, tol, last_atr, score, n, window_offset=win_start)
+
+
+def detect_trading_ranges(
+	df: pd.DataFrame,
+	lookback: Optional[int] = None,
+	min_bars: int = 24,
+	atr_mult: float = 1.2,
+	tail_reserve: int = 12,
+	max_cycles: int = MAX_CYCLES,
+	prefer_start_time: Any = None,
+	range_start_time: Any = None,
+) -> List[Dict[str, Any]]:
+	"""
+	倒序切多段 TradingRange（近→远）。
+	过滤顺序：detect → quality → trend → overlap → accept → mask。
+	返回列表已按时间倒序，调用方将 [0] 标为 ACTIVE。
+	"""
+	if df is None or len(df) < min_bars + 5:
+		return []
+	lb = int(lookback) if lookback is not None else len(df)
+	work = df.tail(lb).reset_index(drop=True)
+	n = len(work)
+	occupied: List[Dict[str, Any]] = []
+	accepted: List[Dict[str, Any]] = []
+
+	# 搜索右端从 n-1 往左收缩；每接受一段后右端移到该段 start 之前
+	search_end = n - 1
+	prefer = prefer_start_time
+	hard_start = range_start_time
+
+	while len(accepted) < max(1, int(max_cycles)) and search_end >= min_bars + 4:
+		# 在剩余历史内从右往左试多个右边界，避免历史箱必须贴住 search_end
+		# （否则中间趋势会挡住更早的真实箱）
+		cand = None
+		step = max(4, min(12, (search_end - min_bars) // 10 or 4))
+		for end_try in range(search_end, min_bars + 4, -step):
+			trial = _detect_in_window(
+				work,
+				0,
+				end_try,
+				min_bars=min_bars,
+				atr_mult=atr_mult,
+				tail_reserve=tail_reserve,
+				prefer_start_time=prefer if len(accepted) == 0 and end_try == search_end else None,
+				range_start_time=hard_start if len(accepted) == 0 and end_try == search_end else None,
+			)
+			# 1) detect
+			if trial is None:
+				continue
+			# 2) quality
+			if not _passes_quality(trial, min_bars):
+				continue
+			# 3) trend contamination
+			if not _passes_trend_filter(work, trial):
+				continue
+			# 4) overlap with accepted
+			a0, a1 = int(trial["abs_start_idx"]), int(trial["abs_end_idx"])
+			overlap_bad = False
+			for occ in occupied:
+				ratio = _overlap_ratio(a0, a1, int(occ["start"]), int(occ["end"]))
+				if ratio >= OVERLAP_RATIO_MAX:
+					overlap_bad = True
+					break
+			if overlap_bad:
+				continue
+			# 取最靠右的合格箱（倒序第一段）
+			cand = trial
+			break
+
+		if cand is None:
+			break
+
+		# 5) accept
+		accepted.append(cand)
+		a0, a1 = int(cand["abs_start_idx"]), int(cand["abs_end_idx"])
+		# 6) mask
+		occupied.append(
+			{
+				"start": a0,
+				"end": max(a1, int(cand.get("abs_scan_end_idx", a1))),
+				"quality": float(cand.get("quality") or 0),
+				"high": float(cand["high"]),
+				"low": float(cand["low"]),
+			}
+		)
+		# 下一轮只在更早窗口搜
+		search_end = int(cand["abs_start_idx"]) - 1
+		hard_start = None
+		prefer = None
+
+	# abs_* 目前相对 work；若 df 比 work 长需加 offset
+	offset = len(df) - len(work)
+	if offset:
+		for tr in accepted:
+			tr["abs_start_idx"] = int(tr["abs_start_idx"]) + offset
+			tr["abs_end_idx"] = int(tr["abs_end_idx"]) + offset
+			tr["abs_scan_end_idx"] = int(tr["abs_scan_end_idx"]) + offset
+
+	return accepted
+
+
+def detect_trading_range(
+	df: pd.DataFrame,
+	lookback: int = 120,
+	min_bars: int = 24,
+	atr_mult: float = 1.2,
+	tail_reserve: int = 12,
+	range_start_time: Any = None,
+	prefer_start_time: Any = None,
+) -> Optional[Dict[str, Any]]:
+	"""兼容旧接口：返回倒序列表中的第一段（ACTIVE 候选）。"""
+	ranges = detect_trading_ranges(
+		df,
+		lookback=lookback,
+		min_bars=min_bars,
+		atr_mult=atr_mult,
+		tail_reserve=tail_reserve,
+		max_cycles=1,
+		prefer_start_time=prefer_start_time,
+		range_start_time=range_start_time,
+	)
+	return ranges[0] if ranges else None

chanlun/analysis/wyckoff/volume_profile.py

@@ -0,0 +1,72 @@
+"""区间内 Volume Profile。"""
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+import numpy as np
+import pandas as pd
+
+
+def compute_volume_profile(
+	df: pd.DataFrame,
+	start_idx: int,
+	end_idx: int,
+	bin_count: int = 50,
+	value_area_pct: float = 0.70,
+) -> Dict[str, Any]:
+	seg = df.iloc[start_idx : end_idx + 1]
+	if seg.empty:
+		return {"bins": [], "poc": None, "vah": None, "val": None, "bin_count": bin_count}
+
+	typical = (seg["high"].astype(float) + seg["low"].astype(float) + seg["close"].astype(float)) / 3.0
+	vol = seg["volume"].astype(float).fillna(0.0)
+	lo = float(seg["low"].min())
+	hi = float(seg["high"].max())
+	if not np.isfinite(lo) or not np.isfinite(hi) or hi <= lo:
+		mid = float(seg["close"].iloc[-1])
+		return {
+			"bins": [{"price": mid, "volume": float(vol.sum())}],
+			"poc": mid,
+			"vah": mid,
+			"val": mid,
+			"bin_count": 1,
+		}
+
+	edges = np.linspace(lo, hi, bin_count + 1)
+	# 右开最后一桶闭合
+	idx = np.clip(np.digitize(typical.values, edges) - 1, 0, bin_count - 1)
+	vols = np.zeros(bin_count, dtype=float)
+	for i, v in zip(idx, vol.values):
+		vols[i] += float(v)
+
+	centers = (edges[:-1] + edges[1:]) / 2.0
+	poc_i = int(np.argmax(vols)) if vols.sum() > 0 else bin_count // 2
+	poc = float(centers[poc_i])
+
+	# Value Area：从 POC 向两侧扩展直到累计 >= value_area_pct
+	total = float(vols.sum()) or 1.0
+	target = total * value_area_pct
+	left = right = poc_i
+	acc = float(vols[poc_i])
+	while acc < target and (left > 0 or right < bin_count - 1):
+		left_v = vols[left - 1] if left > 0 else -1.0
+		right_v = vols[right + 1] if right < bin_count - 1 else -1.0
+		if right_v >= left_v and right < bin_count - 1:
+			right += 1
+			acc += float(vols[right])
+		elif left > 0:
+			left -= 1
+			acc += float(vols[left])
+		else:
+			break
+
+	bins: List[Dict[str, float]] = [
+		{"price": float(centers[i]), "volume": float(vols[i])} for i in range(bin_count)
+	]
+	return {
+		"bins": bins,
+		"poc": poc,
+		"vah": float(centers[right]),
+		"val": float(centers[left]),
+		"bin_count": bin_count,
+	}

chanlun/core/ChanBI.py

@@ -0,0 +1,132 @@
+from decimal import Decimal
+import chanlun.core.ChanKLC as ChanKLC
+from chanlun.core.ChanEnum import Chan_BI_DIR
+class ChanBI():
+	def __init__(self, klc: ChanKLC, index, ddir=Chan_BI_DIR.UP):
+		self.start_klc = klc
+		self.end_klc = klc
+		self.next = None
+		self.pre = None
+		self.dir = ddir
+		self.index = index
+		self.is_sure = False
+		self.high = klc.high
+		self.low = klc.low
+		self.sure_time = None
+		self.klc_list = []
+		self.klc_list.append(klc)
+		self.end_time = klc.end_time
+		self.start_time = klc.start_time
+		self.macd_hist = 0
+		self.macd_div = 0
+		self.seg = None
+		self.height = 0
+		self.width = 0
+		self.slop = 0
+		self.fib_list = []
+		self.seg_index = 0
+		self.bi_zs = None
+		self.seg_zs = None
+	def set_bi_zs(self, bi_zs):
+		for klc in self.klc_list:
+			klc.set_bi_zs(bi_zs)
+	def set_seg(self, seg):
+		self.seg = seg
+		self.seg_index = len(seg.bi_list)-1
+	def set_macdhist(self, macd_hist):
+		self.macd_hist = macd_hist
+	def set_macd_div(self, macd_div):
+		self.macd_div = macd_div
+	def cal_macd_div(self):
+		self.macd_div = 0.0
+		if self.pre and self.pre.pre:
+			if self.pre.pre.macd_hist == 0:
+				self.macd_div = 0.0
+			else:
+				self.macd_div = self.macd_hist / self.pre.pre.macd_hist
+			#print(self.start_time, self.end_time, self.macd_hist, self.pre.pre.macd_hist, self.macd_div)
+	def cal_macdhist(self):
+		self.macd_hist = 0
+		for klc in self.klc_list:
+			for klu in klc.klu_list:
+				if self.dir == Chan_BI_DIR.UP and klu.macdhist > 0: 
+					self.macd_hist += klu.macdhist
+				if self.dir == Chan_BI_DIR.DOWN and klu.macdhist < 0:
+					self.macd_hist -= klu.macdhist
+	def check_bi_zs_overlap(self):
+		if self.next and self.next.next:
+			if self.dir == Chan_BI_DIR.UP:
+				return self.low < self.next.next.high
+			else:
+				return self.high > self.next.next.low
+		else:
+			return False
+	def check_overlap(self):
+		if self.next and self.next.next and self.next.next.is_sure:
+			if self.dir == Chan_BI_DIR.UP:
+				return self.high > self.next.low and self.high < self.next.next.high
+			else:
+				return self.high > self.next.high and self.low > self.next.next.low
+		else:
+			return False
+	def set_end_klc(self, klc, sure_klc):
+		if self.dir == Chan_BI_DIR.UP and klc.high > self.high:
+			self.high = klc.high
+		if self.dir == Chan_BI_DIR.DOWN and klc.low < self.low:
+			self.low = klc.low
+		self.end_klc = klc
+		self.set_is_sure(True, sure_klc.end_time)
+		self.end_time = klc.end_time
+		self.cal_properties()
+		#print(self.start_time, klc.fx, "This bi is ended", len(self.klc_list), klc.index - self.start_klc.index)
+	def cal_properties(self):
+		if self.is_sure:
+			self.height = float(format(self.high - self.low, ".2f"))
+			self.width = self.end_klc.index - self.start_klc.index
+			self.slop = float(format(self.height / self.width, ".2f"))
+			fib_list = [0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0]
+			for fib in fib_list:
+				self.fib_list.append(float(format(self.height * fib + self.low, ".2f")))
+		#print(self.end_time, self.height, self.width, self.slop, self.fib_list)
+	def set_is_sure(self, is_sure, time):
+		self.is_sure = is_sure
+		self.sure_time = time
+	def set_start_klc(self, klc, ddir):
+		self.start_klc = klc
+		self.klc_list = []
+		self.klc_list.append(klc)
+		self.high = klc.high
+		self.low = klc.low
+		self.dir = ddir
+	def set_pre(self, bi):
+		self.pre = bi
+	def set_next(self, bi):
+		self.next = bi
+	def add_klc(self, klc):
+		added = False
+		if len(self.klc_list) > 0:
+			for index in range(0, len(self.klc_list)):
+				if self.klc_list[index].index == klc.index:
+					added = True
+					break
+		if not added:
+			self.klc_list.append(klc)
+			#print(self.start_time, klc.start_time)
+		#print(klc.end_time, klc.index)
+		self.end_klc = klc
+		self.end_time = klc.klu_list[-1].time
+		self.cal_macdhist()
+		self.cal_macd_div()
+	def append_klc_list(self, klc_list):
+		self.klc_list.append(klc_list)
+	def get_decimal(self, value):
+		return Decimal("{:.2f}".format(value))
+	def update_bi(self, klc):
+		self.end_klc = None
+		if self.dir == Chan_BI_DIR.UP and klc.high > self.high:
+			self.high = klc.high
+		if self.dir == Chan_BI_DIR.DOWN and klc.low < self.low:
+			self.low = klc.low
+		self.is_sure = False
+		self.sure_time = None
+		#print(self.start_time, klc.start_time, klc.fx, "This bi is extended")

chanlun/core/ChanBIZS.py

@@ -0,0 +1,161 @@
+from chanlun.core.ChanEnum import Chan_ZS_DIR, Chan_ZS_TYPE, Chan_BI_DIR
+import chanlun.core.ChanBI as ChanBI
+# 中枢
+class ChanBIZS():
+    def __init__(self, start_bi: ChanBI, index, ddir: Chan_ZS_DIR):
+        self.start_klc = start_bi.start_klc
+        self.start_time = self.start_klc.start_time
+        self.end_time = None
+        self.index = index
+        self.start_bi = start_bi
+        self.bi_list = []
+        self.bi_list.append(start_bi)
+        self.end_bi = None
+        self.bi_out = None
+        self.is_sure = False
+        self.zg = 0
+        self.zd = 0
+        self.gg = 0
+        self.dd = 0
+        self.dir = ddir
+        self.sure_time = None
+        self.end_klc = None
+        self.zs_type = Chan_ZS_TYPE.NORMAL
+        start_bi.set_bi_zs(self)
+    def set_end_bi(self, end_bi, sure_time):
+        self.end_bi = end_bi
+        self.set_end_time(end_bi.end_klc.end_time)
+        self.is_sure = True
+        self.sure_time = sure_time
+        end_bi.set_bi_zs(self)
+        #print(self.start_time, self.is_sure, len(self.bi_list), self.dir, self.zs_type)
+    def set_end_time(self, end_time):
+        self.end_time = end_time
+    def set_zg(self, zg):
+        self.zg = zg
+    def set_zd(self, zd):
+        self.zd = zd
+    def set_gg(self, gg):
+        self.gg = gg
+    def set_dd(self, dd):
+        self.dd = dd
+    def add_bi(self, bi: ChanBI):
+        if bi:
+            self.bi_list.append(bi)
+            if bi.high > self.gg:
+                self.gg = bi.high
+            if bi.low < self.dd:
+                self.dd = bi.low
+            bi.set_bi_zs(self)
+            self.classify_zs()
+    def set_pre(self, pre):
+        self.pre = pre
+    def set_next(self, next):
+        self.next = next
+    def classify_zs(self):
+        """
+        根据中枢内笔的高低点变化趋势，对中枢进行分类
+        
+        分类逻辑：
+        - 取中枢内向上笔的高点（peaks）和向下笔的低点（valleys）
+        - 比较前半段和后半段的均值，判断高点和低点的整体趋势
+        
+        分类结果：
+        - RISING    上升中枢：高点抬高 + 低点抬高 → 多方占优，可能向上突破
+        - FALLING   下行中枢：高点降低 + 低点降低 → 空方占优，可能向下突破
+        - CONVERGING 收敛中枢：高点降低 + 低点抬高 → 区间收窄，即将选择方向
+        - DIVERGING  扩散中枢：高点抬高 + 低点降低 → 波动加剧，市场不稳定
+        - NORMAL    常规中枢：无明显趋势 → 多空均衡，区间震荡
+        """
+        if len(self.bi_list) < 3:
+            self.zs_type = Chan_ZS_TYPE.NORMAL
+            return
+
+        # 提取向上笔的高点（peaks）和向下笔的低点（valleys）
+        peaks = [bi.high for bi in self.bi_list if bi.dir == Chan_BI_DIR.UP]
+        valleys = [bi.low for bi in self.bi_list if bi.dir == Chan_BI_DIR.DOWN]
+
+        high_trend = self._calc_trend(peaks)
+        low_trend = self._calc_trend(valleys)
+
+        if high_trend > 0 and low_trend > 0:
+            self.zs_type = Chan_ZS_TYPE.RISING
+        elif high_trend < 0 and low_trend < 0:
+            self.zs_type = Chan_ZS_TYPE.FALLING
+        elif high_trend < 0 and low_trend > 0:
+            self.zs_type = Chan_ZS_TYPE.CONVERGING
+        elif high_trend > 0 and low_trend < 0:
+            self.zs_type = Chan_ZS_TYPE.DIVERGING
+        else:
+            self.zs_type = Chan_ZS_TYPE.NORMAL
+
+    def _calc_trend(self, values):
+        """
+        计算序列的趋势方向
+        将序列分为前后两半，比较均值：
+        - 后半均值 > 前半均值 → 返回 1（上升趋势）
+        - 后半均值 < 前半均值 → 返回 -1（下降趋势）
+        - 相等或数据不足 → 返回 0（无趋势）
+        
+        使用均值比较而非首尾比较，可以过滤单笔异常波动带来的误判
+        """
+        if len(values) < 2:
+            return 0
+        mid = len(values) // 2
+        first_half = values[:mid] if mid > 0 else values[:1]
+        second_half = values[mid:]
+        avg_first = sum(first_half) / len(first_half)
+        avg_second = sum(second_half) / len(second_half)
+        # 使用中枢区间的一定比例作为阈值，避免微小波动误判
+        threshold = abs(avg_first) * 0.005 if avg_first != 0 else 0
+        if avg_second - avg_first > threshold:
+            return 1
+        elif avg_first - avg_second > threshold:
+            return -1
+        else:
+            return 0
+
+    def is_weakening(self):
+        """
+        判断中枢是否在衰弱（即将反向突破的信号）
+        
+        衰弱条件：
+        1. 中枢内笔数 >= 5（有足够的数据判断）
+        2. 最后一笔的MACD面积相比同方向前一笔出现背驰（macd_div < 1）
+        3. 中枢类型为收敛型或常规型
+        
+        返回: True表示中枢力量衰弱，可能反向
+        """
+        if len(self.bi_list) < 5:
+            return False
+        last_bi = self.bi_list[-1]
+        # 最后一笔与同方向前一笔比较MACD面积是否背驰
+        if last_bi.macd_div > 0 and last_bi.macd_div < 1.0:
+            return True
+        return False
+
+    def get_zs_strength(self):
+        """
+        计算中枢强度，用于辅助判断中枢延续还是反向
+        
+        返回字典包含：
+        - type: 中枢类型 (Chan_ZS_TYPE)
+        - bi_count: 中枢内笔数
+        - range_ratio: 中枢区间占比 = (zg - zd) / (gg - dd)，越小说明中枢越紧密
+        - last_bi_div: 最后一笔的MACD背驰比率
+        - is_weakening: 是否衰弱
+        - is_extending: 是否在延伸（笔数 >= 9 可能升级）
+        """
+        total_range = self.gg - self.dd if self.gg != self.dd else 1
+        zs_range = self.zg - self.zd if self.zg != self.zd else 0
+        range_ratio = zs_range / total_range if total_range > 0 else 0
+        last_bi_div = self.bi_list[-1].macd_div if len(self.bi_list) > 0 else 0
+
+        return {
+            'type': self.zs_type,
+            'bi_count': len(self.bi_list),
+            'range_ratio': round(range_ratio, 4),
+            'last_bi_div': round(last_bi_div, 4),
+            'is_weakening': self.is_weakening(),
+            'is_extending': len(self.bi_list) >= 9,  # 9段可能升级
+        }

chanlun/core/ChanBSP.py

@@ -0,0 +1,24 @@
+import chanlun.core.ChanBI as ChanBI
+from chanlun.core.ChanEnum import Chan_BSP_TYPE, Chan_BSP_DIR
+
+class ChanBSP():
+    def __init__(self, bi: ChanBI, index, type: Chan_BSP_TYPE, ddir: Chan_BSP_DIR, sure_time, zs_count, zs, seg):
+        self.bi = bi
+        self.klc = bi.end_klc
+        self.index = index
+        self.type = type
+        self.start_time = self.klc.start_time
+        self.end_time = self.klc.end_time
+        if sure_time:
+            self.is_sure = True
+            self.sure_time = sure_time
+        else:
+            self.is_sure = False
+            self.sure_time = None
+        self.dir = ddir
+        self.zs_count = zs_count
+        self.zs = zs
+        self.seg = bi.seg
+    def set_sure_time(self, sure_time):
+        self.is_sure = True
+        self.sure_time = sure_time

chanlun/core/ChanCTime.py

@@ -0,0 +1,44 @@
+from datetime import datetime
+
+
+class ChanCTime:
+    def __init__(self, year, month, day, hour, minute, second=0, auto=True):
+        self.year = year
+        self.month = month
+        self.day = day
+        self.hour = hour
+        self.minute = minute
+        self.second = second
+        self.auto = auto  # 自适应对天的理解
+        self.set_timestamp()  # set self.ts
+
+    def __str__(self):
+        if self.hour == 0 and self.minute == 0:
+            return f"{self.year:04}/{self.month:02}/{self.day:02}"
+        else:
+            return f"{self.year:04}/{self.month:02}/{self.day:02} {self.hour:02}:{self.minute:02}"
+
+    def to_str(self):
+        if self.hour == 0 and self.minute == 0:
+            return f"{self.year:04}/{self.month:02}/{self.day:02}"
+        else:
+            return f"{self.year:04}/{self.month:02}/{self.day:02} {self.hour:02}:{self.minute:02}"
+
+    def toDateStr(self, splt=''):
+        return f"{self.year:04}{splt}{self.month:02}{splt}{self.day:02}"
+
+    def toDate(self):
+        return ChanCTime(self.year, self.month, self.day, 0, 0, auto=False)
+
+    def set_timestamp(self):
+        if self.hour == 0 and self.minute == 0 and self.auto:
+            date = datetime(self.year, self.month, self.day, 23, 59, self.second)
+        else:
+            date = datetime(self.year, self.month, self.day, self.hour, self.minute, self.second)
+        self.ts = date.timestamp()
+
+    def __gt__(self, t2):
+        return self.ts > t2.ts
+
+    def __ge__(self, t2):
+        return self.ts >= t2.ts

chanlun/core/ChanEnum.py

@@ -0,0 +1,368 @@
+from enum import Enum, auto
+from typing import Literal
+
+
+class Chan_DATA_SRC(Enum):
+    BAO_STOCK = auto()
+    CCXT = auto()
+    CSV = auto()
+
+class Chan_ZS_DIR(Enum):
+    UP = auto()
+    DOWN = auto()
+
+class Chan_ZS_TYPE(Enum):
+    """中枢类型分类"""
+    NORMAL = auto()       # 常规中枢：高低点无明显趋势，区间震荡
+    RISING = auto()       # 上升中枢：高点抬高，低点也抬高，重心上移
+    FALLING = auto()      # 下行中枢：高点降低，低点也降低，重心下移
+    CONVERGING = auto()   # 收敛中枢：高点降低，低点抬高，区间收窄（三角收敛）
+    DIVERGING = auto()    # 扩散中枢：高点抬高，低点降低，区间扩大（喇叭口）
+class Chan_K_DIR(Enum):
+    BULL = auto()
+    BEAR = auto()
+    CROSS = auto()
+
+class Chan_EMA_POS(Enum):
+    """K线与任意EMA的位置关系（与趋势方向无关的客观分类，支持threshold容差）"""
+    ABOVE = auto()              # 完全在EMA上方（远离）：low > ema + threshold
+    NEAR_ABOVE = auto()         # 在EMA上方但接近：ema < low <= ema + threshold
+    CROSS_CLOSE_ABOVE = auto()  # 跨越EMA，收盘在上方：close > ema, low <= ema（含threshold范围内触碰）
+    ON_EMA = auto()             # 收盘价在EMA附近：abs(close - ema) <= threshold
+    CROSS_CLOSE_BELOW = auto()  # 跨越EMA，收盘在下方：close < ema, high >= ema（含threshold范围内触碰）
+    NEAR_BELOW = auto()         # 在EMA下方但接近：ema - threshold <= high < ema
+    BELOW = auto()              # 完全在EMA下方（远离）：high < ema - threshold
+    UNKNOWN = auto()            # 未知（EMA值无效）
+
+class Chan_EMA_SEMANTIC(Enum):
+    """K线与EMA结合趋势方向的语义状态（用于交易判断）"""
+    STRONG_TREND = auto()       # 7: 顺势K线完全在EMA趋势侧（强势，远未及EMA）
+    TREND_SIDE = auto()         # 6: 完全在EMA趋势侧（正常趋势运行）
+    RECOVER = auto()            # 5: 逆势后穿越EMA回到趋势侧（收复EMA，趋势恢复）
+    TOUCH_FAIL = auto()         # 4: 逆势触碰EMA但未穿越（反弹/反抽力度不足）
+    DEEP_COUNTER = auto()       # 3: 完全在EMA逆势侧（深度回调/反抽）
+    BREAK = auto()              # 2: 穿越EMA，收盘在逆势侧（支撑/压力失败）
+    TOUCH_HOLD = auto()         # 1: 触碰EMA，收盘守住趋势侧（支撑/压力有效）
+    WEAK_COUNTER = auto()       # 8: 逆势K线完全在EMA逆势侧（弱势，远未到EMA）
+    APPROACHING = auto()        # 9: K线接近EMA但未触碰（即将测试支撑/压力）
+    NEUTRAL = auto()            # 0: 盘整/无法判断
+class Chan_KL_TYPE(Enum):
+    K_1S = auto()
+    K_1M = auto()
+    K_DAY = auto()
+    K_WEEK = auto()
+    K_MON = auto()
+    K_YEAR = auto()
+    K_5M = auto()
+    K_15M = auto()
+    K_30M = auto()
+    K_60M = auto()
+    K_1H = auto()
+    K_2H = auto()
+    K_4H = auto()
+    K_6H = auto()
+    K_8H = auto()
+    K_12H = auto()
+    K_1D = auto()
+    K_3D = auto()
+    K_3M = auto()
+    K_QUARTER = auto()
+
+
+class Chan_KLINE_DIR(Enum):
+    UP = auto()
+    DOWN = auto()
+    COMBINE = auto()
+    INCLUDED = auto()
+class Chan_KLU_TYPE(Enum):
+    BigBull = auto()
+    MiddleBull = auto()
+    SmallBull = auto()
+    BigBear = auto()
+    MiddleBear = auto()
+    SmallBear = auto()
+    Cross = auto()
+    
+class Chan_KLU_PATTERN(Enum):
+    # 单根K线形态
+    HAMMER = auto()              # 锤子线
+    INVERTED_HAMMER = auto()     # 倒锤子线
+    SHOOTING_STAR = auto()       # 射击之星
+    HANGING_MAN = auto()         # 上吊线
+    DOJI = auto()                # 十字星
+    LONG_LEGGED_DOJI = auto()    # 长腿十字星
+    GRAVESTONE_DOJI = auto()     # 墓碑十字星
+    DRAGONFLY_DOJI = auto()      # 蜻蜓十字星
+    MARUBOZU = auto()            # 光头光脚
+    SPINNING_TOP = auto()        # 纺锤线
+    
+    # 双根K线形态
+    BULLISH_ENGULFING = auto()   # 看涨吞没
+    BEARISH_ENGULFING = auto()   # 看跌吞没
+    PIERCING_LINE = auto()       # 刺透形态
+    DARK_CLOUD_COVER = auto()    # 乌云盖顶
+    TWEEZER_TOP = auto()         # 镊子顶
+    TWEEZER_BOTTOM = auto()      # 镊子底
+    HARAMI = auto()              # 孕线
+    BULLISH_HARAMI = auto()      # 看涨孕线
+    BEARISH_HARAMI = auto()      # 看跌孕线
+    
+    # 三根K线形态
+    MORNING_STAR = auto()        # 早晨之星
+    EVENING_STAR = auto()        # 黄昏之星
+    THREE_WHITE_SOLDIERS = auto() # 红三兵
+    THREE_BLACK_CROWS = auto()   # 三只乌鸦
+    THREE_INNER_UP = auto()      # 上升三法
+    THREE_INNER_DOWN = auto()    # 下降三法
+    ABANDONED_BABY = auto()      # 弃婴形态
+    
+    # 多根K线形态
+    DOUBLE_TOP = auto()          # 双顶
+    DOUBLE_BOTTOM = auto()       # 双底
+    TRIPLE_TOP = auto()          # 三顶
+    TRIPLE_BOTTOM = auto()       # 三底
+    HEAD_AND_SHOULDERS = auto()  # 头肩顶
+    INVERSE_HEAD_SHOULDERS = auto() # 头肩底
+    ROUNDING_BOTTOM = auto()     # 圆弧底
+    ROUNDING_TOP = auto()        # 圆弧顶
+    
+    # 缺口形态
+    BREAKAWAY_GAP = auto()       # 突破缺口
+    RUNAWAY_GAP = auto()         # 持续缺口
+    EXHAUSTION_GAP = auto()      # 衰竭缺口
+    
+    # 特殊形态
+    ISLAND_REVERSAL = auto()     # 岛形反转
+    KEY_REVERSAL = auto()        # 关键反转
+    INSIDE_BAR = auto()          # 内包线
+    OUTSIDE_BAR = auto()         # 外包线
+    
+    # 趋势形态
+    HIGHER_HIGH = auto()         # 更高高点
+    HIGHER_LOW = auto()          # 更高低点
+    LOWER_HIGH = auto()          # 更低高点
+    LOWER_LOW = auto()           # 更低低点
+    
+    # 支撑阻力形态
+    SUPPORT_BOUNCE = auto()      # 支撑反弹
+    RESISTANCE_REJECTION = auto() # 阻力拒绝
+    BREAKOUT = auto()            # 突破
+    BREAKDOWN = auto()           # 跌破
+    
+    # 成交量相关形态
+    VOLUME_SPIKE = auto()        # 成交量激增
+    VOLUME_DECLINE = auto()      # 成交量萎缩
+    
+    # 未知/无形态
+    UNKNOWN = auto()             # 未知形态
+
+
+class Chan_FX_TYPE(Enum):
+    BOTTOM = auto()
+    TOP = auto()
+    UNKNOWN = auto()
+    UP = auto()
+    DOWN = auto()
+    TT = auto()
+    BB = auto()
+    PTOP = auto()
+    PBOTTOM = auto()
+class Chan_FX(Enum):
+    CONTINUATION = auto()
+    REVERSAL = auto()
+    UNKNOWN = auto()
+class Chan_PRICE_TREND(Enum):
+    UP = auto()
+    DOWN = auto()
+    FLAT = auto()
+    UNKNOWN = auto()
+class Chan_KLC_FX(Enum):
+    TOP0 = auto()
+    TOP1 = auto()
+    TOP2 = auto()
+    TOP3 = auto()
+    TOP4 = auto()
+    TOP5 = auto()
+    TOP6 = auto()
+    TOP7 = auto()
+    TOP8 = auto()
+    BOTTOM0 = auto()
+    BOTTOM1 = auto()
+    BOTTOM2 = auto()
+    BOTTOM3 = auto()
+    BOTTOM4 = auto()
+    BOTTOM5 = auto()
+    BOTTOM6 = auto()
+    BOTTOM7 = auto()
+    BOTTOM8 = auto()
+    UNKNOWN = auto()
+# 统一的MACD状态枚举，包含所有可能的状态
+class Chan_MACD_STATE(Enum):
+    """MACD状态枚举 - 包含所有可能的状态"""
+    # 穿越状态
+    CROSS0_UP = auto()      # 穿零轴后快速向上，能量柱呈现一根比一根长的排列方式
+    CROSS0_DOWN = auto()    # 穿零轴后快速向下，能量柱呈现一根比一根短的排列方式
+    
+    CROSS_OS = auto()       # 穿零轴后缠绕/粘合，黄白线沿着能量柱运行，黄白线在运行的过程中没有释放出反向能量柱
+    CROSS_REV = auto()      # 穿零轴后倒挂，MACD黄白线在穿零轴的时候与零轴的距离比较近，同时黄白线沿着能量柱运行，在运行的过程中，能量柱衰减导致它跟黄白线之间形成夹角空位，同时黄白线产生交叉并释放反向能量柱。
+    
+    # 趋势状态
+    NEAR0  = auto()
+    NEAR0_52 = auto()          # 价格在EMA52附近/价格接触EMA52并马上离开，需要观察离开强度
+    NEAR0_DIFF = auto()        # MACD白线接近零轴，价格未到EMA52
+    NEAR0_PERFECT = auto()     # MACD白线接近零轴和价格接触或短暂击穿EMA52，而MACD黄线不穿零轴，完美形态
+    NEAR0_24 = auto()          # MACD黄白线接近零轴和价格在EMA24附近
+    # 位置状态
+    HIGH = auto()           # 高位：MACD黄白线离开能量柱到高点，能量柱最大开始减弱
+    HIGH_EMPTY = auto()     # 高位空：MACD黄白线处于高位，能量柱衰减，与黄白线形成空间夹角
+    RETURN_ZERO = auto()    # 归零轴：能量柱呈现一根比一根短的排列方式
+    RZ_UP = auto()             # 归零轴后的零轴上涨
+    RZ_DOWN = auto()           # 归零轴后的零轴下跌
+    UP = auto()         # 穿零轴后向上
+    DOWN = auto()       # 穿零轴后向下
+    PEAK = auto()       # 峰值：MACD白线处于高位
+    # 基础状态
+    UNKNOWN = auto()        # 未知
+    START = auto()          # 开始
+class Chan_MACDSEG_DIR(Enum):
+    ABOVE = auto()
+    UNDER = auto()
+class Chan_MACDUNITTF_TYPE(Enum):
+    START = auto()
+    CROSS0 = auto()
+    NEAR0 = auto()
+class Chan_MACDUNITTF_JUMP(Enum):
+    CONTUNE = auto()
+    DISCRETE = auto()
+class Chan_MACDUNITTF_DIV(Enum):
+    CONTUNE = auto()
+    DISCRETE = auto()
+    UNDIV = auto()
+class Chan_MACDHISTSET_DIR(Enum):
+    ABOVE = auto()
+    UNDER = auto()
+class Chan_MACDUNITTF_DIR(Enum):
+    ABOVE = auto()
+    UNDER = auto()
+class Chan_MACDHIST_STATE(Enum):
+    UP = auto()
+    DOWN = auto()
+    PEAK = auto()
+    UNKNOWN = auto()
+    
+class Chan_BI_DIR(Enum):
+    UP = auto()
+    DOWN = auto()
+
+class Chan_SEG_DIR(Enum):
+    UP = auto()
+    DOWN = auto()
+
+class Chan_BI_TYPE(Enum):
+    UNKNOWN = auto()
+    STRICT = auto()
+    SUB_VALUE = auto()  # 次高低点成笔
+    TIAOKONG_THRED = auto()
+    DAHENG = auto()
+    TUIBI = auto()
+    UNSTRICT = auto()
+    TIAOKONG_VALUE = auto()
+
+
+Chan_BSP_MAIN_TYPE = Literal['1', '2', '3']
+
+class Chan_BSP_DIR(Enum):
+    BUY = auto()
+    SELL = auto()
+class Chan_BSP_TYPE(Enum):
+    B1 = auto()
+    B2 = auto()
+    B3 = auto()
+    S1 = auto()
+    S2 = auto()
+    S3 = auto()
+    NONE = auto()
+"""
+class Chan_BSP_TYPE(Enum):
+    T1 = '1'
+    T1P = '1p'
+    T2 = '2'
+    T2S = '2s'
+    T3A = '3a'  # 中枢在1类后面
+    T3B = '3b'  # 中枢在1类前面
+    T3 = '3'
+    T3E ='3e' # T3退出点
+    QJT = 'qjt' # 区间套突破
+    QJT1 = 'qjt1' # 区间套一类买点
+    QJT2 = 'qjt2' # 区间套一类卖点 
+    QJT3 = 'qjt3' # 区间套三类买点
+    def main_type(self) -> Chan_BSP_MAIN_TYPE:
+        return self.value[0]  # type: ignore
+
+"""
+class Chan_AUTYPE(Enum):
+    QFQ = auto()
+    HFQ = auto()
+    NONE = auto()
+
+
+class Chan_TREND_TYPE(Enum):
+    MEAN = "mean"
+    MAX = "max"
+    MIN = "min"
+
+
+class Chan_TREND_LINE_SIDE(Enum):
+    INSIDE = auto()
+    OUTSIDE = auto()
+
+
+class Chan_LEFT_SEG_METHOD(Enum):
+    ALL = auto()
+    PEAK = auto()
+
+
+class Chan_FX_CHECK_METHOD(Enum):
+    STRICT = auto()
+    LOSS = auto()
+    HALF = auto()
+    TOTALLY = auto()
+
+
+class Chan_SEG_TYPE(Enum):
+    BI = auto()
+    SEG = auto()
+
+
+class Chan_MACD_ALGO(Enum):
+    AREA = auto()
+    PEAK = auto()
+    FULL_AREA = auto()
+    DIFF = auto()
+    SLOPE = auto()
+    AMP = auto()
+    VOLUMN = auto()
+    AMOUNT = auto()
+    VOLUMN_AVG = auto()
+    AMOUNT_AVG = auto()
+    TURNRATE_AVG = auto()
+    RSI = auto()
+
+
+class Chan_DATA_FIELD:
+    FIELD_TIME = "time_key"
+    FIELD_OPEN = "open"
+    FIELD_HIGH = "high"
+    FIELD_LOW = "low"
+    FIELD_CLOSE = "close"
+    FIELD_VOLUME = "volume"  # 成交量
+    FIELD_TURNOVER = "turnover"  # 成交额
+    FIELD_TURNRATE = "turnover_rate"  # 换手率
+
+class Chan_KLC_STATE:
+    """笔当下状态（缠论笔定理）。任意时刻必属其一。"""
+    S10 = "(1, 0)"   # 顶分型构造中 (1,0)
+    S_10 = "(-1, 0)"   # 底分型构造中 (-1,0)
+    S11 = "(1,1)"  # 向上笔延续中 
+    S_11 = "(-1,1)" # 向下笔延续中
+    UNKNOWN = "Unknown" # 初始状态

chanlun/core/ChanKLC.py

@@ -0,0 +1,620 @@
+import copy
+from typing import Dict, Optional
+
+from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_KLC_FX
+from chanlun.core.ChanEnum import Chan_K_DIR, Chan_MACD_STATE, Chan_PRICE_TREND, Chan_EMA_POS
+from chanlun.core.ChanEnum import Chan_EMA_SEMANTIC, Chan_BSP_TYPE, Chan_KLC_STATE, Chan_FX
+import chanlun.core.ChanKLU as ChanKLU
+import chanlun.core.ChanCTime as ChanCTime
+import chanlun.core.Chan_FX_Box as Chan_FX_Box
+# 根据结合律合并K线后的K线
+class ChanKLC():
+	def __init__(self, klu: ChanKLU, index, ddir=Chan_KLINE_DIR.UP):
+		self.start_time = klu.time
+		self.end_time = None
+		self.high = klu.high
+		self.low = klu.low
+		self.dir = ddir
+		self.index = index
+		self.klu_list = []
+		self.add_klu(klu)
+		self.fx = Chan_FX_TYPE.UNKNOWN
+		self.next = None
+		self.pre = None
+		self.start_klu = klu
+		self.end_klu = None
+		self.state = "00"
+		self.klc_state = Chan_KLC_STATE.UNKNOWN
+		self.open = klu.open
+		self.close = klu.close
+		self.volume = klu.volume
+		self.bi = None
+		self.distance = 0
+		self.klc_fx_type = Chan_KLC_FX.UNKNOWN
+		self.rsi = klu.rsi
+		self.volume_ratio = klu.volume_ratio
+		self.macdhist = klu.macdhist
+		self.body = klu.body
+		self.upper_shadow = klu.upper_shadow
+		self.lower_shadow = klu.lower_shadow
+		self.body_ratio = klu.body_ratio
+		self.upper_shadow_ratio = klu.upper_shadow_ratio
+		self.lower_shadow_ratio = klu.lower_shadow_ratio
+		self.candle_dir = klu.candle_dir
+		self.range = klu.range
+		self.bb_out = True
+		self.macd = klu.macd
+		self.signal = klu.signal
+		self.state = Chan_MACD_STATE.UNKNOWN
+		self.continue_div = False
+		self.separate_div = False
+		self.ema24 = klu.ema24
+		self.ema26 = klu.ema26
+		self.ema52 = klu.ema52
+		self.ema104 = klu.ema104
+		self.ema156 = klu.ema156
+		self.ema208 = klu.ema208
+		self.ema13 = klu.ema13
+		self.ema7 = klu.ema7
+		self.trend = Chan_PRICE_TREND.UNKNOWN
+		self.exception = klu.exception
+		self.klc_dir = Chan_KLINE_DIR.UP if klu.close > klu.open else Chan_KLINE_DIR.DOWN
+		self.ema_dir = klu.ema_dir
+		self.bsp = False
+		self.bsp_type = Chan_BSP_TYPE.NONE
+		# EMA状态字典：key为EMA名称，value为 {'pos': Chan_EMA_POS, 'semantic': Chan_EMA_SEMANTIC}
+		self.ema_status = {}
+		# 向后兼容：保留 ema52_status 和 ema52_pos
+		self.ema52_status = 0
+		self.ema52_pos = Chan_EMA_POS.UNKNOWN
+		self.bb2633upper = klu.bb2633upper
+		self.bb2633lower = klu.bb2633lower
+		self.bb2633middle = klu.bb2633middle
+		self.ema5 = klu.ema5
+		self.ma5 = klu.ma5
+		self.fx_box = None
+		self.in_fx = False
+		self.fx_confirmed = False
+		self.ema52_dis = klu.high - klu.ema52 if klu.close > klu.ema52 else klu.ema52 - klu.low
+		self.ema26_dis = klu.high - klu.ema26 if klu.close > klu.ema26 else klu.ema26 - klu.low
+		self.macd_signal_dis = abs(klu.macd - klu.signal)
+		self.ema52_ema26_dis = abs(klu.ema52 - klu.ema26)
+		self.fx_type = Chan_FX.UNKNOWN
+		self.bi_zs = None
+		self.seg_zs = None
+		self.last_bi_zs = None
+	# ==================== EMA 通用计算方法 ====================
+
+	@staticmethod
+	def cal_ema_pos(high, low, close, ema_value, threshold=0):
+		"""
+		计算K线与任意EMA的客观位置关系（与趋势方向无关，支持threshold容差）
+		
+		参数:
+		  high, low, close: K线的高低收盘价
+		  ema_value: EMA的值
+		  threshold: 容差值（绝对值），在此范围内视为"接近/触碰"
+		             例如 BTC 价格 $100,000 时 threshold=100 表示差100点视为触碰
+		返回:
+		  Chan_EMA_POS 枚举值
+		
+		判断逻辑（以threshold=100, ema=97000为例）：
+		  ema_zone = [96900, 97100]  （EMA上下各扩展threshold）
+		  
+		  ABOVE:             low > 97100     K线完全在zone上方（远离EMA）
+		  NEAR_ABOVE:        97000 < low <= 97100  K线在上方但下影线进入zone（接近EMA）
+		  CROSS_CLOSE_ABOVE: close > 97000, low <= 97000  K线穿越EMA，收盘在上方
+		  ON_EMA:            abs(close - 97000) <= 100  收盘价在zone内
+		  CROSS_CLOSE_BELOW: close < 97000, high >= 97000  K线穿越EMA，收盘在下方
+		  NEAR_BELOW:        96900 <= high < 97000  K线在下方但上影线进入zone（接近EMA）
+		  BELOW:             high < 96900     K线完全在zone下方（远离EMA）
+		"""
+		if ema_value is None or ema_value == 0:
+			return Chan_EMA_POS.UNKNOWN
+
+		ema_upper = ema_value + threshold  # EMA zone 上界
+		ema_lower = ema_value - threshold  # EMA zone 下界
+
+		# 1. 收盘价在EMA附近（zone内）
+		if threshold > 0 and abs(close - ema_value) <= threshold:
+			# 收盘价在zone内，但还需要看是否有实际穿越
+			if low <= ema_value and close >= ema_value:
+				return Chan_EMA_POS.CROSS_CLOSE_ABOVE  # 实际穿越了精确EMA线
+			elif high >= ema_value and close <= ema_value:
+				return Chan_EMA_POS.CROSS_CLOSE_BELOW
+			return Chan_EMA_POS.ON_EMA
+
+		# 2. K线实际穿越了精确的EMA线
+		if close > ema_value and low <= ema_value:
+			return Chan_EMA_POS.CROSS_CLOSE_ABOVE
+		if close < ema_value and high >= ema_value:
+			return Chan_EMA_POS.CROSS_CLOSE_BELOW
+		if close == ema_value:
+			return Chan_EMA_POS.ON_EMA
+
+		# 3. 没有实际穿越，检查是否"接近"（在threshold zone内）
+		if close > ema_value:
+			# K线在EMA上方
+			if threshold > 0 and low <= ema_upper:
+				return Chan_EMA_POS.NEAR_ABOVE   # 下影线进入zone，接近但未触碰
+			return Chan_EMA_POS.ABOVE             # 远离EMA
+		else:
+			# K线在EMA下方
+			if threshold > 0 and high >= ema_lower:
+				return Chan_EMA_POS.NEAR_BELOW   # 上影线进入zone，接近但未触碰
+			return Chan_EMA_POS.BELOW             # 远离EMA
+
+	@staticmethod
+	def cal_ema_semantic(ema_pos, kline_dir, ema_dir):
+		"""
+		根据客观位置 + K线方向 + 趋势方向，计算语义状态
+		
+		参数:
+		  ema_pos: Chan_EMA_POS 客观位置
+		  kline_dir: Chan_KLINE_DIR K线方向 (UP/DOWN/COMBINE/INCLUDED)
+		  ema_dir: int 趋势方向 (1=多头, -1=空头, 0=盘整)
+		返回:
+		  Chan_EMA_SEMANTIC 枚举值
+		
+		语义含义（以多头为例，空头完全对称）：
+		  TOUCH_HOLD:   触碰EMA，收盘守住趋势侧（支撑/压力有效）
+		  BREAK:        穿越EMA，收盘在逆势侧（支撑/压力失败）
+		  DEEP_COUNTER: 完全在EMA逆势侧（深度回调/反抽）
+		  TOUCH_FAIL:   逆势触碰EMA但未穿越（反弹/反抽力度不足）
+		  RECOVER:      逆势后穿越EMA回到趋势侧（收复EMA）
+		  TREND_SIDE:   完全在EMA趋势侧（正常运行）
+		  STRONG_TREND: 顺势K线完全在EMA趋势侧（强势，远未及EMA）
+		  WEAK_COUNTER: 逆势K线完全在EMA逆势侧（弱势，远未到EMA）
+		"""
+		if ema_pos == Chan_EMA_POS.UNKNOWN:
+			return Chan_EMA_SEMANTIC.NEUTRAL
+
+		# 统一处理：将多头/盘整和空头映射到同一套逻辑
+		# is_bull=True 时，"趋势侧"=上方，"逆势侧"=下方
+		# is_bull=False时，"趋势侧"=下方，"逆势侧"=上方
+		is_bull = ema_dir >= 0  # 多头和盘整都按多头逻辑处理
+
+		# K线是否是顺势方向（多头下UP为顺势，空头下DOWN为顺势）
+		is_trend_kline = (kline_dir == Chan_KLINE_DIR.UP) if is_bull else (kline_dir == Chan_KLINE_DIR.DOWN)
+		is_counter_kline = (kline_dir == Chan_KLINE_DIR.DOWN) if is_bull else (kline_dir == Chan_KLINE_DIR.UP)
+
+		# 位置映射：多头下 ABOVE=趋势侧, BELOW=逆势侧; 空头反过来
+		trend_side = Chan_EMA_POS.ABOVE if is_bull else Chan_EMA_POS.BELOW
+		counter_side = Chan_EMA_POS.BELOW if is_bull else Chan_EMA_POS.ABOVE
+		near_trend = Chan_EMA_POS.NEAR_ABOVE if is_bull else Chan_EMA_POS.NEAR_BELOW
+		near_counter = Chan_EMA_POS.NEAR_BELOW if is_bull else Chan_EMA_POS.NEAR_ABOVE
+		cross_to_trend = Chan_EMA_POS.CROSS_CLOSE_ABOVE if is_bull else Chan_EMA_POS.CROSS_CLOSE_BELOW
+		cross_to_counter = Chan_EMA_POS.CROSS_CLOSE_BELOW if is_bull else Chan_EMA_POS.CROSS_CLOSE_ABOVE
+
+		# COMBINE / INCLUDED 方向：只看位置，不区分强弱
+		if not is_trend_kline and not is_counter_kline:
+			if ema_pos == trend_side:
+				return Chan_EMA_SEMANTIC.TREND_SIDE
+			elif ema_pos in (near_trend, cross_to_trend, Chan_EMA_POS.ON_EMA):
+				return Chan_EMA_SEMANTIC.APPROACHING
+			elif ema_pos in (near_counter, cross_to_counter):
+				return Chan_EMA_SEMANTIC.APPROACHING
+			elif ema_pos == counter_side:
+				return Chan_EMA_SEMANTIC.DEEP_COUNTER
+			return Chan_EMA_SEMANTIC.NEUTRAL
+
+		# 逆势K线（多头下的下跌K线 / 空头下的上涨K线）
+		if is_counter_kline:
+			if ema_pos == trend_side:
+				return Chan_EMA_SEMANTIC.STRONG_TREND   # 逆势K线仍在趋势侧（回调很浅）
+			elif ema_pos == near_trend:
+				return Chan_EMA_SEMANTIC.APPROACHING     # 接近EMA，即将测试支撑/压力
+			elif ema_pos == cross_to_trend:
+				return Chan_EMA_SEMANTIC.TOUCH_HOLD      # 触碰EMA后守住趋势侧
+			elif ema_pos == Chan_EMA_POS.ON_EMA:
+				return Chan_EMA_SEMANTIC.TOUCH_HOLD      # 收盘在EMA附近，视为守住
+			elif ema_pos == cross_to_counter:
+				return Chan_EMA_SEMANTIC.BREAK            # 穿越EMA到逆势侧
+			elif ema_pos == near_counter:
+				return Chan_EMA_SEMANTIC.BREAK            # 接近EMA但收盘在逆势侧，也视为击穿
+			elif ema_pos == counter_side:
+				return Chan_EMA_SEMANTIC.DEEP_COUNTER     # 完全在逆势侧
+
+		# 顺势K线（多头下的上涨K线 / 空头下的下跌K线）
+		if is_trend_kline:
+			if ema_pos == counter_side:
+				return Chan_EMA_SEMANTIC.WEAK_COUNTER     # 顺势K线却在逆势侧（弱势）
+			elif ema_pos == near_counter:
+				return Chan_EMA_SEMANTIC.APPROACHING      # 从逆势侧接近EMA
+			elif ema_pos == cross_to_counter:
+				return Chan_EMA_SEMANTIC.TOUCH_FAIL       # 触碰EMA但未穿越回趋势侧
+			elif ema_pos == Chan_EMA_POS.ON_EMA:
+				return Chan_EMA_SEMANTIC.TOUCH_FAIL       # 收盘在EMA附近，未确认突破
+			elif ema_pos == cross_to_trend:
+				return Chan_EMA_SEMANTIC.RECOVER           # 从逆势侧穿越回趋势侧
+			elif ema_pos == near_trend:
+				return Chan_EMA_SEMANTIC.RECOVER           # 接近趋势侧（刚收复EMA附近）
+			elif ema_pos == trend_side:
+				return Chan_EMA_SEMANTIC.TREND_SIDE        # 完全在趋势侧（正常）
+
+		return Chan_EMA_SEMANTIC.NEUTRAL
+
+	@staticmethod
+	def semantic_to_int(semantic):
+		"""将 Chan_EMA_SEMANTIC 枚举转换为整数，兼容旧的 ema52_status 数值"""
+		mapping = {
+			Chan_EMA_SEMANTIC.TOUCH_HOLD: 1,
+			Chan_EMA_SEMANTIC.BREAK: 2,
+			Chan_EMA_SEMANTIC.DEEP_COUNTER: 3,
+			Chan_EMA_SEMANTIC.TOUCH_FAIL: 4,
+			Chan_EMA_SEMANTIC.RECOVER: 5,
+			Chan_EMA_SEMANTIC.TREND_SIDE: 6,
+			Chan_EMA_SEMANTIC.STRONG_TREND: 7,
+			Chan_EMA_SEMANTIC.WEAK_COUNTER: 8,
+			Chan_EMA_SEMANTIC.APPROACHING: 9,
+			Chan_EMA_SEMANTIC.NEUTRAL: 0,
+		}
+		return mapping.get(semantic, 0)
+
+	# threshold_pct: 阈值百分比，用于自动计算绝对阈值
+	# 例如 0.001 表示 EMA 值的 0.1%，BTC $100,000 时 threshold = $100
+	threshold_pct = 0.001
+	def set_bsp_type(self, bsp_type):
+		if bsp_type and bsp_type != Chan_BSP_TYPE.NONE:
+			self.bsp_type = bsp_type
+			self.bsp = True
+	def cal_all_ema_status(self):
+		"""
+		统一计算所有EMA与K线的位置关系和语义状态
+		
+		threshold 自动按 EMA 值的百分比计算（cls.threshold_pct，默认0.1%）
+		  - BTC $100,000 时：threshold ≈ $100
+		  - ETH $3,000 时：threshold ≈ $3
+		  - SOL $200 时：threshold ≈ $0.2
+		
+		结果存储在 self.ema_status 字典中，格式：
+		  {
+		    'ema24':  {'pos': Chan_EMA_POS, 'semantic': Chan_EMA_SEMANTIC, 'value': float, 'threshold': float},
+		    'ema52':  {...},
+		    ...
+		  }
+		
+		同时保持向后兼容：self.ema52_pos 和 self.ema52_status
+		"""
+		ema_configs = {
+			'ema24':  self.ema24,
+			'ema52':  self.ema52,
+			'ema104': self.ema104,
+			'ema156': self.ema156,
+			'ema208': self.ema208,
+		}
+		self.ema_status = {}
+		for name, value in ema_configs.items():
+			# 按 EMA 值的百分比自动计算阈值
+			threshold = abs(value) * self.threshold_pct if value and self.threshold_pct > 0 else 0
+			pos = ChanKLC.cal_ema_pos(self.high, self.low, self.close, value, threshold)
+			semantic = ChanKLC.cal_ema_semantic(pos, self.dir, self.ema_dir)
+			self.ema_status[name] = {
+				'pos': pos,
+				'semantic': semantic,
+				'value': value,
+				'threshold': threshold,
+			}
+		# 向后兼容
+		self.ema52_pos = self.ema_status['ema52']['pos']
+		self.ema52_status = ChanKLC.semantic_to_int(self.ema_status['ema52']['semantic'])
+	def get_ema_pos(self, ema_name):
+		"""获取指定EMA的客观位置，如 klc.get_ema_pos('ema24')"""
+		if ema_name in self.ema_status:
+			return self.ema_status[ema_name]['pos']
+		return Chan_EMA_POS.UNKNOWN
+	def check_ema_pos(self):
+		if len(self.ema_status) > 0:
+			for ema_name, pos in self.ema_status.items():
+				#print(self.end_time, ema_name, pos['pos'])
+				if ((self.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc_fx_type == Chan_KLC_FX.TOP2) and pos['pos'] == Chan_EMA_POS.CROSS_CLOSE_BELOW) or ((self.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc_fx_type == Chan_KLC_FX.BOTTOM2) and pos['pos'] == Chan_EMA_POS.CROSS_CLOSE_ABOVE):
+					#print("---------------------")
+					return ema_name
+		return None
+	def get_ema_semantic(self, ema_name):
+		"""获取指定EMA的语义状态，如 klc.get_ema_semantic('ema52')"""
+		if ema_name in self.ema_status:
+			return self.ema_status[ema_name]['semantic']
+		return Chan_EMA_SEMANTIC.NEUTRAL
+	def set_trend(self, trend):
+		self.trend = trend
+	def to_string(self):
+		out = ""
+		start = self.start_time if self.start_time is not None else ""
+		end = self.end_time if self.end_time is not None else ""
+		price_diff = getattr(self, 'price_diff', None)
+		out += str(start) + " " + str(end) + " " + str(self.close) + " " + str(self.ema24) + " " + str(self.ema52) + " " + str(self.trend) + " " + str(self.close - self.ema52)
+		return out
+	def set_bi_zs(self, bi_zs):
+		if bi_zs:
+			self.bi_zs = bi_zs
+	def set_klc_fx_type(self, klc_fx_type):
+		#print(self.start_time, klc_fx_type, self.get_feature_data()['klu_macd'], self.get_feature_data()['klu_macdhist'], self.get_feature_data()['klu_rsi'])
+		self.klc_fx_type = klc_fx_type
+		#self.cal_fx()
+		ema_name = self.check_ema_pos()
+		hist_div = abs(self.macdhist - self.next.macdhist)
+		#print(self.end_time, self.dir, abs(self.macdhist), hist_div)
+		#if ema_name:
+			#print(self.end_time, ema_name, self.ema_status[ema_name]['semantic'], hist_div)
+		#self.cal_bb_out()
+		#print(self.pre.start_time, self.next.end_time, self.klc_fx_type)
+		if klc_fx_type == Chan_KLC_FX.TOP1 or klc_fx_type == Chan_KLC_FX.TOP2 or klc_fx_type == Chan_KLC_FX.BOTTOM1 or klc_fx_type == Chan_KLC_FX.BOTTOM2:
+			self.cal_fx_box()
+			self.cal_fx_type()
+	def cal_fx_type(self):
+		if self.fx == Chan_FX_TYPE.TOP and self.next:
+			if self.ema52_dis > self.ema26_dis:
+				if self.pre.macd < self.macd and self.macd < self.next.macd:
+					self.fx_type = Chan_FX.CONTINUATION
+				else:
+					self.fx_type = Chan_FX.REVERSAL
+		elif self.fx == Chan_FX_TYPE.BOTTOM and self.next:
+			if self.ema52_dis < self.ema26_dis:
+				if self.pre.macd > self.macd and self.macd > self.next.macd:
+					self.fx_type = Chan_FX.CONTINUATION
+				else:
+					self.fx_type = Chan_FX.REVERSAL
+		#if self.fx_type != Chan_FX.UNKNOWN and self.fx_type != Chan_FX.CONTINUATION:
+			#print(self.end_time, self.fx_type)
+	def cal_fx_box(self):
+		# 每次重算前先清空，避免旧box残留
+		self.fx_box = None
+		start_time = None
+		end_time = None
+		high = 0
+		low = 0
+		display = False
+		if self.pre and self.next and self.next.end_time:
+			self.next.in_fx = True
+			if self.fx == Chan_FX_TYPE.TOP:
+				start_time = self.pre.end_time
+				end_time = self.next.end_time
+				high = self.high
+				low = self.pre.low if self.pre.low < self.next.low else self.next.low
+				if self.next.close < self.pre.low or True:
+					display = True
+			elif self.fx == Chan_FX_TYPE.BOTTOM:
+				start_time = self.pre.end_time
+				end_time = self.next.end_time
+				high = self.pre.high if self.pre.high > self.next.high else self.next.high
+				low = self.low
+				if self.next.close > self.pre.high or True:
+					display = True
+		if high > 0 and self.next.end_time and display:
+			#print(start_time, end_time, high, low)
+			# Chan_FX_BOX 这里导入的是模块，类名在模块内部为 Chan_FX_Box
+			self.fx_confirmed = True
+			self.fx_box = Chan_FX_Box.Chan_FX_Box(start_time, end_time, high, low)
+	def check_fx_confirmed(self, last_top, last_bottom):
+		if last_top and last_bottom and False:
+			if last_top.index > last_bottom.index:
+				if self.in_fx == False and last_top.fx_confirmed == False:
+					pre = last_top.pre
+					if pre.low > self.close:
+						last_top.fx_confirmed = True
+						if last_top.fx_box:
+							last_top.fx_box.end_time = self.end_time
+							#print(self.end_time, "fx_confirmed top")
+						else:
+							high = last_top.high
+							low = self.low
+							last_top.fx_box = Chan_FX_Box.Chan_FX_Box(last_top.pre.start_time, self.end_time, high, low)
+							#print(self.end_time, "fx_confirmed new box top")
+			elif self.in_fx == False and last_bottom.fx_confirmed == False:
+				pre = last_bottom.pre
+				if pre.high < self.close:
+					last_bottom.fx_confirmed = True
+					if last_bottom.fx_box:
+						last_bottom.fx_box.end_time = self.end_time
+						#print(self.end_time, "fx_confirmed bottom")
+					else:
+						high = self.high
+						low = last_bottom.low
+						last_bottom.fx_box = Chan_FX_Box.Chan_FX_Box(last_bottom.pre.start_time, self.end_time, high, low)
+						#print(self.end_time, "fx_confirmed new box bottom")
+	def add_klu(self, klu):
+		self.klu_list.append(klu)
+	def check_klc_state(self, last_fx_klc):
+		if last_fx_klc and last_fx_klc.fx == Chan_FX_TYPE.TOP:
+			if self.high > last_fx_klc.high:
+				self.klc_state = Chan_KLC_STATE.S11
+			else:
+				self.klc_state = Chan_KLC_STATE.S_11
+		elif last_fx_klc and last_fx_klc.fx == Chan_FX_TYPE.BOTTOM:
+			if self.low < last_fx_klc.low:
+				self.klc_state = Chan_KLC_STATE.S_11
+			else:
+				self.klc_state = Chan_KLC_STATE.S11
+		if self.pre and self.pre.fx == Chan_FX_TYPE.TOP:
+			self.klc_state = Chan_KLC_STATE.S10
+		elif self.pre and self.pre.fx == Chan_FX_TYPE.BOTTOM:
+			self.klc_state = Chan_KLC_STATE.S_10
+		#print(self.end_time, self.klc_state)
+	def set_end_klu(self, klu):
+		self.end_klu = klu
+		self.end_time = klu.time
+		self.close = klu.close
+		for klu in self.klu_list:
+			if klu.exception:
+				self.exception = True
+				print(klu.time, "exception")
+			if klu.separate_div > 0:
+				self.separate_div = True
+			if klu.continue_div:
+				self.continue_div = klu.continue_div
+			if klu.macd_state != Chan_MACD_STATE.UNKNOWN:
+				self.state = klu.macd_state
+			klu.set_klc(self)
+		self.klc_dir = Chan_KLINE_DIR.UP if self.close > self.open else Chan_KLINE_DIR.DOWN
+		self.cal_indicators()
+		self.cal_all_ema_status()
+		if self.open > self.high:
+			self.open = self.high
+		if self.close > self.high:
+			self.close = self.high
+		if self.close < self.low:
+			self.close = self.low
+		if self.open < self.low:
+			self.open = self.low
+		#print(self.end_time, self.open, self.close, self.high, self.low)
+		#print(klu.time, klu.open, klu.close, klu.high, klu.low)
+	def cal_fx(self):
+		if  self.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc_fx_type == Chan_KLC_FX.TOP2:
+			#print(self.end_time, self.fx, self.macd, self.macdhist, len(self.klu_list))
+			if self.state == Chan_MACD_STATE.HIGH_EMPTY and self.macd > 0:
+				#print(self.end_time, self.state, self.macd, self.klc_fx_type)
+				self.klc_fx_type = Chan_KLC_FX.TOP6
+			if self.separate_div or self.continue_div:
+				self.klc_fx_type = Chan_KLC_FX.TOP7
+			if self.signal > 0 and self.macd > self.signal:
+				self.klc_fx_type = Chan_KLC_FX.TOP8
+		else:
+			if self.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc_fx_type == Chan_KLC_FX.BOTTOM2:
+				if self.macdhist > 0 and self.macd < 0:
+					self.klc_fx_type = Chan_KLC_FX.BOTTOM5
+					return
+				if self.state == Chan_MACD_STATE.HIGH_EMPTY and self.macd < 0:
+					self.klc_fx_type = Chan_KLC_FX.BOTTOM6
+					#print(self.end_time, self.state, self.macd, self.klc_fx_type)
+				if self.separate_div or self.continue_div:
+					self.klc_fx_type = Chan_KLC_FX.BOTTOM7
+				if self.signal < 0 and self.macd < self.signal:
+					self.klc_fx_type = Chan_KLC_FX.BOTTOM8
+	def cal_bb_out(self):
+		for klu in self.klu_list:
+			if self.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc_fx_type == Chan_KLC_FX.TOP2:
+				#print(self.start_time, self.klc_fx_type, klu.high, klu.bb52upper, self.macd, self.next.macd, klu.time)
+				if self.high >= klu.bb52upper and klu.bb52upper > 0 and self.next and self.high > self.next.high:
+					self.klc_fx_type = Chan_KLC_FX.TOP4
+					print(self.end_time, self.klc_fx_type)
+			if self.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc_fx_type == Chan_KLC_FX.BOTTOM2:
+				#print(self.start_time, self.klc_fx_type, klu.low, klu.bb52lower, self.macd, self.next.macd, klu.time)
+				if self.low <= klu.bb52lower and klu.bb52lower > 0 and self.next and self.low < self.next.low:
+					self.klc_fx_type = Chan_KLC_FX.BOTTOM4
+					print(self.end_time, self.klc_fx_type)
+	def cal_indicators(self):
+		for index in range(1, len(self.klu_list)):
+			self.volume += self.klu_list[index].volume
+			self.rsi += self.klu_list[index].rsi
+			self.volume_ratio += self.klu_list[index].volume_ratio
+			self.macdhist += self.klu_list[index].macdhist
+			self.ema26 += self.klu_list[index].ema26
+			self.ema24 += self.klu_list[index].ema24
+			self.ema52 += self.klu_list[index].ema52
+			self.ema104 += self.klu_list[index].ema104
+			self.ema156 += self.klu_list[index].ema156
+			self.ema208 += self.klu_list[index].ema208
+			self.ema13 += self.klu_list[index].ema13
+			self.ema7 += self.klu_list[index].ema7
+			self.bb2633upper += self.klu_list[index].bb2633upper
+			self.bb2633lower += self.klu_list[index].bb2633lower
+			self.bb2633middle += self.klu_list[index].bb2633middle
+			self.ma5 += self.klu_list[index].ma5
+			self.ema5 += self.klu_list[index].ema5
+			if self.ema_dir != self.klu_list[index].ema_dir:
+				self.ema_dir = 0
+		n = len(self.klu_list)
+		self.rsi = self.rsi / n
+		self.volume_ratio = self.volume_ratio / n
+		self.volume = self.volume / n
+		self.macdhist = self.macdhist / n
+		self.ema26 = self.ema26 / n
+		self.ema24 = self.ema24 / n
+		self.ema52 = self.ema52 / n
+		self.ema104 = self.ema104 / n
+		self.ema156 = self.ema156 / n
+		self.ema208 = self.ema208 / n
+		self.ema13 = self.ema13 / n
+		self.ema7 = self.ema7 / n
+		self.ma5 = self.ma5 / n
+		self.ema5 = self.ema5 / n
+		self.bb2633upper = self.bb2633upper / n
+		self.bb2633lower = self.bb2633lower / n
+		self.bb2633middle = self.bb2633middle / n
+		if len(self.klu_list) > 0:
+			self.macd = self.klu_list[-1].macd
+			self.signal = self.klu_list[-1].signal
+		self.body = abs(self.close - self.open)
+		self.upper_shadow = self.high - max(self.close, self.open)
+		self.lower_shadow = min(self.close, self.open) - self.low
+		self.body_ratio = self.body / self.open
+		self.upper_shadow_ratio = self.upper_shadow / self.open
+		self.lower_shadow_ratio = self.lower_shadow / self.open
+		self.candle_dir = Chan_K_DIR.CROSS if self.close == self.open else Chan_K_DIR.BULL if self.close > self.open else Chan_K_DIR.BEAR
+		self.range = self.high - self.low
+	def set_next(self, klc):
+		self.next = klc
+	def set_pre(self, klc):
+		self.pre = klc
+	def set_state(self, state):
+		self.state = state
+	def check_klu_included(self, klu):
+		if self.high >= klu.high:
+			# high大于，low小于，左包含
+			if self.low <= klu.low:
+				self.add_klu(klu=klu)
+				# gn>gn-1
+				if self.dir == Chan_KLINE_DIR.UP:
+					# UP -> max(dn)
+					self.low = klu.low
+				else:
+					# DOWN -> min(gn)
+					self.high = klu.high
+				#self.print(klu, "Z")
+				return True
+				# high大于，low大于，不包含
+			else:
+				# if self.low > klu.low
+				# high相等，右包含
+				if self.high == klu.high:
+					self.add_klu(klu=klu)
+					# UP -> max(gn)
+					if self.dir == Chan_KLINE_DIR.UP:
+						self.high = klu.high
+					else:
+						# DOWN -> min(dn)
+						self.low = klu.low
+					return True
+				else:
+					return False
+		else:
+			# high小于，low大于，右包含
+			if self.low >= klu.low:
+				self.add_klu(klu=klu)
+				# gn>gn-1
+				if self.dir == Chan_KLINE_DIR.UP:
+					# UP -> max(gn)
+					self.high = klu.high
+				else:
+					# DOWN -> min(dn)
+					self.low = klu.low
+				#self.print(klu, "Y")
+				return True
+			else:
+				# high小于，low小于，不包含
+				return False
+	def set_fx(self, fx: Chan_FX_TYPE):
+		self.fx = fx
+	def cal_invisible(self):
+		if self.fx == Chan_FX_TYPE.TOP:
+			if self.macdhist < 0 and self.macd > 0:
+				self.klc_fx_type = Chan_KLC_FX.TOP5
+		else:
+			if self.fx == Chan_FX_TYPE.BOTTOM:
+				if self.macdhist > 0 and self.macd < 0:
+					self.klc_fx_type = Chan_KLC_FX.BOTTOM5
+	def set_pre_fx(self):
+		if self.pre and self.pre.pre:
+			self.pre.fx = self.check_fx(self.pre.pre, self.pre)
+	def check_fx(self, k1, k2):
+		if k2.high > k1.high and k2.high > self.high:
+			return Chan_FX_TYPE.TOP
+		elif k2.low < k1.low and k2.low < self.low:
+			return Chan_FX_TYPE.BOTTOM
+		else:
+			return Chan_FX_TYPE.UNKNOWN
+	def set_bi(self, bi):
+		self.bi = bi
+		self.distance = self.index - bi.start_klc.index
+		#print(self.start_time, self.distance, bi.index, bi.dir)

chanlun/core/ChanKLU.py

@@ -0,0 +1,388 @@
+from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLU_TYPE, Chan_K_DIR, Chan_MACD_STATE, Chan_MACDHIST_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN, Chan_KLC_FX
+class ChanKLU:
+	def __init__(self, time, open, high, low, close, volume):
+		# _time, _close, _open, _high, _low, _extra_info={}
+		self.kl_type = None
+		self.time = time
+		self.close = close
+		self.open = open
+		self.high = high
+		self.low = low
+		self.volume = volume
+		self.idx = 0
+		self.index = 0
+		self.macd = 0
+		self.signal = 0
+		self.macdhist = 0
+		self.klc = None
+		self.rsi = 0
+		self.volume_ratio = 0
+		self.bb52upper = 0
+		self.bb52lower = 0
+		# === 新增：K线类型 ===
+		self.kline_type = None  # K线类型：大阳线、大阴线、小阳线、小阴线
+		self.pattern = Chan_KLU_PATTERN.UNKNOWN
+  
+		# === 新增：实时分型相关属性 ===
+		self.pre = None  # 前一根K线
+		self.next = None  # 后一根K线  
+		self.fx_type = Chan_FX_TYPE.UNKNOWN      # 分型类型：0=无分型，1=顶分型，-1=底分型
+		self.fx_strength = 0  # 分型强度：0-100
+		self.fx_confirmed = False  # 分型是否确认
+		self.klu_type = None
+		self.range = self.high - self.low
+		self.body = abs(self.close - self.open)
+		self.upper_shadow = self.high - max(self.close, self.open)
+		self.lower_shadow = min(self.close, self.open) - self.low
+		self.body_ratio = self.body / self.range if self.range != 0 else 0
+		self.upper_shadow_ratio = self.upper_shadow / self.body if self.body != 0 else float('inf')
+		self.lower_shadow_ratio = self.lower_shadow / self.body if self.body != 0 else float('inf')
+		self.exception = False
+		#self.cal_exception()
+		self.candle_dir = Chan_K_DIR.CROSS if self.close == self.open else Chan_K_DIR.BULL if self.close > self.open else Chan_K_DIR.BEAR
+		
+		self.continue_div = 0
+		self.separate_div = 0
+		self.near0_return = 0
+		self.ema52 = 0
+		self.ema24 = 0
+		self.ema26 = 0
+		self.ema104 = 0
+		self.ema156 = 0
+		self.ema208 = 0
+		self.macd_slop = 0
+		self.signal_slop = 0
+		self.hist_slop = 0
+		self.hist_state = Chan_MACDHIST_STATE.UNKNOWN
+		self.macd_state = Chan_MACD_STATE.UNKNOWN
+		self.macd_hist_gap = 0
+		self.trend = Chan_PRICE_TREND.UNKNOWN
+		self.seg_histset_index = 0
+		# === 归零轴细化与模式/背离 ===
+		self.zero_axis = False                 # 是否归零轴（穿越或接近）
+		self.zero_axis_state = "none"         # {none,crossing,near}
+		self.zero_axis_side = 0               # 1:above, -1:under, 0:none
+		self.zero_axis_score = 0              # 0-100 综合评分
+		self.mode1_touch_ema52 = False        # 单边后触碰EMA52
+		self.mode2_fast_to_zero = False       # 快线向零收敛
+		self.mode3_double_tf = False          # 双周期归零（近似占位，由上层填充高周期确认）
+		self.mode3_dir = "none"               # {long_strong_rebound, short_strong_rebound, none}
+		self.mode4_touch52_no_zero = False    # 先触碰EMA52但黄白线未归零
+		self.div_type = "none"                # {bearish, bullish, hidden_bearish, hidden_bullish, none}
+		self.div_score = 0.0                  # 背离强度(0-100)
+		self.ema_dir = 0
+		self.get_ema_dir()
+		self.bb2633upper = 0
+		self.bb2633lower = 0
+		self.bb2633middle = 0
+		self.ma5 = 0
+		self.ema5 = 0
+		#print(self.open, self.close, self.high, self.low, self.candle_dir, self.strength)
+	def set_macd_state(self, state):
+		self.macd_state = state
+	def set_pattern(self, pattern):
+		self.pattern = pattern
+	def set_seg_histset_index(self, seg_histset_index):
+		self.seg_histset_index = seg_histset_index
+		#print(self.time, self.seg_histset_index)
+	def to_string(self):
+		return f"{self.time} {self.candle_dir} {self.pattern}"
+	def cal_exception(self):
+		if self.upper_shadow_ratio > 5 or self.lower_shadow_ratio > 5:
+			self.exception = True
+			#print(self.time, self.upper_shadow_ratio, self.lower_shadow_ratio, self.body, self.lower_shadow, self.upper_shadow, self.high, self.low, self.close, self.open)
+		#self.exception = False
+	def set_trend(self, trend):
+		self.trend = trend
+	def set_separate_div(self, separate_div):
+		self.separate_div = separate_div
+		if self.klc and self.klc.pre and self.klc.next:
+			fx = self.check_fx_dir(self.klc.pre, self.klc.next)
+			if fx == Chan_FX_TYPE.TOP:
+				if self.macdhist > 0:
+					self.separate_div = separate_div
+				else:
+					self.separate_div = 0
+			elif fx == Chan_FX_TYPE.BOTTOM:
+				if self.macdhist < 0:
+					self.separate_div = separate_div
+				else:
+					self.separate_div = 0
+	def check_fx_dir(self, pre, next):
+		fx = Chan_FX_TYPE.UNKNOWN
+		if pre.klc_fx_type == Chan_KLC_FX.TOP1 or pre.klc_fx_type == Chan_KLC_FX.TOP2 or next.klc_fx_type == Chan_KLC_FX.TOP1 or next.klc_fx_type == Chan_KLC_FX.TOP2 or self.klc.klc_fx_type == Chan_KLC_FX.TOP1 or self.klc.klc_fx_type == Chan_KLC_FX.TOP2:
+			fx = Chan_FX_TYPE.TOP
+		elif pre.klc_fx_type == Chan_KLC_FX.BOTTOM1 or pre.klc_fx_type == Chan_KLC_FX.BOTTOM2 or next.klc_fx_type == Chan_KLC_FX.BOTTOM1 or next.klc_fx_type == Chan_KLC_FX.BOTTOM2 or self.klc.klc_fx_type == Chan_KLC_FX.BOTTOM1 or self.klc.klc_fx_type == Chan_KLC_FX.BOTTOM2:
+			fx = Chan_FX_TYPE.BOTTOM
+		return fx
+	def set_next(self, next):
+		self.next = next
+		#if self.fx_type != Chan_FX_TYPE.UNKNOWN and self.fx_strength > 1:
+			#print(self.index, self.time, self.fx_type, self.fx_confirmed, self.fx_strength)
+	def set_pre(self, pre):
+		self.pre = pre
+	def set_klc(self, klc):
+		self.klc = klc
+	def set_histset(self, histset):
+		"""设置HistSet关联"""
+		self.histset = histset
+	
+	def set_seg(self, seg):
+		"""设置Seg关联"""
+		self.seg = seg
+		
+	def set_unittf(self, unittf):
+		"""设置UnitTF关联"""
+		self.unittf = unittf
+	def set_idx(self, idx):
+		self.idx = idx
+		self.index = idx
+	def check_price_ema156(self):
+		if self.check_indicators():
+			if self.close > self.ema156:
+				return 1
+			elif self.close < self.ema156:
+				return -1
+			else:
+				return 0
+		else:
+			return 0
+	def get_ema_dir(self):
+		if self.check_indicators():
+			if self.ema24 > self.ema52 and self.ema52 > self.ema104 and self.ema104 > self.ema156:
+				self.ema_dir = 1
+			elif self.ema24 < self.ema52 and self.ema52 < self.ema104 and self.ema104 < self.ema156:
+				self.ema_dir = -1
+			else:
+				self.ema_dir = 0
+	def check_indicators(self):
+		if self.ema156 == 0:
+			return False
+		else:
+			return True
+	def set_indicators(self, item):
+		self.macd = float(item['macd']) if 'macd' in item and item['macd'] else 0
+		self.signal = float(item['macdsignal']) if 'macdsignal' in item and item['macdsignal'] else 0
+		self.macdhist = float(item['macdhist']) if 'macdhist' in item and item['macdhist'] else 0
+		self.ema26 = float(item['ema26']) if 'ema26' in item and item['ema26'] else 0
+		self.ema52 = float(item['ema52']) if 'ema52' in item and item['ema52'] else 0
+		self.ema24 = float(item['ema24']) if 'ema24' in item and item['ema24'] else 0
+		self.ema104 = float(item['ema104']) if 'ema104' in item and item['ema104'] else 0
+		self.ema156 = float(item['ema156']) if 'ema156' in item and item['ema156'] else 0
+		self.ema208 = float(item['ema208']) if 'ema208' in item and item['ema208'] else 0
+		self.ema13 = float(item['ema13']) if 'ema13' in item and item['ema13'] else 0
+		self.ema7 = float(item['ema7']) if 'ema7' in item and item['ema7'] else 0
+		self.rsi = float(item['rsi']) if 'rsi' in item and item['rsi'] else 0
+		self.volume_ratio = float(item['volume_ratio']) if 'volume_ratio' in item and item['volume_ratio'] else 0
+		self.bb52upper = float(item['bb52upper']) if 'bb52upper' in item and item['bb52upper'] else 0
+		self.bb52lower = float(item['bb52lower']) if 'bb52lower' in item and item['bb52lower'] else 0
+		self.bb2633upper = float(item['bb2633upper']) if 'bb2633upper' in item and item['bb2633upper'] else 0
+		self.bb2633lower = float(item['bb2633lower']) if 'bb2633lower' in item and item['bb2633lower'] else 0
+		self.bb2633middle = float(item['bb2633middle']) if 'bb2633middle' in item and item['bb2633middle'] else 0
+		self.ma5 = float(item['ma5']) if 'ma5' in item and item['ma5'] else 0
+		self.ema5 = float(item['ema5']) if 'ema5' in item and item['ema5'] else 0
+	def cal_macd_state(self):
+		# 按定义精简实现：优先级 CROSS0 > 位置(HIGH/HE/RETURN_ZERO) > NEAR0 > UNKNOWN
+		# 首条或缺前一根
+		if not hasattr(self, 'pre') or self.pre is None:
+			self.macd_state = Chan_MACD_STATE.START
+			return self.macd_state
+
+		# 基本校验
+		if (self.macd == 0 and self.signal == 0 and self.macdhist == 0) or self.ema52 == 0:
+			self.macd_state = Chan_MACD_STATE.UNKNOWN
+			return self.macd_state
+		# 归零轴判断
+		if self.signal > 0:
+			if self.macd < self.signal:
+				if 0 < self.low - self.ema52 < 100:
+					self.near0_return = 0
+				elif self.close > self.ema52 and self.low < self.ema52 and self.open > self.ema52:
+					self.near0_return = 0
+				elif self.close < self.ema52 and self.open > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+				elif self.close < self.ema52 and self.open < self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+				elif self.close > self.ema52 and self.open > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+			elif self.close > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+				self.near0_return = 0
+		else:
+			if self.macd > self.signal:
+				if 0 < self.ema52 - self.high < 100:
+					self.near0_return = 0
+				elif self.close < self.ema52 and self.high > self.ema52 and self.open < self.ema52:
+					self.near0_return = 0
+				elif self.close < self.ema52 and self.open > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+				elif self.close > self.ema52 and self.open < self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+				elif self.close > self.ema52 and self.high > self.ema52 and self.open >= self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+				elif self.close > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
+					self.near0_return = 0
+		# 向上穿越EMA52 7
+		if self.close > self.ema52 and self.open < self.ema52:
+			self.near0_return = 0
+		# 向下穿越EMA52 8
+		elif self.close < self.ema52 and self.open > self.ema52:
+			self.near0_return = 0
+		if self.pre.near0_return == 7:
+			# 向上穿越后的一根价格再EMA52上方 9
+			if self.low > self.ema52 and self.close > self.open:
+				self.near0_return = 9
+		if self.pre.near0_return == 8:
+			# 向下穿越后的一根价格再EMA52下方 10
+			if self.high < self.ema52 and self.close < self.open:
+				self.near0_return = 10
+		# CROSS0 仅以 Signal 穿越零轴判定
+		if self.pre.signal >= 0 and self.signal < 0:
+			self.macd_state = Chan_MACD_STATE.CROSS0_DOWN
+			return self.macd_state
+		if self.pre.signal <= 0 and self.signal > 0:
+			self.macd_state = Chan_MACD_STATE.CROSS0_UP
+			return self.macd_state
+		# 穿零轴后的形态：缠绕/倒挂（基于前一状态为CROSS0_*）
+		if self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP or self.pre.macd_state == Chan_MACD_STATE.CROSS0_DOWN:
+			direction = 1 if self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP else -1
+			hist_same_dir = (self.macdhist * direction) > 0
+			hist_decreasing = abs(self.macdhist) < abs(self.pre.macdhist)
+			lines_tight = abs(self.macd - self.signal) <= 12
+			# 倒挂：能量柱衰减且黄白线相对方向不利/出现反向能量释放
+			if hist_decreasing and (((self.macd - self.signal) * direction) < 0 or not hist_same_dir):
+				self.macd_state = Chan_MACD_STATE.CROSS_REV
+				return self.macd_state
+			# 缠绕/粘合：紧贴能量柱运行，无反向能量释放
+			if lines_tight and hist_same_dir:
+				self.macd_state = Chan_MACD_STATE.CROSS_OS
+				return self.macd_state
+		# 趋近零轴：细化 NEAR0_* 判定
+		NEAR0_EPS = 15
+		lines_near_zero = abs(self.macd) <= NEAR0_EPS or abs(self.signal) <= NEAR0_EPS
+		touch_52 = (self.ema52 != 0) and ((abs(self.close - self.ema52) <= NEAR0_EPS) or (self.low <= self.ema52 <= self.high))
+		touch_24 = (self.ema24 != 0) and ((abs(self.close - self.ema24) <= NEAR0_EPS) or (self.low <= self.ema24 <= self.high))
+		# 完美形态：白线接近零轴 + 价格触碰/轻破EMA52 + 黄线不穿零轴
+		if abs(self.macd) <= NEAR0_EPS and touch_52 and (not (self.pre.signal >= 0 and self.signal < 0)) and (not (self.pre.signal <= 0 and self.signal > 0)):
+			self.macd_state = Chan_MACD_STATE.NEAR0_PERFECT
+			#self.near0_return = 1
+			return self.macd_state
+		# EMA24 附近
+		if lines_near_zero and touch_24:
+			self.macd_state = Chan_MACD_STATE.NEAR0_24
+			#self.near0_return = 2
+			return self.macd_state
+		# EMA52 附近
+		if lines_near_zero and touch_52:
+			self.macd_state = Chan_MACD_STATE.NEAR0_52
+			#self.near0_return = 3
+			return self.macd_state
+		# 白线接近零轴但价格未至EMA52
+		if abs(self.macd) <= NEAR0_EPS and not touch_52:
+			self.macd_state = Chan_MACD_STATE.NEAR0_DIFF
+			#self.near0_return = 4
+			return self.macd_state
+		# 一般近零轴
+		if lines_near_zero or touch_52:
+			self.macd_state = Chan_MACD_STATE.NEAR0
+			#self.near0_return = 5
+			return self.macd_state
+		
+		# 穿零轴后离开零轴
+		if self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP and ((self.macd >= self.pre.macd and self.signal >= self.pre.signal) or (abs(self.macdhist) >= abs(self.pre.macdhist))):
+			self.macd_state = Chan_MACD_STATE.UP
+			return self.macd_state
+		if self.pre.macd_state == Chan_MACD_STATE.CROSS0_DOWN and ((self.macd <= self.pre.macd and self.signal <= self.pre.signal) or (abs(self.macdhist) >= abs(self.pre.macdhist))):
+			self.macd_state = Chan_MACD_STATE.DOWN
+			return self.macd_state
+		if self.pre.macd_state == Chan_MACD_STATE.UP and self.macd > self.pre.macd and self.signal > self.pre.signal:
+			self.macd_state = Chan_MACD_STATE.UP
+			return self.macd_state
+		if self.pre.macd_state == Chan_MACD_STATE.DOWN and self.macd < self.pre.macd and self.signal < self.pre.signal:
+			self.macd_state = Chan_MACD_STATE.DOWN
+			return self.macd_state
+		# 趋势兜底：强势同步上行/下行直接进入 UP/DOWN
+		if self.macd > 0 and self.signal > 0 and (self.macd >= self.pre.macd and self.signal >= self.pre.signal):
+			self.macd_state = Chan_MACD_STATE.UP
+			return self.macd_state
+		if self.macd < 0 and self.signal < 0 and (self.macd <= self.pre.macd and self.signal <= self.pre.signal):
+			self.macd_state = Chan_MACD_STATE.DOWN
+			return self.macd_state
+		# 峰值：白线高位出现局部顶
+		if hasattr(self.pre, 'pre') and self.pre and self.pre.pre and self.macd > 0:
+			if self.pre.macd > self.pre.pre.macd and self.pre.macd > self.macd:
+				self.macd_state = Chan_MACD_STATE.PEAK
+				return self.macd_state
+		# 高位状态的位置状态， 高位，高位空，归零轴
+		if (self.pre.macd_state == Chan_MACD_STATE.UP or self.pre.macd_state == Chan_MACD_STATE.HIGH or self.pre.macd_state == Chan_MACD_STATE.RZ_UP or self.pre.macd_state == Chan_MACD_STATE.PEAK or self.pre.macd_state == Chan_MACD_STATE.HIGH_EMPTY) and self.macd > 0:
+			# 高位空（正区间）：能量柱衰减且黄白线间距较大
+			if abs(self.pre.macdhist) > 0 and abs(self.macdhist) < abs(self.pre.macdhist) and abs(self.macd - self.signal) > 5:
+				self.macd_state = Chan_MACD_STATE.HIGH_EMPTY
+				return self.macd_state
+			if abs(self.pre.macd - self.macd) < 10:
+				self.macd_state = Chan_MACD_STATE.HIGH
+				return self.macd_state
+			else:
+				if self.macd > self.pre.macd and self.signal > self.pre.signal:
+					self.macd_state = Chan_MACD_STATE.UP
+					return self.macd_state
+				elif self.macd < self.pre.macd and self.signal < self.pre.signal:
+					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
+					return self.macd_state
+		if (self.pre.macd_state == Chan_MACD_STATE.DOWN or self.pre.macd_state == Chan_MACD_STATE.HIGH or self.pre.macd_state == Chan_MACD_STATE.RZ_DOWN or self.pre.macd_state == Chan_MACD_STATE.PEAK or self.pre.macd_state == Chan_MACD_STATE.HIGH_EMPTY) and self.macd < 0:
+			# 高位空（负区间）：能量柱衰减且黄白线间距较大
+			if abs(self.pre.macdhist) > 0 and abs(self.macdhist) < abs(self.pre.macdhist) and abs(self.macd - self.signal) > 5:
+				self.macd_state = Chan_MACD_STATE.HIGH_EMPTY
+				return self.macd_state
+			if abs(self.pre.macd - self.macd) < 10:
+				self.macd_state = Chan_MACD_STATE.HIGH
+				return self.macd_state
+			else:
+				if self.macd > self.pre.macd and self.signal > self.pre.signal:
+					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
+					return self.macd_state
+				elif self.macd < self.pre.macd and self.signal < self.pre.signal:
+					self.macd_state = Chan_MACD_STATE.DOWN
+					return self.macd_state
+		
+		# 离开0轴开始上涨或者下跌阶段，高位之前的
+		if self.macd > 0 and self.pre:
+			if (self.pre.macd_state == Chan_MACD_STATE.NEAR0 or self.pre.macd_state == Chan_MACD_STATE.RZ_UP or self.pre.macd_state == Chan_MACD_STATE.CROSS0_UP) and (self.signal > self.pre.signal or self.close > self.ema52):
+				self.macd_state = Chan_MACD_STATE.RZ_UP
+				return self.macd_state
+		elif self.macd < 0 and self.pre:
+			if (self.pre.macd_state == Chan_MACD_STATE.NEAR0 or self.pre.macd_state == Chan_MACD_STATE.RZ_DOWN or self.pre.macd_state == Chan_MACD_STATE.CROSS0_DOWN) and (self.signal < self.pre.signal or self.close < self.ema52):
+				self.macd_state = Chan_MACD_STATE.RZ_DOWN
+				return self.macd_state
+		# 归零轴走势
+		if self.pre.macd_state == Chan_MACD_STATE.RETURN_ZERO:
+			if self.macd > 0:
+				if self.pre.macd > self.macd or abs(self.macdhist) <= abs(self.pre.macdhist) or self.signal <= self.pre.signal:
+					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
+					return self.macd_state
+			else:
+				if self.pre.macd < self.macd or abs(self.macdhist) <= abs(self.pre.macdhist) or self.signal >= self.pre.signal:
+					self.macd_state = Chan_MACD_STATE.RETURN_ZERO
+					return self.macd_state
+		# 从 NEAR0 收敛到零轴的归零轴承接（正负两侧）
+		if self.pre.macd_state == Chan_MACD_STATE.NEAR0:
+			# 正区间朝零轴收敛
+			if self.macd > 0 and self.pre.macd > 0 and self.macd <= self.pre.macd and self.signal <= self.pre.signal:
+				self.macd_state = Chan_MACD_STATE.RETURN_ZERO
+				return self.macd_state
+			# 负区间朝零轴收敛
+			if self.macd < 0 and self.pre.macd < 0 and self.macd >= self.pre.macd and self.signal >= self.pre.signal:
+				self.macd_state = Chan_MACD_STATE.RETURN_ZERO
+				return self.macd_state
+		# 其余情况
+		if self.pre.macd_state == Chan_MACD_STATE.UNKNOWN:
+			if self.macd > 0 and self.close > self.ema52 and self.pre.pre and (self.pre.pre.macd_state == Chan_MACD_STATE.UP or self.pre.pre.macd_state == Chan_MACD_STATE.RZ_UP):
+				self.macd_state = self.pre.pre.macd_state
+				return self.macd_state
+			elif self.macd < 0 and self.close < self.ema52 and self.pre.pre and (self.pre.pre.macd_state == Chan_MACD_STATE.DOWN or self.pre.pre.macd_state == Chan_MACD_STATE.RZ_DOWN):
+				self.macd_state = self.pre.pre.macd_state
+				return self.macd_state
+			else:
+				self.macd_state = Chan_MACD_STATE.UNKNOWN
+				return self.macd_state
+		return self.macd_state
+	

chanlun/core/ChanSBI.py

@@ -0,0 +1,91 @@
+import copy
+from typing import Dict, Optional
+
+from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR
+import chanlun.core.ChanKLU as ChanKLU
+from chanlun.core.ChanBI import ChanBI
+
+class ChanSBI():
+    def __init__(self, start_bi: ChanBI, index, dir=Chan_BI_DIR.UP):
+        self.start_bi = start_bi
+        self.end_bi = None
+        self.index = index
+        self.dir = dir
+        self.high = start_bi.high
+        self.low = start_bi.low
+        self.pre = None
+        self.next = None
+        self.fx = Chan_FX_TYPE.UNKNOWN
+        self.bi_list = []
+        self.bi_list.append(start_bi)
+        self.has_fx_gap = False
+    def set_fx(self, fx):
+        self.fx = fx
+    def set_end_bi(self, bi):
+        self.end_bi = bi
+    def set_pre(self, sbi):
+        self.pre = sbi
+    def set_next(self, sbi):
+        self.next = sbi
+    def add_bi(self, bi):
+        self.bi_list.append(bi)
+    def check_fx(self):
+        if self.pre and self.next:
+            #print(self.pre.start_bi.start_time, self.start_bi.start_time, self.end_bi.end_time, self.next.start_bi.start_time, self.pre.high, self.high, self.next.high, self.pre.low, self.low, self.next.low, self.dir)
+            if self.high > self.pre.high and self.high > self.next.high:
+                self.fx = Chan_FX_TYPE.TOP
+                #print(self.start_bi.start_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.fx)
+                if self.low > self.pre.high:
+                    self.has_fx_gap = True
+                #print(self.start_bi.start_time, self.end_bi.end_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.dir, self.has_fx_gap, self.fx)
+                return Chan_FX_TYPE.TOP
+            else:
+                if self.low < self.pre.low and self.low < self.next.low:
+                    self.fx = Chan_FX_TYPE.BOTTOM
+                    #print(self.start_bi.start_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.fx)
+                    if self.high < self.pre.low:
+                        self.has_fx_gap = True
+                    #print(self.start_bi.start_time, self.end_bi.end_time, self.pre.start_bi.start_time, self.next.start_bi.start_time, self.dir, self.has_fx_gap, self.fx)
+                    return Chan_FX_TYPE.BOTTOM
+        return Chan_FX_TYPE.UNKNOWN
+    def check_seg_bi_broken(self):
+        broken = False
+        if self.fx == Chan_FX_TYPE.TOP:
+            if self.next.low < self.pre.high:
+                broken = True
+        elif self.fx == Chan_FX_TYPE.BOTTOM:
+                if self.next.high > self.pre.low:
+                    broken = True
+        return broken
+    def check_bi_included(self, bi):
+        included = False
+        if self.high > bi.high:
+            # high大于，low小于，左包含
+            if self.low < bi.low:
+                included = True
+                # high大于，low大于，不包含
+            else:
+                # if self.low > bi.low
+                # high相等，右包含
+                included = False
+        else:
+            included = False
+            # high小于，low大于，右包含
+            #if self.low > bi.low:
+                #included = True
+        if included:
+            if self.pre:
+                if self.high > self.pre.high and self.low < self.pre.low:
+                    included = True
+        if included:
+            self.add_bi(bi)
+            # gn>gn-1
+            if self.dir == Chan_BI_DIR.DOWN:
+                # UP -> max(dn)
+                self.low = bi.low
+            else:
+                # DOWN -> min(gn)
+                self.high = bi.high
+            #self.print(bi, "Z")
+        #print(self.start_bi.start_time, bi.start_time, included)
+        return included

chanlun/core/ChanSEG.py

@@ -0,0 +1,197 @@
+import copy
+from typing import Dict, Optional
+
+from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_SEG_DIR, Chan_BI_DIR, Chan_ZS_DIR
+import chanlun.core.ChanCTime as ChanCTime
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+class ChanSEG():
+    def __init__(self, start_bi: ChanBI, index, ddir=Chan_SEG_DIR.UP, pre_end_bi: ChanBI = None):
+        self.start_bi = start_bi
+        self.start_time = start_bi.start_time
+        self.end_time = None
+        self.end_bi = None
+        self.dir = ddir
+        self.low = 0
+        self.high = 0
+        if self.dir == Chan_SEG_DIR.UP and start_bi:
+            self.low = start_bi.low
+        else:
+            if start_bi:
+                self.high = start_bi.high
+        self.index = index
+        self.pre = None
+        self.next = None
+        self.bi_list = []
+        self.bi_list.append(start_bi)
+        self.is_sure = False
+        self.sure_time = None
+        self.macd_hist = 0
+        self.macd_div = 0
+        self.start_bi.set_seg(self)
+        self.pre_end_bi = pre_end_bi
+        if self.pre_end_bi:
+            self.ini_seg()
+    def ini_seg(self):
+        next_bi = self.start_bi.next
+        for index in range(self.start_bi.index+1, self.pre_end_bi.index):
+            if next_bi:
+                self.bi_list.append(next_bi)
+                next_bi.set_seg(self)
+                next_bi = next_bi.next
+    def set_macdhist(self, macd_hist):
+        self.macd_hist = macd_hist
+    def set_macd_div(self, macd_div):
+        self.macd_div = macd_div
+    def set_end_bi(self, bi: ChanBI, sure_bi: ChanBI):
+        self.end_bi = bi
+        if bi and bi.is_sure:
+            if self.dir == Chan_SEG_DIR.UP:
+                self.high = bi.high
+            else:
+                self.low = bi.low
+            self.is_sure = True
+            self.end_time = bi.end_klc.end_time
+        if sure_bi.is_sure:
+            self.sure_time = sure_bi.sure_time
+        self.format_bi_list()
+    def pre_set_end_bi(self, bi: ChanBI):
+        self.end_bi = bi
+        if bi and bi.is_sure:
+            if self.dir == Chan_SEG_DIR.UP:
+                self.high = bi.high
+            else:
+                self.low = bi.low
+            self.end_time = bi.end_klc.end_time
+        self.format_bi_list()
+    def set_pre(self, seg):
+        self.pre = seg
+    def set_next(self, seg):
+        self.next = seg
+    def set_sure(self, sure_bi):
+        if sure_bi.is_sure:
+            self.sure_time = sure_bi.sure_time
+        self.is_sure = True
+        self.format_bi_list()
+    def format_bi_list(self):
+        self.bi_list = []
+        self.bi_list.append(self.start_bi)
+        if self.end_bi:
+            next_bi = self.start_bi.next
+            for i in range(self.start_bi.index, self.end_bi.index):
+                if next_bi:
+                    self.bi_list.append(next_bi)
+                    next_bi.set_seg(self)
+                    next_bi = next_bi.next
+    def add_bi(self, bi: ChanBI):
+        if len(self.bi_list) > 0:
+            self.bi_list.append(bi)
+            bi.set_seg(self)
+            self.end_time = bi.end_time
+            self.end_bi = bi
+    def cal_bi_zs(self):
+        zs_list = []
+        if len(self.bi_list) > 3:
+            last_zs = None
+            if self.dir == Chan_SEG_DIR.UP:
+                for index in range(1, len(self.bi_list)):
+                    bi = self.bi_list[index]
+                    #print(bi.end_time, bi.next,"UP SEG BI ZS Index")
+                    if bi.next == None or bi.next.next == None:
+                        if last_zs and (bi.low > last_zs.zg or bi.high < last_zs.zd):
+                                last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
+                        continue
+                    bi2 = bi.next
+                    bi3 = bi.next.next
+                    if len(zs_list) == 0 or (last_zs and last_zs.is_sure):
+                        if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.DOWN:
+                            zg = min(bi.high, bi2.high, bi3.high)
+                            zd = max(bi.low, bi2.low, bi3.low)
+                            gg = max(bi.high, bi2.high, bi3.high)
+                            dd = min(bi.low, bi2.low, bi3.low)
+                            zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.UP)
+                            zs.set_zg(zg)
+                            zs.set_zd(zd)
+                            zs.set_gg(gg)
+                            zs.set_dd(dd)
+                            zs.add_bi(bi2)
+                            zs.add_bi(bi3)
+                            zs_list.append(zs)
+                            last_zs = zs
+                    else:
+                        if bi.index > last_zs.bi_list[-1].index and bi.dir == Chan_BI_DIR.DOWN and bi.is_sure:
+                            if bi.low > last_zs.zg or bi.high < last_zs.zd:
+                                #print(bi.end_time, "UP SEG BI ZS End")
+                                last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
+                                if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.DOWN:
+                                    zg = min(bi.high, bi2.high, bi3.high)
+                                    zd = max(bi.low, bi2.low, bi3.low)
+                                    gg = max(bi.high, bi2.high, bi3.high)
+                                    dd = min(bi.low, bi2.low, bi3.low)
+                                    zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.UP)
+                                    zs.set_zg(zg)
+                                    zs.set_zd(zd)
+                                    zs.set_gg(gg)
+                                    zs.set_dd(dd)
+                                    zs.add_bi(bi2)
+                                    zs.add_bi(bi3)
+                                    zs_list.append(zs)
+                                    last_zs = zs
+                            else:
+                                last_zs.add_bi(bi.pre)
+                                last_zs.add_bi(bi)
+                    if index == len(self.bi_list) - 1 and last_zs and not last_zs.is_sure:
+                        #print(bi.start_time, "BI", last_zs.is_sure)
+                        last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
+            else:
+                for index in range(1, len(self.bi_list)):
+                    bi = self.bi_list[index]
+                    if bi.next == None or bi.next.next == None:
+                        if last_zs and (bi.low > last_zs.zg or bi.high < last_zs.zd):
+                                last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
+                        continue
+                    bi2 = bi.next
+                    bi3 = bi.next.next
+                    if len(zs_list) == 0 or (last_zs and last_zs.is_sure):
+                        if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.UP:
+                            zg = min(bi.high, bi2.high, bi3.high)
+                            zd = max(bi.low, bi2.low, bi3.low)
+                            gg = max(bi.high, bi2.high, bi3.high)
+                            dd = min(bi.low, bi2.low, bi3.low)
+                            zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.DOWN)
+                            zs.set_zg(zg)
+                            zs.set_zd(zd)
+                            zs.set_gg(gg)
+                            zs.set_dd(dd)
+                            zs.add_bi(bi2)
+                            zs.add_bi(bi3)
+                            zs_list.append(zs)
+                            last_zs = zs
+                    else:
+                        if bi.index > last_zs.bi_list[-1].index and bi.dir == Chan_BI_DIR.UP and bi.is_sure:
+                            if bi.low > last_zs.zg or bi.high < last_zs.zd:
+                                last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
+                                if bi3.is_sure and bi3.index <= self.bi_list[-1].index and bi.check_bi_zs_overlap() and bi.dir == Chan_BI_DIR.UP:
+                                    zg = min(bi.high, bi2.high, bi3.high)
+                                    zd = max(bi.low, bi2.low, bi3.low)
+                                    gg = max(bi.high, bi2.high, bi3.high)
+                                    dd = min(bi.low, bi2.low, bi3.low)
+                                    zs = ChanBIZS(bi, len(zs_list), Chan_ZS_DIR.DOWN)
+                                    zs.set_zg(zg)
+                                    zs.set_zd(zd)
+                                    zs.set_gg(gg)
+                                    zs.set_dd(dd)
+                                    zs.add_bi(bi2)
+                                    zs.add_bi(bi3)
+                                    zs_list.append(zs)
+                                    last_zs = zs
+                            else:
+                                last_zs.add_bi(bi.pre)
+                                last_zs.add_bi(bi)
+                    if index == len(self.bi_list) - 1 and last_zs and not last_zs.is_sure:
+                        #print(bi.start_time, "BI", last_zs.is_sure)
+                        last_zs.set_end_bi(last_zs.bi_list[-1], last_zs.bi_list[-1].sure_time)
+                        
+        #print(self.start_time, len(zs_list))
+        #print(self.bi_list[-1].end_time, "end_bi")
+        return zs_list

chanlun/core/ChanZS.py

@@ -0,0 +1,109 @@
+from typing import Dict, Optional
+
+import chanlun.core.ChanKLC as ChanKLC
+import chanlun.core.ChanSEG as ChanSEG
+import chanlun.core.ChanCTime as ChanCTime
+from chanlun.core.ChanEnum import Chan_ZS_DIR
+# 中枢
+class ChanZS():
+    def __init__(self, start_seg: ChanSEG, index, ddir: Chan_ZS_DIR):
+        self.start_klc = start_seg.start_bi.start_klc
+        self.start_time = self.start_klc.start_time
+        self.end_time = None
+        self.index = index
+        self.next = None
+        self.pre = None
+        self.start_seg = start_seg
+        self.seg_list = []
+        self.seg_list.append(start_seg)
+        self.end_seg = None
+        self.last_bi_in = None
+        self.bi_out = None
+        self.is_sure = False
+        self.zg = 0
+        self.zd = 0
+        self.gg = 0
+        self.dd = 0
+        self.dir = ddir
+        self.sure_time = None
+        self.end_klc = None
+        self.bi_out_count = 0
+        self.bi_out_list = []
+        self.bi_out_seg_list = []
+        self.bi_out_seg = None
+        self.is_extended = False
+    def set_last_bi_in(self, last_bi_in):
+        self.last_bi_in = last_bi_in
+    def set_bi_out(self, bi_out, bi_out_seg):
+        if bi_out:
+            #print(bi_out.start_klc.start_time, bi_out.sure_time, bi_out.dir, bi_out_seg.dir, len(self.bi_out_list))
+            if len(self.bi_out_list) > 0:
+                last_bi = self.bi_out_list[-1]
+                if last_bi.index != bi_out.index:
+                    self.bi_out_list.append(bi_out)
+                    self.bi_out_seg_list.append(bi_out_seg)
+            else:
+                self.bi_out_list.append(bi_out)
+                self.bi_out_seg_list.append(bi_out_seg)
+        self.bi_out = bi_out
+        self.bi_out_seg = bi_out_seg
+    def set_end_klc(self, end_klc, sure_time, bi_out_count, seg):
+        self.end_klc = end_klc
+        self.set_end_time(end_klc.end_time)
+        self.is_sure = True
+        self.sure_time = sure_time
+        self.bi_out_count = bi_out_count
+        self.end_seg = seg
+    def set_end_seg(self, end_seg):
+        self.end_seg = end_seg
+    def set_pre(self, pre):
+        self.pre = pre
+    def set_next(self, next):
+        self.next = next
+    def set_end_time(self, end_time):
+        self.end_time = end_time
+    def add_klc(self, klc):
+        self.klc_list.append(klc)
+    def add_seg(self, seg):
+        self.seg_list.append(seg)
+        self.end_time = seg.end_time
+        self.end_seg = seg
+    def set_zg(self, zg):
+        self.zg = zg
+    def set_zd(self, zd):
+        self.zd = zd
+    def set_gg(self, gg):
+        self.gg = gg
+    def set_dd(self, dd):
+        self.dd = dd
+    def extend_zs(self, seg_list):
+        self.is_sure = False
+        self.end_seg = None
+        self.end_klc = None
+        self.sure_time = None
+        for seg in seg_list:
+            if seg.end_bi.high > self.gg:
+                self.set_gg(seg.end_bi.high)
+            if seg.end_bi.low < self.dd:
+                self.set_dd(seg.end_bi.low)
+            self.seg_list.append(seg)
+        self.is_extended = True
+        #print(self.start_time, "extend zs", seg_list[-1].end_time)
+
+# 大级别中枢：由多个区间重叠（扩张）的笔/线段中枢合并而成，用于显示更大级别的震荡区间
+class ChanZS_Big():
+    def __init__(self, zs_list):
+        assert len(zs_list) >= 1
+        self.zs_list = list(zs_list)
+        first = self.zs_list[0]
+        last = self.zs_list[-1]
+        self.start_time = first.start_time
+        self.end_time = last.end_time if last.end_time else None
+        self.start_klc = first.start_klc
+        self.end_klc = last.end_klc
+        # 大级别区间取并集：包住所有子中枢
+        self.zd = min(zs.zd for zs in self.zs_list)
+        self.zg = max(zs.zg for zs in self.zs_list)
+        self.dd = min(zs.dd for zs in self.zs_list)
+        self.gg = max(zs.gg for zs in self.zs_list)
+        self.index = 0  # 由外部设置

chanlun/core/Chan_FX_Box.py

@@ -0,0 +1,7 @@
+class Chan_FX_Box():
+	def __init__(self, start_time, end_time, high, low):
+		self.start_time = start_time
+		self.end_time = end_time
+		self.high = high
+		self.low = low
+	

chanlun/indicators/ChanMACD.py

@@ -0,0 +1,274 @@
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanEnum import Chan_MACD_STATE, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIR, Chan_MACDUNITTF_TYPE
+from chanlun.indicators.ChanMACDSeg import ChanMACDSeg
+from chanlun.indicators.ChanMACDUnitTF import ChanMACDUnitTF
+from chanlun.indicators.ChanMACDHistSet import ChanMACDHistSet
+
+class ChanMACD():
+	def __init__(self, klu_list: list[ChanKLU]):
+		self.klu_list = klu_list
+		self.seg_list = []
+		self.unittf_list = []
+		self.histset_list = []
+		# 状态标记列表
+		self.high_position_list = []      # 高位列表
+		self.high_empty_list = []         # 高位空列表
+		self.return_zero_list = []        # 归零轴列表
+		self.cross0_up_list = []          # 向上穿越零轴列表
+		self.cross0_down_list = []        # 向下穿越零轴列表
+		# 计算段 / UnitTF / HistSet 及状态标记
+		self.cal_macd_state()
+		self.get_klu_sd_list()
+	def get_klu_sd(self):
+		if self.klu_list:
+			sd = self.klu_list[-1].separate_div
+			if sd > 1:
+				print(self.klu_list[-1].time, sd)
+				return True
+		return False
+	def get_klu_sd_list(self):
+		sd_list = []
+		if self.klu_list:
+			for klu in self.klu_list:
+				hist = klu.macdhist
+				signal = False
+				if klu.pre and klu.next:
+					if klu.signal > 0:
+						signal = klu.pre.signal > klu.signal and klu.next.signal < klu.signal
+					else:
+						signal = klu.pre.signal < klu.signal and klu.next.signal > klu.signal
+				sd = klu.separate_div
+				if sd > 1 and ((hist > 0 and hist < 200) or (hist < 0 and hist > -200)):
+					sd_list.append(klu.time)
+					#print(klu.time, sd)
+		return sd_list
+	def cal_macd_state(self):
+		last_seg = None
+		last_unittf = None
+		last_histset = None
+		last_klu = None
+		for klu in self.klu_list:
+			# initialise first histset
+			if klu.macd == 0 and klu.signal == 0 and klu.macdhist == 0:
+				continue
+			if last_histset is None:
+				if klu.macdhist > 0:
+					last_histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.ABOVE)
+					self.histset_list.append(last_histset)
+				else:
+					last_histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.UNDER)
+					self.histset_list.append(last_histset)
+			else:
+				# initialise first seg and unittf
+				if last_seg is None:
+					# create histset afterwards
+					if last_histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
+						if klu.macdhist > 0:
+							last_histset.add_klu(klu)
+						else:
+							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.UNDER)
+							self.histset_list.append(histset)
+							last_histset.set_next(histset)
+							histset.set_pre(last_histset)
+							last_histset.set_end_klu(last_klu)
+							last_histset = histset
+					else:
+						if klu.macdhist < 0:
+							last_histset.add_klu(klu)
+						else:
+							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE)
+							self.histset_list.append(histset)
+							last_histset.set_next(histset)
+							histset.set_pre(last_histset)
+							last_histset.set_end_klu(last_klu)
+							last_histset = histset
+					if last_klu.signal >= 0 and klu.signal < 0:
+						last_unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, None, Chan_MACDUNITTF_DIR.UNDER, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
+						self.unittf_list.append(last_unittf)
+						last_seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, None, Chan_MACDSEG_DIR.UNDER, last_unittf)
+						self.seg_list.append(last_seg)
+					elif last_klu.signal <= 0 and klu.signal > 0:
+						last_unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, None, Chan_MACDUNITTF_DIR.ABOVE, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
+						self.unittf_list.append(last_unittf)
+						last_seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, None, Chan_MACDSEG_DIR.ABOVE, last_unittf)
+						self.seg_list.append(last_seg)
+				# after the first seg and unittf
+				else:
+					# create histset afterwards
+					if last_histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
+						if klu.macdhist > 0:
+							last_histset.add_klu(klu)
+						else:
+							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.UNDER)
+							self.histset_list.append(histset)
+							last_histset.set_next(histset)
+							histset.set_pre(last_histset)
+							last_histset.set_end_klu(last_klu)
+							last_histset = histset
+							if last_unittf:
+								last_unittf.add_histset(last_histset)
+					else:
+						if klu.macdhist < 0:
+							last_histset.add_klu(klu)
+						else:
+							histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE)
+							self.histset_list.append(histset)
+							last_histset.set_next(histset)
+							histset.set_pre(last_histset)
+							last_histset.set_end_klu(last_klu)
+							last_histset = histset
+							if last_unittf:
+								last_unittf.add_histset(last_histset)
+					if last_klu.signal >= 0 and klu.signal < 0:
+						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.CROSS0)
+						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, Chan_MACDUNITTF_DIR.UNDER, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
+						self.unittf_list.append(unittf)
+						last_unittf.set_next(unittf)
+						last_seg.set_end_klu(last_klu)
+						seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, last_seg, Chan_MACDSEG_DIR.UNDER, unittf)
+						self.seg_list.append(seg)
+						last_seg.set_next(seg)
+						last_seg = seg
+						last_unittf = unittf
+					elif last_klu.signal <= 0 and klu.signal > 0:
+						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.CROSS0)
+						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, Chan_MACDUNITTF_DIR.ABOVE, Chan_MACDUNITTF_TYPE.CROSS0, last_histset)
+						self.unittf_list.append(unittf)
+						last_unittf.set_next(unittf)
+						last_seg.set_end_klu(last_klu)
+						seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, last_seg, Chan_MACDSEG_DIR.ABOVE, unittf)
+						self.seg_list.append(seg)
+						last_seg.set_next(seg)
+						last_seg = seg
+						last_unittf = unittf
+					elif last_unittf.is_end and last_klu.macd < klu.macd and klu.macd > klu.signal:
+						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, Chan_MACDUNITTF_DIR.ABOVE, Chan_MACDUNITTF_TYPE.NEAR0, last_histset)
+						self.unittf_list.append(unittf)
+						last_unittf.set_next(unittf)
+						last_seg.add_unittf(unittf)
+						last_unittf = unittf
+						last_seg.add_klu(klu)
+					else:
+						if not last_unittf.is_end:
+							last_unittf.add_klu(klu)
+						last_seg.add_klu(klu)
+			last_klu = klu
+			klu.cal_macd_state()
+			#print(klu.time, klu.macd_state, klu.continue_div, klu.separate_div, klu.macd, klu.signal, klu.macdhist, klu.ema24, klu.ema52, klu.close)  
+		return self.klu_list         
+	def cal_macd(self):
+		last_seg = None
+		last_unittf = None
+		last_histset = None
+		histset = None
+		last_klu = None
+		for klu in self.klu_list:          
+			klu.cal_macd_state()
+			print(klu.time, klu.macd_state)
+			# 1) 只有当 MACD 已可用(非 UNKNOWN)时，才开始初始化段/单元
+			if last_seg is None:
+				if klu.macd_state != Chan_MACD_STATE.UNKNOWN:
+					# 初始化首个直方图集合（根据当前柱体正负）
+					if klu.macdhist >= 0:
+						histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.ABOVE)
+					else:
+						histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, None, Chan_MACDHISTSET_DIR.UNDER)
+					self.histset_list.append(histset)
+					last_histset = histset
+					
+					# 初始化首段
+					seg_dir = Chan_MACDSEG_DIR.ABOVE if klu.signal >= 0 else Chan_MACDSEG_DIR.UNDER
+					seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, None, seg_dir, last_unittf)
+					self.seg_list.append(seg)
+					last_seg = seg
+
+					# 初始化首个UnitTF
+					unittf_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
+					unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, None, unittf_dir, Chan_MACDUNITTF_TYPE.START, histset)
+					self.unittf_list.append(unittf)
+					last_unittf = unittf
+					last_seg.add_unittf(unittf)
+				# 未就绪则继续等下一根；已就绪亦已完成首个结构初始化，继续下一根
+				last_klu = klu
+				continue
+			 # 3) 直方图集合（基于当前 unittf）
+			if klu.macdhist >= 0:
+				if last_histset and last_histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
+					last_histset.add_klu(klu)
+				else:
+					# 结束旧 histset（以前一根结束更合理）
+					if last_histset and last_klu:
+						last_histset.set_end_klu(last_klu)
+					histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE if klu.macdhist >= 0 else Chan_MACDHISTSET_DIR.UNDER)
+					self.histset_list.append(histset)
+					if last_histset:
+						last_histset.set_next(histset)
+					last_histset = histset
+					if last_unittf:
+						last_unittf.add_histset(histset)
+			else:
+				if last_histset and last_histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER:
+					last_histset.add_klu(klu)
+				else:
+					# 结束旧 histset（以前一根结束更合理）
+					if last_histset and last_klu:
+						last_histset.set_end_klu(last_klu)
+					histset = ChanMACDHistSet(len(self.histset_list), klu.time, klu, last_histset, Chan_MACDHISTSET_DIR.ABOVE if klu.macdhist >= 0 else Chan_MACDHISTSET_DIR.UNDER)
+					self.histset_list.append(histset)
+					if last_histset:
+						last_histset.set_next(histset)
+					last_histset = histset
+					if last_unittf:
+						last_unittf.add_histset(histset)
+			# 2) 过零切段（使用KLU中的穿越状态）
+			if (klu.macd_state == Chan_MACD_STATE.CROSS0_UP or 
+				klu.macd_state == Chan_MACD_STATE.CROSS0_DOWN):
+				# 结束旧 unittf
+				last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.CROSS0)
+				# 新的单位时间周期
+				new_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
+				unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, new_dir, Chan_MACDUNITTF_TYPE.CROSS0, histset)
+				self.unittf_list.append(unittf)
+				last_unittf.set_next(unittf)
+				last_unittf = unittf
+				# 收尾旧段
+				last_seg.set_end_klu(last_klu)
+				# 新段方向取反
+				new_dir = Chan_MACDSEG_DIR.UNDER if last_seg.seg_dir == Chan_MACDSEG_DIR.ABOVE else Chan_MACDSEG_DIR.ABOVE
+				seg = ChanMACDSeg(len(self.seg_list), klu.time, klu, last_seg, new_dir, last_unittf)
+				self.seg_list.append(seg)
+				last_seg.set_next(seg)
+				last_seg = seg
+				last_seg.add_unittf(unittf)
+			else:
+				# 4) UnitTF 状态机：用黄线Signal的归零轴
+				if last_klu.macd_state == Chan_MACD_STATE.NEAR0 and last_unittf.div_count > 1:
+					#print(klu.time, klu.macd_state)
+					if klu.macd_state == Chan_MACD_STATE.RZ_UP:
+						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.NEAR0)
+						new_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
+						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, new_dir, Chan_MACDUNITTF_TYPE.NEAR0, histset)
+						self.unittf_list.append(unittf)
+						last_unittf.set_next(unittf)
+						last_unittf = unittf
+						last_seg.add_unittf(unittf)
+					elif klu.macd_state == Chan_MACD_STATE.RZ_DOWN:
+						last_unittf.set_end_klu(last_klu, Chan_MACDUNITTF_TYPE.NEAR0)
+						new_dir = Chan_MACDUNITTF_DIR.ABOVE if klu.signal >= 0 else Chan_MACDUNITTF_DIR.UNDER
+						unittf = ChanMACDUnitTF(len(self.unittf_list), klu.time, klu, last_unittf, new_dir, Chan_MACDUNITTF_TYPE.NEAR0, histset)
+						self.unittf_list.append(unittf)
+						last_unittf.set_next(unittf)
+						last_unittf = unittf
+						last_seg.add_unittf(unittf)
+					else:
+						last_unittf.add_klu(klu)
+						last_seg.add_klu(klu)
+				else:
+					last_unittf.add_klu(klu)
+					last_seg.add_klu(klu)
+
+			last_klu = klu
+		last_histset.set_end_klu(last_klu)
+		last_unittf.set_end_klu(last_klu, None)
+		last_seg.set_end_klu(last_klu)
+		return self.klu_list

chanlun/indicators/ChanMACDHistSet.py

@@ -0,0 +1,117 @@
+from chanlun.core.ChanEnum import Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACD_STATE
+
+class ChanMACDHistSet():
+    def __init__(self, index, start_time, start_klu, pre_histset, dir):
+        self.index = index
+        self.start_time = start_time
+        self.end_time = None
+        self.klu_list = []
+        self.klu_list.append(start_klu)
+        self.histset_dir = dir
+        self.next = None
+        self.pre = pre_histset
+        self.peak_klu = None
+        self.area = start_klu.macdhist
+        self.unittf_div = Chan_MACDUNITTF_DIV.UNDIV
+        self.middle_klu = None
+        self.div_count = 0
+        self.last_klu = start_klu
+        self.start_klu = start_klu
+        self.peak_div_list = []
+        self.middle_area = 0
+        self.total_macdhist = 0
+    def set_next(self, next_histset):
+        self.next = next_histset
+    def set_pre(self, pre_histset):
+        self.pre = pre_histset
+    def set_middle_klu(self, middle_klu):
+        self.middle_klu = middle_klu
+        #self.middle_area = abs(middle_klu.macdhist)
+        #self.middle_klu = None
+    def set_unittf_div(self, unittf_div):
+        self.unittf_div = unittf_div
+    def add_klu(self, klu):
+        klu.set_histset(self)
+        self.klu_list.append(klu)
+        self.area += abs(klu.macdhist)
+        if self.middle_klu:
+            self.middle_area += abs(klu.macdhist)
+        if self.middle_klu and self.middle_klu.index + 1 == klu.index:
+            self.low_klu = None
+            self.peak_klu = None
+            self.div_count = 0
+            self.peak_div_list = []
+        else:
+            if self.last_klu:
+                self.cal_macdhist_klu(klu)
+        self.last_klu = klu
+    def cal_macdhist_klu(self, klu):
+        if self.middle_klu:
+            if klu.index >= self.middle_klu.index + 2:
+                if klu.pre.pre:
+                    if abs(klu.pre.macdhist) > abs(klu.pre.pre.macdhist) and abs(klu.pre.macdhist) > abs(klu.macdhist):
+                        if self.peak_klu:
+                            if abs(klu.pre.macdhist) > abs(self.peak_klu.macdhist):
+                                self.peak_klu = klu.pre
+                                #self.div_count = 0
+                                #self.peak_div_list = []
+                            else:
+                                if klu.pre.macd * klu.pre.macdhist > 0:
+                                    self.peak_div_list.append(klu.pre)
+                                    self.div_count += 1
+                                    klu.pre.continue_div = True
+                        else:
+                            self.peak_klu = klu.pre
+        else:
+            if len(self.klu_list) >= 3:
+                if klu.pre.pre:
+                    if abs(klu.pre.macdhist) > abs(klu.pre.pre.macdhist) and abs(klu.pre.macdhist) > abs(klu.macdhist):
+                        if self.peak_klu:
+                            if abs(klu.pre.macdhist) > abs(self.peak_klu.macdhist):
+                                self.peak_klu = klu.pre
+                                #self.div_count = 0
+                                #self.peak_div_list = []
+                            else:
+                                if klu.pre.macd * klu.pre.macdhist > 0 and klu.pre.signal * klu.pre.macdhist > 0:
+                                    self.peak_div_list.append(klu.pre)
+                                    self.div_count += 1
+                                    klu.pre.continue_div = True
+                        else:
+                            self.peak_klu = klu.pre
+    def set_end_klu(self, end_klu):
+        self.end_klu = end_klu
+        self.end_time = end_klu.time
+        #if len(self.peak_div_list) > 0:
+            #klu = self.peak_div_list[-1]
+            #if klu.macd * klu.macdhist > 0:
+                #end_klu.continue_div = True
+                #print(end_klu.time, "Continue Div")
+        if self.start_klu.index == end_klu.index:
+            self.peak_klu = self.start_klu
+        if self.start_klu.index + 1 == end_klu.index:
+            if abs(self.start_klu.macdhist) > abs(end_klu.macdhist):
+                self.peak_klu = self.start_klu
+            else:
+                self.peak_klu = end_klu
+        if len(self.klu_list) >= 3 and self.peak_klu == None:
+            self.peak_klu = self.klu_list[0]
+            for klu in self.klu_list:
+                if abs(klu.macdhist) > abs(self.peak_klu.macdhist):
+                    self.peak_klu = klu
+        peak_str = ""
+        state_str = ""
+        for peak_div in self.peak_div_list:
+            peak_str += f"{peak_div.time}, "
+            state_str += f"{peak_div.macd_state}, "
+        total_macdhist = 0
+        first_klu = self.klu_list[0]
+        last_klu = self.klu_list[-1]
+        if (first_klu.macd > 0 and last_klu.macd > 0 and first_klu.macdhist > 0) or (first_klu.macd < 0 and last_klu.macd < 0 and first_klu.macdhist < 0):
+            for klu in self.klu_list:
+                self.total_macdhist += klu.macdhist
+            if abs(self.total_macdhist) < 150:
+                #print(self.end_time, "Total MACDHist: ", self.total_macdhist)
+                last_klu.separate_div = 99999
+        #if self.peak_klu and len(self.peak_div_list) > 0:
+            #print("Continue Div: ",self.start_time, "Peak:", self.peak_klu.time, "Div: ", peak_str, state_str)
+        

chanlun/indicators/ChanMACDSeg.py

@@ -0,0 +1,49 @@
+from chanlun.core.ChanEnum import Chan_MACDSEG_DIR
+
+
+class ChanMACDSeg():
+    def __init__(self, index, start_time, start_klu, pre_seg, seg_dir, start_unittf):
+        self.index = index
+        self.start_time = start_time
+        self.end_time = None
+        self.start_klu = start_klu
+        self.end_klu = None
+        self.klu_list = []
+        self.klu_list.append(start_klu)
+        self.unittf_list = []
+        self.seg_dir = seg_dir
+        self.pre = pre_seg
+        self.next = None
+        self.high_klu = start_klu
+        self.low_klu = start_klu
+        self.ref_klu = None
+        self.unittf_list.append(start_unittf)
+    def set_next(self, next_seg):
+        self.next = next_seg
+    def set_pre(self, pre_seg):
+        self.pre = pre_seg
+    def add_klu(self, klu):
+        if klu:
+            self.klu_list.append(klu)
+            klu.set_seg(self)
+            if self.seg_dir == Chan_MACDSEG_DIR.ABOVE:
+                if klu.macdhist > self.high_klu.macdhist:
+                    self.high_klu = klu
+                else:
+                    if klu.macdhist < self.low_klu.macdhist:
+                        self.low_klu = klu
+            else:
+                if klu.macdhist < self.high_klu.macdhist:
+                    self.high_klu = klu
+                else:
+                    if klu.macdhist > self.low_klu.macdhist:
+                        self.low_klu = klu
+            if self.high_klu.index != self.start_klu.index:
+                self.ref_klu = self.high_klu
+    def add_unittf(self, unittf):
+        self.unittf_list.append(unittf)
+        unittf.set_next(self)
+    def set_end_klu(self, end_klu):
+        self.add_klu(end_klu)
+        self.end_klu = end_klu
+        self.end_time = end_klu.time

chanlun/indicators/ChanMACDUnitTF.py

@@ -0,0 +1,141 @@
+from chanlun.core.ChanEnum import Chan_MACD_STATE, Chan_MACDUNITTF_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACDUNITTF_TYPE
+
+
+class ChanMACDUnitTF():
+    def __init__(self, index, start_time, start_klu, pre_unittf, unittf_dir, start_type, start_histset):
+        self.index = index
+        self.start_time = start_time
+        self.end_time = None
+        self.start_klu = start_klu
+        self.end_klu = None
+        self.klu_list = []
+        self.klu_list.append(start_klu)
+        self.histset_list = []
+        self.histset_list.append(start_histset)
+        self.div_count = 0
+        start_histset.set_middle_klu(start_klu)
+        self.next = None
+        self.pre = pre_unittf
+        self.unittf_dir = unittf_dir
+        self.start_type = start_type
+        self.end_type = None
+        self.peak_klu = None
+        self.div_type = Chan_MACDUNITTF_DIV.UNDIV
+        self.div_peak_list = []
+        self.is_end = False
+    def set_next(self, next_unittf):
+        self.next = next_unittf
+    def set_pre(self, pre_unittf):
+        self.pre = pre_unittf
+    def add_histset(self, histset):
+        self.histset_list.append(histset)
+    def add_klu(self, klu):
+        self.klu_list.append(klu)
+        self.cal_peak_div()
+        self.cal_macd_state()
+    def cal_peak_div(self):
+        self.div_count = 0
+        self.div_peak_list = []
+        self.peak_klu = None
+        if len(self.histset_list) == 0:
+            return
+        if len(self.histset_list) == 1:
+            self.div_type = self.histset_list[0].unittf_div
+            self.peak_klu = self.histset_list[0].peak_klu
+        else:
+            
+            for index in range(0, len(self.histset_list)):
+                histset = self.histset_list[index]
+                if self.same_dir(histset):
+                    #if histset.peak_klu:
+                        #print("Unittf: ", self.start_klu.time, len(self.histset_list), histset.peak_klu.time)
+                    if self.peak_klu:
+                        if histset.peak_klu:
+                            if abs(histset.peak_klu.macdhist) >= abs(self.peak_klu.macdhist):
+                                self.peak_klu = histset.peak_klu
+                                self.div_type = Chan_MACDUNITTF_DIV.UNDIV
+                                self.div_count = 0
+                            else:
+                                self.div_type = Chan_MACDUNITTF_DIV.DISCRETE
+                                self.div_count += 1
+                                self.div_peak_list.append(histset.peak_klu)
+                                #print("Unittf: ", self.start_klu.time)
+                                if histset.peak_klu.macd > 0 and histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE:
+                                    histset.peak_klu.set_separate_div(self.div_count)
+                                elif histset.peak_klu.macd < 0 and histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER:
+                                    histset.peak_klu.set_separate_div(self.div_count)
+                    else:
+                        if histset.peak_klu:
+                            self.peak_klu = histset.peak_klu
+    def cal_macd_state(self):
+        if len(self.klu_list) > 0:
+            last_klu = self.klu_list[0]
+            macd_peak_klu = None
+            signal_peak_klu = None
+            for index in range(1, len(self.klu_list)):
+                klu = self.klu_list[index]
+                if klu.macd == 0 and klu.signal == 0 and klu.macdhist == 0:
+                    continue
+                if self.start_type == Chan_MACDUNITTF_TYPE.CROSS0 or self.start_type == Chan_MACDUNITTF_TYPE.NEAR0:
+                    if self.unittf_dir == Chan_MACDUNITTF_DIR.ABOVE:
+                        if macd_peak_klu is None:
+                            if last_klu.macd < klu.macd:
+                                if last_klu.signal < last_klu.macdhist:
+                                    last_klu.set_macd_state(Chan_MACD_STATE.UP)
+                                else:
+                                    last_klu.set_macd_state(Chan_MACD_STATE.HIGH)
+                            else:
+                                macd_peak_klu = last_klu
+                                last_klu.set_macd_state(Chan_MACD_STATE.PEAK)
+                        elif klu.macd > macd_peak_klu.macd:
+                            macd_peak_klu = None
+                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH)
+                        elif last_klu.signal < klu.signal:
+                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH_EMPTY)
+                        elif signal_peak_klu is None:
+                            signal_peak_klu = last_klu
+                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH_EMPTY)
+                        elif klu.signal > signal_peak_klu.signal:
+                            signal_peak_klu = None
+                            last_klu.set_macd_state(Chan_MACD_STATE.HIGH)
+                        elif last_klu.macd < last_klu.signal:
+                            last_klu.set_macd_state(Chan_MACD_STATE.RETURN_ZERO)
+                        if self.return_zero(last_klu, klu):
+                            self.end_type = Chan_MACDUNITTF_TYPE.NEAR0
+                            self.is_end = True
+                            self.end_klu = klu
+                            self.end_time = klu.time
+                            klu.set_macd_state(Chan_MACD_STATE.NEAR0)
+                            #print(self.index, last_klu.time, last_klu.macd, last_klu.signal, last_klu.macd_state, klu.macd_state)
+                            break
+                        #print(self.index, last_klu.time, last_klu.macd, last_klu.signal, last_klu.macd_state)
+                last_klu = klu
+                
+    def return_zero(self, last_klu, klu):
+        return_zero = False
+        if last_klu.close < last_klu.ema52 and klu.close > klu.ema52:
+            return_zero = True
+        return_zero = False
+        return return_zero
+    def same_dir(self, histset):
+        if self.unittf_dir == Chan_MACDUNITTF_DIR.ABOVE:
+            return histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE
+        else:
+            return histset.histset_dir == Chan_MACDHISTSET_DIR.UNDER
+    def set_end_klu(self, end_klu, end_type):
+        self.end_type = end_type
+        self.end_klu = end_klu
+        self.end_time = end_klu.time
+        self.is_end = True
+        self.cal_macd_state()
+        div_time = ""
+        for div in self.div_peak_list:
+            div_time += f"{div.time}, "
+        histset_time = ""
+        for histset in self.histset_list:
+            histset_time += f"{histset.start_time}, "
+        
+        #if self.peak_klu and self.div_type == Chan_MACDUNITTF_DIV.DISCRETE:
+            #print("Cross Div: ", self.start_klu.time, self.peak_klu.time, self.div_count, self.div_type, self.unittf_dir, div_time, len(self.histset_list))
+        
+        

chanlun/pipeline/builders/bi.py

@@ -0,0 +1,682 @@
+"""TF_DF builder mixin — 由 split_tfdf_builders 自动生成，逻辑与原 TF_DF 一致。"""
+from __future__ import annotations
+
+from datetime import timedelta
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+
+
+class BiBuilderMixin:
+	def cal_trend(self, klc_list):
+		"""
+		基于价格与EMA24/EMA52的位置关系、以及MACD/Signal/Hist的方向，
+		为每个KLC打上趋势标签：'UP' / 'DOWN' / 'FLAT'。
+		仅设置 klc.trend，不影响其它字段。
+		"""
+		if not klc_list:
+			return klc_list
+		last_trend = Chan_PRICE_TREND.UNKNOWN
+		# 趋势延续性：参考近 N 根已完成的KLC
+		lookback_n = 5
+		prev_klcs = []
+		for klc in klc_list:
+			price = getattr(klc, 'close', None)
+			ema24 = getattr(klc, 'ema24', None)
+			ema52 = getattr(klc, 'ema52', None)
+			macd_raw = getattr(klc, 'macd', None)
+			signal_raw = getattr(klc, 'signal', None)
+			hist_raw = getattr(klc, 'macdhist', None)
+			macd = macd_raw if macd_raw is not None else 0
+			signal = signal_raw if signal_raw is not None else 0
+			hist = hist_raw if hist_raw is not None else 0
+			rsi = getattr(klc, 'rsi', None)
+			macd_ready = macd_raw is not None and signal_raw is not None
+			hist_ready = hist_raw is not None
+			trend = Chan_PRICE_TREND.UNKNOWN
+			score = 0
+			try:
+				# 有效性
+				price_valid = price is not None and price != 0
+				ema24_valid = ema24 is not None and ema24 != 0
+				ema52_valid = ema52 is not None and ema52 != 0
+				# 多因子投票
+				
+				# 1) 均线结构 + 价位
+				if ema24_valid or ema52_valid:
+					ma_votes = 0
+					if ema24_valid and ema52_valid:
+						ma_votes += 1 if ema24 > ema52 else -1
+					if price_valid and ema24_valid:
+						ma_votes += 1 if price > ema24 else 0
+					if price_valid and ema52_valid:
+						ma_votes += 1 if price > ema52 else -1
+					# 限幅，避免相关因子重复计分
+					score += max(-2, min(2, ma_votes))
+				# 2) MACD结构
+				if macd_ready:
+					score += 1 if macd >= signal else -1
+					if hist_ready and hist != 0:
+						score += 1 if hist > 0 else -1
+				# 3) 动量与均线差分斜率
+				pre = getattr(klc, 'pre', None)
+				pre_hist = getattr(pre, 'macdhist', None) if pre else None
+				if pre:
+					pre_close = getattr(pre, 'close', None)
+					if price_valid and pre_close is not None:
+						score += 1 if price >= pre_close else -1
+					pre_ema24 = getattr(pre, 'ema24', None)
+					pre_ema52 = getattr(pre, 'ema52', None)
+					if ema24_valid and ema52_valid and pre_ema24 not in (None, 0) and pre_ema52 not in (None, 0):
+						spread_now = ema24 - ema52
+						spread_pre = pre_ema24 - pre_ema52
+						score += 1 if spread_now >= spread_pre else -1
+					# 3.1) MACD柱体动量趋势：考虑 macdhist 的斜率与过零
+					if hist_ready and pre_hist is not None:
+						# 柱体斜率：上升加分，下降减分
+						if hist > pre_hist:
+							score += 1
+						elif hist < pre_hist:
+							score -= 1
+						# 过零加权：负转正更偏多，正转负更偏空
+						if pre_hist < 0 and hist > 0:
+							score += 1
+						elif pre_hist > 0 and hist < 0:
+							score -= 1
+					# 3.2) EMA52 突破/跌破加权
+					if ema52_valid and price_valid and pre_close is not None and pre_ema52 not in (None, 0):
+						# 看多突破：从均线下方上破且动量配合
+						if pre_close <= pre_ema52 and price > ema52 and (hist is None or pre_hist is None or hist >= pre_hist):
+							score += 1
+						# 看空跌破：从均线上方下破且动量配合
+						if pre_close >= pre_ema52 and price < ema52 and (hist is None or pre_hist is None or hist <= pre_hist):
+							score -= 1
+				# 3.3) EMA52 支撑/阻力触碰（非强穿越）
+				if ema52_valid and price_valid:
+					low_v = getattr(klc, 'low', None)
+					high_v = getattr(klc, 'high', None)
+					if low_v is not None and high_v is not None and ema52 not in (None, 0):
+						# 触碰容差（相对EMA52的0.15%）
+						touch_tol = 0.0015
+						# 作为支撑：收盘在上，最低靠近EMA52
+						near_support_touch = (price > ema52) and (abs(low_v - ema52) / abs(ema52) <= touch_tol)
+						# 作为阻力：收盘在下，最高靠近EMA52
+						near_resistance_touch = (price < ema52) and (abs(high_v - ema52) / abs(ema52) <= touch_tol)
+						if near_support_touch:
+							# 若动量不弱，则更偏多
+							score += 1 if (hist is None or pre_hist is None or hist >= pre_hist) else 0
+						if near_resistance_touch:
+							# 若动量不强，则更偏空
+							score -= 1 if (hist is None or pre_hist is None or hist <= pre_hist) else 0
+				# 3.4) 多次对 EMA52 的"拒绝"配合 MACD 逆向：易形成压/支并反向
+				# 统计近窗口内的上/下拒绝次数：
+				# - 上拒绝：价格位于 EMA52 下方，最高触及/越过 EMA52 但收盘仍在下方
+				# - 下拒绝：价格位于 EMA52 上方，最低触及/跌破 EMA52 但收盘仍在上方
+				recent_up_rejects = 0
+				recent_down_rejects = 0
+				if ema52_valid:
+					window_rej = prev_klcs[-lookback_n:] if len(prev_klcs) > 0 else []
+					rej_tol = 0.0015
+					for wk in window_rej:
+						wk_close = getattr(wk, 'close', None)
+						wk_ema52 = getattr(wk, 'ema52', None)
+						wk_high = getattr(wk, 'high', None)
+						wk_low = getattr(wk, 'low', None)
+						if wk_close is None or wk_ema52 in (None, 0):
+							continue
+						# 上拒绝（阻力）：下方多次试图上破但未站上
+						if wk_close < wk_ema52 and wk_high is not None:
+							if wk_high >= wk_ema52 or abs(wk_high - wk_ema52) / abs(wk_ema52) <= rej_tol:
+								recent_up_rejects += 1
+						# 下拒绝（支撑）：上方多次试图下破但未跌破
+						if wk_close > wk_ema52 and wk_low is not None:
+							if wk_low <= wk_ema52 or abs(wk_low - wk_ema52) / abs(wk_ema52) <= rej_tol:
+								recent_down_rejects += 1
+				# 定义 MACD 的方向偏好
+				macd_bias_up = macd_ready and (macd >= signal) and (not hist_ready or pre_hist is None or hist >= pre_hist)
+				macd_bias_down = macd_ready and (macd <= signal) and (not hist_ready or pre_hist is None or hist <= pre_hist)
+				# 若多次上拒绝且 MACD 偏空，则更偏向下行；若多次下拒绝且 MACD 偏多，则更偏向上行
+				if recent_up_rejects >= 2 and macd_bias_down:
+					score -= 2
+				if recent_down_rejects >= 2 and macd_bias_up:
+					score += 2
+				# 4) RSI 辅助
+				if rsi is not None:
+					if rsi >= 55:
+						score += 1
+					elif rsi <= 45:
+						score -= 1
+				# 5) 指标未就绪回退（EMA/MACD缺失时，用动量与RSI辅助，延续趋势）
+				has_full_ind = ema24_valid and ema52_valid and not (macd == 0 and signal == 0 and hist == 0)
+				if not has_full_ind:
+					# 仅根据价动量/RSI做轻量判断，默认延续 last_trend，除非出现强反向
+					strong_up = False
+					strong_down = False
+					pre = getattr(klc, 'pre', None)
+					if pre:
+						pre_close = getattr(pre, 'close', None)
+						if price_valid and pre_close is not None:
+							strong_up = (price >= pre_close)
+							strong_down = (price < pre_close)
+					if rsi is not None:
+						if rsi >= 60:
+							strong_up = True
+						elif rsi <= 40:
+							strong_down = True
+					if last_trend == Chan_PRICE_TREND.UP and not strong_down:
+						trend = Chan_PRICE_TREND.UP
+					elif last_trend == Chan_PRICE_TREND.DOWN and not strong_up:
+						trend = Chan_PRICE_TREND.DOWN
+					else:
+						trend = Chan_PRICE_TREND.UP if strong_up and not strong_down else (Chan_PRICE_TREND.DOWN if strong_down and not strong_up else Chan_PRICE_TREND.FLAT)
+				else:
+					# 6) 震荡过滤（仅当极近EMA52且MACD贴合时判作震荡）
+					near_flat = False
+					if price_valid and ema52_valid:
+						near_ema52 = abs(price - ema52) / abs(ema52) <= 0.0005  # 0.05%
+						if macd_ready:
+							macd_scale = max(abs(macd), abs(signal), 1e-6)
+							near_macd = abs(macd - signal) / macd_scale <= 0.05
+						else:
+							near_macd = False
+						near_flat = near_ema52 and near_macd
+					# 7) 动态阈值 + 趋势记忆（更强粘滞：趋势中容忍小幅反分）
+					# 引入过去 N 根KLC 的趋势延续性来动态调整翻转阈值，并结合 EMA52 支撑/阻力触碰强化门槛
+					force_flip_down = False
+					force_flip_up = False
+					if near_flat:
+						trend = Chan_PRICE_TREND.FLAT
+					else:
+						# 计算过去窗口的趋势一致性
+						window = prev_klcs[-lookback_n:] if len(prev_klcs) > 0 else []
+						persist_up = 0
+						persist_down = 0
+						for wk in window:
+							if getattr(wk, 'trend', None) == Chan_PRICE_TREND.UP:
+								persist_up += 1
+							elif getattr(wk, 'trend', None) == Chan_PRICE_TREND.DOWN:
+								persist_down += 1
+						persist_ratio_up = (persist_up / len(window)) if len(window) > 0 else 0
+						persist_ratio_down = (persist_down / len(window)) if len(window) > 0 else 0
+						# 基准阈值
+						down_flip_threshold = -2
+						up_flip_threshold = 2
+						# 若最近多为UP，则从UP翻转需更强反向信号；同理对DOWN
+						if last_trend == Chan_PRICE_TREND.UP and persist_ratio_up >= 0.6:
+							down_flip_threshold = -3
+						elif last_trend == Chan_PRICE_TREND.DOWN and persist_ratio_down >= 0.6:
+							up_flip_threshold = 3
+						# EMA52 触碰强化门槛：UP时若出现支撑触碰，下翻更难；DOWN时若出现阻力触碰，上翻更难
+						if ema52_valid and price_valid:
+							low_v = getattr(klc, 'low', None)
+							high_v = getattr(klc, 'high', None)
+							if low_v is not None and high_v is not None and ema52 not in (None, 0):
+								touch_tol = 0.0015
+								near_support_touch = (price > ema52) and (abs(low_v - ema52) / abs(ema52) <= touch_tol)
+								near_resistance_touch = (price < ema52) and (abs(high_v - ema52) / abs(ema52) <= touch_tol)
+								if last_trend == Chan_PRICE_TREND.UP and near_support_touch:
+									# 强化维持UP：进一步降低向下翻转阈值
+									down_flip_threshold = min(down_flip_threshold - 1, -3)
+								if last_trend == Chan_PRICE_TREND.DOWN and near_resistance_touch:
+									# 强化维持DOWN：进一步提高向上翻转阈值
+									up_flip_threshold = max(up_flip_threshold + 1, 3)
+						# 7.1) 复合拐头信号：MACD/Signal 同向拐头 + hist 连续减弱 + 多次未能越过 EMA52
+						pre_macd = getattr(pre, 'macd', None) if pre else None
+						pre_signal = getattr(pre, 'signal', None) if pre else None
+						macd_slope = (macd - pre_macd) if (macd_ready and pre_macd is not None) else 0
+						signal_slope = (signal - pre_signal) if (macd_ready and pre_signal is not None) else 0
+						# hist 连续减弱（绝对值缩小）
+						hist_seq = []
+						for wk in prev_klcs[-2:]:
+							val = getattr(wk, 'macdhist', None)
+							if val is not None:
+								hist_seq.append(val)
+						if hist is not None:
+							hist_seq.append(hist)
+						weaken_steps = 0
+						for i in range(1, len(hist_seq)):
+							if abs(hist_seq[i]) < abs(hist_seq[i-1]):
+								weaken_steps += 1
+						# 近窗口对 EMA52 的"未能站上/跌破"统计（放宽窗口与条件）
+						window_ema = prev_klcs[-4:] if len(prev_klcs) > 0 else []
+						no_up_break = False
+						no_down_break = False
+						if ema52_valid:
+							# 未能有效上破：最近若干根收盘大多数不在 EMA52 上方，且高点多次触及/接近
+							cnt_touch_up = 0
+							cnt_close_above = 0
+							for wk in window_ema:
+								wk_close = getattr(wk, 'close', None)
+								wk_high = getattr(wk, 'high', None)
+								wk_ema = getattr(wk, 'ema52', None)
+								if wk_close is not None and wk_ema not in (None, 0):
+									if wk_close > wk_ema:
+										cnt_close_above += 1
+									if wk_high is not None and (wk_high >= wk_ema or abs(wk_high - wk_ema) / abs(wk_ema) <= 0.0015):
+										cnt_touch_up += 1
+							no_up_break = (cnt_close_above <= 1 and cnt_touch_up >= 1 and price <= ema52)
+							# 未能有效下破：最近若干根收盘大多数不在 EMA52 下方，且低点多次触及/接近
+							cnt_touch_down = 0
+							cnt_close_below = 0
+							for wk in window_ema:
+								wk_close = getattr(wk, 'close', None)
+								wk_low = getattr(wk, 'low', None)
+								wk_ema = getattr(wk, 'ema52', None)
+								if wk_close is not None and wk_ema not in (None, 0):
+									if wk_close < wk_ema:
+										cnt_close_below += 1
+									if wk_low is not None and (wk_low <= wk_ema or abs(wk_low - wk_ema) / abs(wk_ema) <= 0.0015):
+										cnt_touch_down += 1
+							no_down_break = (cnt_close_below <= 1 and cnt_touch_down >= 1 and price >= ema52)
+						# 若当前为UP趋势，出现明显拐头+hist减弱+未能上破EMA52，则加速看空
+						if last_trend == Chan_PRICE_TREND.UP and macd_slope < 0 and signal_slope < 0 and weaken_steps >= 1 and no_up_break and macd_bias_down:
+							score -= 3
+							down_flip_threshold = max(down_flip_threshold, 0)
+							force_flip_down = True
+						# 若当前为DOWN趋势，出现明显拐头+hist减弱+未能下破EMA52，则加速看多
+						if last_trend == Chan_PRICE_TREND.DOWN and macd_slope > 0 and signal_slope > 0 and weaken_steps >= 1 and no_down_break and macd_bias_up:
+							score += 3
+							up_flip_threshold = min(up_flip_threshold, 0)
+							force_flip_up = True
+						# 多次对 EMA52 的拒绝配合 MACD 逆向：加速反向翻转（降低相反方向阈值）
+						if recent_up_rejects >= 2 and macd_bias_down:
+							# 从 UP 向 DOWN 的翻转更容易
+							down_flip_threshold = max(down_flip_threshold, -1)
+						if recent_down_rejects >= 2 and macd_bias_up:
+							# 从 DOWN 向 UP 的翻转更容易
+							up_flip_threshold = min(up_flip_threshold, 1)
+						if force_flip_down:
+							trend = Chan_PRICE_TREND.DOWN
+						elif force_flip_up:
+							trend = Chan_PRICE_TREND.UP
+						elif last_trend == Chan_PRICE_TREND.UP:
+							if score <= down_flip_threshold:
+								trend = Chan_PRICE_TREND.DOWN
+							else:
+								trend = Chan_PRICE_TREND.UP
+						elif last_trend == Chan_PRICE_TREND.DOWN:
+							if score >= up_flip_threshold:
+								trend = Chan_PRICE_TREND.UP
+							else:
+								trend = Chan_PRICE_TREND.DOWN
+						else:
+							# 初始无记忆时，降低进入门槛
+							if score >= 1:
+								trend = Chan_PRICE_TREND.UP
+							elif score <= -1:
+								trend = Chan_PRICE_TREND.DOWN
+							else:
+								trend = Chan_PRICE_TREND.FLAT
+			except Exception:
+				trend = Chan_PRICE_TREND.UNKNOWN
+			# 写回趋势
+			if klc.end_time is None:
+				trend = Chan_PRICE_TREND.FLAT
+			if hasattr(klc, 'set_trend'):
+				klc.set_trend(trend)
+			else:
+				setattr(klc, 'trend', trend)
+			last_trend = trend
+			# 更新滑窗：仅向后看
+			prev_klcs.append(klc)
+			price_diff = klc.close - klc.pre.close if klc.pre else 0
+			#if klc.index > len(klc_list) - 10:
+				#print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff, score)
+				#print(klc.start_time, klc.end_time, klc.trend, price_diff, score)
+		return klc_list
+
+	def get_bi_list(self, dataframe):
+		bi_list = self.cal_bi_list(self.get_klc_list(dataframe))
+		#bi_list = self.cal_bi_list_chanlun(self.get_klc_list(dataframe))
+		return bi_list
+
+	def cal_bi_list(self, klc_list):
+		bi_list = []
+		last_top = None
+		last_bottom = None
+		bi_klc_min = 4
+		last_fx_klc = None
+		for klc in klc_list:
+			if last_fx_klc:
+				klc.check_klc_state(last_fx_klc)
+			klc.check_fx_confirmed(last_top, last_bottom)
+			fx = self.check_fx(klc)
+			if fx == Chan_FX_TYPE.TOP:
+				if last_bottom:
+					if self.check_top_fx(last_bottom, klc) == False:
+						fx = Chan_FX_TYPE.UNKNOWN
+			if fx == Chan_FX_TYPE.BOTTOM:
+				if last_top:
+					if self.check_bottom_fx(last_top, klc) == False:
+						#print(klc.end_time, last_top.end_time, "---")
+						fx = Chan_FX_TYPE.UNKNOWN
+			# Do nothing
+			if fx == Chan_FX_TYPE.UNKNOWN:
+				if len(bi_list) > 0:
+					bi_list[-1].add_klc(klc)
+					klc.set_bi(bi_list[-1])
+				#continue
+				if len(bi_list) > 0 and klc.end_klu:
+					last_bi = bi_list[-1]
+					#print(klc.start_time, last_bi.start_time, last_bi.end_time, last_bi.dir, last_bi.high, last_bi.low, last_bottom.end_time,  "last bi")
+					if last_top and last_bi.dir == Chan_BI_DIR.DOWN:
+						if last_bottom and klc.high > last_bi.high:
+							#print(klc.end_time, "Top 7, 1", last_bi.start_time, klc.high, last_bi.high)
+							#klc.klc_fx_type = Chan_KLC_FX.TOP7
+							#klc.fx = Chan_FX_TYPE.TOP
+							"""
+							last_bi.set_end_klc(last_bottom, klc)
+							bi = ChanBI(last_bottom, len(bi_list), Chan_BI_DIR.UP)
+							#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM7)
+							#klc.bb_out = True
+							last_bi.set_next(bi)
+							bi.set_pre(last_bi)
+							for klc_index in range(last_bi.end_klc.index, len(klc_list)):
+								bi.add_klc(klc_list[klc_index])
+							bi_list.append(bi)
+							last_top = klc
+							klc.set_bi(bi)
+							#print(klc.start_time, bi.start_time, bi.end_time, bi.dir, bi.high, bi.low, bi.is_sure)
+							"""
+					else:
+						if last_bottom and last_bi.dir == Chan_BI_DIR.UP:
+							if last_top and klc.low < last_bi.low:
+								#print(klc.end_time, "Bottom 8, 2", last_bi.start_time)
+								#klc.klc_fx_type = Chan_KLC_FX.BOTTOM8
+								#klc.fx = Chan_FX_TYPE.BOTTOM
+								"""
+								last_bi.set_end_klc(last_top, klc)
+								bi = ChanBI(last_top, len(bi_list), Chan_BI_DIR.DOWN)
+								#klc.set_klc_fx_type(Chan_KLC_FX.TOP6)
+								#klc.bb_out = True
+								last_bi.set_next(bi)
+								bi.set_pre(last_bi)
+								for klc_index in range(last_bi.end_klc.index, len(klc_list)):
+									bi.add_klc(klc_list[klc_index])
+								bi_list.append(bi)
+								last_bottom = klc
+								klc.set_bi(bi)
+								#print(klc.start_time, bi.start_time, bi.end_time, bi.dir, bi.high, bi.low, bi.is_sure)
+								"""
+			else:
+				last_fx_klc = klc
+				if fx == Chan_FX_TYPE.TOP:
+					#print(klc.end_time, fx, klc.pre.high, klc.high, klc.pre.start_time, klc.pre.end_time)
+					if last_top:
+						if last_bottom:
+							#print(klc.start_time, last_bottom.start_time, last_top.start_time)
+							if last_bottom.index < last_top.index:
+								# Second top lower to be second sell point
+								if last_top.high > klc.high:
+									bi_list[-1].add_klc(klc)
+									klc.set_bi(bi_list[-1])
+									#klc.set_klc_fx_type(Chan_KLC_FX.TOP3)
+									#print(klc.end_time, klc.fx, "二类卖点Sell 1")
+								else:
+									# A new top found
+									#last_top.set_fx(Chan_FX_TYPE.UNKNOWN)
+									last_top = klc
+									#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Change 1")
+									klc.set_klc_fx_type(Chan_KLC_FX.TOP1)
+									self.check_fx_pattern(klc)
+									#print(klc.end_time, klc.fx, "一类卖点Sell 1")
+									bi_list[-1].add_klc(klc)
+									klc.set_bi(bi_list[-1])
+							# 不满足结合律的分型
+							else:
+								#klc.set_klc_fx_type(Chan_KLC_FX.TOP0)
+								#print(klc.end_time, klc.klc_fx_type)
+								if last_bottom.index + bi_klc_min > klc.index:
+									if last_top.high > klc.high:
+										#print(klc.start_time, klc.fx, "二类卖点Sell 1")
+										#klc.set_klc_fx_type(Chan_KLC_FX.TOP8)
+										bi_list[-1].add_klc(klc)
+										klc.set_bi(bi_list[-1])
+									# New TOP Found前面的UKNOWN可能出现TOP7，但是这里的也可能出现TOP8分型
+									else:
+										# 顶分型在出现2之前超过前一个笔的顶 TOP8
+										if last_top.index + bi_klc_min < klc.index and len(bi_list) > 1:
+											pre_last_bi = bi_list[-2]
+											last_bi = bi_list[-1]
+											if pre_last_bi.is_sure and not last_bi.is_sure and pre_last_bi.dir == Chan_BI_DIR.UP and False:
+												pre_last_bi.update_bi(klc)
+												bi_list.remove(last_bi)
+												pre_last_bi.set_next(None)
+												#last_top.set_fx(Chan_FX_TYPE.PTOP)
+												last_top = klc
+												last_bottom = pre_last_bi.start_klc
+												#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Bottom Change 1")
+												klc.set_klc_fx_type(Chan_KLC_FX.TOP2)
+												#print(klc.start_time, last_bi.start_klc.start_time, "New TOP Found reset last bi")
+												#klc.set_state("10")
+												#print(klc.start_time, klc.fx, "笔卖点Sell 1")
+											###klc.set_klc_fx_type(Chan_KLC_FX.TOP2) # when bi is down but the fx is top
+											bi_list[-1].add_klc(klc)
+											klc.set_bi(bi_list[-1])
+											#klc.set_klc_fx_type(Chan_KLC_FX.TOP8)
+											#print(klc.start_time, last_bi.start_klc.start_time, "New TOP Found reset last bi")
+										else:
+											#klc.set_fx(Chan_FX_TYPE.PTOP)
+											bi_list[-1].add_klc(klc)
+											klc.set_bi(bi_list[-1])
+											#print(klc.end_time, klc.fx, "无效顶分型")
+								# 满足结合律
+								else:
+									# New Temp TOP and last bottom confirmed ***** confirm last down bi(last bottom and last top)
+									last_bi = bi_list[-1]
+									if not last_bi.is_sure:
+										last_bi.set_end_klc(last_bottom, klc)
+									bi = ChanBI(last_bottom, len(bi_list), Chan_BI_DIR.UP)
+									last_bi.set_next(bi)
+									bi.set_pre(last_bi)
+									bi.add_klc(klc)
+									bi_list.append(bi)
+									last_top = klc
+									#print(klc.end_time, klc.fx, bi_list[-1].dir, "Last Top Change 2")
+									klc.set_klc_fx_type(Chan_KLC_FX.TOP2)
+									self.check_fx_pattern(klc)
+									#bi_list[-1].add_klc(klc)
+									klc.set_bi(bi_list[-1])
+									#print(klc.start_time, last_bottom.start_time, "Normal TOP Found, Confirm down bi 4")
+						# last bottom = None 初始化的时候用，其他时间不用
+						else:
+							# 初始化的时候用，其他时间不用
+							if last_top.high < klc.high:
+								last_bi = bi_list[-1]
+								last_bi.set_start_klc(klc, Chan_BI_DIR.DOWN)
+								last_top = klc
+								#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Change 3")
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+							# 初始化的时候用，其他时间不用
+							else:
+								#klc.set_fx(Chan_FX_TYPE.TT)
+								#print(klc.start_time, klc.fx, "二类卖点Sell 2")
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+					# last_top == None 初始化的时候用，其他时间不用
+					else:
+						if last_bottom:
+							# 不满足结合律的分型
+							if last_bottom.index + bi_klc_min > klc.index:
+								#klc.set_fx(Chan_FX_TYPE.PTOP)
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+								#print(klc.start_time, klc.fx, "中枢卖点Sell 1")
+							else:
+								# First temp top and last bottom confirmed
+								last_top = klc
+								#print(klc.start_time, klc.fx, bi_list[-1].dir,  "Last Top Change 4")
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+						# Last top = None, last bottom = None, create first down bi 初始化的时候用，其他时间不用
+						else:
+							# First temp top
+							last_top = klc
+							bi = ChanBI(klc, len(bi_list), Chan_BI_DIR.DOWN)
+							bi_list.append(bi)
+							bi.add_klc(klc)
+							klc.set_bi(bi_list[-1])
+							#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Change 5")
+				#klc.fx = Bottom ========================
+				else:
+					if last_bottom:
+						if last_top:
+							# Bottom after top and find a new bottom
+							if last_top.index < last_bottom.index:
+								# Second bottom uppper to be second buy point and confirm last bi
+								if last_bottom.low < klc.low:
+									bi_list[-1].add_klc(klc)
+									klc.set_bi(bi_list[-1])
+									#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM3)
+									#print(last_bottom.start_time, last_bottom.end_time, "--------------------------------1")
+									#print(klc.end_time, klc.fx, "二类买点Buy 1")
+								else:
+									# A new bottom found
+									last_bottom = klc
+									#print(klc.end_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 1")
+									klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM1)
+									self.check_fx_pattern(klc)
+									#print(klc.end_time, klc.fx, "一类买点Buy 1")
+									bi_list[-1].add_klc(klc)
+									klc.set_bi(bi_list[-1])
+							# 不满足结合律的分型
+							else:
+								#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM0)
+								#print(klc.end_time, klc.klc_fx_type)
+								if last_top.index + bi_klc_min > klc.index:
+									if last_bottom.low < klc.low:
+										#print(klc.end_time, klc.fx, "中枢买点Buy 1")
+										bi_list[-1].add_klc(klc)
+										klc.set_bi(bi_list[-1])
+										#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM8)
+									# Found new bottom没有意义，上面UNKNOWN的时候已经是笔破坏了
+									else:
+										#print(klc.end_time, last_bottom.end_time, "Found a new bottom")
+										if last_bottom.index + bi_klc_min < klc.index and len(bi_list) > 1:
+											pre_last_bi = bi_list[-2]
+											last_bi = bi_list[-1]
+											if pre_last_bi.is_sure and not last_bi.is_sure and pre_last_bi.dir == Chan_BI_DIR.DOWN and False: 
+												pre_last_bi.update_bi(klc)
+												bi_list.remove(last_bi)
+												pre_last_bi.set_next(None)
+												last_bottom = klc
+												last_top = pre_last_bi.start_klc
+												#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Top Bottom Change 2")
+												klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM2)
+												#print(klc.start_time, last_bi.start_klc.start_time, "New BOTTOM Found reset last bi")
+												#print(klc.start_time, klc.fx, "笔买点Buy 1")
+											###klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM2) # when bi is up but the fx is bottom
+											bi_list[-1].add_klc(klc)
+											klc.set_bi(bi_list[-1])
+											#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM8)
+										else:
+											#klc.set_fx(Chan_FX_TYPE.UNKNOWN)
+											bi_list[-1].add_klc(klc)
+											klc.set_bi(bi_list[-1])
+											print(klc.end_time, klc.fx, "无效底分型")
+								# 满足结合律的分型
+								else:
+									# New Temp Bottom and last top confirmed ***** confirm last up bi(last bottom and last top)
+									last_bi = bi_list[-1]
+									if not last_bi.is_sure:
+										last_bi.set_end_klc(last_top, klc)
+									bi = ChanBI(last_top, len(bi_list), Chan_BI_DIR.DOWN)
+									last_bi.set_next(bi)
+									bi.set_pre(last_bi)
+									bi.add_klc(klc)
+									bi_list.append(bi)
+									last_bottom = klc
+									#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 2")
+									klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM2)
+									self.check_fx_pattern(klc)
+									#bi_list[-1].add_klc(klc)
+									klc.set_bi(bi_list[-1])
+									#print(klc.start_time, last_top.start_time, "Normal Bottom Found, Confirm up bi 6")
+						# last_top = None 初始化的时候用，其他时间不用
+						else:
+							if last_bottom.low > klc.low:
+								last_bi = bi_list[-1]
+								last_bi.set_start_klc(klc, Chan_BI_DIR.UP)
+								#last_bottom.set_fx(Chan_FX_TYPE.UNKNOWN)
+								last_bottom = klc
+								#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 3")
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+								#print(klc.start_time, klc.fx, "笔买点Buy 3")
+							else:
+								#klc.set_fx(Chan_FX_TYPE.BB)
+								#klc.set_state('-20')
+								#print(klc.start_time, klc.fx, "二类买点Buy 2")
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+					# last_bottom = None 初始化的时候用，其他时间不用
+					else:
+						if last_top:
+							# 不满足结合律的分型
+							if last_top.index + bi_klc_min > klc.index:
+								#klc.set_fx(Chan_FX_TYPE.PBOTTOM)
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+								#print(klc.start_time, klc.fx, "中枢买点Buy 1")
+							else:
+								# First temp bottom and last top confirmed
+								last_bottom = klc
+								#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 4")
+								bi_list[-1].add_klc(klc)
+								klc.set_bi(bi_list[-1])
+								#print(klc.start_time, klc.fx, "一类买点Buy 1")
+						# Last top = None, last bottom = None, create first up bi
+						else:
+							# First temp bottom and no top yet
+							last_bottom = klc
+							bi = ChanBI(klc, len(bi_list), Chan_BI_DIR.UP)
+							#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM7)
+							bi_list.append(bi)
+							bi_list[-1].add_klc(klc)
+							klc.set_bi(bi_list[-1])
+							#print(klc.start_time, klc.fx, bi_list[-1].dir, "Last Bottom Change 5")
+							#print(klc.start_time, klc.fx, "笔买点Buy 4")
+		self.get_above_zero_bsp(klc_list)
+		#print(bi_list[-1].start_time, bi_list[-1].end_time, len(bi_list[-1].klc_list))
+		return bi_list
+
+	def check_top_fx(self, last_bottom, klc):
+		if (last_bottom.high > klc.pre.low or last_bottom.high > klc.next.low) and (klc.index - last_bottom.index < 100):
+			return False
+		return True
+	
+
+	def check_bottom_fx(self, last_top, klc):
+		if (last_top.low < klc.pre.high or last_top.low < klc.next.high) and (klc.index - last_top.index < 100):
+			return False
+		return True
+	# 线段内的中枢

chanlun/pipeline/builders/bsp.py

@@ -0,0 +1,412 @@
+"""TF_DF builder mixin — 由 split_tfdf_builders 自动生成，逻辑与原 TF_DF 一致。"""
+from __future__ import annotations
+
+from datetime import timedelta
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+
+
+class BspBuilderMixin:
+	def get_bsp_state(self, dataframe):
+		klu_list = self.get_klu_list(dataframe)
+		klc_list = self.get_klc_list(klu_list)
+		bi_list = self.cal_bi_list(klc_list)
+		seg_list = self.get_seg_list(bi_list)
+		bi_zs_list = self.cal_bi_zs(seg_list)
+		bsp_list = self.find_all_bsp(bi_list, bi_zs_list)
+		bsp_state_list = [0] * len(dataframe)
+		klc_index = 0
+		for index in range(0, len(dataframe)):
+			if klc_index == len(klc_list):
+				klc_index = len(klc_list) - 1
+			klc = klc_list[klc_index]
+			if klc.end_klu and klc.end_klu.idx == index:
+				if klc.klc_fx_type == Chan_KLC_FX.TOP2:
+					bi = klc.bi.pre
+					if bi and bi.is_sure and bi.end_klc.bsp_type == Chan_BSP_TYPE.B3:
+						# 第三类买点
+						bsp_state_list[index] = -1
+						#print(klc.end_time, "B3")
+					else:
+						bsp_state_list[index] = 0
+				elif klc.klc_fx_type == Chan_KLC_FX.BOTTOM2:
+					bi = klc.bi.pre
+					if bi and bi.is_sure and bi.end_klc.bsp_type == Chan_BSP_TYPE.S3:
+						# 第三类卖点
+						bsp_state_list[index] = 1
+						#print(klc.end_time, "S3")
+					else:
+						bsp_state_list[index] = 0
+				klc_index += 1
+			else:
+				bsp_state_list[index] = 0
+		return bsp_state_list
+
+	def get_above_zero_bsp(self, klc_list):
+		buy_bsp_list = []
+		sell_bsp_list = []
+		above_zero = False
+		buy_bsp = None
+		sell_bsp = None
+		for klc in klc_list:
+			if klc.pre and klc.pre.signal < 0 and klc.signal > 0:
+				above_zero = True
+			if klc.pre and klc.pre.signal > 0 and klc.signal < 0:
+				above_zero = False
+			if above_zero and klc.klc_fx_type == Chan_KLC_FX.BOTTOM2 and klc.macd > 0:
+				buy_bsp = klc
+				buy_bsp_list.append(klc)
+				#print(klc.end_time, "MACD 0轴上穿，回调笔底分型做多")
+			if buy_bsp and klc.pre and klc.pre.macdhist > 0 and klc.macdhist < 0:
+				sell_bsp = klc
+				sell_bsp_list.append(klc)
+				buy_bsp = None
+				#print(klc.end_time, "Sell BSP Found")
+		return buy_bsp_list
+
+	def find_all_bsp(self, bi_list, bi_zs_list):
+		"""
+		笔中枢的三类买卖点识别
+
+		三类买点：中枢形成后，一笔向上离开中枢（低点 > zg），
+				 随后回拉的一笔低点不跌回中枢（低点 >= zg），确认支撑有效。
+		三类卖点：中枢形成后，一笔向下离开中枢（高点 < zd），
+				 随后反弹的一笔高点不回到中枢（高点 <= zd），确认压力有效。
+
+		参数:
+			bi_list: 笔列表
+			bi_zs_list: 笔中枢列表（二维列表，每个seg内的中枢列表）
+
+		返回:
+			bsp_list: ChanBSP 列表，包含所有识别到的三类买卖点
+		"""
+		bsp_list = []
+		if len(bi_list) < 4 or len(bi_zs_list) == 0:
+			return bsp_list
+
+		for zs in bi_zs_list:
+			if not zs.is_sure or len(zs.bi_list) < 3:
+				continue
+			#print(zs.start_time, zs.end_time, zs.dir, zs.is_sure, len(zs.bi_list))
+			# 中枢结束后的第一笔（离开笔）
+			last_zs_bi = zs.bi_list[-1]
+			if last_zs_bi.dir == Chan_BI_DIR.UP:
+				if last_zs_bi.is_sure and last_zs_bi.end_klc.high <= zs.zg or (last_zs_bi.next and last_zs_bi.next.is_sure and last_zs_bi.next.end_klc.low < zs.zd):
+					leave_bi = last_zs_bi.next
+				else:
+					leave_bi = last_zs_bi
+			else:
+				if last_zs_bi.is_sure and last_zs_bi.end_klc.low >= zs.zd or (last_zs_bi.next and last_zs_bi.next.is_sure and last_zs_bi.next.end_klc.high > zs.zg):
+					leave_bi = last_zs_bi.next
+				else:
+					leave_bi = last_zs_bi
+			#print(zs.zg, zs.zd)
+			if leave_bi is None or not leave_bi.is_sure:
+				continue
+			if (zs.dir == Chan_ZS_DIR.UP and leave_bi.dir == Chan_BI_DIR.UP and leave_bi.end_klc.high < zs.zg and leave_bi.end_klc.high > zs.zd) or (zs.dir == Chan_ZS_DIR.DOWN and leave_bi.dir == Chan_BI_DIR.DOWN and leave_bi.end_klc.low < zs.zg and leave_bi.end_klc.low > zs.zd):
+				#print("--------------------", leave_bi.dir, leave_bi.end_klc.high, leave_bi.end_klc.low, zs.zg, zs.zd)
+				leave_bi = leave_bi.next
+			# 三类买点：向上离开中枢后回拉不破 zg
+			#print("Leave bi:", leave_bi.start_time, leave_bi.end_time, leave_bi.dir, leave_bi.is_sure, leave_bi.low, leave_bi.high)
+			if leave_bi.dir == Chan_BI_DIR.UP:
+				first_bsp_bi_div = self.check_bi_div(zs, leave_bi)
+				# 确认一类卖点：离开断能量小于进入段能量
+				if first_bsp_bi_div:
+					bsp = ChanBSP(
+						leave_bi, len(bsp_list),
+						Chan_BSP_TYPE.S1,
+						Chan_BSP_DIR.SELL,
+						leave_bi.sure_time,
+						zs.index+1, zs, None
+					)
+					leave_bi.end_klc.set_bsp_type(Chan_BSP_TYPE.S1)
+					bsp_list.append(bsp)
+				# 回拉笔
+				pullback_bi = leave_bi.next
+				#print(pullback_bi.start_klc.start_time, pullback_bi.dir, pullback_bi.is_sure, pullback_bi.low, pullback_bi.high)
+				if pullback_bi and pullback_bi.is_sure and pullback_bi.dir == Chan_BI_DIR.DOWN:
+					if pullback_bi.low >= zs.zg:
+						# 确认三类买点：回拉笔的低点不跌回中枢
+						bsp = ChanBSP(
+							pullback_bi, len(bsp_list),
+							Chan_BSP_TYPE.B3,
+							Chan_BSP_DIR.BUY,
+							pullback_bi.sure_time,
+							zs.index+1, zs, None
+						)
+						pullback_bi.end_klc.set_bsp_type(Chan_BSP_TYPE.B3)
+						bsp_list.append(bsp)
+				# 二类卖点
+				if first_bsp_bi_div:
+					second_bsp_bi = pullback_bi.next
+					if second_bsp_bi and second_bsp_bi.is_sure and second_bsp_bi.end_klc.high < leave_bi.end_klc.high:
+						# 确认二类卖点：一类卖点后回拉不超过一类卖点高点
+						bsp = ChanBSP(
+							second_bsp_bi, len(bsp_list),
+							Chan_BSP_TYPE.S2,
+							Chan_BSP_DIR.SELL,
+							second_bsp_bi.sure_time,
+							zs.index+1, zs, None
+						)
+						second_bsp_bi.end_klc.set_bsp_type(Chan_BSP_TYPE.B2)
+						bsp_list.append(bsp)
+			# 三类卖点：向下离开中枢后反弹不破 zd
+			elif leave_bi.dir == Chan_BI_DIR.DOWN:
+				first_bsp_bi_div = self.check_bi_div(zs, leave_bi)
+				# 确认一类买点：离开段能量小于进入段
+				if first_bsp_bi_div:
+					bsp = ChanBSP(
+						leave_bi, len(bsp_list),
+						Chan_BSP_TYPE.B1,
+						Chan_BSP_DIR.BUY,
+						leave_bi.sure_time,
+						zs.index+1, zs, None
+					)
+					leave_bi.end_klc.set_bsp_type(Chan_BSP_TYPE.B1)
+					bsp_list.append(bsp)
+				# 反弹笔
+				bounce_bi = leave_bi.next
+				#print(bounce_bi.start_klc.start_time, bounce_bi.dir, bounce_bi.is_sure, bounce_bi.low, bounce_bi.high)
+				if bounce_bi and bounce_bi.is_sure and bounce_bi.dir == Chan_BI_DIR.UP:
+					if bounce_bi.high <= zs.zd:
+						# 确认三类卖点：反弹笔的高点不回到中枢
+						bsp = ChanBSP(
+							bounce_bi, len(bsp_list),
+							Chan_BSP_TYPE.S3,
+							Chan_BSP_DIR.SELL,
+							bounce_bi.sure_time,
+							zs.index+1, zs, None
+						)
+						bounce_bi.end_klc.set_bsp_type(Chan_BSP_TYPE.S3)
+						bsp_list.append(bsp)
+				# 二类卖点
+				if first_bsp_bi_div:
+					second_bsp_bi = bounce_bi.next
+					if second_bsp_bi and second_bsp_bi.is_sure and second_bsp_bi.end_klc.low > leave_bi.end_klc.low:
+						# 确认二类买点：一类买点后回拉不超过一类卖点高点
+						bsp = ChanBSP(
+							second_bsp_bi, len(bsp_list),
+							Chan_BSP_TYPE.B2,
+							Chan_BSP_DIR.BUY,
+							second_bsp_bi.sure_time,
+							zs.index+1, zs, None
+						)
+						second_bsp_bi.end_klc.set_bsp_type(Chan_BSP_TYPE.B2)
+						bsp_list.append(bsp)
+		return bsp_list
+
+	def check_bi_div(self, zs, leave_bi):
+		enter_bi = zs.bi_list[0].pre
+		macdhist_div = 0
+		if enter_bi and enter_bi.dir == leave_bi.dir:
+			macdhist_div = abs(leave_bi.macd_hist) - abs(enter_bi.macd_hist)
+		#print(enter_bi.end_time, leave_bi.end_time, macdhist_div < 0)
+		return macdhist_div < 0
+
+	def find_first_bsp(self, bi_list, bi_zs_list):
+		"""
+		笔中枢的一类买卖点识别
+
+		一类买点：下跌趋势中，最后一个中枢完成后，向下离开中枢的笔创新低，
+				 但该笔与进入中枢前的最后一笔下跌形成底背驰（力度减弱），
+				 即趋势力竭的转折点。
+		一类卖点：上涨趋势中，最后一个中枢完成后，向上离开中枢的笔创新高，
+				 但该笔与进入中枢前的最后一笔上涨形成顶背驰（力度减弱），
+				 即趋势力竭的转折点。
+
+		简化判断：中枢形成后，离开中枢的笔（突破笔）本身即为一类买卖点的触发笔。
+
+		参数:
+			bi_list: 笔列表
+			bi_zs_list: 笔中枢列表（扁平列表，每个元素是一个中枢对象）
+
+		返回:
+			bsp_list: ChanBSP 列表，包含所有识别到的一类买卖点
+		"""
+		bsp_list = []
+		if len(bi_list) < 4 or len(bi_zs_list) == 0:
+			return bsp_list
+
+		for zs in bi_zs_list:
+			if not zs.is_sure or len(zs.bi_list) < 3:
+				continue
+
+			# 找到中枢的最后一笔
+			last_zs_bi = zs.bi_list[-1]
+
+			# 确定离开笔：中枢最后一笔之后的第一笔
+			if last_zs_bi.dir == Chan_BI_DIR.UP:
+				# 中枢最后一笔向上，如果没有真正离开中枢，取下一笔
+				if last_zs_bi.is_sure and last_zs_bi.end_klc.high <= zs.zg:
+					leave_bi = last_zs_bi.next
+				else:
+					leave_bi = last_zs_bi
+			else:
+				# 中枢最后一笔向下，如果没有真正离开中枢，取下一笔
+				if last_zs_bi.is_sure and last_zs_bi.end_klc.low >= zs.zd:
+					leave_bi = last_zs_bi.next
+				else:
+					leave_bi = last_zs_bi
+
+			if leave_bi is None or not leave_bi.is_sure:
+				continue
+
+			# 一类买点：向下离开中枢（leave_bi向下，低点 < zd），趋势力竭
+			if leave_bi.dir == Chan_BI_DIR.DOWN and leave_bi.low < zs.zd:
+				# 背驰判断：比较离开笔与中枢内最后一笔同向笔的MACD柱状累积面积
+				# 缠论原文：两段同向走势的MACD柱状面积比较，面积缩小即为背驰
+				compare_bi = None
+				for bi in reversed(zs.bi_list):
+					if bi.dir == Chan_BI_DIR.DOWN and bi is not leave_bi:
+						compare_bi = bi
+						break
+
+				is_divergence = False
+				if compare_bi:
+					# 笔的macd_hist是该笔内所有KLU的macdhist累积面积
+					leave_macd_area = abs(leave_bi.macd_hist)
+					compare_macd_area = abs(compare_bi.macd_hist)
+
+					# 价格创新低但MACD面积缩小 = 底背驰
+					if leave_bi.low <= compare_bi.low and leave_macd_area < compare_macd_area:
+						is_divergence = True
+					# 即使没创新低，MACD面积明显缩小也算背驰
+					elif leave_macd_area < compare_macd_area * 0.5:
+						is_divergence = True
+				else:
+					# 没有对比笔时，只要离开中枢就算一类买点
+					is_divergence = True
+
+				if is_divergence:
+					bsp = ChanBSP(
+						leave_bi, len(bsp_list),
+						Chan_BSP_TYPE.T1,
+						Chan_BSP_DIR.BUY,
+						leave_bi.sure_time,
+						1, zs, None
+					)
+					bsp_list.append(bsp)
+
+			# 一类卖点：向上离开中枢（leave_bi向上，高点 > zg），趋势力竭
+			elif leave_bi.dir == Chan_BI_DIR.UP and leave_bi.high > zs.zg:
+				# 背驰判断：比较离开笔与中枢内最后一笔同向笔的MACD柱状累积面积
+				compare_bi = None
+				for bi in reversed(zs.bi_list):
+					if bi.dir == Chan_BI_DIR.UP and bi is not leave_bi:
+						compare_bi = bi
+						break
+
+				is_divergence = False
+				if compare_bi:
+					leave_macd_area = abs(leave_bi.macd_hist)
+					compare_macd_area = abs(compare_bi.macd_hist)
+
+					# 价格创新高但MACD面积缩小 = 顶背驰
+					if leave_bi.high >= compare_bi.high and leave_macd_area < compare_macd_area:
+						is_divergence = True
+					# 即使没创新高，MACD面积明显缩小也算背驰
+					elif leave_macd_area < compare_macd_area * 0.5:
+						is_divergence = True
+				else:
+					is_divergence = True
+
+				if is_divergence:
+					bsp = ChanBSP(
+						leave_bi, len(bsp_list),
+						Chan_BSP_TYPE.T1,
+						Chan_BSP_DIR.SELL,
+						leave_bi.sure_time,
+						1, zs, None
+					)
+					bsp_list.append(bsp)
+
+		return bsp_list
+
+
+	def find_second_bsp(self, bi_list, first_bsp_list):
+		"""
+		笔中枢的二类买卖点识别
+
+		二类买点：一类买点出现后，价格向上反弹一笔，再回落一笔，
+				 回落笔的低点不跌破一类买点的低点，确认底部成立。
+		二类卖点：一类卖点出现后，价格向下回落一笔，再反弹一笔，
+				 反弹笔的高点不超过一类卖点的高点，确认顶部成立。
+
+		参数:
+			bi_list: 笔列表
+			first_bsp_list: 一类买卖点列表（find_first_bsp 的返回值）
+
+		返回:
+			bsp_list: ChanBSP 列表，包含所有识别到的二类买卖点
+		"""
+		bsp_list = []
+		if not first_bsp_list or len(bi_list) < 4:
+			return bsp_list
+
+		for first_bsp in first_bsp_list:
+			trigger_bi = first_bsp.bi  # 一类买卖点的触发笔
+
+			if first_bsp.dir == Chan_BSP_DIR.BUY:
+				# 一买之后：trigger_bi 向下 -> 反弹笔(向上) -> 回落笔(向下)
+				# 回落笔的低点 > trigger_bi 的低点 => 二类买点
+				bounce_bi = trigger_bi.next  # 反弹笔（向上）
+				if bounce_bi and bounce_bi.is_sure and bounce_bi.dir == Chan_BI_DIR.UP:
+					pullback_bi = bounce_bi.next  # 回落笔（向下）
+					if pullback_bi and pullback_bi.is_sure and pullback_bi.dir == Chan_BI_DIR.DOWN:
+						if pullback_bi.low > trigger_bi.low:
+							bsp = ChanBSP(
+								pullback_bi, len(bsp_list),
+								Chan_BSP_TYPE.T2,
+								Chan_BSP_DIR.BUY,
+								pullback_bi.sure_time,
+								1, first_bsp.zs, None
+							)
+							bsp_list.append(bsp)
+
+			elif first_bsp.dir == Chan_BSP_DIR.SELL:
+				# 一卖之后：trigger_bi 向上 -> 回落笔(向下) -> 反弹笔(向上)
+				# 反弹笔的高点 < trigger_bi 的高点 => 二类卖点
+				drop_bi = trigger_bi.next  # 回落笔（向下）
+				if drop_bi and drop_bi.is_sure and drop_bi.dir == Chan_BI_DIR.DOWN:
+					bounce_bi = drop_bi.next  # 反弹笔（向上）
+					if bounce_bi and bounce_bi.is_sure and bounce_bi.dir == Chan_BI_DIR.UP:
+						if bounce_bi.high < trigger_bi.high:
+							bsp = ChanBSP(
+								bounce_bi, len(bsp_list),
+								Chan_BSP_TYPE.T2,
+								Chan_BSP_DIR.SELL,
+								bounce_bi.sure_time,
+								1, first_bsp.zs, None
+							)
+							bsp_list.append(bsp)
+
+		return bsp_list

chanlun/pipeline/builders/indicators.py

@@ -0,0 +1,133 @@
+"""TF_DF builder mixin — 由 split_tfdf_builders 自动生成，逻辑与原 TF_DF 一致。"""
+from __future__ import annotations
+
+from datetime import timedelta
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+
+
+class IndicatorsBuilderMixin:
+	def get_ema52(self, index=-1):
+		if self.klu_list:
+			ema52_value = self.klu_list[index].ema52
+			# 处理NaN值
+			if pd.isna(ema52_value) or ema52_value is None:
+				return None
+			return float(ema52_value)
+		return None
+
+	def get_ema24(self, index=-1):
+		if self.klu_list:
+			ema24_value = self.klu_list[index].ema24
+			# 处理NaN值
+			if pd.isna(ema24_value) or ema24_value is None:
+				return None
+			return float(ema24_value)
+		return None
+
+	def add_indicators(self, df):
+		fast = 12
+		slow = 26
+		period = 9
+		macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
+		bb365 = ta.BBANDS(df, timeperiod=365, nbdevup=3.0, nbdevdn=3.0, matype=0)
+		bb120 = ta.BBANDS(df, timeperiod=120, nbdevup=3.0, nbdevdn=3.0, matype=0)
+		bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
+		bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
+		bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
+		bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
+		bb2633 = ta.BBANDS(df, timeperiod=26, nbdevup=3.0, nbdevdn=3.0, matype=0)
+		# 计算布林带中轨（移动平均线）
+		bb30_middle = ta.SMA(df, timeperiod=90)
+		
+		# 手动计算布林带 %B 指标 (BBP)
+		# %B = (Price - Lower Band) / (Upper Band - Lower Band)
+		bbp365 = (df['close'] - bb365['lowerband']) / (bb365['upperband'] - bb365['lowerband'])
+		bbp120 = (df['close'] - bb120['lowerband']) / (bb120['upperband'] - bb120['lowerband'])
+		bbp30 = (df['close'] - bb30['lowerband']) / (bb30['upperband'] - bb30['lowerband'])
+		bbp302 = (df['close'] - bb302['lowerband']) / (bb302['upperband'] - bb302['lowerband'])
+		bbp2633 = (df['close'] - bb2633['lowerband']) / (bb2633['upperband'] - bb2633['lowerband'])
+		df['bb2633upper'] = bb2633['upperband']
+		df['bb2633lower'] = bb2633['lowerband']
+		df['bbp2633'] = bbp2633
+		df['bb2633middle'] = bb2633['middleband']
+		df['atr'] = ta.ATR(df, timeperiod=14)
+		df['bbup365'] = bb365['upperband']
+		df['bblow365'] = bb365['lowerband']
+		df['bbp365'] = bbp365
+		df['bbup120'] = bb120['upperband']
+		df['bblow120'] = bb120['lowerband']
+		df['bbp120'] = bbp120
+		df['bbup30'] = bb30['upperband']
+		df['bblow30'] = bb30['lowerband']
+		df['bbmiddle30'] = bb30_middle  # 添加bb30中轨
+		df['bbp30'] = bbp30
+		df['bbup302'] = bb302['upperband']
+		df['bblow302'] = bb302['lowerband']
+		df['bbp302'] = bbp302
+		df['macd'] = macd['macd']
+		df['macdsignal'] = macd['macdsignal']
+		df['macdhist'] = macd['macdhist']
+		df['ema5'] = ta.EMA(df, timeperiod=5)
+		df['ema10'] = ta.EMA(df, timeperiod=10)
+		df['ema24'] = ta.EMA(df, timeperiod=24)
+		df['ema52'] = ta.EMA(df, timeperiod=52)
+		df['ema104'] = ta.EMA(df, timeperiod=104)
+		df['ema156'] = ta.EMA(df, timeperiod=156)
+		df['ema208'] = ta.EMA(df, timeperiod=208)
+		df['ema26'] = ta.EMA(df, timeperiod=26)
+		df['ema13'] = ta.EMA(df, timeperiod=13)
+		df['ema7'] = ta.EMA(df, timeperiod=7)
+		df['rsi'] = ta.RSI(df, timeperiod=14)
+		df['volume_ratio'] = self.cal_volume_ratio(df)
+		return df
+
+	def get_ema_state(self, dataframe):
+		klu_list = self.get_klu_list(dataframe)
+		klc_list = self.get_klc_list(klu_list)
+		bi_list = self.cal_bi_list(klc_list)
+		klu_state_list = []
+		for klu in klu_list:
+			if klu.near0_return == 1:
+				klu_state_list.append("1")
+			elif klu.near0_return == 9:
+				klu_state_list.append("-1")
+			elif klu.candle_dir == Chan_K_DIR.BULL:
+				klu_state_list.append("2")
+			elif klu.candle_dir == Chan_K_DIR.BEAR:
+				klu_state_list.append("-2")
+			else:
+				klu_state_list.append("0")
+		return klu_state_list
+
+	def get_decimal(self, value):
+		return Decimal("{:.2f}".format(value))

chanlun/pipeline/builders/kline.py

@@ -0,0 +1,549 @@
+"""TF_DF builder mixin — 由 split_tfdf_builders 自动生成，逻辑与原 TF_DF 一致。"""
+from __future__ import annotations
+
+from datetime import timedelta
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+
+
+class KlineBuilderMixin:
+	def get_klu_state(self, dataframe):
+		klc_list = self.get_klc_list(self.get_klu_list(dataframe))
+		bi_list = self.cal_bi_list(klc_list)
+		klu_state_list = []
+		klc_index = 0
+		for index in range(0, len(dataframe)):
+			if klc_index == len(klc_list):
+				klc_index = len(klc_list) - 1
+			klc = klc_list[klc_index]
+			if klc.end_klu and klc.end_klu.idx == index:
+				if klc.klc_state == Chan_KLC_STATE.S10:
+					klu_state_list.append("10")
+					#print(klc.end_time, klc.klc_fx_type)
+				elif klc.klc_state == Chan_KLC_STATE.S_10:
+					klu_state_list.append("-10")
+					#print(klc.end_time, klc.klc_fx_type)
+				elif klc.klc_state == Chan_KLC_STATE.S11:
+					klu_state_list.append("11")
+					#print(klc.end_time, klc.klc_fx_type)
+				elif klc.klc_state == Chan_KLC_STATE.S_11:
+					klu_state_list.append("-11")
+					#print(klc.end_time, klc.klc_fx_type)
+				else:
+					klu_state_list.append("00")
+				klc_index += 1
+			else:
+				klu_state_list.append("00")
+		print(klu_state_list[:20])
+		return klu_state_list
+
+
+	def check_fx1(self, klc):
+		if klc.pre and klc.next:
+			if klc.high > klc.pre.high and klc.high > klc.next.high and klc.low > klc.pre.low and klc.low > klc.next.low:
+				if klc.pre.pre and klc.next.next:
+					if klc.high > klc.pre.pre.high and klc.high > klc.next.next.high:
+						#if (klc.close > klc.ema52 or klc.next.close > klc.next.ema52) and klc.macd > 0:
+						klc.set_fx(Chan_FX_TYPE.TOP)
+						#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "TOP")
+						return Chan_FX_TYPE.TOP
+			elif klc.low < klc.pre.low and klc.low < klc.next.low and klc.high < klc.pre.high and klc.high < klc.next.high:
+				#if (klc.close < klc.ema52 or klc.next.close < klc.next.ema52) and klc.macd < 0:
+				if klc.pre.pre and klc.next.next:
+					if klc.low < klc.pre.pre.low and klc.low < klc.next.next.low:
+						klc.set_fx(Chan_FX_TYPE.BOTTOM)
+						#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "BOTTOM")
+						return Chan_FX_TYPE.BOTTOM
+		return Chan_FX_TYPE.UNKNOWN
+
+	def check_fx(self, klc):
+		if klc.pre and klc.next:
+			if klc.high > klc.pre.high and klc.high > klc.next.high and klc.low > klc.pre.low and klc.low > klc.next.low:
+				#if (klc.close > klc.ema52 or klc.next.close > klc.next.ema52) and klc.macd > 0:
+				klc.set_fx(Chan_FX_TYPE.TOP)
+				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "TOP")
+				return Chan_FX_TYPE.TOP
+			elif klc.low < klc.pre.low and klc.low < klc.next.low and klc.high < klc.pre.high and klc.high < klc.next.high:
+				#if (klc.close < klc.ema52 or klc.next.close < klc.next.ema52) and klc.macd < 0:
+				klc.set_fx(Chan_FX_TYPE.BOTTOM)
+				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "BOTTOM")
+				return Chan_FX_TYPE.BOTTOM
+		return Chan_FX_TYPE.UNKNOWN
+
+	def check_fx2(self, klc):
+		if klc.pre and klc.next:
+			if klc.high > klc.pre.close and klc.close > klc.next.close and klc.close > klc.pre.close and klc.close > klc.next.close:
+				#if (klc.close > klc.ema52 or klc.next.close > klc.next.ema52) and klc.macd > 0:
+				klc.set_fx(Chan_FX_TYPE.TOP)
+				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "TOP")
+				return Chan_FX_TYPE.TOP
+			elif klc.low < klc.pre.close and klc.close < klc.next.close and klc.close < klc.pre.close and klc.close < klc.next.close:
+				#if (klc.close < klc.ema52 or klc.next.close < klc.next.ema52) and klc.macd < 0:
+				klc.set_fx(Chan_FX_TYPE.BOTTOM)
+				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "BOTTOM")
+				return Chan_FX_TYPE.BOTTOM
+		return Chan_FX_TYPE.UNKNOWN
+
+	def check_fx_pattern(self, klc):
+		klu_list = klc.pre.klu_list + klc.klu_list + klc.next.klu_list
+		
+		self.cal_klu_pattern(klu_list)
+		p = ""
+		for klu in klu_list:
+			p += klu.to_string()
+		#print(p)
+
+	def cal_volume_ratio(self, dataframe, window=10):
+		df = dataframe.copy()
+		# 计算过去N根K线的平均成交量
+		df['avg_volume'] = df['volume'].rolling(window=window).mean()
+		# 计算量比
+		df['volume_ratio'] = df['volume'] / df['avg_volume']
+		# 填充缺失值（前N根K线）
+		df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
+		return df['volume_ratio']
+
+	def cal_kl_data(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		klu_list = []
+		last_klu = None
+		for i in range(0, len(dataframe)):
+			item = dataframe.iloc[i]
+			date = item['date']
+			o = item['open']
+			h = item['high']
+			l = item['low']
+			c = item['close']
+			v = item['volume']
+			# time_obj = date.fromtimestamp(date)
+			# date = date + timedelta(hours=8)
+			time_str = date.strftime('%Y-%m-%d %H:%M:%S')
+			item_data = [
+				time_str,
+				o,
+				h,
+				l,
+				c,
+				v
+			]
+			# klu =  KLU(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)))
+			klu = ChanKLU(time_str, o, h, l, c, v)
+			# print(klu.time, klu.open, klu.high, klu.low, klu.close, klu.volume)	
+			klu.set_idx(i)
+			klu_list.append(klu)
+			if last_klu:
+				last_klu.set_next(klu)
+				klu.set_pre(last_klu)
+			last_klu = klu
+			if 'macd' in item:
+				klu.set_indicators(item)
+		return klu_list
+
+	def get_kl_data(self, dataframe:DataFrame):
+		return self.cal_kl_data(dataframe)
+
+	def get_klc_list(self, klu_list):
+		klc_list = []
+		last_klu = None
+		# ChanMACD.__init__ 已调用 cal_macd_state，切勿再调一次（会重复堆积 seg/unittf）
+		macd = ChanMACD(klu_list)
+		klu_list = macd.klu_list
+		self._last_chan_macd = macd
+		ema_up_list = []
+		ema_down_list = []
+		ema_up_count = 0
+		ema_down_count = 0
+		last_klu = None
+		for klu in klu_list:
+			ema = klu.ema52
+			last_ema = last_klu.ema52 if last_klu else 0
+			if klu.close >= ema:
+				ema_up_count += 1
+			elif klu.close < ema:
+				ema_down_count += 1
+			if last_klu and last_klu.close >= last_ema and klu.close < ema:
+				ema_up_list.append(ema_up_count)
+				#print(last_klu.time, ema_up_count, "UP END")
+				ema_up_count = 0
+			elif last_klu and last_klu.close < last_ema and klu.close >= ema:
+				ema_down_list.append(ema_down_count)
+				#print(last_klu.time, ema_down_count, "DOWN END")
+				ema_down_count = 0
+			if len(klc_list) > 0:
+				last_klc = klc_list[-1]
+				if klu.exception:
+					ddir = Chan_KLINE_DIR.DOWN
+					if last_klc.high < klu.high:
+						ddir = Chan_KLINE_DIR.UP
+					klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
+					klc.high = klu.close if klu.close > klu.open else klu.open
+					klc.low = klu.open if klu.close > klu.open else klu.close
+					klc_list.append(klc)
+					last_klc.set_next(klc)
+					klc.set_pre(last_klc)
+					last_klc.set_end_klu(last_klu)
+					klc.set_pre_fx()
+					#print(klu.time, klu.high, klu.low, klu.close, klu.open, klu.exception)
+				else:
+					included = last_klc.check_klu_included(klu)
+					if not included:
+						ddir = Chan_KLINE_DIR.DOWN
+						if last_klc.high < klu.high:
+							ddir = Chan_KLINE_DIR.UP
+						klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
+						klc_list.append(klc)
+						last_klc.set_next(klc)
+						klc.set_pre(last_klc)
+						last_klc.set_end_klu(last_klu)
+						klc.set_pre_fx()
+					else:
+						last_klc.add_klu(klu)
+			else:
+				ddir = Chan_KLINE_DIR.UP
+				if klu.open > klu.close:
+					ddir = Chan_KLINE_DIR.DOWN     
+				klc = ChanKLC(klu, 0, ddir)
+				klc_list.append(klc)
+			last_klu = klu
+		klc_list = self.cal_trend(klc_list)
+		#print(ema52_up_list, ema52_down_list)
+		return klc_list
+
+
+	def get_klu_list(self, dataframe):
+		klu_list = self.get_kl_data(dataframe)
+		#klu_list = self.cal_klu_pattern(klu_list)
+		return klu_list
+
+	def cal_klu_pattern(self, klu_list):
+		"""
+		计算裸K的pattern - 识别反转形态
+		"""
+		if not klu_list or len(klu_list) < 3:
+			return klu_list
+			
+		for i, klu in enumerate(klu_list):
+			# 单根K线反转模式识别
+			self._detect_single_reversal_pattern(klu)
+			
+			# 双根K线形态识别
+			if i >= 1:
+				self._detect_double_pattern(klu_list[i-1], klu)
+			
+			# 三根K线形态识别
+			if i >= 2:
+				self._detect_triple_pattern(klu_list[i-2], klu_list[i-1], klu)
+			
+			#if klu.pattern != Chan_KLU_PATTERN.UNKNOWN:
+				#print(klu.time, klu.pattern, klu.lower_shadow_ratio, klu.upper_shadow_ratio, klu.body_ratio, klu.lower_shadow_ratio/klu.body_ratio, klu.upper_shadow_ratio/klu.body_ratio)
+		return klu_list
+	
+
+	def _detect_single_reversal_pattern(self, klu):
+		"""检测单根K线反转模式"""
+		body = abs(klu.close - klu.open)
+		upper_shadow = klu.high - max(klu.close, klu.open)
+		lower_shadow = min(klu.close, klu.open) - klu.low
+		total_range = klu.high - klu.low
+		
+		# 避免除零
+		if total_range == 0:
+			return
+		
+		body_ratio = body / total_range
+		upper_ratio = upper_shadow / total_range
+		lower_ratio = lower_shadow / total_range
+		#print(klu.time, upper_ratio, lower_ratio, body_ratio, upper_ratio/body_ratio, lower_ratio/body_ratio)
+		# 避免body_ratio为0时的除零错误
+		if body_ratio == 0:
+			return
+		# 锤子线/上吊线 - 反转信号
+		if lower_ratio / body_ratio >= 2:
+			# 锤子线：底部反转，需要前面一段
+			if klu.close > klu.open and klu.pre:
+				klu.set_pattern(Chan_KLU_PATTERN.HAMMER)  # 底部反转
+			# 上吊线：顶部反转，需要前一根是上涨趋势
+			elif klu.close < klu.open and klu.pre:
+				klu.set_pattern(Chan_KLU_PATTERN.HANGING_MAN)  # 顶部反转
+		
+		# 倒锤子线/射击之星 - 反转信号
+		elif upper_ratio / body_ratio >= 2:
+			# 倒锤子线：底部反转，需要前一根是下跌趋势
+			if klu.close > klu.open and klu.pre:
+				klu.set_pattern(Chan_KLU_PATTERN.INVERTED_HAMMER)  # 底部反转
+			# 射击之星：顶部反转，需要前一根是上涨趋势
+			elif klu.close < klu.open and klu.pre:
+				klu.set_pattern(Chan_KLU_PATTERN.SHOOTING_STAR)  # 顶部反转
+		
+		# 十字星 - 反转信号
+		elif body_ratio <= 0.1:
+			if upper_ratio > 0.4 and lower_ratio > 0.4:
+				klu.set_pattern(Chan_KLU_PATTERN.LONG_LEGGED_DOJI)  # 强烈反转信号
+			elif upper_ratio > 0.4 and lower_ratio <= 0.1:
+				# 墓碑十字星：顶部反转，需要前一根是上涨趋势
+				if klu.pre and klu.pre.close > klu.pre.open:
+					klu.set_pattern(Chan_KLU_PATTERN.GRAVESTONE_DOJI)  # 顶部反转
+			elif lower_ratio > 0.4 and upper_ratio <= 0.1:
+				# 蜻蜓十字星：底部反转，需要前一根是下跌趋势
+				if klu.pre and klu.pre.close < klu.pre.open:
+					klu.set_pattern(Chan_KLU_PATTERN.DRAGONFLY_DOJI)  # 底部反转
+			else:
+				klu.set_pattern(Chan_KLU_PATTERN.DOJI)  # 一般反转信号
+	
+
+	def _detect_double_pattern(self, prev_klu, curr_klu):
+		"""检测两根K线形成的形态
+		包括：吞没形态(看涨/看跌)、乌云盖顶、曙光初现
+		"""
+		# 如果前一根K线已经有形态，不再识别双K线形态
+		if prev_klu.pattern != Chan_KLU_PATTERN.UNKNOWN:
+			return
+		
+		# 计算K线实体
+		prev_body = abs(prev_klu.close - prev_klu.open)
+		curr_body = abs(curr_klu.close - curr_klu.open)
+		
+		# 判断K线颜色（阴阳）
+		prev_bullish = prev_klu.close > prev_klu.open
+		curr_bullish = curr_klu.close > curr_klu.open
+		
+		# 检查是否存在长期趋势（至少需要5根K线的趋势）
+		def check_long_trend(klu, bullish_trend=True, min_bars=5):
+			"""检查是否存在长期趋势
+			bullish_trend=True: 检查上涨趋势
+			bullish_trend=False: 检查下跌趋势
+			min_bars: 最少需要多少根K线形成趋势
+			"""
+			if not klu or not klu.pre:
+				return False
+			return True
+				
+			# 使用EMA指标判断长期趋势
+			if klu.ema52 > 0:
+				if bullish_trend and klu.close < klu.ema52:
+					return False
+				if not bullish_trend and klu.close > klu.ema52:
+					return False
+			
+			# 检查连续的K线方向
+			count = 0
+			current = klu.pre
+			
+			while current and count < min_bars:
+				if not current.pre:
+					break
+					
+				if bullish_trend:
+					# 上涨趋势：当前收盘价高于前一根收盘价
+					if current.close <= current.pre.close:
+						break
+				else:
+					# 下跌趋势：当前收盘价低于前一根收盘价
+					if current.close >= current.pre.close:
+						break
+						
+				count += 1
+				current = current.pre
+				
+			return count >= min_bars
+		
+		# 1. 看涨吞没形态：前阴后阳，后者完全吞没前者
+		# 要求前面有明显的下跌趋势
+		if not prev_bullish and curr_bullish and \
+		   abs(curr_klu.open - prev_klu.close) < 10 and \
+		   curr_klu.close > prev_klu.open and \
+		   check_long_trend(prev_klu, bullish_trend=False, min_bars=5):
+			curr_klu.set_pattern(Chan_KLU_PATTERN.BULLISH_ENGULFING)
+			return
+		
+		# 2. 看跌吞没形态：前阳后阴，后者完全吞没前者
+		# 要求前面有明显的上涨趋势
+		if prev_bullish and not curr_bullish and \
+		   abs(curr_klu.open - prev_klu.close) < 10 and \
+		   curr_klu.close < prev_klu.open and \
+		   check_long_trend(prev_klu, bullish_trend=True, min_bars=5):
+			curr_klu.set_pattern(Chan_KLU_PATTERN.BEARISH_ENGULFING)
+			return
+		
+		# 3. 乌云盖顶：前阳后阴，后者开盘价高于前者最高价，收盘价在前者实体中部以下
+		# 要求前面有明显的上涨趋势
+		if prev_bullish and not curr_bullish and \
+		   curr_klu.open > prev_klu.high and \
+		   curr_klu.close < (prev_klu.open + prev_klu.close) / 2 and \
+		   curr_klu.close > prev_klu.open and \
+		   check_long_trend(prev_klu, bullish_trend=True, min_bars=5):
+			curr_klu.set_pattern(Chan_KLU_PATTERN.DARK_CLOUD_COVER)
+			return
+		
+		# 4. 曙光初现：前阴后阳，后者开盘价低于前者最低价，收盘价在前者实体中部以上
+		# 要求前面有明显的下跌趋势
+		if not prev_bullish and curr_bullish and \
+		   curr_klu.open < prev_klu.low and \
+		   curr_klu.close > (prev_klu.open + prev_klu.close) / 2 and \
+		   curr_klu.close < prev_klu.open and \
+		   check_long_trend(prev_klu, bullish_trend=False, min_bars=5):
+			curr_klu.set_pattern(Chan_KLU_PATTERN.PIERCING_LINE)
+			return
+		
+		# 平顶和平底移至三根K线形态中判断
+
+
+	def _detect_triple_pattern(self, first_klu, second_klu, third_klu):
+		"""检测三根K线形成的形态
+		包括：早晨之星、黄昏之星、平顶、平底
+		"""
+		# 如果前两根K线已经有形态，不再识别三K线形态
+		if first_klu.pattern != Chan_KLU_PATTERN.UNKNOWN or \
+		   second_klu.pattern != Chan_KLU_PATTERN.UNKNOWN:
+			return
+		
+		# 判断K线颜色（阴阳）
+		first_bullish = first_klu.close > first_klu.open
+		second_bullish = second_klu.close > second_klu.open
+		third_bullish = third_klu.close > third_klu.open
+		
+		# 计算实体大小
+		first_body = abs(first_klu.close - first_klu.open)
+		second_body = abs(second_klu.close - second_klu.open)
+		third_body = abs(third_klu.close - third_klu.open)
+		
+		# 检查是否存在长期趋势（至少需要5根K线的趋势）
+		def check_long_trend(klu, bullish_trend=True, min_bars=5):
+			"""检查是否存在长期趋势
+			bullish_trend=True: 检查上涨趋势
+			bullish_trend=False: 检查下跌趋势
+			min_bars: 最少需要多少根K线形成趋势
+			"""
+			if not klu or not klu.pre:
+				return False
+				
+			# 使用EMA指标判断长期趋势
+			if klu.ema52 > 0:
+				if bullish_trend and klu.close < klu.ema52:
+					return False
+				if not bullish_trend and klu.close > klu.ema52:
+					return False
+			
+			# 检查连续的K线方向
+			count = 0
+			current = klu.pre
+			
+			while current and count < min_bars:
+				if not current.pre:
+					break
+					
+				if bullish_trend:
+					# 上涨趋势：当前收盘价高于前一根收盘价
+					if current.close <= current.pre.close:
+						break
+				else:
+					# 下跌趋势：当前收盘价低于前一根收盘价
+					if current.close >= current.pre.close:
+						break
+						
+				count += 1
+				current = current.pre
+				
+			return count >= min_bars
+		
+		# 1. 早晨之星：第一根阴线，第二根十字星或小实体，第三根阳线
+		# 要求前面有明显的下跌趋势
+		if not first_bullish and third_bullish and \
+		   second_body < first_body * 0.3 and \
+		   third_body > first_body * 0.5 and \
+		   max(second_klu.open, second_klu.close) < first_klu.close and \
+		   min(second_klu.open, second_klu.close) < third_klu.open and \
+		   third_klu.close > (first_klu.open + first_klu.close) / 2 and \
+		   check_long_trend(first_klu, bullish_trend=False, min_bars=7):
+			third_klu.set_pattern(Chan_KLU_PATTERN.MORNING_STAR)
+			return
+		
+		# 2. 黄昏之星：第一根阳线，第二根十字星或小实体，第三根阴线
+		# 要求前面有明显的上涨趋势
+		if first_bullish and not third_bullish and \
+		   second_body < first_body * 0.3 and \
+		   third_body > first_body * 0.5 and \
+		   min(second_klu.open, second_klu.close) > first_klu.close and \
+		   max(second_klu.open, second_klu.close) > third_klu.open and \
+		   third_klu.close < (first_klu.open + first_klu.close) / 2 and \
+		   check_long_trend(first_klu, bullish_trend=True, min_bars=7):
+			third_klu.set_pattern(Chan_KLU_PATTERN.EVENING_STAR)
+			return
+			
+		# 3. 平顶：三根K线的最高点几乎相同（上升趋势中更有意义）
+		# 要求前面有明显的上涨趋势
+		if (abs(first_klu.high - second_klu.high) / first_klu.high < 0.0002 and 
+			abs(second_klu.high - third_klu.high) / second_klu.high < 0.0002 and
+			check_long_trend(first_klu, bullish_trend=True, min_bars=7)):
+			# 额外确认：价格接近阻力位或关键技术指标
+			is_near_resistance = False
+			
+			# 检查是否接近EMA52阻力位
+			if first_klu.ema52 > 0:
+				resistance_level = first_klu.ema52
+				if abs(first_klu.high - resistance_level) / resistance_level < 0.01:
+					is_near_resistance = True
+			
+			# 检查是否有成交量确认（成交量减少表示上涨动能减弱）
+			volume_confirmation = False
+			if (first_klu.volume > 0 and second_klu.volume > 0 and third_klu.volume > 0 and
+				third_klu.volume < second_klu.volume and second_klu.volume < first_klu.volume):
+				volume_confirmation = True
+				
+			if is_near_resistance or volume_confirmation:
+				third_klu.set_pattern(Chan_KLU_PATTERN.TWEEZER_TOP)
+				return
+		
+		# 4. 平底：三根K线的最低点几乎相同（下降趋势中更有意义）
+		# 要求前面有明显的下跌趋势
+		if (abs(first_klu.low - second_klu.low) / first_klu.low < 0.0002 and 
+			abs(second_klu.low - third_klu.low) / second_klu.low < 0.0002 and
+			check_long_trend(first_klu, bullish_trend=False, min_bars=7)):
+			# 额外确认：价格接近支撑位或关键技术指标
+			is_near_support = False
+			
+			# 检查是否接近EMA52支撑位
+			if first_klu.ema52 > 0:
+				support_level = first_klu.ema52
+				if abs(first_klu.low - support_level) / support_level < 0.01:
+					is_near_support = True
+			
+			# 检查是否有成交量确认（成交量减少表示下跌动能减弱）
+			volume_confirmation = False
+			if (first_klu.volume > 0 and second_klu.volume > 0 and third_klu.volume > 0 and
+				third_klu.volume < second_klu.volume and second_klu.volume < first_klu.volume):
+				volume_confirmation = True
+				
+			if is_near_support or volume_confirmation:
+				third_klu.set_pattern(Chan_KLU_PATTERN.TWEEZER_BOTTOM)
+				return
+	

chanlun/pipeline/builders/seg.py

@@ -0,0 +1,317 @@
+"""TF_DF builder mixin — 由 split_tfdf_builders 自动生成，逻辑与原 TF_DF 一致。"""
+from __future__ import annotations
+
+from datetime import timedelta
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+
+
+class SegBuilderMixin:
+	def get_seg_list(self, bi_list):
+		seg_list = []
+		up_bi_list = []
+		down_bi_list = []
+		last_up_bi = None
+		last_down_bi = None
+		last_up_sbi = None
+		last_down_sbi = None
+		last_seg = None
+		up_sbi_list = []
+		down_sbi_list = []
+		look_for_bottom = False
+		look_for_top = False
+		for bi in bi_list:
+			#print(len(up_sbi_list), len(down_sbi_list))
+			if len(seg_list) > 0:
+				# Last seg is up
+				if last_seg.dir == Chan_SEG_DIR.UP:
+					if bi.dir == Chan_BI_DIR.DOWN:
+						if len(down_sbi_list) > 1:
+							# Check down sbi inclusion
+							included = last_down_sbi.check_bi_included(bi)
+							if not included:
+								down_sbi = ChanSBI(bi, len(down_sbi_list), bi.dir)
+								last_down_sbi.set_next(down_sbi)
+								last_down_sbi.set_end_bi(last_down_bi)
+								down_sbi.set_pre(last_down_sbi)
+								down_sbi_list.append(down_sbi)
+								fx = last_down_sbi.check_fx()
+								# Found top
+								if fx == Chan_FX_TYPE.TOP:
+									if look_for_top:
+										seg_list[-2].set_sure(bi)
+										look_for_top = False
+										#print(bi.start_time, look_for_top, "UP 1")
+									# Has gap and search for bottom fx
+									if last_down_sbi.has_fx_gap:
+										look_for_bottom = True
+										last_seg.pre_set_end_bi(bi_list[last_down_sbi.start_bi.index - 1])
+										seg = ChanSEG(last_down_sbi.start_bi, len(seg_list), Chan_SEG_DIR.DOWN, bi)
+										seg_list.append(seg)
+										last_seg.set_next(seg)
+										seg.set_pre(last_seg)
+										last_seg = seg
+										up_sbi_list = []
+										last_up_sbi = ChanSBI(last_up_bi, len(up_sbi_list), last_up_bi.dir)
+										up_sbi_list.append(last_up_sbi)
+										#up_sbi_list.append(last_up_sbi)
+										#print(last_up_bi.start_time, last_up_sbi.start_bi.start_time, "Reset up sbi list 1")
+										#print(bi.start_time, look_for_top, "UP 2")
+									# No gap end SEG
+									else:
+										if look_for_bottom:
+											look_for_bottom = False
+											last_seg.set_start_bi(last_down_sbi.start_bi)
+											seg_list[-2].set_end_bi(bi_list[last_down_sbi.start_bi.index - 1], bi)
+											up_sbi_list = []
+											last_up_sbi = ChanSBI(last_up_bi, len(up_sbi_list), last_up_bi.dir)
+											up_sbi_list.append(last_up_sbi)
+											last_seg.add_bi(bi)
+											#up_sbi_list.append(last_up_sbi)
+											#print(last_up_bi.start_time, last_up_sbi.start_bi.start_time, "Reset up sbi list 2")
+											#print(bi.start_time, look_for_top, "UP 3")
+										else:
+											last_seg.set_end_bi(bi_list[last_down_sbi.start_bi.index - 1], bi)
+											seg = ChanSEG(last_down_sbi.start_bi, len(seg_list), Chan_SEG_DIR.DOWN, bi)
+											seg_list.append(seg)
+											last_seg.set_next(seg)
+											seg.set_pre(last_seg)
+											last_seg = seg
+											#print(last_down_sbi.end_bi.start_time, "Normal UP SEG", last_up_sbi.start_bi.start_time, bi.start_time)
+											#l_up_sbi = up_sbi_list[-1]
+											up_sbi_list = []
+											last_up_sbi = ChanSBI(last_up_bi, len(up_sbi_list), last_up_bi.dir)
+											up_sbi_list.append(last_up_sbi)
+											#up_sbi_list.append(last_up_sbi)
+											#print(last_up_bi.start_time, last_up_sbi.start_bi.start_time, "Reset up sbi list 3")
+								last_down_sbi = down_sbi
+							last_seg.add_bi(bi)
+						else:
+							if len(down_sbi_list) == 1:
+								included = last_down_sbi.check_bi_included(bi)
+								if not included:
+									down_sbi = ChanSBI(bi, len(down_sbi_list), bi.dir)
+									last_down_sbi.set_next(down_sbi)
+									last_down_sbi.set_end_bi(last_down_bi)
+									down_sbi.set_pre(last_down_sbi)
+									down_sbi_list.append(down_sbi)
+									last_down_sbi = down_sbi
+									#print(bi.start_time, look_for_top, "UP 4")
+								last_seg.add_bi(bi)
+									
+							else:
+								last_down_sbi = ChanSBI(bi, len(down_sbi_list), bi.dir)
+								down_sbi_list.append(last_down_sbi)
+								last_seg.add_bi(bi)
+								#print(bi.start_time, look_for_top, "UP 5")
+					else:
+						if last_up_sbi:
+							included = last_up_sbi.check_bi_included(bi)
+							if not included:
+								up_sbi = ChanSBI(bi, len(up_sbi_list), bi.dir)
+								last_up_sbi.set_next(up_sbi)
+								last_up_sbi.set_end_bi(last_up_bi)
+								up_sbi.set_pre(last_up_sbi)
+								up_sbi_list.append(up_sbi)
+								last_up_sbi = up_sbi
+								#print(bi.start_time, look_for_top, "UP 6")
+							last_seg.add_bi(bi)
+								
+				# Last seg is down
+				else:
+					if bi.dir == Chan_BI_DIR.UP:
+						if len(up_sbi_list) > 1:
+							# Check down sbi inclusion
+							included = last_up_sbi.check_bi_included(bi)
+							if not included:
+								up_sbi = ChanSBI(bi, len(up_sbi_list), bi.dir)
+								last_up_sbi.set_next(up_sbi)
+								last_up_sbi.set_end_bi(last_up_bi)
+								up_sbi.set_pre(last_up_sbi)
+								up_sbi_list.append(up_sbi)
+								fx = last_up_sbi.check_fx()
+								# Found bottom
+								if fx == Chan_FX_TYPE.BOTTOM:
+									if look_for_bottom:
+										seg_list[-2].set_sure(bi)
+										look_for_bottom = False
+										#print(bi.start_time, look_for_top, "DOWN 1")
+									# Has gap and search for bottom fx
+									if last_up_sbi.has_fx_gap:
+										look_for_top = True
+										last_seg.pre_set_end_bi(bi_list[last_up_sbi.start_bi.index - 1])
+										seg = ChanSEG(last_up_sbi.start_bi, len(seg_list), Chan_SEG_DIR.UP, bi)
+										seg_list.append(seg)
+										last_seg.set_next(seg)
+										seg.set_pre(last_seg)
+										last_seg = seg
+										down_sbi_list = []
+										last_down_sbi = ChanSBI(last_down_bi, len(down_sbi_list), last_down_bi.dir)
+										down_sbi_list.append(last_down_sbi)
+										#down_sbi_list.append(last_down_sbi)
+										#print(last_down_bi.start_time, last_down_sbi.start_bi.start_time, "Reset down sbi list 1")
+										#print(bi.start_time, look_for_top, "DOWN 2")
+									# No gap end SEG
+									else:
+										if look_for_top:
+											look_for_top = False
+											last_seg.set_start_bi(last_up_sbi.start_bi)
+											seg_list[-2].set_end_bi(bi_list[last_up_sbi.start_bi.index - 1], bi)
+											down_sbi_list = []
+											last_down_sbi = ChanSBI(last_down_bi, len(down_sbi_list), last_down_bi.dir)
+											down_sbi_list.append(last_down_sbi)
+											last_seg.add_bi(bi)
+											#down_sbi_list.append(last_down_sbi)
+											#print(last_down_bi.start_time, last_down_sbi.start_bi.start_time, "Reset down sbi list 2")
+											#print(bi.start_time, look_for_top, "DOWN 3")
+										else:
+											last_seg.set_end_bi(bi_list[last_up_sbi.start_bi.index - 1], bi)
+											seg = ChanSEG(last_up_sbi.start_bi, len(seg_list), Chan_SEG_DIR.UP, bi)
+											#print(last_up_sbi.start_bi.start_time)
+											last_seg.set_next(seg)
+											seg.set_pre(last_seg)
+											seg_list.append(seg)
+											last_seg = seg
+											#print(last_up_sbi.end_bi.start_time, "Normal DOWN SEG", last_down_sbi.start_bi.start_time, bi.start_time)
+											down_sbi_list = []
+											last_down_sbi = ChanSBI(last_down_bi, len(down_sbi_list), last_down_bi.dir)
+											down_sbi_list.append(last_down_sbi)
+											#down_sbi_list.append(last_down_sbi)
+											#print(last_down_bi.start_time, last_down_sbi.start_bi.start_time, "Reset down sbi list 3")
+								last_up_sbi = up_sbi
+							last_seg.add_bi(bi)	
+						else:
+							if len(up_sbi_list) == 1:
+								#last_up_sbi = up_sbi_list[-1]
+								included = last_up_sbi.check_bi_included(bi)
+								if not included:
+									up_sbi = ChanSBI(bi, len(up_sbi_list), bi.dir)
+									last_up_sbi.set_next(up_sbi)
+									last_up_sbi.set_end_bi(last_up_bi)
+									up_sbi.set_pre(last_up_sbi)
+									up_sbi_list.append(up_sbi)
+									last_up_sbi = up_sbi
+								last_seg.add_bi(bi)
+									#print(bi.start_time, look_for_top, "DOWN 4")
+							else:
+								last_up_sbi = ChanSBI(bi, len(up_sbi_list), bi.dir)
+								up_sbi_list.append(last_up_sbi)
+								last_seg.add_bi(bi)
+								#print(bi.start_time, look_for_top, "DOWN 5")
+					else:
+						if last_down_sbi:
+							included = last_down_sbi.check_bi_included(bi)
+							if not included:
+								down_sbi = ChanSBI(bi, len(down_sbi_list), bi.dir)
+								last_down_sbi.set_next(down_sbi)
+								last_down_sbi.set_end_bi(last_down_bi)
+								down_sbi.set_pre(last_down_sbi)
+								down_sbi_list.append(down_sbi)
+								last_down_sbi = down_sbi
+							last_seg.add_bi(bi)
+								#print(bi.start_time, look_for_top, look_for_bottom, "DOWN 6")
+			# len(seg_list) = 0
+			else:
+				if bi.check_overlap():
+					if bi.dir == Chan_BI_DIR.UP:
+						seg = ChanSEG(bi, len(seg_list), Chan_SEG_DIR.UP, bi)
+						last_up_bi = bi
+						last_up_sbi = ChanSBI(bi, len(up_sbi_list), bi.dir)
+						seg_list.append(seg)
+						last_seg = seg
+						#print(bi.start_time, 'Create first UP SEG')
+					else:
+						seg = ChanSEG(bi, len(seg_list), Chan_SEG_DIR.DOWN, bi)
+						last_down_bi = bi
+						last_down_sbi = ChanSBI(bi, len(down_sbi_list), bi.dir)
+						seg_list.append(seg)
+						last_seg = seg
+						#print(bi.start_time, 'Create first DOWN SEG')
+			if bi.dir == Chan_BI_DIR.UP:
+				last_up_bi = bi
+				up_bi_list.append(bi)
+			else:
+				last_down_bi = bi
+				down_bi_list.append(bi)
+		"""
+		if len(seg_list) > 1:
+			seg = seg_list[-1]
+			last_seg = seg_list[-2]
+			last_seg_bi = last_seg.bi_list[-3]
+			bi_index = seg.start_bi.index
+			for i in range(bi_index, len(bi_list) - 1):
+				# last seg is down
+				if seg.dir == Chan_SEG_DIR.UP:
+					if bi_list[i].dir == Chan_BI_DIR.UP:
+						last_seg_peak = last_seg_bi.high
+						if bi_list[i].high > last_seg_peak:
+							# The confirmed
+							print("Last UP seg is broken, create a new seg. 1")
+							seg.pre_set_end_bi(bi_list[i])
+							seg = ChanSEG(bi_list[i+1], len(seg_list), Chan_SEG_DIR.DOWN, bi)
+							seg_list.append(seg)
+							last_seg = seg_list[-2]
+							if len(last_seg.bi_list) > 3:
+								last_seg_bi = last_seg.bi_list[-3]
+
+				else:
+					if bi_list[i].dir == Chan_BI_DIR.DOWN:
+						last_seg_peak = last_seg_bi.low
+						if bi_list[i].low < last_seg_peak:
+							print("Last DOWN seg is broken, create a new seg. 1")
+							seg.pre_set_end_bi(bi_list[i])
+							seg = ChanSEG(bi_list[i+1], len(seg_list), Chan_SEG_DIR.UP, bi)
+							seg_list.append(seg)
+							last_seg = seg_list[-2]
+							if len(last_seg.bi_list) > 3:
+								last_seg_bi = last_seg.bi_list[-3]
+		else:
+			if len(seg_list) == 1:
+				last_seg = seg_list[-1]
+				bi_index = last_seg.bi_list[0].index
+				for i in range(bi_index, len(bi_list) - 1):
+					if i > bi_index + 2:
+						last_seg_peak = bi_list[i-2].high
+						# last seg is down
+						if last_seg.dir == Chan_SEG_DIR.DOWN:
+							if bi_list[i].dir == Chan_BI_DIR.UP:
+								if bi_list[i].high > last_seg_peak:
+									print("Last seg is broken, create a new seg. 2")
+									last_seg.pre_set_end_bi(bi_list[i-1])
+									seg = ChanSEG(bi_list[i], len(seg_list), Chan_SEG_DIR.UP, bi)
+									seg_list.append(seg)
+									last_seg = seg
+									last_seg_bi = bi_list[i]
+									break
+		"""
+		#self.cal_bi_zs(seg_list)
+		return seg_list

chanlun/pipeline/builders/zs.py

@@ -0,0 +1,699 @@
+"""TF_DF builder mixin — 由 split_tfdf_builders 自动生成，逻辑与原 TF_DF 一致。"""
+from __future__ import annotations
+
+from datetime import timedelta
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+
+
+class ZsBuilderMixin:
+	def get_zs_state(self, df):
+		bi_list = self.cal_bi_list(self.get_klc_list(self.get_kl_data(df)))
+		seg_list = self.get_seg_list(bi_list)
+		zs_list = self.calculate_zs(seg_list)
+		for zs in zs_list:
+			last_zs = zs
+		return zs_list
+
+	def cal_bi_zs(self, seg_list):
+		bi_zs_list = []
+		for seg in seg_list:
+			zs_list = seg.cal_bi_zs()
+			if len(zs_list) > 0:
+				bi_zs_list = list(bi_zs_list) + list(zs_list)
+		return bi_zs_list
+	# 跨段不相连的中枢
+
+	def cal_bi_zs_list(self, bi_list):
+		"""
+		根据缠论笔中枢定义计算中枢（参照 get_zs_list 线段中枢判断规则）
+		从第4根笔开始（索引3），每3根笔为一组检查
+		上涨中枢：后中枢 zd > 前中枢 zg（不重叠上移）
+		下跌中枢：后中枢 zg < 前中枢 zd（不重叠下移）
+		中枢可按两笔一组继续扩展到5根、7根...
+		"""
+		bi_zs_list = []
+		if len(bi_list) < 3:
+			return bi_zs_list
+
+		last_zs = None
+		start_idx = 3
+
+		while start_idx < len(bi_list):
+			if start_idx + 2 >= len(bi_list):
+				break
+
+			bi1 = bi_list[start_idx]
+			bi2 = bi_list[start_idx + 1]
+			bi3 = bi_list[start_idx + 2]
+
+			if not (bi1.is_sure and bi2.is_sure and bi3.is_sure):
+				start_idx += 1
+				continue
+
+			zg = min(bi1.high, bi2.high, bi3.high)
+			zd = max(bi1.low, bi2.low, bi3.low)
+
+			if zg <= zd:
+				start_idx += 1
+				continue
+
+			valid = False
+			if last_zs is None:
+				if bi1.dir == Chan_BI_DIR.DOWN:
+					zs_dir = Chan_ZS_DIR.UP
+					valid = (bi2.dir == Chan_BI_DIR.UP and bi3.dir == Chan_BI_DIR.DOWN)
+				else:
+					zs_dir = Chan_ZS_DIR.DOWN
+					valid = (bi2.dir == Chan_BI_DIR.DOWN and bi3.dir == Chan_BI_DIR.UP)
+			else:
+				is_up_zs = zg > last_zs.zg
+				is_down_zs = zd < last_zs.zd
+
+				if is_up_zs:
+					zs_dir = Chan_ZS_DIR.UP
+					valid = (bi1.dir == Chan_BI_DIR.DOWN and bi2.dir == Chan_BI_DIR.UP and bi3.dir == Chan_BI_DIR.DOWN)
+				elif is_down_zs:
+					zs_dir = Chan_ZS_DIR.DOWN
+					valid = (bi1.dir == Chan_BI_DIR.UP and bi2.dir == Chan_BI_DIR.DOWN and bi3.dir == Chan_BI_DIR.UP)
+
+			if not valid:
+				start_idx += 1
+				continue
+			gg = max(bi1.high, bi2.high, bi3.high)
+			dd = min(bi1.low, bi2.low, bi3.low)
+			zs = ChanBIZS(bi1, len(bi_zs_list), zs_dir)
+			zs.set_zg(zg)
+			zs.set_zd(zd)
+			zs.set_gg(gg)
+			zs.set_dd(dd)
+			zs.is_sure = False
+			zs.bi_list = [bi1, bi2, bi3]
+
+			added_after_leave = []
+			leave_index = start_idx + 4
+			while leave_index < len(bi_list):
+				b = bi_list[leave_index]
+				if not b.is_sure:
+					break
+				if b.high >= zs.zd and b.low <= zs.zg:
+					added_after_leave.append(b.pre)
+					added_after_leave.append(b)
+				else:
+					break
+				leave_index += 2
+
+			if added_after_leave:
+				bis_for_zs = list(zs.bi_list) + list(added_after_leave)
+				bi_highs = [bi.high for bi in bis_for_zs]
+				bi_lows = [bi.low for bi in bis_for_zs]
+				zs.set_gg(max(bi_highs))
+				zs.set_dd(min(bi_lows))
+				zs.bi_list = bis_for_zs
+				bi = bis_for_zs[-1]
+				if bi.is_sure:
+					zs.set_end_bi(bi, bi.sure_time)
+
+				start_idx = start_idx + len(added_after_leave)
+			else:
+				zs.set_end_bi(bi3, bi3.sure_time)
+
+			if last_zs:
+				last_zs.set_next(zs)
+				zs.set_pre(last_zs)
+
+			bi_zs_list.append(zs)
+			last_zs = zs
+
+			start_idx += 4
+
+		if last_zs:
+			last_zs.is_sure = bi_list[-1].is_sure
+
+		if last_zs and not last_zs.is_sure:
+			if last_zs.bi_list and len(last_zs.bi_list) > 0:
+				last_bi_of_zs = last_zs.bi_list[-1]
+				last_bi_idx = -1
+				for i, bi in enumerate(bi_list):
+					if bi == last_bi_of_zs:
+						last_bi_idx = i
+						break
+
+				has_leave = False
+				if last_bi_idx >= 0 and last_bi_idx + 1 < len(bi_list):
+					for i in range(last_bi_idx + 1, len(bi_list)):
+						bi = bi_list[i]
+						if bi.is_sure:
+							leave = (bi.low > last_zs.zg and bi.high > last_zs.zg) or \
+									(bi.high < last_zs.zd and bi.low < last_zs.zd)
+							if leave:
+								has_leave = True
+								break
+
+				if has_leave:
+					if last_bi_of_zs.is_sure:
+						last_zs.set_end_bi(last_bi_of_zs, last_bi_of_zs.sure_time)
+		return bi_zs_list
+
+	def get_bi_zs_list(self, bi_list):
+		"""
+		根据缠论笔中枢定义计算中枢（完全参照 get_seg_zs_list 线段中枢判断规则）
+		从第4根笔开始（索引3），每3根笔为一组检查
+		上涨中枢：后中枢 zd > 前中枢 zg（不重叠上移）
+		下跌中枢：后中枢 zg < 前中枢 zd（不重叠下移）
+		盘整/扩张：后中枢与前中枢整体区间有交集 → 合并扩展
+		中枢可按两笔一组继续扩展到5根、7根...
+		"""
+		bi_zs_list = []
+		if len(bi_list) < 3:
+			return bi_zs_list
+
+		last_zs = None
+		start_idx = 3
+
+		while start_idx < len(bi_list):
+			if start_idx + 2 >= len(bi_list):
+				break
+
+			bi1 = bi_list[start_idx]
+			bi2 = bi_list[start_idx + 1]
+			bi3 = bi_list[start_idx + 2]
+
+			if not (bi1.is_sure and bi2.is_sure and bi3.is_sure):
+				start_idx += 1
+				continue
+
+			zg = min(bi1.high, bi2.high, bi3.high)
+			zd = max(bi1.low, bi2.low, bi3.low)
+
+			if zg <= zd:
+				start_idx += 1
+				continue
+
+			valid = False
+			if last_zs is None:
+				if bi1.dir == Chan_BI_DIR.DOWN:
+					zs_dir = Chan_ZS_DIR.UP
+					valid = (bi2.dir == Chan_BI_DIR.UP and bi3.dir == Chan_BI_DIR.DOWN)
+				else:
+					zs_dir = Chan_ZS_DIR.DOWN
+					valid = (bi2.dir == Chan_BI_DIR.DOWN and bi3.dir == Chan_BI_DIR.UP)
+			else:
+				is_up_zs = zd > last_zs.zg
+				is_down_zs = zg < last_zs.zd
+
+				if is_up_zs:
+					zs_dir = Chan_ZS_DIR.UP
+					valid = (bi1.dir == Chan_BI_DIR.DOWN and bi2.dir == Chan_BI_DIR.UP and bi3.dir == Chan_BI_DIR.DOWN)
+				elif is_down_zs:
+					zs_dir = Chan_ZS_DIR.DOWN
+					valid = (bi1.dir == Chan_BI_DIR.UP and bi2.dir == Chan_BI_DIR.DOWN and bi3.dir == Chan_BI_DIR.UP)
+
+			create_new_zs = False
+			if not valid:
+				# 如果新中枢和前一个中枢的中枢区间有重叠，不形成新中枢，合并扩展
+				if last_zs is not None:
+					is_in_last_zs = (zd > last_zs.zd and zd < last_zs.zg) or \
+									(zg < last_zs.zg and zg > last_zs.zd) or \
+									(zg > last_zs.zg and zd < last_zs.zd) or \
+									(zg < last_zs.zg and zd > last_zs.zd)
+					if is_in_last_zs:
+						# 扩展当前中枢：将 bi1-bi3 加入 last_zs
+						for bi in [bi1, bi2, bi3]:
+							if bi not in last_zs.bi_list:
+								last_zs.add_bi(bi)
+						create_new_zs = False
+					else:
+						start_idx += 1
+						continue
+				else:
+					start_idx += 1
+					continue
+			else:
+				create_new_zs = True
+
+			# 新中枢形成时确认前一个中枢
+			if last_zs and create_new_zs:
+				last_bi = last_zs.bi_list[-1]
+				if last_bi and last_bi.is_sure:
+					last_zs.is_sure = True
+					last_zs.set_end_bi(last_bi, last_bi.sure_time)
+
+			zs = last_zs
+			if create_new_zs:
+				gg = max(bi1.high, bi2.high, bi3.high)
+				dd = min(bi1.low, bi2.low, bi3.low)
+				zs = ChanBIZS(bi1, len(bi_zs_list), zs_dir)
+				zs.set_zg(zg)
+				zs.set_zd(zd)
+				zs.set_gg(gg)
+				zs.set_dd(dd)
+				zs.is_sure = False
+				zs.bi_list = [bi1, bi2, bi3]
+
+			# 离开后回抽扩展检查
+			added_after_leave = []
+			leave_index = start_idx + 4
+			while leave_index < len(bi_list):
+				b = bi_list[leave_index]
+				if not b.is_sure:
+					break
+				if b.high >= zs.zd and b.low <= zs.zg:
+					added_after_leave.append(b.pre)
+					added_after_leave.append(b)
+				else:
+					break
+				leave_index += 2
+
+			if added_after_leave:
+				bis_for_zs = list(zs.bi_list) + list(added_after_leave)
+				bi_highs = [bi.high for bi in bis_for_zs]
+				bi_lows = [bi.low for bi in bis_for_zs]
+				zs.set_gg(max(bi_highs))
+				zs.set_dd(min(bi_lows))
+				zs.bi_list = bis_for_zs
+				bi = bis_for_zs[-1]
+				if bi.is_sure:
+					zs.set_end_bi(bi, bi.sure_time)
+				start_idx = start_idx + len(added_after_leave)
+			else:
+				if create_new_zs:
+					zs.set_end_bi(bi3, bi3.sure_time)
+
+			if create_new_zs:
+				if last_zs:
+					last_zs.set_next(zs)
+					zs.set_pre(last_zs)
+				bi_zs_list.append(zs)
+				last_zs = zs
+
+			start_idx += 4
+
+		# 最后一个中枢：根据 bi_list 最后一笔确认状态
+		if last_zs:
+			last_zs.is_sure = bi_list[-1].is_sure
+
+		if last_zs and not last_zs.is_sure:
+			if last_zs.bi_list and len(last_zs.bi_list) > 0:
+				last_bi_of_zs = last_zs.bi_list[-1]
+				last_bi_idx = -1
+				for i, bi in enumerate(bi_list):
+					if bi == last_bi_of_zs:
+						last_bi_idx = i
+						break
+
+				has_leave = False
+				if last_bi_idx >= 0 and last_bi_idx + 1 < len(bi_list):
+					for i in range(last_bi_idx + 1, len(bi_list)):
+						bi = bi_list[i]
+						if bi.is_sure:
+							leave = (bi.low > last_zs.zg and bi.high > last_zs.zg) or \
+									(bi.high < last_zs.zd and bi.low < last_zs.zd)
+							if leave:
+								has_leave = True
+								break
+
+				if has_leave:
+					if last_bi_of_zs.is_sure:
+						last_zs.set_end_bi(last_bi_of_zs, last_bi_of_zs.sure_time)
+
+		return bi_zs_list
+
+
+	def cal_bi_zs_list_pure(self, bi_list):
+		bi_zs_list = []
+		if len(bi_list) < 3:
+			return bi_zs_list
+
+		def get_zs_range(bis):
+			zg = min(bi.high for bi in bis)
+			zd = max(bi.low for bi in bis)
+			return zg, zd
+
+		def is_bi_overlap_range(bi, zg, zd):
+			return bi.high >= zd and bi.low <= zg
+
+		def check_zs_position_filter(last_zs, zg, zd, bis):
+			if last_zs is None:
+				return True
+			if zg <= last_zs.zd:
+				return bis[0].dir == Chan_BI_DIR.UP and bis[-1].dir == Chan_BI_DIR.UP
+			if zd >= last_zs.zg:
+				return bis[0].dir == Chan_BI_DIR.DOWN and bis[-1].dir == Chan_BI_DIR.DOWN
+			return True
+
+		def set_zs_bi_list(zs, bis):
+			zs.bi_list = list(bis)
+			for bi in zs.bi_list:
+				bi.set_bi_zs(zs)
+			zs.set_gg(max(bi.high for bi in zs.bi_list))
+			zs.set_dd(min(bi.low for bi in zs.bi_list))
+			zs.classify_zs()
+
+		last_zs = None
+		start_idx = 0
+		while start_idx + 2 < len(bi_list):
+			bi1 = bi_list[start_idx]
+			bi2 = bi_list[start_idx + 1]
+			bi3 = bi_list[start_idx + 2]
+
+			if not (bi1.is_sure and bi2.is_sure and bi3.is_sure):
+				start_idx += 1
+				continue
+
+			if not (bi1.dir != bi2.dir and bi1.dir == bi3.dir):
+				start_idx += 1
+				continue
+
+			zg, zd = get_zs_range([bi1, bi2, bi3])
+			if zg <= zd:
+				start_idx += 1
+				continue
+
+			bis_for_zs = [bi1, bi2, bi3]
+			extend_idx = start_idx + 3
+			while extend_idx + 1 < len(bi_list):
+				leave_bi = bi_list[extend_idx]
+				back_bi = bi_list[extend_idx + 1]
+				if not (leave_bi.is_sure and back_bi.is_sure):
+					break
+				if not is_bi_overlap_range(back_bi, zg, zd):
+					break
+				bis_for_zs.append(leave_bi)
+				bis_for_zs.append(back_bi)
+				extend_idx += 2
+
+			if not check_zs_position_filter(last_zs, zg, zd, bis_for_zs):
+				start_idx += 1
+				continue
+
+			zs_dir = Chan_ZS_DIR.UP if bi1.dir == Chan_BI_DIR.DOWN else Chan_ZS_DIR.DOWN
+			zs = ChanBIZS(bi1, len(bi_zs_list), zs_dir)
+			zs.set_zg(zg)
+			zs.set_zd(zd)
+
+			set_zs_bi_list(zs, bis_for_zs)
+			zs.set_end_bi(bis_for_zs[-1], bis_for_zs[-1].sure_time)
+
+			if last_zs:
+				last_zs.set_next(zs)
+				zs.set_pre(last_zs)
+
+			bi_zs_list.append(zs)
+			last_zs = zs
+			start_idx = start_idx + len(bis_for_zs)
+
+		# 与 cal_bi_zs_list 一致：最后一笔未确认时末中枢标为未完成；若其后已出现确认的离开笔，仍按离开前最后一笔确认中枢结束
+		if last_zs:
+			last_zs.is_sure = bi_list[-1].is_sure
+
+		if last_zs and not last_zs.is_sure:
+			if last_zs.bi_list and len(last_zs.bi_list) > 0:
+				last_bi_of_zs = last_zs.bi_list[-1]
+				last_bi_idx = -1
+				for i, bi in enumerate(bi_list):
+					if bi == last_bi_of_zs:
+						last_bi_idx = i
+						break
+
+				has_leave = False
+				if last_bi_idx >= 0 and last_bi_idx + 1 < len(bi_list):
+					for i in range(last_bi_idx + 1, len(bi_list)):
+						bi = bi_list[i]
+						if bi.is_sure:
+							leave = (bi.low > last_zs.zg and bi.high > last_zs.zg) or \
+									(bi.high < last_zs.zd and bi.low < last_zs.zd)
+							if leave:
+								has_leave = True
+								break
+
+				if has_leave:
+					if last_bi_of_zs.is_sure:
+						last_zs.set_end_bi(last_bi_of_zs, last_bi_of_zs.sure_time)
+
+		return bi_zs_list
+
+	def get_zs_list(self, bi_list, seg_list):
+		"""兼容历史 API：线段中枢列表。"""
+		return self.get_seg_zs_list(seg_list)
+
+	def calculate_seg_zs(self, seg_list):
+		return self.get_seg_zs_list(seg_list)
+
+	def get_seg_zs_list(self, seg_list):
+		"""
+		根据缠论线段中枢定义计算中枢
+		从第4根线段开始（索引3），每3根线段为一组检查
+		上涨中枢：后中枢 zd > 前中枢 zg（不重叠上移）
+		下跌中枢：后中枢 zg < 前中枢 zd（不重叠下移）
+		盘整/扩张：后中枢与前中枢整体区间（GG/DD）有交集
+		中枢可按两段一组继续扩展到5根、7根...
+		"""
+		zs_list = []
+		if len(seg_list) < 3:
+			return zs_list
+
+		last_zs = None
+
+		# 从第4根线段开始（索引3），每3根为一组
+		start_idx = 3
+
+		while start_idx < len(seg_list):
+			# 取连续3个线段
+			if start_idx + 2 >= len(seg_list):
+				break
+
+			seg1 = seg_list[start_idx]
+			seg2 = seg_list[start_idx + 1]
+			seg3 = seg_list[start_idx + 2]
+
+			# 三个线段都必须是已确认的
+			if not (seg1.is_sure and seg2.is_sure and seg3.is_sure):
+				start_idx += 1
+				continue
+
+			# 计算这3个线段的中枢区间
+			zg = min(seg1.high, seg2.high, seg3.high)
+			zd = max(seg1.low, seg2.low, seg3.low)
+
+			if zg <= zd:
+				start_idx += 1
+				#print(seg1.start_bi.start_klc.end_time, "not valid", zg, zd)
+				continue
+
+			# 判断中枢类型（按注释定义）
+			# 上涨中枢：后中枢 zd > 前中枢 zg（不重叠上移）
+			# 下跌中枢：后中枢 zg < 前中枢 zd（不重叠下移）
+			# 盘整/扩张：后中枢与前中枢区间有交集
+			if last_zs is None:
+				# 第一个中枢仅按线段形态判定方向
+				if seg1.dir == Chan_SEG_DIR.DOWN:
+					# 下跌+上涨+下跌，对应上涨中枢
+					zs_dir = Chan_ZS_DIR.UP
+					valid = (seg2.dir == Chan_SEG_DIR.UP and seg3.dir == Chan_SEG_DIR.DOWN)
+				else:
+					# 上涨+下跌+上涨，对应下跌中枢
+					zs_dir = Chan_ZS_DIR.DOWN
+					valid = (seg2.dir == Chan_SEG_DIR.DOWN and seg3.dir == Chan_SEG_DIR.UP)
+			else:
+				is_up_zs = zd > last_zs.zg
+				is_down_zs = zg < last_zs.zd
+
+				if is_up_zs:
+					# 不重叠上移
+					zs_dir = Chan_ZS_DIR.UP
+					valid = (seg1.dir == Chan_SEG_DIR.DOWN and seg2.dir == Chan_SEG_DIR.UP and seg3.dir == Chan_SEG_DIR.DOWN)
+				elif is_down_zs:
+					# 不重叠下移
+					zs_dir = Chan_ZS_DIR.DOWN
+					valid = (seg1.dir == Chan_SEG_DIR.UP and seg2.dir == Chan_SEG_DIR.DOWN and seg3.dir == Chan_SEG_DIR.UP)
+			create_new_zs = False
+			# 验证是否有效
+			if not valid:
+				# 如果新中枢和前一个中枢的中枢区间有重叠，不行成新中枢需要合并两个中枢
+				is_in_last_zs = (zd > last_zs.zd and zd < last_zs.zg) or (zg < last_zs.zg and zg > last_zs.zd) or (zg > last_zs.zg and zd < last_zs.zd) or (zg < last_zs.zg and zd > last_zs.zd)
+				if is_in_last_zs:
+					#print(seg1.start_time, "New zs is in last zs, not valid")
+					last_zs.extend_zs(seg_list[last_zs.seg_list[-1].index:(seg3.index + 1)])
+					create_new_zs = False
+				else:
+					start_idx += 1
+					continue
+			else:
+				create_new_zs = True
+			if last_zs and create_new_zs:
+				last_seg = last_zs.seg_list[-1]
+				last_bi = last_seg.end_bi
+				if last_bi:
+					last_zs.is_sure = True
+					last_zs.set_end_klc(last_bi.end_klc, last_bi.sure_time, 0, last_seg)
+					last_zs.set_end_seg(last_seg)
+			zs = last_zs
+			if create_new_zs:
+				# 创建新中枢
+				gg = max(seg1.high, seg2.high, seg3.high)
+				dd = min(seg1.low, seg2.low, seg3.low)
+
+				zs = ChanZS(seg1, len(zs_list), zs_dir)
+				zs.set_zg(zg)
+				zs.set_zd(zd)
+				zs.set_gg(gg)
+				zs.set_dd(dd)
+				zs.is_sure = False
+				zs.seg_list = [seg1, seg2, seg3]
+			# 若第二线段与 [zd,zg] 重叠（如离开后回抽回到前中枢）则并入扩展
+			added_after_leave = []
+			leave_index = start_idx + 4
+			is_break = False
+			while leave_index < len(seg_list):
+				s = seg_list[leave_index]
+				if not s.is_sure:
+					break
+				sh = max(s.start_bi.high, s.end_bi.high) if s.end_bi else s.start_bi.high
+				sl = min(s.start_bi.low, s.end_bi.low) if s.end_bi else s.start_bi.low
+				if sh >= zs.zd and sl <= zs.zg:
+					added_after_leave.append(s.pre)
+					added_after_leave.append(s)
+					leave_index += 2
+				else:
+					next_seg = s.next
+					if next_seg and next_seg.is_sure:
+						if next_seg.dir == Chan_SEG_DIR.UP:
+							if next_seg.high <= zs.zg and next_seg.low >= zs.zd:
+								leave_index += 2
+								continue
+							else:
+								is_break = True
+						else:
+							if next_seg.low >= zs.zd and next_seg.low <= zs.zg:
+								leave_index += 2
+								continue
+							else:
+								is_break = True
+					else:
+						break
+				if is_break:
+					break
+			if added_after_leave:
+				#print(len(added_after_leave))
+				segs_for_zs = list(zs.seg_list) + list(added_after_leave)
+				seg_highs = [s.high for s in segs_for_zs]
+				seg_lows = [s.low for s in segs_for_zs]
+				zs.set_gg(max(seg_highs))
+				zs.set_dd(min(seg_lows))
+				zs.seg_list = segs_for_zs
+				seg = segs_for_zs[-1]
+				#if seg.end_bi:
+					#zs.set_end_klc(seg.end_bi.end_klc, seg.sure_time, 0, seg)
+					#zs.set_end_seg(seg)
+					#zs.is_sure = True
+				start_idx = start_idx + len(added_after_leave)
+			if last_zs and last_zs.index != zs.index:
+				last_zs.set_next(zs)
+				zs.set_pre(last_zs)
+
+			zs_list.append(zs)
+			last_zs = zs
+
+			# 移动到下一组
+			start_idx += 4
+		if last_zs:
+			last_zs.is_sure = seg_list[-1].is_sure
+		"""
+		# 处理最后一个未确认的中枢 - 不自动扩展，保持未完成状态
+		if last_zs and not last_zs.is_sure:
+			# 获取中枢最后一个线段的索引
+			if last_zs.seg_list and len(last_zs.seg_list) > 0:
+				last_seg_of_zs = last_zs.seg_list[-1]
+				# 找到这个线段在seg_list中的索引
+				last_seg_idx = -1
+				for i, seg in enumerate(seg_list):
+					if seg == last_seg_of_zs:
+						last_seg_idx = i
+						break
+
+				# 从中枢最后一个线段之后检查是否有离开
+				has_leave = False
+				if last_seg_idx >= 0 and last_seg_idx + 1 < len(seg_list):
+					for i in range(last_seg_idx + 1, len(seg_list)):
+						seg = seg_list[i]
+						if seg.is_sure:
+							# 检查是否离开中枢
+							leave = (seg.low > last_zs.zg and seg.high > last_zs.zg) or \
+									(seg.high < last_zs.zd and seg.low < last_zs.zd)
+							if leave:
+								has_leave = True
+								break
+
+				if not has_leave:
+					# 没有离开，保持未完成状态
+					pass
+				else:
+					# 有离开，确认中枢
+					if last_seg_of_zs.end_bi:
+						#print(last_seg_of_zs.start_time, "last_seg_of_zs.end_time", last_seg_of_zs.end_time)
+						last_zs.set_end_klc(last_seg_of_zs.end_bi.end_klc, last_seg_of_zs.sure_time, 0, last_seg_of_zs)
+						last_zs.set_end_seg(last_seg_of_zs)
+						last_zs.is_sure = True
+		"""
+		return zs_list
+
+
+	def get_big_zs_list(self, zs_list):
+		"""
+		中枢扩张：将区间重叠的连续中枢合并为大级别中枢，便于显示更大级别的震荡区间。
+		重叠定义：两中枢 [zd,zg] 有交集，即 (zs_i.zg >= zs_j.zd and zs_i.zd <= zs_j.zg)。
+		"""
+		big_list = []
+		if len(zs_list) < 2:
+			return big_list
+		i = 0
+		while i < len(zs_list):
+			group = [zs_list[i]]
+			j = i + 1
+			while j < len(zs_list):
+				cur = zs_list[j]
+				# 与当前组内任一中枢有重叠即算扩张（通常只需与组内最后一个比）
+				last_in_group = group[-1]
+				overlap = (last_in_group.zg >= cur.zd and last_in_group.zd <= cur.zg)
+				if overlap:
+					group.append(cur)
+					j += 1
+				else:
+					break
+			if len(group) >= 2:
+				big = ChanZS_Big(group)
+				big.index = len(big_list)
+				big_list.append(big)
+			i = j if len(group) >= 2 else i + 1
+		return big_list
+

chanlun/pipeline/orchestrator.py

@@ -0,0 +1,188 @@
+import warnings
+
+# 抑制 Docker 内 technical.util 的 fillna/ffill/bfill 的 pandas FutureWarning（pandas 2.x 弃用 object 静默 downcast）
+warnings.filterwarnings(
+    "ignore",
+    category=FutureWarning,
+    message=".*Downcasting object dtype arrays on \\.fillna.*",
+)
+
+from datetime import timedelta
+from pandas import DataFrame
+from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX, Chan_MACD_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS
+from chanlun.core.ChanBSP import ChanBSP
+import talib.abstract as ta
+import pandas as pd
+from technical.util import resample_to_interval
+from decimal import Decimal
+import numpy as np
+from chanlun.indicators.ChanMACD import ChanMACD
+from chanlun.pipeline.timeframe import TF_DF
+from chanlun.analysis.ChanZone import StructureZone, StructureZoneConfig, analyze_structure_zones
+
+class ChanLun():
+	def __init__(self):
+		self.time2m = 2
+		self.time3m = 3
+		self.time5m = 5
+		self.time10m = 10
+		self.time20m = 20
+		self.time_m_intervals = [2, 3, 5, 10, 20]
+		self.time_m_symbols = ['2m', '3m', '5m', '10m', '20m']
+		self.time30m = 30
+		self.time45m = 45
+		self.time_m15_intervals = [30, 45]
+		self.time_m15_symbols = ['30m', '45m']
+		self.time2h = 2*60
+		self.time4h = 4*60
+		self.time6h = 6*60
+		self.time8h = 8*60
+		self.time12h = 12*60
+		self.time16h = 16*60
+		self.time_h_intervals = [2*60, 4*60, 6*60, 8*60, 12*60, 16*60]
+		self.time_h_symbols = ['2h', '4h', '6h', '8h', '12h', '16h']
+		self.time2d = 2*24*60
+		self.time3d = 3*24*60
+		self.time_d_intervals = [2*24*60, 3*24*60]
+		self.time_d_symbols	 = ['2d', '3d']
+		self.time1w = 7*24*60
+		self.time2w = 14*24*60
+		self.time_w_intervals = [14*24*60]
+		self.time_w_symbols = ['2w']
+		self.time2M = 2*30*24*60
+		self.time3M = 3*30*24*60
+		self.time6M = 6*30*24*60
+		self.time1y = 12*30*24*60
+		self.time_M_intervals = [2*30*24*60, 3*30*24*60, 6*30*24*60, 12*30*24*60]
+		self.time_M_symbols	 = ['2M', '3M', '6M', '1y']
+		self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
+		self.tf_df_dict = {}
+		self.ema_symbols = ['5m', '15m', '30m', '45m', '1h', '2h', '4h', '8h', '12h', '1d', '2d', '3d']
+		self.tf_df = TF_DF()
+	def init_data(self, dataframe, intervals, timeframes):
+		for index in range(0, len(intervals)):
+			timeframe = timeframes[index]
+			interval = intervals[index]
+			self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
+	def init_dataframes(self, dataframe_m=None, dataframe_15m=None, dataframe_h=None, dataframe_d=None, dataframe_w=None, dataframe_M=None):
+		self.tf_df_dict = {}
+		if dataframe_m is not None:
+			self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
+			self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
+		if dataframe_15m is not None:
+			self.tf_df_dict['15m'] = TF_DF(dataframe_15m, 1, '15m')
+			self.init_data(dataframe_15m, self.time_m15_intervals, self.time_m15_symbols)
+		if dataframe_h is not None:
+			self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h')
+			self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols)
+		if dataframe_d is not None:
+			self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
+			self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
+		if dataframe_w is not None and False:
+			self.tf_df_dict['1w'] = TF_DF(dataframe_w, 1, '1w')
+			self.init_data(dataframe_w, self.time_w_intervals, self.time_w_symbols)
+		if dataframe_M is not None and False:
+			self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
+			self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
+	def get_ema52_dict(self):
+		if len(self.tf_df_dict) > 0:
+			return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols}
+		return None
+	def get_ema24_dict(self):
+		if len(self.tf_df_dict) > 0:
+			return {key: self.tf_df_dict[key].get_ema24() for key in self.ema_symbols}
+		return None
+	def get_current_klc_dict(self):
+		if len(self.tf_df_dict) > 0:
+			return {key: self.tf_df_dict[key].get_current_klc() for key in self.ema_symbols}
+		return None
+	def get_tf_df_by_timeframe(self, timeframe):
+		if timeframe in self.tf_df_dict:
+			return self.tf_df_dict[timeframe]
+		return None
+	def check_price_ema52(self, price):
+		key_list = []
+		if len(self.tf_df_dict) > 0:
+			ema52_dict = self.get_ema52_dict()
+			for key in self.ema_symbols:
+				if ema52_dict[key] is not None:
+					if abs(price - ema52_dict[key]) < 100:
+						key_list.append(key)
+		return key_list
+	def get_ema_bsp(self, long_tf='1h', short_tf='15m'):
+		if long_tf in self.tf_df_dict and short_tf in self.tf_df_dict:
+			long_df = self.tf_df_dict[long_tf]
+			short_df = self.tf_df_dict[short_tf]
+			return long_df.get_ema_bsp(short_df)
+		return None
+
+
+ 
+ 
+ 
+	def get_bsp_state(self, dataframe):
+		return self.tf_df.get_bsp_state(dataframe)
+
+	def get_structure_zones(self, current_price=None, config=None):
+		if config is None:
+			config = StructureZoneConfig()
+		return analyze_structure_zones(
+			self.tf_df_dict,
+			self.ema_symbols,
+			current_price=current_price,
+			config=config,
+		)
+ 	# TF_DF methods ------------------------------------------
+	def get_ema_state(self, dataframe):
+		return self.tf_df.get_ema_state(dataframe)
+	def get_klu_state(self, dataframe):
+		return self.tf_df.get_klu_state(dataframe)
+	def check_fx(self, klc):
+		return self.tf_df.check_fx(klc)
+	def add_indicators1(self, df):
+		return self.tf_df.add_indicators(df)
+	def get_bi_list(self, dataframe):
+		return self.tf_df.get_bi_list(dataframe)
+	def get_kl_data(self, dataframe:DataFrame):
+		return self.tf_df.cal_kl_data(dataframe)
+	def cal_volume_ratio(self, dataframe, window=10):
+		return self.tf_df.cal_volume_ratio(dataframe, window)
+	def calculate_seg_zs(self, bi_list, seg_list):
+		return self.get_seg_zs_list(bi_list, seg_list)
+	def get_seg_list(self, bi_list):
+		return self.tf_df.get_seg_list(bi_list)
+	def cal_trend(self, klc_list):
+		return self.tf_df.cal_trend(klc_list)
+	def check_top_fx(self, last_bottom, klc):
+		return self.tf_df.check_top_fx(last_bottom, klc)
+	def check_bottom_fx(self, last_top, klc):
+		return self.tf_df.check_bottom_fx(last_top, klc)
+	def cal_bi_list(self, klc_list):
+		return self.tf_df.cal_bi_list(klc_list)
+	def find_first_bsp(self, bi_list, bi_zs_list):
+		return self.tf_df.find_first_bsp(bi_list, bi_zs_list)
+	def find_second_bsp(self, bi_list, first_bsp_list):
+		return self.tf_df.find_second_bsp(bi_list, first_bsp_list)
+	def find_all_bsp(self, bi_list, bi_zs_list):
+		return self.tf_df.find_all_bsp(bi_list, bi_zs_list)
+	def get_zs_list(self, bi_list, seg_list):
+		return self.tf_df.get_zs_list(bi_list, seg_list)
+	def cal_bi_zs(self, seg_list):
+		return self.tf_df.cal_bi_zs(seg_list)
+	def cal_bi_zs_list(self, bi_list):
+		#return self.tf_df.cal_bi_zs(bi_list)
+		return self.tf_df.cal_bi_zs_list(bi_list)
+	def get_bi_zs_list(self, bi_list):
+		return self.tf_df.get_bi_zs_list(bi_list)
+	def get_decimal(self, value):
+		return Decimal("{:.2f}".format(value))
+	def get_klc_list(self, klu_list):
+		return self.tf_df.get_klc_list(klu_list)
+	def get_klu_list(self, dataframe):
+		return self.tf_df.cal_klu_pattern(self.get_kl_data(dataframe))

chanlun/pipeline/timeframe.py

@@ -0,0 +1,81 @@
+from datetime import timedelta
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval
+
+from chanlun.core.ChanBI import ChanBI
+from chanlun.core.ChanBIZS import ChanBIZS
+from chanlun.core.ChanBSP import ChanBSP
+from chanlun.core.ChanEnum import (
+    Chan_BI_DIR,
+    Chan_BSP_DIR,
+    Chan_BSP_TYPE,
+    Chan_FX_TYPE,
+    Chan_K_DIR,
+    Chan_KLC_FX,
+    Chan_KLC_STATE,
+    Chan_KLINE_DIR,
+    Chan_KLU_PATTERN,
+    Chan_PRICE_TREND,
+    Chan_SEG_DIR,
+    Chan_ZS_DIR,
+)
+from chanlun.core.ChanKLC import ChanKLC
+from chanlun.core.ChanKLU import ChanKLU
+from chanlun.core.ChanSBI import ChanSBI
+from chanlun.core.ChanSEG import ChanSEG
+from chanlun.core.ChanZS import ChanZS, ChanZS_Big
+from chanlun.indicators.ChanMACD import ChanMACD
+from chanlun.pipeline.builders.bi import BiBuilderMixin
+from chanlun.pipeline.builders.bsp import BspBuilderMixin
+from chanlun.pipeline.builders.indicators import IndicatorsBuilderMixin
+from chanlun.pipeline.builders.kline import KlineBuilderMixin
+from chanlun.pipeline.builders.seg import SegBuilderMixin
+from chanlun.pipeline.builders.zs import ZsBuilderMixin
+
+class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilderMixin, ZsBuilderMixin, BspBuilderMixin):
+	def __init__(self, df=None, interval=0, timeframe=None):
+		if df is not None:
+			self.init_TF_DF(df, interval, timeframe)
+	def init_TF_DF(self, df, interval, timeframe):
+		self.timeframe = timeframe
+		self.interval = interval
+		# 检查 DataFrame 是否为空或没有 date 列
+		if df is None or df.empty:
+			raise ValueError(f"DataFrame for {timeframe} is empty. Please download data first.")
+		if 'date' not in df.columns:
+			raise ValueError(f"DataFrame for {timeframe} missing 'date' column. Columns: {df.columns.tolist()}")
+		# interval=1 时不需要重采样
+		if interval == 1:
+			self.dataframe = df.copy()
+		else:
+			self.dataframe = resample_to_interval(df, interval)
+		#print(self.timeframe, len(self.dataframe))
+		self.dataframe = self.add_indicators(self.dataframe)
+		self.klu_list = []
+		self.klc_list = []
+		self.bi_list = []
+		self.zs_list = []
+		self.bsp_list = []
+		self.seg_list = []
+		self.klc_fx_list = []
+		self.klu_list = self.cal_kl_data(self.dataframe)
+		self.klc_list = self.get_klc_list(self.klu_list)
+		self.bi_list = self.cal_bi_list(self.klc_list)
+		self.seg_list = self.get_seg_list(self.bi_list)
+		self.zs_list = self.get_zs_list(self.bi_list, self.seg_list)
+		self.big_zs_list = self.get_big_zs_list(self.zs_list)
+		# get_klc_list 内已算过 ChanMACD，直接复用
+		self.chanmacd = getattr(self, '_last_chan_macd', None)
+		if self.chanmacd is None:
+			self.chanmacd = ChanMACD(self.klu_list)
+		self.klu_list = self.chanmacd.klu_list
+	
+						
+	def get_current_klc(self):
+		if len(self.klc_list) > 0:
+			return self.klc_list[-2]
+		return None

config/ChanLun_BTC_1m.json

@@ -0,0 +1,89 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 1,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.chanlun_btc_1m.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short" : true,
+    "timeframe" : "1m",
+    "process_only_new_candles" : false,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "order_types": {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": false
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {
+            "proxies": {
+                "http": "http://127.0.0.1:7897",                                                                                     
+                "https": "http://127.0.0.1:7897" 
+            }
+        },
+        "ccxt_async_config": {
+            "aiohttp_proxy": "http://127.0.0.1:7897"
+        },
+        "pair_whitelist": [
+            "BTC/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": true,
+        "token": "8197349375:AAH208JghCq8raFYF-IpnobYknCr6iGDH_0",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8814,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
+        "ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "FreqTrade007"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 1
+    }
+}

config/ChanLun_BTC_5m.json

@@ -0,0 +1,68 @@
+{
+    "max_open_trades": 1,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.chanlun_btc_5m.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short" : true,
+    "timeframe" : "5m",
+    "process_only_new_candles" : false,
+    "unfilledtimeout": {
+        "entry": 5,
+        "exit": 5,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "order_types": {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": false
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {
+            "proxies": {
+                "http": "http://127.0.0.1:7897",
+                "https": "http://127.0.0.1:7897"
+            }
+        },
+        "ccxt_async_config": {
+            "aiohttp_proxy": "http://127.0.0.1:7897"
+        },
+        "pair_whitelist": [
+            "BTC/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

data_provider/Dockerfile

@@ -1,20 +0,0 @@
-FROM python:3.11-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    PIP_NO_CACHE_DIR=1
-
-WORKDIR /app
-
-COPY requirements.txt /app/requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . /app
-
-ENV CONFIG_PATH=/app/config.json \
-    UVICORN_HOST=0.0.0.0 \
-    UVICORN_PORT=9009
-
-EXPOSE 9009
-
-CMD ["python", "-m", "main"]
-

data_provider/config.json

@@ -1,18 +0,0 @@
-{
-  "exchange": "binance",
-  "symbols": [
-    "BTC/USDT:USDT", 
-    "ETH/USDT:USDT", 
-    "SOL/USDT:USDT",
-    "WIF/USDT:USDT",  
-    "AAVE/USDT:USDT", 
-    "SUI/USDT:USDT", 
-    "1INCH/USDT:USDT", 
-    "DOGE/USDT:USDT", 
-    "UNI/USDT:USDT"
-  ],
-  "start_time": "2024-01-01T00:00:00Z",
-  "timeframes": ["1m", "1h", "1d", "1w"],
-  "data_dir": "./data"
-}
-

data_provider/docker-compose.yml

@@ -1,15 +0,0 @@
-services:
-  data_provider:
-    build: .
-    container_name: data-provider
-    restart: unless-stopped
-    environment:
-      CONFIG_PATH: /app/config.json
-      UVICORN_HOST: 0.0.0.0
-      UVICORN_PORT: "9009"
-    volumes:
-      - ./config.json:/app/config.json:ro
-      - ./data:/app/data
-    ports:
-      - "9009:9009"
-

data_provider/main.py

@@ -1,947 +0,0 @@
-"""
-Chan 数据服务：用 ccxt 从交易所拉取 K 线，内存缓存 + CSV 落盘；
-后台线程定期增量刷新，断线时记录 resume_since 以免漏 K；
-配置中的基础周期（如 1m/1h）可合成 DERIVED_TIMEFRAME_PLAN 中的衍生周期。
-"""
-import asyncio
-import csv
-import json
-import logging
-import os
-import threading
-import time
-from contextlib import asynccontextmanager
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Dict, Iterable, List, Optional
-
-import ccxt  # type: ignore
-import pandas as pd  # type: ignore
-from fastapi import FastAPI, HTTPException, Query, WebSocket, WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-import uvicorn
-from technical.util import resample_to_interval
-
-# docker compose logs --tail=200
-# docker compose down && docker compose build --no-cache && docker compose up -d
-
-# 基础周期枚举顺序（用于衍生周期展示顺序）；仅允许集合内周期作为交易所直接拉取的 tf
-TIMEFRAME_ORDER = ["1m", "1h", "1d", "1w"]
-ALLOWED_TIMEFRAMES = set(TIMEFRAME_ORDER)
-# 各基础周期一根 K 线的毫秒长度（用于历史分页与断线回退）
-TIMEFRAME_TO_MS: Dict[str, int] = {
-    "1m": 60_000,
-    "1h": 3_600_000,
-    "1d": 86_400_000,
-    "1w": 604_800_000,
-}
-# 每个基础周期可派生出的合成周期列表（由该基础周期 K 线 resample 得到）
-DERIVED_TIMEFRAME_PLAN: Dict[str, List[str]] = {
-    "1m": ["2m", "3m", "4m", "5m", "10m", "15m", "20m", "25m", "30m", "45m"],
-    "1h": ["2h", "3h", "4h", "5h", "6h", "7h", "8h", "9h", "10", "11h", "12h", "16h", "20h"],
-    "1d": ["2d", "3d", "4d", "5d", "6d"],
-    "1w": ["2w", "3w"],
-}
-CSV_FIELDNAMES = ["timestamp", "datetime", "open", "high", "low", "close", "volume"]
-DEFAULT_LIMIT = 500
-RECENT_CANDLE_LIMIT = 10
-RECENT_FETCH_INTERVAL = 5  # 后台刷新循环休眠秒数
-PERSIST_INTERVAL = 600  # 全量落盘周期（秒）
-WS_UPDATE_CANDLE_COUNT = 2  # WebSocket 增量推送最近 K 线根数
-
-
-logger = logging.getLogger("data_provider")
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-)
-
-
-def to_utc_iso(timestamp_ms: int) -> str:
-    """将毫秒时间戳格式化为 UTC ISO 字符串（末尾 Z）。"""
-    dt = datetime.fromtimestamp(timestamp_ms / 1000, tz=timezone.utc)
-    return dt.isoformat().replace("+00:00", "Z")
-
-
-def parse_timestamp(value: Optional[object]) -> Optional[int]:
-    """解析查询参数中的时间为 UTC 毫秒时间戳；支持数字或 ISO 字符串。"""
-    if value is None:
-        return None
-    if isinstance(value, (int, float)):
-        return int(value)
-    if isinstance(value, str):
-        text = value.strip()
-        if not text:
-            return None
-        if text.isdigit():
-            return int(text)
-        if text.endswith("Z"):
-            text = text[:-1] + "+00:00"
-        try:
-            dt = datetime.fromisoformat(text)
-        except ValueError as exc:  # pragma: no cover - informative logging
-            raise ValueError(f"无法解析时间字符串: {value}") from exc
-        if dt.tzinfo is None:
-            dt = dt.replace(tzinfo=timezone.utc)
-        else:
-            dt = dt.astimezone(timezone.utc)
-        return int(dt.timestamp() * 1000)
-    raise ValueError(f"不支持的时间格式: {value}")
-
-
-def candle_to_dict(candle: Iterable[float]) -> Dict[str, float]:
-    """ccxt OHLCV 单根 [ts, o, h, l, c, v] 转为内部字典结构。"""
-    ts = int(candle[0])
-    return {
-        "timestamp": ts,
-        "datetime": to_utc_iso(ts),
-        "open": float(candle[1]),
-        "high": float(candle[2]),
-        "low": float(candle[3]),
-        "close": float(candle[4]),
-        "volume": float(candle[5]),
-    }
-
-
-def timeframe_to_minutes(tf: str) -> Optional[int]:
-    """将如 15m、2h 转为「分钟数」，供 resample 与衍生周期计算。"""
-    if not tf:
-        return None
-    unit = tf[-1]
-    try:
-        value = int(tf[:-1])
-    except ValueError:
-        return None
-    multiplier = {
-        "m": 1,
-        "h": 60,
-        "d": 1_440,
-        "w": 10_080,
-    }.get(unit)
-    if multiplier is None:
-        return None
-    return value * multiplier
-
-
-class WebSocketManager:
-    """管理 WebSocket 连接及订阅，线程安全地广播 K 线更新。"""
-
-    def __init__(self) -> None:
-        self._subscriptions: Dict[tuple, set] = {}
-        self._async_lock = asyncio.Lock()
-        self._loop: Optional[asyncio.AbstractEventLoop] = None
-
-    def set_loop(self, loop: asyncio.AbstractEventLoop) -> None:
-        self._loop = loop
-
-    async def connect(self, ws: WebSocket) -> None:
-        await ws.accept()
-        logger.info("WebSocket 客户端已连接")
-
-    async def disconnect(self, ws: WebSocket) -> None:
-        async with self._async_lock:
-            for key in list(self._subscriptions):
-                self._subscriptions[key].discard(ws)
-                if not self._subscriptions[key]:
-                    del self._subscriptions[key]
-        logger.info("WebSocket 客户端已断开")
-
-    async def subscribe(self, ws: WebSocket, symbol: str, timeframe: str) -> None:
-        key = (symbol, timeframe)
-        async with self._async_lock:
-            self._subscriptions.setdefault(key, set()).add(ws)
-        logger.info("WebSocket 订阅: %s %s", symbol, timeframe)
-
-    async def unsubscribe(self, ws: WebSocket, symbol: str, timeframe: str) -> None:
-        key = (symbol, timeframe)
-        async with self._async_lock:
-            if key in self._subscriptions:
-                self._subscriptions[key].discard(ws)
-                if not self._subscriptions[key]:
-                    del self._subscriptions[key]
-
-    def has_subscribers(self, symbol: str, timeframe: str) -> bool:
-        """非异步快速检查（供同步线程调用）。"""
-        return bool(self._subscriptions.get((symbol, timeframe)))
-
-    async def broadcast(
-        self, symbol: str, timeframe: str, candles: List[Dict], msg_type: str = "kline",
-    ) -> None:
-        key = (symbol, timeframe)
-        async with self._async_lock:
-            subscribers = list(self._subscriptions.get(key, set()))
-        if not subscribers:
-            return
-        message = json.dumps(
-            {"type": msg_type, "symbol": symbol, "timeframe": timeframe, "data": candles},
-            ensure_ascii=False,
-        )
-        dead: list = []
-        for ws in subscribers:
-            try:
-                await ws.send_text(message)
-            except Exception:
-                dead.append(ws)
-        if dead:
-            async with self._async_lock:
-                for ws in dead:
-                    self._subscriptions.get(key, set()).discard(ws)
-
-    def broadcast_from_thread(
-        self, symbol: str, timeframe: str, candles: List[Dict], msg_type: str = "kline",
-    ) -> None:
-        """供同步后台线程调用，将广播提交到 asyncio 事件循环。"""
-        if self._loop is None or self._loop.is_closed():
-            return
-        asyncio.run_coroutine_threadsafe(
-            self.broadcast(symbol, timeframe, candles, msg_type),
-            self._loop,
-        )
-
-
-class DataProvider:
-    """封装交易所连接、本地 CSV、内存缓存、断线恢复与衍生周期聚合。"""
-
-    def __init__(self, config_path: Path) -> None:
-        self.config_path = config_path
-        self.config = self._load_config()
-        self.exchange_name: str = self.config["exchange"]
-        self.symbols: List[str] = self._load_symbols(self.config)
-        self.timeframes: List[str] = self._validate_timeframes(self.config.get("timeframes"))
-        self.data_dir = Path(self.config.get("data_dir", "./data")).expanduser()
-        start = parse_timestamp(self.config.get("start_time"))
-        if start is None:
-            raise ValueError("配置文件必须包含 start_time 字段")
-        self.start_time_ms: int = start
-        self.exchange = self._init_exchange()
-        self.data: Dict[str, Dict[str, List[Dict[str, float]]]] = {
-            symbol: {tf: [] for tf in self.timeframes} for symbol in self.symbols
-        }
-        # 衍生周期 -> 用于合成的交易所基础周期（每个衍生只对应一个 base）
-        self.derived_map: Dict[str, str] = {}
-        for base_tf in self.timeframes:
-            for derived_tf in DERIVED_TIMEFRAME_PLAN.get(base_tf, []):
-                self.derived_map.setdefault(derived_tf, base_tf)
-        # 衍生周期展示顺序：按 TIMEFRAME_ORDER 中的基础周期依次展开
-        derived_order: List[str] = []
-        for base_tf in TIMEFRAME_ORDER:
-            if base_tf not in self.timeframes:
-                continue
-            for derived_tf in DERIVED_TIMEFRAME_PLAN.get(base_tf, []):
-                if derived_tf in self.derived_map and derived_tf not in derived_order:
-                    derived_order.append(derived_tf)
-        self.available_timeframes: List[str] = list(self.timeframes) + derived_order
-        self._lock = threading.RLock()
-        self._ready = threading.Event()
-        self._stop_event = threading.Event()
-        self._fetch_thread: Optional[threading.Thread] = None
-        self._persist_thread: Optional[threading.Thread] = None
-        # 记录断线后需要从哪个 since 重新拉取（symbol -> timeframe -> since_ms）
-        self._resume_since: Dict[str, Dict[str, int]] = {}
-        # 恢复点持久化文件
-        self._resume_file: Path = self.data_dir / "resume_since.json"
-        # 尝试加载历史恢复点
-        self._load_resume_since()
-        self._update_callbacks: List = []
-
-    def _load_config(self) -> Dict[str, object]:
-        """读取 JSON 配置文件。"""
-        if not self.config_path.exists():
-            raise FileNotFoundError(f"未找到配置文件: {self.config_path}")
-        with self.config_path.open("r", encoding="utf-8") as fp:
-            return json.load(fp)
-
-    def _load_symbols(self, config: Dict[str, object]) -> List[str]:
-        """从 symbols 列表、逗号分隔字符串或单字段 symbol 解析交易对，去重保序。"""
-        raw_symbols: List[str] = []
-        symbols_value = config.get("symbols")
-        if isinstance(symbols_value, list):
-            raw_symbols = [str(item).strip() for item in symbols_value if isinstance(item, str) and item.strip()]
-        elif isinstance(symbols_value, str) and symbols_value.strip():
-            raw_symbols = [item.strip() for item in symbols_value.split(",") if item.strip()]
-        symbol_single = config.get("symbol")
-        if not raw_symbols and isinstance(symbol_single, str) and symbol_single.strip():
-            raw_symbols = [symbol_single.strip()]
-        if not raw_symbols:
-            raise ValueError("配置文件必须提供 symbols（列表或逗号分隔字符串）或 symbol 字段")
-        unique: List[str] = []
-        for item in raw_symbols:
-            if item not in unique:
-                unique.append(item)
-        return unique
-
-    def _validate_timeframes(self, configured: Optional[Iterable[str]]) -> List[str]:
-        """校验周期在允许集合内；未配置则默认 TIMEFRAME_ORDER 全部；顺序优先按 TIMEFRAME_ORDER。"""
-        if not configured:
-            return list(TIMEFRAME_ORDER)
-        invalid = [tf for tf in configured if tf not in ALLOWED_TIMEFRAMES]
-        if invalid:
-            raise ValueError(f"不支持的时间周期: {invalid}. 允许值: {sorted(ALLOWED_TIMEFRAMES)}")
-        unique = []
-        seen = set()
-        for tf in TIMEFRAME_ORDER:
-            if tf in configured and tf not in seen:
-                unique.append(tf)
-                seen.add(tf)
-        for tf in configured:
-            if tf not in seen:
-                unique.append(tf)
-                seen.add(tf)
-        return unique
-
-    def _init_exchange(self):
-        """实例化 ccxt 交易所，币安期货默认 defaultType=future，并 load_markets。"""
-        if not hasattr(ccxt, self.exchange_name):
-            raise ValueError(f"不支持的交易所: {self.exchange_name}")
-        exchange_class = getattr(ccxt, self.exchange_name)
-        exchange = exchange_class({"enableRateLimit": True})
-        if exchange.id == "binance":
-            exchange.options.setdefault("defaultType", "future")
-        exchange.load_markets()
-        logger.info("已初始化交易所 %s", exchange.id)
-        return exchange
-
-    def _data_file_path(self, symbol: str, timeframe: str) -> Path:
-        """单交易对单周期的 CSV 路径：data_dir/tf/exchange_symbol_tf.csv。"""
-        symbol_safe = symbol.replace("/", "_").replace(":", "_")
-        return self.data_dir / timeframe / f"{self.exchange.id}_{symbol_safe}_{timeframe}.csv"
-
-    def _load_local(self, symbol: str, timeframe: str) -> List[Dict[str, float]]:
-        """启动时从磁盘加载已有 K 线，损坏行跳过，按时间排序。"""
-        path = self._data_file_path(symbol, timeframe)
-        if not path.exists():
-            return []
-        loaded: List[Dict[str, float]] = []
-        with path.open("r", encoding="utf-8", newline="") as fp:
-            reader = csv.DictReader(fp)
-            for row in reader:
-                try:
-                    loaded.append(
-                        {
-                            "timestamp": int(row["timestamp"]),
-                            "datetime": row.get("datetime") or to_utc_iso(int(row["timestamp"])),
-                            "open": float(row["open"]),
-                            "high": float(row["high"]),
-                            "low": float(row["low"]),
-                            "close": float(row["close"]),
-                            "volume": float(row["volume"]),
-                        }
-                    )
-                except (KeyError, ValueError):
-                    logger.warning("忽略损坏的行: %s", row)
-        loaded.sort(key=lambda item: item["timestamp"])
-        logger.info("交易对 %s 时间周期 %s 加载本地K线数量: %s", symbol, timeframe, len(loaded))
-        return loaded
-
-    def _merge_candles(
-        self,
-        timeframe: str,
-        base: List[Dict[str, float]],
-        new_candles: Iterable[Iterable[float]],
-    ) -> List[Dict[str, float]]:
-        """按 timestamp 去重合并，新数据覆盖同时间戳旧数据。"""
-        merged = {entry["timestamp"]: entry for entry in base}
-        for candle in new_candles:
-            entry = candle_to_dict(candle)
-            merged[entry["timestamp"]] = entry
-        ordered = list(sorted(merged.values(), key=lambda item: item["timestamp"]))
-        logger.debug("时间周期 %s 合并后K线数量: %s", timeframe, len(ordered))
-        return ordered
-
-    def _write_to_disk(self, symbol: str, timeframe: str, data: List[Dict[str, float]]) -> None:
-        """先写临时文件再 replace，避免写入中断导致 CSV 损坏。"""
-        path = self._data_file_path(symbol, timeframe)
-        path.parent.mkdir(parents=True, exist_ok=True)
-        tmp_path = path.with_suffix(path.suffix + ".tmp")
-        try:
-            with tmp_path.open("w", encoding="utf-8", newline="") as fp:
-                writer = csv.DictWriter(fp, fieldnames=CSV_FIELDNAMES)
-                writer.writeheader()
-                writer.writerows(data)
-            os.replace(tmp_path, path)
-        finally:
-            if tmp_path.exists():
-                try:
-                    tmp_path.unlink()
-                except OSError:
-                    pass
-        logger.info("交易对 %s 时间周期 %s 已写入磁盘 (%s 根K线)", symbol, timeframe, len(data))
-
-    def _fetch_history(self, symbol: str, timeframe: str, since_ms: int) -> List[List[float]]:
-        """从 since_ms 分页拉取直到接近当前时间；遇限频则 sleep 重试。"""
-        results: List[List[float]] = []
-        limit = 1500
-        now_ms = self.exchange.milliseconds()
-        tf_ms = TIMEFRAME_TO_MS[timeframe]
-        fetch_since = since_ms
-        max_rounds = 5000
-        rounds = 0
-        while fetch_since < now_ms and rounds < max_rounds:
-            rounds += 1
-            try:
-                candles = self.exchange.fetch_ohlcv(
-                    symbol,
-                    timeframe=timeframe,
-                    since=fetch_since,
-                    limit=limit,
-                )
-            except ccxt.RateLimitExceeded as exc:
-                logger.warning("触发频率限制，等待: %s", exc)
-                time.sleep(self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 1)
-                continue
-            except ccxt.BaseError as exc:
-                logger.error("拉取历史K线失败 (%s, %s): %s", timeframe, fetch_since, exc)
-                time.sleep(5)
-                continue
-            if not candles:
-                break
-            results.extend(candles)
-            last_ts = candles[-1][0]
-            fetch_since = last_ts + tf_ms
-            if last_ts >= now_ms - tf_ms:
-                break
-            time.sleep(self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 0.2)
-        logger.info("交易对 %s 时间周期 %s 拉取历史K线数量: %s", symbol, timeframe, len(results))
-        return results
-
-    def initialize(self) -> None:
-        """阻塞式启动：加载本地、从倒数第二根或配置起点补历史、写盘并 set _ready。"""
-        logger.info("开始初始化数据提供商")
-        for symbol in self.symbols:
-            for timeframe in self.timeframes:
-                existing = self._load_local(symbol, timeframe)
-                tf_ms = TIMEFRAME_TO_MS[timeframe]
-                last_ts = existing[-1]["timestamp"] if existing else None
-                if last_ts is not None:
-                    if len(existing) >= 2:
-                        # 从倒数第二根起拉，避免最后一根未收盘重复/缺口
-                        fetch_since = existing[-2]["timestamp"]
-                    else:
-                        fetch_since = max(0, last_ts - tf_ms)
-                else:
-                    fetch_since = self.start_time_ms
-                logger.debug(
-                    "初始化拉取参数",
-                    extra={
-                        "symbol": symbol,
-                        "timeframe": timeframe,
-                        "existing_last": last_ts,
-                        "fetch_since": fetch_since,
-                        "tf_ms": tf_ms,
-                    },
-                )
-                history = self._fetch_history(symbol, timeframe, fetch_since)
-                merged = self._merge_candles(timeframe, existing, history)
-                with self._lock:
-                    self.data.setdefault(symbol, {})[timeframe] = merged
-                self._write_to_disk(symbol, timeframe, merged)
-        self._ready.set()
-        logger.info("数据初始化完成")
-        
-    def resample_df(self, df: pd.DataFrame, interval: int) -> pd.DataFrame:
-        """将基础周期 DataFrame 聚合为 interval 分钟周期（freqtrade technical.util）。"""
-        return resample_to_interval(df, interval)
-    
-    def _save_resume_since(self) -> None:
-        """将断线恢复点持久化到 resume_since.json（原子替换）。"""
-        path = self._resume_file
-        path.parent.mkdir(parents=True, exist_ok=True)
-        tmp_path = path.with_suffix(path.suffix + ".tmp")
-        with self._lock:
-            snapshot = {
-                symbol: {tf: int(since) for tf, since in tf_map.items()}
-                for symbol, tf_map in self._resume_since.items()
-            }
-        try:
-            with tmp_path.open("w", encoding="utf-8") as fp:
-                json.dump(snapshot, fp, ensure_ascii=False, separators=(",", ":"))
-            os.replace(tmp_path, path)
-        finally:
-            if tmp_path.exists():
-                try:
-                    tmp_path.unlink()
-                except OSError:
-                    pass
-        logger.debug("恢复点已保存到磁盘: %s", path)
-    
-    def _load_resume_since(self) -> None:
-        """启动时加载恢复点；与内存合并时取更早的 since，避免漏拉。"""
-        path = self._resume_file
-        if not path.exists():
-            return
-        try:
-            with path.open("r", encoding="utf-8") as fp:
-                raw = json.load(fp)
-        except Exception as exc:
-            logger.warning("恢复点文件读取失败，忽略: %s (%s)", path, exc)
-            return
-        if not isinstance(raw, dict):
-            logger.warning("恢复点文件格式错误，忽略: %s", path)
-            return
-        loaded: Dict[str, Dict[str, int]] = {}
-        for symbol, tf_map in raw.items():
-            if not isinstance(tf_map, dict):
-                continue
-            per_symbol: Dict[str, int] = {}
-            for timeframe, since in tf_map.items():
-                try:
-                    per_symbol[str(timeframe)] = int(since)
-                except Exception:
-                    continue
-            if per_symbol:
-                loaded[str(symbol)] = per_symbol
-        if not loaded:
-            return
-        with self._lock:
-            # 合并为更早的 since，避免遗漏
-            for symbol, tf_map in loaded.items():
-                cur = self._resume_since.setdefault(symbol, {})
-                for timeframe, since in tf_map.items():
-                    prev = cur.get(timeframe)
-                    if prev is None or since < prev:
-                        cur[timeframe] = since
-        logger.info("已加载恢复点: %s", path)
-    
-    def _get_resume_since(self, symbol: str, timeframe: str) -> Optional[int]:
-        """若曾断线，返回应从哪一毫秒起补拉该 symbol/tf。"""
-        with self._lock:
-            return self._resume_since.get(symbol, {}).get(timeframe)
-    
-    def _set_resume_since(self, symbol: str, timeframe: str, since_ms: int) -> None:
-        """断线时写入恢复点（取更早的 since 以免漏数据），并持久化到磁盘。"""
-        with self._lock:
-            per_symbol = self._resume_since.setdefault(symbol, {})
-            prev = per_symbol.get(timeframe)
-            # 取更早的 since，避免跳过数据
-            if prev is None or since_ms < prev:
-                per_symbol[timeframe] = since_ms
-                logger.warning(
-                    "记录断线恢复点: %s %s since=%s (%s)",
-                    symbol,
-                    timeframe,
-                    since_ms,
-                    to_utc_iso(since_ms),
-                )
-        # 同步写盘
-        self._save_resume_since()
-    
-    def _clear_resume_since(self, symbol: str, timeframe: str) -> None:
-        """补数成功后清除该 symbol/tf 的恢复点。"""
-        with self._lock:
-            if symbol in self._resume_since and timeframe in self._resume_since[symbol]:
-                del self._resume_since[symbol][timeframe]
-                if not self._resume_since[symbol]:
-                    del self._resume_since[symbol]
-                logger.info("清除断线恢复点: %s %s", symbol, timeframe)
-        # 同步写盘
-        self._save_resume_since()
-    
-    def on_update(self, callback) -> None:
-        """注册数据更新回调（签名: callback(symbol, timeframe)）。"""
-        self._update_callbacks.append(callback)
-
-    def _notify_update(self, symbol: str, timeframe: str) -> None:
-        """通知所有回调：某 symbol/timeframe 数据已更新。"""
-        for cb in self._update_callbacks:
-            try:
-                cb(symbol, timeframe)
-            except Exception as exc:
-                logger.error("数据更新回调异常: %s", exc)
-
-    def start_background_workers(self) -> None:
-        """启动增量刷新线程与周期性落盘线程。"""
-        if self._fetch_thread and self._fetch_thread.is_alive():
-            return
-        self._stop_event.clear()
-        self._fetch_thread = threading.Thread(target=self._refresh_loop, name="refresh-loop", daemon=True)
-        self._persist_thread = threading.Thread(target=self._persist_loop, name="persist-loop", daemon=True)
-        self._fetch_thread.start()
-        self._persist_thread.start()
-        logger.info("后台线程已启动")
-
-    def stop(self) -> None:
-        """停止后台线程（应用关闭时 lifespan finally 调用）。"""
-        self._stop_event.set()
-        if self._fetch_thread:
-            self._fetch_thread.join(timeout=5)
-        if self._persist_thread:
-            self._persist_thread.join(timeout=5)
-        logger.info("数据提供商已停止")
-
-    def _refresh_loop(self) -> None:
-        """轮询各 symbol/tf：有恢复点则先补历史，否则 fetch 最近 RECENT_CANDLE_LIMIT 根。"""
-        while not self._stop_event.is_set():
-            for symbol in self.symbols:
-                for timeframe in self.timeframes:
-                    try:
-                        # 若存在断线恢复点，则优先从该 since 补齐历史数据
-                        resume_since = self._get_resume_since(symbol, timeframe)
-                        if resume_since is not None:
-                            logger.info(
-                                "开始断线后补数: %s %s since=%s (%s)",
-                                symbol,
-                                timeframe,
-                                resume_since,
-                                to_utc_iso(resume_since),
-                            )
-                            history = self._fetch_history(symbol, timeframe, resume_since)
-                            with self._lock:
-                                current = self.data.setdefault(symbol, {}).get(timeframe, [])
-                                merged = self._merge_candles(timeframe, current, history)
-                                self.data[symbol][timeframe] = merged
-                            self._notify_update(symbol, timeframe)
-                            self._clear_resume_since(symbol, timeframe)
-                        else:
-                            # 正常增量获取最近若干根K线
-                            candles = self.exchange.fetch_ohlcv(
-                                symbol,
-                                timeframe=timeframe,
-                                limit=RECENT_CANDLE_LIMIT,
-                            )
-                            if not candles:
-                                continue
-                            with self._lock:
-                                current = self.data.setdefault(symbol, {}).get(timeframe, [])
-                                merged = self._merge_candles(timeframe, current, candles)
-                                self.data[symbol][timeframe] = merged
-                            self._notify_update(symbol, timeframe)
-                    except ccxt.BaseError as exc:
-                        logger.error("更新最新K线失败 (%s %s): %s", symbol, timeframe, exc)
-                        # 记录应当从何时恢复拉取，避免重连后从当前时间开始导致丢K
-                        with self._lock:
-                            current = self.data.get(symbol, {}).get(timeframe, [])
-                        if current:
-                            last_ts = int(current[-1]["timestamp"])
-                        else:
-                            last_ts = self.start_time_ms
-                        tf_ms = TIMEFRAME_TO_MS[timeframe]
-                        # 回退一个周期，确保包含可能未完全收盘的K线，去重由 _merge_candles 处理
-                        since_ms = max(self.start_time_ms, last_ts - tf_ms)
-                        self._set_resume_since(symbol, timeframe, since_ms)
-                        time.sleep(2)
-                        continue
-            if self._stop_event.wait(RECENT_FETCH_INTERVAL):
-                break
-
-    def _persist_loop(self) -> None:
-        """每隔 PERSIST_INTERVAL 秒把内存快照写 CSV 并保存恢复点。"""
-        while not self._stop_event.wait(PERSIST_INTERVAL):
-            self._persist_all()
-
-    def _persist_all(self) -> None:
-        """在锁内复制 data 后落盘，避免长时间持锁。"""
-        if not self._ready.is_set():
-            return
-        with self._lock:
-            snapshot = {
-                symbol: {tf: list(data) for tf, data in tf_map.items()}
-                for symbol, tf_map in self.data.items()
-            }
-        for symbol, tf_map in snapshot.items():
-            for timeframe, data in tf_map.items():
-                self._write_to_disk(symbol, timeframe, data)
-        # 周期性也保存一次恢复点，保证一致性
-        self._save_resume_since()
-
-    def is_ready(self) -> bool:
-        return self._ready.is_set()
-
-    def wait_ready(self, timeout: Optional[float] = None) -> bool:
-        return self._ready.wait(timeout)
-
-    def get_available_timeframes(self) -> List[str]:
-        return list(self.available_timeframes)
-
-    def get_derived_timeframes(self) -> List[str]:
-        return list(self.derived_map.keys())
-
-    def _get_base_klines(
-        self,
-        symbol: str,
-        timeframe: str,
-        start_ms: Optional[int],
-        end_ms: Optional[int],
-        limit: Optional[int],
-    ) -> List[Dict[str, float]]:
-        """从内存读取已缓存的基础周期 K 线并按时间/limit 裁剪。"""
-        with self._lock:
-            candles = list(self.data.get(symbol, {}).get(timeframe, []))
-        if start_ms is not None:
-            candles = [row for row in candles if row["timestamp"] >= start_ms]
-        if end_ms is not None:
-            candles = [row for row in candles if row["timestamp"] <= end_ms]
-        if limit:
-            candles = candles[-limit:]
-        return candles
-
-    def get_klines(
-        self,
-        symbol: str,
-        timeframe: str,
-        start_time: Optional[object] = None,
-        end_time: Optional[object] = None,
-        limit: Optional[int] = None,
-    ) -> List[Dict[str, float]]:
-        """对外查询：基础周期直接返回；衍生周期从 derived_map 取 base，resample 后对齐时间戳再裁剪。"""
-        if symbol not in self.symbols:
-            raise HTTPException(status_code=404, detail=f"symbol {symbol} 不可用")
-        self.wait_ready()
-        start_ms = parse_timestamp(start_time)
-        end_ms = parse_timestamp(end_time)
-        if timeframe in self.timeframes:
-            return self._get_base_klines(symbol, timeframe, start_ms, end_ms, limit)
-        base_tf = self.derived_map.get(timeframe)
-        if not base_tf:
-            raise HTTPException(status_code=404, detail=f"{symbol} 时间周期 {timeframe} 不可用")
-        target_minutes = timeframe_to_minutes(timeframe)
-        if target_minutes is None:
-            raise HTTPException(status_code=400, detail=f"不支持的时间周期: {timeframe}")
-        target_ms = target_minutes * 60_000
-        # 起点前移一根目标周期长度，保证首根合成 K 边界完整
-        adjusted_start = None if start_ms is None else max(0, start_ms - target_ms)
-        base_candles = self._get_base_klines(symbol, base_tf, adjusted_start, end_ms, None)
-        if not base_candles:
-            return []
-        df = pd.DataFrame(base_candles)
-        if df.empty:
-            return []
-        df = df.drop_duplicates(subset=["timestamp"], keep="last").sort_values("timestamp")
-        df["date"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
-        # resample_to_interval 按「分钟」目标周期聚合 OHLCV
-        resampled = self.resample_df(df, target_minutes)
-        if resampled is None or resampled.empty:
-            return []
-        # 统一得到毫秒 timestamp 列（resample 可能返回 date 或 DatetimeIndex）
-        if "timestamp" in resampled.columns:
-            resampled_df = resampled.copy()
-        else:
-            resampled_df = resampled.copy()
-            if "date" in resampled_df.columns:
-                dates = pd.to_datetime(resampled_df["date"], utc=True, errors="coerce")
-                resampled_df["timestamp"] = (dates.astype("int64", copy=False) // 1_000_000).astype("int64")
-            elif isinstance(resampled_df.index, pd.DatetimeIndex):
-                idx = resampled_df.index
-                if idx.tz is None:
-                    idx = idx.tz_localize("UTC")
-                else:
-                    idx = idx.tz_convert("UTC")
-                resampled_df["timestamp"] = (idx.astype("int64", copy=False) // 1_000_000).astype("int64")
-            else:
-                raise HTTPException(status_code=500, detail=f"聚合结果缺少 timestamp 列 ({timeframe})")
-        resampled_df = resampled_df.dropna(subset=["timestamp"]).sort_values("timestamp")
-        if start_ms is not None:
-            resampled_df = resampled_df[resampled_df["timestamp"] >= start_ms]
-        if end_ms is not None:
-            resampled_df = resampled_df[resampled_df["timestamp"] <= end_ms]
-        if resampled_df.empty:
-            return []
-        resampled_df["datetime"] = resampled_df["timestamp"].apply(to_utc_iso)
-        for column in ["open", "high", "low", "close", "volume"]:
-            if column not in resampled_df.columns:
-                resampled_df[column] = 0.0
-        resampled_df = resampled_df[["timestamp", "datetime", "open", "high", "low", "close", "volume"]]
-        result = resampled_df.to_dict("records")
-        if limit:
-            result = result[-limit:]
-        logger.debug(
-            "衍生周期返回",
-            extra={
-                "symbol": symbol,
-                "timeframe": timeframe,
-                "base_timeframe": base_tf,
-                "count": len(result),
-            },
-        )
-        return result
-
-
-def create_app(provider: DataProvider) -> FastAPI:
-    """构造 FastAPI 应用：lifespan 内同步 initialize 并启动后台拉数；WebSocket 实时推送。"""
-    ws_manager = WebSocketManager()
-
-    def _on_data_update(symbol: str, base_tf: str) -> None:
-        """后台刷新线程回调：广播基础及衍生周期更新给 WebSocket 订阅者。"""
-        with provider._lock:
-            base_data = list(provider.data.get(symbol, {}).get(base_tf, []))
-        recent = base_data[-WS_UPDATE_CANDLE_COUNT:] if base_data else []
-        if recent:
-            ws_manager.broadcast_from_thread(symbol, base_tf, recent)
-        for derived_tf, src_base in provider.derived_map.items():
-            if src_base != base_tf or not ws_manager.has_subscribers(symbol, derived_tf):
-                continue
-            try:
-                target_min = timeframe_to_minutes(derived_tf)
-                if target_min is None:
-                    continue
-                now_ms = int(time.time() * 1000)
-                window_ms = target_min * 60_000 * (WS_UPDATE_CANDLE_COUNT + 2)
-                derived = provider.get_klines(
-                    symbol, derived_tf, start_time=now_ms - window_ms, limit=WS_UPDATE_CANDLE_COUNT,
-                )
-                if derived:
-                    ws_manager.broadcast_from_thread(symbol, derived_tf, derived)
-            except Exception as exc:
-                logger.debug("衍生周期广播失败 %s %s: %s", symbol, derived_tf, exc)
-
-    @asynccontextmanager
-    async def lifespan(app: FastAPI):
-        loop = asyncio.get_running_loop()
-        ws_manager.set_loop(loop)
-        provider.on_update(_on_data_update)
-        await loop.run_in_executor(None, provider.initialize)
-        provider.start_background_workers()
-        try:
-            yield
-        finally:
-            provider.stop()
-
-    app = FastAPI(title="Chan 数据提供商", version="1.0.0", lifespan=lifespan)
-
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=["*"],
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-
-    @app.get("/health")
-    async def health() -> Dict[str, object]:
-        """存活检查：交易所、交易对、基础/衍生周期、是否已完成冷启动。"""
-        return {
-            "status": "ok",
-            "exchange": provider.exchange_name,
-            "symbols": provider.symbols,
-            "base_timeframes": provider.timeframes,
-            "derived_timeframes": provider.get_derived_timeframes(),
-            "timeframes": provider.get_available_timeframes(),
-            "ready": provider.is_ready(),
-        }
-
-    @app.get("/timeframes")
-    async def list_timeframes() -> Dict[str, List[str]]:
-        """返回配置的基础周期与可合成的衍生周期列表。"""
-        provider.wait_ready()
-        return {
-            "base_timeframes": provider.timeframes,
-            "derived_timeframes": provider.get_derived_timeframes(),
-            "timeframes": provider.get_available_timeframes(),
-        }
-
-    @app.get("/api/candles")
-    async def api_candles(
-        symbol: str = Query(..., description="如 BTC/USDT"),
-        tf: str = Query("1m", description="时间周期"),
-        start: Optional[int] = Query(None, description="开始时间戳(ms)"),
-        end: Optional[int] = Query(None, description="结束时间戳(ms)"),
-        limit: Optional[int] = Query(None, description="可选，限制返回数量"),
-    ):
-        """按交易对与时间周期返回 OHLCV；tf 支持配置的基础周期及衍生合成周期。"""
-        data = provider.get_klines(symbol=symbol, timeframe=tf, start_time=start, end_time=end, limit=limit)
-        return data
-
-    @app.get("/")
-    async def root() -> Dict[str, object]:
-        """根路径：服务名、交易所、交易对与可用周期（含 ready 标志）。"""
-        return {
-            "service": "Data Provider",
-            "exchange": provider.exchange_name,
-            "symbols": provider.symbols,
-            "base_timeframes": provider.timeframes,
-            "derived_timeframes": provider.get_derived_timeframes(),
-            "timeframes": provider.get_available_timeframes(),
-            "ready": provider.is_ready(),
-        }
-
-    @app.websocket("/ws")
-    async def websocket_endpoint(ws: WebSocket):
-        """WebSocket 实时 K 线推送。
-
-        客户端发送 JSON:
-            {"action": "subscribe",   "symbol": "BTC/USDT:USDT", "timeframe": "1m"}
-            {"action": "unsubscribe", "symbol": "BTC/USDT:USDT", "timeframe": "1m"}
-            {"action": "ping"}
-        服务端推送:
-            {"type": "subscribed",   "symbol": "...", "timeframe": "..."}
-            {"type": "snapshot",     "symbol": "...", "timeframe": "...", "data": [...]}
-            {"type": "kline",        "symbol": "...", "timeframe": "...", "data": [...]}
-            {"type": "pong"}
-            {"type": "error",        "message": "..."}
-        """
-        await ws_manager.connect(ws)
-        try:
-            while True:
-                raw = await ws.receive_text()
-                try:
-                    msg = json.loads(raw)
-                except json.JSONDecodeError:
-                    await ws.send_text(json.dumps({"type": "error", "message": "invalid JSON"}))
-                    continue
-
-                action = msg.get("action", "")
-                symbol = str(msg.get("symbol", "")).strip()
-                timeframe = str(msg.get("timeframe", "")).strip()
-
-                if action == "ping":
-                    await ws.send_text(json.dumps({"type": "pong"}))
-
-                elif action == "subscribe":
-                    if not symbol or not timeframe:
-                        await ws.send_text(json.dumps(
-                            {"type": "error", "message": "需要 symbol 和 timeframe 字段"}
-                        ))
-                        continue
-                    await ws_manager.subscribe(ws, symbol, timeframe)
-                    await ws.send_text(json.dumps(
-                        {"type": "subscribed", "symbol": symbol, "timeframe": timeframe},
-                        ensure_ascii=False,
-                    ))
-                    try:
-                        snapshot = provider.get_klines(symbol, timeframe, limit=DEFAULT_LIMIT)
-                        if snapshot:
-                            await ws.send_text(json.dumps(
-                                {"type": "snapshot", "symbol": symbol, "timeframe": timeframe, "data": snapshot},
-                                ensure_ascii=False,
-                            ))
-                    except Exception as exc:
-                        await ws.send_text(json.dumps({"type": "error", "message": str(exc)}))
-
-                elif action == "unsubscribe":
-                    await ws_manager.unsubscribe(ws, symbol, timeframe)
-                    await ws.send_text(json.dumps(
-                        {"type": "unsubscribed", "symbol": symbol, "timeframe": timeframe},
-                        ensure_ascii=False,
-                    ))
-
-                else:
-                    await ws.send_text(json.dumps({"type": "error", "message": f"未知 action: {action}"}))
-
-        except WebSocketDisconnect:
-            pass
-        finally:
-            await ws_manager.disconnect(ws)
-
-    return app
-
-
-def build_app() -> FastAPI:
-    """默认入口：从环境变量 CONFIG_PATH（或 config.json）加载配置并创建 FastAPI app。"""
-    config_path = Path(os.getenv("CONFIG_PATH", "config.json"))
-    provider = DataProvider(config_path)
-    return create_app(provider)
-
-
-app = build_app()
-
-
-def main() -> None:
-    """直接运行本模块时启动 uvicorn（监听 UVICORN_HOST / UVICORN_PORT）。"""
-    host = os.getenv("UVICORN_HOST", "0.0.0.0")
-    port = int(os.getenv("UVICORN_PORT", "9009"))
-
-    uvicorn.run(app, host=host, port=port, log_level=os.getenv("UVICORN_LOG_LEVEL", "info"))
-
-
-if __name__ == "__main__":
-    main()
-

data_provider/requirements.txt

@@ -1,5 +0,0 @@
-ccxt>=4.0.0,<5.0.0
-fastapi>=0.110.0,<1.0.0
-uvicorn[standard]>=0.23.0,<1.0.0
-pandas>=2.0.0,<3.0.0
-technical==1.5.0

docs/ADR/ADR-001-package-layout.md

@@ -0,0 +1,22 @@
+# ADR-001: 包布局与兼容 shim
+
+**Status:** Accepted  
+**Date:** 2026-08-05  
+**ECR:** ECR-001  
+
+## Context
+
+根目录扁平模块被 strategies 与 web 通过模块名直接 import；完全改名会破坏 Freqtrade 策略。需要正式包边界，同时零改 `strategies/`。
+
+## Decision
+
+1. 正式包名：`chanlun`（`core` / `pipeline` / `indicators` / `analysis`）。
+2. 根目录保留同名 shim 文件，再导出公共符号。
+3. Web 使用 Flask blueprints + services；前端 JS 模块化，不引入 TS 构建。
+4. `TF_DF` 保留门面类名与公开方法，内部委托 builders。
+
+## Consequences
+
+- 策略无需修改。
+- 长期可逐步引导新代码 `from chanlun import ...`。
+- shim 需保持至策略侧显式迁移（另立 ECR）。

docs/AGENT_MEMORY.md

@@ -0,0 +1,44 @@
+# AGENT_MEMORY — chan
+
+> Agent 短记忆。先读 `PROJECT_PROFILE.md`，再读本文件。不要把猜测写进这里。
+
+## 双前端
+
+| 入口 | 引擎 | 实时 |
+|------|------|------|
+| `/` | Lightweight Charts | HTTP 定时自动刷新（增量 + 每 6 次全量） |
+| `/chan_tv` | Charting Library 全版 | datafeed `subscribeBars` → WS |
+
+勿把主站 `live_feed` 方案与 chan_tv datafeed 混为一谈；主站 WS 实时已回退。
+
+## 版本
+
+- `system_version`：`v1.0.0`（ECR-001）
+- `strategy_version`：与 system 解耦；默认不改 `config/` / `strategies/`
+
+## 近期变更
+
+- IDEA-002 / `9f1e736`：主站内存泄漏 dispose、首屏单次 analyze、ChanMACD 复用、chan_tv 体验
+- ECR-002 Reviewed：拆 `web/services/runtime/`、加深 analyze 契约
+- ECR-003 Reviewed：主站威科夫叠层（`chanlun/analysis/wyckoff/` + `include_wyckoff`）→ `081a57a`
+- ECR-004 Reviewed：TR 评分硬化 + VP 少系列 + 阶段/门闩/单测（无币种参数）
+- ECR-007 Final Approval / `276481e`：Wyckoff Live Structure（`live.py`）；Confirmed ≠ Live；execution 仅 confirmed
+- ECR-008 Reviewed：主站 `chart_tv.js` → `chart_tv_{lifecycle,shell,indicators,chan,overlays,finalize}.js` + 薄门面
+- 威科夫数据随主 analyze 默认返回；UI 开关仅显隐叠层
+- Live 观察：主图左下角 Cycle Summary（「形成中」= FORMING）；无单独 Live 图层
+
+## 硬约束提醒
+
+- `/api/analyze` 字段可增不可删
+- 无 ADR 不改笔/段/中枢/买卖点语义
+- 威科夫为独立叠层（ECR-003/007）；勿借机改缠论算法
+- Live candidate **不得**进入 execution；交易 L2+ → RISK_REVIEW + EXP；Live 须 Human
+
+## 已知债务
+
+- analyze 契约已加深（mock HTTP + wyckoff opt-in）；可再加固定 JSON 快照文件
+- 内存泄漏尚无自动化 heap/监听断言
+- `macd_config` POST 写本地 global 的历史 quirks（未改）
+- 威科夫启发式参数未做 UI 调参
+- ECR-007 待 Human 在 Gitea 开 PR 合入 `dev`
+- `chart_tv_overlays.js` 仍偏大，可后续再拆

docs/BACKEND_DESIGN/BD-2026-007-wyckoff-live-structure.md

@@ -0,0 +1,70 @@
+# Backend Design: ECR-007 Wyckoff Live Structure
+
+| Field | Value |
+|-------|-------|
+| ID | BD-2026-007 |
+| ECR | ECR-007 |
+| Change Level | L2 |
+| Status | Approved |
+| Author | Architect (LOOP-RUN-005 Planner) |
+| Date | 2026-08-07 |
+| Risk | High (domain / execution boundary) |
+
+---
+
+## Context
+
+- 问题：Confirmed 引擎已存在；需要独立 Live 推演层供观察，且不得成为交易执行输入。
+- 非目标：改 Confirmed 门槛；自动交易；策略。
+- 依赖：ECR-003/004 威科夫；WYCKOFF-LIVE-STRUCTURE-001（FROZEN）。
+
+## Architecture Change / Change Boundary
+
+```text
+OHLCV
+  → detect_trading_ranges (Confirmed path)
+  → detect_bias_and_events / build_phases   ← Confirmed（阈值不降）
+  → analyze_live_structure                   ← Live（只读 confirmed）
+  → cycles[i] = { lifecycle, confirmed, live }
+  → API analyze + Summary UI
+  → execution_signal_from_wyckoff(confirmed only)
+```
+
+| Layer | May change | Must not |
+|-------|------------|----------|
+| Confirmed | assemble into `confirmed{}` | relax Spring/SOS rules |
+| Live | `live.py` heuristics | write into confirmed.events |
+| Execution helper | source=confirmed gate | consume candidates |
+| UI | Summary partition | treat Live as order |
+
+## Backend Change Boundary
+
+Live outputs are **observation**. Execution boundary:
+
+```python
+assert execution_signal.source == "confirmed"
+# live-only payload → None
+```
+
+## Data contract
+
+See WYCKOFF-LIVE-STRUCTURE-001. Top-level `phases`/`events` mirror **Confirmed** only.
+
+## delivery_constraints
+
+- BD Status Approved
+- TEST_REPORT commands/result/date
+- CODE_REVIEW handoff
+- TRACEABILITY commit
+- out_of_scope + execution_source_confirmed_only
+
+## Test Plan
+
+1. Live candidates not in confirmed.events  
+2. CONFIRMED lifecycle when Spring+SOS confirmed  
+3. execution_signal source=confirmed; live-only → None  
+4. analyze contract keys include live/lifecycle  
+
+## Rollback
+
+Remove live assembly path; Summary falls back to confirmed-only.

docs/CHANGELOG/CHANGELOG.md

@@ -0,0 +1,88 @@
+# CHANGELOG
+
+## Unreleased — 2026-08-07
+
+### ECR-008（L3，Reviewed）
+
+- 主站 `chart_tv.js` 拆为 lifecycle / shell / indicators / chan / overlays / finalize + 薄门面
+- 行为冻结；`initTradingView` / `disposeTradingViewCharts` 对外不变；无 Vite/TS
+
+### ECR-007（L2，LOOP-RUN-005）
+
+- Wyckoff **Live Structure**：`live.py` + engine 组装 `lifecycle` / `confirmed` / `live`
+- Event candidates（Spring/SOS/LPS/UTAD）+ 可解释 confidence；Summary Confirmed/Live 分区
+- `execution_signal_from_wyckoff` **仅** `source=confirmed`；Live-only → None
+- **No** Confirmed 门槛降低；**No** strategies / 自动交易
+
+## Unreleased — 2026-08-06
+
+### ECR-004（L2，Reviewed）
+
+- 威科夫 TR 评分选段（防吞前置趋势）；阶段非重叠最小跨度
+- 主站 VP Top-8 + bins≤24；填充线减负
+- `elements_only` 时不跑威科夫；收紧单测（无币种独立参数）
+- **后续**：威科夫随主 `/api/analyze` 默认一并返回；前端开关只控制绘制（不再勾选才加载）
+
+### ECR-003（L2，Reviewed）
+
+- 新增 `chanlun/analysis/wyckoff/`：交易区间、阶段 A–E、Spring/SOS/LPS/UTAD 等事件、区间 VP（POC/VAH/VAL）、量能确认
+- `/api/analyze` 按需 `include_wyckoff=1` 返回顶层 `wyckoff`
+- 主站「威科夫」开关与 Lightweight 叠层（区间/阶段/事件/VP）
+- 单测与 analyze 契约 opt-in 断言
+
+### ECR-002（L3，Reviewed）
+
+- 拆分 `web/services/runtime.py` 为包 `web/services/runtime/`（state / timeframes / market_data / indicators / analyze / serialize）
+- 加深 analyze 契约测试（mock HTTP + analyze_chan 键集 + serialize JSON）
+- 新增 TF_DF 全量 init 冒烟与 runtime 门面测试
+
+### IDEA-002（L1 补档）
+
+对应 commit `9f1e736`。无新 system tag（仍为 `v1.0.0`）。
+
+#### Fixed
+
+- 主站自动刷新内存泄漏：`disposeTradingViewCharts`、去掉重复 sync 监听、默认增量刷新（每 6 次全量重建笔/段/中枢）
+- 加密货币首屏重复调用 `/api/analyze`
+- ChanMACD 同周期重复全量分析（复用 `get_klc_list` 结果）
+
+#### Changed
+
+- `/chan_tv`：WS/REST 可分离配置、指标布局 localStorage、未完成中枢与 datafeed 实时 tick 行为完善
+- `PROJECT_PROFILE` Realtime 条目与 chan_tv WS 对齐（文档）
+
+#### Docs
+
+- ESS：IDEA-002、AGENT_MEMORY、AGENTS；ECR-002 实现与报告
+
+---
+
+## v1.0.0 — 2026-08-05（首个正式 Release）
+
+对应 ECR-001 / tag `v1.0.0`。详见 `docs/RELEASE/ECR-001-v1.0.0.md`。
+
+### Added
+
+- 正式包 `chanlun/`（core / pipeline / builders / indicators / analysis）
+- ESS 文档树 `docs/`（PROFILE / ECR / SPEC / ADR / HANDOFF / CODE_REVIEW / RELEASE）
+- Web `config.py`、`services/`、`api/` blueprints
+- 前端 `web/static/js/app/` 模块
+- Golden 回归 `tests/generate_golden.py` + fixtures
+
+### Changed
+
+- 根目录 `Chan*.py` / `TF_DF.py` 改为兼容 shim
+- `TF_DF` 实现拆至 builders，门面签名保持
+- `web/app.py` 瘦身为 `create_app()`
+- `index.html` 去掉巨型 inline 业务 JS
+- HTTP 代理改为环境变量配置
+- strategies / web / tests / examples → `from chanlun...` 导入
+
+### Fixed
+
+- 恢复缺失的 `TF_DF.get_zs_list`（委托 `get_seg_zs_list`）
+
+### Moved
+
+- 示例 → `examples/`；笔记 → `docs/notes/`
+- 未接线 TSX/TS → `static/js/_unused/`

docs/CODE_REVIEW/ECR-001.md

@@ -0,0 +1,75 @@
+# CODE_REVIEW — ECR-001
+
+**Role:** REVIEWER  
+**Date:** 2026-08-05  
+**Commit:** `74dec4e` (`refactor: 缠论引擎包化与 Web 分层（ECR-001）`)  
+**Decision:** Approve  
+
+## Evidence loaded
+
+- `docs/ECR/ECR-001-chan-web-restructure.md`
+- `docs/ENGINEERING_SPEC/ECR-001-restructure.md`
+- `docs/IMPLEMENTATION_REPORT/ECR-001.md`
+- `docs/TEST_REPORT/ECR-001.md`
+- `docs/HANDOFF/ECR-001-engineer-to-reviewer.md`
+- Diff `e2e45bc..74dec4e`；本地复跑测试
+
+## Acceptance ↔ Evidence
+
+| Acceptance | Verdict | Evidence |
+|------------|---------|----------|
+| `from ChanLun import ChanLun` / `ChanEnum` 仍可用 | PASS | 复跑 shim+package 同一对象；`test_compat_shim_still_works` |
+| Golden bi/seg/zs/bsp 与基线一致 | PASS | `python tests/generate_golden.py --check` → GOLDEN OK；pytest 含 golden |
+| `/api/analyze` 关键字段兼容 | PASS | `analyze_contract_keys.json` + 路由注册冒烟（未做实盘拉行情 E2E，见 Findings） |
+| `web/app.py` 瘦身 factory | PASS | `web/app.py` 32 行；`create_app` + blueprints |
+| `index.html` 无大体量 inline 业务 JS | PASS | ~1482 行；业务在 `static/js/app/*` |
+| ESS docs / TEST / IMPL / CHANGELOG | PASS | `docs/` 齐全 |
+| `config/` 无内容变更 | PASS | `git diff e2e45bc..HEAD -- config` 空 |
+| `strategies/` 无内容变更 | **AMENDED** | 见下「范围修订」 |
+
+## 范围修订（Human 后续指示）
+
+原 ECR Forbidden 写「不改 strategies/」。实现后期 Human 要求「一次性做完」导入迁移：strategies 仅改 import / `sys.path`（26 files, +75/−75），**无策略交易逻辑变更**。  
+审阅结论：视为 **L3 结构收尾的允许增补**，不构成交易语义 L2；建议 ECR Acceptance 改为「strategies 仅允许 import/path 迁移，禁止改买卖逻辑」。  
+**不据此 Request changes。**
+
+## 复跑结果（Reviewer）
+
+```text
+shim+package OK
+GOLDEN OK {klu:400, klc:208, bi:14, seg:2, zs:0, bsp:3}
+pytest tests/test_golden_pipeline.py web/tests/test_analyze_contract.py → 6 passed
+```
+
+## Findings
+
+### Non-blocking（记入债务，需新 ECR 再动）
+
+1. **`web/services/runtime.py` ~1176 行** — 已从 app 抽出但仍是大模块；facade 再导出符合计划 → **已起草 `docs/ECR/ECR-002-runtime-split.md`（Draft）**。
+2. **`web/static/js/app/chart_tv.js` ~4664 行** — `initTradingView` 单体；行为冻结下可接受；ECR-002 可选范围。
+3. **`/api/analyze` 契约测试偏浅** — 仅关键字段清单 + 路由存在；无固定 fixture 的端到端 JSON 快照（需 mock 行情）→ ECR-002。
+4. **TEST_REPORT 写「5 passed」** — 现为 6（含 shim 兼容测）；Release 前可改正文（L0 docs）。
+5. **L1：`TF_DF.get_zs_list` 恢复** — 合理兼容修复；golden 走 analyze 路径未覆盖 `TF_DF(df,...)` 全量 `__init__` → ECR-002 Acceptance。
+
+### No blockers
+
+未发现违反「算法语义冻结 / API 可增不可删 / 无 Vite-React / config 未改」的证据。
+
+## Decision
+
+**Approve**
+
+- ECR-001 可进入 Release（本变更无交易 EXP 门禁）。
+- 非阻断项进入 backlog / 未来 ECR，不阻塞 tag。
+
+## Next owner
+
+`release_manager` — 写 RELEASE_REPORT、打 tag（需 Human 确认发布动作）。
+
+## Traceability
+
+| Item | Updated |
+|------|---------|
+| Acceptance mapping | 本文件 |
+| STATE.owner | → release_manager |
+| ECR Status | → Done (Reviewed) |

docs/CODE_REVIEW/ECR-002.md

@@ -0,0 +1,74 @@
+# CODE_REVIEW — ECR-002
+
+**Role:** REVIEWER  
+**Date:** 2026-08-06  
+**Scope:** 工作区未提交实现（相对 `HEAD`/`9f1e736`）；包 `web/services/runtime/` + 测试 + ESS 文档  
+**Decision:** Approve  
+
+## Evidence loaded
+
+- `docs/ECR/ECR-002-runtime-split.md`
+- `docs/ENGINEERING_SPEC/ECR-002-runtime-split.md`
+- `docs/IMPLEMENTATION_REPORT/ECR-002.md`
+- `docs/TEST_REPORT/ECR-002.md`
+- `docs/HANDOFF/ECR-002-engineer-to-reviewer.md`
+- 包源码：`web/services/runtime/{__init__,state,timeframes,market_data,indicators,analyze,serialize}.py`
+- Diff：删除 `web/services/runtime.py`；新增包与测试
+
+## Acceptance ↔ Evidence
+
+| Acceptance | Verdict | Evidence |
+|------------|---------|----------|
+| runtime 门面公开符号兼容（含历史 `import *` 漏出） | PASS | 手工核对 api 所需符号；`timezone`/`OrderedDict`/`np`/`StructureZone*`/`ThreadPoolExecutor` 等在门面；`test_runtime_facade` |
+| Golden 通过 | PASS | 复跑 `tests/test_golden_pipeline.py` |
+| Analyze 契约加深 | PASS | `test_analyze_contract`：键清单 + analyze_chan 键集 + serialize JSON + mock HTTP |
+| TF_DF 全量 init 冒烟 | PASS | `tests/test_tf_df_init.py`（`interval=1`） |
+| config/strategies 无交易逻辑 diff | PASS | 工作区无 `config/`/`strategies/` 变更 |
+| IMPL / TEST / CHANGELOG / TRACEABILITY | PASS | docs 已落盘 |
+| CODE_REVIEW Approve | PASS | 本文件 |
+
+## 复跑结果（Reviewer）
+
+```text
+PYTHONPATH=.:web python -m pytest \
+  tests/test_golden_pipeline.py \
+  tests/test_tf_df_init.py \
+  web/tests/test_runtime_facade.py \
+  web/tests/test_analyze_contract.py -q
+→ 13 passed
+```
+
+算法冻结抽查：`analyze.py` 仍为 `cal_bi_zs(seg_list)` + `_last_chan_macd` 复用；未改笔段中枢语义。
+
+## Findings
+
+### Non-blocking（不挡 Approve）
+
+1. **门面标量同步只做一次** — `__init__` 在首次 `refresh` 后把 `DATA_SERVICE_AVAILABLE` / `macd_*` 写入模块 dict；之后 `refresh_data_service_metadata` 只改 `state.*`。通过 `R.DATA_SERVICE_AVAILABLE` 读取可能与 state 短期不一致；`from services.runtime import *` 的 bool 拷贝问题在 monolith 时代已存在。建议后续 L1：在 `refresh` 末尾同步写回门面模块，或让标量只经 `state`/`__getattr__` 暴露。
+2. **`__getattr__` 对已绑定名无效** — 与上条相关；属清理项。
+3. **`chart_tv.js` 拆分未做** — ECR 明确可选；继续记入 backlog。
+4. **契约测试仍无「固定 JSON 快照文件」** — 已有 mock HTTP + 键集，比 ECR-001 深；完整响应快照可另开 L1/ECR。
+5. **`web/tests/test_cn_stock_data_fetch.py` 仍因旧 `user_data.Chan...` 路径无法收集** — 既有问题，非本 ECR 引入。
+
+### No blockers
+
+未发现违反「算法语义冻结 / API 可增不可删 / 无 Vite-React / 未动 strategies·config / 未引主站 WS」的证据。
+
+## Decision
+
+**Approve**
+
+- ECR-002 可标 Done（Reviewed）；不强制新 system tag（仍为 `v1.0.0` Unreleased 文档变更）。
+- 非阻断项进 backlog；不阻塞合并本实现。
+
+## Next owner
+
+`engineer` / Human — 提交合并；若要发版再交 `release_manager`（本 ECR 未要求 bump tag）。
+
+## Traceability
+
+| Item | Updated |
+|------|---------|
+| Acceptance mapping | 本文件 |
+| STATE.owner | → idle / merge |
+| ECR Status | → Done (Reviewed) |

docs/CODE_REVIEW/ECR-003.md

@@ -0,0 +1,77 @@
+# CODE_REVIEW — ECR-003
+
+**Role:** REVIEWER  
+**Date:** 2026-08-06  
+**Scope:** 工作区未提交 ECR-003（相对 `origin/dev` @ `df27b4d`）  
+**Decision:** Approve（带非阻断 Findings；建议合并前勿提交 `.DS_Store`）
+
+## Evidence loaded
+
+- `chanlun/analysis/wyckoff/{engine,range,events,volume_profile}.py`
+- `web/api/analyze.py`（`include_wyckoff`）
+- `web/templates/index.html`、`chart_view.js`、`macd_ui.js`、`chart_tv.js` 威科夫块
+- `tests/test_wyckoff.py`、`web/tests/test_analyze_contract.py`
+- ESS：ECR/PRODUCT/ENG/IMPL/TEST/HANDOFF
+
+## Acceptance ↔ Evidence
+
+| Acceptance | Verdict | Evidence |
+|------------|---------|----------|
+| `include_wyckoff=1` 返回约定键；默认不强制 | PASS | 契约测试；默认无 `wyckoff` 键 |
+| 合成 TR + 事件；VP POC | PASS | `test_wyckoff.py`（12 相关套件全绿） |
+| 主站可开关绘制 | PASS | 主开关按需拉取；子项本地重绘 |
+| golden 不变 | PASS | `test_golden_pipeline` |
+| 未改缠论算法 / strategies / chan_tv | PASS | diff 范围核对 |
+| ESS 闭环 | PASS | IMPL/TEST/TRACE/CHANGELOG/本文件 |
+
+## 复跑
+
+```text
+PYTHONPATH=.:web python -m pytest \
+  tests/test_wyckoff.py tests/test_golden_pipeline.py \
+  web/tests/test_analyze_contract.py -q
+→ 12 passed
+```
+
+## Findings
+
+### Important（不挡 Approve，建议跟进）
+
+1. **交易区间易吞并前置趋势**  
+   `detect_trading_range` 从最长窗口向下搜，合成夹具下 `abs_start_idx=0`，箱体前下跌段被算进 TR。单测只断言「有区间 + 有事件」，未锁定高低/起点。  
+   *建议：* 用「宽度/触边密度」评分取最优段，或要求近端触边；测试断言 `high≈60/low≈40` 与起点靠近箱体。
+
+2. **VP 叠层系列数偏多，可能加压自动刷新内存**  
+   开启 VP 时约每个 bin 一条 `addLineSeries`（默认 ~50），再加区间填充/阶段。与 IDEA-002 内存修复同路径全量重建时放大。  
+   *建议：* 只画非零 bin 或合并为少量 series / histogram；或限制 `vp_bins` 上限到 24。
+
+### Medium
+
+3. **阶段 C–E 在事件扎堆时常退化重叠**  
+   夹具输出中 D/E 起止几乎相同；状态机按事件锚点硬切，缺少最小阶段长度。展示可用，语义偏弱。
+
+4. **`elements_only=true` 仍可能跑威科夫**  
+   威科夫挂在路由末尾，不依赖 `not elements_only`。主站当前不这么发，但契约上奇怪；建议与主周期分析同门闩。
+
+5. **单测断言偏松**  
+   `Spring in types or SOS`、`abs(poc-50)<2` 对回归保护不足。
+
+### Low
+
+6. 失败时 `wyckoff.error` 回传异常字符串（与结构区 print 风格一致，信息暴露轻微）。
+7. 事件 marker 一律 `arrowUp`（跌破类也可 `arrowDown`）。
+8. 工作区 `.DS_Store` 脏文件——**勿纳入 commit**。
+
+### No blockers
+
+未发现：契约删键、缠论语义改动、策略/config 改动、未鉴权危险写操作、主站误引 WS。
+
+## Decision
+
+**Approve**
+
+可合并提交（排除 `.DS_Store`）。Important #1/#2 可开后续 L1/L2，不阻塞本 ECR 着陆。
+
+## Next owner
+
+`engineer` / Human — commit（勿含 `.DS_Store`）；可选跟进 TR 评分与 VP 绘图优化。

docs/CODE_REVIEW/ECR-004.md

@@ -0,0 +1,53 @@
+# CODE_REVIEW — ECR-004
+
+**Role:** REVIEWER  
+**Date:** 2026-08-06  
+**Scope:** `d3188ca`（相对 ECR-003）威科夫硬化  
+**Decision:** Approve  
+
+## Evidence loaded
+
+- Diff `d3188ca`：`range.py` / `events.py` / `analyze.py` / `chart_tv.js` / tests / ESS
+- 复跑：`tests/test_wyckoff.py` + golden + analyze contract → **14 passed**
+- 合成夹具抽查：`abs_start_idx=20`，low/high≈40.1/59.9（相对 003 的 bar0 已修好）
+
+## Acceptance ↔ Evidence
+
+| Acceptance | Verdict | Evidence |
+|------------|---------|----------|
+| TR 不吞明显前置趋势；边界近箱体 | PASS | 评分选段；单测 low/high 带 + `abs_start≥12` + start 时间容差 |
+| VP series 减负 | PASS | Top-8 + 填充 3 + POC/VAH/VAL；API bins≤24 |
+| 阶段最小跨度 / 不重合 | PASS | 链式 cursor；unique (start,end) 断言 |
+| elements_only 门闩 | PASS | `include_wyckoff and not elements_only` + 契约测试 |
+| golden 不变 / 无策略改动 / 无币种表 | PASS | golden 绿；diff 无 config/strategies |
+
+## Findings
+
+### Medium（不挡 Approve）
+
+1. **同分 tie-break 偏向更长窗口**  
+   循环从长到短，`score <= best_score` 时保留已有（更长）。多数情况分数拉开；若实盘出现「长窗与短窗同分」，仍可能略偏长。可选：同分取更短，或加 `1/length` 微项。
+
+2. **阶段常截断为 A–C**  
+   Spring/SOS 落在尾部时 D/E 因 `min_span` 被吃掉——与 ENG「空间不足截断」一致，但 UI 勾选「阶段」时用户可能期望总见 D/E。属产品预期，非缺陷；可在 UI/文档标明「尾部不足则省略」。
+
+### Low
+
+3. **`abs_start_idx >= 12` 弱于「箱体起点」** —— 主测已用时间容差；该断言可再收紧到 `>= 16` 一类。  
+4. **VP Top-N 无自动化 series 计数** —— 靠代码审查 + ENG 约定。  
+5. 事件 marker 仍一律 `arrowUp`（003 遗留）。  
+6. 失败路径仍回传 `wyckoff.error` 字符串。
+
+### No blockers
+
+未发现契约删键、缠论语义改动、策略改动、或回归红灯。
+
+## Decision
+
+**Approve**
+
+ECR-004 可维持 Done (Reviewed)。Medium 项进 backlog，不必立刻新 ECR，除非实盘 TR 仍偏长。
+
+## Next owner
+
+Human — 主站 BTC 勾选威科夫目测；无发版要求则保持 `v1.0.0` Unreleased 累计。

docs/CODE_REVIEW/ECR-008.md

@@ -0,0 +1,25 @@
+# CODE_REVIEW — ECR-008
+
+**Role:** REVIEWER  
+**Date:** 2026-08-07  
+**Scope:** chart_tv 物理拆分  
+**Decision:** Approve  
+
+## Checklist
+
+| Item | Result | Notes |
+|------|--------|-------|
+| 行为冻结（仅搬移） | PASS | ctx 编排；无绘制算法改写意图 |
+| 对外 API | PASS | `initTradingView` / `disposeTradingViewCharts` 保留 |
+| Forbidden | PASS | 无 Vite/TS；无 strategies/config；无 analyze 契约改动 |
+| script 顺序 | PASS | lifecycle→shell→indicators→chan→overlays→finalize→门面→sync |
+| 测试证据 | PASS | `node --check` ALL_CHECK_OK |
+
+## Findings
+
+1. **Low：** 浏览器硬刷新冒烟仍建议 Human 点一次（自动刷新 + Cycle Summary）。不挡 Approve。  
+2. **Low：** `chart_tv_overlays.js` 仍偏大（~2.3k 行）；可后续再拆，非本 ECR 范围。
+
+## Decision
+
+**Approve**

docs/ECR/ECR-001-chan-web-restructure.md

@@ -0,0 +1,70 @@
+# ECR-001
+
+**Title:** 缠论引擎包化 + Web 分层重构（行为冻结）  
+**Status:** Approved  
+**Date:** 2026-08-05  
+**Change Level:** L3  
+
+## Change
+
+将根目录扁平 `Chan*.py` / `TF_DF.py` 包化为 `chanlun/`，拆分 `web/app.py` 与巨型 `index.html` inline JS；算法与 `/api/analyze` 契约冻结；`config/` / `strategies/` 不动。
+
+## Motivation
+
+根目录与 Web 单体过大、职责混杂，难以维护与测试；需在不影响 Freqtrade 策略导入的前提下重整结构。
+
+## Scope
+
+### Allowed
+
+- 创建 `chanlun/`（core / pipeline / indicators / analysis）与根目录兼容 shim
+- 拆分 `TF_DF` 为 builders + 门面（公开方法签名不变）
+- Web：`config` + `services` + `api` blueprints；配置外置（proxy / DATA_SERVICE）
+- 前端：`index.html` 业务 JS 外置到 `static/js/app/`；隔离未接线 TS/TSX
+- 示例与笔记迁入 `examples/` / `docs/notes/`
+- Golden / 契约回归测试
+
+### Forbidden
+
+- 修改笔 / 线段 / 中枢 / 买卖点算法语义
+- 破坏 `/api/analyze` JSON 字段（可增不可删）
+- 修改 `config/`；修改 `strategies/` 交易逻辑或参数（import/`sys.path` 迁移除外，见 CODE_REVIEW）
+- 引入 Vite/React/TS 构建
+- 重做 UI 视觉或更换 TradingView
+
+## Risk
+
+| Risk | Mitigation |
+|------|------------|
+| 策略 import 断裂 | 根 shim + 冒烟导入 |
+| 拆文件改算法 | 仅搬移；golden fixture |
+| 前端事件遗漏 | 按块抽取 + 手工/冒烟 |
+| API 字段漂移 | analyze 契约测试 |
+
+## Acceptance Criteria
+
+- [x] `from ChanLun import ChanLun` / `from ChanEnum import ...` 仍可用
+- [x] Golden：同一 fixture 下 bi/seg/zs/bsp 序列化结果与基线一致
+- [x] `/api/analyze` 关键字段集合兼容（契约冒烟）
+- [x] `web/app.py` 瘦身为 factory；业务在 services/api
+- [x] `index.html` 不再含大体量业务 inline JS
+- [x] ESS docs 齐全；TEST_REPORT / IMPLEMENTATION_REPORT / CHANGELOG
+- [x] `config/` 无内容变更；`strategies/` 仅允许 import/`sys.path` 迁移（Human 增补，无交易逻辑变更）
+
+**Status:** Done (Released as `v1.0.0`)
+
+## Rollback
+
+单分支 / 单 PR 回滚；shim 期可整体 `git revert`。
+
+## Risk Review
+
+- Path: `docs/RISK_REVIEW/ECR-001.md` — N/A（不改交易语义）
+
+## Linked
+
+- PRD / PRODUCT_SPEC: `docs/PRODUCT_SPEC/ECR-001-restructure.md`
+- ENGINEERING_SPEC: `docs/ENGINEERING_SPEC/ECR-001-restructure.md`
+- ADR: `docs/ADR/ADR-001-package-layout.md`
+- EXPERIMENT: N/A
+- TRACEABILITY: Yes

docs/ECR/ECR-002-runtime-split.md

@@ -0,0 +1,74 @@
+# ECR-002
+
+**Title:** 拆分 `web/services/runtime.py` + 加深 `/api/analyze` 契约测试  
+**Status:** Done (Reviewed)  
+**Date:** 2026-08-06  
+**Change Level:** L3（行为冻结；若 golden 漂移则升 L2）  
+
+## Change
+
+将仍偏大的 `web/services/runtime.py` 按职责拆为可维护子模块；加深 analyze API 契约/快照测试；可选拆分主站巨型 `chart_tv.js`（本轮未做）。
+
+## Motivation
+
+ECR-001 CODE_REVIEW 非阻断债务：runtime 过大、契约测试偏浅、chart_tv 单体。不处理会继续抬高 Web 改动风险。
+
+## Scope
+
+### Allowed
+
+- 物理拆分 `web/services/runtime.py` → 包 `web/services/runtime/`（state / timeframes / market_data / indicators / analyze / serialize + 门面）
+- 加深 `web/tests/`：固定 fixture / mock 行情下的关键字段快照与契约
+- 补 `TF_DF(..., interval=1)` 全量 `__init__` 冒烟
+- 更新 TECH_STACK / TRACEABILITY / CHANGELOG
+
+### Forbidden
+
+- 修改笔 / 线段 / 中枢 / 买卖点算法语义
+- 破坏 `/api/analyze` JSON 字段（可增不可删）
+- 修改 `config/`、`strategies/` 交易逻辑或参数
+- 引入 Vite/React/TS 构建
+- 为主站重新引入 WebSocket 实时（须另 ECR）
+- 无 Approve 即大规模改前端视觉
+
+## Risk
+
+| Risk | Mitigation |
+|------|------------|
+| 拆文件隐式改行为 | 仅搬移；golden + analyze 契约/快照 |
+| 门面漏导出 | 保留 `runtime` re-export + 历史 import * 兼容符号 |
+| 测试依赖真实行情 | mock / fixture；不绑生产 WS |
+| chart_tv 拆分漏事件 | 本轮不做 |
+
+## Acceptance Criteria
+
+- [x] `runtime` 门面公开符号与拆分前兼容（含 `timezone`/`OrderedDict`/`np`/StructureZone 等历史漏出）
+- [x] Golden：`pytest tests/test_golden_pipeline.py` 通过
+- [x] Analyze 契约/快照测试通过且覆盖关键字段清单以上
+- [x] TF_DF 全量 init 冒烟通过
+- [x] `config/` / `strategies/` 无交易逻辑 diff
+- [x] IMPLEMENTATION_REPORT / TEST_REPORT / CHANGELOG / TRACEABILITY 更新
+- [x] CODE_REVIEW Approve
+
+## Rollback
+
+`git revert` 本 ECR 提交；门面保留期可整包回滚。
+
+## Risk Review
+
+- Path: `docs/RISK_REVIEW/ECR-002.md` — N/A（不改交易决策语义）
+
+## Linked
+
+- IDEA: `docs/IDEA/IDEA-003-runtime-split.md`
+- PRODUCT_SPEC: `docs/PRODUCT_SPEC/ECR-002-runtime-split.md`
+- ENGINEERING_SPEC: `docs/ENGINEERING_SPEC/ECR-002-runtime-split.md`
+- ADR: 引用 ADR-001（包内再拆，无新顶层布局 ADR）
+- EXPERIMENT: N/A
+- TRACEABILITY: Yes
+- IMPLEMENTATION_REPORT: `docs/IMPLEMENTATION_REPORT/ECR-002.md`
+- TEST_REPORT: `docs/TEST_REPORT/ECR-002.md`
+
+## Origin
+
+- `docs/CODE_REVIEW/ECR-001.md` Findings 1–3、5

docs/ECR/ECR-003-wyckoff-main.md

@@ -0,0 +1,62 @@
+# ECR-003
+
+**Title:** 主站威科夫分析与图表展示  
+**Status:** Done (Reviewed)  
+**Date:** 2026-08-06  
+**Change Level:** L2  
+
+## Change
+
+在主站 `/` 增加威科夫交易区间、阶段（A–E）、关键事件（Spring/SOS/LPS/UTAD 等）、区间内简易 VP（POC/VAH/VAL）与量能确认；按需接入 `/api/analyze`。
+
+## Motivation
+
+用户需要在缠论图上叠加威科夫结构解读；与现有结构区语义分离。
+
+## Scope
+
+### Allowed
+
+- 新建 `chanlun/analysis/wyckoff/`
+- `/api/analyze` 增加可选 `include_wyckoff` 与响应字段 `wyckoff`（可增不可删既有字段）
+- 主站 UI 开关与 Lightweight 绘图
+- 单测 + ESS 文档
+
+### Forbidden
+
+- 修改笔/段/中枢/买卖点算法语义
+- 改 `config/` / `strategies/`
+- `/chan_tv` Study
+- Vite/React、主站 WebSocket 实时（另 ECR）
+
+## Risk
+
+| Risk | Mitigation |
+|------|------------|
+| 启发式误标 | 规格写明启发式；UI 可关；单测合成形态 |
+| 负载 | 默认关闭，勾选才计算 |
+| 与结构区混淆 | 独立开关与字段名 |
+
+## Acceptance Criteria
+
+- [x] `include_wyckoff=1` 返回约定 `wyckoff` 键；默认不强制计算
+- [x] 合成 fixture：能检出 TR + 至少一类事件；VP POC 可测
+- [x] 主站可开关绘制区间/阶段/事件/VP
+- [x] golden 缠论基线不变
+- [x] TEST/IMPL/CHANGELOG/TRACEABILITY + CODE_REVIEW
+
+## Rollback
+
+`git revert`；关闭 UI 开关即可无图面影响。
+
+## Risk Review
+
+- `docs/RISK_REVIEW/ECR-003.md` — N/A（展示分析，非 Live 策略）
+
+## Linked
+
+- IDEA: `docs/IDEA/IDEA-004-wyckoff-main.md`
+- PRODUCT_SPEC / ENGINEERING_SPEC: 同目录 ECR-003-*
+- EXPERIMENT: N/A
+- TRACEABILITY: Yes
+- CODE_REVIEW: `docs/CODE_REVIEW/ECR-003.md` — Approve

docs/ECR/ECR-004-wyckoff-harden.md

@@ -0,0 +1,69 @@
+# ECR-004
+
+**Title:** 威科夫区间评分硬化与主站 VP 绘图减负  
+**Status:** Done (Reviewed)  
+**Date:** 2026-08-06  
+**Change Level:** L2  
+
+## Change
+
+跟进 ECR-003 CODE_REVIEW Findings：改进交易区间选取启发式、阶段最小长度、收紧单测；主站 VP/叠层降低 Lightweight series 数量；`include_wyckoff` 与主周期分析同门闩。
+
+## Motivation
+
+003 已 Approve 合入；质量与内存项不得回塞已审变更，须独立可审闭环。
+
+## Scope
+
+### Allowed
+
+- `chanlun/analysis/wyckoff/range.py` / `events.py`（阶段）启发式与单测  
+- `web/static/js/app/chart_tv.js` 威科夫 VP/填充绘制路径  
+- `web/api/analyze.py`：`elements_only` 时不跑威科夫；默认 `vp_bins` 上限 24  
+- ESS 文档与契约测试补充断言（不删既有 `wyckoff` 键）
+
+### Forbidden
+
+- 改笔/段/中枢/买卖点语义  
+- `config/` / `strategies/`  
+- `/chan_tv`  
+- 新数据源 / 订单流  
+- **按币种独立参数表**（全局 ATR 相对即可；当前以 BTC 场景验证）
+
+## Decisions（Approve 时锁定）
+
+- VP：**A+C**（前端 Top-N 有量 bin + 服务端 bins 上限 24）  
+- 不做 per-symbol 参数  
+
+## Risk
+
+| Risk | Mitigation |
+|------|------------|
+| TR 结果相对 003 漂移 | 合成夹具锁定高低与起点；文档标明启发式迭代 |
+| 前端 VP 观感变化 | 保留 POC/VAH/VAL；密度用 Top-N |
+| 回归 | 扩展 `tests/test_wyckoff.py` + 既有契约套件 |
+
+## Acceptance Criteria
+
+- [x] 合成箱体夹具：`trading_range` 高低接近箱体边界，起点不落入明显前置趋势段  
+- [x] 开启 VP 时主图新增 series 数显著低于「每 bin 一条」（目标：填充+VP ≤ ~15 或等价合并策略）  
+- [x] 阶段输出满足最小跨度或合并退化段；文档说明规则  
+- [x] `elements_only=true` 即使 `include_wyckoff=1` 也不返回 `wyckoff`  
+- [x] golden 缠论基线不变；相关 pytest 绿  
+- [x] TEST/IMPL/CHANGELOG/TRACEABILITY + CODE_REVIEW  
+
+## Rollback
+
+`git revert`；UI 关威科夫即可无图面影响。
+
+## Risk Review
+
+- `docs/RISK_REVIEW/ECR-004.md` — N/A（展示/启发式，非 Live 策略）
+
+## Linked
+
+- IDEA: `docs/IDEA/IDEA-005-wyckoff-harden.md`  
+- 上游: `docs/CODE_REVIEW/ECR-003.md` Findings 1–5  
+- PRODUCT_SPEC / ENGINEERING_SPEC: 同目录 ECR-004-*  
+- TRACEABILITY: Yes  
+- CODE_REVIEW: `docs/CODE_REVIEW/ECR-004.md` — Approve  

docs/ECR/ECR-007-wyckoff-live-structure.md

@@ -0,0 +1,60 @@
+# ECR-007
+
+**Title:** Wyckoff Live Structure  
+**Status:** Approved  
+**Date:** 2026-08-07  
+**Change Level:** L2  
+**Human:** Approved (LOOP-RUN-005 Start Authorization)
+
+## Change
+
+Add **Live / Developing** structure layer beside **Confirmed** Wyckoff engine: lifecycle, FORMING candidates (Spring/SOS/LPS/UTAD), explainable confidence, Summary partition. Keep Confirmed thresholds unchanged; execution may only consume Confirmed.
+
+## Motivation
+
+LOOP-RUN-005 — domain-state complexity under Adapter v0.1 STABLE (Confirmed ≠ Live ≠ execution).
+
+## Scope
+
+### Allowed (IN)
+
+- `chanlun/analysis/wyckoff/live.py` + engine assembly
+- lifecycle / confirmed / live payload
+- Event candidates + confidence
+- API contract + Summary UI
+- tests + docs notes (WYCKOFF-LIVE-STRUCTURE-001)
+
+### Forbidden (OUT)
+
+- execution signal automation / auto trading
+- strategy / maker / decide_quotes / `strategies/**`
+- lowering Confirmed thresholds
+- Live candidate replacing Confirmed
+- ESS / Loop / Adapter changes
+
+## Risk
+
+| Risk | Mitigation |
+|------|------------|
+| Live → execution | `execution_signal_from_wyckoff` source=confirmed only; live-only → None |
+| Confirmed pollution | candidates never written to confirmed.events |
+| Domain confusion in UI | Summary Confirmed vs Live partitions |
+
+## Acceptance Criteria
+
+- [ ] Approved BD-2026-007
+- [ ] Confirmed logic not relaxed
+- [ ] Live ≠ execution signal (tests)
+- [ ] Lifecycle verifiable
+- [ ] Artifact chain + Gate PASS
+
+## Rollback
+
+- Disable live assembly; remove live.py; revert Summary partition
+
+## Linked
+
+- Note: `docs/notes/WYCKOFF-LIVE-STRUCTURE-001.md` (FROZEN)
+- BACKEND_DESIGN: `docs/BACKEND_DESIGN/BD-2026-007-wyckoff-live-structure.md`
+- ENGINEERING_SPEC: `docs/ENGINEERING_SPEC/ECR-007-wyckoff-live-structure.md`
+- Loop: LOOP-RUN-005

docs/ECR/ECR-008-chart-tv-split.md

@@ -0,0 +1,61 @@
+# ECR-008
+
+**Title:** 拆分主站巨型 `chart_tv.js`（行为冻结）  
+**Status:** Done (Reviewed)  
+**Date:** 2026-08-07  
+**Change Level:** L3（结构重构；行为冻结）  
+
+## Change
+
+将 `web/static/js/app/chart_tv.js`（≈4700 行）按职责拆为多个无打包 script；薄门面保留 `initTradingView` / `disposeTradingViewCharts` 供 `ui.js` 调用。
+
+## Motivation
+
+ECR-001/002 CODE_REVIEW 非阻断债务；威科夫与 Live 叠层继续堆入单体，审阅与回归成本上升。
+
+## Scope
+
+### Allowed
+
+- 新增：`chart_tv_lifecycle.js` / `chart_tv_shell.js` / `chart_tv_indicators.js` / `chart_tv_chan.js` / `chart_tv_overlays.js` / `chart_tv_finalize.js`
+- `chart_tv.js` 改为编排门面；`index.html` 调整 script 顺序与 cache bust
+- `node --check`；主站手动冒烟
+
+### Forbidden
+
+- Vite / React / TS 构建流水线
+- 修改笔 / 线段 / 中枢 / 买卖点算法语义或绘制语义（仅搬移）
+- 破坏 `/api/analyze` JSON 字段
+- 修改 `config/` / `strategies/`
+- 为主站重新引入 WebSocket 实时
+
+## Risk
+
+| Risk | Mitigation |
+|------|------------|
+| 拆分漏变量 / 作用域错误 | ctx 显式传参；冒烟 dispose + 三周期元素 + 威科夫 |
+| script 顺序错误 | index.html 固定 lifecycle→…→门面→sync |
+| 缓存旧单体 | bump `?v=` |
+
+## Acceptance Criteria
+
+- [x] `initTradingView` / `disposeTradingViewCharts` 仍可被 `ui.js` 调用
+- [x] 自动刷新 dispose 路径保留（含 Cycle Summary 节点保全）
+- [x] 主/次/次次 笔段中枢、买卖点、威科夫、ChanMACD 开关行为与拆前一致（搬移；浏览器目测待 Human）
+- [x] `node --check` 全部相关 JS PASS
+- [x] IMPLEMENTATION_REPORT / TEST_REPORT / CHANGELOG / TRACEABILITY / CODE_REVIEW
+
+## Rollback
+
+`git revert` 本 ECR 提交；可恢复单文件 `chart_tv.js`。
+
+## Risk Review
+
+N/A（不改交易决策语义）
+
+## Linked
+
+- IDEA: `docs/IDEA/IDEA-006-chart-tv-split.md`
+- ENGINEERING_SPEC: `docs/ENGINEERING_SPEC/ECR-008-chart-tv-split.md`
+- HANDOFF: `docs/HANDOFF/ECR-008-architect-to-engineer.md`
+- TRACEABILITY: Yes

docs/ENGINEERING_SPEC/ECR-001-restructure.md

@@ -0,0 +1,105 @@
+# Engineering Spec: ECR-001 引擎 + Web 重构
+
+## Related
+
+| Doc | Link |
+|-----|------|
+| ECR | `docs/ECR/ECR-001-chan-web-restructure.md` |
+| PRODUCT_SPEC | `docs/PRODUCT_SPEC/ECR-001-restructure.md` |
+| ADR | `docs/ADR/ADR-001-package-layout.md` |
+
+## Module Design
+
+### `chanlun/core/*`
+
+| 项 | 值 |
+|----|-----|
+| Responsibility | K 线单元、合并 K、笔/段/中枢/买卖点数据结构与枚举 |
+| Can | 表达缠论基础对象 |
+| Cannot | 拉行情、写 Flask 路由 |
+| Layer | domain |
+
+### `chanlun/pipeline/*`
+
+| 项 | 值 |
+|----|-----|
+| Responsibility | `ChanLun` 编排、`TF_DF` 门面、builders |
+| Can | 从 DataFrame 构建结构 |
+| Cannot | 改公开方法语义 |
+| Layer | application |
+
+### `chanlun/indicators/*` / `chanlun/analysis/*`
+
+| 项 | 值 |
+|----|-----|
+| Responsibility | MACD 状态、Zone/Classifier/Trend 等分析 |
+| Layer | domain / application |
+
+### Root shims (`ChanLun.py`, `ChanEnum.py`, …)
+
+| 项 | 值 |
+|----|-----|
+| Responsibility | 转发到 `chanlun.*`，兼容 strategies |
+| Cannot | 含业务逻辑 |
+
+### `web/config.py` + `web/services/*` + `web/api/*`
+
+| 项 | 值 |
+|----|-----|
+| Responsibility | 配置、行情、分析、序列化、HTTP |
+| Cannot | 修改缠论算法 |
+
+### `web/static/js/app/*`
+
+| 项 | 值 |
+|----|-----|
+| Responsibility | 图表、overlay、UI、API 客户端 |
+| Cannot | 本轮引入 React 构建 |
+
+## Interfaces
+
+```python
+class TF_DF:
+    # 公开方法签名保持与重构前一致
+    def get_kl_data(self, dataframe): ...
+    def get_bi_list(self, dataframe): ...
+    # ... 门面委托 builders
+
+class ChanLun:
+    def init_dataframes(...): ...
+```
+
+`/api/analyze`：查询参数与 JSON 顶层字段兼容。
+
+## Algorithm Sketch
+
+1. 物理迁移模块到 `chanlun/`，修正 import
+2. 根 shim 再导出
+3. `TF_DF` 方法体迁移到 builders，门面调用
+4. Web 按职责拆文件，路由注册不变
+5. HTML 抽 JS；未接线 TS 入 `_unused`
+
+## Error Handling
+
+| Case | Behavior |
+|------|----------|
+| 行情失败 | 保持现有 JSON error |
+| 分析异常 | 保持现有日志与错误返回 |
+
+## Config / Constants
+
+| Name | Source |
+|------|--------|
+| DATA_SERVICE_URL | env / config |
+| HTTP(S)_PROXY | env / config（替代硬编码 7897） |
+| MACD periods | config |
+
+## Test Plan Pointer
+
+- `tests/test_golden_pipeline.py` + `tests/fixtures/golden_*`
+- `web/tests/` analyze 契约冒烟
+- `python -c "from ChanLun import ChanLun"`
+
+## Migration / API Impact
+
+无破坏性 API 变更；仅内部路径变化。

docs/ENGINEERING_SPEC/ECR-002-runtime-split.md

@@ -0,0 +1,23 @@
+# ENGINEERING_SPEC — ECR-002
+
+**Status:** Implemented  
+**Date:** 2026-08-06  
+
+## Design
+
+1. **包目录** `web/services/runtime/`（不用平铺 `runtime_*.py`）
+2. **边界**
+   - `state`：可变全局与客户端
+   - `timeframes`：周期工具
+   - `market_data`：行情
+   - `indicators`：技术指标列
+   - `analyze`：缠论编排 + 趋势分类
+   - `serialize`：JSON 整形
+   - `__init__`：门面 + 历史 `import *` 兼容再导出
+3. **测试**：facade / analyze_chan 键 / serialize / HTTP mock 契约 / TF_DF init / golden
+4. **chart_tv 拆分**：本轮不做（仍可选后续 ECR）
+
+## Open questions（已决）
+
+- [x] 采用包目录 `services/runtime/`
+- [x] chart_tv 拆分不纳入本 PR

docs/ENGINEERING_SPEC/ECR-003-wyckoff-main.md

@@ -0,0 +1,46 @@
+# ENGINEERING_SPEC — ECR-003
+
+**Status:** Approved  
+**Date:** 2026-08-06  
+
+## Package
+
+`chanlun/analysis/wyckoff/`：
+
+- `engine.py` — `analyze_wyckoff(df) -> dict`
+- `range.py` — 交易区间检测（ATR 容差震荡箱）
+- `phases.py` — A–E 状态机
+- `events.py` — Spring/SOS/LPS/UTAD（及 distribution 对称）
+- `volume_profile.py` — 区间内分桶 VP
+- `__init__.py` — 导出 `analyze_wyckoff`
+
+## API
+
+`GET /api/analyze?include_wyckoff=1` → `result["wyckoff"]`：
+
+```json
+{
+  "trading_range": {"start_time","end_time","high","low","mid","active"},
+  "bias": "accumulation|distribution|unknown",
+  "phases": [{"phase","label","start_time","end_time"}],
+  "events": [{"type","time","price","note","volume_ratio","volume_ok"}],
+  "volume_profile": {"bins":[{"price","volume"}],"poc","vah","val","bin_count"},
+  "volume_confirm": {"avg_volume","event_checks":{}}
+}
+```
+
+默认 `include_wyckoff` 假：可不返回或返回 `null`（实现选：不返回键以减负）。
+
+## Detection heuristics
+
+1. ATR(14) 容差；扫描最近窗口找高低点接近的连续段作为 TR。
+2. 阶段：价格在 TR 内相对位置 + 假破/真破时间序。
+3. Spring：下破 TR.low 后收回且收盘回到区间内；量能相对均量判断。
+4. SOS：收盘站上 TR.high 且放量。
+5. LPS：SOS 后回踩不破 mid/high 带且缩量。
+6. UTAD：上破后跌回区间内（派发）。
+7. VP：typical=(H+L+C)/3，volume 加权分桶，VA≈70% 围绕 POC。
+
+## Frontend
+
+主站 checkbox + `chart_view` 传参；`chart_tv.js` 绘制。

docs/ENGINEERING_SPEC/ECR-004-wyckoff-harden.md

@@ -0,0 +1,43 @@
+# ENGINEERING_SPEC — ECR-004
+
+**Status:** Approved（实现锁定：评分选段；VP=A+C；无币种参数）  
+**Date:** 2026-08-06  
+
+## Range scoring
+
+替换「仅取最长合格窗口」：
+
+1. 仍在 `lookback` + `tail_reserve` 框架内扫描候选段（步长 -4）。  
+2. 硬门槛不变：near_hi/lo≥2、inside≥0.75、宽度上限等。  
+3. 分数：`touch_density*50 + inside*30 - (width/ATR)*3 + min(length/40, 2)`，取最高。  
+4. 单测：`low∈[38,42]`、`high∈[58,62]`，起点不早于箱体（容差 8 根）；`abs_start_idx >= 12`。
+
+## Phases
+
+- 非重叠链式切分；每段至少 `min_bars=3`。  
+- 尾部空间不足则延长上一段并停止新增（避免 D/E 完全重合双画）。
+
+## API gate
+
+```text
+if include_wyckoff and not elements_only:
+    result["wyckoff"] = analyze_wyckoff(..., vp_bins∈[10,24])
+```
+
+默认 `wyckoff_vp_bins=24`，上限 24。
+
+## Frontend VP（A+C）
+
+- 填充线 6→3  
+- 有量 bin 按 volume Top-8 绘制 + POC/VAH/VAL  
+- 目标：区间填充+边框+VP ≈ ≤15 series 量级  
+
+## Tests
+
+- `tests/test_wyckoff.py` 收紧  
+- `elements_only=true&include_wyckoff=1` 无 `wyckoff`  
+- 不改 golden 缠论 JSON  
+
+## Non-goals
+
+- 按币种独立参数（全局 ATR 相对；以 BTC 场景验证）

docs/ENGINEERING_SPEC/ECR-007-wyckoff-live-structure.md

@@ -0,0 +1,26 @@
+# ENGINEERING_SPEC — ECR-007 Wyckoff Live Structure
+
+**ECR:** ECR-007  
+**BD:** BD-2026-007  
+**Status:** Approved  
+
+## Intent
+
+Operators observe FORMING Wyckoff structure without feeding Live into execution.
+
+## Modules
+
+| Module | Role |
+|--------|------|
+| `events.py` / `range.py` | Confirmed facts |
+| `live.py` | Live candidates + confidence + lifecycle hint |
+| `engine.py` | Assemble cycles[].confirmed / .live |
+| `execution_signal_from_wyckoff` | Confirmed-only gate |
+
+## Lifecycle
+
+`UNKNOWN → FORMING → CONFIRMED → COMPLETED`
+
+## Non-goals
+
+strategies, maker, Live-as-signal, Confirmed threshold cuts.

docs/ENGINEERING_SPEC/ECR-008-chart-tv-split.md

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+# ENGINEERING_SPEC — ECR-008 chart_tv 拆分
+
+**ECR:** ECR-008  
+**Level:** L3 · 行为冻结  
+**Date:** 2026-08-07  
+
+## Goal
+
+物理拆分主站 Lightweight Charts 绘制单体，不改变可见行为。
+
+## Module map
+
+| File | Responsibility |
+|------|----------------|
+| `chart_tv_lifecycle.js` | `disposeTradingViewCharts`；cleanup 数组与 chart.remove |
+| `chart_tv_shell.js` | `chartTvBuildShell(ctx)`：容器、createChart、K 线主系列 |
+| `chart_tv_indicators.js` | `chartTvRenderIndicators(ctx)`：成交量 / ATR / ChanMACD |
+| `chart_tv_chan.js` | `chartTvRenderChan(ctx)`：笔 / 线段 / 中枢（含未完成与 BI） |
+| `chart_tv_overlays.js` | `chartTvRenderOverlays(ctx)`：结构区、威科夫、BSP/分型、布林等 |
+| `chart_tv_finalize.js` | `chartTvFinalize(ctx)`：时间轴同步、bindSync、视图恢复、tooltip |
+| `chart_tv.js` | `initTradingView`：组 ctx → 顺序调用上述步骤 |
+
+## Context object
+
+`ctx` 至少携带：`symbol`、`timeframe`、`symbolConfig`、周期开关、`candles`、各 chart/container、`showMacd`。全局 `currentData` / `tvWidget` 仍按现网约定使用。
+
+## HTML load order
+
+`lifecycle → shell → indicators → chan → overlays → finalize → chart_tv.js → chart_sync.js → …`
+
+## Tests
+
+1. `node --check` 各新文件 + 门面  
+2. 人工：首屏、自动刷新、威科夫开关、三周期笔段中枢、Cycle Summary  
+
+## Out of scope
+
+Live 验证批跑、威科夫算法调参、analyze JSON 快照、`chart_sync` 大改。

docs/HANDOFF/ECR-001-architect-to-engineer.md

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+# Handoff
+
+**From:** ARCHITECT  
+**To:** ENGINEER  
+**ECR:** ECR-001  
+**State:** design → coding  
+**Date:** 2026-08-05  
+
+## Artifacts
+
+- [x] ECR (Approved)
+- [x] PRODUCT_SPEC
+- [x] ENGINEERING_SPEC
+- [x] RISK_REVIEW (N/A)
+- [x] ADR-001
+- [x] TRACEABILITY
+- [ ] TEST_REPORT / CODE_REVIEW / EXP (engineer / reviewer)
+
+## Restrictions — Do not modify
+
+- `config/`、`strategies/` 内容
+- 缠论算法语义、`/api/analyze` 破坏性变更
+- 计划文件本身
+
+## Goal for receiver
+
+按 ENGINEERING_SPEC 完成包化、TF_DF 拆分、Web 分层、前端模块化、golden 测试与文档收尾。
+
+## Done for this hop
+
+- [x] ESS docs 落盘
+- [x] ECR Approved（计划实施授权）
+
+## References
+
+- `docs/ECR/ECR-001-chan-web-restructure.md`
+- `docs/ENGINEERING_SPEC/ECR-001-restructure.md`
+- Plan: ESS Chan Refactor（1B+2B）