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

关门收尾：归档 loop/gate 产物至 docs/runs，同步 CURRENT/MEMORY/PROFILE，并忽略工作目录 .gates/loop。

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

.gitignore

@@ -1,59 +1,46 @@
 # MacOS
 .DS_Store
 
-# Python
+# Python编译文件和缓存
 __pycache__/
 *.py[cod]
 *$py.class
 *.pyc
 *.pyo
-.pytest_cache/
 
-# Logs & databases
+# 策略文件的缓存
+strategies/__pycache__/
+
+# Machine Learning / AI model files
+*_model*_xgb_model.json
+*modelchan*.json
+*.libsvm
+feature_meta
+*_model_feature_data.csv
+*.pem
+
+# Log files
 *.log
+
+# Database files
 *.sqlite
 *.sqlite-shm
 *.sqlite-wal
-
-# Office documents kept alongside the repo but not part of it.
-# "~$" files are Excel's lock files, recreated every time a workbook is opened.
-*.xlsx
-*.xls
-~$*
-
-# Local data
-data/
-
-# Exchange history fetched by research/live/*.py. 40MB and re-fetchable from
-# the venue, so it stays local; the small result CSVs it feeds are committed.
-research/live/cache/
-
-# Scratch outputs from short shakedown runs, superseded by the real collection.
-research/out/archive/
-
-# Per-trade simulation dumps from research/step*.py. 70MB+ and regenerable by
-# rerunning the step; the summaries they feed live in HANDOFF.md.
-research/out/*.feather
-
-# Virtualenvs. venv writes its own .gitignore since 3.11, but only for the
-# directory it creates — declare it here so other layouts are covered too.
-.venv/
-venv/
-
-# Local tooling
+.DS_Store
+交易记录/~$交易规则.docx
+/datasvc/data
+.DS_Store
+.DS_Store
+/data_provider/data
+.DS_Store
+.DS_Store
+.DS_Store
+.DS_Store
+.DS_Store
+.DS_Store
+data_provider/._config.json
 .gstack/
-research/out/*.jsonl.gz
-research/out/penetration.csv
-research/out/shadow_*.csv
-research/out/run_meta_*.json
 
-# Telegram 凭据。**不要提交**
-research/live/deploy/tg.env
-
-# 生产状态与信号总线。刻意放在仓库外（LIVE_HOME / BUS_DIR），这几条只防
-# 有人把它们指回仓库里：里面是日亏损累计与已处理信号键，被 git clean
-# 清掉等于两道闸静默失忆
-/live/deploy/live.env
-live_state.json
-live_trades.jsonl
-signals_live.jsonl
+# 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

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

ChanBIZS.py

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

ChanBSP.py

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

ChanCTime.py

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

ChanEnum.py

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

ChanHeng.py

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

ChanKLC.py

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

ChanKLU.py

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

ChanLun.py

@@ -0,0 +1,3 @@
+"""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

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

ChanMACD.py

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

ChanMACDHistSet.py

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

ChanMACDSeg.py

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

ChanMACDUnitTF.py

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

ChanPY.py

@@ -0,0 +1,2 @@
+"""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

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

ChanSEG.py

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

ChanZS.py

@@ -0,0 +1,2 @@
+"""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

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

Find_Trend.py

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

README.md

@@ -1,130 +0,0 @@
-# Chan — 缠论分析引擎
-
-把 OHLCV K 线拆成缠论结构（K 线单元 → 合并 K 线 → 分型 → 笔 → 线段 → 中枢 → 买卖点），
-配一个 TradingView Charting Library 的 Web 界面，外加一套验证信号有效性的回测脚本。
-
-标的不限：加密永续（ccxt）与 A 股（akshare）都走同一条分析链路。
-
-```
-chanlun/      缠论引擎，纯 pandas/numpy，无外部指标库依赖
-web/          Flask API + 图表界面
-research/     信号有效性验证脚本（step1 ~ step30）
-data/         本地 K 线（freqtrade 的 feather 格式）
-```
-
-## 安装
-
-需要 Python ≥ 3.11（pandas 3.x / numpy 2.x 的要求，不是本项目代码的限制）。
-
-```bash
-python -m venv .venv
-.venv/bin/pip install -r requirements.txt          # 核心运行时，8 个包
-.venv/bin/pip install -r requirements-dev.txt      # 另加测试与 research/ 所需
-```
-
-## 跑 Web
-
-```bash
-cd web
-../.venv/bin/python app.py        # 默认 http://0.0.0.0:8128
-```
-
-从仓库根跑 `.venv/bin/python web/app.py` 也可以——Python 会把脚本所在目录放进 `sys.path`。
-但**不能用 `python -m web.app`**，也不能 `import web.app`：`web/` 内部是无前缀导入
-（`import config`、`from api.analyze import bp`），`-m` 方式下 `sys.path` 里是仓库根而不是
-`web/`，会 `ModuleNotFoundError: No module named 'config'`。
-
-配置全部走环境变量，见 `web/config.py`：
-
-| 变量 | 默认值 | 用途 |
-|------|--------|------|
-| `FLASK_HOST` / `FLASK_PORT` | `0.0.0.0` / `8128` | 监听地址 |
-| `DATA_SERVICE_URL` | `https://provider.jackyu66.com` | 行情 REST 源 |
-| `DATA_SERVICE_WS_URL` | `wss://jackyu66.com/ws` | 行情 WebSocket 源 |
-| `ASHARE_DP_URL` | `http://103.179.242.166:8000` | A 股数据源 |
-| `CHAN_HTTP_PROXY` | 未设置则不走代理 | ccxt / HTTP 代理 |
-| `MACD_FACTOR` / `MACD_SMOOTH` | `1` / `1` | MACD 周期倍数，默认 12/26/9 |
-
-主要接口：`GET /api/analyze` 返回某标的某周期的完整缠论结构，`/api/klines/recent`
-取最新 K 线，`/api/trend_filter` 与 `/api/trend_detail` 做多周期趋势筛选，
-`/api/symbols`、`/api/search_stock`、`/api/sectors` 等负责标的检索。页面在 `/` 与 `/chan_tv`。
-
-## 作为库使用
-
-```python
-import pandas as pd
-from chanlun import TF_DF
-
-# 需要 date/open/high/low/close/volume 六列，date 为 datetime
-df = pd.read_feather("data/binance/futures/BTC_USDT_USDT-1h-futures.feather")
-
-tf = TF_DF(df, interval=1, timeframe="1h")
-
-len(tf.klu_list)    # K 线单元
-len(tf.klc_list)    # 合并 K 线（处理包含关系后）
-len(tf.bi_list)     # 笔
-len(tf.seg_list)    # 线段
-len(tf.zs_list)     # 中枢
-tf.bi_list[-1].dir  # Chan_BI_DIR.DOWN
-tf.chanmacd         # MACD 结构分析（背驰判定用）
-```
-
-**`interval` 的单位是分钟**，对传入的 df 做重采样；`interval=1` 是特例，表示原样使用、
-不重采样。所以拿 1h 的 feather 要传 `interval=1`，拿 1m 数据想看 1h 才传 `interval=60`：
-
-```python
-df1m = pd.read_feather("data/binance/futures/BTC_USDT_USDT-1m-futures.feather")
-TF_DF(df1m, interval=5,  timeframe="5m")
-TF_DF(df1m, interval=60, timeframe="1h")
-```
-
-传错不会报错，只会静默给出错误周期的结构——1h 数据配 `interval=4` 相当于按 4 分钟
-重采样，结果与 `interval=1` 完全相同。
-
-## 分析流程
-
-`TF_DF.init_TF_DF()` 按顺序做这几步，每步的实现在 `chanlun/pipeline/builders/` 下同名文件：
-
-1. `resample_to_interval` — 重采样（`interval != 1` 时）
-2. `add_indicators` — 追加 33 列指标（MACD / BBANDS / EMA / RSI / ATR 等）
-3. `cal_kl_data` → `klu_list` — K 线单元
-4. `get_klc_list` → `klc_list` — 按包含关系合并 K 线，并标记分型
-5. `cal_bi_list` → `bi_list` — 笔
-6. `cal_bi_zs_list_pure` → `bi_zs_list` — 笔中枢
-7. `get_seg_list` → `seg_list` — 线段
-8. `get_zs_list` / `get_big_zs_list` — 中枢与大级别中枢
-9. `ChanMACD(klu_list)` — MACD 段 / 柱堆结构，供背驰判定
-
-## 数据
-
-`data/<交易所>/futures/<SYMBOL>-<周期>-futures.feather`，即 freqtrade 的下载格式，
-如 `data/binance/futures/BTC_USDT_USDT-1h-futures.feather`。
-`research/lib/data.py` 负责定位：`BTC/USDT:USDT` + `1h` 会解析到上面这个路径，
-找不到本地文件则回落到远端拉取。
-
-## 测试
-
-```bash
-.venv/bin/python -m pytest chanlun/tests web/tests -q
-```
-
-`chanlun/tests/test_ta_compat.py` 有个**需要注意的陷阱**：它把 `chanlun/indicators/ta.py`
-的输出逐 bar 钉在 TA-Lib 上，但 **TA-Lib 不存在时会静默跳过**。也就是说改了 `ta.py`
-之后在没装 TA-Lib 的环境里跑，测试会显示通过，其实一项都没验证。改动那个文件时请先装：
-
-```bash
-sudo apt-get install -y libta-lib0 ta-lib-dev
-.venv/bin/pip install TA-Lib technical
-```
-
-## 已知问题
-
-- **`web/DEPLOY_GUIDE.md` 已失效**：它引用的 `deploy_venv.sh`、`stop_venv.sh`、
-  `status_venv.sh` 等 6 个脚本都在 `7f393b9` 精简提交里删掉了，目前没有部署脚本。
-  两个 systemd unit 文件（`web/chanlun-web*.service`）仍可参考，但它们用 gunicorn
-  且写死端口 8123，与 `config.py` 默认的 8128 不一致，gunicorn 也不在依赖清单里。
-- **`web/README.txt` 已过时**：它说的 `web/requirements.txt` 不存在，依赖清单在仓库根目录。
-- **四个零引用的死文件**：`chanlun/analysis/` 下的 `ChanPY.py`、`ChanLun_Classifier.py`、
-  `Find_Trend.py`、`ChanHeng.py` 全项目无人引用。`ChanPY.py` 依赖未安装的外部 chan.py 库，
-  另外三个需要 matplotlib / mplfinance / xgboost / scikit-learn——这些都**不在**依赖清单里，
-  是有意为之。要用得自行安装。

TF_DF.py

@@ -0,0 +1,2 @@
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.pipeline.timeframe import TF_DF  # noqa: F401

chanlun/analysis/ChanLun_Classifier.py

@@ -14,10 +14,12 @@ 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

chanlun/analysis/Find_Trend.py

@@ -2,7 +2,7 @@ import ccxt
 import pandas as pd
 import numpy as np
 import mplfinance as mpf
-from chanlun.indicators import ta
+from talib import MACD, SMA
 from datetime import datetime, timedelta
 import logging
 import datetime as dt
@@ -249,9 +249,8 @@ def analyze_higher_timeframe(df_30m):
 # 8. Back-divergence detection (enhanced)
 def detect_back_divergence(df, strokes, higher_trend):
     try:
-        macd_df = ta.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
-        macd, hist = macd_df['macd'], macd_df['macdhist']
-        sma20 = ta.SMA(df['Close'], timeperiod=20)
+        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

chanlun/analysis/fast_bsp.py

@@ -1,384 +0,0 @@
-"""快速三类买卖点（引擎内称第四类，B4/S4）。
-
-原本长在 `research/lib/fast_bsp3.py`，现移入引擎作为唯一实现，研究脚本改为转发导入，
-这样回测口径与 web 图表永远一致。
-
-命名说明：缠论原文里没有「第四类买卖点」，但 B4/S4 也不只是「B3/S3 提前几根」——
-两者的选样口径不同，统计性质符号相反，故单独立类。形态条件是实时可判的：
-
-    中枢已成 -> 收盘突破 zg -> 收盘未跌回中枢 -> 重新上行
-
-最后一步发生的当根就能下单，滞后约 2 根，而引擎 B3/S3 要等 pullback_bi.sure_time，
-滞后 9~10 根。但差别不止滞后：
-
-  判据   引擎用笔端点事后判（回拉笔低点 >= zg），本函数用收盘价实时判
-  方向   引擎由离开笔方向决定，本函数由收盘从哪一侧突破决定
-  口径   627 个中枢里引擎发 625 个信号（几乎不筛），本函数只认 212 个（34%）；
-         被拒的多数是「中枢确认时价格早已离开、此后再没回来」的历史区间
-
-step30 同条件对拍（同一套 pure 笔中枢、同一组过滤器、同样的 1.5/3.0/48 出场）：
-
-                 原始 PF   +大级别同向   +同向+阶梯   胜率    t值
-    引擎 B3/S3     0.66      0.66         0.71      27.4%  -18.76
-    本函数 B4/S4   1.59      1.85         2.26      47.1%  +10.09
-
-同一组过滤器对 B4 有效、对 B3 无效，滞后差解释不了这一点（入场后移 1~4 根只是
-PF 3.18->2.53 的平滑衰减）。且 SL1.5/TP3.0 下随机入场胜率约 33%，引擎那 27.4%
-低于随机——它选中的是一批系统性反向的样本，不是「晚了所以差」。
-
-require_touch 控制是否强求回抽碰到中枢边界。step32/33 的实测表明强求反而更差：
-这等于排除掉「突破后一去不回头」的强势段，而那正是缠论里最强的趋势形态。
-故默认 False（笔数 +21%、滞后 -1 根、PF 2.26→2.37）。
-
-判据本身（收盘越过前一根极值）没有独立预测力：裸用 15 万笔样本 PF 0.95，
-把中枢换成「近20根高点」这类伪阻力位后 PF 0.90。alpha 全部来自中枢结构，
-判据只负责在这个已知价位上确认动能恢复。它也不能用于识别笔端点——
-入场前的回抽极值命中笔端点±2根的比例 19.3%，低于随机基准 22%。
-
-全部判定只使用当根及之前的数据，无未来函数。
-"""
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from chanlun.core.ChanEnum import Chan_FX_TYPE
-
-# 中枢的「可用时刻」取第几笔的确认时间。
-#   -1  = 最后一笔（历史默认）。中枢每吸收一根K线，最后一笔就可能后移，
-#         于是 available_ts 跟着漂——这是右边缘重画的根因（见 HANDOFF §5.41）。
-#    2  = 第三笔。三笔重叠即中枢成立，此后不再变，理论上更早也更稳。
-# ⛔ step47 已判定 bis[2] 作废（毛 R 0.933 → −0.000，见 HANDOFF §5.42）。
-# 开关保留只为可复现那次 A/B，**不要改默认值**。
-import os as _os
-
-AVAIL_BI_INDEX = -1
-
-
-def _resolve_avail_bi(avail_bi: int | None) -> int:
-    """优先级：显式入参 > 环境变量 CHAN_AVAIL_BI > 模块默认。
-
-    环境变量在调用时读取而非 import 时——ProcessPoolExecutor 在 fork 启动方式下
-    子进程会继承已 import 的模块，import 时读就固化成父进程的值了。
-    """
-    if avail_bi is not None:
-        return avail_bi
-    return int(_os.environ.get("CHAN_AVAIL_BI", AVAIL_BI_INDEX))
-
-
-def timestamps_ms(src: pd.DataFrame) -> np.ndarray:
-    """取毫秒时间戳。研究侧的 df 自带 timestamp，web 侧的不一定，故按 date 回退。
-
-    回退写法不能用 `date.astype("int64") // 10**6`：该值单位取决于列精度，
-    对毫秒精度的列会把时间戳砸平。
-    """
-    if "timestamp" in src.columns:
-        return src["timestamp"].to_numpy()
-    d = pd.to_datetime(src["date"])
-    if getattr(d.dt, "tz", None) is None:
-        d = d.dt.tz_localize("UTC")
-    return (d.dt.tz_convert("UTC").dt.tz_localize(None)
-             .astype("datetime64[ms]").astype("int64").to_numpy())
-
-
-def ensure_timestamp(df: pd.DataFrame) -> pd.DataFrame:
-    """保证 df 带 timestamp 列，缺失时补一份副本，不改动调用方的对象。"""
-    if "timestamp" in df.columns:
-        return df
-    out = df.copy()
-    out["timestamp"] = timestamps_ms(out)
-    return out
-
-
-def build_htf_zones(df_htf: pd.DataFrame, tf: str, chan=None, avail_bi: int | None = None) -> pd.DataFrame:
-    """算 pure 笔中枢，返回带生效时间的区间表。
-
-    available_ts —— 该中枢最早可被使用的时间戳（其确认时刻）。
-    传入已构建好的 chan 可避免重复跑一遍 pipeline（大数据集上省一半时间）。
-    """
-    if chan is None:
-        from chanlun import TF_DF
-
-        chan = TF_DF(df_htf, 1, tf)
-    zs_list = chan.cal_bi_zs_list_pure(chan.bi_list)
-    # 用引擎自己的 dataframe 对齐，避免调用方传入的 df 与引擎内部行数不一致
-    src = chan.dataframe if getattr(chan, "dataframe", None) is not None else df_htf
-    return zones_from_zs_list(zs_list, src, avail_bi=avail_bi)
-
-
-def zones_from_zs_list(zs_list, src: pd.DataFrame, avail_bi: int | None = None) -> pd.DataFrame:
-    """把已算好的 pure 笔中枢转成区间表。
-
-    调用方手工跑过 cal_bi_zs_list_pure 时走这里，免得再算一遍（web 的 analyze_chan
-    就是这种用法）。
-    """
-    ts_of = dict(zip(src["date"].dt.strftime("%Y-%m-%d %H:%M:%S"), timestamps_ms(src)))
-    # 中枢可用时刻取第几笔。在循环外解析一次，别让每个中枢都去读一遍环境变量。
-    i = _resolve_avail_bi(avail_bi)
-
-    rows = []
-    for zs in zs_list:
-        bis = getattr(zs, "bi_list", [])
-        if not bis:
-            continue
-        # 笔数不够时退回最后一笔（i=2 需要至少 3 笔才成立）
-        key_bi = bis[i] if (i == -1 or len(bis) > i) else bis[-1]
-        sure_key = str(getattr(key_bi, "sure_time", "") or "")
-        end_key = str(getattr(key_bi, "end_time", "") or "")
-        avail = ts_of.get(sure_key) or ts_of.get(end_key)
-        if avail is None:
-            continue
-        start_key = str(bis[0].start_time)
-        rows.append({
-            "zg": float(zs.zg), "zd": float(zs.zd),
-            "gg": float(getattr(zs, "gg", zs.zg)), "dd": float(getattr(zs, "dd", zs.zd)),
-            "start_ts": ts_of.get(start_key, avail),
-            "available_ts": int(avail),
-        })
-    out = pd.DataFrame(rows)
-    return out.sort_values("available_ts").reset_index(drop=True) if not out.empty else out
-
-
-def add_zone_ladder(zones: pd.DataFrame) -> pd.DataFrame:
-    """标注每个中枢相对前一个中枢是否同向推进（缠论「趋势 vs 盘整」）。
-
-    z_above / z_below 分别对应向上、向下推进。买信号要求 z_above、卖信号要求 z_below
-    时，30m/2h 的 PF 从 2.72 升到 3.41。
-    """
-    out = zones.copy()
-    if out.empty:
-        out["z_above"], out["z_below"] = pd.Series(dtype=bool), pd.Series(dtype=bool)
-        return out
-    pg, pdn = out["zg"].shift(), out["zd"].shift()
-    out["z_above"] = (out["zd"] > pg).fillna(False)
-    out["z_below"] = (out["zg"] < pdn).fillna(False)
-    return out
-
-
-def htf_fx_timeline(chan_htf, df_htf: pd.DataFrame | None = None) -> pd.DataFrame:
-    """把大级别分型压成一条按确认时间排序的时间线。
-
-    confirm_ts 是该分型最早可被使用的时刻。timestamp 是K线开盘时刻，而分型要等这根K线
-    收盘才算数，所以整体后移一个大级别周期；否则小级别会提前一整根大级别K线拿到信号。
-
-    只取方向与确认时刻——同向过滤用不到背驰强度，省掉 MACD 面积计算。
-    """
-    src = chan_htf.dataframe if getattr(chan_htf, "dataframe", None) is not None else df_htf
-    if src is None or len(src) == 0:
-        return pd.DataFrame(columns=["confirm_ts", "fx_ts", "direction", "price"])
-    ts = timestamps_ms(src)
-    idx_of = {t: i for i, t in enumerate(src["date"].dt.strftime("%Y-%m-%d %H:%M:%S"))}
-    period = int(np.median(np.diff(ts))) if len(ts) > 1 else 0
-
-    rows = []
-    for klc in getattr(chan_htf, "klc_list", []):
-        if klc.fx not in (Chan_FX_TYPE.TOP, Chan_FX_TYPE.BOTTOM):
-            continue
-        if klc.next is None or klc.next.end_klu is None:
-            continue
-        e_key, c_key = str(klc.end_time), str(klc.next.end_klu.time)
-        if e_key not in idx_of or c_key not in idx_of:
-            continue
-        fx_idx, confirm_idx = idx_of[e_key], idx_of[c_key]
-        if confirm_idx <= fx_idx:
-            continue
-        d = 1 if klc.fx == Chan_FX_TYPE.BOTTOM else -1
-        rows.append({
-            "confirm_ts": int(ts[confirm_idx]) + period,
-            "fx_ts": int(ts[fx_idx]),
-            "direction": d,
-            "price": float(klc.low if d == 1 else klc.high),
-        })
-    out = pd.DataFrame(rows)
-    if out.empty:
-        return pd.DataFrame(columns=["confirm_ts", "fx_ts", "direction", "price"])
-    return out.sort_values("confirm_ts").reset_index(drop=True)
-
-
-def attach_htf_agree(sig: pd.DataFrame, df_ltf: pd.DataFrame, tl: pd.DataFrame) -> pd.DataFrame:
-    """给每个小级别信号挂上「入场时刻之前最近的大级别分型」是否同向。
-
-    产出 htf_dir（+1 底 / -1 顶）与 htf_agree（1 同向 / 0 反向 / NaN 无可用分型）。
-    """
-    out = sig.copy()
-    if sig.empty or tl.empty:
-        out["htf_dir"] = np.nan
-        out["htf_agree"] = np.nan
-        return out
-
-    ts_ltf = timestamps_ms(df_ltf)
-    entry_ts = ts_ltf[out["entry_idx"].to_numpy().astype(int)]
-    k = np.searchsorted(tl["confirm_ts"].to_numpy(), entry_ts, side="right") - 1
-    valid = k >= 0
-    k_safe = np.clip(k, 0, len(tl) - 1)
-
-    fx_dir = tl["direction"].to_numpy()[k_safe].astype(float)
-    out["htf_dir"] = np.where(valid, fx_dir, np.nan)
-    out["htf_agree"] = np.where(
-        valid, (fx_dir == out["direction"].to_numpy()).astype(float), np.nan
-    )
-    return out
-
-
-def attach_zone_ladder(sig: pd.DataFrame, zones: pd.DataFrame) -> pd.DataFrame:
-    """按信号方向取该中枢的阶梯标记：买看 z_above、卖看 z_below。
-
-    zone_i 是 find_fast_bsp3 内 enumerate 出的位置序号，故用 iloc 定位。
-    """
-    out = sig.copy()
-    if sig.empty:
-        out["ladder_ok"] = pd.Series(dtype=bool)
-        return out
-    z = zones if "z_above" in zones.columns else add_zone_ladder(zones)
-    above = z["z_above"].to_numpy()
-    below = z["z_below"].to_numpy()
-    zi = out["zone_i"].to_numpy().astype(int)
-    ok = np.where(out["direction"].to_numpy() == 1, above[zi], below[zi])
-    out["ladder_ok"] = ok.astype(bool)
-    return out
-
-
-def find_fast_bsp3(
-    df: pd.DataFrame,
-    zones: pd.DataFrame,
-    scan: int = 200,
-    pullback_win: int = 30,
-    tol: float = -1.0,
-    max_per_zone: int = 1,
-    diag: dict | None = None,
-    require_touch: bool = False,
-) -> pd.DataFrame:
-    """扫描每个中枢，找突破后回抽不回中枢的入场点。
-
-    zones 需含 zg / zd / available_ts，且 available_ts 已是可用时刻。
-    max_per_zone > 1 时，同一中枢在首次入场后继续往后找二次、三次突破回抽，
-    用来检验「趋势里同一中枢反复给机会」是否值得做。
-
-    tol 是「算作回抽中」的边界容差。require_touch=False 时它不再是入场门槛，
-    仅决定哪些K线被视为回抽中而跳过转强判定，故 tol 越大入场越晚。
-    默认负值 = 完全禁用该跳过，突破后每根都检查转强，滞后压到 2.2 根。
-    step34 实测滞后与收益严格单调：9.9根 PF1.88 / 5.8根 2.37 / 2.2根 2.86。
-
-    返回列：
-      entry_idx   实时可下单的K线
-      direction   +1 三买 / -1 三卖
-      bo_idx      突破根
-      pb_idx      回抽极值根
-      lag         entry_idx - bo_idx
-      depth       回抽深度（相对中枢边界，负值表示曾插入中枢）
-      occ         这是该中枢的第几次入场
-    """
-    if zones.empty:
-        return pd.DataFrame()
-
-    ts = df["timestamp"].to_numpy()
-    close = df["close"].to_numpy(dtype=float)
-    high = df["high"].to_numpy(dtype=float)
-    low = df["low"].to_numpy(dtype=float)
-    n = len(df)
-    rows = []
-
-    def note(key: str) -> None:
-        if diag is not None:
-            diag[key] = diag.get(key, 0) + 1
-
-    for zone_i, (_, z) in enumerate(zones.iterrows()):
-        note("中枢总数")
-        zg, zd = float(z["zg"]), float(z["zd"])
-        if zg <= zd:
-            note("×无效中枢")
-            continue
-        start = int(np.searchsorted(ts, z["available_ts"], side="left"))
-        if start >= n - 2:
-            note("×中枢太靠后")
-            continue
-        # 允许多次入场时按比例放宽扫描窗口，否则后几次机会会被窗口截断
-        scan_end = min(start + scan * max_per_zone, n)
-        cursor = start
-
-        for occ in range(1, max_per_zone + 1):
-            if cursor >= n - 2:
-                break
-            # 第一步：找突破。要求突破前确实待在中枢内，避免把远处的价格当突破。
-            was_inside = False
-            bo_idx, d = None, 0
-            for j in range(cursor, scan_end):
-                c = close[j]
-                if zd <= c <= zg:
-                    was_inside = True
-                    continue
-                if not was_inside:
-                    continue
-                bo_idx, d = j, (1 if c > zg else -1)
-                break
-            if bo_idx is None:
-                if occ == 1:
-                    note("×窗口内未突破")
-                break
-
-            edge = zg if d == 1 else zd
-            # 第二步：突破后监控回抽，回抽不跌回中枢且重新顺势 -> 入场
-            touched = False
-            pb_idx = None
-            pb_ext = None
-            entry_idx = None
-            fell_back = False
-            for j in range(bo_idx + 1, min(bo_idx + pullback_win + 1, n)):
-                # 收盘跌回中枢 -> 突破失效
-                if zd <= close[j] <= zg:
-                    fell_back = True
-                    break
-                # 回抽触及边界附近（允许 tol 的毛刺）
-                near = (low[j] <= edge * (1 + tol)) if d == 1 else (high[j] >= edge * (1 - tol))
-                if near:
-                    touched = True
-                    ext = low[j] if d == 1 else high[j]
-                    if pb_ext is None or ((ext < pb_ext) if d == 1 else (ext > pb_ext)):
-                        pb_ext, pb_idx = ext, j
-                    continue
-                # 回抽后重新顺势：收盘创出前一根之上（三买）/ 之下（三卖）
-                # require_touch=False 时不强求回抽碰到中枢边界，
-                # 这样「突破后一去不回头」的强势段也能收进来。
-                if (touched and pb_idx is not None) or not require_touch:
-                    go = close[j] > high[j - 1] if d == 1 else close[j] < low[j - 1]
-                    if go:
-                        entry_idx = j
-                        break
-            if entry_idx is None:
-                if occ == 1:
-                    note("×突破后跌回中枢" if fell_back
-                         else "×回抽未触及边界" if not touched
-                         else "×触及边界但未转强")
-                # 这次突破没走成，从突破点之后继续找下一次
-                cursor = bo_idx + 1
-                continue
-            if pb_ext is None:
-                # 未触及边界就转强（require_touch=False）：取突破至入场间的实际极值
-                seg = slice(bo_idx + 1, entry_idx + 1)
-                pb_ext = float(low[seg].min() if d == 1 else high[seg].max())
-                pb_idx = int((low[seg].argmin() if d == 1 else high[seg].argmax())
-                             + bo_idx + 1)
-            if occ == 1:
-                note("√成交")
-
-            # 回抽深度：>0 表示未插入中枢，越大表示回抽越浅
-            depth = (pb_ext - zg) / zg if d == 1 else (zd - pb_ext) / zd
-            rows.append({
-                "entry_idx": entry_idx, "direction": d,
-                "bo_idx": bo_idx, "pb_idx": pb_idx,
-                "lag": entry_idx - bo_idx,
-                "depth": depth,
-                "zg": zg, "zd": zd,
-                "width_pct": (zg - zd) / close[bo_idx],
-                "occ": occ,
-                "zone_i": zone_i,
-            })
-            cursor = entry_idx + 1
-
-    out = pd.DataFrame(rows)
-    if out.empty:
-        return out
-    # 相邻中枢可能突破到同一根K线，同一时刻只能有一个仓位，保留最早成型的那个
-    return (out.sort_values(["entry_idx", "occ", "zone_i"])
-               .drop_duplicates("entry_idx", keep="first")
-               .reset_index(drop=True))

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

@@ -15,12 +15,10 @@ class ChanBI():
 		self.sure_time = None
 		self.klc_list = []
 		self.klc_list.append(klc)
-		self._klc_idx = {klc.index}
 		self.end_time = klc.end_time
 		self.start_time = klc.start_time
-		self._macd_hist = 0
-		self._macd_div = 0
-		self._macd_dirty = False
+		self.macd_hist = 0
+		self.macd_div = 0
 		self.seg = None
 		self.height = 0
 		self.width = 0
@@ -35,57 +33,26 @@ class ChanBI():
 	def set_seg(self, seg):
 		self.seg = seg
 		self.seg_index = len(seg.bi_list)-1
-	# macd_hist / macd_div 改为惰性。原来 add_klc 每加一根 KLC 就把整笔的
-	# 所有 KLU 重新累加一遍，是 O(k²)；而这两个值只有背驰判定（bsp.py）在读，
-	# lean 模式下 bsp 根本不算，等于全程白算。这里只标脏，取值时才算。
-	# 语义不变：klc_list 只增不减（set_start_klc 会重置并同时清脏），
-	# dir 在 klc 累加期间固定，所以延后到读取时算与逐次重算结果相同。
-	@property
-	def macd_hist(self):
-		if self._macd_dirty:
-			self.cal_macdhist()
-		return self._macd_hist
-
-	@macd_hist.setter
-	def macd_hist(self, v):
-		self._macd_hist = v
-		self._macd_dirty = False
-
-	@property
-	def macd_div(self):
-		self.cal_macd_div()
-		return self._macd_div
-
-	@macd_div.setter
-	def macd_div(self, v):
-		self._macd_div = v
-
 	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
+		self.macd_div = 0.0
 		if self.pre and self.pre.pre:
 			if self.pre.pre.macd_hist == 0:
-				self._macd_div = 0.0
+				self.macd_div = 0.0
 			else:
-				self._macd_div = self.macd_hist / self.pre.pre.macd_hist
+				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_dirty = False
-		self._macd_hist = 0
-		up = self.dir == Chan_BI_DIR.UP
-		acc = 0
+		self.macd_hist = 0
 		for klc in self.klc_list:
 			for klu in klc.klu_list:
-				h = klu.macdhist
-				if up:
-					if h > 0:
-						acc += h
-				elif h < 0:
-					acc -= h
-		self._macd_hist = acc
+				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:
@@ -128,10 +95,6 @@ class ChanBI():
 		self.start_klc = klc
 		self.klc_list = []
 		self.klc_list.append(klc)
-		# klc_list 被整个换掉，去重集合与惰性缓存都要跟着重置，
-		# 否则后续 add_klc 会以为旧下标还在里面而漏加
-		self._klc_idx = {klc.index}
-		self._macd_dirty = True
 		self.high = klc.high
 		self.low = klc.low
 		self.dir = ddir
@@ -140,14 +103,20 @@ class ChanBI():
 	def set_next(self, bi):
 		self.next = bi
 	def add_klc(self, klc):
-		# 去重原本是对 klc_list 线性扫描，配合下面每次全量重算的 macdhist，
-		# 让「往一笔里加 k 根 KLC」变成 O(k²)。改用下标集合，O(1)。
-		if klc.index not in self._klc_idx:
-			self._klc_idx.add(klc.index)
+		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._macd_dirty = True
+		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):

chanlun/core/ChanEnum.py

@@ -281,10 +281,6 @@ class Chan_BSP_TYPE(Enum):
     S1 = auto()
     S2 = auto()
     S3 = auto()
-    # 第四类：三类买卖点的低滞后变体，几何位置同 B3/S3，但不等笔确认。
-    # 见 chanlun/analysis/fast_bsp.py
-    B4 = auto()
-    S4 = auto()
     NONE = auto()
 """
 class Chan_BSP_TYPE(Enum):

chanlun/core/ChanFastBSP.py

@@ -1,40 +0,0 @@
-from chanlun.core.ChanEnum import Chan_BSP_DIR, Chan_BSP_TYPE
-
-
-class ChanFastBSP():
-	"""第四类买卖点（B4/S4）。
-
-	与 ChanBSP 的区别在于它不挂在笔上：fast_bsp 刻意不等笔确认，入场点是一根具体的
-	K线而非一笔的端点，所以时间与价格直接取自 K 线，没有 bi / klc 可依附。
-
-	htf_agree 与 ladder_ok 是两个独立的过滤标志，不在这里合成——上层（图表或策略）
-	自己决定要不要用、怎么组合。
-	"""
-
-	def __init__(self, time, price, ddir, entry_idx, bo_time=None, pb_time=None,
-	             lag=0, depth=0.0, zg=None, zd=None, occ=1,
-	             htf_dir=None, htf_agree=None, ladder_ok=None):
-		self.time = time
-		self.price = float(price)
-		self.dir = ddir
-		self.type = Chan_BSP_TYPE.B4 if ddir == Chan_BSP_DIR.BUY else Chan_BSP_TYPE.S4
-		self.entry_idx = int(entry_idx)
-		self.bo_time = bo_time
-		self.pb_time = pb_time
-		self.lag = int(lag)
-		self.depth = float(depth)
-		self.zg = float(zg) if zg is not None else None
-		self.zd = float(zd) if zd is not None else None
-		self.occ = int(occ)
-		self.htf_dir = htf_dir
-		self.htf_agree = htf_agree
-		self.ladder_ok = ladder_ok
-		# 入场即成立，没有「等待确认」这个状态；留此字段是为了与 ChanBSP 的序列化对齐
-		self.is_sure = True
-		self.start_time = time
-		self.end_time = time
-		self.sure_time = time
-
-	def __repr__(self):
-		name = str(self.type).replace('Chan_BSP_TYPE.', '')
-		return f"<ChanFastBSP {name} {self.time} {self.price} lag={self.lag}>"

chanlun/core/ChanKLC.py

@@ -63,11 +63,10 @@ class ChanKLC():
 		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 = {}
-		self._ema_status_dirty = False
+		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.ema52_status = 0
+		self.ema52_pos = Chan_EMA_POS.UNKNOWN
 		self.bb2633upper = klu.bb2633upper
 		self.bb2633lower = klu.bb2633lower
 		self.bb2633middle = klu.bb2633middle
@@ -284,44 +283,21 @@ class ChanKLC():
 			'ema156': self.ema156,
 			'ema208': self.ema208,
 		}
-		self._ema_status_dirty = False
-		self._ema_status = {}
+		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] = {
+			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'])
-
-	# 以下三个改成惰性求值。原来 set_end_klu 每次合并 KLU 都会立刻重算一遍，
-	# 实测占 TF_DF 构建的约 25%，而全仓（含前端）没有任何地方读取它的产出。
-	# 保留属性形式是为了任何外部读取仍拿到正确值，只是推迟到真被读时才算。
-	@property
-	def ema_status(self):
-		if self._ema_status_dirty:
-			self.cal_all_ema_status()
-		return self._ema_status
-
-	@property
-	def ema52_pos(self):
-		if self._ema_status_dirty:
-			self.cal_all_ema_status()
-		return self._ema52_pos
-
-	@property
-	def ema52_status(self):
-		if self._ema_status_dirty:
-			self.cal_all_ema_status()
-		return self._ema52_status
-
+		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:
@@ -472,7 +448,7 @@ class ChanKLC():
 			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._ema_status_dirty = True
+		self.cal_all_ema_status()
 		if self.open > self.high:
 			self.open = self.high
 		if self.close > self.high:

chanlun/core/ChanKLU.py

@@ -160,40 +160,27 @@ class ChanKLU:
 			return False
 		else:
 			return True
-	# 属性名 ← dataframe 列名。两条赋值路径（逐行 Series / 预取 ndarray）共用这张表，
-	# 免得将来加指标时只改一处、另一处静默漏掉。
-	INDICATOR_FIELDS = (
-		('macd', 'macd'), ('signal', 'macdsignal'), ('macdhist', 'macdhist'),
-		('ema26', 'ema26'), ('ema52', 'ema52'), ('ema24', 'ema24'),
-		('ema104', 'ema104'), ('ema156', 'ema156'), ('ema208', 'ema208'),
-		('ema13', 'ema13'), ('ema7', 'ema7'), ('ema5', 'ema5'),
-		('rsi', 'rsi'), ('volume_ratio', 'volume_ratio'),
-		('bb52upper', 'bb52upper'), ('bb52lower', 'bb52lower'),
-		('bb2633upper', 'bb2633upper'), ('bb2633lower', 'bb2633lower'),
-		('bb2633middle', 'bb2633middle'), ('ma5', 'ma5'),
-	)
-
 	def set_indicators(self, item):
-		"""单根赋值。增量追加时每次只有一根，走这条即可。
-
-		原写法是 `float(item[c]) if c in item and item[c] else 0`。其中的真值判断
-		是空转：值为 0.0 时 float(0.0) 仍是 0，值为 NaN 时 NaN 为真值、照样透传。
-		唯一起作用的是「列不存在则填 0」，所以这里只保留那一层。
-		"""
-		for attr, col in self.INDICATOR_FIELDS:
-			v = item[col] if col in item else 0
-			setattr(self, attr, float(v) if v else 0)
-
-	def set_indicators_from(self, cols, i):
-		"""从预取的 {列名: ndarray} 按下标赋值，语义与 set_indicators 相同。
-
-		全量构建时用这条：避免每根 `df.iloc[i]` 构造一个 Series，再在其上做
-		几十次逐键查找——那是 TF_DF 构建 96% 的耗时所在。
-		"""
-		for attr, col in self.INDICATOR_FIELDS:
-			arr = cols.get(col)
-			v = arr[i] if arr is not None else 0
-			setattr(self, attr, float(v) if v else 0)
+		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

chanlun/indicators/ta.py

@@ -1,196 +0,0 @@
-"""Drop-in replacement for the `talib.abstract` calls this project makes.
-
-Same call signatures, same column names, same NaN warm-up lengths, so call
-sites only change their import line.
-
-Only what the codebase actually uses is implemented: SMA, MA, EMA, RSI, ATR,
-MACD, BBANDS. Numerical agreement with TA-Lib is enforced by
-`chanlun/tests/test_ta_compat.py`, which skips when talib is absent.
-
-The warm-up conventions below are TA-Lib's, not the textbook ones, and they
-differ between functions — getting them wrong shifts every downstream Chan
-structure by a bar:
-
-  SMA/BBANDS  first value at index period-1
-  EMA         seeded with the SMA of the first `period` values, at index period-1
-  RSI/ATR     Wilder smoothing (alpha = 1/period), first value at index period
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-__all__ = ["SMA", "MA", "EMA", "RSI", "ATR", "MACD", "BBANDS"]
-
-
-def _series(data, price: str = "close") -> pd.Series:
-    """Accept the abstract-API shapes: DataFrame, Series, or ndarray."""
-    if isinstance(data, pd.DataFrame):
-        return data[price].astype(float)
-    if isinstance(data, pd.Series):
-        return data.astype(float)
-    return pd.Series(np.asarray(data, dtype=float))
-
-
-def _recursive(values: np.ndarray, seed: float, start: int, alpha: float, n: int) -> np.ndarray:
-    """out[start] = seed; out[i] = alpha*values[i] + (1-alpha)*out[i-1].
-
-    Delegates the recursion to pandas' C implementation rather than a Python
-    loop — `research/` runs this over long histories.
-    """
-    out = np.full(n, np.nan)
-    if start >= n:
-        return out
-    tail = values[start:].astype(float).copy()
-    tail[0] = seed
-    out[start:] = pd.Series(tail).ewm(alpha=alpha, adjust=False).mean().to_numpy()
-    return out
-
-
-def SMA(data, timeperiod: int = 30, price: str = "close") -> pd.Series:
-    s = _series(data, price)
-    return s.rolling(window=timeperiod, min_periods=timeperiod).mean()
-
-
-def MA(data, timeperiod: int = 30, matype: int = 0, price: str = "close") -> pd.Series:
-    if matype != 0:
-        raise NotImplementedError(f"MA matype={matype} is not used by this codebase")
-    return SMA(data, timeperiod, price=price)
-
-
-def _ema(x: np.ndarray, period: int, start: int) -> np.ndarray:
-    """EMA whose first output lands on `start`, seeded by the SMA of the
-    `period` values ending there.
-
-    `start` is a parameter because MACD needs the fast EMA to begin later than
-    it naturally would; see the note in MACD().
-    """
-    n = x.size
-    if n <= start or start < period - 1:
-        return np.full(n, np.nan)
-    seed = x[start - period + 1: start + 1].mean()
-    return _recursive(x, seed, start, 2.0 / (period + 1.0), n)
-
-
-def EMA(data, timeperiod: int = 30, price: str = "close") -> pd.Series:
-    s = _series(data, price)
-    x = s.to_numpy(dtype=float)
-    return pd.Series(_ema(x, timeperiod, timeperiod - 1), index=s.index)
-
-
-def RSI(data, timeperiod: int = 14, price: str = "close") -> pd.Series:
-    s = _series(data, price)
-    x = s.to_numpy(dtype=float)
-    n = x.size
-    out = np.full(n, np.nan)
-    if n <= timeperiod:
-        return pd.Series(out, index=s.index)
-
-    delta = np.diff(x)
-    gain = np.where(delta > 0.0, delta, 0.0)
-    loss = np.where(delta < 0.0, -delta, 0.0)
-
-    # delta[k] corresponds to bar k+1, so the first `timeperiod` deltas seed bar `timeperiod`.
-    alpha = 1.0 / timeperiod
-    avg_gain = _recursive(gain, gain[:timeperiod].mean(), timeperiod - 1, alpha, n - 1)
-    avg_loss = _recursive(loss, loss[:timeperiod].mean(), timeperiod - 1, alpha, n - 1)
-
-    ag = avg_gain[timeperiod - 1:]
-    al = avg_loss[timeperiod - 1:]
-    with np.errstate(divide="ignore", invalid="ignore"):
-        rsi = np.where(al == 0.0, 100.0, 100.0 - 100.0 / (1.0 + ag / al))
-    out[timeperiod:] = rsi
-    return pd.Series(out, index=s.index)
-
-
-def ATR(data, timeperiod: int = 14) -> pd.Series:
-    if not isinstance(data, pd.DataFrame):
-        raise TypeError("ATR needs a DataFrame with high/low/close")
-    high = data["high"].to_numpy(dtype=float)
-    low = data["low"].to_numpy(dtype=float)
-    close = data["close"].to_numpy(dtype=float)
-    n = high.size
-    out = np.full(n, np.nan)
-    if n <= timeperiod:
-        return pd.Series(out, index=data.index)
-
-    prev_close = close[:-1]
-    tr = np.maximum.reduce([
-        high[1:] - low[1:],
-        np.abs(high[1:] - prev_close),
-        np.abs(low[1:] - prev_close),
-    ])
-
-    # tr[k] is bar k+1; the first `timeperiod` true ranges seed bar `timeperiod`.
-    smoothed = _recursive(tr, tr[:timeperiod].mean(), timeperiod - 1, 1.0 / timeperiod, n - 1)
-    out[timeperiod:] = smoothed[timeperiod - 1:]
-    return pd.Series(out, index=data.index)
-
-
-def MACD(
-    data,
-    fastperiod: int = 12,
-    slowperiod: int = 26,
-    signalperiod: int = 9,
-    price: str = "close",
-) -> pd.DataFrame:
-    if slowperiod < fastperiod:
-        fastperiod, slowperiod = slowperiod, fastperiod
-
-    s = _series(data, price)
-    x = s.to_numpy(dtype=float)
-    n = x.size
-
-    macd = np.full(n, np.nan)
-    signal = np.full(n, np.nan)
-    hist = np.full(n, np.nan)
-    empty = pd.DataFrame({"macd": macd, "macdsignal": signal, "macdhist": hist}, index=s.index)
-
-    # Both EMAs emit their first value on the same bar. That makes the slow one
-    # ordinary, but re-seeds the fast one from the SMA of the `fastperiod`
-    # values ending there instead of carrying the recursion forward from bar
-    # fastperiod-1 — the two disagree by ~0.2 on a 100-priced series.
-    macd_start = slowperiod - 1
-    if n <= macd_start:
-        return empty
-    line = _ema(x, fastperiod, macd_start) - _ema(x, slowperiod, macd_start)
-
-    # The signal EMA runs over the MACD line, so everything shifts by another
-    # signalperiod-1 bars, and TA-Lib trims the MACD line to match.
-    valid = line[macd_start:]
-    if valid.size < signalperiod:
-        return empty
-    sig = _recursive(
-        valid, valid[:signalperiod].mean(), signalperiod - 1, 2.0 / (signalperiod + 1.0), valid.size
-    )
-    start = macd_start + signalperiod - 1
-    macd[start:] = line[start:]
-    signal[macd_start:] = sig
-    hist = macd - signal
-
-    return pd.DataFrame({"macd": macd, "macdsignal": signal, "macdhist": hist}, index=s.index)
-
-
-def BBANDS(
-    data,
-    timeperiod: int = 5,
-    nbdevup: float = 2.0,
-    nbdevdn: float = 2.0,
-    matype: int = 0,
-    price: str = "close",
-) -> pd.DataFrame:
-    if matype != 0:
-        raise NotImplementedError(f"BBANDS matype={matype} is not used by this codebase")
-    s = _series(data, price)
-    middle = s.rolling(window=timeperiod, min_periods=timeperiod).mean()
-    # TA-Lib uses the population standard deviation.
-    std = s.rolling(window=timeperiod, min_periods=timeperiod).std(ddof=0)
-    return pd.DataFrame(
-        {
-            "upperband": middle + nbdevup * std,
-            "middleband": middle,
-            "lowerband": middle - nbdevdn * std,
-        },
-        index=s.index,
-    )

chanlun/pipeline/builders/bi.py

@@ -6,7 +6,9 @@ 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
@@ -465,7 +467,7 @@ class BiBuilderMixin:
 											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)
+												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)
@@ -583,7 +585,7 @@ class BiBuilderMixin:
 											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)
+												pre_last_bi.update_bi(klc)
 												bi_list.remove(last_bi)
 												pre_last_bi.set_next(None)
 												last_bottom = klc

chanlun/pipeline/builders/bsp.py

@@ -6,7 +6,9 @@ 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

chanlun/pipeline/builders/fast_bsp.py

@@ -1,148 +0,0 @@
-"""第四类买卖点（B4/S4）接入 TF_DF。
-
-判定逻辑全在 chanlun/analysis/fast_bsp.py，这里只负责把引擎的中枢/K线喂进去，
-再把结果包成 ChanFastBSP。
-
-刻意不在 init_TF_DF 里默认计算：现有构造路径的开销保持不变，由调用方按需触发。
-"""
-from __future__ import annotations
-
-import re
-
-import pandas as pd
-
-from chanlun.analysis.fast_bsp import (
-	add_zone_ladder,
-	attach_htf_agree,
-	attach_zone_ladder,
-	ensure_timestamp,
-	find_fast_bsp3,
-	htf_fx_timeline,
-	zones_from_zs_list,
-)
-from chanlun.core.ChanEnum import Chan_BSP_DIR
-from chanlun.core.ChanFastBSP import ChanFastBSP
-
-# 区间套配对：小级别出中枢与买卖点，大级别只出分型定方向。
-# 取值来自 research/HANDOFF.md §1.5，是回测里实际用过的组合。
-FAST_BSP_HTF_PAIR = {
-	'1m': '5m',
-	'5m': '30m',
-	'15m': '1h',
-	'30m': '2h',
-}
-
-# 未列入配对表的周期回落到这个倍数
-FAST_BSP_HTF_FALLBACK_RATIO = 4
-
-_TF_UNIT_MINUTES = {'m': 1, 'h': 60, 'd': 1440, 'w': 10080}
-
-
-def timeframe_minutes(tf: str) -> int | None:
-	"""'30m' -> 30，'2h' -> 120。无法解析时返回 None。"""
-	if not tf:
-		return None
-	m = re.fullmatch(r'(\d+)\s*([mhdw])', str(tf).strip().lower())
-	if not m:
-		return None
-	return int(m.group(1)) * _TF_UNIT_MINUTES[m.group(2)]
-
-
-def resolve_htf(tf: str) -> tuple[str, int] | None:
-	"""给小级别找配套的大级别，返回 (标签, 分钟数)。"""
-	minutes = timeframe_minutes(tf)
-	if minutes is None:
-		return None
-	paired = FAST_BSP_HTF_PAIR.get(str(tf).strip().lower())
-	if paired:
-		return paired, timeframe_minutes(paired)
-	return f'{minutes * FAST_BSP_HTF_FALLBACK_RATIO}m', minutes * FAST_BSP_HTF_FALLBACK_RATIO
-
-
-class FastBspBuilderMixin:
-	def build_fast_bsp_htf(self, df, timeframe=None):
-		"""对同一份 df 重采样得到大级别，不额外拉数据。
-
-		大级别只用来取分型方向，样本太少就没有过滤意义，故重采样后不足 60 根时放弃。
-		"""
-		tf = timeframe or getattr(self, 'timeframe', None)
-		htf = resolve_htf(tf)
-		ltf_minutes = timeframe_minutes(tf)
-		if htf is None or not ltf_minutes:
-			return None
-		label, minutes = htf
-		if not minutes or len(df) * ltf_minutes < minutes * 60:
-			return None
-		try:
-			from chanlun.pipeline.timeframe import TF_DF
-
-			return TF_DF(df, minutes, label)
-		except Exception:
-			return None
-
-	def cal_fast_bsp(self, df=None, bi_zs_list=None, htf_chan=None, with_htf=True,
-	                 timeframe=None, **kw):
-		"""算第四类买卖点，返回 ChanFastBSP 列表。
-
-		bi_zs_list 传入已算好的 pure 笔中枢可免去重复计算。
-		with_htf=False 时跳过大级别构建，只留 ladder_ok 这一个过滤标志。
-		kw 透传给 find_fast_bsp3（scan / pullback_win / tol / require_touch 等）。
-		"""
-		src = df if df is not None else getattr(self, 'dataframe', None)
-		if src is None or len(src) == 0:
-			self.fast_bsp_list = []
-			return self.fast_bsp_list
-
-		src = ensure_timestamp(src)
-		if bi_zs_list is None:
-			bi_zs_list = getattr(self, 'bi_zs_list', None)
-			if not bi_zs_list:
-				bi_zs_list = self.cal_bi_zs_list_pure(self.cal_bi_list(self.get_klc_list(self.cal_kl_data(src))))
-
-		zones = zones_from_zs_list(bi_zs_list, src)
-		if zones.empty:
-			self.fast_bsp_list = []
-			return self.fast_bsp_list
-
-		zones = add_zone_ladder(zones)
-		sig = find_fast_bsp3(src, zones, **kw)
-		if sig.empty:
-			self.fast_bsp_list = []
-			return self.fast_bsp_list
-
-		sig = attach_zone_ladder(sig, zones)
-
-		if with_htf:
-			if htf_chan is None:
-				htf_chan = self.build_fast_bsp_htf(src, timeframe)
-			sig = attach_htf_agree(sig, src, htf_fx_timeline(htf_chan) if htf_chan is not None else pd.DataFrame())
-		else:
-			sig['htf_dir'] = None
-			sig['htf_agree'] = None
-
-		times = src['date'].dt.strftime('%Y-%m-%d %H:%M:%S').to_numpy()
-		close = src['close'].to_numpy(dtype=float)
-
-		out = []
-		for r in sig.itertuples(index=False):
-			entry_idx = int(r.entry_idx)
-			agree = getattr(r, 'htf_agree', None)
-			htf_dir = getattr(r, 'htf_dir', None)
-			out.append(ChanFastBSP(
-				time=times[entry_idx],
-				price=close[entry_idx],
-				ddir=Chan_BSP_DIR.BUY if r.direction == 1 else Chan_BSP_DIR.SELL,
-				entry_idx=entry_idx,
-				bo_time=times[int(r.bo_idx)],
-				pb_time=times[int(r.pb_idx)] if r.pb_idx == r.pb_idx else None,
-				lag=r.lag,
-				depth=r.depth,
-				zg=r.zg,
-				zd=r.zd,
-				occ=r.occ,
-				htf_dir=None if htf_dir is None or htf_dir != htf_dir else int(htf_dir),
-				htf_agree=None if agree is None or agree != agree else bool(agree),
-				ladder_ok=bool(r.ladder_ok),
-			))
-		self.fast_bsp_list = out
-		return out

chanlun/pipeline/builders/incremental.py

@@ -1,171 +0,0 @@
-"""增量更新：新K只追加 KLU/KLC，笔与笔中枢在当前列表上重算。
-
-不改 init_TF_DF 的整段语义。笔必须整表重扫：最后一笔 is_sure 允许收回
-（OWN_CHAN_ZS_001 上 60 天出现 7 次）。笔中枢用 cal_bi_zs_list_pure。
-"""
-from __future__ import annotations
-
-from datetime import datetime
-
-import pandas as pd
-from pandas import DataFrame
-from chanlun.pipeline.resample import resample_to_interval
-
-from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_KLC_STATE
-from chanlun.core.ChanKLU import ChanKLU
-
-
-class IncrementalBuilderMixin:
-	def init_stream(self, df, interval=1, timeframe=None):
-		"""用历史K线初始化流式状态，之后用 append_bar / replace_last_bar。"""
-		if df is None or df.empty:
-			raise ValueError("DataFrame for stream is empty.")
-		if "date" not in df.columns:
-			raise ValueError(f"DataFrame missing 'date' column. Columns: {df.columns.tolist()}")
-		self.timeframe = timeframe
-		self.interval = interval
-		if interval == 1:
-			self.dataframe = df.copy()
-		else:
-			self.dataframe = resample_to_interval(df, interval)
-		self.dataframe = self.add_indicators(self.dataframe)
-		self.klu_list = []
-		self.klc_list = []
-		self.bi_list = []
-		self.bi_zs_list = []
-		self.seg_list = []
-		self.zs_list = []
-		self.bsp_list = []
-		self.klc_fx_list = []
-		self.big_zs_list = []
-		self._klc_feed_last_klu = None
-		for i in range(len(self.dataframe)):
-			self._append_row_at(i, rebuild=False)
-		self.rebuild_bi_zs()
-		return self
-
-	def append_bar(self, row):
-		"""追加一根已收盘K线。同一时间戳则改为替换最后一根。"""
-		self._ensure_stream_state()
-		item = self._normalize_row(row)
-		if self.klu_list and self.klu_list[-1].time == self._row_time_str(item):
-			return self.replace_last_bar(item)
-		self._append_item_to_dataframe(item)
-		self.dataframe = self.add_indicators(self.dataframe)
-		self._append_row_at(len(self.dataframe) - 1, rebuild=True)
-		return self
-
-	def replace_last_bar(self, row):
-		"""更新最后一根K（未完成K线走新OHLC）。包含关系从 KLU 列表重放。"""
-		self._ensure_stream_state()
-		if not self.klu_list:
-			return self.append_bar(row)
-		item = self._normalize_row(row)
-		idx = self.dataframe.index[-1]
-		for key, val in item.items():
-			self.dataframe.at[idx, key] = val
-		self.dataframe = self.add_indicators(self.dataframe)
-		self._apply_item_to_klu(self.klu_list[-1], self.dataframe.iloc[-1])
-		self._rebuild_klc_from_klu()
-		self.rebuild_bi_zs()
-		return self
-
-	def rebuild_bi_zs(self):
-		"""在当前 KLC 上重算笔 + cal_bi_zs_list_pure。会先清分型标记。"""
-		self._reset_klc_bi_marks(self.klc_list)
-		self.bi_list = self.cal_bi_list(self.klc_list) if self.klc_list else []
-		self.bi_zs_list = self.cal_bi_zs_list_pure(self.bi_list) if self.bi_list else []
-		return self.bi_zs_list
-
-	def _ensure_stream_state(self):
-		if not hasattr(self, "klu_list") or self.klu_list is None:
-			self.klu_list = []
-		if not hasattr(self, "klc_list") or self.klc_list is None:
-			self.klc_list = []
-		if not hasattr(self, "dataframe") or self.dataframe is None:
-			self.dataframe = DataFrame(
-				columns=["date", "open", "high", "low", "close", "volume"]
-			)
-		if not hasattr(self, "_klc_feed_last_klu"):
-			self._klc_feed_last_klu = self.klu_list[-1] if self.klu_list else None
-		if not hasattr(self, "bi_zs_list"):
-			self.bi_zs_list = []
-
-	def _rebuild_klc_from_klu(self):
-		self.klc_list = []
-		last_klu = None
-		for klu in self.klu_list:
-			self._push_klu_into_klc_list(self.klc_list, klu, last_klu)
-			last_klu = klu
-		self._klc_feed_last_klu = last_klu
-
-	def _append_row_at(self, idx, rebuild=True):
-		item = self.dataframe.iloc[idx]
-		klu = self._klu_from_item(item, idx)
-		if self.klu_list:
-			self.klu_list[-1].set_next(klu)
-			klu.set_pre(self.klu_list[-1])
-		self._push_klu_into_klc_list(self.klc_list, klu, self._klc_feed_last_klu)
-		self._klc_feed_last_klu = klu
-		self.klu_list.append(klu)
-		if rebuild:
-			self.rebuild_bi_zs()
-
-	def _klu_from_item(self, item, idx):
-		klu = ChanKLU(
-			self._item_time_str(item),
-			item["open"],
-			item["high"],
-			item["low"],
-			item["close"],
-			item["volume"],
-		)
-		klu.set_idx(idx)
-		if not hasattr(klu, "ema13"):
-			klu.ema13 = 0
-		if "macd" in item:
-			klu.set_indicators(item)
-		return klu
-
-	def _apply_item_to_klu(self, klu, item):
-		klu.time = self._item_time_str(item)
-		klu.open = item["open"]
-		klu.high = item["high"]
-		klu.low = item["low"]
-		klu.close = item["close"]
-		klu.volume = item["volume"]
-		klu.range = klu.high - klu.low
-		klu.body = abs(klu.close - klu.open)
-		if "macd" in item:
-			klu.set_indicators(item)
-
-	def _reset_klc_bi_marks(self, klc_list):
-		for klc in klc_list:
-			klc.fx = Chan_FX_TYPE.UNKNOWN
-			klc.klc_fx_type = Chan_KLC_FX.UNKNOWN
-			klc.klc_state = Chan_KLC_STATE.UNKNOWN
-			klc.bi = None
-			klc.fx_confirmed = False
-
-	def _item_time_str(self, item):
-		date = item["date"]
-		if hasattr(date, "to_pydatetime"):
-			date = date.to_pydatetime()
-		if isinstance(date, datetime):
-			return date.strftime("%Y-%m-%d %H:%M:%S")
-		return str(date)
-
-	def _row_time_str(self, item):
-		return self._item_time_str(item)
-
-	def _normalize_row(self, row):
-		if isinstance(row, pd.Series):
-			return row
-		return pd.Series(row)
-
-	def _append_item_to_dataframe(self, item):
-		row_df = DataFrame([item])
-		if self.dataframe is None or self.dataframe.empty:
-			self.dataframe = row_df
-		else:
-			self.dataframe = pd.concat([self.dataframe, row_df], ignore_index=True)

chanlun/pipeline/builders/indicators.py

@@ -6,8 +6,9 @@ from decimal import Decimal
 
 import numpy as np
 import pandas as pd
-from chanlun.indicators import ta
+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
@@ -54,14 +55,8 @@ class IndicatorsBuilderMixin:
 		return None
 
 	def add_indicators(self, df):
-		"""算指标并一次性挂到 df 上。
-
-		这里不逐列 `df['x'] = ...`：那样每一列都触发一次 BlockManager 插入，
-		30 多列的开销比全部 TA 计算本身还大（2001 行实测 TA 合计 2.5ms，
-		逐列赋值 3.6ms）。增量路径每根都要走一遍，这笔开销是白付的。
-		"""
-		fast = 26
-		slow = 52
+		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)
@@ -81,43 +76,40 @@ class IndicatorsBuilderMixin:
 		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'])
-		cols = {
-			'bb2633upper': bb2633['upperband'],
-			'bb2633lower': bb2633['lowerband'],
-			'bbp2633': bbp2633,
-			'bb2633middle': bb2633['middleband'],
-			'atr': ta.ATR(df, timeperiod=14),
-			'bbup365': bb365['upperband'],
-			'bblow365': bb365['lowerband'],
-			'bbp365': bbp365,
-			'bbup120': bb120['upperband'],
-			'bblow120': bb120['lowerband'],
-			'bbp120': bbp120,
-			'bbup30': bb30['upperband'],
-			'bblow30': bb30['lowerband'],
-			'bbmiddle30': bb30_middle,
-			'bbp30': bbp30,
-			'bbup302': bb302['upperband'],
-			'bblow302': bb302['lowerband'],
-			'bbp302': bbp302,
-			'macd': macd['macd'],
-			'macdsignal': macd['macdsignal'],
-			'macdhist': macd['macdhist'],
-		}
-		for _p, _n in ((5, 'ema5'), (10, 'ema10'), (24, 'ema24'), (52, 'ema52'),
-		               (104, 'ema104'), (156, 'ema156'), (208, 'ema208'),
-		               (26, 'ema26'), (13, 'ema13'), (7, 'ema7')):
-			cols[_n] = ta.EMA(df, timeperiod=_p)
-		cols['rsi'] = ta.RSI(df, timeperiod=14)
-		cols['volume_ratio'] = self.cal_volume_ratio(df)
-
-		# 重复调用（增量路径每根都会）时先摘掉旧列，否则 concat 出重名列。
-		# 摘掉再接回末尾，列序与逐列覆盖的结果一致。
-		new = pd.DataFrame(cols, index=df.index)
-		dup = [c for c in new.columns if c in df.columns]
-		if dup:
-			df = df.drop(columns=dup)
-		return pd.concat([df, new], axis=1)
+		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)

chanlun/pipeline/builders/kline.py

@@ -6,7 +6,9 @@ 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
@@ -84,8 +86,7 @@ class KlineBuilderMixin:
 		return Chan_FX_TYPE.UNKNOWN
 
 	def check_fx(self, klc):
-		# 右K未完成（仍在包含合并）时不分型：否则确认笔会随 next 扩区间被 check_*_fx 收回
-		if klc.pre and klc.next and klc.next.end_klu is not None:
+		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)
@@ -98,21 +99,6 @@ class KlineBuilderMixin:
 				return Chan_FX_TYPE.BOTTOM
 		return Chan_FX_TYPE.UNKNOWN
 
-	def check_fx3(self, klc):
-		# 右K未完成（仍在包含合并）时不分型：否则确认笔会随 next 扩区间被 check_*_fx 收回
-		if klc.pre and klc.next and klc.next.end_klu is not None:
-			next_klu = klc.next.end_klu.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 and klc.high > next_klu.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 and klc.low < next_klu.low:
-				#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:
@@ -128,138 +114,128 @@ class KlineBuilderMixin:
 		return Chan_FX_TYPE.UNKNOWN
 
 	def check_fx_pattern(self, klc):
-		"""给分型前后三根 KLC 的裸 K 打 pattern 标记。
-
-		原本还会把 `klu.to_string()` 拼成一个字符串——那是给下面那行注释掉的
-		print 用的，拼完就丢。它在 cal_bi_list 的内层，2000 根上要跑近三万次
-		f-string + 六万次 enum 格式化，是纯废动作，已删。
-
-		`klu.pattern` 只被 cal_klu_pattern 自己的双 K / 三 K 判定读，
-		不出这个模块，也不进 web 序列化。所以 lean 下整个调用可跳。
-		"""
-		if getattr(self, 'lean', False):
-			return
 		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):
-		"""当根量 / 前 window 根均量。前 window-1 根无基准，填 1.0。
-
-		原写法先 `dataframe.copy()` 再挂两列——为算一列 rolling 复制了整张
-		四十列的表。直接在 Series 上算，结果逐值相同。
-		"""
-		vol = dataframe['volume']
-		return (vol / vol.rolling(window=window).mean()).fillna(1.0).rename('volume_ratio')
+		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):
-		"""按行构造 KLU 链。
-
-		这里刻意不用 `dataframe.iloc[i]`：那会为每一根新建一个几十列的 Series，
-		随后 set_indicators 再在其上做几十次逐键查找。实测这两件事合计占 TF_DF
-		构建耗时的 96%。改为先把用到的列取成 ndarray，循环里只做整数下标访问。
-		"""
-		n = len(dataframe)
-		if n == 0:
-			return []
-
-		times = self._format_times(dataframe['date'])
-		o_a = dataframe['open'].to_numpy(dtype=float)
-		h_a = dataframe['high'].to_numpy(dtype=float)
-		l_a = dataframe['low'].to_numpy(dtype=float)
-		c_a = dataframe['close'].to_numpy(dtype=float)
-		v_a = dataframe['volume'].to_numpy(dtype=float)
-
-		has_ind = 'macd' in dataframe.columns
-		ind_cols = {}
-		if has_ind:
-			for _attr, col in ChanKLU.INDICATOR_FIELDS:
-				if col in dataframe.columns:
-					ind_cols[col] = dataframe[col].to_numpy(dtype=float)
-
+		fields = "time,open,high,low,close,volume"
 		klu_list = []
 		last_klu = None
-		for i in range(n):
-			klu = ChanKLU(times[i], o_a[i], h_a[i], l_a[i], c_a[i], v_a[i])
+		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 has_ind:
-				klu.set_indicators_from(ind_cols, i)
+			if 'macd' in item:
+				klu.set_indicators(item)
 		return klu_list
 
-	@staticmethod
-	def _format_times(col):
-		"""向量化 strftime。非 datetime 列（少见）退回逐个格式化。"""
-		fmt = '%Y-%m-%d %H:%M:%S'
-		try:
-			return col.dt.strftime(fmt).to_numpy()
-		except AttributeError:
-			return np.array([d.strftime(fmt) for d in col], dtype=object)
-
 	def get_kl_data(self, dataframe:DataFrame):
 		return self.cal_kl_data(dataframe)
 
-	def _push_klu_into_klc_list(self, klc_list, klu, last_klu):
-		"""把一根 KLU 并入包含K线列表。与 get_klc_list 的几何规则相同。"""
-		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()
-			else:
-				included = last_klc.check_klu_included(klu)
-				if not included:
+	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:
-					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)
-
-	def get_klc_list(self, klu_list):
-		klc_list = []
-		# ChanMACD.__init__ 已调用 cal_macd_state，切勿再调一次（会重复堆积 seg/unittf）。
-		# lean 模式跳过整套 MACD 状态机：它只服务于 bsp/背驰/web 展示，笔与中枢不依赖它。
-		if getattr(self, 'lean', False):
-			self._last_chan_macd = None
-		else:
-			macd = ChanMACD(klu_list)
-			klu_list = macd.klu_list
-			self._last_chan_macd = macd
-
-		last_klu = None
-		for klu in klu_list:
-			self._push_klu_into_klc_list(klc_list, klu, last_klu)
+					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
-		# cal_trend 只产出 klc.trend，而全仓只有它自己（经 prev_klcs 读自身序列
-		# 状态）、一个 __str__ 和 web 的 klc_trend 图层消费它——笔与中枢不读。
-		# 增量路径的 klc 其 trend 恒为 UNKNOWN 却与批量构建逐字段相同，是实证。
-		# 所以 lean 下可跳；web 走非 lean，图层不受影响。
-		if not getattr(self, 'lean', False):
-			klc_list = self.cal_trend(klc_list)
+		klc_list = self.cal_trend(klc_list)
+		#print(ema52_up_list, ema52_down_list)
 		return klc_list
 
 

chanlun/pipeline/builders/seg.py

@@ -6,7 +6,9 @@ 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

chanlun/pipeline/builders/zs.py

@@ -6,7 +6,9 @@ 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
@@ -353,12 +355,9 @@ class ZsBuilderMixin:
 			return bi_zs_list
 
 		def get_zs_range(bis):
-			bis_list = bis[0:3]
-			zg = min(bi.high for bi in bis_list)
-			zd = max(bi.low for bi in bis_list)
-			dd = min(bi.low for bi in bis_list)
-			gg = max(bi.high for bi in bis_list)
-			return zg, zd, dd, gg
+			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
@@ -376,8 +375,8 @@ class ZsBuilderMixin:
 			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.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
@@ -395,7 +394,7 @@ class ZsBuilderMixin:
 				start_idx += 1
 				continue
 
-			zg, zd, dd, gg = get_zs_range([bi1, bi2, bi3])
+			zg, zd = get_zs_range([bi1, bi2, bi3])
 			if zg <= zd:
 				start_idx += 1
 				continue
@@ -421,8 +420,6 @@ class ZsBuilderMixin:
 			zs = ChanBIZS(bi1, len(bi_zs_list), zs_dir)
 			zs.set_zg(zg)
 			zs.set_zd(zd)
-			zs.set_dd(dd)
-			zs.set_gg(gg)
 
 			set_zs_bi_list(zs, bis_for_zs)
 			zs.set_end_bi(bis_for_zs[-1], bis_for_zs[-1].sure_time)

chanlun/pipeline/orchestrator.py

@@ -17,7 +17,9 @@ 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
@@ -169,8 +171,6 @@ class ChanLun():
 		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 cal_fast_bsp(self, df=None, bi_zs_list=None, htf_chan=None, with_htf=True, timeframe=None, **kw):
-		return self.tf_df.cal_fast_bsp(df, bi_zs_list, htf_chan, with_htf, timeframe, **kw)
 	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):
@@ -178,15 +178,6 @@ class ChanLun():
 	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 cal_bi_zs_list_pure(self, bi_list):
-		return self.tf_df.cal_bi_zs_list_pure(bi_list)
-	def init_stream(self, dataframe, interval=1, timeframe=None):
-		self.tf_df.init_stream(dataframe, interval, timeframe)
-		return self.tf_df
-	def append_bar(self, row):
-		return self.tf_df.append_bar(row)
-	def replace_last_bar(self, row):
-		return self.tf_df.replace_last_bar(row)
 	def get_bi_zs_list(self, bi_list):
 		return self.tf_df.get_bi_zs_list(bi_list)
 	def get_decimal(self, value):

chanlun/pipeline/resample.py

@@ -1,52 +0,0 @@
-"""OHLCV resampling — replaces `technical.util.resample_to_interval`.
-
-That was the only symbol this project imported from `technical`, which in turn
-pulled in the freqtrade dependency chain. Behaviour is preserved exactly,
-including the left-labelled bins (rows are candle *open* times) and the
-`dropna()` that drops empty intervals.
-"""
-
-from __future__ import annotations
-
-import pandas as pd
-
-__all__ = ["TICKER_INTERVAL_MINUTES", "resample_to_interval"]
-
-TICKER_INTERVAL_MINUTES: dict[str, int] = {
-    "1m": 1,
-    "5m": 5,
-    "15m": 15,
-    "30m": 30,
-    "1h": 60,
-    "60m": 60,
-    "2h": 120,
-    "4h": 240,
-    "6h": 360,
-    "12h": 720,
-    "1d": 1440,
-    "1w": 10080,
-}
-
-_OHLC_AGG = {
-    "open": "first",
-    "high": "max",
-    "low": "min",
-    "close": "last",
-    "volume": "sum",
-}
-
-
-def resample_to_interval(dataframe: pd.DataFrame, interval: int | str) -> pd.DataFrame:
-    """Resample OHLCV rows to `interval` minutes (or a timeframe string).
-
-    Merging the result back onto a finer frame requires care to avoid lookahead
-    bias; this function only resamples.
-    """
-    if isinstance(interval, str):
-        interval = TICKER_INTERVAL_MINUTES[interval]
-
-    df = dataframe.copy()
-    df = df.set_index(pd.DatetimeIndex(df["date"]))
-    df = df.resample(f"{interval}min", label="left").agg(_OHLC_AGG).dropna()
-    df.reset_index(inplace=True)
-    return df

chanlun/pipeline/timeframe.py

@@ -2,8 +2,9 @@ from datetime import timedelta
 
 import numpy as np
 import pandas as pd
+import talib.abstract as ta
 from pandas import DataFrame
-from chanlun.pipeline.resample import resample_to_interval
+from technical.util import resample_to_interval
 
 from chanlun.core.ChanBI import ChanBI
 from chanlun.core.ChanBIZS import ChanBIZS
@@ -30,26 +31,16 @@ 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.fast_bsp import FastBspBuilderMixin
-from chanlun.pipeline.builders.incremental import IncrementalBuilderMixin
 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, FastBspBuilderMixin, IncrementalBuilderMixin):
-	def __init__(self, df=None, interval=0, timeframe=None, lean=False):
-		"""lean=True 只构建到中枢，跳过线段/走势中枢/MACD 状态机。
-
-		研究与实盘只吃 bi_list → 中枢 → fast_bsp 这条链；线段、zs、big_zs 和整套
-		MACD 背驰状态机是 web 展示与 bsp_list 才用的。实测这些占全量构建的约四成。
-		注意 lean 下 bsp_list/seg_list/chanmacd 均为空，**不要给 web 用**。
-		"""
-		self.lean = lean
+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, lean=lean)
-	def init_TF_DF(self, df, interval, timeframe, lean=False):
-		self.lean = lean
+			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
@@ -68,19 +59,12 @@ class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilde
 		self.klc_list = []
 		self.bi_list = []
 		self.zs_list = []
-		self.bi_zs_list = []
 		self.bsp_list = []
-		self.fast_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.bi_zs_list = self.cal_bi_zs_list_pure(self.bi_list)
-		if self.lean:
-			self.big_zs_list = []
-			self.chanmacd = None
-			return
 		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)

chanlun/tests/__init__.py

@@ -1 +0,0 @@
-from __future__ import annotations

chanlun/tests/test_incremental.py

@@ -1,141 +0,0 @@
-from __future__ import annotations
-
-import sys
-import unittest
-from pathlib import Path
-
-import pandas as pd
-
-_CHAN = Path(__file__).resolve().parents[2]
-if str(_CHAN) not in sys.path:
-	sys.path.insert(0, str(_CHAN))
-
-from chanlun.pipeline.timeframe import TF_DF  # noqa: E402
-
-
-def _zigzag_df(n=160, step=8):
-	dates = pd.date_range("2024-01-01", periods=n, freq="5min")
-	rows = []
-	price = 100.0
-	for i, date in enumerate(dates):
-		up = (i // step) % 2 == 0
-		if up:
-			o = price
-			c = price + 1.5
-			h = c + 0.3
-			l = o - 0.2
-		else:
-			o = price
-			c = price - 1.5
-			h = o + 0.2
-			l = c - 0.3
-		price = c
-		rows.append(
-			{
-				"date": date,
-				"open": o,
-				"high": h,
-				"low": l,
-				"close": c,
-				"volume": 1.0,
-			}
-		)
-	return pd.DataFrame(rows)
-
-
-def _sure_bi_key(bi):
-	return (str(bi.start_time), bi.dir.name, round(float(bi.high), 6), round(float(bi.low), 6))
-
-
-def _zs_key(zs):
-	return (
-		str(zs.start_time),
-		round(float(zs.zg), 6),
-		round(float(zs.zd), 6),
-		len(zs.bi_list),
-	)
-
-
-class TestIncremental(unittest.TestCase):
-	def test_init_stream_matches_batch_push(self):
-		df = _zigzag_df()
-		stream = TF_DF()
-		stream.init_stream(df, 1, "5m")
-
-		batch = TF_DF()
-		indexed = batch.add_indicators(df.copy())
-		klu = batch.cal_kl_data(indexed)
-		klc = []
-		last = None
-		for k in klu:
-			batch._push_klu_into_klc_list(klc, k, last)
-			last = k
-		batch.klc_list = klc
-		batch.rebuild_bi_zs()
-
-		self.assertEqual(len(stream.klu_list), len(klu))
-		self.assertEqual(len(stream.klc_list), len(klc))
-		self.assertEqual(
-			[_sure_bi_key(b) for b in stream.bi_list if b.is_sure],
-			[_sure_bi_key(b) for b in batch.bi_list if b.is_sure],
-		)
-		self.assertEqual(
-			[_zs_key(z) for z in stream.bi_zs_list],
-			[_zs_key(z) for z in batch.bi_zs_list],
-		)
-
-	def test_append_bar_matches_init_stream(self):
-		df = _zigzag_df()
-		stream = TF_DF()
-		stream.init_stream(df, 1, "5m")
-
-		inc = TF_DF()
-		for _, row in df.iterrows():
-			inc.append_bar(row)
-
-		self.assertEqual(len(inc.klu_list), len(stream.klu_list))
-		self.assertEqual(len(inc.klc_list), len(stream.klc_list))
-		self.assertEqual(
-			[_sure_bi_key(b) for b in inc.bi_list if b.is_sure],
-			[_sure_bi_key(b) for b in stream.bi_list if b.is_sure],
-		)
-		self.assertEqual(
-			[_zs_key(z) for z in inc.bi_zs_list],
-			[_zs_key(z) for z in stream.bi_zs_list],
-		)
-
-	def test_replace_last_bar_keeps_count(self):
-		df = _zigzag_df(n=80)
-		tf = TF_DF()
-		tf.init_stream(df, 1, "5m")
-		n_klu = len(tf.klu_list)
-		last = df.iloc[-1].copy()
-		last["close"] = float(last["close"]) + 0.01
-		last["high"] = max(float(last["high"]), float(last["close"]))
-		tf.replace_last_bar(last)
-		self.assertEqual(len(tf.klu_list), n_klu)
-		self.assertGreater(len(tf.klc_list), 0)
-
-	def test_check_fx_skips_forming_right_wing(self):
-		from types import SimpleNamespace
-
-		from chanlun.core.ChanEnum import Chan_FX_TYPE
-
-		tf = TF_DF()
-		pre = SimpleNamespace(high=10, low=8)
-		nxt_open = SimpleNamespace(high=11, low=7, end_klu=None)
-		nxt_done = SimpleNamespace(high=11, low=7, end_klu=object())
-		center = SimpleNamespace(
-			pre=pre,
-			next=nxt_open,
-			high=12,
-			low=9,
-			set_fx=lambda *_a, **_k: None,
-		)
-		self.assertEqual(tf.check_fx(center), Chan_FX_TYPE.UNKNOWN)
-		center.next = nxt_done
-		self.assertEqual(tf.check_fx(center), Chan_FX_TYPE.TOP)
-
-
-if __name__ == "__main__":
-	unittest.main()

chanlun/tests/test_ta_compat.py

@@ -1,198 +0,0 @@
-"""Pin chanlun.indicators.ta to TA-Lib's output, bar for bar.
-
-These indicators feed the Chan structure builders, so a one-bar shift in the
-warm-up or a different smoothing seed silently changes every downstream
-bi/seg/zs. Equality against the reference implementation is the only check
-that catches that.
-
-Skipped when talib is unavailable — which is the point of the replacement, so
-the suite still has to pass without it. Run in an environment that has talib
-whenever chanlun/indicators/ta.py changes.
-"""
-
-from __future__ import annotations
-
-import sys
-import unittest
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
-
-from chanlun.indicators import ta  # noqa: E402
-from chanlun.pipeline.resample import resample_to_interval  # noqa: E402
-
-try:
-    import talib.abstract as reference
-except ImportError:  # pragma: no cover
-    reference = None
-
-requires_talib = unittest.skipIf(reference is None, "talib not installed")
-
-
-def make_ohlcv(n: int = 900, seed: int = 7) -> pd.DataFrame:
-    """Random walk with enough range for BBANDS(365) and EMA(208) to warm up."""
-    rng = np.random.default_rng(seed)
-    close = 100.0 + np.cumsum(rng.normal(0.0, 1.0, n))
-    spread = np.abs(rng.normal(0.0, 0.6, n)) + 0.05
-    high = close + spread
-    low = close - spread
-    open_ = np.concatenate([[close[0]], close[:-1]])
-    return pd.DataFrame(
-        {
-            "date": pd.date_range("2024-01-01", periods=n, freq="1min", tz="UTC"),
-            "open": open_,
-            "high": np.maximum.reduce([high, open_, close]),
-            "low": np.minimum.reduce([low, open_, close]),
-            "close": close,
-            "volume": rng.uniform(1.0, 100.0, n),
-        }
-    )
-
-
-class TAEquivalence(unittest.TestCase):
-    def setUp(self) -> None:
-        self.df = make_ohlcv()
-
-    def assertSameSeries(self, got, expected, label: str) -> None:
-        g = np.asarray(got, dtype=float)
-        e = np.asarray(expected, dtype=float)
-        self.assertEqual(g.shape, e.shape, f"{label}: shape")
-        np.testing.assert_array_equal(
-            np.isnan(g), np.isnan(e), err_msg=f"{label}: NaN warm-up differs"
-        )
-        mask = ~np.isnan(e)
-        np.testing.assert_allclose(
-            g[mask], e[mask], rtol=1e-9, atol=1e-8, err_msg=f"{label}: values differ"
-        )
-
-    @requires_talib
-    def test_sma(self) -> None:
-        for period in (5, 20, 90, 250):
-            self.assertSameSeries(
-                ta.SMA(self.df, timeperiod=period),
-                reference.SMA(self.df, timeperiod=period),
-                f"SMA({period})",
-            )
-
-    @requires_talib
-    def test_ma(self) -> None:
-        for period in (5, 10, 250):
-            self.assertSameSeries(
-                ta.MA(self.df, timeperiod=period),
-                reference.MA(self.df, timeperiod=period),
-                f"MA({period})",
-            )
-
-    @requires_talib
-    def test_ema(self) -> None:
-        for period in (5, 7, 10, 13, 24, 26, 30, 52, 104, 156, 208):
-            self.assertSameSeries(
-                ta.EMA(self.df, timeperiod=period),
-                reference.EMA(self.df, timeperiod=period),
-                f"EMA({period})",
-            )
-
-    @requires_talib
-    def test_rsi(self) -> None:
-        for period in (7, 14, 21):
-            self.assertSameSeries(
-                ta.RSI(self.df, timeperiod=period),
-                reference.RSI(self.df, timeperiod=period),
-                f"RSI({period})",
-            )
-
-    @requires_talib
-    def test_atr(self) -> None:
-        for period in (7, 14, 30):
-            self.assertSameSeries(
-                ta.ATR(self.df, timeperiod=period),
-                reference.ATR(self.df, timeperiod=period),
-                f"ATR({period})",
-            )
-
-    @requires_talib
-    def test_macd(self) -> None:
-        for fast, slow, signal in ((12, 26, 9), (26, 52, 9), (5, 35, 5)):
-            got = ta.MACD(self.df, fastperiod=fast, slowperiod=slow, signalperiod=signal)
-            exp = reference.MACD(self.df, fastperiod=fast, slowperiod=slow, signalperiod=signal)
-            for col in ("macd", "macdsignal", "macdhist"):
-                self.assertSameSeries(got[col], exp[col], f"MACD({fast},{slow},{signal}).{col}")
-
-    @requires_talib
-    def test_bbands(self) -> None:
-        cases = (
-            (365, 3.0, 3.0),
-            (120, 3.0, 3.0),
-            (41, 2.3, 2.3),
-            (41, 2.0, 2.0),
-            (26, 3.0, 3.0),
-            (20, 2.0, 2.0),
-            (14, 2.0, 2.0),
-        )
-        for period, up, dn in cases:
-            got = ta.BBANDS(self.df, timeperiod=period, nbdevup=up, nbdevdn=dn, matype=0)
-            exp = reference.BBANDS(self.df, timeperiod=period, nbdevup=up, nbdevdn=dn, matype=0)
-            for col in ("upperband", "middleband", "lowerband"):
-                self.assertSameSeries(got[col], exp[col], f"BBANDS({period},{up},{dn}).{col}")
-
-    @requires_talib
-    def test_bbands_is_more_accurate_than_talib_on_tiny_windows(self) -> None:
-        """A deliberate divergence, documented so nobody "fixes" it back.
-
-        TA-Lib derives the variance from sumsq/n - mean**2, which cancels
-        catastrophically when the window is short and prices are far from zero;
-        at timeperiod=2 it drifts ~1e-6. Rolling std is accurate there, so the
-        two disagree. No timeperiod below 14 is used in this codebase, and the
-        periods that are used agree to ~1e-10 (covered by test_bbands).
-        """
-        got = ta.BBANDS(self.df, timeperiod=2, nbdevup=1.0, nbdevdn=1.0, matype=0)["upperband"]
-        exp = reference.BBANDS(self.df, timeperiod=2, nbdevup=1.0, nbdevdn=1.0, matype=0)["upperband"]
-
-        window = self.df["close"].rolling(2)
-        truth = window.mean() + window.std(ddof=0)
-        ours = np.nanmax(np.abs((got - truth).to_numpy()))
-        theirs = np.nanmax(np.abs((exp - truth).to_numpy()))
-        self.assertLess(ours, 1e-9)
-        self.assertLess(ours, theirs)
-
-    @requires_talib
-    def test_matches_on_real_price_scale(self) -> None:
-        """Guard against tolerances that only hold near 100."""
-        df = self.df.copy()
-        for col in ("open", "high", "low", "close"):
-            df[col] *= 900.0
-        self.assertSameSeries(
-            ta.ATR(df, timeperiod=14), reference.ATR(df, timeperiod=14), "ATR@scale"
-        )
-        self.assertSameSeries(
-            ta.RSI(df, timeperiod=14), reference.RSI(df, timeperiod=14), "RSI@scale"
-        )
-
-
-class ResampleEquivalence(unittest.TestCase):
-    @unittest.skipIf(
-        __import__("importlib").util.find_spec("technical") is None,
-        "technical not installed",
-    )
-    def test_matches_technical(self) -> None:
-        from technical.util import resample_to_interval as ref_resample
-
-        df = make_ohlcv(600)
-        for interval in (5, 15, 60, "5m", "1h"):
-            got = resample_to_interval(df, interval)
-            exp = ref_resample(df, interval)
-            pd.testing.assert_frame_equal(got, exp, check_exact=False, rtol=1e-12)
-
-    def test_shapes_without_reference(self) -> None:
-        df = make_ohlcv(120)
-        out = resample_to_interval(df, 5)
-        self.assertEqual(list(out.columns), ["date", "open", "high", "low", "close", "volume"])
-        self.assertLessEqual(len(out), 120 // 5 + 1)
-        self.assertTrue((out["high"] >= out["low"]).all())
-
-
-if __name__ == "__main__":
-    unittest.main()

config/BTC_Chan.json

@@ -0,0 +1,84 @@
+
+{
+    "$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.btc_chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT",
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800,
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8882,
+        "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": 2
+    }
+}

config/BTC_Perpetual_Futures.json

@@ -0,0 +1,83 @@
+{
+    "$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.btc_perpetual.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "YOUR_BINANCE_API_KEY",
+        "secret": "YOUR_BINANCE_API_SECRET",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": "YOUR_TELEGRAM_BOT_TOKEN",
+        "chat_id": "YOUR_TELEGRAM_CHAT_ID"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8820,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "change_me_to_a_random_secret_key",
+        "ws_token": "change_me_to_a_random_ws_token",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "FreqTrade007"
+    },
+    "bot_name": "BTC_Perpetual_Bot",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 2
+    }
+}

config/Chan.json

@@ -0,0 +1,84 @@
+
+{
+    "$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.chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8800,
+        "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": 2
+    }
+}

config/ChanLun_BTC_15.json

@@ -0,0 +1,83 @@
+{
+    "$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_15.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8811,
+        "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": 2
+    }
+}

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_30.json

@@ -0,0 +1,83 @@
+{
+    "$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_15.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8813,
+        "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": 2
+    }
+}

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
+    }
+}

config/ChanLun_BTC_60.json

@@ -0,0 +1,83 @@
+{
+    "$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_60.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "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": 2
+    }
+}

config/ChanLun_BTC_K.json

@@ -0,0 +1,83 @@
+{
+    "$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_k.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8815,
+        "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": 2
+    }
+} 

config/ChanLun_BTC_K_protect.json

@@ -0,0 +1,87 @@
+{
+  "$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_k.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"
+  },
+  "entry_pricing": {
+    "price_side": "same",
+    "use_order_book": true,
+    "order_book_top": 1,
+    "price_last_balance": 0.0,
+    "check_depth_of_market": {
+      "enabled": false,
+      "bids_to_ask_delta": 1
+    }
+  },
+  "exit_pricing": {
+    "price_side": "same",
+    "use_order_book": true,
+    "order_book_top": 1
+  },
+  "exchange": {
+    "name": "binance",
+    "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+    "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "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": false,
+    "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+    "chat_id": "580807463"
+  },
+  "api_server": {
+    "enabled": true,
+    "listen_ip_address": "127.0.0.1",
+    "listen_port": 8815,
+    "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": 2
+  }
+}
+
+
+

config/ChanLun_EMA52.json

@@ -0,0 +1,83 @@
+{
+    "$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_15.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8820,
+        "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": 2
+    }
+}

config/ChanLun_EMA_Align.json

@@ -0,0 +1,83 @@
+{
+    "$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_15.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8820,
+        "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": 2
+    }
+}

config/ChanLun_ETH_60.json

@@ -0,0 +1,83 @@
+{
+    "$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_eth_60.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "ETH/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": true,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8813,
+        "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": 2
+    }
+}

config/ChanLun_MACD.json

@@ -0,0 +1,83 @@
+{
+    "$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_15.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": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "other",
+        "use_order_book": false,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "other",
+        "use_order_book": false,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8813,
+        "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": 2
+    }
+}

config/ChanLun_SOL.json

@@ -0,0 +1,83 @@
+{
+    "$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_sol.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8811,
+        "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": 2
+    }
+}

config/ChanLun_SOL_Optimized.json

@@ -0,0 +1,151 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 2,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.95,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.chanlun_sol_optimized.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": 2,
+        "exit": 2,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {
+            "options": {"defaultType": "swap"}
+        },
+        "ccxt_async_config": {
+            "enableRateLimit": true,
+            "rateLimit": 1000,
+            "timeout": 30000
+        },
+        "pair_whitelist": [
+            "SOL/USDT:USDT"
+        ],
+        "pair_blacklist": []
+    },
+    "telegram": {
+        "enabled": false,
+        "token": "",
+        "chat_id": ""
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "jwt_secret_key": "",
+        "username": "",
+        "password": ""
+    },
+    "discord": {
+        "enabled": false,
+        "webhook": "",
+        "webhook_avatar": "",
+        "poll_delay_seconds": 10
+    },
+    "notification_settings": {
+        "status": "on",
+        "status_inactive_after": 7,
+        "timeframe_condition_change": "on",
+        "telegram": { },
+        "discord": { },
+        "notify_all": true
+    },
+    "bot_name": "SOL_Chan_Optimized",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    },
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.007,
+        "stoploss_range_step": 0.001,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "order_types": {
+        "entry": "limit",
+        "exit": "market",
+        "emergency_exit": "market",
+        "force_exit": "market",
+        "force_entry": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": false,
+        "stoploss_on_exchange_interval": 60
+    },
+    "order_time_in_force": {
+        "entry": "GTC",
+        "exit": "GTC"
+    },
+    "strategy_path": "./user_data/Chan/strategies/",
+    "strategy": "ChanLun_SOL_Optimized",
+    "minimal_roi": {
+        "0": 0.012,
+        "120": 0.010,
+        "240": 0.007,
+        "360": 0.005
+    },
+    "stoploss": -0.007,
+    "trailing_stop": true,
+    "trailing_stop_positive": 0.003,
+    "trailing_stop_positive_offset": 0.005,
+    "trailing_only_offset_is_reached": true,
+    "use_custom_stoploss": true,
+    "max_open_trades_per_pair": 1,
+    "dry_run_wallet_refresh_time": 5,
+    "caches": {
+        "dataframe": {
+            "enabled": true,
+            "refresh_period": 60
+        },
+        "strategy": {
+            "enabled": true,
+            "refresh_period": 300
+        }
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "config": {
+                "pairs": ["SOL/USDT:USDT"]
+            }
+        }
+    ]
+} 

config/ChanLun_XGB.json

@@ -0,0 +1,125 @@
+{
+    "$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_sol.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" : true,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": true,
+        "train_period_days": 30,
+        "backtest_period_days": 7,
+        "identifier": "chanLun1",
+        "live_retrain_hours": 1,
+        "expiration_hours": 48,
+        "fit_live_predictions_candles": 0,
+        "data_kitchen_thread_count": 4,
+        "save_backtest_models": true,
+        "save_metadata": true,
+        "feature_parameters": {
+            "include_timeframes": [
+                "1m",
+                "5m",
+                "15m"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT:USDT",
+                "ETH/USDT:USDT"
+            ],
+            "label_period_candles": 24,
+            "include_shifted_candles": 2,
+            "indicator_periods_candles": [10, 20, 30],
+            "allow_duplicate_train": true
+        },
+        "data_split_parameters": {
+            "test_size": 0.25
+        },
+        "model_training_parameters": {
+            "n_estimators": 100, 
+            "learning_rate": 0.1,
+            "max_depth": 5,
+            "subsample": 0.8, 
+            "colsample_bytree": 0.8, 
+            "use_label_for_weight": true,
+            "booster": "gbtree",
+            "num_class": 2
+        }
+    },
+    "freqaimodel": "XGBoostClassifier",
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8811,
+        "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": 2
+    }
+}

config/Chan_5m.json

@@ -0,0 +1,103 @@
+
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 3,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.btc_chan.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short" : true,
+    "timeframe" : "30m",
+    "process_only_new_candles" : false,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "ocQUqAPSD9PDhIL2lTMlMan0wMFwvvu5Fv8eYF3wUM8yPytm2jBgz51cgiHXw7J6",
+        "secret": "yHIc6FOnSoOI2FvygpRKKku4FKaZGI5DSwC83Ip4wRfUcxszennF6hy2vhbVuLYJ",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "BTC/USDT:USDT", 
+            "ETH/USDT:USDT", 
+            "SOL/USDT:USDT", 
+            "WIF/USDT:USDT", 
+            "1000PEPE/USDT:USDT", 
+            "DOGS/USDT:USDT", 
+            "ORDI/USDT:USDT", 
+            "AAVE/USDT:USDT", 
+            "REEF/USDT:USDT", 
+            "1000SATS/USDT:USDT", 
+            "SUI/USDT:USDT", 
+            "1INCH/USDT:USDT", 
+            "DOGE/USDT:USDT", 
+            "TON/USDT:USDT", 
+            "UNI/USDT:USDT", 
+            "XRP/USDT:USDT", 
+            "SUN/USDT:USDT", 
+            "NOT/USDT:USDT", 
+            "RARE/USDT:USDT", 
+            "RDNT/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "VolumePairList",
+            "number_assets": 10,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": true,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8088,
+        "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": 2
+    }
+}

config/Chan_BTC_30m.json

@@ -0,0 +1,83 @@
+
+{
+    "$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,
+    "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": 1,
+        "exit": 1,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "5985766683:AAEx2Nm_4y2IC0Tj4Hhz7djVRJRso0JKaj0",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8088,
+        "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": 2
+    }
+}

config/Chan_ETH.json

@@ -0,0 +1,84 @@
+
+{
+    "$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.btc_chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT",
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800,
+        }
+    ],
+    "telegram": {
+        "enabled": true,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8888,
+        "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": 2
+    }
+}

config/Chan_SOL.json

@@ -0,0 +1,84 @@
+{
+    "$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,
+    "strategy": "ChanStrategy",
+    "db_url": "sqlite:///tradesv3.btc_chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8818,
+        "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": 2
+    }
+}

config/Chan_SOL_15.json

@@ -0,0 +1,84 @@
+
+{
+    "$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.chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT",
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800,
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8815,
+        "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": 2
+    }
+}

config/Chan_SOL_15_HL.json

@@ -0,0 +1,84 @@
+
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 1,
+    "stake_currency": "USDC",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "hyperliquid",
+        "walletAddress": "0xA834b6d3Fa1D8A55ea8e502685ef5cbD2b2D3343",
+        "privateKey": "0xa399cea4c01be67c16b88e1d2121ed6e72bab6b4e8d81684ed03ce78f8b4f827",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "PURR/USDC:USDC",
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800,
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8815,
+        "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": 2
+    }
+}

config/Chan_SOL_30.json

@@ -0,0 +1,84 @@
+
+{
+    "$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.chan_sol_30.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT",
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800,
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8818,
+        "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": 2
+    }
+}

config/Chan_SOL_Day.json

@@ -0,0 +1,84 @@
+
+{
+    "$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.chan.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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8801,
+        "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": 2
+    }
+}

config/Deepseek_Trader.json

@@ -0,0 +1,93 @@
+
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 3,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "db_url": "sqlite:///tradesv3.deepseek_trader.sqlite",
+    "dry_run": true,
+    "dry_run_wallet": 10000,
+    "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": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "BTC/USDT:USDT",
+            "ETH/USDT:USDT", 
+            "SOL/USDT:USDT", 
+            "WIF/USDT:USDT", 
+            "1000PEPE/USDT:USDT", 
+            "DOGS/USDT:USDT", 
+            "ORDI/USDT:USDT", 
+            "AAVE/USDT:USDT", 
+            "REEF/USDT:USDT", 
+            "1000SATS/USDT:USDT",   
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 10,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": true,
+        "token": "5985766683:AAEx2Nm_4y2IC0Tj4Hhz7djVRJRso0JKaj0",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8001,
+        "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": true,
+    "internals": {
+        "process_throttle_secs": 15
+    }
+}

config/EMA26_EMA52_Cross.json

@@ -0,0 +1,83 @@
+{
+    "$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.ema26_ema52_cross.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": true,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8820,
+        "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": 2
+    }
+}

config/EMA_Pattern.json

@@ -0,0 +1,83 @@
+{
+    "$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.ema_pattern.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short" : true,
+    "timeframe" : "1h",
+    "process_only_new_candles" : false,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8888,
+        "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": 2
+    }
+}

config/ElliottWaveBTC.json

@@ -0,0 +1,70 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 2,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.elliottwave_btc.sqlite",
+    "dry_run_wallet": 10000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "unfilledtimeout": {
+        "entry": 5,
+        "exit": 5,
+        "exit_timeout_count": 3,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "BTC/USDT:USDT"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "",
+        "chat_id": ""
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "freqtrade_secret",
+        "ws_token": "freqtrade_ws",
+        "username": "freqtrade",
+        "password": "freqtrade"
+    },
+    "bot_name": "ElliottWaveBTC"
+}

config/FreqAI_Test.json

@@ -0,0 +1,123 @@
+{
+    "$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.freqai_sol.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": true,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList"
+        }
+    ],
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": 2,
+        "train_period_days": 10,
+        "backtest_period_days": 7,
+        "live_retrain_hours": 1,
+        "identifier": "sol_futures_lgbm_v1",
+        "feature_parameters": {
+            "include_timeframes": [
+                "5m",
+                "15m"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT:USDT"
+            ],
+            "label_period_candles": 12,
+            "include_shifted_candles": 1,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": true,
+            "indicator_periods_candles": [
+                14
+            ],
+            "plot_feature_importances": 0
+        },
+        "data_split_parameters": {
+            "test_size": 0.15,
+            "random_state": 42
+        },
+        "model_training_parameters": {
+            "n_estimators": 300,
+            "learning_rate": 0.05,
+            "max_depth": 5,
+            "num_leaves": 31,
+            "min_child_samples": 20,
+            "subsample": 0.8,
+            "colsample_bytree": 0.8,
+            "reg_alpha": 0.1,
+            "reg_lambda": 0.1,
+            "n_jobs": 1,
+            "verbosity": -1
+        }
+    },
+    "telegram": {
+        "enabled": false,
+        "token": "",
+        "chat_id": ""
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8822,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
+        "ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "FreqTrade007"
+    },
+    "bot_name": "freqai_sol",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

config/HeikinAshi_BTC.json

@@ -0,0 +1,83 @@
+{
+    "$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.heikinashi_btc.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"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "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": 2
+    }
+}

config/Local_Test.json

@@ -0,0 +1,83 @@
+{
+    "$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.ema26_ema52_cross.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "timeframe": "1m",
+    "can_short" : true,
+    "process_only_new_candles" : true,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "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": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8821,
+        "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": 2
+    }
+}

config/SOL5m.json

@@ -0,0 +1,81 @@
+{
+    "$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.sol5m.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short": true,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "SOL/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8822,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
+        "ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "FreqTrade007"
+    },
+    "bot_name": "SOL5m",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

config/ThreeFilterEMA_BTC_15.json

@@ -0,0 +1,83 @@
+{
+    "$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_15.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short" : true,
+    "timeframe" : "15m",
+    "process_only_new_candles" : false,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "WIF/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8811,
+        "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": 2
+    }
+}

deploy.sh

@@ -0,0 +1,82 @@
+#!/bin/bash
+# 缠论分析系统生产环境部署脚本
+
+# 显示执行的命令
+set -x
+
+# 确保脚本在错误时停止
+set -e
+
+# 项目根目录
+PROJECT_DIR=$(pwd)
+echo "项目将部署在: $PROJECT_DIR"
+
+# 创建虚拟环境
+echo "创建Python虚拟环境..."
+python3 -m venv venv
+source venv/bin/activate
+
+# 安装依赖
+echo "安装依赖包..."
+pip install --upgrade pip
+pip install flask pandas matplotlib ccxt pytz talib-binary gunicorn
+
+# 安装任何额外的系统依赖
+# sudo apt-get update
+# sudo apt-get install -y python3-dev
+
+# 检查目录结构
+echo "检查目录结构..."
+mkdir -p web/templates
+
+# 创建日志目录
+mkdir -p logs
+
+# 创建启动脚本
+echo "创建启动脚本..."
+cat > start_service.sh << 'EOF'
+#!/bin/bash
+# 缠论分析系统启动脚本
+
+# 项目根目录
+PROJECT_DIR=$(pwd)
+cd $PROJECT_DIR
+
+# 激活虚拟环境
+source venv/bin/activate
+
+# 启动服务
+cd web
+echo "启动缠论分析系统服务..."
+gunicorn app:app --bind=0.0.0.0:8123 --workers=4 --timeout=120 --log-level=info --log-file=logs/chanlun.log --daemon
+
+echo "服务已启动，端口8123"
+echo "日志文件位置: $PROJECT_DIR/web/logs/chanlun.log"
+EOF
+
+# 创建停止脚本
+echo "创建停止脚本..."
+cat > stop_service.sh << 'EOF'
+#!/bin/bash
+# 停止缠论分析系统服务
+
+echo "停止缠论分析系统服务..."
+pkill -f "gunicorn app:app"
+echo "服务已停止"
+EOF
+
+# 添加执行权限
+chmod +x start_service.sh
+chmod +x stop_service.sh
+
+# 修改app.py中的调试模式（生产环境应关闭调试模式）
+if [ -f web/app.py ]; then
+    echo "配置app.py为生产环境模式..."
+    sed -i 's/app.run(debug=True, host='\''0.0.0.0'\'', port=8123)/# 在生产环境中，使用gunicorn启动服务\n# app.run(debug=False, host='\''0.0.0.0'\'', port=8124)/' user_data/Chan/web/app.py
+else
+    echo "警告: 未找到app.py文件"
+fi
+
+echo "部署完成！"
+echo "使用 ./start_service.sh 启动服务"
+echo "使用 ./stop_service.sh 停止服务"

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 +1,43 @@
+# 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
+- 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
+
+- `chart_tv.js` 单体巨大 → 后续可选 ECR
+- 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 PR 合入 `dev`

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 +1,83 @@
+# CHANGELOG
+
+## Unreleased — 2026-08-07
+
+### 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) |