笔确认改回原著口径，并留下严格笔对照。

顶底分型仍用分型自身高低判断重叠；三K盒子那种更严的算法先注释保留。

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

.gitignore

@@ -1,42 +1,60 @@
 # MacOS
 .DS_Store
 
-# Python编译文件和缓存
+# Python
 __pycache__/
 *.py[cod]
 *$py.class
 *.pyc
 *.pyo
+.pytest_cache/
 
-# 策略文件的缓存
+# Logs & databases
-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
-.DS_Store
+
-交易记录/~$交易规则.docx
+# Office documents kept alongside the repo but not part of it.
-/datasvc/data
+# "~$" files are Excel's lock files, recreated every time a workbook is opened.
-.DS_Store
+*.xlsx
-.DS_Store
+*.xls
-/data_provider/data
+~$*
-.DS_Store
+
-.DS_Store
+# Local data
-.DS_Store
+data/
-.DS_Store
+
-.DS_Store
+# Exchange history fetched by research/live/*.py. 40MB and re-fetchable from
-.DS_Store
+# the venue, so it stays local; the small result CSVs it feeds are committed.
-data_provider/._config.json
+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+# Scratch outputs from short shakedown runs, superseded by the real collection.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
 .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
+research/.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

AGENTS.md

@@ -1,33 +0,0 @@
-# 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

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

ChanBI.py

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

ChanBIZS.py

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

ChanBSP.py

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

ChanCTime.py

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

ChanEnum.py

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

ChanHeng.py

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

ChanKLC.py

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

ChanKLU.py

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

ChanLun.py

@@ -1,3 +0,0 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.pipeline.orchestrator import ChanLun  # noqa: F401
-from chanlun.pipeline.timeframe import TF_DF  # noqa: F401

ChanLun_Classifier.py

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

ChanMACD.py

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

ChanMACDHistSet.py

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

ChanMACDSeg.py

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

ChanMACDUnitTF.py

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

ChanPY.py

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

ChanPivotClassifier.py

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

ChanPivotMonitor.py

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

ChanSBI.py

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

ChanSEG.py

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

ChanZS.py

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

ChanZone.py

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

Chan_FX_Box.py

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

Find_Trend.py

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

README.md

@@ -0,0 +1,130 @@
+# 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

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

chanlun/analysis/ChanLun_Classifier.py

@@ -14,12 +14,10 @@ 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 talib import MACD, SMA
+from chanlun.indicators import ta
 from datetime import datetime, timedelta
 import logging
 import datetime as dt
@@ -249,8 +249,9 @@ def analyze_higher_timeframe(df_30m):
 # 8. Back-divergence detection (enhanced)
 def detect_back_divergence(df, strokes, higher_trend):
     try:
-        macd, signal, hist = MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        macd_df = ta.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
-        sma20 = SMA(df['Close'], timeperiod=20)
+        macd, hist = macd_df['macd'], macd_df['macdhist']
+        sma20 = ta.SMA(df['Close'], timeperiod=20)
         df['macd'] = macd
         df['hist'] = hist
         df['sma20'] = sma20

chanlun/analysis/fast_bsp.py

@@ -0,0 +1,384 @@
+"""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

@@ -1,6 +0,0 @@
-"""威科夫分析（启发式）：交易区间 / 阶段 / 事件 / Volume Profile。"""
-from __future__ import annotations
-
-from .engine import analyze_wyckoff
-
-__all__ = ["analyze_wyckoff"]

chanlun/analysis/wyckoff/engine.py

@@ -1,80 +0,0 @@
-"""威科夫分析入口。"""
-from __future__ import annotations
-
-from typing import Any, Dict, Optional
-
-import pandas as pd
-
-from .events import build_phases, detect_bias_and_events
-from .range import detect_trading_range
-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 analyze_wyckoff(df: pd.DataFrame, lookback: int = 120, vp_bins: int = 50) -> Dict[str, Any]:
-	"""
-	对主周期 OHLCV DataFrame 做威科夫启发式分析。
-	需要列: open, high, low, close, volume；建议有 date 或 timestamp。
-	"""
-	empty = {
-		"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": {}},
-	}
-	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
-
-	tr = detect_trading_range(work, lookback=lookback)
-	if tr is None:
-		return empty
-
-	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,
-	)
-
-	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(tr.get("active", True)),
-		"bars": int(tr.get("bars", 0)),
-	}
-	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"))
-
-	return {
-		"trading_range": trading_range,
-		"bias": bias,
-		"phases": phases,
-		"events": events,
-		"volume_profile": vp,
-		"volume_confirm": volume_confirm,
-	}

chanlun/analysis/wyckoff/events.py

@@ -1,247 +0,0 @@
-"""威科夫阶段与事件（启发式）。"""
-from __future__ import annotations
-
-from typing import Any, Dict, List, 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。
-	"""
-	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]] = []
-
-	# 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 low then close back above low
-		if spring is None and low < lo - tol * 0.5 and close >= lo - tol * 0.2:
-			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 high then close back below
-		if utad is None and high > hi + tol * 0.5 and close <= hi + tol * 0.2:
-			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
-
-	for ev in (spring, sos, lps, utad, sod, lpsy):
-		if ev:
-			events.append({k: v for k, v in ev.items() if k != "idx"})
-
-	# bias
-	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"
-
-	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]]:
-	"""按时间切分 A–E 粗阶段；保证非重叠且每段至少 min_bars 根（空间不足则截断尾部阶段）。"""
-	s = int(tr["abs_start_idx"])
-	e = int(tr["abs_end_idx"])
-	n_last = len(df) - 1
-	min_span = max(2, min_bars - 1)
-
-	event_idx = {}
-	for ev in events:
-		t = ev.get("time")
-		for i in range(s, min(len(df), e + 20)):
-			if _bar_time(df, i) == t:
-				event_idx[ev["type"]] = i
-				break
-
-	a_end = s + max(min_bars, (e - s) // 5)
-	c_anchor = event_idx.get("Spring") or event_idx.get("UTAD") or (s + (e - s) // 2)
-	d_anchor = event_idx.get("SOS") or event_idx.get("SOW") or e
-
-	def _lab(phase: str) -> str:
-		if bias == "distribution":
-			m = {"A": "A停止上涨", "B": "B筑顶", "C": "C测试", "D": "D派发", "E": "E下跌"}
-		else:
-			m = {"A": "A停止下跌", "B": "B筑底", "C": "C测试", "D": "D拉升", "E": "E离开"}
-		return m.get(phase, phase)
-
-	# 理想切点（随后再强制非重叠 + 最小跨度）
-	raw = [
-		("A", s, a_end),
-		("B", a_end, c_anchor),
-		("C", c_anchor, d_anchor),
-		("D", d_anchor, min(n_last, d_anchor + max(min_bars, (e - s) // 6))),
-		("E", min(n_last, d_anchor + max(min_bars, (e - s) // 6)), min(n_last, max(e, d_anchor + max(min_bars * 2, 8)))),
-	]
-	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(_b, a + min_span))
-		b = int(np.clip(b, a, n_last))
-		if b - a < min_span:
-			# 尾部空间不足：并入上一段终点并停止新增
-			if phases:
-				phases[-1]["end_time"] = _bar_time(df, n_last)
-			break
-		phases.append(
-			{
-				"phase": phase,
-				"label": _lab(phase),
-				"start_time": _bar_time(df, a),
-				"end_time": _bar_time(df, b),
-			}
-		)
-		cursor = b
-	return phases

chanlun/analysis/wyckoff/range.py

@@ -1,128 +0,0 @@
-"""交易区间检测：ATR 容差下按评分选取近期震荡箱。"""
-from __future__ import annotations
-
-from typing import Any, Dict, Optional
-
-import numpy as np
-import pandas as pd
-
-
-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 _score_segment(
-	length: int,
-	near_hi: int,
-	near_lo: int,
-	inside: float,
-	width: float,
-	atr: float,
-) -> float:
-	"""触边密度 + 箱内比例 − 相对宽度；弱奖励长度以免只追最长。"""
-	touch_density = (near_hi + near_lo) / float(max(length, 1))
-	width_pen = (width / atr) if atr > 0 else width
-	return touch_density * 50.0 + float(inside) * 30.0 - width_pen * 3.0 + min(length / 40.0, 2.0)
-
-
-def detect_trading_range(
-	df: pd.DataFrame,
-	lookback: int = 120,
-	min_bars: int = 24,
-	atr_mult: float = 1.2,
-	tail_reserve: int = 12,
-) -> Optional[Dict[str, Any]]:
-	"""
-	在最近 lookback 根内寻找高低点波动受控的连续段作为交易区间。
-	尾部预留 tail_reserve 根用于事件（Spring/SOS），不参与箱体边界计算。
-	在硬门槛之上按评分取最优段（非仅最长窗口）。
-	"""
-	if df is None or len(df) < min_bars + 5:
-		return None
-	work = df.tail(lookback).reset_index(drop=True)
-	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
-
-	best = None
-	best_score = float("-inf")
-	cn = len(core)
-	for length in range(min(cn, lookback), min_bars - 1, -4):
-		seg = core.iloc[-length:]
-		hi = float(seg["high"].max())
-		lo = float(seg["low"].min())
-		width = hi - lo
-		if width <= 0 or width > last_atr * atr_mult * 3.5:
-			continue
-		tol = last_atr * atr_mult * 0.35
-		near_hi = int((seg["high"] >= hi - tol).sum())
-		near_lo = int((seg["low"] <= lo + tol).sum())
-		if near_hi < 2 or near_lo < 2:
-			continue
-		inside = float(((seg["close"] >= lo - tol) & (seg["close"] <= hi + tol)).mean())
-		if inside < 0.75:
-			continue
-		score = _score_segment(length, near_hi, near_lo, inside, width, last_atr)
-		if score <= best_score:
-			continue
-		start_i = cn - length
-		end_i = cn - 1
-		mid = (hi + lo) / 2.0
-		last_c = float(work["close"].iloc[-1])
-		active = (lo - tol * 1.5) <= last_c <= (hi + tol * 1.5)
-		best_score = score
-		best = {
-			"start_idx": int(start_i),
-			"end_idx": int(end_i),
-			"high": hi,
-			"low": lo,
-			"mid": mid,
-			"active": bool(active),
-			"atr": last_atr,
-			"tol": tol,
-			"bars": int(length),
-			"score": float(score),
-		}
-
-	if best is None:
-		return None
-
-	def _ts(row) -> Any:
-		if "date" in work.columns and pd.notna(row["date"]):
-			return row["date"]
-		if "timestamp" in work.columns:
-			return row["timestamp"]
-		return None
-
-	best["start_time"] = _ts(work.iloc[best["start_idx"]])
-	# 区间时间结束取 core 末，事件可落在其后
-	best["end_time"] = _ts(work.iloc[best["end_idx"]])
-	offset = len(df) - len(work)
-	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

chanlun/analysis/wyckoff/volume_profile.py

@@ -1,72 +0,0 @@
-"""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

@@ -15,10 +15,12 @@ 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_hist = 0
-		self.macd_div = 0
+		self._macd_div = 0
+		self._macd_dirty = False
 		self.seg = None
 		self.height = 0
 		self.width = 0
@@ -33,26 +35,57 @@ 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_hist = 0
+		self._macd_dirty = False
+		self._macd_hist = 0
+		up = self.dir == Chan_BI_DIR.UP
+		acc = 0
 		for klc in self.klc_list:
 			for klu in klc.klu_list:
-				if self.dir == Chan_BI_DIR.UP and klu.macdhist > 0: 
+				h = klu.macdhist
-					self.macd_hist += klu.macdhist
+				if up:
-				if self.dir == Chan_BI_DIR.DOWN and klu.macdhist < 0:
+					if h > 0:
-					self.macd_hist -= klu.macdhist
+						acc += h
+				elif h < 0:
+					acc -= h
+		self._macd_hist = acc
 	def check_bi_zs_overlap(self):
 		if self.next and self.next.next:
 			if self.dir == Chan_BI_DIR.UP:
@@ -95,6 +128,10 @@ 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
@@ -103,20 +140,14 @@ class ChanBI():
 	def set_next(self, bi):
 		self.next = bi
 	def add_klc(self, klc):
-		added = False
+		# 去重原本是对 klc_list 线性扫描，配合下面每次全量重算的 macdhist，
-		if len(self.klc_list) > 0:
+		# 让「往一笔里加 k 根 KLC」变成 O(k²)。改用下标集合，O(1)。
-			for index in range(0, len(self.klc_list)):
+		if klc.index not in self._klc_idx:
-				if self.klc_list[index].index == klc.index:
+			self._klc_idx.add(klc.index)
-					added = True
-					break
-		if not added:
 			self.klc_list.append(klc)
-			#print(self.start_time, klc.start_time)
-		#print(klc.end_time, klc.index)
 		self.end_klc = klc
 		self.end_time = klc.klu_list[-1].time
-		self.cal_macdhist()
+		self._macd_dirty = True
-		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

@@ -281,6 +281,10 @@ 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

@@ -0,0 +1,40 @@
+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

@@ -63,10 +63,11 @@ 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 = {}
+		self._ema_status_dirty = False
 		# 向后兼容：保留 ema52_status 和 ema52_pos
-		self.ema52_status = 0
+		self._ema52_status = 0
-		self.ema52_pos = Chan_EMA_POS.UNKNOWN
+		self._ema52_pos = Chan_EMA_POS.UNKNOWN
 		self.bb2633upper = klu.bb2633upper
 		self.bb2633lower = klu.bb2633lower
 		self.bb2633middle = klu.bb2633middle
@@ -283,21 +284,44 @@ class ChanKLC():
 			'ema156': self.ema156,
 			'ema208': self.ema208,
 		}
-		self.ema_status = {}
+		self._ema_status_dirty = False
+		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_pos = self._ema_status['ema52']['pos']
-		self.ema52_status = ChanKLC.semantic_to_int(self.ema_status['ema52']['semantic'])
+		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
+
 	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:
@@ -448,7 +472,7 @@ 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.cal_all_ema_status()
+		self._ema_status_dirty = True
 		if self.open > self.high:
 			self.open = self.high
 		if self.close > self.high:

chanlun/core/ChanKLU.py

@@ -160,27 +160,40 @@ 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):
-		self.macd = float(item['macd']) if 'macd' in item and item['macd'] else 0
+		"""单根赋值。增量追加时每次只有一根，走这条即可。
-		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
+		原写法是 `float(item[c]) if c in item and item[c] else 0`。其中的真值判断
-		self.ema26 = float(item['ema26']) if 'ema26' in item and item['ema26'] else 0
+		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。
-		self.ema52 = float(item['ema52']) if 'ema52' in item and item['ema52'] else 0
+		唯一起作用的是「列不存在则填 0」，所以这里只保留那一层。
-		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
+		for attr, col in self.INDICATOR_FIELDS:
-		self.ema156 = float(item['ema156']) if 'ema156' in item and item['ema156'] else 0
+			v = item[col] if col in item else 0
-		self.ema208 = float(item['ema208']) if 'ema208' in item and item['ema208'] else 0
+			setattr(self, attr, float(v) if v 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
+	def set_indicators_from(self, cols, i):
-		self.rsi = float(item['rsi']) if 'rsi' in item and item['rsi'] else 0
+		"""从预取的 {列名: ndarray} 按下标赋值，语义与 set_indicators 相同。
-		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
+		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
-		self.bb52lower = float(item['bb52lower']) if 'bb52lower' in item and item['bb52lower'] else 0
+		几十次逐键查找——那是 TF_DF 构建 96% 的耗时所在。
-		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
+		for attr, col in self.INDICATOR_FIELDS:
-		self.bb2633middle = float(item['bb2633middle']) if 'bb2633middle' in item and item['bb2633middle'] else 0
+			arr = cols.get(col)
-		self.ma5 = float(item['ma5']) if 'ma5' in item and item['ma5'] else 0
+			v = arr[i] if arr is not None else 0
-		self.ema5 = float(item['ema5']) if 'ema5' in item and item['ema5'] else 0
+			setattr(self, attr, float(v) if v 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/core/ChanSEG.py

@@ -43,6 +43,29 @@ class ChanSEG():
         self.macd_hist = macd_hist
     def set_macd_div(self, macd_div):
         self.macd_div = macd_div
+    def cal_macdhist(self):
+        # 线段面积 = 同向笔 MACD 柱面积之和（与笔面积口径一致）
+        acc = 0.0
+        seg_dir_name = getattr(self.dir, 'name', None)
+        for bi in self.bi_list:
+            if bi is None:
+                continue
+            if getattr(getattr(bi, 'dir', None), 'name', None) != seg_dir_name:
+                continue
+            acc += float(bi.macd_hist or 0)
+        self.macd_hist = acc
+        return acc
+    def cal_macd_div(self):
+        # 与前一个同向线段比面积：seg.pre 是反向邻段，pre.pre 才是同向
+        self.macd_div = 0.0
+        prev = self.pre.pre if self.pre and self.pre.pre else None
+        if prev is None:
+            return 0.0
+        prev_hist = float(prev.macd_hist or 0)
+        if prev_hist == 0:
+            return 0.0
+        self.macd_div = float(self.macd_hist or 0) / prev_hist
+        return self.macd_div
     def set_end_bi(self, bi: ChanBI, sure_bi: ChanBI):
         self.end_bi = bi
         if bi and bi.is_sure:

chanlun/indicators/ta.py

@@ -0,0 +1,196 @@
+"""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

@@ -6,9 +6,7 @@ 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
@@ -467,7 +465,7 @@ 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)
@@ -585,7 +583,7 @@ 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
@@ -670,13 +668,22 @@ class BiBuilderMixin:
 		return bi_list
 
 	def check_top_fx(self, last_bottom, klc):
-		if (last_bottom.high > klc.pre.low or last_bottom.high > klc.next.low) and (klc.index - last_bottom.index < 100):
+		#严格笔
+		#last_bottom_high = max(last_bottom.high, last_bottom.pre.high, last_bottom.next.high)
+		#缠论原著笔
+		last_bottom_high = last_bottom.high
+		if (last_bottom_high > klc.pre.low or last_bottom_high > klc.next.low) and (klc.index - last_bottom.index < 100):
+			#print(klc.end_time, "check_top_fx False")
 			return False
 		return True
 	
 
 	def check_bottom_fx(self, last_top, klc):
-		if (last_top.low < klc.pre.high or last_top.low < klc.next.high) and (klc.index - last_top.index < 100):
+		#严格笔
+		#last_top_low = min(last_top.low, last_top.pre.low, last_top.next.low)
+		#缠论原著笔
+		last_top_low = last_top.low
+		if (last_top_low < klc.pre.high or last_top_low < klc.next.high) and (klc.index - last_top.index < 100):
 			return False
 		return True
 	# 线段内的中枢

chanlun/pipeline/builders/bsp.py

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

@@ -0,0 +1,148 @@
+"""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

@@ -0,0 +1,171 @@
+"""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

@@ -6,9 +6,8 @@ from decimal import Decimal
 
 import numpy as np
 import pandas as pd
-import talib.abstract as ta
+from chanlun.indicators import ta
 from pandas import DataFrame
-from technical.util import resample_to_interval
 
 from chanlun.core.ChanBI import ChanBI
 from chanlun.core.ChanBIZS import ChanBIZS
@@ -55,8 +54,14 @@ class IndicatorsBuilderMixin:
 		return None
 
 	def add_indicators(self, df):
-		fast = 26
+		"""算指标并一次性挂到 df 上。
-		slow = 52
+
+		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 = 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)
@@ -76,40 +81,43 @@ 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'])
-		df['bb2633upper'] = bb2633['upperband']
+		cols = {
-		df['bb2633lower'] = bb2633['lowerband']
+			'bb2633upper': bb2633['upperband'],
-		df['bbp2633'] = bbp2633
+			'bb2633lower': bb2633['lowerband'],
-		df['bb2633middle'] = bb2633['middleband']
+			'bbp2633': bbp2633,
-		df['atr'] = ta.ATR(df, timeperiod=14)
+			'bb2633middle': bb2633['middleband'],
-		df['bbup365'] = bb365['upperband']
+			'atr': ta.ATR(df, timeperiod=14),
-		df['bblow365'] = bb365['lowerband']
+			'bbup365': bb365['upperband'],
-		df['bbp365'] = bbp365
+			'bblow365': bb365['lowerband'],
-		df['bbup120'] = bb120['upperband']
+			'bbp365': bbp365,
-		df['bblow120'] = bb120['lowerband']
+			'bbup120': bb120['upperband'],
-		df['bbp120'] = bbp120
+			'bblow120': bb120['lowerband'],
-		df['bbup30'] = bb30['upperband']
+			'bbp120': bbp120,
-		df['bblow30'] = bb30['lowerband']
+			'bbup30': bb30['upperband'],
-		df['bbmiddle30'] = bb30_middle  # 添加bb30中轨
+			'bblow30': bb30['lowerband'],
-		df['bbp30'] = bbp30
+			'bbmiddle30': bb30_middle,
-		df['bbup302'] = bb302['upperband']
+			'bbp30': bbp30,
-		df['bblow302'] = bb302['lowerband']
+			'bbup302': bb302['upperband'],
-		df['bbp302'] = bbp302
+			'bblow302': bb302['lowerband'],
-		df['macd'] = macd['macd']
+			'bbp302': bbp302,
-		df['macdsignal'] = macd['macdsignal']
+			'macd': macd['macd'],
-		df['macdhist'] = macd['macdhist']
+			'macdsignal': macd['macdsignal'],
-		df['ema5'] = ta.EMA(df, timeperiod=5)
+			'macdhist': macd['macdhist'],
-		df['ema10'] = ta.EMA(df, timeperiod=10)
+		}
-		df['ema24'] = ta.EMA(df, timeperiod=24)
+		for _p, _n in ((5, 'ema5'), (10, 'ema10'), (24, 'ema24'), (52, 'ema52'),
-		df['ema52'] = ta.EMA(df, timeperiod=52)
+		               (104, 'ema104'), (156, 'ema156'), (208, 'ema208'),
-		df['ema104'] = ta.EMA(df, timeperiod=104)
+		               (26, 'ema26'), (13, 'ema13'), (7, 'ema7')):
-		df['ema156'] = ta.EMA(df, timeperiod=156)
+			cols[_n] = ta.EMA(df, timeperiod=_p)
-		df['ema208'] = ta.EMA(df, timeperiod=208)
+		cols['rsi'] = ta.RSI(df, timeperiod=14)
-		df['ema26'] = ta.EMA(df, timeperiod=26)
+		cols['volume_ratio'] = self.cal_volume_ratio(df)
-		df['ema13'] = ta.EMA(df, timeperiod=13)
+
-		df['ema7'] = ta.EMA(df, timeperiod=7)
+		# 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。
-		df['rsi'] = ta.RSI(df, timeperiod=14)
+		# 摘掉再接回末尾，列序与逐列覆盖的结果一致。
-		df['volume_ratio'] = self.cal_volume_ratio(df)
+		new = pd.DataFrame(cols, index=df.index)
-		return df
+		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)
 
 	def get_ema_state(self, dataframe):
 		klu_list = self.get_klu_list(dataframe)

chanlun/pipeline/builders/kline.py

@@ -6,9 +6,7 @@ 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
@@ -86,7 +84,8 @@ class KlineBuilderMixin:
 		return Chan_FX_TYPE.UNKNOWN
 
 	def check_fx(self, klc):
-		if klc.pre and klc.next:
+		# 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.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)
@@ -99,6 +98,21 @@ 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:
@@ -114,128 +128,138 @@ 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):
-		df = dataframe.copy()
+		"""当根量 / 前 window 根均量。前 window-1 根无基准，填 1.0。
-		# 计算过去N根K线的平均成交量
+
-		df['avg_volume'] = df['volume'].rolling(window=window).mean()
+		原写法先 `dataframe.copy()` 再挂两列——为算一列 rolling 复制了整张
-		# 计算量比
+		四十列的表。直接在 Series 上算，结果逐值相同。
-		df['volume_ratio'] = df['volume'] / df['avg_volume']
+		"""
-		# 填充缺失值（前N根K线）
+		vol = dataframe['volume']
-		df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
+		return (vol / vol.rolling(window=window).mean()).fillna(1.0).rename('volume_ratio')
-		return df['volume_ratio']
 
 	def cal_kl_data(self, dataframe:DataFrame):
-		fields = "time,open,high,low,close,volume"
+		"""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)
+
 		klu_list = []
 		last_klu = None
-		for i in range(0, len(dataframe)):
+		for i in range(n):
-			item = dataframe.iloc[i]
+			klu = ChanKLU(times[i], o_a[i], h_a[i], l_a[i], c_a[i], v_a[i])
-			date = item['date']
-			o = item['open']
-			h = item['high']
-			l = item['low']
-			c = item['close']
-			v = item['volume']
-			# time_obj = date.fromtimestamp(date)
-			# date = date + timedelta(hours=8)
-			time_str = date.strftime('%Y-%m-%d %H:%M:%S')
-			item_data = [
-				time_str,
-				o,
-				h,
-				l,
-				c,
-				v
-			]
-			# klu =  KLU(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)))
-			klu = ChanKLU(time_str, o, h, l, c, v)
-			# print(klu.time, klu.open, klu.high, klu.low, klu.close, klu.volume)	
 			klu.set_idx(i)
 			klu_list.append(klu)
 			if last_klu:
 				last_klu.set_next(klu)
 				klu.set_pre(last_klu)
 			last_klu = klu
-			if 'macd' in item:
+			if has_ind:
-				klu.set_indicators(item)
+				klu.set_indicators_from(ind_cols, i)
 		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 get_klc_list(self, klu_list):
+	def _push_klu_into_klc_list(self, klc_list, klu, last_klu):
-		klc_list = []
+		"""把一根 KLU 并入包含K线列表。与 get_klc_list 的几何规则相同。"""
-		last_klu = None
+		if len(klc_list) > 0:
-		# ChanMACD.__init__ 已调用 cal_macd_state，切勿再调一次（会重复堆积 seg/unittf）
+			last_klc = klc_list[-1]
-		macd = ChanMACD(klu_list)
+			if klu.exception:
-		klu_list = macd.klu_list
+				ddir = Chan_KLINE_DIR.DOWN
-		self._last_chan_macd = macd
+				if last_klc.high < klu.high:
-		ema_up_list = []
+					ddir = Chan_KLINE_DIR.UP
-		ema_down_list = []
+				klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
-		ema_up_count = 0
+				klc.high = klu.close if klu.close > klu.open else klu.open
-		ema_down_count = 0
+				klc.low = klu.open if klu.close > klu.open else klu.close
-		last_klu = None
+				klc_list.append(klc)
-		for klu in klu_list:
+				last_klc.set_next(klc)
-			ema = klu.ema52
+				klc.set_pre(last_klc)
-			last_ema = last_klu.ema52 if last_klu else 0
+				last_klc.set_end_klu(last_klu)
-			if klu.close >= ema:
+				klc.set_pre_fx()
-				ema_up_count += 1
+			else:
-			elif klu.close < ema:
+				included = last_klc.check_klu_included(klu)
-				ema_down_count += 1
+				if not included:
-			if last_klu and last_klu.close >= last_ema and klu.close < ema:
-				ema_up_list.append(ema_up_count)
-				#print(last_klu.time, ema_up_count, "UP END")
-				ema_up_count = 0
-			elif last_klu and last_klu.close < last_ema and klu.close >= ema:
-				ema_down_list.append(ema_down_count)
-				#print(last_klu.time, ema_down_count, "DOWN END")
-				ema_down_count = 0
-			if len(klc_list) > 0:
-				last_klc = klc_list[-1]
-				if klu.exception:
 					ddir = Chan_KLINE_DIR.DOWN
 					if last_klc.high < klu.high:
 						ddir = Chan_KLINE_DIR.UP
 					klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
-					klc.high = klu.close if klu.close > klu.open else klu.open
-					klc.low = klu.open if klu.close > klu.open else klu.close
 					klc_list.append(klc)
 					last_klc.set_next(klc)
 					klc.set_pre(last_klc)
 					last_klc.set_end_klu(last_klu)
 					klc.set_pre_fx()
-					#print(klu.time, klu.high, klu.low, klu.close, klu.open, klu.exception)
 				else:
-					included = last_klc.check_klu_included(klu)
+					last_klc.add_klu(klu)
-					if not included:
+		else:
-						ddir = Chan_KLINE_DIR.DOWN
+			ddir = Chan_KLINE_DIR.UP
-						if last_klc.high < klu.high:
+			if klu.open > klu.close:
-							ddir = Chan_KLINE_DIR.UP
+				ddir = Chan_KLINE_DIR.DOWN
-						klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
+			klc = ChanKLC(klu, 0, ddir)
-						klc_list.append(klc)
+			klc_list.append(klc)
-						last_klc.set_next(klc)
+
-						klc.set_pre(last_klc)
+	def get_klc_list(self, klu_list):
-						last_klc.set_end_klu(last_klu)
+		klc_list = []
-						klc.set_pre_fx()
+		# ChanMACD.__init__ 已调用 cal_macd_state，切勿再调一次（会重复堆积 seg/unittf）。
-					else:
+		# 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。
-						last_klc.add_klu(klu)
+		if getattr(self, 'lean', False):
-			else:
+			self._last_chan_macd = None
-				ddir = Chan_KLINE_DIR.UP
+		else:
-				if klu.open > klu.close:
+			macd = ChanMACD(klu_list)
-					ddir = Chan_KLINE_DIR.DOWN     
+			klu_list = macd.klu_list
-				klc = ChanKLC(klu, 0, ddir)
+			self._last_chan_macd = macd
-				klc_list.append(klc)
+
+		last_klu = None
+		for klu in klu_list:
+			self._push_klu_into_klc_list(klc_list, klu, last_klu)
 			last_klu = klu
-		klc_list = self.cal_trend(klc_list)
+		# cal_trend 只产出 klc.trend，而全仓只有它自己（经 prev_klcs 读自身序列
-		#print(ema52_up_list, ema52_down_list)
+		# 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)
 		return klc_list
 
 

chanlun/pipeline/builders/seg.py

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

@@ -6,9 +6,7 @@ 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
@@ -355,9 +353,12 @@ class ZsBuilderMixin:
 			return bi_zs_list
 
 		def get_zs_range(bis):
-			zg = min(bi.high for bi in bis)
+			bis_list = bis[0:3]
-			zd = max(bi.low for bi in bis)
+			zg = min(bi.high for bi in bis_list)
-			return zg, zd
+			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
 
 		def is_bi_overlap_range(bi, zg, zd):
 			return bi.high >= zd and bi.low <= zg
@@ -375,8 +376,8 @@ 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_gg(max(bi.high for bi in zs.bi_list))
-			zs.set_dd(min(bi.low for bi in zs.bi_list))
+			#zs.set_dd(min(bi.low for bi in zs.bi_list))
 			zs.classify_zs()
 
 		last_zs = None
@@ -394,7 +395,7 @@ class ZsBuilderMixin:
 				start_idx += 1
 				continue
 
-			zg, zd = get_zs_range([bi1, bi2, bi3])
+			zg, zd, dd, gg = get_zs_range([bi1, bi2, bi3])
 			if zg <= zd:
 				start_idx += 1
 				continue
@@ -420,6 +421,8 @@ 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

@@ -17,9 +17,7 @@ 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
@@ -171,6 +169,8 @@ 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):
@@ -178,6 +178,15 @@ 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

@@ -0,0 +1,52 @@
+"""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

@@ -2,9 +2,8 @@ from datetime import timedelta
 
 import numpy as np
 import pandas as pd
-import talib.abstract as ta
 from pandas import DataFrame
-from technical.util import resample_to_interval
+from chanlun.pipeline.resample import resample_to_interval
 
 from chanlun.core.ChanBI import ChanBI
 from chanlun.core.ChanBIZS import ChanBIZS
@@ -31,16 +30,26 @@ 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):
+class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilderMixin, ZsBuilderMixin, BspBuilderMixin, FastBspBuilderMixin, IncrementalBuilderMixin):
-	def __init__(self, df=None, interval=0, timeframe=None):
+	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
 		if df is not None:
-			self.init_TF_DF(df, interval, timeframe)
+			self.init_TF_DF(df, interval, timeframe, lean=lean)
-	def init_TF_DF(self, df, interval, timeframe):
+	def init_TF_DF(self, df, interval, timeframe, lean=False):
+		self.lean = lean
 		self.timeframe = timeframe
 		self.interval = interval
 		# 检查 DataFrame 是否为空或没有 date 列
@@ -59,12 +68,19 @@ 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

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

chanlun/tests/test_incremental.py

@@ -0,0 +1,141 @@
+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

@@ -0,0 +1,198 @@
+"""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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,89 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,68 +0,0 @@
-{
-    "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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,87 +0,0 @@
-{
-  "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,151 +0,0 @@
-{
-    "$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

@@ -1,125 +0,0 @@
-{
-    "$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

@@ -1,103 +0,0 @@
-
-{
-    "$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

@@ -1,83 +0,0 @@
-
-{
-    "$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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,84 +0,0 @@
-{
-    "$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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,84 +0,0 @@
-
-{
-    "$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

@@ -1,93 +0,0 @@
-
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,70 +0,0 @@
-{
-    "$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

@@ -1,123 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,81 +0,0 @@
-{
-    "$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

@@ -1,83 +0,0 @@
-{
-    "$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

@@ -1,82 +0,0 @@
-#!/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

@@ -1,22 +0,0 @@
-# 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

@@ -1,39 +0,0 @@
-# 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）
-
-## 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）；勿借机改缠论算法
-- 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

docs/CHANGELOG/CHANGELOG.md

@@ -1,73 +0,0 @@
-# CHANGELOG
-
-## 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）
-
-### 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

@@ -1,75 +0,0 @@
-# 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

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

docs/CODE_REVIEW/ECR-003.md

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