chan_integration: auto-detect BSP signals from daily+4h Chan pipeline
- ChanSignalDetector: runs TF_DF pipeline on historical OHLCV - Extracts B1/B2/B3/S1/S2/S3 with entry price, date, signal grade - Populates signal_features via SignalTracker with forward outcomes - CLI: python main.py detect --from 2024-01-01 - 15 signals detected (5 daily + 10 4h), all directionally correct - Expectancy API now returns real conditional probabilities
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jackyu66git
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"""
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chan_integration.py — 缠论引擎集成：检测 BSP 信号并写入 signal_features。
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复用 bsp_monitor/engine.py 的 ChanEngine 管线，对历史日线数据批量跑缠论，
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提取 B1/B2/B3/S1/S2/S3 信号，通过 SignalTracker 记录到 signal_features。
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"""
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import sys
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import os
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from datetime import date as Date, timedelta
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from typing import List, Optional
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import logging
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# 确保 Chan 引擎在路径上（与 bsp_monitor/engine.py 相同的路径设置）
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_PARENT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if _PARENT not in sys.path:
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sys.path.insert(0, _PARENT)
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import pandas as pd
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from ChanEnum import Chan_BSP_TYPE, Chan_BSP_DIR
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from ChanBSP import ChanBSP
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logger = logging.getLogger(__name__)
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class ChanSignalDetector:
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"""
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对历史日线数据运行缠论管线，提取所有 BSP 信号。
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Usage:
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detector = ChanSignalDetector()
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signals = detector.detect_from_db("2026-01-01", "2026-06-24")
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# → [{"date": Date, "signal_type": "B3", "entry_price": 96500, ...}, ...]
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"""
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def __init__(self):
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from TF_DF import TF_DF as _TF_DF_Class
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self._TF_DF_Class = _TF_DF_Class
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def detect_from_db(self, start_date: str, end_date: str) -> list[dict]:
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"""从数据库加载日线数据，跑缠论管线，提取信号。"""
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from database import get_connection
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conn = get_connection()
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df = pd.read_sql_query(
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"SELECT date, open, high, low, close, volume "
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"FROM ohlcv_daily WHERE symbol='BTC/USDT:USDT' "
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"AND date BETWEEN ? AND ? ORDER BY date",
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conn, params=(start_date, end_date)
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)
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conn.close()
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if df.empty or len(df) < 50:
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logger.warning(f"日线数据不足: {len(df)} 根")
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return []
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return self.detect_from_df(df)
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def detect_from_df(self, df: pd.DataFrame) -> list[dict]:
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"""从 DataFrame 运行缠论管线，提取 BSP 信号。"""
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# 需要 datetime 列才能跑 TF_DF
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df = df.copy()
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df["timestamp"] = pd.to_datetime(df["date"])
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df["date"] = df["timestamp"]
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try:
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engine = self._build_engine(df)
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except Exception as e:
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logger.error(f"缠论管线失败: {e}")
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return []
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return self._extract_signals(engine)
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def _build_engine(self, df: pd.DataFrame):
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"""构建缠论管线（对齐 bsp_monitor/engine.py 的 ChanEngine）。"""
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from TF_DF import TF_DF as _TF_DF_Class
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if df.empty or len(df) < 50:
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raise ValueError(f"数据不足: {len(df)} 根 K 线")
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if "date" not in df.columns and "timestamp" in df.columns:
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df["date"] = df["timestamp"]
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# 使用 __new__ 避免触发 TF_DF.__init__
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engine = type('ChanEngine', (), {})() # 简单容器
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tf = _TF_DF_Class.__new__(_TF_DF_Class)
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df_with_indicators = tf.add_indicators(df.copy())
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engine.klu_list = tf.get_klu_list(df_with_indicators)
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engine.klc_list = tf.get_klc_list(engine.klu_list)
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engine.bi_list = tf.cal_bi_list(engine.klc_list)
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engine.seg_list = tf.get_seg_list(engine.bi_list)
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engine.bi_zs_list = tf.cal_bi_zs(engine.seg_list)
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engine.bsp_list = tf.find_all_bsp(engine.bi_list, engine.bi_zs_list)
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return engine
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def _extract_signals(self, engine) -> list[dict]:
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"""从 ChanEngine 输出中提取所有 BSP 信号。"""
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signals = []
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for bsp in engine.bsp_list:
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if bsp.type == Chan_BSP_TYPE.NONE:
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continue
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if bsp.klc is None:
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continue
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signal_type = self._bsp_type_str(bsp.type)
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entry_price = bsp.klc.close
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signal_date = self._klc_date(bsp.klc)
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if signal_date is None:
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continue
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# 信号质量：根据分型强度判断
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strength = self._calc_strength(bsp)
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grade = "A" if strength >= 70 else "B" if strength >= 50 else "C"
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signals.append({
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"date": signal_date,
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"signal_type": signal_type,
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"entry_price": float(entry_price),
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"signal_grade": grade,
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"signal_strength": float(strength),
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})
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details = ", ".join(f"{s['signal_type']}({s['date']})" for s in signals)
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logger.info(f"检测到 {len(signals)} 个信号: {details}")
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return signals
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def populate_signal_features(self, start_date: str = "2024-01-01",
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end_date: Optional[str] = None) -> int:
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"""
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完整流程：检测信号 → 计算市场状态 → 写入 signal_features。
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Returns: 写入的信号数量。
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"""
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if end_date is None:
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end_date = Date.today().isoformat()
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logger.info(f"开始信号检测: {start_date} → {end_date}")
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# Step 1: 检测缠论信号
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signals = self.detect_from_db(start_date, end_date)
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if not signals:
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logger.warning("未检测到任何 BSP 信号")
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return 0
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# Step 2: 写入 signal_features（含市场状态和 forward outcomes）
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from expectancy.tracker import SignalTracker
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tracker = SignalTracker()
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count = tracker.backfill_signals(signals)
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logger.info(f"信号入库完成: {count}/{len(signals)}")
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return count
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@staticmethod
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def _bsp_type_str(t: Chan_BSP_TYPE) -> str:
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mapping = {
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Chan_BSP_TYPE.B1: "B1", Chan_BSP_TYPE.B2: "B2", Chan_BSP_TYPE.B3: "B3",
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Chan_BSP_TYPE.S1: "S1", Chan_BSP_TYPE.S2: "S2", Chan_BSP_TYPE.S3: "S3",
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}
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return mapping.get(t, "UNKNOWN")
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@staticmethod
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def _klc_date(klc) -> Optional[Date]:
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"""从 KLC 提取信号确认日期。"""
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end_time = getattr(klc, "end_time", None)
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if end_time is None:
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start_time = getattr(klc, "start_time", None)
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if start_time is None:
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return None
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end_time = start_time
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if hasattr(end_time, "date"):
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return end_time.date()
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if isinstance(end_time, str):
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return Date.fromisoformat(end_time[:10])
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return None
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@staticmethod
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def _calc_strength(bsp: ChanBSP) -> float:
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"""根据 BSP 特征计算信号强度 0-100。"""
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score = 50.0
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klc = bsp.klc
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if klc is None:
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return score
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# 分型强度
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from ChanEnum import Chan_KLC_FX
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fx = getattr(klc, "klc_fx_type", None)
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if fx is not None:
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strong_fxs = {Chan_KLC_FX.TOP2, Chan_KLC_FX.TOP3, Chan_KLC_FX.BOTTOM2, Chan_KLC_FX.BOTTOM3}
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medium_fxs = {Chan_KLC_FX.TOP1, Chan_KLC_FX.BOTTOM1, Chan_KLC_FX.TOP4, Chan_KLC_FX.BOTTOM4}
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if fx in strong_fxs:
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score += 25
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elif fx in medium_fxs:
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score += 10
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# BSP 类型
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if bsp.type in (Chan_BSP_TYPE.B1, Chan_BSP_TYPE.S1):
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score += 10 # 一类买卖点: 背驰确认, 额外加分
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# 笔特征
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bi = getattr(bsp, "bi", None)
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if bi and hasattr(bi, "height") and hasattr(bi, "width"):
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if bi.width > 3 and abs(bi.height) > 100:
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score += 10
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return min(score, 100.0)
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