chore: 移除不再使用的 ChanMacro、system、tests。
这些目录已废弃，从仓库中清理。 Co-authored-by: Cursor <cursoragent@cursor.com>
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jackyu66git
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"""
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scoring/volatility_regime.py — Volatility Regime Classification.
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4 regimes from OHLCV data:
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LOW_VOL: ATR/Close < 2% → compression, breakout imminent
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NORMAL_VOL: ATR/Close 2-5% → normal trading
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HIGH_VOL: ATR/Close 5-10% → trend acceleration, wider stops
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EXPLOSIVE_VOL: ATR/Close > 10% → extreme, reduce or wait
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Uses: ATR(14)/Close, HV(20)/HV(60) ratio, BB width ratio.
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OHLCV-only — never goes offline.
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"""
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from datetime import date as Date
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import sqlite3
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import numpy as np
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import pandas as pd
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from .base import BaseScorer
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from .constants import (
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VOL_LOW, VOL_HIGH, VOL_REGIME_SCORES, HV_RATIO_LOW, HV_RATIO_HIGH,
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)
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from models import FactorScore, VolatilityRegimeScore, VolRegime, MacroDirection
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from config import config
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class VolatilityRegimeScorer(BaseScorer):
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"""Classifies volatility regime from OHLCV data."""
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def compute(self, target_date: Date) -> VolatilityRegimeScore:
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conn = self.get_connection()
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try:
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df = pd.read_sql_query(
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"SELECT * FROM ohlcv_daily WHERE date <= ? ORDER BY date DESC LIMIT 120",
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conn, params=(str(target_date),)
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)
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if df.empty:
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return VolatilityRegimeScore(
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name="Volatility Regime",
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score=50.0,
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label="No Data",
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)
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df = df.sort_values("date").reset_index(drop=True)
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# 1. ATR/Close %
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latest = df.iloc[-1]
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atr = latest.get("atr_14")
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close = float(latest["close"])
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atr_pct = (atr / close * 100) if atr and not pd.isna(atr) and close > 0 else 3.0
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# 2. HV(20) / HV(60) ratio
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hv_ratio = self._compute_hv_ratio(df)
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# 3. BB width ratio
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bb_ratio = self._compute_bb_ratio(df)
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# Classify regime
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regime = self._classify(atr_pct, hv_ratio, bb_ratio)
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# Score
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score = VOL_REGIME_SCORES.get(regime.value, 50)
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# Narrative
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narrative = self._build_narrative(regime, atr_pct, hv_ratio, bb_ratio)
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return VolatilityRegimeScore(
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name="Volatility Regime",
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score=float(score),
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label=regime.value,
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direction=MacroDirection.NEUTRAL,
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vol_regime=regime,
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atr_pct=round(atr_pct, 2),
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hv_ratio=round(hv_ratio, 2),
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bb_width_ratio=round(bb_ratio, 2),
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sub_scores={
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"atr_pct": round(atr_pct, 2),
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"hv_ratio": round(hv_ratio, 2),
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"bb_width_ratio": round(bb_ratio, 2),
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},
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narrative=narrative,
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)
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finally:
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conn.close()
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def _compute_hv_ratio(self, df: pd.DataFrame) -> float:
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"""Compute HV(20) / HV(60) ratio."""
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closes = df["close"].astype(float)
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returns = closes.pct_change().dropna()
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if len(returns) < 60:
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return 1.0
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hv20 = returns.tail(20).std() * np.sqrt(365) * 100
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hv60 = returns.tail(60).std() * np.sqrt(365) * 100
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if hv60 == 0:
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return 1.0
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return hv20 / hv60
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def _compute_bb_ratio(self, df: pd.DataFrame) -> float:
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"""Compute current BB width / 20d average BB width."""
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bb_widths = df["bb_width"].dropna().tail(40)
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if len(bb_widths) < 20:
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return 1.0
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current = bb_widths.iloc[-1]
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avg = bb_widths.tail(20).mean()
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if avg == 0:
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return 1.0
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return current / avg
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@staticmethod
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def _classify(atr_pct: float, hv_ratio: float, bb_ratio: float) -> VolRegime:
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"""Classify volatility regime from multiple indicators."""
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# Primary: ATR/Close %
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if atr_pct > 10.0:
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return VolRegime.EXPLOSIVE_VOL
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elif atr_pct > VOL_HIGH:
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return VolRegime.HIGH_VOL
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elif atr_pct < VOL_LOW:
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return VolRegime.LOW_VOL
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# Secondary: HV ratio and BB ratio for edge cases
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if hv_ratio > HV_RATIO_HIGH and bb_ratio > 1.3:
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return VolRegime.HIGH_VOL
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elif hv_ratio < HV_RATIO_LOW and bb_ratio < 0.8:
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return VolRegime.LOW_VOL
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return VolRegime.NORMAL_VOL
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@staticmethod
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def _build_narrative(regime: VolRegime, atr_pct: float,
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hv_ratio: float, bb_ratio: float) -> str:
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mapping = {
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VolRegime.LOW_VOL: f"低波动(ATR={atr_pct:.1f}%), 布林带收窄, 突破前兆",
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VolRegime.NORMAL_VOL: f"正常波动(ATR={atr_pct:.1f}%), 正常交易环境",
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VolRegime.HIGH_VOL: f"高波动(ATR={atr_pct:.1f}%), 趋势加速, 放宽止损",
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VolRegime.EXPLOSIVE_VOL: f"极端波动(ATR={atr_pct:.1f}%), 减仓或等待",
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}
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return mapping.get(regime, "Unknown")
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