模型，评分，修复网格策略市场状态监听

2026-06-22 10:48:40 +08:00
parent 2e3202968d
commit f938c453e1
42 changed files with 3803 additions and 108 deletions
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+"""
+情绪弹性特征 (4维) — calculate_relaxed_emotion_features(df, market_regime)
+180 日窗口，筛选 advance_ratio < 0.20 恐慌日 + advance_ratio > 0.55 贪婪日。
+"""
+import numpy as np
+import pandas as pd
+from core.scoring.config import PANIC_ADVANCE_RATIO, GREED_ADVANCE_RATIO, WINDOW_180D
+
+
+def calculate_relaxed_emotion_features(ctx) -> pd.DataFrame:
+    """计算情绪弹性特征 (4维)"""
+    kline = ctx.kline.copy()
+    mkt = ctx.market_regime.copy() if ctx.market_regime is not None else pd.DataFrame()
+
+    if kline.empty or mkt.empty:
+        return pd.DataFrame()
+
+    # 筛选恐慌日和贪婪日
+    panic_dates = set()
+    greed_dates = set()
+
+    for _, row in mkt.iterrows():
+        td = row['trade_date']
+        ar = row.get('advance_ratio', 0.5)
+        if ar < PANIC_ADVANCE_RATIO:
+            panic_dates.add(td.date() if hasattr(td, 'date') else td)
+        if ar > GREED_ADVANCE_RATIO:
+            greed_dates.add(td.date() if hasattr(td, 'date') else td)
+
+    kline = kline.sort_values(['stock_code', 'trade_date'])
+    kline['td'] = (kline['trade_date'].dt.date
+                   if hasattr(kline['trade_date'], 'dt')
+                   else pd.to_datetime(kline['trade_date']).dt.date)
+
+    candidates = ctx.candidates
+
+    # 计算全市场平均振幅 (用于恐慌/贪婪 ratio)
+    kline['amp'] = (kline['high'] - kline['low']) / np.where(
+        kline['open'] > 0, kline['open'], 1
+    )
+    market_amp = kline.groupby('td')['amp'].mean().to_dict()
+
+    features = {}
+    grouped = kline.groupby('stock_code')
+
+    for code, group in grouped:
+        if code not in candidates:
+            continue
+        if len(group) < 20:
+            continue
+
+        g = group.sort_values('trade_date').tail(WINDOW_180D)
+        closes = g['close'].values
+        opens = g['open'].values
+        highs = g['high'].values
+        lows = g['low'].values
+        dates = g['td'].values
+        amps = (highs - lows) / np.where(opens > 0, opens, 1)
+
+        feat = {}
+
+        # 恐慌日分析
+        panic_indices = [i for i, d in enumerate(dates) if d in panic_dates]
+        if panic_indices:
+            panic_amp_ratios = []
+            panic_drop_ratios = []
+            panic_rebounds = []
+
+            for pi in panic_indices:
+                td = dates[pi]
+                mkt_amp_val = market_amp.get(td, amps[pi])
+                if mkt_amp_val > 0:
+                    panic_amp_ratios.append(amps[pi] / mkt_amp_val)
+
+                # 恐慌日跌幅
+                if pi > 0 and closes[pi - 1] > 0:
+                    drop = (closes[pi] - closes[pi - 1]) / closes[pi - 1]
+                    # 全市场跌幅: 用 advance_ratio 估算
+                    ar = _get_advance_ratio(mkt, td)
+                    market_drop = -0.02 if ar < PANIC_ADVANCE_RATIO else -0.005
+                    if market_drop != 0:
+                        panic_drop_ratios.append(drop / market_drop)
+
+                # 恐慌次日反弹
+                if pi + 1 < len(g) and closes[pi] > 0:
+                    panic_rebounds.append(highs[pi + 1] / closes[pi] - 1)
+
+            # 64. relaxed_panic_amplitude_ratio
+            feat['relaxed_panic_amplitude_ratio'] = (
+                np.median(panic_amp_ratios) if panic_amp_ratios else 1.0
+            )
+
+            # 65. relaxed_panic_rebound_strength
+            feat['relaxed_panic_rebound_strength'] = (
+                np.median(panic_rebounds) if panic_rebounds else 0.0
+            )
+        else:
+            feat['relaxed_panic_amplitude_ratio'] = 1.0
+            feat['relaxed_panic_rebound_strength'] = 0.0
+
+        # 贪婪日分析
+        greed_indices = [i for i, d in enumerate(dates) if d in greed_dates]
+        if greed_indices:
+            greed_gains = []
+            greed_amp_ratios = []
+
+            for gi in greed_indices:
+                td = dates[gi]
+                if gi > 0 and closes[gi - 1] > 0:
+                    gain = (closes[gi] - closes[gi - 1]) / closes[gi - 1]
+                    # 全市场收益
+                    ar = _get_advance_ratio(mkt, td)
+                    market_gain = 0.01 if ar > GREED_ADVANCE_RATIO else 0.003
+                    if market_gain > 0:
+                        greed_gains.append(gain / market_gain)
+
+                mkt_amp_val = market_amp.get(td, amps[gi])
+                if mkt_amp_val > 0:
+                    greed_amp_ratios.append(amps[gi] / mkt_amp_val)
+
+            # 66. relaxed_greed_relative_gain
+            feat['relaxed_greed_relative_gain'] = (
+                np.median(greed_gains) if greed_gains else 0.0
+            )
+
+            # 67. relaxed_greed_amplitude_ratio
+            feat['relaxed_greed_amplitude_ratio'] = (
+                np.median(greed_amp_ratios) if greed_amp_ratios else 1.0
+            )
+        else:
+            feat['relaxed_greed_relative_gain'] = 0.0
+            feat['relaxed_greed_amplitude_ratio'] = 1.0
+
+        features[code] = feat
+
+    return pd.DataFrame.from_dict(features, orient='index')
+
+
+def _get_advance_ratio(mkt_df, trade_date):
+    """获取指定日期的 advance_ratio"""
+    if mkt_df is None or mkt_df.empty:
+        return 0.5
+    td = trade_date
+    match = mkt_df[mkt_df['trade_date'] == td]
+    if not match.empty:
+        return match.iloc[0].get('advance_ratio', 0.5)
+    return 0.5