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
@@ -0,0 +1,84 @@
+"""
+负向指标 (5维) — calculate_negative_features(df)
+注意: #53 trend_consistency_20d 与 #39 同名不同义，更新字典时会覆盖 #39
+"""
+import numpy as np
+import pandas as pd
+
+
+def calculate_negative_features(ctx) -> pd.DataFrame:
+    """计算负向指标 (5维)"""
+    kline = ctx.kline.copy()
+    if kline.empty:
+        return pd.DataFrame()
+
+    kline = kline.sort_values(['stock_code', 'trade_date'])
+    candidates = ctx.candidates
+
+    features = {}
+    grouped = kline.groupby('stock_code')
+
+    for code, group in grouped:
+        if code not in candidates:
+            continue
+        g = group.tail(120)
+        if len(g) < 20:
+            continue
+
+        closes = g['close'].values
+        opens = g['open'].values
+        highs = g['high'].values
+        lows = g['low'].values
+        volumes = g['volume'].values
+
+        feat = {}
+
+        # 53. trend_consistency_20d (负向版本): |mean(return>0) - 0.5| × 100
+        # 衡量偏离均衡的程度，越接近50%越弱
+        rets_20 = np.diff(closes[-21:]) / np.where(closes[-21:-1] > 0, closes[-21:-1], 1)
+        feat['trend_consistency_20d'] = abs(np.mean(rets_20 > 0) - 0.5) * 100
+
+        # 54. max_consecutive_direction_20d: 最大连续同向天数
+        rets_sign = np.sign(np.diff(closes[-21:]))
+        max_consec = 0
+        curr_consec = 0
+        curr_sign = 0
+        for s in rets_sign:
+            if s != 0 and s == curr_sign:
+                curr_consec += 1
+            elif s != 0:
+                curr_sign = s
+                curr_consec = 1
+            else:
+                curr_consec = 0
+            max_consec = max(max_consec, curr_consec)
+        feat['max_consecutive_direction_20d'] = max_consec
+
+        # 55. gap_risk_20d: mean(|open_t - close_{t-1}|/close_{t-1} > 0.02) × 100
+        gap_count = 0
+        n = 0
+        for i in range(max(0, len(g) - 20), len(g)):
+            if i > 0 and closes[i - 1] > 0:
+                gap_pct = abs(opens[i] - closes[i - 1]) / closes[i - 1]
+                if gap_pct > 0.02:
+                    gap_count += 1
+                n += 1
+        feat['gap_risk_20d'] = gap_count / n * 100 if n > 0 else 0
+
+        # 56. liquidity_drying_up_20d: min(vol_20d)/mean(vol_60d)
+        vol_20 = volumes[-20:]
+        vol_60 = volumes[-60:] if len(volumes) >= 60 else volumes
+        feat['liquidity_drying_up_20d'] = (
+            np.min(vol_20) / np.mean(vol_60) if np.mean(vol_60) > 0 else 1
+        )
+
+        # 57. price_stagnation_20d: (max(high_20d)-min(low_20d))/close × 100
+        h20 = np.max(highs[-20:])
+        l20 = np.min(lows[-20:])
+        feat['price_stagnation_20d'] = (
+            (h20 - l20) / closes[-1] * 100 if closes[-1] > 0 else 0
+        )
+
+        features[code] = feat
+
+    return pd.DataFrame.from_dict(features, orient='index')