haha

strategy/feature_engine.py

@@ -11,6 +11,37 @@ from .data_loader import load_kline
 from .indicators import compute_all_indicators, get_indicator_names
 
 
+def _add_intra15m_1m_pullback_features(df: pd.DataFrame, primary_df: pd.DataFrame,
+                                        one_min_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    用 1 分钟数据在每根 15m K 线内的最高/最低，计算「冲高回落」「探底回升」程度，
+    便于模型识别 15 分钟涨到一半又回调的情况。
+    """
+    if one_min_df.empty or 'high' not in one_min_df.columns or 'low' not in one_min_df.columns:
+        return df
+    # 每根 1m K 线归属到其所在 15m 的结束时间
+    one_min = one_min_df[['high', 'low']].copy()
+    one_min['15m_end'] = one_min.index.ceil('15min')
+    agg = one_min.groupby('15m_end').agg(intra_high_1m=('high', 'max'), intra_low_1m=('low', 'min'))
+    # 对齐到主周期索引
+    agg = agg.reindex(primary_df.index)
+    agg = agg.ffill().bfill()  # 边界缺失时前后填充
+    h15 = primary_df['high'].values
+    l15 = primary_df['low'].values
+    c15 = primary_df['close'].values
+    range_15 = h15 - l15
+    range_15 = np.where(range_15 <= 0, np.nan, range_15)
+    pullback = (agg['intra_high_1m'].values - c15) / range_15  # 收盘相对 15m 内 1m 最高回落比例
+    recovery = (c15 - agg['intra_low_1m'].values) / range_15  # 收盘相对 15m 内 1m 最低回升比例
+    pullback = np.clip(np.nan_to_num(pullback, nan=0), 0, 1)
+    recovery = np.clip(np.nan_to_num(recovery, nan=0), 0, 1)
+    df = df.copy()
+    df['pullback_ratio_1m'] = pullback
+    df['recovery_ratio_1m'] = recovery
+    logger.info("已加入 1m 周期内冲高回落特征: pullback_ratio_1m, recovery_ratio_1m")
+    return df
+
+
 def build_features(primary_df: pd.DataFrame,
                    aux_dfs: dict = None,
                    has_volume: bool = False) -> pd.DataFrame:
@@ -51,6 +82,10 @@ def build_features(primary_df: pd.DataFrame,
         if aux_frames:
             df = pd.concat([df] + aux_frames, axis=1)
 
+    # 3.5 1 分钟周期内「冲高回落」特征（15m 内 1m 最高/最低 vs 15m 收盘，用于判断涨到一半又回调）
+    if aux_dfs and 1 in aux_dfs:
+        df = _add_intra15m_1m_pullback_features(df, primary_df, aux_dfs[1])
+
     # 4. 去除全NaN列
     before_cols = len(df.columns)
     df.dropna(axis=1, how='all', inplace=True)