haha

strategy/feature_engine.py

@@ -19,13 +19,14 @@ def _add_intra15m_1m_pullback_features(df: pd.DataFrame, primary_df: pd.DataFram
     """
     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
+    # 每根 1m K 线归属到其所在 15m 的起始时间（数据库 15m id 与 bar 起始时刻对齐）
     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'))
+    one_min['15m_start'] = one_min.index.floor('15min')
+    agg = one_min.groupby('15m_start').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
+    # 仅前向填充，避免使用未来数据回填到过去
+    agg = agg.ffill()
     h15 = primary_df['high'].values
     l15 = primary_df['low'].values
     c15 = primary_df['close'].values
@@ -126,10 +127,10 @@ def generate_labels(df: pd.DataFrame, forward_periods: int = None,
 
 
 def prepare_dataset(period: int = None, start_date: str = None, end_date: str = None,
-                    has_volume: bool = False) -> tuple:
+                    has_volume: bool = False, normalize: bool = None) -> tuple:
     """
     一键准备训练数据集
-    :return: (X, y, feature_names) — 已去NaN、已标准化
+    :return: (X, y, feature_names, scaler) — 已去NaN；是否标准化由 normalize 决定
     """
     if period is None:
         period = PRIMARY_PERIOD
@@ -193,9 +194,11 @@ def prepare_dataset(period: int = None, start_date: str = None, end_date: str =
     X = df[feature_cols].copy()
     y = df['label'].astype(int)
 
-    # 标准化
+    # 标准化（可选）
     scaler = None
-    if FC['normalize']:
+    if normalize is None:
+        normalize = FC['normalize']
+    if normalize:
         scaler = StandardScaler()
         X = pd.DataFrame(scaler.fit_transform(X), columns=feature_cols, index=X.index)
         logger.info("特征已标准化")
@@ -228,12 +231,14 @@ def get_latest_feature_row(period: int = None, feature_cols: list = None,
         except Exception:
             pass
     df = build_features(primary_df, aux_dfs, has_volume=False)
-    labels = generate_labels(df)
-    df = df.copy()
-    df['label'] = labels
-    df.dropna(inplace=True)
     missing = [c for c in feature_cols if c not in df.columns]
     if missing:
         logger.warning(f"get_latest_feature_row: 缺少特征列 {missing[:5]}{'...' if len(missing) > 5 else ''}")
         return pd.DataFrame()
-    return df[feature_cols].iloc[-1:].copy()
+    X = df[feature_cols].copy()
+    X.replace([np.inf, -np.inf], np.nan, inplace=True)
+    X.dropna(inplace=True)
+    if X.empty:
+        logger.warning("get_latest_feature_row: 可用于预测的特征为空（数据不足或存在NaN）")
+        return pd.DataFrame()
+    return X.iloc[-1:].copy()