feat(factor): alpha_lab compute_factors 完整化

添加 compute_factors 方法到 AlphaLabSession：
- 在 __init__ 添加 _loaded_symbols 和 _loaded_bars 缓存
- load_symbols 现在缓存 bar 数据供 compute_factors 使用
- compute_factors 使用缓存的 bars 调用 AlphaDataset
- 使用懒导入避免本地 Python 3.14 缺少 polars/vnpy.alpha 的 ImportError

测试 (container only):
- test_compute_factors_calls_prepare_and_fetch 验证 AlphaDataset 调用

Co-Authored-By: Claude <noreply@anthropic.com>

sanguo_factor/alpha_lab.py

@@ -8,23 +8,25 @@ if _VNPY_SRC not in sys.path:
 
 class AlphaLabSession:
     """Session manager for vnpy.alpha AlphaLab operations."""
-    
+
     def __init__(self, lab_path: str):
         """
         Initialize AlphaLab session.
-        
+
         Args:
             lab_path: Path to AlphaLab directory
         """
         from vnpy.alpha.lab import AlphaLab
-        
+
         self.lab_path = lab_path
         self.lab = AlphaLab(lab_path)
+        self._loaded_symbols: list[str] = []
+        self._loaded_bars: dict[str, list] = {}
     
     def load_symbols(self, symbols: list[str], start: str, end: str, cfg) -> None:
         """
         Load symbol data from database and save to AlphaLab.
-        
+
         Args:
             symbols: List of vt_symbols to load
             start: Start date (YYYY-MM-DD)
@@ -33,8 +35,55 @@ class AlphaLabSession:
         """
         from sanguo_data.datareader import read_db_daily
         from .data_adapter import save_alpha_lab_data
-        
+
         for symbol in symbols:
             bars = read_db_daily(symbol, start, end, cfg)
             if bars:
                 save_alpha_lab_data(bars, self.lab_path)
+                # Cache bars for compute_factors
+                if symbol not in self._loaded_symbols:
+                    self._loaded_symbols.append(symbol)
+                self._loaded_bars[symbol] = bars
+
+    def compute_factors(self, factor_names: list[str], train_period: tuple, valid_period: tuple, test_period: tuple):
+        """
+        Compute factors using cached bars and vnpy.alpha AlphaDataset.
+
+        Args:
+            factor_names: List of factor names to compute
+            train_period: Training period tuple (start, end)
+            valid_period: Validation period tuple (start, end)
+            test_period: Test period tuple (start, end)
+
+        Returns:
+            polars DataFrame with computed factors for test period
+        """
+        # Lazy imports to avoid ImportError on local Python 3.14 without polars/vnpy.alpha
+        import polars as pl
+        from vnpy.alpha.dataset import AlphaDataset, Segment
+        from .registry import get_factor
+        from .data_adapter import convert_bars_to_alpha_df
+
+        # Gather all cached bars across loaded symbols
+        all_bars = []
+        for symbol in self._loaded_symbols:
+            all_bars.extend(self._loaded_bars.get(symbol, []))
+
+        # Convert bars to AlphaLab DataFrame format
+        df = convert_bars_to_alpha_df(all_bars)
+
+        # Create AlphaDataset with the specified periods
+        ds = AlphaDataset(df, train_period, valid_period, test_period)
+
+        # Add each factor to the dataset
+        for name in factor_names:
+            factor = get_factor(name)
+            if factor is None:
+                continue  # Skip unknown factors
+            ds.add_feature(name, factor["expression"])
+
+        # Prepare data (compute features)
+        ds.prepare_data(max_workers=1)
+
+        # Return test period data
+        return ds.fetch_raw(Segment.TEST)