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>
2026-07-06 19:14:12 +08:00
parent efaac41c06
commit 8972d1058f
2 changed files with 143 additions and 5 deletions
@@ -35,3 +35,92 @@ def test_load_symbols_calls_read_db_daily():
            session = AlphaLabSession(lab_path=lab_path)
            # Verify load_symbols method exists
            assert hasattr(session, "load_symbols")
+
+
+def test_compute_factors_calls_prepare_and_fetch(tmp_path):
+    """Test compute_factors calls AlphaDataset methods correctly."""
+    from pathlib import Path
+    from datetime import datetime
+    from unittest.mock import MagicMock
+    from sanguo_factor.alpha_lab import AlphaLabSession
+    import polars as pl
+
+    # Create mock bar data
+    mock_bar = MagicMock()
+    mock_bar.vt_symbol = "600000.SSE"
+    mock_bar.datetime = datetime(2024, 1, 1)
+    mock_bar.open_price = 1.0
+    mock_bar.high_price = 1.0
+    mock_bar.low_price = 1.0
+    mock_bar.close_price = 1.0
+    mock_bar.volume = 1
+    mock_bar.turnover = 0
+    mock_bar.open_interest = 0
+
+    # Mock AlphaLab session initialization
+    with patch("vnpy.alpha.lab.AlphaLab"), \
+         patch("sanguo_data.datareader.read_db_daily") as mock_read, \
+         patch("sanguo_factor.data_adapter.save_alpha_lab_data"), \
+         patch("sanguo_factor.data_adapter.convert_bars_to_alpha_df") as mock_convert, \
+         patch("sanguo_factor.registry.get_factor") as mock_get_factor, \
+         patch("vnpy.alpha.dataset.AlphaDataset") as mock_dataset_class:
+
+        # Setup mock returns
+        mock_read.return_value = [mock_bar]
+        mock_get_factor.return_value = {"expression": "ts_mean(close,5)"}
+
+        # Create mock AlphaDataset instance
+        mock_dataset = MagicMock()
+        mock_dataset.fetch_raw.return_value = pl.DataFrame({
+            "datetime": [],
+            "vt_symbol": [],
+            "ma5": []
+        })
+        mock_dataset_class.return_value = mock_dataset
+
+        # Create DataFrame for convert_bars_to_alpha_df
+        mock_df = pl.DataFrame({
+            "vt_symbol": ["600000.SSE"],
+            "datetime": [datetime(2024, 1, 1)],
+            "open": [1.0],
+            "high": [1.0],
+            "low": [1.0],
+            "close": [1.0],
+            "volume": [1.0],
+            "turnover": [0.0],
+            "open_interest": [0.0]
+        })
+        mock_convert.return_value = mock_df
+
+        # Create session and load symbols
+        lab_path = str(tmp_path / "alpha_lab")
+        session = AlphaLabSession(lab_path=lab_path)
+        session.load_symbols(["600000"], "2024-01-01", "2024-06-30", cfg=MagicMock())
+
+        # Compute factors
+        df = session.compute_factors(
+            ["ma5"],
+            ("2024-01-01", "2024-04-30"),
+            ("2024-05-01", "2024-05-15"),
+            ("2024-05-16", "2024-06-30")
+        )
+
+        # Verify AlphaDataset was created with correct periods
+        mock_dataset_class.assert_called_once_with(
+            mock_df,
+            ("2024-01-01", "2024-04-30"),
+            ("2024-05-01", "2024-05-15"),
+            ("2024-05-16", "2024-06-30")
+        )
+
+        # Verify add_feature was called for the factor
+        mock_dataset.add_feature.assert_called_once_with("ma5", "ts_mean(close,5)")
+
+        # Verify prepare_data was called
+        mock_dataset.prepare_data.assert_called_once_with(max_workers=1)
+
+        # Verify fetch_raw was called
+        assert mock_dataset.fetch_raw.called
+
+        # Verify return value is a DataFrame
+        assert isinstance(df, pl.DataFrame)