feat(polish): 因子报告 IC 提取 + periods 提参 + 缓存上限 + 代码整洁

- analyzer.py: 提取 IC 值到 ic_summary (mean/std/icir/t_stat)，periods 提参 (默认 1,5,10)
- alpha_lab.py: _loaded_bars 缓存 LRU 上限 (_MAX_CACHED_SYMBOLS=50)
- runner.py: 统一阶段文案 (参数优化中/因子分析中)，worker 类型标注，_wait_future 文档
- pool.py: submit_work 添加 task_id debug 日志

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

tests/factor/test_analyzer.py

@@ -100,7 +100,13 @@ def test_run_factor_analysis_adds_features():
 
 
 def test_run_factor_analysis_calls_tears(tmp_path):
-    """Test that run_factor_analysis calls alphalens tears pipeline."""
+    """Test that run_factor_analysis calls alphalens tears pipeline.
+
+    Requires polars - runs in container, skips locally.
+    """
+    import pytest
+    pytest.importorskip("polars")
+
     from pathlib import Path
     from sanguo_factor.analyzer import run_factor_analysis
 
@@ -121,3 +127,130 @@ def test_run_factor_analysis_calls_tears(tmp_path):
         assert report.factor_names == ["ma5"]
         MC.assert_called_once()
         MT.assert_called_once()
+
+
+def test_run_factor_analysis_extracts_ic_values(tmp_path):
+    """Test that run_factor_analysis extracts IC values from alphalens.
+
+    Requires polars/alphalens - runs in container, skips locally.
+    """
+    import pytest
+    pytest.importorskip("polars")
+    pytest.importorskip("alphalens")
+
+    import pandas as pd
+    from datetime import datetime
+    from sanguo_factor.analyzer import run_factor_analysis
+
+    # Create mock factor_data with MultiIndex (datetime, asset) and IC columns
+    dates = pd.date_range("2024-01-01", periods=10, freq="D")
+    assets = ["AAPL", "GOOGL"]
+    index = pd.MultiIndex.from_product([dates, assets], names=["datetime", "asset"])
+
+    # Mock factor_data with forward returns
+    mock_factor_data = pd.DataFrame(index=index)
+    mock_factor_data["factor"] = [0.5] * 20  # Factor values
+    mock_factor_data["1D"] = [0.01] * 20      # 1-day forward returns
+    mock_factor_data["5D"] = [0.05] * 20      # 5-day forward returns
+    mock_factor_data["10D"] = [0.10] * 20    # 10-day forward returns
+
+    # Mock IC DataFrame returned by factor_information_coefficient
+    mock_ic_df = pd.DataFrame({
+        "1D": [0.05, 0.03, 0.07, 0.04, 0.06, 0.05, 0.04, 0.06, 0.05, 0.04],
+        "5D": [0.08, 0.06, 0.09, 0.07, 0.08, 0.07, 0.08, 0.06, 0.07, 0.08],
+        "10D": [0.12, 0.10, 0.13, 0.11, 0.12, 0.11, 0.12, 0.10, 0.11, 0.12]
+    }, index=dates)
+
+    # Mock polars DataFrame
+    mock_pl_df = MagicMock()
+    mock_pl_df.to_pandas.return_value = pd.DataFrame({
+        "datetime": [d.isoformat() for d in dates for _ in assets],
+        "vt_symbol": assets * len(dates),
+        "ma5": [0.5] * 20,
+        "close": [100.0] * 20
+    })
+
+    with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
+         patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
+         patch("sanguo_factor.analyzer.create_full_tear_sheet") as MT, \
+         patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC:
+
+        # Mock compute_factors to return polars DataFrame
+        MS.return_value.compute_factors.return_value = mock_pl_df
+
+        # Mock get_clean_factor_and_forward_returns to return our factor_data
+        MC.return_value = mock_factor_data
+
+        # Mock IC function to return our IC DataFrame
+        MIC.return_value = mock_ic_df
+
+        report = run_factor_analysis(
+            ["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
+            cfg=MagicMock(), output_dir=str(tmp_path)
+        )
+
+        # Verify IC values were extracted
+        assert "ma5" in report.ic_summary
+        assert "ic" in report.ic_summary["ma5"]
+
+        # Check IC structure contains expected periods
+        ic_data = report.ic_summary["ma5"]["ic"]
+        assert "1D" in ic_data
+        assert "5D" in ic_data
+        assert "10D" in ic_data
+
+        # Verify IC statistics are computed
+        assert "mean" in ic_data["1D"]
+        assert "icir" in ic_data["1D"]
+        assert "std" in ic_data["1D"]
+
+        # Verify approximate values (mean should be around 0.05 for 1D)
+        assert abs(ic_data["1D"]["mean"] - 0.05) < 0.01  # Allow small rounding errors
+
+
+def test_run_factor_analysis_ic_extraction_fails_gracefully(tmp_path):
+    """Test that IC extraction failures don't crash the pipeline.
+
+    Requires polars/alphalens - runs in container, skips locally.
+    """
+    import pytest
+    pytest.importorskip("polars")
+    pytest.importorskip("alphalens")
+
+    from sanguo_factor.analyzer import run_factor_analysis
+    import pandas as pd
+
+    # Mock polars DataFrame
+    mock_pl_df = MagicMock()
+    mock_pl_df.to_pandas.return_value = pd.DataFrame({
+        "datetime": ["2024-01-01"],
+        "vt_symbol": ["AAPL"],
+        "ma5": [0.5],
+        "close": [100.0]
+    })
+
+    with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
+         patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
+         patch("sanguo_factor.analyzer.create_full_tear_sheet") as MT, \
+         patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC:
+
+        MS.return_value.compute_factors.return_value = mock_pl_df
+
+        # Mock get_clean_factor_and_forward_returns to return valid data
+        mock_factor_data = MagicMock()
+        MC.return_value = mock_factor_data
+
+        # Mock IC function to raise an exception
+        MIC.side_effect = Exception("IC calculation failed")
+
+        report = run_factor_analysis(
+            ["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
+            cfg=MagicMock(), output_dir=str(tmp_path)
+        )
+
+        # Verify IC error is captured but status/report still exist
+        assert "ma5" in report.ic_summary
+        assert "ic" in report.ic_summary["ma5"]
+        assert "error" in report.ic_summary["ma5"]["ic"]
+        # Status and report should still be present
+        assert "status" in report.ic_summary["ma5"]