feat(factor): tears报告方案A原生重构+「加入对比」做实(用户08-29拍板) [vps]

①后端tears序列化:sanguo_factor/tears_data.py新模块,alphalens已算分层序列(日度IC/月度聚合/十分组累计净值/多空Q10−Q1净值+最大回撤/去重叠年化/因子秩自相关)在分析时序列化为{factor}_tears.json,与tearsheet同源;FactorReport加tears_paths,GET /task/{id}/tears/{factor}端点(token header),_persist_factor同步落DB
②前端tears页:TearsPanel.vue按设计稿tab①——指标条7格+月度IC柱(红正绿负)/累计IC线双轴+月度IC热力图(年×月,CSS格)+分组累计净值Q1/Q5/Q10+多空净值(琥珀+面积+○最大回撤标注)+十分组年化(±5%虚线)+IC衰减(1/5/10D),1/5/10D全页联动;Result.vue的iframe→原生渲染;旧任务404自动回退iframe旧alphalens报告
③加入对比(设计稿tab②纯前端):factorCompare store(localStorage持久化,2~6个)+排行榜行内「＋对比/✓已选」列+详情页死按钮做实(选中青色态)+全局底部托盘CompareTray(chips可删/清空/对比N因子→)+对比页Compare.vue(指标并排·行最优青色高亮/累计IC叠加多线/月度IC序列Pearson相关性矩阵前端算/十分组小倍数SVG)

测试:tears_data纯函数5+全链真实alphalens6(合成因子IC>0/分层单调/JSON可序列化)+analyzer写盘/容错2+端点401/404/200共3;factor+api+orchestrator 317全绿;npm run build绿

tests/factor/test_analyzer.py

@@ -232,6 +232,102 @@ def test_run_factor_analysis_extracts_ic_values(tmp_path):
         assert abs(ic_data["1D"]["mean"] - 0.05) < 0.01  # Allow small rounding errors
 
 
+def test_run_factor_analysis_writes_tears_json(tmp_path):
+    """tears JSON(方案A)写盘 + FactorReport.tears_paths 记录 + factor/generated_at 补齐."""
+    import pytest
+    pytest.importorskip("polars")
+    pytest.importorskip("alphalens")
+
+    import json
+    import pandas as pd
+    from sanguo_factor.analyzer import run_factor_analysis
+
+    dates = pd.date_range("2024-01-01", periods=6, freq="D", tz="Asia/Shanghai")
+    mock_factor_data = pd.DataFrame(index=pd.MultiIndex.from_product(
+        [dates, ["AAPL"]], names=["datetime", "asset"]))
+    mock_factor_data["factor"] = [0.5] * 6
+    mock_factor_data["1D"] = [0.01] * 6
+    mock_factor_data["5D"] = [0.05] * 6
+    mock_factor_data["10D"] = [0.10] * 6
+
+    mock_pl_df = MagicMock()
+    mock_pl_df.to_pandas.return_value = pd.DataFrame({
+        "datetime": [d.isoformat() for d in dates],
+        "vt_symbol": ["AAPL"] * 6,
+        "ma5": [0.5] * 6,
+    })
+
+    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"), \
+         patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
+         patch("sanguo_factor.tears_data.build_tears_data",
+               return_value={"factor_autocorr": 0.9, "periods": {"1D": {"ic_mean": 0.1}}}) as MB, \
+         patch("sanguo_data.datareader.read_db_daily",
+               return_value=_fake_bars("AAPL", range(1, 7))):
+        MS.return_value.compute_factors.return_value = mock_pl_df
+        MC.return_value = mock_factor_data
+        MIC.return_value = pd.DataFrame(
+            {"1D": [0.05, 0.04, 0.06, 0.05, 0.04, 0.06]}, index=dates)
+
+        report = run_factor_analysis(
+            ["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
+            cfg=MagicMock(), output_dir=str(tmp_path)
+        )
+        MB.assert_called_once()
+        assert "ma5" in report.tears_paths
+        assert report.tears_paths["ma5"].endswith("ma5_tears.json")
+        assert os.path.exists(report.tears_paths["ma5"])
+        with open(report.tears_paths["ma5"], encoding="utf-8") as f:
+            data = json.load(f)
+        assert data["factor"] == "ma5"
+        assert data["periods"]["1D"]["ic_mean"] == 0.1
+        assert data["generated_at"]
+
+
+def test_run_factor_analysis_tears_json_error_not_fatal(tmp_path):
+    """build_tears_data 抛错 → 只标注 tears_json_error,IC 结果保留."""
+    import pytest
+    pytest.importorskip("polars")
+    pytest.importorskip("alphalens")
+
+    import pandas as pd
+    from sanguo_factor.analyzer import run_factor_analysis
+
+    dates = pd.date_range("2024-01-01", periods=4, freq="D", tz="Asia/Shanghai")
+    mock_factor_data = pd.DataFrame(index=pd.MultiIndex.from_product(
+        [dates, ["AAPL"]], names=["datetime", "asset"]))
+    for c in ("factor", "1D", "5D", "10D"):
+        mock_factor_data[c] = [0.5] * 4
+
+    mock_pl_df = MagicMock()
+    mock_pl_df.to_pandas.return_value = pd.DataFrame({
+        "datetime": [d.isoformat() for d in dates],
+        "vt_symbol": ["AAPL"] * 4,
+        "ma5": [0.5] * 4,
+    })
+
+    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"), \
+         patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
+         patch("sanguo_factor.tears_data.build_tears_data",
+               side_effect=RuntimeError("boom")), \
+         patch("sanguo_data.datareader.read_db_daily",
+               return_value=_fake_bars("AAPL", range(1, 5))):
+        MS.return_value.compute_factors.return_value = mock_pl_df
+        MC.return_value = mock_factor_data
+        MIC.return_value = pd.DataFrame({"1D": [0.05, 0.04, 0.06, 0.05]}, index=dates)
+
+        report = run_factor_analysis(
+            ["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
+            cfg=MagicMock(), output_dir=str(tmp_path)
+        )
+        assert report.ic_summary["ma5"]["status"] == "success"
+        assert "tears_json_error" in report.ic_summary["ma5"]
+        assert report.tears_paths == {}
+
+
 def test_run_factor_analysis_ic_extraction_fails_gracefully(tmp_path):
     """Test that IC extraction failures don't crash the pipeline.