fix(factor): 因子分析端到端修复(依赖缺失+vt_symbol+IC/tears解耦)

根因链:
1. alphalens + scikit-learn 没装 → analyzer:76 软错误 return {error:alphalens not installed}(已 pip 装 alphalens-reloaded 0.4.6 + scikit-learn 1.7.2,requirements-lock 已锁)。
2. symbols 传 vt_symbol(600000.SSE)但 read_db_daily 查 DB 用裸代码 → bars 空 → factor/prices 全空 → alphalens infer_trading_calendar weekmask 全零 → busdaycal 崩。
3. statistics 含 date/numpy(之前 optimize 已修 result_store _json_default)。
4. alphalens-reloaded × pandas2 的 groupby.transform 兼容:demean_forward_returns tears 崩 No objects to concatenate。

适配修复(不动 vnpy/alphalens 源码):
- symbols 归一:vt_symbol → 裸代码(split '.'),read_db_daily 能查到。
- IC/tears 解耦:IC 算完先存 ic_summary success,tears 独立 try(失败标 tears_error 不覆盖 IC)——IC 核心数值可用,tears 报告作为已知限制。
- error 加 traceback 字段(调试友好)。
验证: 3 symbol(600000/000001/600519) ma5 因子 IC 成功(1D/5D/10D mean/icir/t_stat count=49),浏览器 /factor/result 页表格渲染。单 symbol IC NaN 是截面用法(需≥2标的),非 bug。

sanguo_factor/analyzer.py

@@ -2,6 +2,7 @@
 import sys
 import os
 import warnings
+import traceback
 _VNPY_SRC = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "vnpy_v4.4.0"))
 if _VNPY_SRC not in sys.path:
     sys.path.insert(0, _VNPY_SRC)
@@ -66,6 +67,11 @@ def run_factor_analysis(
     """
     from .registry import get_factor
 
+    # symbols 兼容:前端/vnpy 可能传 vt_symbol("600000.SSE"),read_db_daily 查 DB 的 key
+    # 是裸代码(同 cta_engine),带后缀查不到 → bars 空 → factor/prices 全空 → alphalens 崩。
+    # 统一归一成裸代码(DB key)。
+    symbols = [str(s).split(".")[0] for s in symbols]
+
     # API path passes cfg=None → load default data_platform.yaml (so read_db_daily
     # and AlphaLabSession can find the A-share DB).
     if cfg is None:
@@ -203,6 +209,18 @@ def run_factor_analysis(
             # Ensure datetime index for prices
             prices_df.index = pd.to_datetime(prices_df.index)
 
+            # busdaycal 调试:定位 factor/prices 数据是否空或日期不对齐
+            if prices_df.empty or len(factor_dates) == 0:
+                fmin = factor_pd['datetime'].min() if len(factor_pd) else None
+                fmax = factor_pd['datetime'].max() if len(factor_pd) else None
+                cmin = close_pd['datetime'].min() if len(close_pd) else None
+                cmax = close_pd['datetime'].max() if len(close_pd) else None
+                ic_summary[factor_name] = {
+                    "status": "error",
+                    "error": f"DBG empty: factor_df.height={factor_df.height}, factor_dates={len(factor_dates)}, factor_series={len(factor_series)}, close_rows={len(close_pd)}, prices_df={prices_df.shape}, factor_dt={fmin}~{fmax}, close_dt={cmin}~{cmax}",
+                }
+                continue
+
             # Call get_clean_factor_and_forward_returns
             merged_data = get_clean_factor_and_forward_returns(
                 factor=factor_series,
@@ -248,12 +266,18 @@ def run_factor_analysis(
                 # Capture IC extraction error but continue with tears report
                 ic_data = {"error": f"IC extraction failed: {type(ic_error).__name__}: {ic_error}"}
 
-            # Generate tears sheet
+            # IC 已算完(ic_data)——先存成功。因子分析的核心指标(IC/ICIR/t_stat)可用,
+            # 即使下面的 tears 报告因 alphalens-reloaded × pandas2 的 groupby.transform
+            # 兼容问题崩,也不影响 IC 数值。
+            ic_summary[factor_name] = {
+                "status": "success",
+                "ic": ic_data,
+            }
+
+            # Generate tears sheet（独立 try：tears 失败只标注，不覆盖上面的 IC 成功）
             from io import StringIO
-            import sys
             old_stdout = sys.stdout
             sys.stdout = StringIO()  # Capture stdout to avoid display issues
-
             try:
                 create_full_tear_sheet(
                     merged_data,
@@ -261,27 +285,21 @@ def run_factor_analysis(
                     group_neutral=False,
                     by_group=False
                 )
+                factor_report_path = os.path.join(output_dir, f"{factor_name}_tears.html")
+                plt.savefig(factor_report_path.replace(".html", ".png"))  # Save as PNG
+                report_paths[factor_name] = factor_report_path.replace(".png", ".html")
+                ic_summary[factor_name]["report"] = factor_report_path
+            except Exception as tears_e:
+                ic_summary[factor_name]["tears_error"] = f"{type(tears_e).__name__}: {tears_e}"
             finally:
                 sys.stdout = old_stdout  # Restore stdout
 
-            # Save the tears report
-            factor_report_path = os.path.join(output_dir, f"{factor_name}_tears.html")
-            plt.savefig(factor_report_path.replace(".html", ".png"))  # Save as PNG
-            report_paths[factor_name] = factor_report_path.replace(".png", ".html")  # Mark HTML as report
-
-            # Store basic IC summary (simplified) — close prices sourced from DB (real)
-            status = "success"
-            ic_summary[factor_name] = {
-                "status": status,
-                "report": factor_report_path,
-                "ic": ic_data  # Add IC statistics
-            }
-
         except Exception as e:
             err_type = type(e).__name__
             ic_summary[factor_name] = {
                 "status": "error",
-                "error": f"{err_type}: {e}"
+                "error": f"{err_type}: {e}",
+                "traceback": traceback.format_exc()
             }
 
     return FactorReport(