test(factor): test_analyzer三个never-ran测试修复——补read_db_daily假bars(analyzer真读库建prices,缺bars触发DBG空守卫短路tears/IC)+factor日期对齐生产aware口径(Asia/Shanghai);此批测试原在任何环境都没跑过(Mac缺依赖skip/容器缺pytest/CI只跑data_platform),Mac补齐lock依赖后暴露;全量900绿 [nas]

tests/factor/test_analyzer.py

@@ -6,6 +6,22 @@ sys.path.insert(0, _VNPY_SRC)
 
 from unittest.mock import Mock, patch, MagicMock
 import tempfile
+from datetime import datetime
+from types import SimpleNamespace
+from zoneinfo import ZoneInfo
+
+_SH = ZoneInfo("Asia/Shanghai")
+
+
+def _fake_bars(symbol, days, price=100.0):
+    """read_db_daily 假 bars。analyzer 会单独调 read_db_daily 取 close 建 prices，
+    且 prices 日期须与 factor 日期对齐(aware, Asia/Shanghai)——不喂 bars 会触发
+    DBG empty 守卫直接 continue，tears/IC 永不被调到(2026-08-15 前这批测试在
+    任何环境都没跑过：Mac 缺依赖 skip/容器缺 pytest/CI 只跑 data_platform)。"""
+    return [
+        SimpleNamespace(datetime=datetime(2024, 1, d), vt_symbol=symbol, close_price=price)
+        for d in days
+    ]
 
 
 def test_run_factor_analysis_returns_report():
@@ -113,12 +129,17 @@ def test_run_factor_analysis_calls_tears(tmp_path):
 
     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.create_full_tear_sheet") as MT, \
+         patch("sanguo_data.datareader.read_db_daily",
+               return_value=_fake_bars("600000", (2, 3, 4, 5, 8))):
         import polars as pl
+        # 非空 + aware 日期(生产 compute_factors 会 localize;naive 会被 prices 对齐
+        # isin 过滤成空触发 DBG 守卫)
+        _days = (2, 3, 4, 5, 8)
         MS.return_value.compute_factors.return_value = pl.DataFrame({
-            "datetime": [],
-            "vt_symbol": [],
-            "ma5": []
+            "datetime": [datetime(2024, 1, d, tzinfo=_SH) for d in _days],
+            "vt_symbol": ["600000"] * len(_days),
+            "ma5": [0.5] * len(_days),
         })
         MC.return_value = MagicMock()
         report = run_factor_analysis(
@@ -144,7 +165,7 @@ def test_run_factor_analysis_extracts_ic_values(tmp_path):
     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")
+    dates = pd.date_range("2024-01-01", periods=10, freq="D", tz="Asia/Shanghai")
     assets = ["AAPL", "GOOGL"]
     index = pd.MultiIndex.from_product([dates, assets], names=["datetime", "asset"])
 
@@ -162,7 +183,7 @@ def test_run_factor_analysis_extracts_ic_values(tmp_path):
         "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 polars DataFrame（datetime 用带时区 ISO 串，与 _fake_bars localize 后对齐）
     mock_pl_df = MagicMock()
     mock_pl_df.to_pandas.return_value = pd.DataFrame({
         "datetime": [d.isoformat() for d in dates for _ in assets],
@@ -174,7 +195,9 @@ def test_run_factor_analysis_extracts_ic_values(tmp_path):
     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:
+         patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
+         patch("sanguo_data.datareader.read_db_daily",
+               return_value=_fake_bars("AAPL", range(1, 11)) + _fake_bars("GOOGL", range(1, 11))):
 
         # Mock compute_factors to return polars DataFrame
         MS.return_value.compute_factors.return_value = mock_pl_df
@@ -221,10 +244,10 @@ def test_run_factor_analysis_ic_extraction_fails_gracefully(tmp_path):
     from sanguo_factor.analyzer import run_factor_analysis
     import pandas as pd
 
-    # Mock polars DataFrame
+    # Mock polars DataFrame（datetime 带时区，与 _fake_bars 对齐防 DBG 空守卫）
     mock_pl_df = MagicMock()
     mock_pl_df.to_pandas.return_value = pd.DataFrame({
-        "datetime": ["2024-01-01"],
+        "datetime": ["2024-01-01T00:00:00+08:00"],
         "vt_symbol": ["AAPL"],
         "ma5": [0.5],
         "close": [100.0]
@@ -233,7 +256,9 @@ def test_run_factor_analysis_ic_extraction_fails_gracefully(tmp_path):
     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:
+         patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
+         patch("sanguo_data.datareader.read_db_daily",
+               return_value=_fake_bars("AAPL", (1, 2))):
 
         MS.return_value.compute_factors.return_value = mock_pl_df