fix(factor): WARMUP_BARS 60→120 恢复spec剔新口径(修夹具130根预热而非改口径)+evaluation_filter改datetime对象比较(9M行免逐行strftime) [vps]

tests/factor/test_universe.py

@@ -32,10 +32,12 @@ def _row(sym, ex, day, close, volume=100.0, turnover=None):
 
 @pytest.fixture()
 def db(tmp_path):
+    from datetime import date, timedelta
     rows = []
-    # 600000:60根(2017-10~2017-12) + 评估窗内 3 根
-    for i in range(60):
-        rows.append(_row("600000", "SSE", f"2017-10-{(i % 28) + 1:02d}", 10.0 + i * 0.01))
+    # 600000:130根预热(2017-08~2017-12,唯一日期) + 评估窗内 3 根
+    for i in range(130):
+        day = (date(2017, 8, 1) + timedelta(days=i)).isoformat()
+        rows.append(_row("600000", "SSE", day, 10.0 + i * 0.01))
     for d in ("2018-01-02", "2018-01-03", "2018-01-04"):
         rows.append(_row("600000", "SSE", d, 11.0))
     # 000001:只有 2 根(次新,bar_idx<WARMUP 应被 evaluation_filter 排除)
@@ -58,8 +60,8 @@ def test_load_filters_prefix_and_interval(db):
 
 def test_load_lookback_buffer_and_vwap(db):
     df = load_universe_bars(db, "2018-01-01", "2018-01-31")
-    # lookback 生效:2017-10 的 bar 也在(因子窗口预热需要)
-    assert str(df["datetime"].min()).startswith("2017-10")
+    # lookback 生效:2017-08 的 bar 也在(因子窗口预热需要)
+    assert str(df["datetime"].min()).startswith("2017-08")
     # vwap = turnover/volume
     row = df.filter(df["vt_symbol"] == "600000.SSE").sort("datetime").row(0, named=True)
     assert row["vwap"] == pytest.approx(10.0)