feat(factor): 批量评估引擎——进程内逐因子calculate_by_expression(弃spawn池整df pickle),单因子失败不中断,结果增量落盘 [vps]

2026-08-25 00:21:54 +08:00
parent afe969e307
commit 3f9a4fc681
2 changed files with 198 additions and 0 deletions
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+# tests/factor/test_batch_eval.py
+"""批量引擎端到端:合成小库 → 2因子计算 → 指标落盘可查."""
+import sqlite3
+import sys, os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))
+
+import pytest
+
+from sanguo_factor import alpha_datasets  # 挂载
+from sanguo_factor.batch_eval import run_batch_eval
+from sanguo_factor import eval_store
+
+_DDL = """
+CREATE TABLE dbbardata(
+  symbol TEXT, exchange TEXT, datetime TEXT, interval TEXT,
+  volume REAL, turnover REAL, open_interest REAL,
+  open_price REAL, high_price REAL, low_price REAL, close_price REAL)
+"""
+
+
+@pytest.fixture(scope="module")
+def db(tmp_path_factory):
+    """3 只 × ~400 交易日(2017-06~2018-12)合成库:一只单调涨/一只震荡/一只反着走."""
+    # Mount alpha datasets first to ensure factors are available
+    alpha_datasets.mount_all()
+
+    p = tmp_path_factory.mktemp("b")
+    db = str(p / "qt.db")
+    conn = sqlite3.connect(db)
+    conn.execute(_DDL)
+    import pandas as pd
+    days = pd.bdate_range("2017-06-01", "2018-12-28")
+    for i, day in enumerate(days):
+        d = day.strftime("%Y-%m-%d")
+        rows = [
+            ("600000", "SSE", f"{d} 00:00:00", "d", 100.0, 1_000_000.0, 0,
+             10 + i * 0.01, 10 + i * 0.01, 10 + i * 0.01, 10 + i * 0.01),      # 单调涨
+            ("000001", "SZSE", f"{d} 00:00:00", "d", 100.0, 500_000.0, 0,
+             5, 5, 5, 5),                                                      # 平盘(截面另一端)
+            ("300001", "SZSE", f"{d} 00:00:00", "d", 200.0, 900_000.0, 0,
+             20 - i * 0.005, 20 - i * 0.005, 20 - i * 0.005, 20 - i * 0.005),  # 缓跌
+        ]
+        conn.executemany("INSERT INTO dbbardata VALUES(?,?,?,?,?,?,?,?,?,?,?)", rows)
+    conn.commit(); conn.close()
+    return db
+
+
+def test_run_batch_eval_end_to_end(db, tmp_path, monkeypatch):
+    eval_db = str(tmp_path / "factor_eval.db")
+    out = run_batch_eval(
+        factor_names=["ma_20", "roc_5"], start="2018-01-01", end="2018-06-30",
+        eval_db=eval_db, label="t", symbols=None, cfg=None, vnpy_db_override=db,
+    )
+    assert out["factors_done"] == 2
+    assert out["errors"] == []
+    rows = eval_store.get_rows(eval_db, out["run_id"])
+    assert {r["factor"] for r in rows} == {"ma_20", "roc_5"}
+    # ma_20 = ts_mean(close,20)/close:涨股该值持续低(均价低于现价) 跌股高 → 与次日收益负相关为主
+    m = eval_store.get_detail(eval_db, out["run_id"], "ma_20")["metrics"]
+    assert "1" in m and "5" in m and "10" in m and "turnover" in m
+    assert isinstance(m["1"]["ic_mean"], float)
+
+
+def test_bad_factor_recorded_not_fatal(db, tmp_path):
+    eval_db = str(tmp_path / "factor_eval.db")
+    out = run_batch_eval(
+        factor_names=["不存在的因子", "kmid"], start="2018-01-01", end="2018-03-31",
+        eval_db=eval_db, label="t2", cfg=None, vnpy_db_override=db,
+    )
+    assert out["factors_done"] == 2
+    assert len(out["errors"]) == 1
+    detail = eval_store.get_detail(eval_db, out["run_id"], "不存在的因子")
+    assert "error" in detail["metrics"]