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
@@ -0,0 +1,124 @@
+# sanguo_factor/batch_eval.py
+"""批量评估引擎:全A bars → 进程内逐因子 calculate_by_expression → 指标 → 落盘.
+
+不走 AlphaDataset.prepare_data(其 spawn 池对每个表达式 pickle 整个 DataFrame,
+9M 行 × 258 因子的传输开销不可接受);calculate_by_expression 纯进程内 polars,
+内存随单因子天然有界。现有单因子分析链路(analyzer/alphalens tears)零改动.
+"""
+import sys
+import os
+import time
+
+_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)
+
+import pandas as pd
+import polars as pl
+
+from .universe import load_universe_bars, evaluation_filter
+from .registry import get_factor
+from . import eval_store
+from .metrics import summarize_factor
+
+
+def _forward_return_matrices(close_wide: pd.DataFrame, periods=(1, 5, 10)) -> dict[int, pd.DataFrame]:
+    out = {}
+    for p in periods:
+        out[p] = close_wide.shift(-p) / close_wide - 1.0
+    return out
+
+
+def run_batch_eval(
+    factor_names: list[str],
+    start: str,
+    end: str,
+    eval_db: str,
+    label: str,
+    universe: str = "all_a",
+    symbols: list[str] | None = None,
+    limit: int | None = None,
+    cfg=None,
+    progress_cb=None,
+    vnpy_db_override: str | None = None,
+) -> dict:
+    """跑一轮批量评估,结果增量写入 eval_db,返回摘要."""
+    from vnpy.alpha.dataset.utility import calculate_by_expression
+
+    if cfg is None:
+        from sanguo_data.config import load_config, find_config_path
+        cfg = load_config(find_config_path())
+    vnpy_db = vnpy_db_override or cfg.data_paths["vnpy_db"]
+
+    t0 = time.time()
+    bars = load_universe_bars(vnpy_db, start, end, symbols=symbols, limit=limit)
+    if bars.height == 0:
+        raise ValueError(f"股票池为空: vnpy_db={vnpy_db} window={start}~{end}")
+
+    alpha_df = bars.select(["vt_symbol", "datetime", "open", "high", "low", "close",
+                            "volume", "turnover", "open_interest", "vwap"])
+    eval_rows = evaluation_filter(bars, start, end).select(["vt_symbol", "datetime"])
+
+    close_wide = (
+        bars.select(["datetime", "vt_symbol", "close"])
+        .pivot(index="datetime", on="vt_symbol", values="close")
+        .sort("datetime").to_pandas().set_index("datetime")
+    )
+    close_wide.index = pd.to_datetime(close_wide.index)
+    rets = _forward_return_matrices(close_wide)
+
+    universe_label = universe if symbols is None else "custom"
+    eval_store.init_db(eval_db)
+    run_id = eval_store.create_run(
+        eval_db, label=label, universe=universe_label,
+        symbols_count=bars["vt_symbol"].n_unique(), factors_total=len(factor_names),
+        start=start, end=end, params={"limit": limit, "symbols": symbols[:20] if symbols else None},
+    )
+
+    errors: list[str] = []
+    done = 0
+    buffer: list[dict] = []
+    for i, name in enumerate(factor_names):
+        row = _eval_one(name, alpha_df, eval_rows, rets, calculate_by_expression)
+        if "error" in row["metrics"]:
+            errors.append(name)
+        buffer.append(row)
+        done += 1
+        if len(buffer) >= 20 or done == len(factor_names):
+            eval_store.save_results(eval_db, run_id, buffer)
+            buffer = []
+        if progress_cb:
+            progress_cb(done, len(factor_names), name)
+
+    eval_store.finish_run(eval_db, run_id, "done", factors_done=done)
+    return {
+        "run_id": run_id,
+        "factors_total": len(factor_names),
+        "factors_done": done,
+        "errors": errors,
+        "elapsed_sec": round(time.time() - t0, 1),
+        "symbols_count": int(bars["vt_symbol"].n_unique()),
+    }
+
+
+def _eval_one(name: str, alpha_df: pl.DataFrame, eval_rows: pl.DataFrame,
+              rets: dict[int, pd.DataFrame], calculate_by_expression) -> dict:
+    factor = get_factor(name)
+    if factor is None:
+        return {"factor": name, "category": "unknown", "expression": "",
+                "metrics": {"error": f"因子未注册: {name}"}}
+    try:
+        res = calculate_by_expression(alpha_df, factor["expression"])
+        f_long = res.join(eval_rows, on=["vt_symbol", "datetime"], how="inner")
+        F = (
+            f_long.pivot(index="datetime", on="vt_symbol", values="data")
+            .sort("datetime").to_pandas().set_index("datetime")
+        )
+        F.index = pd.to_datetime(F.index)
+        metrics = summarize_factor(F, rets[1], rets[5], rets[10])
+        return {"factor": name, "category": factor["category"],
+                "expression": factor["expression"], "metrics": metrics}
+    except Exception as e:  # 单因子失败不拖垮整批
+        return {"factor": name, "category": factor["category"],
+                "expression": factor["expression"],
+                "metrics": {"error": f"{type(e).__name__}: {e}"}}
@@ -0,0 +1,74 @@
+# 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"]