feat(factor): 向量化评估指标——RankIC逐行秩相关/ICIR/t/胜率/多空年化/换手/十分组/月度IC/结论分级 [vps]

2026-08-25 00:12:35 +08:00
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+# tests/factor/test_metrics.py
+"""向量化指标:已知输入的精确断言."""
+import sys, os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from sanguo_factor.metrics import (
+    rank_corr_rows, factor_turnover, long_short_annual_return,
+    decile_annual_returns, monthly_ic, classify, summarize_factor,
+    TRADING_DAYS_PER_YEAR,
+)
+
+
+def _mat(values, cols=("A", "B", "C")):
+    n_rows = len(values)
+    idx = pd.to_datetime([f"2024-01-{i+1:02d}" for i in range(n_rows)])
+    return pd.DataFrame(values, index=idx, columns=list(cols), dtype=float)
+
+
+def test_rank_corr_perfect_monotonic():
+    F = _mat([[1, 2, 3], [3, 2, 1], [1, 3, 2]])
+    R = _mat([[10, 20, 30], [30, 20, 10], [10, 30, 20]])
+    ic = rank_corr_rows(F, R)
+    assert (ic == 1.0).all()
+
+
+def test_rank_corr_inverse():
+    F = _mat([[1, 2, 3], [1, 2, 3]])
+    R = _mat([[3, 2, 1], [30, 20, 10]])
+    ic = rank_corr_rows(F, R)
+    assert (ic == -1.0).all()
+
+
+def test_rank_corr_nan_propagates_row():
+    F = _mat([[1, 2, 3], [np.nan, 2, 3]])
+    R = _mat([[1, 2, 3], [1, 2, 3]])
+    ic = rank_corr_rows(F, R)
+    assert not np.isnan(ic.iloc[0])
+    assert np.isnan(ic.iloc[1])  # 2个有效值秩恒定 → 无方差 → nan
+
+
+def test_turnover_zero_for_static():
+    F = _mat([[1, 2, 3]] * 4)
+    assert factor_turnover(F) == pytest.approx(0.0)
+
+
+def test_turnover_full_for_shuffled():
+    F = _mat([[1, 2, 3], [3, 2, 1]])  # 完全逆序 → 秩相关-1 → 换手=2
+    assert factor_turnover(F) == pytest.approx(2.0)
+
+
+def test_long_short_direction():
+    F = _mat([[1, 2, 3, 4, 5]] * 2, cols=tuple("ABCDE"))
+    R = _mat([[0.01, 0.02, 0.03, 0.04, 0.05],
+              [0.01, 0.02, 0.03, 0.04, 0.05]], cols=tuple("ABCDE"))
+    # 每日 top10%(1只)=E bottom10%=A → 日均 0.04
+    expected = (1 + 0.04) ** TRADING_DAYS_PER_YEAR - 1
+    assert long_short_annual_return(F, R) == pytest.approx(expected)
+
+
+def test_decile_monotonic():
+    F = _mat([list(range(1, 11))] * 3, cols=tuple("ABCDEFGHIJ"))
+    R = _mat([[c / 100 for c in range(1, 11)]] * 3, cols=tuple("ABCDEFGHIJ"))
+    dec = decile_annual_returns(F, R)
+    assert len(dec) == 10
+    assert all(d is not None for d in dec)
+    assert dec == sorted(dec)  # D1最低收益 → D10最高收益 单调
+
+
+def test_monthly_ic_shape():
+    idx = pd.to_datetime(["2024-01-05", "2024-01-10", "2024-02-01"])
+    out = monthly_ic(pd.Series([0.1, 0.2, -0.1], index=idx))
+    assert out[0] == {"month": "2024-01", "ic": pytest.approx(0.15)}
+    assert out[1] == {"month": "2024-02", "ic": pytest.approx(-0.1)}
+
+
+def test_classify_rules():
+    assert classify(0.5, 5.0) == "effective"
+    assert classify(-0.4, -3.0) == "effective"   # 负 IC 强因子同样有效(反向)
+    assert classify(0.1, 1.8) == "watch"
+    assert classify(0.0, 0.5) == "eliminated"
+
+
+def test_summarize_factor_structure():
+    rng = np.random.default_rng(7)
+    idx = pd.bdate_range("2024-01-01", periods=60)
+    base = np.tile(np.arange(10.0, 90.0, 1.0), (60, 1))
+    F = pd.DataFrame(base + rng.normal(0, 0.5, base.shape), index=idx)
+    R1 = pd.DataFrame(-0.001 * base + rng.normal(0, 0.001, base.shape), index=idx, columns=F.columns)
+    R5, R10 = R1 * 5, R1 * 10
+    out = summarize_factor(F, R1, R5, R10)
+    assert set(out) == {"1", "5", "10", "turnover"}
+    p1 = out["1"]
+    for key in ("count", "ic_mean", "ic_std", "icir", "t_stat", "win_rate",
+                "ls_annual", "monthly_ic", "deciles", "conclusion"):
+        assert key in p1
+    assert p1["count"] == 60
+    assert p1["ic_mean"] < 0  # 构造为负相关