mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 22:44:22 +08:00
Justin Gu
5fc398255d
mypy (13 errors → 0): - portfolio/optimizer: register_optimizer 返回类型改为 Callable 装饰器签名 (原标注 type[WeightOptimizer] 导致 4 个子类 Too many arguments) - factor/engine: _datetime_to_int 用 isinstance 收窄替代 object→int 强转 - factor/analysis: 删多余 type:ignore（改由 mypy override 统一处理 scipy） - backtest/orders, execution: np.sqrt 表达式用 float() 包裹消除 no-any-return - MyTT.pyi: MACD 签名删除错误的 LOW/HIGH 参数（与 MyTT.py 实际签名对齐） - pyproject: 新增 scipy mypy override (ignore_missing_imports) ruff format: 8 个 test 文件格式化 验证: 564 passed, mypy 192 文件零错误, ruff check/format 全绿
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
# tests/unit/test_factor_transform.py
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"""Test factor preprocessing functions."""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from easy_tdx.factor.transform import (
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fill_missing,
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orthogonalize,
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preprocess,
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rank_normalize,
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winsorize,
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zscore,
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)
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def _make_cross_section(n_dates: int = 20, n_stocks: int = 30, seed: int = 42) -> pd.DataFrame:
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rng = np.random.default_rng(seed)
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rows = []
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for d in range(n_dates):
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for s in range(n_stocks):
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rows.append(
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{
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"date": 20240101 + d,
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"code": f"{s:06d}",
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"momentum_20d": rng.normal(0.02, 0.05),
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"volatility_20d": abs(rng.normal(0.02, 0.01)),
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}
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)
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df = pd.DataFrame(rows)
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df.loc[0, "momentum_20d"] = 10.0
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df.loc[1, "momentum_20d"] = -10.0
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df.loc[2, "momentum_20d"] = np.nan
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return df
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class TestWinsorize:
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def test_mad_clips_extremes(self):
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df = _make_cross_section()
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result = winsorize(df, ["momentum_20d"], method="mad", threshold=3.0)
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assert result["momentum_20d"].max() < 10.0
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assert result["momentum_20d"].min() > -10.0
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def test_preserves_shape(self):
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df = _make_cross_section()
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result = winsorize(df, ["momentum_20d"])
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assert len(result) == len(df)
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assert list(result.columns) == list(df.columns)
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class TestZscore:
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def test_cross_section_standardization(self):
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df = _make_cross_section()
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result = zscore(df, ["momentum_20d"], cross_section=True)
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for date in result["date"].unique():
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sub = result[result["date"] == date]["momentum_20d"].dropna()
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if len(sub) > 2:
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assert abs(sub.mean()) < 0.5
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def test_preserves_nan(self):
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df = _make_cross_section()
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result = zscore(df, ["momentum_20d"])
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assert result["momentum_20d"].isna().sum() >= 1
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class TestRankNormalize:
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def test_output_range(self):
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df = _make_cross_section()
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result = rank_normalize(df, ["momentum_20d"])
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valid = result["momentum_20d"].dropna()
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assert valid.min() >= 0
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assert valid.max() <= 1
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class TestFillMissing:
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def test_cross_mean_fills(self):
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df = _make_cross_section()
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na_before = df["momentum_20d"].isna().sum()
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result = fill_missing(df, ["momentum_20d"], method="cross_mean")
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na_after = result["momentum_20d"].isna().sum()
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assert na_after < na_before
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def test_forward_fill(self):
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df = _make_cross_section()
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result = fill_missing(df, ["momentum_20d"], method="forward_fill")
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assert len(result) == len(df)
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class TestOrthogonalize:
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def test_residual_differs(self):
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df = _make_cross_section()
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df = zscore(df, ["momentum_20d", "volatility_20d"])
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df = fill_missing(df, ["momentum_20d", "volatility_20d"], method="cross_mean")
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result = orthogonalize(df, target="momentum_20d", by="volatility_20d")
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assert "momentum_20d" in result.columns
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assert not result["momentum_20d"].equals(df["momentum_20d"])
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class TestPreprocess:
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def test_default_pipeline(self):
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df = _make_cross_section()
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result = preprocess(df, ["momentum_20d"])
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assert len(result) == len(df)
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assert "momentum_20d" in result.columns
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assert result["momentum_20d"].isna().sum() <= df["momentum_20d"].isna().sum()
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def test_custom_steps(self):
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df = _make_cross_section()
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result = preprocess(df, ["momentum_20d"], steps=["winsorize", "zscore"])
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assert len(result) == len(df)
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def test_preserves_other_columns(self):
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df = _make_cross_section()
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result = preprocess(df, ["momentum_20d"])
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assert "date" in result.columns
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assert "code" in result.columns
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assert "volatility_20d" in result.columns
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