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117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
# tests/unit/test_factor_analysis.py
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"""Test FactorAnalyzer and FactorReport."""
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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.analysis import FactorAnalyzer, FactorReport
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def _make_factor_and_return(
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n_dates: int = 50,
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n_stocks: int = 20,
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seed: int = 42,
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ic: float = 0.05,
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) -> tuple[pd.DataFrame, pd.DataFrame]:
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rng = np.random.default_rng(seed)
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rows_f, rows_r = [], []
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for d in range(n_dates):
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factor_vals = rng.normal(0, 1, n_stocks)
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noise = rng.normal(0, 1, n_stocks)
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returns = ic * factor_vals + (1 - ic) * noise
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for s in range(n_stocks):
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rows_f.append({"date": 20240101 + d, "code": f"{s:06d}", "test_factor": factor_vals[s]})
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rows_r.append({"date": 20240101 + d, "code": f"{s:06d}", "forward_5d": returns[s]})
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return pd.DataFrame(rows_f), pd.DataFrame(rows_r)
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class TestFactorReport:
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def test_report_fields(self):
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report = FactorReport(
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name="test",
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ic_mean=0.05,
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ic_std=0.1,
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ir=0.5,
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ic_positive_rate=0.6,
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quantile_returns={"q1": -0.01, "q2": 0.0, "q3": 0.01, "q4": 0.02, "q5": 0.03},
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top_minus_bottom=0.04,
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turnover_rate=0.3,
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autocorr=0.8,
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ic_series=pd.Series([0.1, 0.05, -0.02]),
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)
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assert report.name == "test"
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assert report.ir == 0.5
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class TestFactorAnalyzerIC:
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def test_compute_ic_returns_series(self):
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fd, rd = _make_factor_and_return(ic=0.1)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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ic_series = analyzer.compute_ic()
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assert isinstance(ic_series, pd.Series)
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assert len(ic_series) == 50
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def test_positive_ic_detected(self):
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fd, rd = _make_factor_and_return(ic=0.3)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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ic_series = analyzer.compute_ic()
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assert ic_series.mean() > 0.05
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def test_zero_ic_detected(self):
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fd, rd = _make_factor_and_return(ic=0.0)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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ic_series = analyzer.compute_ic()
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assert abs(ic_series.mean()) < 0.15
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class TestFactorAnalyzerQuantile:
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def test_quantile_returns(self):
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fd, rd = _make_factor_and_return(ic=0.1)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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qr = analyzer.compute_quantile_returns()
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assert isinstance(qr, pd.DataFrame)
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assert len(qr.columns) == 5
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def test_monotonic_with_positive_ic(self):
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fd, rd = _make_factor_and_return(ic=0.3)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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qr = analyzer.compute_quantile_returns()
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means = qr.mean()
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assert means.iloc[-1] > means.iloc[0]
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class TestFactorAnalyzerReport:
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def test_full_report(self):
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fd, rd = _make_factor_and_return(ic=0.1)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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report = analyzer.full_report()
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assert isinstance(report, FactorReport)
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assert report.name == "test_factor"
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assert isinstance(report.ic_mean, float)
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assert len(report.quantile_returns) == 5
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assert "q1" in report.quantile_returns
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def test_report_ic_positive_rate(self):
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fd, rd = _make_factor_and_return(ic=0.3)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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report = analyzer.full_report()
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assert report.ic_positive_rate > 0.5
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class TestFactorAnalyzerDecay:
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def test_decay_returns_dataframe(self):
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fd, rd = _make_factor_and_return(ic=0.1)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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decay = analyzer.compute_decay(max_lag=5)
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assert isinstance(decay, pd.DataFrame)
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assert len(decay) == 5
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class TestFactorAnalyzerTurnover:
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def test_turnover_in_range(self):
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fd, rd = _make_factor_and_return(ic=0.1)
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analyzer = FactorAnalyzer(fd, rd, factor_col="test_factor", return_col="forward_5d")
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to = analyzer.compute_turnover()
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assert 0 <= to <= 1
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