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GitHubandClaude
e6a69d51e4
- WeightOptimizer base class with registry (equal, factor_weighted, risk_parity, mean_variance) - RiskModel with shrinkage covariance estimation and portfolio risk metrics - RebalanceEngine for multi-period backtesting with commission/slippage - 20 unit tests covering all components Co-Authored-By: Claude <noreply@anthropic.com>
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
"""Test RiskModel."""
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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.portfolio.risk import RiskModel
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def _make_returns(n_dates: int = 100, n_stocks: int = 5, seed: int = 42) -> pd.DataFrame:
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rng = np.random.default_rng(seed)
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codes = [f"{i:06d}" for i in range(n_stocks)]
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return pd.DataFrame(rng.normal(0.001, 0.02, (n_dates, n_stocks)), columns=codes)
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class TestCovarianceEstimation:
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def test_shape(self):
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cov = RiskModel().estimate_covariance(_make_returns())
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assert cov.shape == (5, 5)
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def test_symmetric(self):
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cov = RiskModel().estimate_covariance(_make_returns())
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assert np.allclose(cov.to_numpy(), cov.to_numpy().T)
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def test_shrinkage_reduces_offdiag(self):
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rm = RiskModel()
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ret = _make_returns()
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shrunk = rm.estimate_covariance(ret, method="shrinkage")
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sample = rm.estimate_covariance(ret, method="sample")
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off_shrunk = shrunk.values[~np.eye(5, dtype=bool)]
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off_sample = sample.values[~np.eye(5, dtype=bool)]
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assert np.abs(off_shrunk).mean() <= np.abs(off_sample).mean()
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class TestPortfolioRisk:
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def test_total_volatility(self):
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cov = RiskModel().estimate_covariance(_make_returns())
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risk = RiskModel().portfolio_risk({"000000": 0.5, "000001": 0.5}, cov)
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assert risk["total_volatility"] > 0
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assert risk["n_positions"] == 2
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def test_empty_weights(self):
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risk = RiskModel().portfolio_risk({}, pd.DataFrame())
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assert risk["total_volatility"] == 0.0
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