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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>
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
"""Test portfolio optimizers."""
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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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import pytest
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from easy_tdx.portfolio.optimizer import (
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EqualWeightOptimizer,
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FactorWeightedOptimizer,
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RiskParityOptimizer,
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get_optimizer,
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)
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def _make_scores(n: int = 20, seed: int = 42) -> pd.DataFrame:
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rng = np.random.default_rng(seed)
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return pd.DataFrame({
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"code": [f"{i:06d}" for i in range(n)],
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"score": rng.normal(0.02, 0.05, n),
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})
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class TestEqualWeight:
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def test_weights_sum_to_one(self):
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w = EqualWeightOptimizer().optimize(_make_scores(), n_stocks=10)
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assert abs(sum(w.values()) - 1.0) < 1e-8
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def test_n_stocks_selected(self):
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w = EqualWeightOptimizer().optimize(_make_scores(), n_stocks=5)
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assert len(w) == 5
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def test_all_equal(self):
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w = EqualWeightOptimizer().optimize(_make_scores(), n_stocks=10)
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vals = list(w.values())
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assert all(abs(v - vals[0]) < 1e-8 for v in vals)
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def test_empty_input(self):
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w = EqualWeightOptimizer().optimize(pd.DataFrame(columns=["code", "score"]), n_stocks=5)
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assert len(w) == 0
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class TestFactorWeighted:
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def test_weights_sum_to_one(self):
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w = FactorWeightedOptimizer().optimize(_make_scores(), n_stocks=10)
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assert abs(sum(w.values()) - 1.0) < 1e-6
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def test_higher_score_higher_weight(self):
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scores = pd.DataFrame({"code": ["A", "B", "C"], "score": [3.0, 2.0, 1.0]})
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w = FactorWeightedOptimizer().optimize(scores, n_stocks=3)
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assert w["A"] > w["C"]
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class TestRiskParity:
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def test_weights_sum_to_one(self):
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w = RiskParityOptimizer().optimize(_make_scores(), n_stocks=10)
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assert abs(sum(w.values()) - 1.0) < 1e-6
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def test_with_volatility_column(self):
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scores = pd.DataFrame({"code": ["A", "B", "C"], "score": [1.0, 1.0, 1.0], "volatility": [0.1, 0.2, 0.4]})
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w = RiskParityOptimizer().optimize(scores, n_stocks=3)
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assert w["A"] > w["C"]
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class TestRegistry:
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def test_get_optimizer(self):
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assert isinstance(get_optimizer("equal"), EqualWeightOptimizer)
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def test_unknown_raises(self):
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with pytest.raises(ValueError, match="未知优化器"):
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get_optimizer("nonexistent")
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