Adriano
b61bbaf13c
Aggiunge expected_max_sharpe e deflated_sharpe_ratio per correggere multiple testing nella valutazione di strategie. Considera skewness, kurtosis e numero di trial. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
33 lines
1.0 KiB
Python
33 lines
1.0 KiB
Python
import numpy as np
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import pandas as pd
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from multi_swarm.metrics.dsr import deflated_sharpe_ratio, expected_max_sharpe
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def test_expected_max_sharpe_grows_with_n_trials():
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e1 = expected_max_sharpe(n_trials=1, sharpe_var=1.0)
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e10 = expected_max_sharpe(n_trials=10, sharpe_var=1.0)
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e100 = expected_max_sharpe(n_trials=100, sharpe_var=1.0)
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assert e1 < e10 < e100
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def test_dsr_zero_when_sharpe_equals_expected_max():
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np.random.seed(0)
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returns = pd.Series(np.random.normal(0, 0.01, 500))
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_dsr, p = deflated_sharpe_ratio(
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returns, n_trials=10, periods_per_year=8760, sharpe_var=0.0
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)
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# Con sharpe_var=0 e Sharpe stimato vicino a 0, p-value deve essere alto.
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assert 0.0 <= p <= 1.0
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def test_dsr_significant_for_strong_sharpe():
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np.random.seed(42)
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returns = pd.Series(np.random.normal(0.005, 0.005, 1000))
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dsr, p = deflated_sharpe_ratio(
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returns, n_trials=5, periods_per_year=8760, sharpe_var=1.0
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)
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# Sharpe atteso > 0 e p-value basso
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assert dsr > 0
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assert p < 0.5
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