Adriano
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Metriche base per valutazione strategie: Sharpe annualizzato (default 8760 periodi/anno per dati orari), max drawdown percentuale dalla curva equity, total return cumulativo. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from multi_swarm.metrics.basic import max_drawdown, sharpe_ratio, total_return
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def test_sharpe_zero_returns():
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r = pd.Series([0.0] * 100)
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assert sharpe_ratio(r, periods_per_year=8760) == 0.0
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def test_sharpe_positive_returns():
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np.random.seed(42)
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r = pd.Series(np.random.normal(0.001, 0.01, 1000))
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s = sharpe_ratio(r, periods_per_year=8760)
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assert s > 0
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def test_sharpe_negative_returns():
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np.random.seed(42)
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r = pd.Series(np.random.normal(-0.001, 0.01, 1000))
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s = sharpe_ratio(r, periods_per_year=8760)
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assert s < 0
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def test_max_drawdown_monotonic_up():
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eq = pd.Series([100.0, 105.0, 110.0, 115.0, 120.0])
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assert max_drawdown(eq) == pytest.approx(0.0)
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def test_max_drawdown_known_curve():
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eq = pd.Series([100.0, 110.0, 90.0, 95.0, 105.0])
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# peak 110, trough 90, drawdown = (110-90)/110 ≈ 0.1818
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assert max_drawdown(eq) == pytest.approx(20.0 / 110.0)
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def test_total_return():
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eq = pd.Series([100.0, 110.0, 105.0, 120.0])
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assert total_return(eq) == pytest.approx(0.20)
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