feat(metrics): Deflated Sharpe Ratio (Bailey & Lopez de Prado)

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>

tests/unit/test_metrics_dsr.py

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+import numpy as np
+import pandas as pd
+
+from multi_swarm.metrics.dsr import deflated_sharpe_ratio, expected_max_sharpe
+
+
+def test_expected_max_sharpe_grows_with_n_trials():
+    e1 = expected_max_sharpe(n_trials=1, sharpe_var=1.0)
+    e10 = expected_max_sharpe(n_trials=10, sharpe_var=1.0)
+    e100 = expected_max_sharpe(n_trials=100, sharpe_var=1.0)
+    assert e1 < e10 < e100
+
+
+def test_dsr_zero_when_sharpe_equals_expected_max():
+    np.random.seed(0)
+    returns = pd.Series(np.random.normal(0, 0.01, 500))
+    _dsr, p = deflated_sharpe_ratio(
+        returns, n_trials=10, periods_per_year=8760, sharpe_var=0.0
+    )
+    # Con sharpe_var=0 e Sharpe stimato vicino a 0, p-value deve essere alto.
+    assert 0.0 <= p <= 1.0
+
+
+def test_dsr_significant_for_strong_sharpe():
+    np.random.seed(42)
+    returns = pd.Series(np.random.normal(0.005, 0.005, 1000))
+    dsr, p = deflated_sharpe_ratio(
+        returns, n_trials=5, periods_per_year=8760, sharpe_var=1.0
+    )
+    # Sharpe atteso > 0 e p-value basso
+    assert dsr > 0
+    assert p < 0.5