backtest.py
import pandas as pd from scipy import stats hurst = compute_hurst(returns) if hurst > 0.55: signal = "trend"
results.log
// Backtest Summary Sharpe 2.41 Sortino 3.18 MaxDD -8.2% Win Rate 61.4% CAGR 34.7%
analysis.py
corr = df.rolling(252).corr() eigenvals = np.linalg.eig(corr) rmt_filtered = denoise(eigenvals) print("Signal extracted")
risk_model.py
var_95 = norm.ppf(0.05) * vol position = capital * 0.02 / var_95 # Kelly criterion sizing kelly = (p * b - q) / b

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