Statistical arbitrage engine that screens S&P 500 pairs using Engle-Granger and Johansen cointegration tests, fits Ornstein-Uhlenbeck dynamics via MLE, and trades spreads with a Kalman filter hedge ratio. Includes a walk-forward backtest with monthly pair re-screening, continuous position carry-over, and a full performance dashboard.
python numpy pandas quantitative-finance algorithmic-trading kalman-filter cointegration pairs-trading time-series-analysis ornstein-uhlenbeck backtesting statistical-arbitrage mean-reversion sp500 johansen-test engle-granger walk-forward-validation hedge-ratio
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Updated
Jun 17, 2026 - Jupyter Notebook