MATLAB-Based Cointegration Arbitrage Strategy Testing Model

We have developed a MATLAB-based cointegration arbitrage strategy testing model designed to analyze long-term relationships between multiple securities and identify their cointegration patterns. The implementation utilizes statistical techniques like the Engle-Granger two-step method or Johansen test to detect stable long-run equilibriums, with portfolio construction algorithms that calculate optimal hedge ratios through ordinary least squares (OLS) regression. Our testing with historical market data demonstrates the model's capability to uncover numerous potential arbitrage opportunities by monitoring deviations from cointegrated relationships. The system incorporates mean-reversion trading logic that triggers entries when price spreads exceed statistical thresholds, with risk management modules controlling position sizing. We plan to deploy this model for analyzing future market trends and guiding trading decisions, with ongoing development focused on enhancing prediction accuracy through machine learning integration and dynamic parameter optimization. Future improvements will include Kalman filter adaptations for real-time coefficient updates and multivariate cointegration testing to identify more complex arbitrage opportunities across broader asset baskets.