Stock Pair Trading Arbitrage Strategy with MATLAB Implementation

Application Background: This paper presents research on stock pair trading strategies implemented in MATLAB, complete with detailed PDF documentation. The project serves as a valuable reference for quantitative finance researchers and algorithmic trading developers. The MATLAB codebase includes modular functions for data preprocessing, statistical analysis, and strategy backtesting. Key Technologies: The feasibility and implementation of stock pair trading require sophisticated statistical techniques such as correlation process analysis and cointegration analysis. The MATLAB implementation typically involves: - Calculating rolling correlations using corr() function with time-series data - Performing Engle-Granger cointegration tests to identify stable pairs - Implementing z-score normalization for mean-reversion signals - Developing threshold-based entry/exit algorithms using conditional statements Additionally, the system can be enhanced with machine learning and AI techniques for market prediction and analysis. The code architecture allows integration of predictive models like SVM or LSTM networks for improved signal accuracy. For robust risk management, the implementation includes portfolio optimization modules using mean-variance optimization techniques and position sizing algorithms. Successful pair trading requires mastering multiple investment management skills, including dynamic hedging implementations and Monte Carlo risk simulation methods, all demonstrable through the provided MATLAB code examples.