Development and Testing of Multiple Technical Analysis Trading Strategies in MATLAB

This article explores the methodology for developing and testing multiple technical analysis trading strategies using MATLAB. Our approach incorporates statistical analysis and algorithmic implementations to enhance decision-making in stock trading. We detail the systematic process of technical analysis using MATLAB's Financial Toolbox, including data preprocessing, indicator calculation (such as moving averages, RSI, and MACD), and strategy formulation through conditional logic. The implementation section covers key functions like macd for trend analysis and backtest for strategy validation, with code snippets demonstrating parameter optimization and risk management features. Additionally, we provide practical guidance on developing robust trading strategies using MATLAB's object-oriented framework and conducting historical backtests with backtestEngine to evaluate performance metrics like Sharpe ratio and maximum drawdown. The article concludes with real-market application scenarios showing how these tested strategies can improve investment capabilities and generate better returns in stock markets through quantitative analysis and automated execution systems.