Optimizing Algorithmic Trading with Machine Learning and Entropy-Based Decision Making

This study explores the use of Shannon entropy as a filtering mechanism to enhance the trading signals produced by the LVQ (Learning Vector Quantization) machine learning algorithm in algorithmic trading. The integration of Shannon entropy aims to improve trade entry accuracy by reducing market noise and identifying stronger trends. A trading bot was developed and tested on Bitcoin using a three-minute timeframe on the TradingView platform, with backtesting conducted from February 1 to February 18, 2025. The fully automated strategy used 100% of available capital per trade, reinvesting profits for compounding. Positions were closed when an opposite signal was generated. A comparative analysis revealed that incorporating Shannon entropy outperformed a baseline strategy. These findings demonstrate that entropy-based filtering improves trade selection and profitability by reducing market noise and focusing on reliable trends, suggesting its potential for broader application in algorithmic trading.