PREDICTIVE ANALYTICS IN CRYPTO TRADING: MACHINE LEARNING'S NEW FRONTIER

This research explores the application of machine learning (ML) in predicting cryptocurrency price movements, focusing on enhancing predictive accuracy to support informed trading decisions in volatile markets. Employing a quantitative methodology, historical price data and real-time indicators were analyzed using Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Random Forest (RF) models. LSTM networks outperformed other models with an 88% accuracy rate for Bitcoin, owing to their ability to retain temporal patterns crucial for crypto market predictions. RF and SVM, achieving accuracies of 85% and 82%, respectively, demonstrated balanced performance with lower computational demands. Integrating social media sentiment data further improved model precision by up to 6%, underscoring the importance of non-traditional data sources. Recommendations include prioritizing LSTM for high-volatility assets, utilizing RF for cost-effective applications, and incorporating sentiment data to enhance model robustness. This study demonstrates the utility of ML models in navigating crypto market complexities, offering adaptive tools for traders.