On Integrating Technical Analysis with Machine Learning for Cryptocurrency Price Forecasting

Abstract

This study explores the integration of technical indicators, specifically Exponential Moving Averages (EMA) and Volume Weighted Average Price (VWAP), into machine learning models for cryptocurrency price forecasting. Our findings reveal that including these indicators can complicate the modeling process without necessarily improving performance. Support Vector Regression (SVR) and Random Forest Regressor (RFR) models outperform deep learning approaches such as Long Short-Term Memory (LSTM) networks, demonstrating higher predictive accuracy with simpler feature sets. These findings emphasize the challenges of high-dimensional data and the critical role of rigorous feature selection and preprocessing in financial forecasting. Practical implications and trade-offs between model complexity and prediction accuracy are discussed, providing valuable insights for researchers and practitioners in financial analytics.