Machine Learning techniques for predictive modeling of Bitcoin price dynamics

This study investigates the application of machine learning techniques for Bitcoin price prediction, addressing the fundamental challenge of forecasting in highly volatile cryptocurrency markets. Using comprehensive daily data spanning 2014-2024, we systematically compare traditional econometric models (Random Walk, ARIMA, Ridge Regression) with advanced machine learning approaches (Random Forest, XGBoost, LSTM) and develop a novel Tiered Temporal Ensemble (TTE) framework. The research incorporates 115 engineered features across four dimensions: technical indicators, on-chain metrics, macroeconomic variables, and sentiment data. Our findings reveal that XGBoost achieves superior risk-adjusted performance with a Sharpe ratio of 1.679 (143% improvement over random walk baseline) and the lowest maximum drawdown of -17.3%. While LSTM demonstrates the highest directional accuracy at 52.4%, it exhibits elevated drawdown risk. Feature importance analysis indicates the dominance of technical indicators and lagged returns (75% of total importance), with on-chain metrics contributing only 10-20%, challenging assumptions about blockchain-specific predictive advantages. The TTE framework reveals pronounced regime-dependent performance, with bull market Sharpe ratios (3.780) dramatically exceeding neutral periods (0.512). However, all models underperform buy-and-hold strategy, highlighting the persistent challenge of market timing in trending assets. These findings contribute to understanding cryptocurrency market efficiency while demonstrating that despite statistically significant improvements, economic significance remains constrained by implementation challenges and market dynamics.