This paper conducts a thorough analysis of machine learning models for algorithmic trading of Bitcoin, evaluating 41 models including 21 classifiers and 20 regressors. The study aims to predict Bitcoin prices by assessing model performance under diverse market conditions, focusing on accuracy, robustness, and adaptability to the volatile cryptocurrency environment. It employs a dual-metric approach, using both machine learning metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), and trading metrics such as Profit and Loss (P&L) percentage and Sharpe Ratio to provide a holistic view of model effectiveness.

The methodology involves extensive testing through backtesting on historical data, forward testing on recent unseen data, and real-world trading scenarios to ensure the models' practical applicability. Key findings reveal that certain models, particularly Random Forest and Stochastic Gradient Descent, excel in generating profits and managing risks compared to others. These insights are valuable for traders and researchers seeking to develop effective algorithmic trading strategies in the cryptocurrency market, highlighting the importance of model selection based on performance metrics and real-world testing.