  This study evaluates the performance of 41 machine learning models, including
21 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic
trading. By examining these models under various market conditions, we
highlight their accuracy, robustness, and adaptability to the volatile
cryptocurrency market. Our comprehensive analysis reveals the strengths and
limitations of each model, providing critical insights for developing effective
trading strategies. We employ both machine learning metrics (e.g., Mean
Absolute Error, Root Mean Squared Error) and trading metrics (e.g., Profit and
Loss percentage, Sharpe Ratio) to assess model performance. Our evaluation
includes backtesting on historical data, forward testing on recent unseen data,
and real-world trading scenarios, ensuring the robustness and practical
applicability of our models. Key findings demonstrate that certain models, such
as Random Forest and Stochastic Gradient Descent, outperform others in terms of
profit and risk management. These insights offer valuable guidance for traders
and researchers aiming to leverage machine learning for cryptocurrency trading.
