A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin

TL;DR

This paper systematically evaluates 41 machine learning models for Bitcoin price prediction, assessing their accuracy, robustness, and practical trading performance to guide effective algorithmic trading strategies in volatile markets.

Contribution

It provides a comprehensive comparison of multiple ML models for Bitcoin trading, highlighting their strengths, limitations, and practical applicability based on extensive backtesting and real-world testing.

Findings

Random Forest and SGD outperform others in profit and risk management

Certain models demonstrate robustness across different market conditions

The study offers practical insights for deploying ML in cryptocurrency trading

Abstract

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.