Quantitative Finance > Trading and Market Microstructure
arXiv:2407.18334 (q-fin)
[Submitted on 9 Jul 2024]
Title:A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin
View a PDF of the paper titled A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin, by Abdul Jabbar and Syed Qaisar Jalil
View PDFHTML (experimental)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.
| Subjects: | Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2407.18334 [q-fin.TR] |
| (orarXiv:2407.18334v1 [q-fin.TR] for this version) | |
| https://doi.org/10.48550/arXiv.2407.18334 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin, by Abdul Jabbar and Syed Qaisar Jalil
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