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

arXiv: https://arxiv.org/abs/2407.18334 · HTML: https://mlsanity.com/paper/2407.18334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators](https://mlsanity.com/paper/1909.01268/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization](https://mlsanity.com/paper/2511.17963/) (2025)

## Abstract (arXiv)

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.