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

Abdul Jabbar, Syed Qaisar Jalil

TL;DR

This work tackles the challenge of selecting effective ML models for Bitcoin algorithmic trading under volatile market conditions. It applies a comprehensive evaluation framework that combines ML performance metrics with trading outcomes, using backtesting, forward testing, and real-world data across 41 models (21 classifiers, 20 regressors) and rolling-window training. The main contributions include a flexible data pipeline with technical indicators and log-difference features, Optuna-driven hyperparameter optimization, and a thorough cross-phase analysis that reveals Random Forest and SGD-based models as the most profitable and risk-managed options. The results offer practical guidance for traders and researchers on model selection, robust evaluation, and deployment of ML-assisted cryptocurrency trading strategies.

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.

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

TL;DR

This work tackles the challenge of selecting effective ML models for Bitcoin algorithmic trading under volatile market conditions. It applies a comprehensive evaluation framework that combines ML performance metrics with trading outcomes, using backtesting, forward testing, and real-world data across 41 models (21 classifiers, 20 regressors) and rolling-window training. The main contributions include a flexible data pipeline with technical indicators and log-difference features, Optuna-driven hyperparameter optimization, and a thorough cross-phase analysis that reveals Random Forest and SGD-based models as the most profitable and risk-managed options. The results offer practical guidance for traders and researchers on model selection, robust evaluation, and deployment of ML-assisted cryptocurrency trading strategies.

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.
Paper Structure (30 sections, 1 equation, 3 figures, 2 tables)

This paper contains 30 sections, 1 equation, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Data
  5. Machine Learning Models
  6. Classifiers
  7. Regressors
  8. Rolling Windows and Training Process
  9. Hyperparameter Optimization
  10. Backtest and Forward Test Procedures
  11. Results and Discussion
  12. Evaluation Metrics
  13. Classifier Results Interpretation
  14. Backtest Insights
  15. Forward Test Observations
  16. ...and 15 more sections

Figures (3)

  • Figure 1: Overview of the Methodology: This flowchart illustrates the comprehensive process used in our study, encompassing three main modules: data, machine learning, and evaluation. The data module includes all the steps from data collection to dataset creation, preparing the data for use by the machine learning module. The machine learning module covers model development and training, including hyperparameter optimization for both classifiers and regressors. The evaluation module involves rigorous backtesting on historical data, forward testing on recent unseen data, and real-world testing to validate model performance and ensure practical applicability.
  • Figure 2: Profit and Loss (PNL) Trajectories of Top Classifiers in Real-World Trading Scenarios
  • Figure 3: Real-World Profit and Loss (PNL) Performance of Top Regressors