Paper: arXiv 2407.18334

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.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 6.0/10 (capped from 9.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on evaluating 41 standard ML models using accessible metrics like MAE and Sharpe Ratio, resulting in low math complexity, while its emphasis on backtesting, forward testing, and real-world trading scenarios ensures high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Evaluate ML models for Bitcoin trading"] --> B["Data Collection<br>Historical & recent Bitcoin price data"]
  B --> C["Model Training<br>21 Classifiers & 20 Regressors<br>Random Forest, SGD, etc."]
  C --> D["Evaluation Process<br>Backtesting, Forward Testing, Real-world Trading"]
  D --> E{"Performance Metrics"}
  E --> F["Machine Learning Metrics<br>MAE, RMSE"]
  E --> G["Trading Metrics<br>PnL%, Sharpe Ratio"]
  F & G --> H["Key Findings & Outcomes"]
  H --> I["Random Forest & SGD<br>best for profit & risk"]
  H --> J["Comprehensive guide for<br>effective trading strategies"]

Cited References (37)

  1. The Sharpe Ratio (1994)
  2. Mutual Fund Performance (1966)
  3. Optuna: A Next-generation Hyperparameter Optimization Framework (2019)
  4. Forecasting the movements of Bitcoin prices: an application of machine learning algorithms — → 2303.04642
  5. Empirical evaluation of machine learning performance in forecasting cryptocurrencies (2023)
  6. Analyzing the machine Learning methods to predict Bitcoin Pricing (2024)
  7. Height-end Multi-layer Perceptron and Machine Learning Methods of Forecasting Bitcoin Price Time Series (2023)
  8. Performance of machine learning methods in predicting trend in price and trading volume of cryptocurrencies (2023)
and 29 more
  1. Artificial intelligence techniques in financial trading: A systematic literature review (2024)
  2. Cryptocurrency trading using machine learning (2020)
  3. Forecasting and trading cryptocurrencies with machine learning under changing market conditions (2021)
  4. Machine learning for cryptocurrency market prediction and trading (2022)
  5. Real-world model for bitcoin price prediction (2022)
  6. Bitcoin USD Closing Price (BTC-USD) Comparison Using Simple Moving Average And Radial Basis Function Neural Network Methods (2022)
  7. Bitcoin price forecasting and trading: Data analytics approaches (2022)
  8. Cryptocurrency trading: a comprehensive survey (2022)
  9. Model-free reinforcement learning for financial portfolios: a brief survey (2019) — link
  10. Comparative performance of machine learning algorithms for cryptocurrency forecasting (2018)
  11. A comparative study for Bitcoin cryptocurrency forecasting in period 2017-2019 (2020)
  12. Time-series prediction of cryptocurrency market using machine learning techniques (2021)
  13. Your Sentiment Matters: A Machine Learning Approach for Predicting Regime Changes in the Cryptocurrency Market (2023)
  14. Predicting Bitcoin Prices Using Machine Learning (2023)
  15. Short-term bitcoin market prediction via machine learning (2021)
  16. Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach (2020)
  17. Bitcoin Price Prediction Using Machine Learning Algorithms (2023)
  18. Signal Prediction in Cryptocurrency Tradeoperations: A Machine Learning-Based Approach
  19. Forecasting mid-price movement of Bitcoin futures using machine learning (2021)
  20. Cryptocurrency Price Prediction Using Deep Learning and Machine Learning (2022)
  21. On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles (2023)
  22. Daily cryptocurrency returns forecasting and trading via machine Learning (2021)
  23. Price movement prediction of cryptocurrencies using sentiment analysis and machine learning (2019)
  24. Bitcoin price prediction based on other cryptocurrencies using machine learning and time series analysis (2020)
  25. Comparative Analysis Of Machine Learning Models For Predicting Bitcoin Price Rate (2021)
  26. Analysis of bitcoin price prediction using machine learning (2023)
  27. Prediction of Bitcoin Price Movements Based on Machine Learning Method and Strategy Construction
  28. Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators (2019) — link
  29. Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniques (2021) — link

1 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.