Proceedings of International Conference on Applied Innovation in IT
2024/03/07, Volume 12, Issue 1, pp.65-70

Machine Learning-Based Forecasting of Bitcoin Price Movements


Darko Angelovski, Bojana Velichkovska, Goran Jakimovski, Danijela Efnusheva and Marija Kalendar


Abstract: In the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks, Gradient Boosting, LightGBM (LGBM) Regressor, and Random Forest algorithms to unravel the complexities of price movements. We perform a comprehensive analysis, and observe patterns and dependencies within historical data at hour-long intervals in the last 30 and 45 days, by using a holdout technique with 80% of the data used for training and 20% used for testing. We evaluate the models using four standard regression metrics. The training data incorporates a diverse range of features capturing hourly trends, day-of-the-week variations, and the correlation between opening and closing prices. Our study delves into the ability for forecasting Bitcoin price movements using ensemble algorithms and LSTM. The results show best performance for the LSTM models, especially when trained on longer training intervals. Namely, our LSTM model obtains R2 of 0.98 when trained on 30 days and 0.99 when trained on 45 days. In comparison, the ensemble methods show volatility and lower predictive ability.

Keywords: Cryptocurrency, Bitcoin, Machine Learning, Long Short-Term Memory, Random Forest, Gradient Boosting, Light Gradient Boosting

DOI: 10.25673/115643; PPN 1884680054

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References:

  1. S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash System," 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf.
  2. Coinmarketcap, [Online]. Available: https://coinmarketcap.com/.
  3. P. Morgen, "Reinforcing the links of Blockchain," IEEE Spectrum Magazine special edition "Blockchain World," 2017.
  4. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-80, 1997.
  5. A. Natekin and A. Knoll, "Gradient boosting machines, a tutorial," Frontiers in Neurorobotics, vol. 7, 2013.
  6. G. Ke et al., "LightGBM: A Highly Efficient Gradient Boosting Decision Tree," in Advances in Neural Information Processing Systems, vol. 30, 2017.
  7. G. Louppe, "Understanding Random Forests: From Theory to Practice," 2015. arXiv:1407.7502.
  8. T. Phaladisailoed and T. Numnonda, "Machine learning models comparison for bitcoin price prediction," in 2018 10th International Conference on Information Technology and Electrical Engineering, pp. 506-511, 2018.
  9. P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, "Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach," Fractal and Fractional, vol. 7, no. 2, pp. 203, 2023.
  10. R. Chowdhury, M. A. Rahman, M. S. Rahman, and M. R. C. Mahdy, "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning," Physica A: Statistical Mechanics and its Applications, vol. 551, pp. 124569, 2020.
  11. C. Betancourt and W. H. Chen, "Reinforcement learning with self-attention networks for cryptocurrency trading," Appl Sci, vol. 11, pp. 7377, 2021.
  12. S. McNally, J. Roche, and S. Caton, "Predicting the price of bitcoin using machine learning," in Proceedings of 2018 Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 339-343, 2018.
  13. P. Jaquart, S. Kopke, and C. Weinhardt, "Machine learning for cryptocurrency market prediction and trading," The Journal of Finance and Data Science, vol. 8, pp. 331-352, 2022.
  14. M. Iqbal, M. S. Iqbal, et al., "Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques," Endorsed Transactions on Creative Technologies, vol. 8, no. 28, 2021.
  15. A. Dutta, S. Kumar, and M. Basu, "A gated recurrent unit approach to bitcoin price prediction," J Risk Financ Manag, vol. 13, 2020.
  16. W. Yiying, "Cryptocurrency Price Analysis With Artificial Intelligence," in 2019 5th Int. Conf. Inf. Manag., pp. 97-101, 2019.
  17. K. Rathan, S. V. Sai, and T. S. Manikanta, "Crypto-Currency price prediction using Decision Tree and Regression techniques," in 2019 3rd Int. Conf. Trends Electron. Informatics, pp. 190-194, 2019.
  18. Python-Binance API, [Online]. Available: https://python-binance.readthedocs.io/en/latest.


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