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Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared

Author

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  • Oluwadamilare Omole

    (Missouri University of Science and Technology)

  • David Enke

    (Missouri University of Science and Technology)

Abstract

This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network–long short-term memory (CNN–LSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methods—i.e., Boruta, genetic algorithm, and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNN–LSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading.

Suggested Citation

  • Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
  • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00643-1
    DOI: 10.1186/s40854-024-00643-1
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    References listed on IDEAS

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    1. Bhaskar Tripathi & Rakesh Kumar Sharma, 2023. "Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1919-1945, December.
    2. Jacques Vella Critien & Albert Gatt & Joshua Ellul, 2022. "Bitcoin price change and trend prediction through twitter sentiment and data volume," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    3. Smales, L.A., 2019. "Bitcoin as a safe haven: Is it even worth considering?," Finance Research Letters, Elsevier, vol. 30(C), pages 385-393.
    4. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    5. Yingjie Zhu & Jiageng Ma & Fangqing Gu & Jie Wang & Zhijuan Li & Youyao Zhang & Jiani Xu & Yifan Li & Yiwen Wang & Xiangqun Yang, 2023. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    6. Shahzad, Syed Jawad Hussain & Anas, Muhammad & Bouri, Elie, 2022. "Price explosiveness in cryptocurrencies and Elon Musk's tweets," Finance Research Letters, Elsevier, vol. 47(PB).
    7. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    8. Zi Ye & Yinxu Wu & Hui Chen & Yi Pan & Qingshan Jiang, 2022. "A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
    9. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    10. Jiri Kukacka & Ladislav Kristoufek, 2023. "Fundamental and speculative components of the cryptocurrency pricing dynamics," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    11. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    12. Sumit Ranjan & Parthajit Kayal & Malvika Saraf, 2023. "Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1617-1636, April.
    13. Dong-Hee Cho & Seung-Hyun Moon & Yong-Hyuk Kim, 2021. "Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    14. Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
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    Cited by:

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