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.
Recommended Citation
O. Omole and D. Enke, "Deep Learning for Bitcoin Price Direction Prediction: Models and Trading Strategies Empirically Compared," Financial Innovation, vol. 10, no. 1, article no. 117, SpringerOpen, Dec 2024.
The definitive version is available at https://doi.org/10.1186/s40854-024-00643-1
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Backtesting; Bitcoin; Cryptocurrency; Deep learning; Feature selection; On-chain data
International Standard Serial Number (ISSN)
2199-4730
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2024
Comments
Missouri University of Science and Technology, Grant None