Skip to main content
SpringerLink
Log in

Deep Reinforcement Learning for Bitcoin Trading

  • Conference paper
  • First Online:
Business Intelligence (CBI 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 449))

Included in the following conference series:

Abstract

Artificial intelligence (AI) is showing its success in various types of applications. Motivated by this trend, automatic trading has taken a keen interest in applying of artificial intelligence methods to predict the future price of a financial asset to overcome trading challenges including asset price fluctuations and dynamics, Investors must therefore understand when it is appropriate to use the optimal strategy that maximizes their investment return. But achieving a perfect strategy is difficult for an asset with a complex and dynamic price. To overcome these challenges, In this study, we apply a new rule-based strategy technique to train one of the successful machine learning algorithms, known as Deep Reinforcement Learning (DRL) for bitcoin trading. Our proposed method is based on dueling double deep q-learning networks, proximal policy optimization, and advantage actor-critic to achieve an optimal policy. The profit reward functions and Sharpe ratio are used to assess the proposed DRL. The results of the experiments demonstrate that combining three agents is the most efficient strategy for automatic bitcoin trading.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad, I., Ahmad, M.O., Alqarni, M.A., Almazroi, A.A., Khalil, M.I.: Using algorithmic trading to analyze short term profitability of bitcoin. Peer J. Comput. Sci. 7, e337 (2021)

    Google Scholar 

  2. El Akraoui, B., Daoui, C.: Deep learning for medical image segmentation. In: Fakir, M., Baslam, M., El Ayachi, R. (eds.) CBI 2021. LNBIP, vol. 416, pp. 294–303. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76508-8_21

  3. Alessandretti, L., ElBahrawy, A., Aiello, L.M., Baronchelli, A.: Anticipating cryptocurrency prices using machine learning. Complexity 2018, 1 (2018)

    Google Scholar 

  4. Arévalo, A., Niño, J., Hernández, G., Sandoval, J.: High-frequency trading strategy based on deep neural networks. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS (LNAI), vol. 9773, pp. 424–436. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42297-8_40

  5. Murillo, A.R.A.: High-frequency trading strategy based on deep neural networks. In: Ingeniería de Sistemas (2019)

    Google Scholar 

  6. Azhikodan, A.R., Bhat, A.G.K., Jadhav, M.V.: Stock trading bot using deep reinforcement learning. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds.) Innovations in Computer Science and Engineering. LNNS, vol. 32, pp. 41–49. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-8201-6_5

  7. Britz, D.: Introduction to learning to trade with reinforcement learning (2018). https://www.wildml.com/2018/02/introduction-to-learning-to-tradewith-reinforcement-learning

  8. Chaboud, A.P., Chiquoine, B., Hjalmarsson, E., Vega, C.: Rise of the machines: algorithmic trading in the foreign exchange market. J. Finan. 69(5), 2045–2084 (2014)

    Google Scholar 

  9. Chan, E.P.: Quantitative Trading: How To Build Your Own Algorithmic Trading Business. John Wiley and Sons, New York (2021)

    Google Scholar 

  10. Colianni, S., Rosales, S., Signorotti, M.: Algorithmic trading of cryptocurrency based on twitter sentiment analysis. CS229 Project 1(5), 1–4 (2015)

    Google Scholar 

  11. Dash, R., Dash, P.K.: A hybrid stock trading framework integrating technical analysis with machine learning techniques. J. Finan. Data Sci. 2(1), 42–57 (2016)

    Google Scholar 

  12. Goldkamp, J., Dehghanimohammadabadi, M.: Evolutionary multi-objective optimization for multivariate pairs trading. Expert Syst. Appl. 135, 113–128 (2019)

    Google Scholar 

  13. Guo, Y., Fu, X., Shi, Y., Liu, M.: Robust log-optimal strategy with reinforcement learning. arXiv preprint arXiv:1805.00205 (2018)

  14. Hegazy, K., Mumford, S.: Comparitive automated bitcoin trading strategies. CS229 Project 27, 1–6 (2016)

    Google Scholar 

  15. Huang, C.Y.: Financial trading as a game: a deep reinforcement learning approach. arXiv preprint arXiv:1807.02787 (2018)

  16. Isensee, F., Jaeger, P.F., Simon, A.A., Kohl, J.P., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Google Scholar 

  17. Jang, H., Lee, J.: An empirical study on modeling and prediction of bitcoin prices with Bayesian neural networks based on blockchain information. IEEE Access 6, 5427–5437 (2017)

    Google Scholar 

  18. Jiang, Z., Liang, J.: Cryptocurrency portfolio management with deep reinforcement learning. In: 2017 Intelligent Systems Conference (IntelliSys), pp. 905–913. IEEE (2017)

    Google Scholar 

  19. Kim, Y., Ahn, W., Oh, K.J., Enke, D.: An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms. Appl. Soft Comput. 55, 127–140 (2017)

    Google Scholar 

  20. Lucarelli, G., Borrotti, M.: A deep reinforcement learning approach for automated cryptocurrency trading. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 247–258. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19823-7_20

  21. Ma, W., Xuemin, T., Luo, B., Wang, G.: Semantic clustering based deduction learning for image recognition and classification. Pattern Recogn. 124, 108440 (2022)

    Google Scholar 

  22. Markowitz, H.: Portfolio selection in the journal of finance, vol. 7 (1952)

    Google Scholar 

  23. McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: 2018 26th EuroMicro International Conference On Parallel, Distributed and Network-based Processing (PDP), pp. 339–343. IEEE (2018)

    Google Scholar 

  24. Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937. PMLR (2016)

    Google Scholar 

  25. Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)

  26. Mosavi, A., et al.: Comprehensive review of deep reinforcement learning methods and applications in economics. Mathematics 8(10), 1640 ( (2020))

    Google Scholar 

  27. Murphy, J.J.: Technical Analysis of The Financial Markets: A Comprehensive Guide To Trading Methods and Applications. Penguin (1999)

    Google Scholar 

  28. ÖZYEŞİL, M.: Comparison of technical and fundamental analysis trading disciplines on portfolio performance: short and long term backtest analysis on Borsa Istanbul national stock indices. J. Contempor. Res. Bus. Econ. Finan. 3(3), 128–143 (2021)

    Google Scholar 

  29. Sattarov, O., et al.: Recommending cryptocurrency trading points with deep reinforcement learning approach. Appl. Sci. 10(4), 1506 (2020)

    Google Scholar 

  30. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)

  31. Schwager, J.D.: A Complete Guide To The Futures Market: Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads, and Trading Principles. John Wiley & Sons, New York (2017)

    Google Scholar 

  32. Théate, T., Ernst, D.: An application of deep reinforcement learning to algorithmic trading. Expert Syst. App. 173, 114632 (2021)

    Google Scholar 

  33. Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M., Freitas, N.: Dueling network architectures for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1995–2003. PMLR (2016)

    Google Scholar 

  34. Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279–292 (1992)

    Google Scholar 

  35. Yang, H., Liu, X.-Y., Zhong, S., Walid, A.: Deep reinforcement learning for automated stock trading: an ensemble strategy. In: Proceedings of the First ACM International Conference on AI in Finance, pp. 1–8 (2020)

    Google Scholar 

  36. Yu, P., Yan, X.: Stock price prediction based on deep neural networks. Neural Comput. App. 32(6), 1609–1628 (2019). https://doi.org/10.1007/s00521-019-04212-x

  37. Zhang, D.: Deep reinforcement learning in medical object detection and segmentation (2020)

    Google Scholar 

  38. Zhang, Z., Zohren, S., Roberts, S.: Deep learning for portfolio optimization. J. Finan. Data Sci. 2(4), 8–20 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Information Processing and Decision Support, Sultan Moulay Slimane University, B.P. 523, Beni Mellal, Morocco

    Bouchra El Akraoui & Cherki Daoui

Authors
  1. Bouchra El Akraoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cherki Daoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bouchra El Akraoui .

Editor information

Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  2. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Baslam

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El Akraoui, B., Daoui, C. (2022). Deep Reinforcement Learning for Bitcoin Trading. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06458-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06457-9

  • Online ISBN: 978-3-031-06458-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation