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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2020: Intelligent Data Engineering and Automated Learning – IDEAL 2020 pp 245–256Cite as

Trading Cryptocurrency with Deep Deterministic Policy Gradients

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12489)

Abstract

The volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the best strategy is to buy a cryptocurrency and hold it until the price rises over a long period. This project aims to automate short term trading using Reinforcement Learning (RL), predominantly using the Deep Deterministic Policy Gradient (DDPG) algorithm. The algorithm integrates with the BitMEX cryptocurrency exchange and uses Technical Indicators (TIs) to create an abundance of features. Training on these different features and using diverse environments proved to have mixed results, many of them being exceptionally interesting. The most peculiar model shows that it is possible to create a strategy that can beat a buy and hold strategy relatively effortlessly in terms of profit made.

Keywords

  • Reinforcement Learning
  • Cryptocurrency
  • Trading

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References

  1. Aboussalah, A.M., Lee, C.G.: Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization. Expert Syst. Appl. 140 (2020)

    Google Scholar 

  2. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework (2019)

    Google Scholar 

  3. Alessandretti, L., ElBahrawy, A., Aiello, L.M., Baronchelli, A.: Anticipating cryptocurrency prices using machine learning. Complexity 2018, 16 (2018)

    CrossRef  Google Scholar 

  4. Baird, L.: The swirlds hashgraph consensus algorithm: fair, fast, byzantine fault tolerance. Swirlds Inc, Technical report SWIRLDS-TR-2016 1 (2016)

    Google Scholar 

  5. Bebarta, D.K., Rout, A.K., Biswal, B., Dash, P.K.: Efficient prediction of stock market indices using adaptive neural network. In: Deep, K., Nagar, A., Pant, M., Bansal, J.C. (eds.) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. AISC, vol. 131, pp. 287–294. Springer, New Delhi (2012). https://doi.org/10.1007/978-81-322-0491-6_28

    CrossRef  Google Scholar 

  6. Choi, H.K.: Stock price correlation coefficient prediction with ARIMA-LSTM hybrid model, 05 August 2018

    Google Scholar 

  7. Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning, 09 September 2015

    Google Scholar 

  8. Milutinovic, M.: Cryptocurrency. 2334–9190 (2018). http://ageconsearch.umn.edu/record/290219/

  9. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Manubot (2008)

    Google Scholar 

  10. Nevmyvaka, Y., Feng, Y., Kearns, M.: Reinforcement learning for optimized trade execution. In: Proceedings of the 23rd international conference on Machine learning, pp. 673–680 (2006)

    Google Scholar 

  11. OpenAI: Gym: A toolkit for developing and comparing reinforcement learning algorithms, 21 October 2019. https://gym.openai.com/

  12. Schnaubelt, M.: Deep reinforcement learning for the optimal placement of cryptocurrency limit orders. FAU Discussion Papers in Economics (2020)

    Google Scholar 

  13. Shin, W., Bu, S.J., Cho, S.B.: Automatic financial trading agent for low-risk portfolio management using deep reinforcement learning, 07 September 2019

    Google Scholar 

  14. Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., Riedmiller, M.: Deterministic policy gradient algorithms (2014)

    Google Scholar 

  15. Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big data: deep learning for financial sentiment analysis. J. Big Data 5(1), 3 (2018)

    CrossRef  Google Scholar 

  16. Sutton, R.S.: Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning, second edn. The MIT Press, Cambridge (2018)

    Google Scholar 

  17. TA-Lib: Ta-lib: Technical analysis library - home (2020). https://ta-lib.org/

  18. Xiong, Z., Liu, X.Y., Zhong, S., Yang, H., Walid, A.: Practical deep reinforcement learning approach for stock trading, 19 November 2018

    Google Scholar 

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Author information

Authors and Affiliations

  1. Avaya, Mervue, Galway, Ireland

    Evan Tummon

  2. Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Muhammad Adil Raja & Conor Ryan

Authors
  1. Evan Tummon
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  2. Muhammad Adil Raja
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  3. Conor Ryan
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Corresponding author

Correspondence to Evan Tummon .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Dr. Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Dr. Hujun Yin

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Cite this paper

Tummon, E., Raja, M.A., Ryan, C. (2020). Trading Cryptocurrency with Deep Deterministic Policy Gradients. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_22

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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