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The Recurrent Reinforcement Learning Crypto Agent

Author

Listed:
  • Gabriel Borrageiro
  • Nick Firoozye
  • Paolo Barucca

Abstract

We demonstrate a novel application of online transfer learning for a digital assets trading agent. This agent uses a powerful feature space representation in the form of an echo state network, the output of which is made available to a direct, recurrent reinforcement learning agent. The agent learns to trade the XBTUSD (Bitcoin versus US Dollars) perpetual swap derivatives contract on BitMEX on an intraday basis. By learning from the multiple sources of impact on the quadratic risk-adjusted utility that it seeks to maximise, the agent avoids excessive over-trading, captures a funding profit, and can predict the market's direction. Overall, our crypto agent realises a total return of 350\%, net of transaction costs, over roughly five years, 71\% of which is down to funding profit. The annualised information ratio that it achieves is 1.46.

Suggested Citation

  • Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2022. "The Recurrent Reinforcement Learning Crypto Agent," Papers 2201.04699, arXiv.org, revised May 2022.
  • Handle: RePEc:arx:papers:2201.04699
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    References listed on IDEAS

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    1. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2021. "Reinforcement Learning for Systematic FX Trading," Papers 2110.04745, arXiv.org, revised May 2022.
    2. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
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    Cited by:

    1. V. Lanzetta, 2024. "Transfer learning for financial data predictions: a systematic review," Papers 2409.17183, arXiv.org.

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