The Recurrent Reinforcement Learning Crypto Agent

Gabriel Borrageiro, Nikan Firoozye and Paolo Barucca

Papers from arXiv.org

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.

Date: 2022-01, Revised 2022-05
New Economics Papers: this item is included in nep-cmp and nep-pay
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Published in IEEE Access, vol. 10, pp. 38590-38599, 2022

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