Borrageiro, Gabriel;
Firoozye, Nick;
Barucca, Paolo;
(2022)
The Recurrent Reinforcement Learning Crypto Agent.
IEEE Access
, 10
pp. 38590-38599.
10.1109/access.2022.3166599.
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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.
| Type: | Article |
|---|---|
| Title: | The Recurrent Reinforcement Learning Crypto Agent |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/access.2022.3166599 |
| Publisher version: | http://dx.doi.org/10.1109/ACCESS.2022.3166599 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Online learning, transfer learning, echo state networks, recurrent reinforcement learning, financial time series |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147117 |
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