The Recurrent Reinforcement Learning Crypto Agent
- URL: http://arxiv.org/abs/2201.04699v1
- Date: Wed, 12 Jan 2022 21:00:43 GMT
- Title: The Recurrent Reinforcement Learning Crypto Agent
- Authors: Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
- Abstract summary: We demonstrate an application of online transfer learning as a digital assets trading agent.
The agent learns to trade the XBTUSD (Bitcoin versus US dollars) perpetual swap derivatives contract on BitMEX.
Overall, our crypto agent realises a total return of 350%, net of transaction costs, over roughly five years.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We demonstrate an application of online transfer learning as a digital assets
trading agent. This agent makes use of 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. It learns to trade intraday on five minutely sampled data, avoids
excessive over-trading, captures a funding profit and is also able to predict
the direction of the market. 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.
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