The Recurrent Reinforcement Learning Crypto Agent
This work addresses the challenge of automated cryptocurrency trading for investors, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of intraday trading of Bitcoin derivatives by developing a recurrent reinforcement learning agent that uses an echo state network for feature representation, achieving a total return of 350% over five years with an annualized information ratio of 1.46.
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.