The Recurrent Reinforcement Learning Crypto Agent

【Author】 Borrageiro, Gabriel; Firoozye, Nick; Barucca, Paolo

【Source】IEEE ACCESS

【影响因子】3.476

【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.

【Keywords】Reinforcement learning; Transfer learning; Costs; Reservoirs; Time series analysis; Cryptography; Training; Online learning; transfer learning; echo state networks; recurrent reinforcement learning; financial time series

【发表时间】2022

【收录时间】2022-04-28

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币