We demonstrate an application of online transfer learning for 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 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.
翻译:我们展示了对数字资产交易代理机构进行在线转移学习的应用。该代理机构以回声状态网络的形式使用强大的地物空间代表,其产出提供给直接的、经常性的强化学习代理机构。该代理机构学会在比特墨西哥公司内部交易XBT$(比特币对美元)永久互换衍生工具合同。通过从多种影响来源学习,该代理机构力求最大化的四面风险调整效用,避免过度交易,获取资金利润,并能够预测市场方向。总体而言,我们的加密代理机构在大约5年中实现了350%的总回报率,扣除交易成本,其中71%的回报率下降到为盈利提供资金。它实现的年度信息比率为1.46。