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The Recurrent Reinforcement Learning Crypto Agent. (arXiv:2201.04699v1 [cs.LG])
Jan. 14, 2022, 2:10 a.m. | Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
cs.LG updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.LG updates on arXiv.org
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