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Reinforcement Learning for Systematic FX Trading

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posted on 28.12.2021, 20:44 authored by Gabriel BorrageiroGabriel Borrageiro, Nick FiroozyeNick Firoozye, Paolo BaruccaPaolo Barucca
We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pairs, where we accurately account for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to the agent via a quadratic utility, who learns to target a position directly. We improve upon earlier work by learning to target a risk position in an online transfer learning context. Our agent achieves an annualised portfolio information ratio of 0.52 with a compound return of 9.3%, net of execution and funding cost, over a 7-year test set; this is despite forcing the model to trade at the close of the trading day at 5 pm EST when trading costs are statistically the most expensive.

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Email Address of Submitting Author

gabriel.borrageiro.20@ucl.ac.uk

ORCID of Submitting Author

https://orcid.org/0000-0002-0063-7103

Submitting Author's Institution

University College London

Submitting Author's Country

United Kingdom

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