Borrageiro, G;
Firoozye, N;
Barucca, P;
(2022)
Reinforcement Learning for Systematic FX Trading.
IEEE Access
, 10
pp. 5024-5036.
10.1109/ACCESS.2021.3139510.
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Abstract
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 targeting 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.
| Type: | Article |
|---|---|
| Title: | Reinforcement Learning for Systematic FX Trading |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ACCESS.2021.3139510 |
| Publisher version: | https://doi.org/10.1109/ACCESS.2021.3139510 |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | policy gradients, recurrent reinforcement learning, online learning, transfer learning, financial time series |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10141040 |
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