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Reinforcement learning for high-frequency market making

Lim, YS; Gorse, D; (2018) Reinforcement learning for high-frequency market making. In: ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. (pp. pp. 521-526). ESANN: Bruges, Belgium. Green open access

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Abstract

In this paper we present the first practical application of reinforcement learning to optimal market making in high-frequency trading. States, actions, and reward formulations unique to high-frequency market making are proposed, including a novel use of the CARA utility as a terminal reward for improving learning. We show that the optimal policy trained using Q-learning outperforms state-of-the-art market making algorithms. Finally, we analyse the optimal reinforcement learning policies, and the influence of the CARA utility from a trading perspective.

Type: Proceedings paper
Title: Reinforcement learning for high-frequency market making
Event: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ISBN-13: 9782875870476
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.esann.org/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10116730
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