Mbt-gym: Reinforcement learning for model-based limit order book trading

Jerome, Joseph ORCID: 0000-0002-8312-0053, Sánchez-Betancourt, Leandro ORCID: 0000-0001-6447-7105, Savani, Rahul ORCID: 0000-0003-1262-7831 and Herdegen, Martin ORCID: 0000-0002-2092-7167 (2023) Mbt-gym: Reinforcement learning for model-based limit order book trading. In: ICAIF '23: 4th ACM International Conference on AI in Finance.

Access the full-text of this item by clicking on the Open Access link.

Official URL: http://dx.doi.org/10.1145/3604237.3626873

Abstract

Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems such as market making and optimal execution. This paper introduces mbt_gym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems in limit order books. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorised environments to allow faster training of RL agents. In this paper, we motivate the use of RL to solve such model-based limit order book problems, we explain the design of our gym environment, and then demonstrate its use and resulting insights from solving standard and novel problems. Finally, we lay out a roadmap for further development and use of our module for research into limit-order-book trading.

Item Type:	Conference or Workshop Item (Unspecified)
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	27 Nov 2023 09:07
Last Modified:	29 Dec 2023 14:48
DOI:	10.1145/3604237.3626873
Open Access URL:	https://dl.acm.org/doi/10.1145/3604237.3626873
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177015