Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness

Coletta, Andrea, Jerome, Joseph, Savani, Rahul ORCID: 0000-0003-1262-7831 and Vyetrenko, Svitlana (2023) Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness In: 4th ACM International Conference on AI in Finance.

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

Abstract

Limit order books are a fundamental and widespread market mechanism. This paper investigates the use of conditional generative models for order book simulation. For developing a trading agent, this approach has drawn recent attention as an alternative to traditional backtesting, due to its ability to react to the presence of the trading agent. We explore the dependence of a state-of-the-art conditional generative adversarial network (CGAN) upon its input features, highlighting both strengths and weaknesses. To do this, we use "adversarial attacks"on the model's features and its mechanism. We then show how these insights can be used to improve the CGAN, both in terms of its realism and robustness. We finish by laying out a roadmap for future work.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	GANs, synthetic data, time-series, financial markets
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	27 Nov 2023 09:07
Last Modified:	23 May 2026 08:13
DOI:	10.1145/3604237.3626854
Open Access URL:	https://dl.acm.org/doi/10.1145/3604237.3626854
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177014
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.