Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Trading and Market Microstructure
2023-06-23 v1 Artificial Intelligence
Computational Engineering, Finance, and Science
Machine Learning
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. Using a state-of-the-art CGAN (from Coletta et al. (2022)), we explore its dependence upon input features, which highlights 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.
Cite
@article{arxiv.2306.12806,
title = {Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness},
author = {Andrea Coletta and Joseph Jerome and Rahul Savani and Svitlana Vyetrenko},
journal= {arXiv preprint arXiv:2306.12806},
year = {2023}
}