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Limit order books are a fundamental and widespread market mechanism. Thispaper investigates the use of conditional generative models for order booksimulation. For developing a trading agent, this approach has drawn recentattention as an alternative to traditional backtesting due to its ability toreact to the presence of the trading agent. Using a state-of-the-art CGAN (fromColetta et al. (2022)), we explore its dependence upon input features, whichhighlights both strengths and weaknesses. To do this, we use "adversarialattacks" on the model's features and its mechanism. We then show how theseinsights can be used to improve the CGAN, both in terms of its realism androbustness. We finish by laying out a roadmap for future work.

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