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

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  • Andrea Coletta
  • Joseph Jerome
  • Rahul Savani
  • Svitlana Vyetrenko

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.

Suggested Citation

  • Andrea Coletta & Joseph Jerome & Rahul Savani & Svitlana Vyetrenko, 2023. "Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness," Papers 2306.12806, arXiv.org.
    • Handle: RePEc:arx:papers:2306.12806
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    References listed on IDEAS

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    12. repec:hal:spmain:info:hdl:2441/13thfd12aa8rmplfudlgvgahff is not listed on IDEAS
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    Cited by:

    1. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.
    2. Vamsi K. Potluru & Daniel Borrajo & Andrea Coletta & Niccol`o Dalmasso & Yousef El-Laham & Elizabeth Fons & Mohsen Ghassemi & Sriram Gopalakrishnan & Vikesh Gosai & Eleonora Kreav{c}i'c & Ganapathy Ma, 2023. "Synthetic Data Applications in Finance," Papers 2401.00081, arXiv.org, revised Mar 2024.

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