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

Author

Listed:
  • 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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    5. Andrea Coletta & Matteo Prata & Michele Conti & Emanuele Mercanti & Novella Bartolini & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2021. "Towards Realistic Market Simulations: a Generative Adversarial Networks Approach," Papers 2110.13287, arXiv.org.
    6. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
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    8. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "Towards Robust Representation of Limit Orders Books for Deep Learning Models," Papers 2110.05479, arXiv.org, revised Dec 2022.
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    11. Zijian Shi & John Cartlidge, 2023. "Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology," Papers 2303.00080, arXiv.org.
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    Citations

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    Cited by:

    1. Hamza Bodor & Laurent Carlier, 2024. "A Novel Approach to Queue-Reactive Models: The Importance of Order Sizes," Papers 2405.18594, arXiv.org.
    2. Bokai Cao & Xueyuan Lin & Yiyan Qi & Chengjin Xu & Cehao Yang & Jian Guo, 2025. "Financial Wind Tunnel: A Retrieval-Augmented Market Simulator," Papers 2503.17909, arXiv.org.
    3. Leonardo Berti & Bardh Prenkaj & Paola Velardi, 2025. "TRADES: Generating Realistic Market Simulations with Diffusion Models," Papers 2502.07071, arXiv.org, revised Nov 2025.
    4. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.
    5. 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.
    6. Yu-Hao Huang & Chang Xu & Yang Liu & Weiqing Liu & Wu-Jun Li & Jiang Bian, 2024. "Controllable Financial Market Generation with Diffusion Guided Meta Agent," Papers 2408.12991, arXiv.org, revised Jan 2026.
    7. Bokai Cao & Saizhuo Wang & Xinyi Lin & Xiaojun Wu & Haohan Zhang & Lionel M. Ni & Jian Guo, 2025. "From Deep Learning to LLMs: A survey of AI in Quantitative Investment," Papers 2503.21422, arXiv.org.
    8. Jian Guo & Heung-Yeung Shum, 2024. "Large Investment Model," Papers 2408.10255, arXiv.org, revised Aug 2024.

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