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

ArXiv ID: 2306.12806 “View on arXiv”

Authors: Unknown

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.

Keywords: limit order books, conditional generative adversarial networks (CGAN), order book simulation, adversarial attacks, market microstructure

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced generative models (GANs), feature dependence analysis, and adversarial training, reflecting high mathematical complexity. It uses historical order data, focuses on model robustness and realism, and discusses implementation for trading agents, indicating strong empirical rigor.

  flowchart TD
    A["Research Goal"] --> B["Model: Conditional GAN<br>(CGAN) for LOB Simulation"]
    B --> C["Adversarial Attacks<br>on Features & Mechanism"]
    C --> D["Analysis of Strengths<br>& Weaknesses"]
    D --> E{"Key Outcomes"}
    E --> F["Improved CGAN Realism<br>& Robustness"]
    E --> G["Roadmap for<br>Future Work"]
    
    subgraph "Data & Input"
        H["Limit Order Book Data"]
        H --> B
    end
    
    style A fill:#e1f5fe
    style E fill:#fff3e0