Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation

ArXiv ID: 2501.08822 “View on arXiv”

Authors: Unknown

Abstract

The Queue-Reactive model introduced by Huang et al. (2015) has become a standard tool for limit order book modeling, widely adopted by both researchers and practitioners for its simplicity and effectiveness. We present the Multidimensional Deep Queue-Reactive (MDQR) model, which extends this framework in three ways: it relaxes the assumption of queue independence, enriches the state space with market features, and models the distribution of order sizes. Through a neural network architecture, the model learns complex dependencies between different price levels and adapts to varying market conditions, while preserving the interpretable point-process foundation of the original framework. Using data from the Bund futures market, we show that MDQR captures key market properties including the square-root law of market impact, cross-queue correlations, and realistic order size patterns. The model demonstrates particular strength in reproducing both conditional and stationary distributions of order sizes, as well as various stylized facts of market microstructure. The model achieves this while maintaining the computational efficiency needed for practical applications such as strategy development through reinforcement learning or realistic backtesting.

Keywords: Queue-Reactive Model, Limit Order Book, Multidimensional Deep Queue-Reactive, Market Impact, Neural Network, Equity Execution

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper presents a sophisticated neural network extension (MDQR) of the Queue-Reactive model, requiring advanced stochastic process theory and deep learning mathematics, while demonstrating strong empirical validation using real market data (Bund futures), reproducing key stylized facts like the square-root law, and emphasizing computational efficiency for practical backtesting.

  flowchart TD
    A["Research Goal: Extend Queue-Reactive Model"] --> B["Key Methodology: Multidimensional Deep Queue-Reactive Model"]
    B --> C["Data Input: Bund Futures Market Limit Order Book Data"]
    C --> D["Computational Process: Neural Network Architecture"]
    D --> E["Learn Complex Dependencies &<br>Order Size Distributions"]
    E --> F["Key Findings: Captures Market Impact,<br>Cross-Queue Correlations, &<br>Stylized Facts of Microstructure"]