2025/01/15 by Hamza Bodor, Bodor, Hamza, Laurent Carlier +1 · 1 citation
Computer Science · Decision Sciences · #Artificial Intelligence in Games #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Mathematics, Computing, and Information Processing #Simulation Techniques and Applications #Trading and Market Microstructure (q-fin.TR)
paper · pdf · doi:10.48550/arxiv.2501.08822
openalex publication_date 2025/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.