用深度学习改进经典订单簿模型，更真实模拟市场行为。
Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation
- 用神经网络建模不同价格档位间的复杂依赖关系。
- 准确复现订单大小分布、市场冲击平方根定律等市场特征。
- 兼顾可解释性与计算效率，适合策略开发与回测。
Huang 等人（2015）提出的队列响应模型已成为限价订单簿建模的标准工具，因其简洁有效被广泛采用。本文提出多维深度队列响应（MDQR）模型，从三个方面扩展该框架：放松队列独立性假设，引入市场特征丰富状态空间，建模订单规模分布。通过神经网络架构，模型学习不同价格层级间的复杂依赖关系，并适应变化的市场环境，同时保持原框架的可解释点过程基础。基于德国国债期货（Bund futures）数据，实验表明MDQR能准确捕捉市场冲击平方根定律、跨队列相关性及真实的订单规模模式。模型在再现订单规模的条件与平稳分布方面表现突出，且符合多种市场微观结构的典型事实。其计算效率高，适用于强化学习策略开发或真实回测等实际应用。
原文摘要 · Abstract (English)
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.
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