Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation (2501.08822v1)

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.

• The paper introduces the MDQR model that integrates deep learning with queue-reactive dynamics to enhance the realism of limit order book simulations.
• It relaxes the independence assumptions of price levels, enriches the state space using neural networks, and models order size variability for greater accuracy.
• Empirical validation shows the model replicates market impact patterns and predicts short-term price movements, offering actionable insights for trading strategies.

An Expert Overview of "Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation"

This paper presents a novel extension of the Queue-Reactive (QR) model for simulating limit order books (LOB), termed the Multidimensional Deep Queue-Reactive (MDQR) model. This research offers significant advancements in limit order book simulation by incorporating deep learning into the QR framework, enabling the capture of complex market dynamics with enhanced realism.

The QR model, as established by Huang et al. (2015), has been a foundational tool due to its analytical tractability and capacity to model order flow dependent on queue sizes. Nonetheless, it simplifies market dynamics by assuming queue independence and constant order sizes. To address these limitations, the MDQR expands on these foundations in several crucial ways.

Enhancements in the MDQR Model

1. Relaxation of Queue Independence: The proposed MDQR model dispels the assumption of independence between price levels found in earlier models. This is crucial because empirical data indicates significant interactions between different price levels, impacting the dynamics across the order book.
2. State Space Enrichment: By embracing a neural network architecture, the MDQR model can integrate a richer set of market features into its state space. It captures intricate dependencies between price levels and adapts to fluctuating market conditions. These enhancements are pivotal for simulating markets that respond dynamically to both liquidity and order flow.
3. Modeling Order Sizes: Incorporating order size distributions within the MDQR framework significantly improves the model's fidelity to real market conditions. Traditional models overlook the variability and impact of order sizes, a gap that MDQR addresses by conditionally modeling sizes based on market context.
4. Machine Learning Integration: The use of neural networks facilitates learning complex patterns from data, providing automated feature extraction and modeling flexibility without sacrificing the QR model’s interpretability and foundational stochastic process characteristics.

Key Findings and Implications

The MDQR model demonstrates a robust ability to replicate key market dynamics, as evidenced by empirical validation with Bund futures market data. It captures sophisticated market impact patterns, such as the concave-convex shape associated with large order executions, and adheres to the square-root law of market impact, highlighting the real-world applicability of the framework for strategy development and risk assessment. Furthermore, it reveals strong performance in predicting short-term price movements, indicating potential applications in developing high-frequency trading strategies.

Moreover, the introduction of trade imbalance as a feature in the MDQR highlights its ability to capture intra-day seasonal patterns, seamlessly integrating temporal dynamics into its framework. This improvement, alongside the model’s computational efficiency, makes it highly appealing for practical applications, particularly in reinforcement learning environments where large volumes of simulated market data are required efficiently.

Conclusion and Future Directions

Through a rigorous methodological framework and sophisticated modeling, the MDQR model represents a significant step toward more realistic and adaptable market simulation tools. The detailed treatment of order book interactions, augmented state spaces, and effective order size modeling contribute to its improved accuracy and applicability.

Looking ahead, validation of the MDQR framework in small-tick asset environments and its adaptation for use in reinforcement learning applications could further enhance its utility. Additionally, exploring its generalizability across various market conditions will provide insights into its broader impact and guide future research trajectories in financial market simulations using deep learning methodologies.

The MDQR model thus serves as a bridge between traditional stochastic models and modern machine learning approaches, offering a versatile platform for simulating and analyzing complex market dynamics in financial markets.