Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking

TL;DR

This paper systematically compares different methods for learning representations from limit order book data, introduces a benchmark dataset and evaluation framework, and demonstrates the importance of effective LOB features for various financial tasks.

Contribution

It provides the first comprehensive benchmark and analysis of LOB representation learning, promoting better understanding and generalization of models in financial markets.

Findings

Effective LOB representations improve downstream task performance.

Traditional end-to-end models are less flexible than learned representations.

The LOBench benchmark facilitates standardized evaluation and comparison.

Abstract

The Limit Order Book (LOB), the mostly fundamental data of the financial market, provides a fine-grained view of market dynamics while poses significant challenges in dealing with the esteemed deep models due to its strong autocorrelation, cross-feature constrains, and feature scale disparity. Existing approaches often tightly couple representation learning with specific downstream tasks in an end-to-end manner, failed to analyze the learned representations individually and explicitly, limiting their reusability and generalization. This paper conducts the first systematic comparative study of LOB representation learning, aiming to identify the effective way of extracting transferable, compact features that capture essential LOB properties. We introduce LOBench, a standardized benchmark with real China A-share market data, offering curated datasets, unified preprocessing, consistent evaluation metrics, and strong baselines. Extensive experiments validate the sufficiency and necessity of LOB representations for various downstream tasks and highlight their advantages over both the traditional task-specific end-to-end models and the advanced representation learning models for general time series. Our work establishes a reproducible framework and provides clear guidelines for future research. Datasets and code will be publicly available at https://github.com/financial-simulation-lab/LOBench.