Benchmark Dataset for Short-Term Market Prediction of Limit Order Book in China Markets

Abstract

Limit order books (LOBs) have generated big financial data for analysis and prediction from both academic community and industry practitioners. This article presents a benchmark LOB dataset from the Chinese stock market, covering a few thousand stocks for the period of June to September 2020. Experiment protocols are designed for model performance evaluation: at the end of every second, to forecast the upcoming volume-weighted average price change and volume over 12 horizons ranging from 1 second to 300 seconds. Results based on a linear regression model and deep learning models are compared. A practical short-term trading strategy framework based on the alpha signal generated is presented. The data and code are available on Github (github.com/HKGSAS). Key Findings ▪ There is a gap between benchmarking a high-frequency LOB dataset and model for researchers to objectively assess prediction performances, which this article serves to bridge. ▪ A more practically effective set of features is proposed to capture both LOB snapshots and periodic data. The prediction target is similarly too simplistic in the published literature—mid-price direction change for the next few events, which is not suitable for a practical trading strategy. The authors propose to predict the price change and volume magnitude over 12 short-term horizons. ▪ This article proposes comparing the performance of baseline linear regression and state-of-the-art deep learning models, based on both accuracy statistics and trading profits.