Price movement prediction using deep learning: a case study of the China futures market

Financial time series prediction has always been a tricky problem due to the uncertainty in the market. It has attracted attention from industry to academia. In recent years, deep learning has shown excellent performance in many different fields. More and more researchers try to apply deep learning on financial markets. In this paper, the complete modeling process of price movement prediction is introduced. Based on high frequency data Limit Order Books, an improved deep learning model combining the local feature extraction ability of Convolutional Neural Network (CNN) with the sequential feature extraction ability of Long Short-Term Memory (LSTM) is proposed and evaluated on RB dominant contracts in the China futures market. Based on the experimental results, it is concluded that our model’s performance on prediction is better than that of single CNN and LSTM models. Through back testing, trading based on the predicted results of the proposed model can yield significantly more returns than other models.