Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting.

预测短期内的价格变动是高频交易中的一个具有挑战性的问题。深度学习方法最近被用来通过限价订单簿（LOB）数据来预测短期价格。在本文中，我们提出了一个框架，将 LOB 数据转换为二维矩阵中的一系列标准图像，并通过基于图像的卷积神经网络 (CNN) 预测中间价格变动。实证研究表明，基于图像的 CNN 模型优于其他基于原始 LOB 数据的传统机器学习和深度学习方法。我们的研究结果表明，LOB 图像中隐含的附加信息有助于短期价格预测。