Automatic Market Prediction System Via an Ensembled Model

The rapid development of computer technology has enabled thousands of stock tradings to happen within a fraction of a second. In this fast-moving market, it is crucial to automate trading with the help of machine learning and deep learning algorithms to free operators from repetitive work and to maximize profit. Buying low and selling high sounds easy but can be difficult to achieve in a highly volatile market. This paper proposes a method that ensembles ResNet, Multi-layer Perceptron and XGBoost to make profitable short-term trading decisions in real-time. The ensemble model outperforms each of the individual model, achieving a reward of 9052 on the Jane Street Market Prediction dataset.