Predicting Futures Market Movement using Deep Neural Networks

Recently there have been many efforts to study the predictability of financial market trend using various machine learning approaches. In this paper we explore the idea of using deep neural networks to analyze and predict futures market movements. Our approach adopts deep long short term memory (LSTM) as the main model architecture and predicts futures market movement using augmented market trading data. Training and testing of our model is performed in a rolling fashion to ensure the validity and reliability of the prediction. We discuss the design trade-off of several configurations and variations of our model, and evaluate the impact of various parameter choices as well as how model and backtesting perform under different parameter settings. We further design and implement a complete trading platform to evaluate our approach. Backtesting and live paper trading of our model on this platform achieves promising returns. Moreover, a total return of 58.69% is obtained with live paper trading for a twelve-month period when taking into account of slippage and commissions, which demonstrates the effectiveness of our proposed approach.