Index Futures Trading with Stable Profits using Deep Learning Models

This research presents a transfer learning approach for deep learning models to predict monthly average index of Standard and Poor's 500(S\&P 500) and Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX) and use it to simulate trading E-mini S\&P 500 and Mini-TAIEX futures contracts for evaluation. It conducts three experiments to show that the approach can gain stable profits. The first experiment is to analyze the results of different types of data preprocessing and trading strategies and find a general one for the following experiments. Second, we compared the results between the original and transfer learning methods to prove that our techniques are able to get consistent earnings. Finally, we proposed some ensemble models and found that the ensemble methods were more effective and stable to make profits.