Embedding Technical Analysis Into Neural Network Based Trading Systems

We have recently proposed a promising trading system for the S&P 500 index, which consists of a feature selection component and a simple filter for data preprocessing, two specialized neural networks for return prediction, and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data filtering and return integration leads to further improvements in the system. The new system uses a well-known technical indicator to split the data, and an additional indicator for reducing the number of unprofitable trades. Several system combinations are explored and tested over a 5-year trading period. The most promising system yielded an annual rate of return (ARR) of 15.99% with 54 trades. This compares favorably to the ARR for the buy and hold strategy (11.05%) and to the best results obtained using the system with no technical analysis knowledge embedded (13.35% with 126 trades).