Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models

Abstract

Technical analysis indicators are widely used in the field of quantitative investment, and they are usually utilized to assist in the search for profitable buy and sell points. In order to make better use of technical indicators, a method of trying to improve the performance of technical indicators by using neural network models is proposed in this work. The method tries to utilize neural network models to learn the possible patterns or features of price and volume before the profitable buy or sell points indicated by technical indicators. In modeling, a certain length of historical market data before the buy and sell points indicated by technical indicators is taken as model inputs, and whether or not can these buy and sell points meet certain profit standard is taken as labels. We validate our method on stock indexes, stocks and futures, and the results show that our method can improve the performance of several simple but common strategies based on technical analysis indicators.