A Stock Pattern Recognition Algorithm Based on Neural Networks

Recent studies show that stock patterns might implicate useful information for stock price forecasting. The patterns underlying the price time series can not be discovered exhaustively by the pure man power in a limited time, thus the computer algorithm for stock price pattern recognition becomes more and more popular. Currently, there are mainly two kinds of stock price pattern recognition algorithms: the algorithm based on rule-matching and the algorithm based on template-matching. However, both of the two algorithms highly require the participation of domain experts, as well as their lacks of the learning ability. To solve these problems, the paper proposes a stock price pattern recognition approach based upon the artificial neural network. The experiment shows that the neural network can effectively learn the characteristics of the patterns, and accurately recognize the patterns.