Predicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns

Candlestick chart pattern is a technical tool that encapsulates the price of the asset for multiple time frames into a single price bar. The expertized trader can predict the price trend of the asset by looking at the pattern of some adjacent candlesticks. This paper proposes the architecture for predicting the short trend of the stocks by using the convolutional neural network and the candlestick patterns. The experiments are conducted with a set of candlestick pattern images collected from various stocks in the stock exchange of Thailand (SET). Each image captures six to twelve adjacent candlesticks. The experimental results indicate that the proposed method can correctly predict the short trend for most stocks with acceptable accuracy. In addition, the proposed architecture achieves better accuracy and training time than that of the well-known architecture, ResNet-18.