Designing an Automated Trading System Using Convolutional Neural Network

In recent years, many articles and researches have been published on the use of machine learning methods and algorithmic trading in financial markets in order to earn returns. The aim of this study is to create an automated trading system using image processing by convolutional neural network. For this purpose, initially, after receiving the data required for the selected stocks, 28 technical analysis indicators were selected and the values of each were calculated separately for each stock. Then the time series of these indicators were converted to 2D images, and as a result, for each data on the time series of the stock price, a 2D image with dimensions of 28 x 28 was created. After labeling each image with one of the buy, sell, or hold labels, these images entered the convolutional neural network. Also, to evaluate the return and risk of the proposed system, a method for buying and selling based on the results of the model in the past has been introduced. The results show that in 80% of cases, this method is more effective than the buy and hold strategy. It also always performs better in terms of standard deviation risk and maximum drawdown. Also, the results show the high impact of trading commission on the Tehran Stock Exchange on the return of the model. In such a way that the model loses many times the profit earned for the payment of the commission.