Designing an Algorithmic Trading System, Based on Deep Learning (Case Study: Tehran Stock Exchange)

With the development of computer systems in recent years, transactions in financial markets have been made available for investors. Artificial intelligence (AI) based models have also used in the financial markets due to the development of information systems and their ability to store and retrieve the large volumes of financial data. As a result, lots of research has been done using artificial intelligence methods to design algorithmic trading systems. In this regard, deep learning, one of the newest subfields of artificial intelligence, has also attracted attentions. This research presents a new approach for modeling the buying and selling process in the stock market based on deep learning and LSTM and CNN methods. In the proposed method, the forecast of the future value of stock indicators obtained using the LSTM algorithm is used as the input features of a CNN network. The CNN network as a classification model provides the buy/sell signal for the algorithmic trading system. In addition, the EC-FS model has been used to determine the most appropriate input indicators for the classification model. The proposed model has been evaluated on the Tehran Stock Exchange market and five selected stocks. The results of this model have been compared with other models such as LSTM-MLP, RNN-CNN and RNN-MLP. As a result, it can be concluded that the LSTM algorithm performs better for forecasting the indicators of selected stocks. According to this study, deep learning models are more efficient than the surface neural network models, demonstrating higher performance in predicting time series indicators and determining buy/sell signals. In four out of five tested stocks, the combined LSTM-CNN method had significantly higher accuracy than the other mixed methods. It is a general statement that the collaborative LSTM-CNN method is more effective than other methods at training the buying and selling process. This study can help the stock market of Iran participants for designing the most effective trading strategy.