Forecasting Stock market behavior through implementing technical indicators , based on deep reinforcement learning and convolutional network approaches Case study: Iran Stock Market

Nowadays, the stock market plays an important role in the economy of different countries. The abundance of data in the stock market and the need for fast and correct data processing and making appropriate decisions have made the use of computers inevitable. In this article, using deep reinforcement learning, a model is designed to present the duties of a trader in the Iranian stock market with regard to liquidated shares. In the first step, the history of stock prices along with the indicators based on it are given as inputs to the convolutional neural network. In the next step, in order to calculate the matching rate of the convolution output with the expected output, the sum of squared error cost function is used, which, in turn, is minimized in the optimization process. Since the data in the Iranian stock market is limited, using the convolution model instead of the Q table in the deep reinforcement model prevents the over fitting of the model. In order to evaluate the model, the data of Tehran Stock Exchange was used in the period of 1390 to 1400. The performance of the proposed method was compared with the buy and hold strategy. The results show, in some cases, the profit from the proposed method in contrast with buy and hold strategy is 21% and -7%, respectively.