A trading algorithm to establish a suitable investment system with a reasonable return (Case study: Tehran Stock Exchange)

One of the most important issues in modern financial markets is finding efficient ways to summarize and visualize stock market information. The purpose of this paper is to discover a method to reduce risk and increase investment returns. By analyzing the mass volume of Tehran stock market data as a case study, and finding the relationships between the data and the discovery of their hidden information that has a significant impact on investors' decisions; an algorithm was designed. Moreover, the data from the automobile industry and oil products and the index of various industries were utilized from 2018 to 2022, and modeling was done by twenty technical indicators. The results of this research showed that mentioned model has a significant performance in identifying and predicting the sales signals issued at the maximum points and the prediction is done with acceptable accuracy. Portfolio management and capital supply companies can use this trading algorithm to make decisions regarding the sale, purchase or holding of securities.