Abstarct: Profitability and investment in the stock market have a constructive role in economic prosperity and creating a suitable job and welfare environment in society. On the other hand, the stock market and capital markets are volatile and unpredictable. Therefore, it is important to provide solutions and techniques that reduce the risk of investing in the stock market and increase profitability. In the stock market, trading rules are used to make more profit, but this alone is not enough and a combination of different and appropriate strategies must be used for high returns in the stock market. In the present research, an attempt has been made to present a strategy baxsed on the performance of the bat algorithm. The proposed strategy, called SMBAT, is baxsed on turbulence theory, and by combining it with the bat algorithm, we tried to take advantage of both to gain returns from the stock market. In the literature, the use of genetic and pso algorithms is common and in this research we have used the pso algorithm to compare the results of the two algorithms and we have studied the combination of the stock calculation algorithm using the bat algorithm for research. The weighting of the algorithm is done by the bat algorithm and the weights are determined with each update and the results are provided to the central stock exchange calculation algorithm after each step, so that the amount of profit can be calculated by the algorithm. The Bat Algorithm has been performed to determine the most optimal weights with the aim of maximizing profits and testing the algorithm on the data extracted from the official website of the Tehran Stock Exchange. The results of the implementation of the algorithm indicate that the algorithm is able to achieve appropriate and significant profitability in conditions where the linear trend of stocks is range, up-trend and down-up-range, but in conditions where stocks are down- The algorithm is not able to make a profit and even increases the investment risk. The proposed algorithm was significantly more profitable compared to the pso algorithm, but in cases where the share of the downtrend was trending, the pso algorithm performed better and made a small profit. By calculating the precision, the algorithm obtained a value of 76% and with an accuracy of 83% managed to obtain an F-score of approximately 76%. The proposed algorithm performed worse than the hybrid pso algorithm, which achieved 90% accuracy, but was able to obtain a more reliable profit margin.