The research aims to assess the necessity and the extent to which advanced tools for processing and analyzing big data are used in stock trading. Forecasting prices on the stock exchange is an important task due to two factors. First, it is necessary to earn money through trading. Second, forecasting market trends enables investment funds to plan their investments, while manufacturing companies can better anticipate material costs and set prices for their products. The main research methods include analysis, synthesis, and statistical methods. The research examined the quotes of ordinary shares of Sberbank. By studying quotes from historical data, the authors trained a system to predict future stock prices. While not optimal, the result lays a solid foundation for further enhancing the accuracy of forecasts. The research results led to well-founded conclusions about the strong relevance of using big data analysis in stock trading. Additionally, the authors outlined practical areas where the proposed methods can be most effective.