Application of Machine Learning Methods in the Development of Stock Market Trading Strategies

The development of information technology and e-commerce has led to significant changes in the financial industry. Electronic trading has not only facilitated access to markets, but also improved operational processes, which has contributed to increased investment efficiency. In addition, modern technologies such as machine learning are actively developing and are able to provide new tools for market analysis and stock price forecasting, which significantly improves the understanding of risks and investment prospects. This work investigates various machine learning methods and proposes a new model based on convolutional neural networks to improve the accuracy of forecasts. The aim of the research paper is to investigate and compare machine learning methods for predicting stock prices and to develop a new model to improve trading strategies. The results demonstrate the advantages of the CNN model over other architectures, emphasizing the importance of choosing the right model for a specific task. The presented trading strategy, based on the analysis of stock price minima and maxima, provides automation of the trading process and improves the efficiency of investment decisions.