Algorithmic Trading using Machine Learning

Predicting the value of a company's stocks is the motive of stock market predictions. Today’s market use Machine Learning to forecast current market values by training on their prior values. As part of the machine learning process, various models are used to facilitate and authenticate forecasting. In the paper, we present results from machine learning and regression based on LSTMs for predicting stock value. A number of factors come into play, such as the open, the close, the low, the high, and the volume. Data quality, overfitting, and interpretability are examined as challenges and limitations of machine learning in algorithmic trading. Our final section discusses the future directions for algorithmic trading and machine learning research. Fusion can be viewed as a method of combining data or qualities in general in order to enhance prediction using a combinational method that can support one another. Financial market machine learning models are also examined for interpretability. The final part of our discussion discusses the future of algorithmic trading and machine learning research. Fusion can be viewed as a method of combining data or qualities in general in order to enhance prediction using a combinational method that can support one another. Ultimately, the integration of machine learning into algorithmic trading has the potential to revolutionize financial markets and improve trading decision-making efficiency