STOCK TRADE PREDICTION USING Y-FINANCE AND LONG SHORT-TERM MEMORY (LSTM).

Abstract: In the pursuit of accurately predicting stock market movements, researchers have increasingly turned to advanced machine learning techniques. This study explores the application of Long Short-Term Memory (LSTM) networks to stock price forecasting, leveraging financial data obtained through the  y-finance library. The research methodology involved ingesting historical stock price data, macroeconomic indicators, and other relevant features into an LSTM network architecture. The model was trained to learn the complex temporal dependencies and patterns inherent in the financial time series data, with the goal of generating accurate buy/sell signal predictions. Experimental results on a diverse portfolio of publicly traded stocks demonstrated the superior performance of the LSTM-based approach compared to traditional time series analysis methods. The model was able to capture subtle market dynamics and achieve notably higher accuracy in forecasting future stock price movements. The findings of this study suggest that the integration of LSTM networks and accessible financial data sources, such as y-finance, can provide a powerful tool for investors and traders seeking to optimize their investment strategies. The technique holds promise for further advancements in the field of automated financial decision-making. The technique holds promise for further advancements in the field of automated financial decision-making. This final sentence suggests that the LSTM based approach has the potential to drive further progress in the area of automated financial analysis and decision-making. By utilizing readily available tools and technologies, one can embark on their journey towards stock market prediction, potentially making informed investment decisions based on  y-finance sentiment analysis and long short-term memory modelling. This research provides a valuable tool for market participants to gain a deeper understanding of market sentiment and make data-driven investment decisions.

Keywords: Stock Market, Machine Learning, Analysing, Prediction & Education etc. Cite: Prof. Raksha Kardak., Aniket Chandore, Piyush Borkar, Pranay Alikane, Priyanshu Ramteke, Purushottam Kakde, "STOCK TRADE PREDICTION USING Y-FINANCE AND LONG SHORT-TERM MEMORY (LSTM).", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13428.