Stock Market Analysis, Forecasting, and Automated Trading Using Deep Learning

Stock price prediction remains a prominent area of interest among investors due to its potential impact on financial decision making.We developed a deep learning-based system for stock market analysis, forecasting, and automated trading.Utilizing historical financial data, technical indicators, and sentiment information, long short-term memory (LSTM) networks were employed to model and predict stock price movements.The predicted outcomes were integrated into a rule-based automated trading system to simulate real-time buy and sell decisions.Experimental evaluations conducted on the Taiwan Stock Exchange (TWSE) indicate that the developed model surpasses baseline models in both prediction accuracy and trading profitability.The system presents the capability of deep learning to improve forecasting precision and facilitate intelligent, automated trading strategies within contemporary financial markets.