Technical Analysis of Stock Market Trends using LSTM for Price Prognosis

This study proposes the utilization of Long Short Term Memory (LSTM) brain networks for specialized analysis of stock exchange patterns and trends to anticipate future stock prices. In this particular study, a comparison is done with three other Machine Learning techniques to show that LSTM is the best implementation. The LSTM model is trained on the previous stock market data, including prices and trading volumes, to learn complex patterns and relationships with stock price trends. The model's accuracy in predicting future stock prices is evaluated, and the results show that LSTM is an effective tool for technical analysis, outperforming traditional time-series forecasting methods. LSTM's ability to capture long term dependencies and unpredictable relationships in the dataset makes it a valuable tool for traders and investors seeking to make informed decisions based on technical analysis of stock market trends. The study demonstrates the potential of LSTM in stock market forecasting, improving investment decision making with accurate price predictions.