Design and Implementation of Technical Analysis Based LSTM Model for Stock Price Prediction

Abstract

The paper describes the design and implementation of a Long Short-Term Memory (LSTM) model for stock price prediction based on technical analysis. The model use technical indicators such as moving averages and Bollinger Bands to discover trends in the stock market and forecast future stock values. Historical stock data was used to extract technical indicators for the model. These indicators were then used as input features to train the LSTM model using a supervised learning strategy. Metrics such as mean absolute error, mean squared error, and root mean squared error were used to assess the model's performance. However, as investment became more accessible, the stock market became more difficult and volatile. This paper proposes a stock price prediction system that employs a (LSTM) oriented neural network to forecast the next-day closing price of APPLE shares. Regression and LONG SHORT-TERM MEMORY models are constructed using selected input variables, and their performance is evaluated using RMSE, MAPE, and R squared error metrics to analyze the stock's trend for buying and selling.