LSTM Deep Learning Based Stock Price Prediction with Bollinger Band, RSI, MACD, and OHLC Features.
Keywords:
Deep Learning, Machine Learning, LSTM, Stock Price PredictionAbstract
The prediction of stock prices is a challenging task due to the volatility of stock prices. This research article aims to identify the effectiveness of using different technical indicators and the LSTM neural network machine learning algorithm for predicting trends and stock prices. This study used historical stock price data from the National Stock Exchange of India (NSE) for the period from January 1, 2020, to July 10, 2023, and used the Yahoo Finance API, which provides Open, High, Low, and Close (OHLC) values. By using these values, we calculated different technical indicators such as the Relative Strength Index (RSI), Bollinger Bands, and Moving Average Convergence Divergence (MACD) and used these indicators as features. In this study, the next day's closing price of stocks and trend are predicted using the Long Short-Term Memory (LSTM) algorithm. The performance of this model is evaluated using different metrics such as R-squared (R2 score), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The trend identified is measured with the help of the confusion matrix. Sample stocks such as RELIANCE, ASIAN PAINTS, HINDUSTAN UNILEVER, KOTAK BANK, and INFOSYS were selected for study purposes. The results of this study demonstrate the ability of combining technical indicators and LSTM neural networks for stock price prediction and trend prediction.
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