Indian Journal of Science and Technology
Year: 2022, Volume: 15, Issue: 11, Pages: 474-480
S Pandikumar1,*, S Bharani Sethupandian2, M Sakthi Saravanan3, S Navin Prasad4, M Arun5
1 Assistant Professor, Department of Computer Science, The American College, Madurai, Tamil Nadu, India
2 Assistant Professor, Department of Computer Science, Mannar Thirumalai Naicker College, Madurai, Tamil Nadu, India
3 Assistant Professor, Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, Tamil Nadu, India
4 Assistant Professor, Department of Computer Science, Nagarathinam Angalammal Arts and
Science College, Madurai, Tamil Nadu, India
5 Assistant Professor, Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamil Nadu, India
*Corresponding author email: [email protected]
Received Date:04 January 2022, Accepted Date:04 February 2022, Published Date:14 March 2022
Background/Objectives: Stock price movement prediction is a difficult task that is simulated using machine learning algorithms to anticipate stock returns. Methods: This study uses the Long-Short-Term Memory (LSTM) Recurrent Neural Network deep learning algorithm combined with the stock’s price action method to predict the movement of intraday price for short-term forecasting. The dataset uses data points such as date, open, high, low, close, volume. The predictions of price movement accuracy were tested on State Bank of India (SBI) stock and one year of the trading dataset used for training the algorithm. Findings: The proposed algorithm gives the prediction of price movement accuracy is up to 98.9%, MSE is 0.918 and MAPE 0.987 with one year of the training dataset. The SBI share price can be predicted one day before and the price prediction can be range level, which means upward or downward. The proposed method has proven to be better than traditional machine learning methods in terms of prediction accuracy and speed. Novelty: This research suggested a fine-tuned and personalized deep learning prediction system that coupled the price action technique with LSTM to make predictions. The combination of the price action method with deep learning algorithm in forecasting is not tried before but this paper does.
Keywords: LongShortTerm Memory, Stock Trading, Stock Price prediction, Recurrent Neural Network, Deep Learning, Machine Learning
© 2022 Pandikumar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Published By Indian Society for Education and Environment (iSee)
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