From Technical Indicators to Trading Decisions: A Deep Learning Model Combining CNN and LSTM

Abstract: Stock market prediction is a highly attractive and popular field within finance, driven by the potential for significant profits that come with substantial risks due to data non-linearity and complex economic principles. Extracting features from trading data is crucial in this domain, and numerous strategies have been developed. Among these, deep learning has achieved impressive results in financial applications because of its robust data processing capabilities. In our study, we propose a hybrid deep learning model, the CNN-LSTM, which combines the 2D Convolutional Neural Network (CNN) for image processing with the Long Short-Term Memory (LSTM) network for managing image sequences and classification. We transformed the top 15 of 21 technical indicators from financial time series into 15x15 images for 21 different day periods. Each image is then categorized as Sell, Hold, or Buy based on the trading data. Our model demonstrates superior performance in stock predictions over other deep learning models.

SAHIB Mohamed Rida, ELKINA Hamza and ZAKI Taher. “From Technical Indicators to Trading Decisions: A Deep Learning Model Combining CNN and LSTM”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150685

@article{Rida2024,
title = {From Technical Indicators to Trading Decisions: A Deep Learning Model Combining CNN and LSTM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150685},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150685},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {SAHIB Mohamed Rida and ELKINA Hamza and ZAKI Taher}
}