Abstract: This paper proposes a deep learning based predictive model for forecasting and classifying the price of cryptocurrency and the direction of its movement. These two tasks are challenging to address since cryptocurrencies prices fluctuate with extremely high volatile behavior. However, it has been proven that cryptocurrency trading market doesn’t show a perfect market property, i.e., price is not totally a random walk phenomenon. Based upon this, this study proves that the price value forecast and price movement direction classification is both predictable. A recurrent neural networks based predictive model is built to regress and classify prices. With adaptive dynamic features selection and the use of external dependable factors with a potential degree of predictability, the proposed model achieves unprecedented performance in terms of movement classification. A naïve simulation of a trading scenario is developed and it shows a 69% profitability score a cross a six months trading period for bitcoin.

Ahmed Saied El-Berawi, Mohamed Abdel Fattah Belal and Mahmoud Mahmoud Abd Ellatif, “Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121264

@article{El-Berawi2021,
title = {Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121264},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121264},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Ahmed Saied El-Berawi and Mohamed Abdel Fattah Belal and Mahmoud Mahmoud Abd Ellatif}
}