Adaptive Deep Learning based Cryptocurrency Price Fluctuation Classification

【Author】 El-Berawi, Ahmed Saied; Belal, Mohamed Abdel Fattah; Abd Ellatif, Mahmoud Mahmoud

【Source】INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS

【影响因子】0.000

【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 naive simulation of a trading scenario is developed and it shows a 69% profitability score a cross a six months trading period for bitcoin.

【Keywords】Computer intelligence; cryptocurrency; deep learning; market movement; recurrent neural network; timeseries forecasting

【发表时间】2021 DEC

【收录时间】2022-01-13

【文献类型】期刊

【主题类别】

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