Ethereum Cryptocurrency Entry Point and Trend Prediction using Bitcoin Correlation and Multiple Data Combination

Deep learning methods have achieved significant success in various applications, including trend signal prediction financial markets. However, most existing approaches only utilize price action data. In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the of Ethereum cryptocurrency. We conduct experiments investigate relationship between action, candlestick patterns, Ethereum-Bitcoin correlation, aiming achieve highly accurate predictions. evaluate compare two different training strategies for Convolutional Neural Networks (CNNs), one based on transfer other from scratch. Our proposed 1-Dimensional CNN (1DCNN) model can also identify inflection points trends during specific periods through analysis statistical indicators. demonstrate our produces more reliable predictions when utilizing representations. show by combining types data, it is possible accurately both signals with an accuracy 98%.