Navigating XRP Volatility: A Deep Learning Perspective on Technical Indicators

Abstract

The rise of cryptocurrency has dramatically changed. Cryptocurrencies have dramatically reshaped the landscape of financial transactions, enabling seamless crossborder exchanges without centralized oversight. This revolutionary shift, powered by blockchain technology, has democratized currency control, entrusting it to a widespread network of participants rather than a single entity. Originating from Satoshi Nakamoto's introduction of Bitcoin, this digital currency model operates on a decentralized framework, contrasting starkly with traditional, centrally governed monetary systems. This research delves into forecasting the price of Ripple (XRP) by leveraging advanced deep-learning approaches and various technical indicators. This study achieves remarkable precision in its predictions through the meticulous preprocessing of data and the application of neural networks, particularly the convolutional neural network-gated recurrent unit hybrid model. Technical indicators further refined these forecasts, highlighting the effective collaboration between machine learning techniques and financial market analysis. Despite the volatile nature of the cryptocurrency market, this work makes a substantial contribution to the field of cryptocurrency prediction strategies, advocating for further investigations into the effects of macroeconomic factors and the utilization of more extensive datasets to deepen our understanding of market dynamics.