Title:	DATA-ENABLED CRYPTOCURRENCY MARKET ANALYSIS AND VISUALIZATION PLATFORM
Author(s):	Ningbo Zhu, Fei Yang, Mingzhi Zhu, Xinyao Sun and Irene Cheng
ISBN:	978-989-8704-32-0
Editors:	Yingcai Xiao, Ajith Abraham and Guo Chao Peng
Year:	2021
Edition:	Single
Keywords:	Cryptocurrency, Price Prediction, Data Visualization, Neural Language Processing, Sentiment Analysis, Machine Learning
Type:	Short
First Page:	133
Last Page:	137
Language:	English
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Paper Abstract:	The cryptocurrency industry has evolved rapidly in recent years, and it is increasingly popular as a convenient tool to complement the traditional stock and futures exchanges. Accurate market research enables traders to make more informed decisions and benefit from their investments. Our objective is to introduce a web platform for aggregating various types of cryptocurrency data, both on- and off-chain. Its novelty lies in offering a visual representation of market data analysis, which is driven by multi-modal data fusion and representation techniques, as well as artificial intelligence. We propose a full-stack framework that consists of a front-end web application for user interaction and visualization, and a backend server for data fetching, preprocessing, and analysis. In our implementation, we used data from the cryptocurrency market, on-chain statistics, and textual data from social media, to create a deep-learning-based market trend model. For market prediction, our data analysis module processed high-frequency vocabulary extracted from social media, sentiment analysis of social media content, historical price trend, and historical hash rates. Investors and market analysts can benefit from our platform by directly observing the dynamic of multi-modal cryptocurrency data and easily exploring market trends, generated by our market prediction model delivered by a front-end application. The complete implementation can be found in our publicly available GitHub link upon request.
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