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Crypto compare api output format9/24/2023 ![]() ![]() ![]() This data can presumably be used to infer future human behavior, and therefore could be used to develop advantageous trading strategies as has been shown in recent attempts to detect speculative bubbles in the cryptocurrency market using sentiment analysis. Īnother point of interest in the cryptocurrency market is the large-scale of available public sentiment data, particularly from social networks. These characteristics have attracted a considerable amount of capital, however up to now there are few studies that have attempted to create profitable trading strategies in the cryptocurrency market. Part of the appeal behind this market is that the technology used for mining cryptocurrency provides feasible alternative to more traditional markets such as gold. The cryptocurrency market seems to behave independently from the other financial markets, but there is a strongly influenced by Asian economies. The financial feasibility of the cryptocurrency market in relation to other markets has been documented and the algorithms upon which the cryptocurrencies operate have been validated in other fields as well. This market is characterized by high volatility, no closed trading periods, relatively smaller capitalization, and high market data availability. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.Īlthough there are some studies that deal with both the task of predicting stock market price movements, as well as the development of profitable trading strategies based on those predictions, it is important to verify the applicability of such studies in new and emerging markets in particular the cryptocurrency market. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. ![]() Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. ![]()
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