Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning
This addresses the need for accurate cryptocurrency price prediction for investors and traders, but it is an incremental study focusing on combining existing metrics.
The paper tackles the problem of predicting Bitcoin price movements by combining market sentiment (Fear & Greed Index) with technical analysis indicators using machine learning, resulting in initial experiments that show promising returns surpassing the Buy & Hold baseline.
Cryptocurrencies have gained significant attention in recent years due to their decentralized nature and potential for financial innovation. Thus, the ability to accurately predict its price has become a subject of great interest for investors, traders, and researchers. Some works in the literature show how Bitcoin's market sentiment correlates with its price fluctuations in the market. However, papers that consider the sentiment of the market associated with financial Technical Analysis indicators in order to predict Bitcoin's price are still scarce. In this paper, we present a novel approach for predicting Bitcoin price movements by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms. This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. Our initial experiments demonstrate promising results considering investment returns, surpassing the Buy & Hold baseline, and offering valuable insights about the combination of indicators of sentiment and market in a cryptocurrency prediction model.