PREDICTING BITCOIN PRICE FLUCTUATIONS THROUGH ON-CHAIN DATA AND WHALE-ALERT TWEET ANALYSIS WITH Q-LEARNING ALGORITHM
DOI:
https://doi.org/10.1808/zx88he37Keywords:
Bitcoin trend prediction; data features; historical price, CryptoQuant data; sentiment analysis; Q-learningAbstract
As cryptocurrency adoption, particularly Bitcoin (BTC), grows in the digital economy, understanding its volatility becomes increasingly crucial. This paper addresses this by exploring the unpredictable nature of the cryptocurrency market, focusing on Bitcoin trend forecasting using on-chain data and whale-alert tweets. Utilizing a Q-learning algorithm, a form of reinforcement learning, the study examines factors such as transaction volume, network activity, and major Bitcoin transactions highlighted by whale-alert tweets. The results show that integrating on-chain and Twitter data enhances the algorithm's ability to predict Bitcoin trends. This research provides valuable insights for investors, aiding in more informed Bitcoin investment decisions and contributing to cryptocurrency risk management.
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