Forecasting Bitcoin Volatility Through on-Chain and Whale-Alert Tweet Analysis Using the Q-Learning Algorithm

【Author】 Azamjon, Muminov; Sattarov, Otabek; Cho, Jinsoo

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】As the adoption of cryptocurrencies, especially Bitcoin (BTC) continues to rise in today's digital economy, understanding their unpredictable nature becomes increasingly critical. This research paper addresses this need by investigating the volatile nature of the cryptocurrency market, mainly focusing on Bitcoin trend prediction utilizing on-chain data and whale-alert tweets. By employing a Q-learning algorithm, a type of reinforcement learning, we analyze variables such as transaction volume, network activity, and significant Bitcoin transactions highlighted in whale-alert tweets. Our findings indicate that the algorithm effectively predicts Bitcoin trends when integrating on-chain and Twitter data. Consequently, this study offers valuable insights that could potentially guide investors in informed Bitcoin investment decisions, thereby playing a pivotal role in the realm of cryptocurrency risk management.

【Keywords】Bitcoin trend prediction; data features; historical price; cryptoquant data; sentiment analysis; Q-learning

【发表时间】2023

【收录时间】2023-11-01

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测