Crypto Price Analysis: An AI Perspective

Abstract

Accurate prediction of Bitcoin market prices is challenging because of extreme volatility, nonlinear dynamics and rapid sentiment-driven fluctuations observed in the cryptocurrency market, especially Bitcoin. In this research, the authors investigate the use of machine learning and deep learning methods for forecasting Bitcoin prices using historical market data, technical indicators, and sentiment analysis from social media. A comprehensive dataset was used to develop and test three experimental setups from 2015 to 2024. The first configuration, an LSTM model was trained on historical OHLCV (Open, High, Low, Close, Volume) data only. In the second setup, the model was improved with the inclusion of technical indicators based on market behavior, including momentum indicators and trend indicators. The third configuration used social media sentiment features (extracted from over 2.5 million tweets related to Bitcoin, employing polarity and subjectivity scoring via TextBlob), added to the previous configuration. The results show that the LSTM model with technical indicators gave the best prediction performance with R2 of 0.9084 and Mean Absolute Error (MAE) of 0.0387, which is 8.5% better than the historical data-only model. By comparison, the accuracy of prediction did not significantly improve with the addition of Twitter sentiment features. The feature importance analysis also revealed that Relative Strength Index (RSI) and MACD histogram were the most significant features in predicting the price movements of Bitcoin. The study brings three significant findings: (1) a methodological framework based on LSTM neural networks for comparing the performance of various sets of features in predicting Bitcoin prices; (2) the discovery of the most relevant features in cryptocurrency markets; and (3) proof that technical indicators have a significant impact on the predictive accuracy of this model, while social media sentiment has a minor impact under the circumstances. The results offer valuable implications for researchers and practitioners building data-driven models for predicting cryptocurrencies.