A Cryptocurrency Market Dynamics: A Hybrid Deep Learning Framework for Bitcoin Price Prediction Using Sentiment Analysis, Technical Indicators, and On-Chain Data

Abstract

The cryptocurrency market, particularly Bitcoin, has emerged as one of the most volatile and speculative financial assets in modern history, attracting significant attention from investors, regulators, and researchers. The extreme price volatility, driven by a complex interplay of market sentiment, macroeconomic factors, technological developments, and regulatory announcements, presents both opportunities and challenges for prediction and risk management. This paper presents CryptoPredict, a novel hybrid deep learning framework that integrates three complementary data sources and analytical approaches for comprehensive Bitcoin price prediction: (1) a sentiment analysis module that extracts market sentiment from Twitter, Reddit, and news articles using fine-tuned transformer-based language models, (2) a technical analysis module that processes over 50 technical indicators derived from price and volume data using attention-based recurrent neural networks, and (3) an on-chain data module that analyzes blockchain transaction data, including network activity, transaction volume, and miner behavior, using graph neural networks. The framework employs a sophisticated multi-modal fusion mechanism that dynamically weights the contributions of each data source based on market conditions and predictive performance. We evaluate CryptoPredict on a comprehensive dataset comprising over 5 years of Bitcoin price data (2019-2024), 500 million social media posts, and complete on-chain transaction records. Our empirical results demonstrate that CryptoPredict achieves a Mean Absolute Percentage Error (MAPE) of 1.87% and a Root Mean Squared Error (RMSE) of 342.1 for 24-hour price predictions, significantly outperforming state-of-the-art baseline models including LSTM (MAPE: 2.89%), Transformer (MAPE: 2.71%), and ensemble methods (MAPE: 2.34%). The multi-modal integration proves particularly effective, improving prediction accuracy by 23.4% over the best single-modality model. We also evaluate the framework's performance for longer-term predictions (7-day and 30-day horizons), achieving MAPEs of 4.32% and 8.76%, respectively. We critically examine the limitations of our approach, including the challenge of handling black swan events, the computational cost of real-time multi-modal processing, and the ethical implications of cryptocurrency prediction, and propose a lightweight deployment variant that reduces computational requirements by 65% while maintaining 91% of the prediction accuracy. Our findings suggest that effective cryptocurrency prediction requires not merely sophisticated algorithms, but a principled integration of diverse data sources, domain expertise, and rigorous model evaluation—a lesson with profound implications for algorithmic trading, risk management, and regulatory oversight