Live and Realtime bitcoin price prediction using AI

Abstract

The growing popularity of cryptocurrencies has attracted significant attention from investors, researchers, and financial analysts, while simultaneously introducing new challenges due to the highly dynamic and unpredictable nature of digital asset markets. Among cryptocurrencies, Bitcoin remains the most actively traded and widely studied asset, exhibiting substantial price fluctuations influenced by market sentiment, trading volume, macroeconomic conditions, regulatory developments, and investor behavior. These characteristics make accurate price forecasting a complex task that requires advanced analytical techniques capable of capturing both temporal dependencies and non-linear relationships within financial data. This paper presents an intelligent Bitcoin Price Prediction framework that combines Long Short-Term Memory (LSTM) neural networks and Extreme Gradient Boosting (XGBoost) to improve short-term forecasting performance. The proposed system collects real-time and historical OHLCV (Open, High, Low, Close, Volume) data from cryptocurrency exchange APIs and processes the information through a comprehensive data preparation pipeline that includes cleansing, normalization, feature extraction, and technical indicator generation. A diverse set of market indicators, including moving averages, Relative Strength Index (RSI), volatility measures, momentum signals, and trend-related features, is employed to enhance the predictive capability of the models. The LSTM component is designed to learn sequential patterns and long-term temporal dependencies present in historical price movements, while the XGBoost model analyzes engineered features to identify complex non-linear interactions that influence market behavior. By leveraging the complementary strengths of both approaches, the framework provides a more comprehensive understanding of Bitcoin price dynamics than single-model prediction systems. The architecture further incorporates a Flaskbased web application that enables users to access forecasts through an interactive dashboard featuring real-time visualizations, performance metrics, historical trend analysis, and prediction summaries. Additional components such as automated model evaluation, system health monitoring, error handling, and fallback mechanisms improve the reliability and usability of the platform. Functional validation confirms the effectiveness of data acquisition, preprocessing operations, technical indicator computation, model execution, and result presentation. The proposed system serves as an educational and analytical platform for exploring machine learning applications in cryptocurrency forecasting and demonstrates how hybrid artificial intelligence techniques can be integrated into financial prediction environments. Although the framework provides valuable insights into market behavior, it is intended solely for research and learning purposes and should not be interpreted as a source of financial advice or guaranteed investment recommendations.