We gather data from multiple sources including exchanges, on-chain metrics, sentiment analysis, and technical indicators every hour. This includes real-time price feeds from CoinGecko and Binance, trading volume data, blockchain metrics, social sentiment from Twitter and Reddit, and technical analysis signals from TradingView. The diverse data sources ensure comprehensive market coverage and reduce reliance on any single data point.

Our AI models analyze historical patterns, market sentiment, and real-time data to generate price range predictions. The system employs machine learning algorithms trained on years of Bitcoin price history, incorporating technical indicators like RSI, MACD, and Bollinger Bands. Sentiment analysis weighs social media trends and news impact, while on-chain metrics provide insights into network activity and holder behavior. The models continuously learn and adapt to evolving market conditions.

Predictions are refreshed every hour to provide the most accurate forecast for the upcoming 60-minute window. Each prediction includes a low and high price range, calculated with a confidence interval based on current market volatility. The system automatically adjusts prediction ranges during periods of high volatility or significant market events. Historical predictions are stored and compared against actual price movements to continuously improve model accuracy.

All predictions are recorded and compared against actual price movements to continuously improve our models. We track whether the actual price range falls within our predicted range for each hour, calculating an overall success rate. This feedback loop allows the AI to identify patterns in prediction errors and adjust its algorithms accordingly. Performance metrics are publicly displayed on the Accuracy page, ensuring transparency and accountability in our prediction methodology.