📈 AI Stock & Crypto Forecasts

This tool uses advanced AI time-series forecasting to predict short-term price movements for stocks and cryptocurrencies. It's designed for learning and exploration — not financial advice.

• 🔍 Search by ticker (e.g., MSFT or BTC-USD).
• 🤖 Choose Stock or Crypto mode.
• 📊 View historical prices, median forecast, and prediction interval.

🤖 How Our AI Forecasting Works

Understanding the technology behind the forecasts helps you interpret the results better and use the tool effectively for learning purposes.

What Is Chronos-Bolt?

Our forecaster uses Amazon Chronos-Bolt, a state-of-the-art AI transformer model specifically designed for time-series predictions. Think of it as GPT for numbers — instead of predicting the next word, it predicts the next price point based on historical patterns.

• 8 million parameters — Large enough to capture complex patterns, efficient enough for real-time predictions
• Pre-trained on diverse datasets — Learns from millions of time-series patterns across different markets
• Fine-tuned for financial markets — Specialized training on 64 stocks and cryptocurrencies across 11 sectors

The Ensemble Approach: Multiple Models, Better Predictions

Rather than relying on a single model, we use AutoGluon ensemble learning that combines predictions from 4 different models:

1. Chronos-Bolt (Transformer): Learns complex non-linear patterns from historical data
2. ETS (Exponential Smoothing): Captures trend and seasonality in traditional time series
3. Theta Method: Statistical approach for short-term forecasts
4. AutoARIMA: Automatic regression model selection for optimal parameter tuning

This ensemble approach reduces overfitting and improves reliability by leveraging the strengths of different methodologies. If one model struggles with a particular pattern, others may capture it better.

Training Data & Features

Our model is trained on comprehensive market data to understand diverse market conditions:

• 64 tickers across 11 sectors: Technology, Finance, Energy, Healthcare, Consumer Goods, and more
• 5 years of historical data (2020-2025): Includes bull markets, bear markets, COVID crash, and recovery
• 23 exogenous features: Market-wide indicators that influence individual stocks:
  • VIX (Volatility Index) — Market fear gauge
  • SPY (S&P 500 ETF) — Overall market direction
  • TNX (10-Year Treasury Yield) — Interest rate environment
  • DXY (US Dollar Index) — Currency strength
  • Calendar patterns (day of week, month, quarter) — Temporal effects
• Technical indicators post-prediction: RSI (momentum), MACD (trend), Moving Averages (support/resistance)

How We Measure Accuracy

We use multiple metrics to evaluate forecast quality, ensuring predictions are both accurate and reliable:

• MASE (Mean Absolute Scaled Error): Primary metric — compares our forecasts to a naive baseline (yesterday's price). Lower is better.
• sMAPE (Symmetric Mean Absolute Percentage Error): Measures percentage error symmetrically between over/under predictions
• RMSE (Root Mean Squared Error): Penalizes large errors more heavily than small ones
• Directional Accuracy: How often we correctly predict whether price goes up or down

Current Performance: Our ensemble achieves a MASE of approximately -4.5 to -4.2, meaning it performs significantly better than a naive "use yesterday's price" baseline across diverse market conditions.

Walk-Forward Validation: Testing in Real Conditions

We don't just test on historical data — we simulate real-world trading conditions using walk-forward validation:

1. Train the model on historical data up to a certain date
2. Make predictions for the next period (as if we don't know the future)
3. Compare predictions to actual prices that occurred
4. Move forward in time and repeat

This approach ensures the model isn't "cheating" by using future information and provides realistic accuracy estimates.

What Makes Our Forecasts Unique?

• Ensemble of 4 models instead of single-model predictions
• 23 market-wide features provide broader context beyond just historical prices
• Technical indicators applied post-prediction for refined estimates
• Trained on diverse sectors and market conditions (2020-2025 includes extreme volatility)
• Prediction intervals show uncertainty ranges, not just single-point estimates
• Continuously updated with latest market data via automated pipelines

Understanding the Limitations

No AI model can predict the future with certainty. Here's what you should know:

• Short-term forecasts only: Designed for 7-30 day predictions, not long-term investing
• Uncertainty increases over time: Next-day predictions are more reliable than 30-day forecasts
• Cannot predict black swan events: Unexpected news, policy changes, or market shocks aren't in historical data
• Past performance doesn't guarantee future results: Market patterns can change
• Educational tool, not trading advice: Use for learning about AI and time-series forecasting

Best Use Cases: Understanding AI forecasting methodology, comparing different stocks/cryptos, identifying potential trends for further research, learning about ensemble models and time-series analysis.

🚀 Ready to Explore?

Now that you understand how the AI works, try it yourself! Search for any stock or cryptocurrency ticker and see the ensemble predictions in action.

⚠️ Important Notice

The forecasts are AI model estimates with inherent uncertainty and can be wrong. Markets are influenced by countless factors including news, policy changes, geopolitical events, and investor sentiment that may not be captured in historical patterns.

Do not make trading or investment decisions based solely on this tool. This is an educational resource for learning about AI forecasting, not financial advice. Always do your own research and consult with qualified financial advisors before making investment decisions.

This tool is for educational purposes only. Not financial advice.