Crypto Volatility Watcher
Predict which cryptocurrency is most likely to experience the highest absolute price movement tomorrow using advanced ML algorithms.
Volatility Predictions
Our AI model analyzes historical data to predict which cryptocurrency will experience the highest volatility in the next 24 hours. These predictions can help traders anticipate market movements.
Understanding Our Predictions
Our predictions show which cryptocurrency is likely to experience the highest absolute price movement in the next 24 hours, regardless of direction (up or down). Volatility represents the degree of price fluctuation, not the direction. Higher volatility typically means higher risk but also potential trading opportunities.
Market Analytics
Explore historical price data and understand the key factors driving our model's predictions. These insights help visualize market trends and model decision-making processes.
Select a cryptocurrency to view its chart
Features influencing model predictions with largest impact shown as bigger pie segments
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Model Evolution
See how our prediction model has evolved through rigorous feature engineering and hyperparameter tuning. The comparison below shows the significant performance improvements between our initial and current models.
Performance Metrics Comparison
Key Improvements
Advanced Feature Engineering
Implementation of sophisticated features like Realized Volatility, EWMA, RSI, and Bollinger Bands significantly improved model performance.
Hyperparameter Tuning
Optimization using RandomizedSearchCV with time-series aware cross-validation enhanced model accuracy by 32%.
Feature Selection
Identifying and retaining only the most impactful features reduced noise and improved generalization capability.
Time-Series Cross-Validation
Implementation of proper time-series validation prevented data leakage and ensured realistic performance estimation.
Performance in Context
Achieving 89% accuracy and 70% precision/recall in cryptocurrency volatility prediction is particularly impressive considering the inherent challenges: high noise-to-signal ratio, non-stationarity, external influence factors, market inefficiency, and susceptibility to black swan events.
Feature Engineering
Our model uses carefully engineered features derived from raw price and volume data. Understanding these features helps provide insight into the model's prediction process.
Realized Volatility (5d, 10d, 30d)
Measures the historical volatility of prices over different time windows. Calculated as the standard deviation of log returns, providing insight into recent and medium-term price fluctuations.
EWMA Volatility (5d, 10d, 30d)
Exponentially Weighted Moving Average Volatility gives more weight to recent observations. This helps capture regime changes in volatility more quickly than simple realized volatility.
Parkinson Volatility
Uses high and low prices to estimate volatility. Often considered more efficient than close-to-close volatility as it incorporates intra-period price movements that might be missed in daily closing prices.
Feature Importance
Not all features contribute equally to predictions. Through careful feature selection, our model prioritizes the most informative signals while discarding those that add noise. The Feature Importance chart (available in the Market Analytics section) visualizes which features contribute most to the model's decisions.
Why Feature Engineering Matters
Raw price data alone provides limited predictive power. By transforming this data into meaningful signals through feature engineering, we extract patterns that might not be immediately obvious. This approach led to a 32% improvement in predictive accuracy compared to our initial model.
Project Overview
Learn about the technologies and methodologies used in building this full-stack machine learning application.
About This Project
The Crypto Volatility Watcher is a full-stack machine learning application designed to predict daily cryptocurrency volatility using advanced ML techniques. The system analyzes historical data and uses a carefully tuned XGBoost model to forecast which cryptocurrency is most likely to experience significant price movements in the next 24 hours.
This project demonstrates an automated ML deployment pipeline, from data ingestion and feature engineering to model tuning, prediction, and API deployment. The frontend visualizes these predictions and provides insights into the model's decision-making process.
Built with modern web technologies and ML best practices, this application showcases the integration of data science with robust web development to create a practical, user-friendly cryptocurrency analysis tool.
Key Project Achievements
- •HTTPS-secured, deployed ML application, serving daily predictions
- •32% ML accuracy improvement through feature engineering
- •Scalable FastAPI backend with efficient data pipeline
- •Responsive Next.js frontend with interactive visualizations
- •Real-world deployment with debugging and performance optimization