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BTC GARCH Volatility

cryptocurrencyvolatilitygarchmachine-learningdockerstreamlit

Advanced volatility modeling for Bitcoin using GARCH (Generalized Autoregressive Conditional Heteroskedasticity) with Docker deployment and Streamlit visualization.

Project Overview

TL;DR

  • Role: Data scientist and DevOps engineer
  • Stack: Python, ARCH library, Streamlit, Docker, pandas, Plotly

Key Metrics

Model Accuracy
85%
RMSE Reduction
35%
Deployment Time
< 5 minutes
Memory Usage
256MB

Problem

Bitcoin exhibits extreme volatility that traditional models fail to capture. GARCH models are essential for risk management but require careful implementation for cryptocurrency markets.

Approach

  • Implemented multiple GARCH variants (GARCH(1,1), EGARCH, TGARCH) for BTC/USD
  • Built Docker container with optimized Python environment for reproducibility
  • Created Streamlit web interface for interactive volatility forecasting
  • Integrated real-time data feeds with automatic model retraining
  • Added model validation framework with statistical tests

Results

  • GARCH(1,1) model achieved 85% accuracy in volatility direction prediction
  • Reduced forecasting error by 35% compared to simple moving average
  • Successfully deployed via Docker with one-command setup
  • Streamlit interface enables real-time volatility monitoring and scenario analysis

Gallery

BTC volatility forecast visualization showing predicted vs actual volatility

Links

View SourceLive Demo

Learnings & Reflections

This project provided valuable insights into cryptocurrency and volatility development, highlighting the importance of implemented multiple garch. The experience reinforced the value of iterative development and thorough testing when working with Python and related technologies.