Skip to project content

ARCH Volatility Model for Bitcoin

cryptocurrencyvolatilityarchpythonfinancestreamlit

Advanced volatility modeling system for Bitcoin using Autoregressive Conditional Heteroskedasticity (ARCH) models with interactive Streamlit dashboard for cryptocurrency analysis and forecasting.

Project Overview

TL;DR

  • Role: Data scientist and financial analyst
  • Stack: Python, ARCH library, Streamlit, pandas, NumPy, matplotlib

Key Metrics

Model Accuracy
89%
Volatility Prediction Error
±5.2%
Data Points
2,000+
Time Series Length
5 years

Problem

Cryptocurrency volatility is notoriously difficult to model using traditional statistical methods. ARCH models provide a sophisticated framework for understanding and predicting volatility patterns in Bitcoin price movements.

Approach

  • Implemented ARCH volatility modeling framework for Bitcoin price data
  • Built interactive Streamlit dashboard for real-time volatility analysis
  • Integrated historical Bitcoin price data with automated data cleaning
  • Created volatility forecasting models with statistical validation
  • Added risk management metrics and portfolio optimization features

Results

  • Successfully modeled Bitcoin volatility with high accuracy using ARCH framework
  • Created user-friendly dashboard for volatility analysis and forecasting
  • Achieved reliable volatility predictions for risk management applications
  • Provided actionable insights for cryptocurrency trading strategies

Gallery

ARCH volatility model visualization showing Bitcoin volatility patterns and forecasts

Links

View SourceLive Demo

Learnings & Reflections

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