VolScope

VolScope is an interactive realized volatility analytics dashboard built in Python.
It is designed for exploratory analysis of volatility dynamics in financial markets, with a focus on volatility regimes, persistence, and forward-looking behavior.

The dashboard fetches market data, computes rolling volatility metrics, and presents them through clear, interactive visualizations.

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🔧 Technologies

• Python
• pandas, NumPy
• matplotlib
• Streamlit
• Yahoo Finance API (yfinance)

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📊 Features

Here’s what you can do with VolScope:

• Market Data Ingestion
  Fetch daily adjusted price data for equities using Yahoo Finance.

• Realized Volatility Calculation
  Compute annualized realized volatility from daily log returns using configurable rolling windows.

• Multi-Horizon Analysis
  Compare short-term and long-term volatility dynamics side-by-side.

• Volatility Regime Classification
  Classify current volatility levels using rolling percentile ranks (e.g. low, normal, high volatility).

• Forward Volatility Analysis
  Analyze volatility persistence by comparing current volatility to future realized volatility averages.

• Interactive Visualization
  Explore price levels, volatility time series, and cross-sectional scatter plots in an interactive dashboard.

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🧠 The Process

I started by designing a clean data pipeline that separates data ingestion, transformation, and visualization.

First, daily closing prices are retrieved and cleaned. From these prices, log returns are computed and used to estimate annualized realized volatility over rolling windows.

Next, I implemented a percentile-based framework to contextualize current volatility relative to historical observations. This allows the dashboard to identify volatility regimes rather than relying on absolute thresholds.

To study volatility persistence, I added a forward-looking component that compares today’s volatility with the average realized volatility over the next 20 trading days, visualized through scatter plots and a fitted regression line.

Finally, I wrapped the analytics into a Streamlit application, focusing on clarity, responsiveness, and ease of experimentation.

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📐 Methodology

Realized volatility is computed as:

HVₜ = std( log(Pₜ / Pₜ₋₁) ) × √252

Volatility regimes are determined using rolling percentile ranks of short-horizon volatility.
Forward volatility is defined as the average realized volatility over the next 20 trading days.

This setup allows inspection of volatility clustering and mean reversion behavior commonly observed in financial markets.

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📈 What I Learned

Through this project, I strengthened my understanding of:

• Time series volatility dynamics and clustering
• Rolling-window statistics and percentile-based normalization
• Designing reproducible financial data pipelines
• Translating quantitative concepts into clear visual tools
• Building interactive analytical dashboards for exploration rather than static reporting

I also gained experience balancing quantitative rigor with practical usability.

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🔄 Possible Improvements

Planned or potential extensions include:

• Integration of implied volatility from options data
• Cross-asset volatility comparison
• Volatility regime backtesting
• Export functionality for downstream analysis
• Additional statistical diagnostics for persistence and regime shifts

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▶️ Running the Project

To run VolScope locally:

pip install streamlit yfinance pandas numpy matplotlib streamlit run streamlit_volatility_dashboard.py

The app will open in your browser at:
http://localhost:8501

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📌 Project Structure

• streamlit_volatility_dashboard.py — Streamlit application
• iv_dashboard.py — Core analysis and experimentation
• README.md — Project documentation

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📬 Contact

If you’re interested in quantitative finance, volatility modeling, or data-driven tooling, feel free to connect:

• LinkedIn: https://www.linkedin.com/in/alexanderzee/