VolFlux

VolFlux is a quantitative volatility forecasting and risk adaptive trading platform built using Python, FastAPI, GARCH modeling, and interactive financial dashboards.

The project analyzes financial time series data, detects volatility regimes, forecasts future market volatility, generates trading signals, and evaluates strategy performance through backtesting.

Features Volatility Forecasting using GARCH Models Time Series Diagnostics Regime Detection Risk Adaptive Trading Strategy Backtesting Engine Performance Evaluation Metrics Interactive FastAPI Dashboard Live Market Data using Yahoo Finance API Candlestick & Volatility Charts CSV Upload Support Multi Asset Support (Stocks, Crypto, Indices) Pipeline Architecture Dataset ↓ Preprocessing ↓ Time Series Diagnostics ↓ Volatility Modeling ↓ Forecasting ↓ Regime Detection ↓ Trading Strategy ↓ Backtesting ↓ Performance Evaluation ↓ Interactive Dashboard Tech Stack Backend FastAPI Python Data Science & Quantitative Finance Pandas NumPy Statsmodels ARCH Scikit Learn Visualization Plotly Matplotlib Seaborn Frontend HTML CSS Jinja2 Templates Project Structure VolFlux/ │ ├── app.py ├── pipeline.py ├── setup.py ├── requirements.txt │ ├── data/ │ ├── notebooks/ │ ├── src/ │ ├── preprocessing.py │ ├── diagnostics.py │ ├── modeling.py │ ├── forecasting.py │ ├── regime.py │ ├── strategy.py │ ├── backtesting.py │ └── metrics.py │ ├── templates/ │ ├── index.html │ └── dashboard.html │ ├── static/ │ └── style.css │ └── README.md Installation

Clone the repository:

git clone https://github.com/Naman21036/VolFlux.git

Move into the project directory:

cd VolFlux

Create virtual environment:

python -m venv .venv

Activate virtual environment:

Windows .venv\Scripts\activate Linux / Mac source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt Running the Application

Start the FastAPI server:

uvicorn app:app --reload

Open browser:

http://127.0.0.1:8000 Supported Functionalities Dataset Upload

Upload custom CSV datasets directly from the dashboard.

Live Market Data

Analyze live assets directly from Yahoo Finance API.

Examples:

/live/AAPL /live/BTC-USD /live/ETH-USD Time Series Diagnostics Stationarity Testing ACF Analysis PACF Analysis ARCH Effect Detection Volatility Clustering Detection Volatility Modeling ARMA Mean Modeling GARCH Volatility Modeling Forecasting

Predict future market volatility for upcoming trading periods.

Regime Detection

Classify market conditions into:

Stable Neutral High Risk Trading Strategy

Generate:

Buy Signals Reduce Exposure Signals Dynamic Position Sizing Backtesting

Evaluate strategy performance against market benchmark.

Performance Metrics Sharpe Ratio Sortino Ratio Maximum Drawdown Total Return Annualized Volatility Win Rate Example Dashboard Components Interactive Volatility Charts Forecast Visualization Candlestick Charts Equity Curves Risk Regime Dashboard Trading Signals Performance Cards Future Improvements Real Time Streaming WebSocket Integration LSTM Volatility Forecasting Portfolio Optimization Monte Carlo Simulations Docker Deployment Cloud Deployment User Authentication Multi Asset Portfolio Analytics

Author

Naman Gupta, Ananya Hadimani