Volatility Forecasting & Risk Adaptive Trading Platform

VolFlux is an end to end quantitative finance platform that forecasts market volatility, detects volatility regimes, generates risk adaptive trading signals, and evaluates strategy performance through backtesting.

Built using FastAPI, GARCH modeling, statistical time series analysis, and interactive financial dashboards, VolFlux transforms raw market data into actionable risk insights for traders, analysts, and quantitative researchers.

Application: https://volflux-ce323960.fastapicloud.dev/

GitHub Repository: https://github.com/Naman21036/VolFlux

• CSV based market data ingestion
• Live market data integration using Yahoo Finance
• Automated preprocessing and feature engineering
• Log return computation
• Missing value handling

• Augmented Dickey Fuller (ADF) Stationarity Test
• Autocorrelation Function (ACF) Analysis
• Partial Autocorrelation Function (PACF) Analysis
• Volatility Clustering Detection
• ARCH Effect Detection

• ARMA Mean Modeling
• GARCH(1,1) Volatility Modeling
• Conditional Volatility Estimation
• Multi Step Volatility Forecasting

• Stable Market Regime
• Neutral Market Regime
• Risky Market Regime

• Dynamic Position Sizing
• Risk Adaptive Trading Signals
• Volatility Driven Exposure Control
• Buy / Reduce Exposure Recommendations

• Strategy Backtesting
• Benchmark Comparison
• Equity Curve Analysis
• Risk Adjusted Performance Evaluation

• Volatility Visualization
• Forecast Visualization
• Candlestick Charts
• Strategy Performance Tracking
• Market Regime Dashboard

Market Dataset
      │
      ▼
Preprocessing
      │
      ▼
Time Series Diagnostics
      │
      ▼
ARMA + GARCH Modeling
      │
      ▼
Volatility Forecasting
      │
      ▼
Market Regime Detection
      │
      ▼
Trading Signal Generation
      │
      ▼
Backtesting
      │
      ▼
Performance Evaluation
      │
      ▼
Interactive Dashboard

• FastAPI
• Python

• Statsmodels
• ARCH
• NumPy
• Pandas

• Scikit Learn

• Plotly
• Matplotlib
• Seaborn

• HTML
• CSS
• Jinja2

• Yahoo Finance API

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

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

python -m venv .venv

Windows:

.venv\Scripts\activate

Linux / macOS:

source .venv/bin/activate

pip install -r requirements.txt

Start the FastAPI server:

uvicorn app:app --reload

Open:

http://127.0.0.1:8000

Upload custom market datasets directly through the dashboard.

Examples:

/live/AAPL
/live/BTC-USD
/live/ETH-USD

• Stationarity Testing
• ACF Analysis
• PACF Analysis
• ARCH Detection
• Volatility Clustering Detection

Predict future market volatility for upcoming trading sessions.

VolFlux evaluates strategies using:

• Sharpe Ratio
• Sortino Ratio
• Maximum Drawdown
• Annualized Return
• Annualized Volatility
• Win Rate
• Total Return

• LSTM Based Volatility Forecasting
• EGARCH & TGARCH Models
• Real Time Streaming Data
• WebSocket Integration
• Portfolio Optimization
• Monte Carlo Risk Simulation
• Docker Containerization
• Cloud Native Deployment
• Multi Asset Portfolio Analytics
• User Authentication & Profiles

• Quantitative Research
• System Design
• Backend Development

• Research
• Analytics
• Project Development

This project was developed to explore practical applications of quantitative finance, volatility forecasting, statistical modeling, and risk adaptive trading systems using modern Python based technologies.