VolFlux 📈

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.

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🌐 Live Demo

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

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

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🚀 Key Features

Market Data Processing

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

Time Series Diagnostics

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

Volatility Modeling

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

Market Regime Detection

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

Trading Engine

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

Backtesting & Evaluation

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

Interactive Dashboard

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

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🏗 System Architecture

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

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🛠 Tech Stack

Backend

• FastAPI
• Python

Quantitative Finance

• Statsmodels
• ARCH
• NumPy
• Pandas

Machine Learning & Analytics

• Scikit Learn

Visualization

• Plotly
• Matplotlib
• Seaborn

Frontend

• HTML
• CSS
• Jinja2

Data Sources

• Yahoo Finance API

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📂 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

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⚙️ Installation

Clone Repository

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

Create Virtual Environment

python -m venv .venv

Activate Environment

Windows:

.venv\Scripts\activate

Linux / macOS:

source .venv/bin/activate

Install Dependencies

pip install -r requirements.txt

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▶️ Running the Application

Start the FastAPI server:

uvicorn app:app --reload

Open:

http://127.0.0.1:8000

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📊 Supported Functionalities

Dataset Upload

Upload custom market datasets directly through the dashboard.

Live Market Analysis

Examples:

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

Diagnostics Dashboard

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

Forecasting Engine

Predict future market volatility for upcoming trading sessions.

Regime Classification

Regime	Market Condition
Stable	Low Volatility
Neutral	Moderate Volatility
Risky	High Volatility

Trading Signal Engine

Market State	Action
Low Volatility	Increase Exposure
Neutral	Hold
High Volatility	Reduce Exposure

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📈 Performance Metrics

VolFlux evaluates strategies using:

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

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🔮 Future Roadmap

• 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

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👥 Contributors

Naman Gupta

• Quantitative Research
• System Design
• Backend Development

Ananya Hadimani

• Research
• Analytics
• Project Development

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⭐ Acknowledgements

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.