Quant Enthusiasts Risk Engine

A high-performance quantitative finance platform for portfolio risk management and options pricing. Built with C++17 core, Python bindings, and an interactive web dashboard.

Overview

Cross-language quantitative finance platform offering:

• Multiple Pricing Models: Black-Scholes, Binomial Tree, Merton Jump Diffusion
• Risk Analytics: Greeks calculation, Value at Risk (Monte Carlo), Portfolio aggregation
• Live Market Data: Automatic fetching from Yahoo Finance with caching
• RESTful API: Flask-based endpoints with comprehensive validation
• Web Dashboard: Interactive portfolio builder and risk visualizer

Quick Start

# Clone repository
git clone https://github.com/Quant-Enthusiasts/Quant-Enthusiasts-Risk-Engine.git
cd Quant-Enthusiasts-Risk-Engine

# Build C++ engine
cd cpp_engine
mkdir build && cd build
cmake .. -GNinja -DCMAKE_BUILD_TYPE=Release
cmake --build .
cmake --install .

# (Optional) Use CMake presets
# Presets are defined under cpp_engine/. Run them from this folder, e.g.
# cmake --preset quantlib

# Setup Python API
cd ../../python_api
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python setup.py build_ext --inplace

# Start API server
python app.py

Server runs at http://127.0.0.1:5000

For detailed setup instructions, see INSTALLATION.md.

Architecture

JavaScript Dashboard (Frontend)
         |
         | HTTP/JSON
         v
  Python API Layer (Flask)
         |
         | pybind11
         v
   C++ Risk Engine (Core)

Project Structure:

Quant-Enthusiasts-Risk-Engine/
├── cpp_engine/          # C++ core with pricing models
├── python_api/          # Flask API + Python bindings
├── js_dashboard/        # Web interface
└── docs/                # Additional documentation

Core Features

Pricing Models

Model	Type	Options	Key Features
Black-Scholes	Analytical	European	Fast, Greeks calculation
Binomial Tree	Numerical	European/American	Early exercise, configurable steps
Merton Jump Diffusion	Analytical	European	Discontinuous jumps

Risk Metrics

• Greeks: Delta, Gamma, Vega, Theta, Rho
• VaR: Monte Carlo simulation (configurable paths), std::thread used for concurrency
• Expected Shortfall: 95%/99% confidence levels
• Portfolio Analytics: Net positions, PV aggregation

Market Data Integration

• Automatic fetching from Yahoo Finance
• SQLite-based caching (24-hour expiration)
• Bulk ticker updates
• Volatility calculation from historical data

See MARKET_DATA.md for details.

Usage Example

# Fetch market data
curl -X POST http://127.0.0.1:5000/update_market_data \
  -H "Content-Type: application/json" \
  -d '{"tickers": ["AAPL", "GOOGL"]}'

# Calculate portfolio risk (auto-fetches missing data)
curl -X POST http://127.0.0.1:5000/calculate_risk \
  -H "Content-Type: application/json" \
  -d '{
    "portfolio": [
      {
        "type": "call",
        "strike": 180,
        "expiry": 1.0,
        "asset_id": "AAPL",
        "quantity": 100,
        "style": "european"
      }
    ],
    "market_data": {}
  }'

Full API documentation: API.md

Performance

Operation	Latency	Notes
Single option pricing	< 1 microsecond	Black-Scholes
Portfolio (100 options)	< 1 ms	Full Greeks
VaR (100K simulations)	< 1 s	95% confidence

Documentation

• Installation Guide - Detailed setup for all platforms
• API Reference - Complete endpoint documentation
• Development Guide - Testing, contributing, CI/CD
• Market Data Guide - YFinance integration details
• Deployment Guide - Docker, production setup

Contributing

We welcome contributions! Please see DEVELOPMENT.md for:

• Development setup
• Code style guidelines
• Testing requirements
• Pull request process

Support

• GitHub Issues: Report bugs or request features
• Discord: Join our community

License

This project is licensed under the MIT License. See LICENSE for details.

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Made by Quant Enthusiasts | Documentation | GitHub