QuantEdge is a production-grade quantitative analysis platform delivering institutional-quality market intelligence tools for professional traders and analysts. Built with modern Python and featuring a Bloomberg-inspired terminal interface, it combines real-time data processing, machine learning, and advanced analytics in a unified system.
🔴 Real-time Market Analysis Sub-second latency from multiple exchanges
🤖 AI-Powered Insights ML models for sentiment and pattern recognition
📊 Portfolio Optimization Advanced risk management and asset allocation
🔬 Quantitative Research Comprehensive backtesting and strategy development
- Professional Research Terminal - AI-powered stock analysis with institutional-grade data
- Real-time Market Overview - Live indices, sectors, commodities, and currencies
- Live Dashboard - Auto-refresh streaming data with sub-second updates
- Sector Performance Analysis - Cross-sector correlation matrices
- Earnings Calendar - Historical surprise tracking and estimates
- Dividend Tracker - Yield analysis and payment schedules
- Advanced Portfolio Tracker - Real-time P&L analytics and performance metrics
- Watchlist Management - Customizable alerts and monitoring
- Multi-factor Stock Screener - Quantitative filtering engine
- Technical Analysis - 10+ indicators and pattern recognition
- Options Pricing - Derivatives analytics and Greeks calculation
- Risk Metrics - VaR, Sharpe ratio, and position sizing
- Sentiment Analysis - NLP on news and social media feeds
- Pattern Recognition - Anomaly detection and trend identification
- Predictive Modeling - Ensemble methods and neural networks
- Insider Trading Tracker - Executive and institutional activity monitoring
- Smart Money Flow - Hedge fund and institutional holdings analysis
- Python 3.8+
- API Keys (free tier available):
- Alpha Vantage - Market data
- News API - News aggregation
- Financial Modeling Prep - Fundamentals
# Clone the repository
git clone https://github.com/yourusername/quantedge.git
cd quantedge
# Install dependencies
pip install -r requirements.txt
# Configure API keys
./setup_api_keys.sh
# Launch platform
./START_HERE.shAlternative launch methods:
# Quick launcher
./run.sh
# Direct execution
python3 MAIN_MENU.py# Verify installation
python3 scripts/verify_installation.py
# Test API connectivity
python3 scripts/test_api_keys.py
# Run test suite
pytest tests/quantedge/
│
├── apps/ # Core applications (18+ tools)
│ ├── PROFESSIONAL_RESEARCH_TERMINAL.py
│ ├── AI_STOCK_PICKER.py
│ ├── PORTFOLIO_PRO.py
│ ├── MARKET_OVERVIEW.py
│ └── ...
│
├── src/ # Core engine
│ ├── ml/ # Machine learning models
│ ├── data/ # Data processing pipeline
│ ├── api/ # API integrations
│ └── analysis/ # Analytics engines
│
├── scripts/ # Utility scripts
│ ├── bloomberg_terminal.py # Terminal interface
│ ├── verify_installation.py # System diagnostics
│ └── test_api_keys.py # API validation
│
├── tests/ # Test suite
│ ├── test_api.py
│ ├── test_ml.py
│ └── test_complete_system.py
│
├── docs/ # Documentation
│ ├── GET_API_KEYS.md
│ └── PRODUCTION_BEST_PRACTICES.md
│
├── config/ # Configuration
├── data/ # Data storage
├── MAIN_MENU.py # Application entry point
└── requirements.txt # Dependencies
| Layer | Technologies |
|---|---|
| Core | Python 3.8+, NumPy, Pandas |
| ML/AI | scikit-learn, TensorFlow, PyTorch |
| Data | yfinance, Alpha Vantage, FMP API, News API |
| Visualization | Rich, Plotly, Matplotlib |
| UI | Bloomberg-inspired terminal (Rich library) |
| Testing | pytest, unittest |
| Deployment | Docker, docker-compose |
- ⚡ Data Retrieval: Sub-second latency
- 🔄 Concurrent Processing: Intelligent API request pooling
- 📊 Portfolio Analysis: Optimized for 1000+ position portfolios
- 🎯 Real-time Streaming: <100ms update intervals
- 💾 Caching: Smart local cache with TTL management
| Use Case | Features |
|---|---|
| Day Trading | Real-time analysis, technical indicators, live dashboard |
| Swing Trading | Multi-day analysis, risk management, pattern recognition |
| Portfolio Management | Asset allocation, rebalancing, performance tracking |
| Quantitative Research | Strategy backtesting, correlation analysis, factor modeling |
| Market Intelligence | Sector rotation, institutional flow, macro analysis |
- S&P 500: All 503 constituents
- Sectors: 11 GICS sectors
- Dividend Aristocrats: 47 stocks with 25+ year dividend history
- High Growth Tech: 15+ leading technology stocks
- FAANG+: 8 mega-cap technology leaders
- Market Cap Coverage: $500B+ companies
Create a .env file in the root directory:
# API Keys
ALPHA_VANTAGE_KEY=your_alpha_vantage_key
NEWS_API_KEY=your_news_api_key
FMP_API_KEY=your_fmp_api_key
# Optional: Advanced Configuration
CACHE_TTL=3600
LOG_LEVEL=INFO
MAX_CONCURRENT_REQUESTS=5- Stock Universe: Edit
apps/stock_universe.py - Watchlists: Configure in
data/watchlist.json - Portfolio: Manage in
data/portfolio.json - Themes: Customize in individual app files
# Run full test suite
pytest tests/
# Run specific test module
pytest tests/test_api.py
# Run with coverage
pytest --cov=src tests/
# Verify installation
python3 scripts/verify_installation.pyWe welcome contributions from the community! Please see our Contributing Guidelines for details.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Alpha Vantage - Market data APIs
- Financial Modeling Prep - Fundamental data
- News API - News aggregation
- yfinance - Yahoo Finance integration
- Rich - Terminal UI framework
- Always conduct your own due diligence before making investment decisions
- Past performance does not guarantee future results
- Trading and investing involve risk of loss
- Consult with a qualified financial advisor before making investment decisions
Built with 🐍 Python • 📊 Machine Learning • 💹 Financial APIs • 🎯 Professional Analytics
Institutional-grade market intelligence at your fingertips