Metadata-Version: 2.4
Name: quantlib-pro
Version: 1.0.3
Summary: Advanced quantitative finance library implementing modern portfolio theory, stochastic calculus, derivative pricing models, and risk management methodologies for institutional financial applications
Home-page: https://github.com/quantlib-pro/quantlib-pro
Author: QuantLib Pro Team
Author-email: Guerson Dukens Jr Joseph <guersondukensjrjoseph@gmail.com>
Maintainer-email: gdukens <guersondukensjrjoseph@gmail.com>
License: MIT
Keywords: quantitative-finance,portfolio-optimization,risk-management,options-pricing,derivatives,stochastic-calculus,monte-carlo,black-scholes,var,cvar,efficient-frontier,martingale,ito-calculus,pde,mathematical-finance,computational-finance
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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License-File: LICENSE
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# QuantLib Pro: Advanced Quantitative Finance Library

Professional quantitative finance toolkit implementing modern portfolio theory, stochastic calculus, derivative pricing, and risk management for institutional financial applications.

## Quick Start

```bash
# Install complete platform
pip install quantlib-pro

# Basic usage
from quantlib_pro import QuantLibSDK
sdk = QuantLibSDK()

# Portfolio optimization
portfolio = sdk.portfolio.optimize_portfolio(returns, covariance_matrix)
risk_metrics = sdk.risk.calculate_var(returns, confidence_level=0.05)
option_price = sdk.options.black_scholes(S=100, K=105, T=0.25, r=0.05, sigma=0.2)
```

## Key Features

**Mathematical Foundation**: Built on measure-theoretic probability theory, stochastic calculus, and martingale theory

**Core Modules**:
- **Portfolio Theory**: Mean-variance optimization, efficient frontier, Black-Litterman model
- **Risk Management**: VaR/CVaR calculation, stress testing, copula modeling, GARCH volatility  
- **Options Pricing**: Black-Scholes, Monte Carlo, binomial trees, Greeks calculation
- **Volatility Modeling**: GARCH models, realized volatility, regime detection
- **Market Data**: Multi-provider integration (Alpha Vantage, FRED, FactSet)
- **Macro Economics**: Yield curve construction, economic indicators, scenario generation
- **Analytics**: PCA/ICA, machine learning models, backtesting framework
- **Execution**: Transaction cost analysis, optimal execution algorithms

**Platform Components**:
- **Unified SDK**: `from quantlib_pro import QuantLibSDK` - centralized interface
- **Web Interface**: `streamlit run streamlit_app.py` - interactive dashboard
- **REST API**: `uvicorn main_api:app` - production FastAPI server  
- **CLI Tools**: `quantlib` command - automated processing capabilities

## Installation Options

```bash
# Complete installation
pip install quantlib-pro

# Minimal SDK only
pip install quantlib-pro[sdk]

# Full platform (API + UI)
pip install quantlib-pro[full]

# Development environment
pip install quantlib-pro[dev]

# All optional features
pip install quantlib-pro[all]
```

## Platform Deliverables

**What You Get**:
- 8 mathematical modules with rigorous scientific foundations
- Unified SDK with lazy loading and configuration management  
- Production-ready Streamlit web application for interactive analysis
- FastAPI server with JWT authentication and OpenAPI documentation
- Professional CLI with batch processing and automation capabilities
- Docker containerization and Kubernetes deployment manifests
- Comprehensive testing suite with 90%+ code coverage

**Integration Support**:
- Database: PostgreSQL, Redis caching
- Data Formats: CSV, Excel, Parquet, JSON
- Cloud Platforms: AWS, Azure, GCP compatible
- Authentication: JWT tokens, role-based access control
- Monitoring: Prometheus metrics, OpenTelemetry tracing

## Scientific Rigor

**Mathematical Implementations**:
- Measure theory and stochastic processes
- Ito calculus and stochastic differential equations  
- Numerical PDE methods and Monte Carlo simulation
- Convex optimization and statistical inference
- Time series econometrics and machine learning

**Quality Assurance**:
- Numerical accuracy verified against academic literature
- Performance benchmarked against industry standards
- Code quality maintained with automated testing and peer review
- Cross-platform compatibility and regulatory compliance considerations

## Usage Examples

```python
# Portfolio optimization with constraints
weights = sdk.portfolio.max_sharpe_portfolio(returns, covariance_matrix)
frontier = sdk.portfolio.efficient_frontier(returns, num_portfolios=1000)

# Risk analysis and stress testing  
var_95 = sdk.risk.calculate_var(returns, confidence_level=0.05)
stress_results = sdk.risk.stress_test(portfolio, scenarios)

# Options pricing and Greeks
call_price = sdk.options.black_scholes(100, 105, 0.25, 0.05, 0.2)
greeks = sdk.options.calculate_greeks(100, 105, 0.25, 0.05, 0.2)

# Volatility modeling
garch_model = sdk.volatility.fit_garch(returns, model_type='GARCH')
forecasts = sdk.volatility.forecast_volatility(garch_model, horizon=10)
```

## Professional Deployment

```bash
# Web application
streamlit run streamlit_app.py

# API server  
uvicorn main_api:app --host 0.0.0.0 --port 8000

# Docker deployment
docker-compose up -d

# CLI processing
quantlib portfolio optimize --symbols AAPL,MSFT --method max_sharpe
```

## Technical Specifications

- **Python**: 3.10+ with type hints and async support
- **Performance**: Vectorized operations with NumPy/SciPy, optional GPU acceleration
- **Architecture**: Microservices with containerized deployment
- **Documentation**: Complete API reference with mathematical derivations
- **Testing**: Comprehensive unit/integration tests with CI/CD pipelines

## Author

**Guerson Dukens Jr Joseph** (gdukens)  
Contact: guersondukensjrjoseph@gmail.com

## License

MIT License - Open source with institutional use considerations

---

**Professional quantitative finance platform combining academic rigor with production-ready implementation for institutional financial applications.**
