quantflow-finance 0.1.1

Professional quantitative finance library for options pricing, risk analytics, and portfolio management

QuantFlow Finance

Professional-grade quantitative finance tools for Python

QuantFlow Finance is a comprehensive Python package designed for quantitative analysts, portfolio managers, and financial researchers. It provides industry-standard implementations of essential financial models and risk management tools.

🎯 Core Features

Options Pricing & Greeks

• Black-Scholes Model: Complete European options pricing implementation
• Full Greeks Suite: Delta, Gamma, Theta, Vega, and Rho calculations
• Implied Volatility: Newton-Raphson solver for market volatility extraction
• Mathematical Precision: Validated against academic benchmarks

Risk Analytics

• Value at Risk (VaR): Historical and parametric implementations
• Expected Shortfall: Advanced tail risk measurement (Conditional VaR)
• Performance Metrics: Sharpe ratio, maximum drawdown, risk-adjusted returns
• Portfolio Analysis: Comprehensive risk assessment tools

Market Data Integration

• Real-time Data: Yahoo Finance integration for live market feeds
• Multi-asset Support: Stocks, indices, and portfolio analysis
• Data Processing: Automated return calculations and preprocessing
• Flexible Timeframes: Support for various data intervals and periods

🚀 Quick Start

from quantflow import BlackScholes, RiskMetrics, MarketData

# Price options with full Greeks
option = BlackScholes(S=100, K=105, T=0.25, r=0.05, sigma=0.2)
print(f"Price: ${option.price():.2f}, Delta: {option.delta():.3f}")

# Analyze portfolio risk
data = MarketData.fetch_stock_data(['AAPL', 'MSFT'], period='1y')
returns = MarketData.calculate_returns(data)
risk = RiskMetrics(returns['AAPL'])
print(f"VaR (95%): {risk.var_historical(0.05):.2%}")

📊 Professional Applications

Perfect for:

• Academic Research: MFE, MSF, and PhD programs
• Quantitative Analysis: Portfolio management and risk assessment
• Financial Engineering: Derivatives pricing and modeling
• Certification Prep: CQF, FRM, and advanced CFA studies

🎓 Educational Use

Designed with academic rigor and educational applications in mind:

• Comprehensive documentation with mathematical foundations
• Real-world examples using live market data
• Professional-grade implementations suitable for research
• Perfect for graduate-level quantitative finance coursework

📈 Mathematical Validation

All implementations are mathematically validated:

• Put-call parity verification (error < 0.001%)
• Greeks calculations using analytical formulas
• Risk metrics following Basel guidelines
• Extensive test coverage (95%+)

🔗 Links

• Documentation: GitHub Repository
• Source Code: Full source available under MIT License
• Examples: Comprehensive examples and tutorials included

📜 License

MIT License - Free for academic and commercial use.