derivflow-finance 0.1.1

Advanced derivatives analytics platform for quantitative finance

🚀 DERIVFLOW-FINANCE

Advanced Derivatives Analytics Platform for Quantitative Finance

DERIVFLOW-FINANCE is a comprehensive, professional-grade derivatives analytics platform built for quantitative finance professionals, researchers, and institutions. It provides advanced pricing models, risk analytics, and portfolio management tools with institutional-quality accuracy and performance.

🌟 Key Features

📊 Advanced Pricing Models

• Multiple Methodologies: Black-Scholes analytical, Binomial trees, Monte Carlo simulation
• Exotic Options: Barrier options (all variants), Asian options (arithmetic/geometric)
• Stochastic Models: Heston stochastic volatility model with calibration
• Greeks Calculation: Complete 1st, 2nd, and 3rd order Greeks with advanced sensitivities

💹 Real-Time Market Data

• Live Data Integration: Yahoo Finance API with intelligent caching
• Options Chains: Complete options data with implied volatilities
• Historical Analytics: Volatility calculation and risk-free rate extraction
• Market Status: Real-time market hours and trading status

📈 Professional Risk Management

• Portfolio Analytics: Multi-asset portfolio construction and valuation
• VaR Calculation: Parametric and Monte Carlo Value-at-Risk
• Scenario Analysis: Stress testing with custom scenarios
• Hedging Optimization: Delta hedging and risk minimization

🎨 Interactive Visualizations

• 3D Volatility Surfaces: Professional volatility modeling with interpolation
• Greeks Dashboards: Interactive sensitivity analysis
• Payoff Diagrams: Option payoff and P&L visualization
• Risk Charts: Portfolio risk decomposition and analytics

🚀 Quick Start

Installation

pip install derivflow-finance

Basic Usage

from derivflow import PricingEngine, GreeksCalculator, VolatilitySurface

# Price a European option
from derivflow.core import price_european_option
price = price_european_option(S=100, K=105, T=0.25, r=0.05, sigma=0.2, option_type='call')
print(f"Option Price: ${price:.2f}")

# Calculate Greeks
from derivflow.greeks import GreeksCalculator
calc = GreeksCalculator()
greeks = calc.calculate_greeks(S=100, K=105, T=0.25, r=0.05, sigma=0.2, option_type='call')
print(f"Delta: {greeks.delta:.4f}")

# Price exotic options
from derivflow.exotic import BarrierOptions, AsianOptions

# Barrier option
barrier = BarrierOptions()
result = barrier.price(S=100, K=105, H=95, T=0.25, r=0.05, sigma=0.2, 
                      barrier_type='down_and_out', option_type='call')
print(f"Barrier Option: ${result.price:.4f}")

# Asian option with variance reduction
asian = AsianOptions()
result = asian.price(S=100, K=105, T=0.25, r=0.05, sigma=0.2, 
                    option_type='call', asian_type='arithmetic')
print(f"Asian Option: ${result.price:.4f} ± {result.std_error:.4f}")

Portfolio Risk Analytics

from derivflow.portfolio import PortfolioRiskAnalyzer

# Create portfolio
portfolio = PortfolioRiskAnalyzer()

# Add positions
portfolio.add_stock_position('AAPL', quantity=100, current_price=150, volatility=0.25)
portfolio.add_option_position('AAPL', quantity=10, current_price=150, 
                             strike=155, expiry=0.25, option_type='call', volatility=0.25)

# Calculate risk metrics
portfolio_value = portfolio.calculate_portfolio_value()
greeks = portfolio.calculate_portfolio_greeks()
var_95, es_95 = portfolio.calculate_var_parametric(0.95)

print(f"Portfolio Value: ${portfolio_value:,.2f}")
print(f"Portfolio Delta: {greeks['delta']:.2f}")
print(f"95% VaR: ${var_95:,.2f}")

Volatility Surface Modeling

from derivflow.volatility import create_sample_surface

# Create and build volatility surface
surface = create_sample_surface()
surface.build_surface()

# Get volatility smile
smile = surface.get_smile(expiry=0.25, num_points=10)

# Interpolate volatility
vol = surface.interpolate(strike=102, expiry=0.33)
print(f"Interpolated Volatility: {vol:.1%}")

🎯 Advanced Features

Stochastic Volatility Models

from derivflow.models import HestonModel

# Heston stochastic volatility
heston = HestonModel()
heston.set_parameters(v0=0.04, kappa=2.0, theta=0.04, sigma=0.3, rho=-0.7)

# Price with stochastic volatility
result = heston.price_option(S=100, K=105, T=0.25, r=0.05, 
                            option_type='call', method='monte_carlo')
print(f"Heston Price: ${result.price:.4f}")

Real-Time Market Data

from derivflow.utils import AdvancedMarketData

# Get live market data
market_data = AdvancedMarketData()
price, timestamp = market_data.get_current_price('AAPL')
vol = market_data.get_historical_volatility('AAPL', days=30)

print(f"Current AAPL: ${price:.2f}")
print(f"30-day Volatility: {vol:.1%}")

📊 Performance Benchmarks

DERIVFLOW-FINANCE is optimized for institutional-grade performance:

• Black-Scholes Pricing: 4,000+ options per second
• Monte Carlo Simulation: 10,000 paths in <0.2 seconds
• Asian Options: 1,500x variance reduction with control variates
• Greeks Calculation: Complete sensitivity analysis in milliseconds

🎓 Use Cases

Investment Banking & Trading

• Derivatives structuring and pricing
• Real-time risk management
• Volatility trading strategies
• Exotic products development

Academic Research

• Financial engineering research
• Quantitative finance education
• PhD dissertations and papers
• Teaching materials and examples

Portfolio Management

• Multi-asset portfolio construction
• Risk analytics and VaR calculation
• Hedging strategy optimization
• Stress testing and scenario analysis

Fintech Development

• Pricing engines for trading platforms
• Risk management systems
• Regulatory compliance tools
• API development for financial services

🛠️ Installation Options

Standard Installation

pip install derivflow-finance

Development Installation

pip install derivflow-finance[dev]

Full Installation (all features)

pip install derivflow-finance[visualization,testing,docs]

📚 Documentation

• API Reference: Complete function and class documentation
• User Guide: Step-by-step tutorials and examples
• Theory Guide: Mathematical foundations and model explanations
• Examples: Jupyter notebooks with real-world applications

🤝 Contributing

We welcome contributions from the quantitative finance community! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/jeevanba273/derivflow-finance.git
cd derivflow-finance
pip install -e .[dev]
pytest tests/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Acknowledgments

• Built with modern Python scientific computing stack
• Inspired by quantitative finance research and industry best practices
• Designed for both academic research and commercial applications

📞 Contact & Support

• Author: Jeevan B A
• Email: [e-mail]
• GitHub: @jeevanba273
• Issues: GitHub Issues

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⭐ Star this repository if DERIVFLOW-FINANCE helps your quantitative finance projects!

🚀 Built for the global quantitative finance community by Jeevan B A