Advanced Quantitative Finance Library for Institutional-Grade Analytics
Classical Black-Scholes framework with geometric Brownian motion. Euler-Maruyama and Milstein schemes for numerical integration.
Use Cases:
- Equity price modeling
- Options pricing
- Risk neutral simulation
Stochastic volatility with mean reversion and correlation structure. Full truncation scheme for variance process positivity.
Use Cases:
- Volatility smile modeling
- Exotic option pricing
- Risk management
Mean-reverting process with analytical solutions and parameter estimation via maximum likelihood and method of moments.
Use Cases:
- Interest rate modeling
- Pairs trading
- Commodity prices
Merton jump-diffusion with Poisson jump arrivals and log-normal jump sizes for crisis modeling.
Use Cases:
- Crisis risk modeling
- Option pricing
- Tail risk analysis
Stochastic Alpha, Beta, Rho model for interest rate derivatives with volatility smile dynamics.
Use Cases:
- Swaption pricing
- Interest rate options
- Volatility surface
Multi-dimensional SDE systems with Cholesky decomposition for correlation structure preservation.
Use Cases:
- Portfolio simulation
- Basket options
- Risk aggregation
Advanced portfolio construction using Modern Portfolio Theory, Black-Litterman, and risk parity approaches. Mean-variance optimization with transaction costs, constraints, and robust estimation techniques for institutional applications.
Comprehensive risk metrics including Value-at-Risk (VaR), Conditional VaR, Maximum Drawdown, and stress testing. Monte Carlo simulation with variance reduction techniques for accurate tail risk estimation.
Black-Scholes framework with extensions for American options, barrier options, and exotic derivatives. Finite difference methods, binomial trees, and Monte Carlo pricing with Greeks calculation for risk management.
Hidden Markov Models and regime-switching frameworks for identifying bull/bear markets, volatility regimes, and structural breaks. Automatic model selection and regime probability estimation.
Professional market data feeds with real-time processing, data cleaning, and transformation pipelines. Support for multiple data vendors and asset classes with institutional-grade data quality controls.
Robust backtesting engine with realistic transaction costs, market impact, and slippage modeling. Performance attribution, risk decomposition, and statistical significance testing for strategy validation.