OptPricing: A Quantitative Finance Library for Derivative Pricing and Analysis¶
optpricing is a Python library for pricing, calibrating, and analyzing financial derivatives. It is built with a focus on architectural clarity, model breadth, and practical usability through a robust API, command-line interface, and an interactive dashboard.
Diljit Singh LinkedIn
Core Features¶
Model Library: Implements a comprehensive set of models, including:
- Stochastic Volatility: Heston, SABR
- Jump-Diffusion: Merton, Bates, Kou, SABR with Jumps
- Pure Levy Processes: Variance Gamma (VG), Normal Inverse Gaussian (NIG), CGMY, Hyperbolic
- Interest Rate Models: Vasicek, Cox-Ingersoll-Ross (CIR), Put-Call Parity Implied Rate
- Local Volatility: Dupire's Equation
Pricing Engines: Provides a suite of numerical methods, allowing for technique comparison and validation:
- Analytic closed-form solutions
- Numerical integration and FFT-based pricing via characteristic functions
- Finite difference (PDE) solver using a Crank-Nicolson scheme
- Binomial and trinomial tree methods (CRR, TOPM, Leisen-Reimer) for European and American options
- High-performance Monte Carlo engine for European and American options, accelerated with
numba, featuring variance reduction techniques (e.g., antithetic variates, control variates, importance sampling)
Interfaces:
- Programmatic API: Use the package as a Python library to build custom financial models in your scripts. Define options, stocks, rates, and models programmatically to compute prices and other metrics.
- Command-Line Interface (CLI): A robust CLI for live pricing, data management, model calibration, and historical backtesting.
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Interactive Dashboard (UI): A Streamlit application for visual analysis of option chains, implied volatility surfaces, and model calibrations.
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Workflow Automation: High-level classes that orchestrate complex tasks like daily calibration runs and out-of-sample performance evaluation.