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Introduction

Welcome to optpricing, a Python toolkit for pricing and calibrating financial derivatives. This library was created to implement and understand the mathematical and computational foundations of quantitative finance, growing into a robust, extensible framework.

Guiding Principles

The library is organized around four core concepts; understanding these will help you navigate the codebase and documentation.

  1. Atoms: Immutable data structures for core financial concepts like Option, Stock, and Rate. They provide a consistent foundation for every calculation. This ensures clarity of inputs across the entire library.

  2. Models: An extensible module of financial models, including classical option pricing models, advanced stochastic volatility models, jump-diffusion processes, and interest rate models. In addition to pricing options, some models support valuation of implied rates, volatility-focused analysis, and put-call parity. Each model is a self-contained representation of a specific financial theory.

  3. Techniques: These are the numerical algorithms used for pricing models, with bespoke Greek calculations or fallback to numerical differentiation. The separation of model (the "what") from technique (the "how") is a core design feature. The library includes a wide arrange of techniques.

  4. Workflows & Tooling: High-level orchestrators that combine data, models, and techniques to perform complex, real-world tasks like daily model calibration or historical backtesting. These power the command-line interface and the Streamlit dashboard.

Who Is This For?

This library is designed for anyone interested in the intersection of finance, mathematics, and software engineering. In particular, the object-oriented design, centered around the BaseModel and BaseTechnique abstract classes, makes it straightforward to add new models or pricing methods. Benchmarks facilitate performance comparisons with existing models.

If you are ready to get started head to the Installation guide.