Python has become the standard language of the financial community. Despite, this, there are but a few open-source packages available to us. In this talk, we present a new library that aims to provide a solid foundation for those whose work involves pricing and risk management of financial derivatives.

This library in turn builds on the foundation of the Python scientific stack, thus providing a familiar look and feel that can easily be extended. The goal is an intuitive, rigorous, framework for practitioners, not an introductory, or academic, learning tool.

We will first motivate the importance of, and then develop, the fundamental building blocks - yield and forward curves. We will describe specification, calibration, and risk, and then show how these curves can be plugged into your models to provide accurate sensitivities to interest rates and spreads.

Finally, we will provide a motivating example of how models can be plugged into the library.