Financial Modelling in Python (PDF)

"Fletcher and Gardner have created a comprehensive resource that
will be of interest not only to those working in the field of
finance, but also to those using numerical methods in other fields
such as engineering, physics, and actuarial mathematics. By showing
how to combine the high-level elegance, accessibility, and
flexibility of Python, with the low-level computational efficiency
of C++, in the context of interesting financial modeling problems,
they have provided an implementation template which will be useful
to others seeking to jointly optimize the use of computational and
human resources. They document all the necessary technical details
required in order to make external numerical libraries available
from within Python, and they contribute a useful library of their
own, which will significantly reduce the start-up costs involved in
building financial models. This book is a must read for all those
with a need to apply numerical methods in the valuation of
financial claims."

-David Louton, Professor of Finance, Bryant University

This book is directed at both industry practitioners and
students interested in designing a pricing and risk management
framework for financial derivatives using the Python programming
language.

It is a practical book complete with working, tested code that
guides the reader through the process of building a flexible,
extensible pricing framework in Python. The pricing frameworks'
loosely coupled fundamental components have been designed to
facilitate the quick development of new models. Concrete
applications to real-world pricing problems are also provided.

Topics are introduced gradually, each building on the last. They
include basic mathematical algorithms, common algorithms from
numerical analysis, trade, market and event data model
representations, lattice and simulation based pricing, and model
development. The mathematics presented is kept simple and to the
point.

The book also provides a host of information on practical
technical topics such as C++/Python hybrid development (embedding
and extending) and techniques for integrating Python based programs
with Microsoft Excel.