Andy Fundinger

In the last few years a number of large dealers and many other financial institutions have made big investments in Python. We will examine the reasons behind this trend and discuss techniques that facilitate successful projects.   Python's strength is in its flexibility. Starting with dynamic typing and extending to more advanced meta-programming capabilities, Python allows code to be written many different ways and is especially good at integrating with C and C++ for optimized code. This makes Python a great fit for finance (especially Risk projects) as it is pretty common to have different groups that want to use different languages for the same project: e.g. Quants usually write in C/C++, while Application Development prefers to use a language with better support for plumbing and UI functionality.   We will demonstrate the design and use of an environment for quantative researchers building a market risk simulation first as a basic system and then adding a hypothetical systemic shock. We will also discuss how we can leverage the dynamic typing of the language without sacrificing some of the benefits of a strongly typed languages.