Python in Finance (and Derivative Analytics)

The Logilab team attended (and co-organized) EuroScipy 2011, at the end of August in Paris.

We saw some interesting posters and a presentation dealing with Python in finance and derivative analytics [1].

In order to debunk the idea that "all computation libraries dedicated to financial applications must be written in C/C++ or some other compiled programming language", I would like to introduce a more Pythonic way.

You may know that financial applications such as risk management have in most cases high computational needs. For instance, it can be necessary to quickly perform a large number of Monte Carlo simulations to evaluate an American option in a few seconds.

The Python community provides several reliable and efficient libraries and packages dedicated to numerical computations:

• the well-known SciPy and NumPy libraries. They provide a complete set of tools to work with matrix, linear algebra operations, singular values decompositions, multi-variate regression models, ...
• scikits is a set of add-on toolkits for SciPy. For instance there are statistical models in statsmodels packages, a toolkit dedicated to timeseries manipulation and another one dedicated to numerical optimization;
• pandas is a recent Python package which provides "fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.". pandas uses Cython to improve its performance. Moreover, pandas has been used extensively in production in financial applications;

• Cython is a way to write C extensions for the Python language. Since you write Cython code in the same way as you write Python code, it's easy to use it. Is it fast? Yes ! I compared a simple example from Cython's official documentation with a full Python code -- a piece of code which computes the first kth prime numbers. The Cython code is almost thirty times faster than the full-Python code (non-official). Furthermore, you can use NumPy in Cython code !

I believe that thanks to several useful tools and libraries, Python can be used in numerical computation, even in Finance (both research and production). It is easy-to-maintain without sacrificing performances.

Note that you can find some other references on Visixion webpages:

• slides and paper about automating numerical research with Python at PyPhy 2011 ;
• Rapid Financial Engineering and Automating Numerical Research at PyCon DE 2011.