Quantitative finance research language [closed]

Question

I am working on an interpreted quant finance library for rapid prototyping of equity derivatives mostly. I do not have any experience with such languages (I've heard of Goldman-Sach's Slang, but have never seen it).

What sort of functionality is found in such languages, and do they have some unique features which correspond to the financial markets?

Answer 1

Have you ever considered Python? There are many mature libraries that can be used for statistical analysis, data acquisition and cleaning. To name a few:

Numpy         - N-dim array objects
Scipy         - library of statistical and optimisation tools
statsmodels   - statistical modeling
Pandas        - data structures for time series, cross-sectional, or any other form of “labeled” data
matplotlib    - MATLAB-like plotting tools
PyTables      - hierarchical database package designed to efficiently manage very large amounts of data
CVXOPT        - convex optimization routines

I've personally implemented some pretty complex derivatives pring models in python, including a jump-diffusion Vasicek interest rate lattice, many stochastic processes, and even managed to write a genetic optimizer.

One of my professors is director of research ( PhD. in math ) at a Chicago hedge fund who uses Python exclusively.