Quantitative finance research language
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.
Perhaps, every company has something on their own, but there are some materials available on the web ( mainly about DSL-s ):
- Going functional on exotic trades
- Composing contracts: an adventure in financial engineering
As for your own language ( and libraries / runtime! ) - there is not too much to say whithout knowing your requirements ( to name just few, which immediately came to my mind when I started to think about it ):
- Who will use it - sales or traders or quants or all
- How will it be used - just pricing of predefined blocks and/or solving optimization problems. It would lead to an ability to define workflows.
- Interaction with underlying infrastructure and its level of abstractions
- Extensibility ( to what an extent )
- Live calculations or simulation
- I/O support