Python in financial services

[Johann Hibschman]

Rustom Mody <rustompmody at gmail.com> writes:

> Ive been asked to formulate a python course for financial services
> folk.
>
> If I actually knew about the subject, I'd have fatter pockets!
> Anyway heres some thoughts. What I am missing out?

Good luck!  It's a pretty broad field, so everyone probably has
different needs.

> - Libraries -- Decimal?

I've never seen decimal used, even though it makes sense for
accounting-style finance.  I've mostly been looking at forecasts,
trading, and risk, where floats are fine.  So maybe mention that it
exists, so people know where to look if they need it, but don't stress
it.

> - scripts -- philosophy and infrastructure eg argparse, os.path

Basic argparse is very handy, but, again, I wouldn't spend too much time
on it.

> - Pandas
> - Numpy Scipy (which? how much?)

For me, pandas is huge, numpy is a nice fundamental substrate, while
only bits and pieces of scipy are used (mostly optimization).
statsmodels may also be worth a mention, as the answer to "how do I do a
regression".

> - ipython + matplotlib + ??

Ipython notebook + matplotlib is great.  At least show that it exists.
pandas plots may be enough, though.

> - Database interfacing

Definitely mention.

> - Excel interfacing (couple of libraries.. which?)

Meh, maybe.  At least give a strategy.  It always seems like a fool's
errand, though: I end up just dumping data to CSV and using that.

> - C(C++?) interfacing paradigms -- ranging from ctypes, cython to
>   classic lo-level

Probably not, but it depends on the audience.  The overview, like
"ctypes will link to C-like libraries, cython lets you write python-like
code that runs fast, and there's SWIG and Boost.Python if you want to
write your own modules" is about all you need.

Hope that helps,
Johann