Rustom Mody <rustompm...@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 -- https://mail.python.org/mailman/listinfo/python-list
