I haven't read all the messages in detail, and I'm a consumer not a producer, but I'll comment anyways.
I'd love to see additional "financial" functionality, but I'd like to see them in a scikit, not in numpy. I think to be useful they are too complicated to go into numpy. A couple of my many reasons: 1. Doing a precise, bang-up job with dates is paramount to any interesting implementation of many financial functions. I've found timeseries to be a great package - there are some things I'd like to see, but overall it is at the foundation of all of my financial analysis. Any moderately interesting extension of the current capabilities would rapidly end up trying to duplicate much of the timeseries functionality, IMO. Rather than partially re-implement the wheel in numpy, as a consumer I'd like to see financial stuff built on a common basis, and timeseries would be a great start. 2. I've read enough of this discussion to hear a requirement for both good date handling and capable solvers - just for xirr. To do a really interesting job on an interesting amount of capability requires even more dependencies, I think. Although it might be tempting to include a few more "lightweight" financial functions in numpy, I doubt they will be that useful. Most of the lightweight ones are easy enough to whip up when you need them. Also, an approximation that's good today isn't the right one tomorrow - only the really robust stuff seems to survive the test of time, in my limited experience. A start on a really solid scikits financial package would be awesome, though. A few months ago, when the open source software for pricing CDS's was released (http://www.cdsmodel.com/information/cds-model) I took a look and noticed that it had a ton of code for dealing with dates. (I also didn't see any tests in the code. I wonder what that means. Scary for anybody that might want to modify it.) I thought if I had an extra 100 hours in every day it would be fun to re-write that code in numpy/scipy and release it. -r _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion