On Mon, May 25, 2009 at 23:59, Joe Harrington <j...@physics.ucf.edu> wrote: > Let's keep this thread focussed on the original issue: > > just add a floating array of times to irr or a new xirr > continuous interest > no more > > Anyone can use the timeseries package to produce a floating array of > times from normal dates, if those are the dates they want. If they > want some specialized financial date, they may want a different > conversion, however. All we should provide in NumPy would be the > simplest tool. Specialized dates and date-time conversion belong > elsewhere. > > If we're *not* skipping dates, there is no need for xirr, just use > irr, which exists. > > scikits.financial seems like a great idea, and then knock yourselves > out for date conversions and definitions of compounding. Just think > big and design it first. But let's keep this thread on the simple > question for NumPy.
Then let's just say "No" and move on. I see no compelling reason to extend numpy's financial capabilities (of course, I spoke against their original addition in the first place, so take that as you will). Handling this by asking, "here are the constraints for numpy; what can we shoehorn in there?" is the wrong approach. Figure out what you want to achieve, then figure out what you need to solve the problem best. I don't think that including xirr in numpy, with its constraints, serves the problem best. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion