[Numpy-discussion] add xirr to numpy financial functions?
Tue May 26 00:12:33 CDT 2009
On Mon, May 25, 2009 at 23:59, Joe Harrington <email@example.com> 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
> 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.
"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
More information about the Numpy-discussion