[Numpy-discussion] add xirr to numpy financial functions?

[Robert Kern]

On Mon, May 25, 2009 at 23:59, Joe Harrington <jh at 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