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

[Robert Ferrell]

On May 25, 2009, at 10:59 PM, Joe Harrington 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.

My vote is against adding xirr to NumPy.  In my experience, if you  
want internal rate of return, then you also want time weighted return,  
for instance, and all of sudden it becomes surprising that NumPy  
tantalizes with a some of the needed capability but not all of it.

I read in an old thread that irr was included partly because OLPC was  
including NumPy and it was great that kids would have a tool to help  
them understand the present value of money.  In my opinion, cumprod()  
is an even better teaching tool for that.  I'm not advocating reducing  
functionality in NumPy, but I prefer the idea of keeping NumPy as an  
array core, and having higher-level capability available as add-ons  
(scipy, scikit, etc...)

-r