[Numpy-discussion] add xirr to numpy financial functions?
Mon May 25 10:50:16 CDT 2009
On Sun, 24 May 2009 18:14:42 -0400 email@example.com wrote:
> On Sun, May 24, 2009 at 4:33 PM, Joe Harrington <firstname.lastname@example.org> wrote:
> > I hate to ask for another function in numpy, but there's an obvious
> > one missing in the financial group: xirr. ?It could be done as a new
> > function or as an extension to the existing np.irr.
> > The internal rate of return (np.irr) is defined as the growth rate
> > that would give you a zero balance at the end of a period of
> > investment given a series of cash flows into or out of the investment
> > at regular intervals (the first and last cash flows are usually an
> > initial deposit and a withdrawal of the current balance).
> > This is useful in academics, but if you're tracking a real investment,
> > you don't just withdraw or add money on a perfectly annual basis, nor
> > do you want a calc with thousands of days of zero entries just so you
> > can handle the uneven intervals by evening them out. ?Both excel and
> > openoffice define a "xirr" function that pairs each cash flow with a
> > date. ?Would there be an objection to either a xirr or adding an
> > optional second arg (or a keyword arg) to np.irr in numpy? ?Who writes
> > the code is a different question, but that part isn't hard.
> 3 comments:
> * open office has also the other function in an x??? version, so it
> might be good to add it consistently to all functions
> * date type: scikits.timeseries and the gsoc for implementing a date
> type would be useful to have a clear date type, or would you want to
> base it only on python standard library
> * real life accuracy: given that there are large differences in the
> definition of a year for financial calculations, any simple
> implementation would be only approximately accurate. for example in
> the open office help, oddlyield list the following option
> Basis is chosen from a list of options and indicates how the year is
> to be calculated.
> Basis Calculation
> 0 or missing US method (NASD), 12 months of 30 days each
> 1 Exact number of days in months, exact number of days in year
> 2 Exact number of days in month, year has 360 days
> 3 Exact number of days in month, year has 365 days
> 4 European method, 12 months of 30 days each
> So, my question: what's the purpose of the financial function in numpy?
> Currently it provides convenient functions for (approximate) interest
> If they get expanded to a "serious" implementation of, for example,
> the main financial functions listed in the open office help (just for
> reference) then maybe numpy is not the right location for it.
> I started to do something similar in matlab, and once I tried to use
> real dates instead of just counting months, the accounting rules get
> quickly very messy.
> Using dates as you propose would be very convenient, but the users
> shouldn't be surprised that their actual payments at the end of the
> year don't fully match up with what numpy told them.
> my 3cents
First point: agreed. I wish this community had a design review
process for numpy and scipy, so that these things could get properly
hashed out, and not just one person (even Travis) suggesting something
and everyone else saying yeah-sure-whatever.
Does anyone on the list have the financial background to suggest what
functions "should" be included in a basic set of financial routines?
xirr is the only one I've ever used in a spreadsheet, myself.
Other points: Yuk. You're right.
When these first came up for discussion, I had a Han Solo moment
("I've got a baaad feeling about this...") but I couldn't put my
finger on why. They seemed like simple and limited functions with
high utility. Certainly anything as open-ended as financial-industry
rules should go elsewhere (scikits, scipy, monpy, whatever).
But, that doesn't prevent a user-supplied, floating-point time array
from going into a function in numpy. The rate of return would be in
units of that array. Functions that convert date/time in some format
(or many) and following some rule (or one of many) to such a floating
array can still go elsewhere, maintained by people who know the
definitions, if they have interest (pun intended). That would make
the functions in numpy much more useful without bloating them or
making them a maintenance nightmare.
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