[Numpy-discussion] add xirr to numpy financial functions?
Mon May 25 12:51:38 CDT 2009
On Mon, May 25, 2009 at 11:50 AM, Joe Harrington <email@example.com> wrote:
> On Sun, 24 May 2009 18:14:42 -0400 firstname.lastname@example.org wrote:
>> On Sun, May 24, 2009 at 4:33 PM, Joe Harrington <email@example.com> 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.
If you think of time just as a regularly spaced, e.g. days, but with
sparse points on it, or as a continuous variable, then extending the
current functions should be relatively easy. I guess the only
questions are compounding, annual, quarterly or at each payment, and
whether the annual rate is calculated as real compounded annualized
rate or as accounting annual rate, e.g. quarterlyrate*4.
This leaves "What is the present value, if you get 100 Dollars at the
10th day of each month (or at the next working day if the 10th day is
a holiday or a weekend) for the next 5 years and the monthly interest
rate is 5/12%?" for another day.
Initially I understood you wanted the date as a string or date type as
in e.g open office. What would be the units of the user-supplied,
floating-point time array?
It is still necessary to know the time units to provide an annualized
rate, unless the rate is in continuous time, exp(r*t). I don't know
whether this would apply to all functions in numpy.finance, it's a
while since I looked at the code. Maybe there are some standard
simplifications in open office or excel.
I briefly skimmed the list of function in the open office help, and it
would be useful to have them available, e.g. as a package in scipy.
But my google searches in the past for applications in finance with a
compatible license didn't provide much useful code that could form the
basis of a finance package.
Adding more convenience and functionality to numpy.finance is useful,
but if they get extended with slow feature creep, then another
location (scipy) might be more appropriate and would be more
expandable, even if it happens only slowly.
That's just my opinion (obviously), I'm a relative newbie to
numpy/scipy and still working my way through all the different
More information about the Numpy-discussion