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

Skipper Seabold jsseabold@gmail....
Mon May 25 11:29:55 CDT 2009


On Mon, May 25, 2009 at 11:50 AM, Joe Harrington <jh@physics.ucf.edu> wrote:
> On Sun, 24 May 2009 18:14:42 -0400 josef.pktd@gmail.com wrote:
>> On Sun, May 24, 2009 at 4:33 PM, Joe Harrington <jh@physics.ucf.edu> 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
>> calculations.
>> 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
>>
>> Josef
>
> 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.
>

Again, I think it depends on what exactly you want to do.  While I've
certainly never worked in a quant shop, I am familiar with some of the
academic/CFA-type usages.  On my todo list for the summer is to
provide some Cookbook examples for some options pricing and yield
curve models (some of which will be based on past work), so I might be
in a somewhat better position to answer this later.  There is always
quantlib over here <http://quantlib.org/reference/index.html>, which
is certainly a good place to look for what *could* be included... but
this is of course much too field-specific to go into Numpy or Scipy.

> 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 remember.  Han shot first ;)

> 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.
>

This seems like a good direction, if I understand you correctly.  That
way the user could just supply a list of trading days or whatever for
the instrument they're interested in.  Anything else could be
maintained elsewhere, and I think this would be an interesting project
personally.

Cheers,

Skipper


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