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

Skipper Seabold jsseabold@gmail....
Mon May 25 15:27:49 CDT 2009

On Mon, May 25, 2009 at 3:40 PM, Joe Harrington <jh@physics.ucf.edu> wrote:
> On Mon, 25 May 2009 13:51:38 -0400, josef.pktd@gmail.com wrote:
>> 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.
>> >> >
>> >>
>> >>
>> >>
>> >>
>> >> * 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.
>> >
>> > Other points: Yuk. ?You're right.
>> >
>> > When these first came up for discussion, I had a Han Solo moment
>> > 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
>> subpackages.
>
> np.irr is defined on (anonymous) constant time intervals and gives you
> the growth per time interval.  The code is very short, basically a
> call to np.roots(values):
>
> def irr(values):
>    """
>    Return the Internal Rate of Return (IRR).
>
>    This is the rate of return that gives a net present value of 0.0.
>
>    Parameters
>    ----------
>    values : array_like, shape(N,)
>        Input cash flows per time period.  At least the first value would be
>        negative to represent the investment in the project.
>
>    Returns
>    -------
>    out : float
>        Internal Rate of Return for periodic input values.
>
>    Examples
>    --------
>    >>> np.irr([-100, 39, 59, 55, 20])
>    0.2809484211599611
>
>    """
>    res = np.roots(values[::-1])
>    # Find the root(s) between 0 and 1
>    mask = (res.imag == 0) & (res.real > 0) & (res.real <= 1)
>    if res.size == 0:
>        return np.nan
>    rate = 1.0/res - 1
>    if rate.size == 1:
>        rate = rate.item()
>    return rate
>
> So, I think this is a continuous definition of growth, not some
> periodic compounding.
>
> I'd propose the time array would be in anonymous units, and the result
> would be in terms of those units.  For example, if an interval of 1.0
> in the time array were one fortnight, it would give interest in units
> of continuous growth per fortnight, etc.  Anything with many more
> options than that does not belong in numpy (but it would be
> interesting to have elsewhere).
>

Here is my stab at xirr.  It depends on the python datetime module and
the Newton - Raphson algorithm in scipy.optimize, but it could be
taken as a starting point if someone wants to get rid of the
dependencies (I haven't worked too much with dates or NR before).  The
reference for the open office version is here
<http://wiki.services.openoffice.org/wiki/Documentation/How_Tos/Calc:_XIRR_function>,
and it performs in exactly the same way (assumes 365 days a year).  It
also doesn't take a 'begin' or 'end' argument for when the payments
are made. but this is already in the numpy.financial and could be

def _discf(rate, pmts, dates):
import numpy as np
dcf=[]
for i,cf in enumerate(pmts):
d=dates[i]-dates[0]
dcf.append(cf*(1+rate)**(-d.days/365.))

def xirr(pmts, dates, guess=.10):
'''
IRR function that accepts irregularly spaced cash flows

Parameters
----------
values: array_like
Contains the cash flows including the initial investment
dates: array_like
Contains the dates of payments as in the form (year, month, day)

Returns: Float
Internal Rate of Return

Notes
----------
In general the xirr is the solution to

.. math:: \sum_{t=0}^M{\frac{v_t}{(1+xirr)^{(date_t-date_0)/365}}} = 0

Examples
--------------
dates=[[2008,2,5],[2008,7,5],[2009,1,5]]
pmts=[-2750,1000,2000]
print xirr(pmts,dates)
'''
from datetime import date
from scipy.optimize import newton

for i,dt in enumerate(dates):
dates[i]=date(*dt)

f = lambda x: _discf(x, pmts, dates)

return newton(f, guess)

if __name__=="__main__":
dates=[[2008,2,5],[2008,7,5],[2009,1,5]]
pmts=[-2750,1000,2000]
print xirr(pmts,dates)

Cheers,

Skipper