[Numpy-discussion] add xirr to numpy financial functions?
josef.pktd@gmai...
josef.pktd@gmai...
Mon May 25 20:06:48 CDT 2009
On Mon, May 25, 2009 at 8:30 PM, Pierre GM <pgmdevlist@gmail.com> wrote:
>
> On May 25, 2009, at 8:06 PM, josef.pktd@gmail.com wrote:
>>>>
>>>> The problem is, if the functions are enhanced in the current numpy,
>>>> then scikits.timeseries is not (yet) available.
>>>
>>> Mmh, I'm not following you here...
>>
>> The original question was how we can enhance numpy.financial, eg.
>> np.irr
>> So we are restricted to use only what is available in numpy and in
>> standard python.
>
> Ah OK. But it seems that you're now running into a pb w/ dates
> handling, which might be a bit too specialized for numpy. Anyway, the
> call isn't mine.
>
>>>
>> I looked at your moving functions, autocorrelation function and so on
>> a while ago. That's were I learned how to use np.correlate or the
>> scipy versions of it, and the filter functions. I've written the
>> standard array versions for the moving functions and acf, ccf, in one
>> of my experiments.
>
> The moving functions were written in C and they work even w/
> timeseries (they work quite OK w/ pure MaskedArraysP. We put them in
> scikits.timeseries because it was easier to have them there than in
> scipy, for example.
>
>
>> If Skipper has enough time in his google summer of code, we would like
>> to include some basic timeseries econometrics (ARMA, VAR, ...?)
>> however most likely only for regularly spaced data.
>
> Well, we can easily restrict the functions to the case were there's no
> missing data nor missing dates. Checking the mask is easy, and we have
> a method to chek the dates (is_valid)
>
>
>>> Anyhow, if the pb you have are just to specify dates, I really think
>>> you should give the scikits a try. And send feedback, of course...
>>
>> Skipper intends to write some examples to show how to work with the
>> extensions to scipy.stats, which, I think, will include examples using
>> time series, besides recarrays, and other array types.
>
>
> Dealing with TimeSeries is pretty much the same thing as dealing with
> MaskedArray, with the extra convenience of converting from one
> frequency to another and so forth.... Quite often, an analysis can be
> performed by dropping the .dates part, working on the .series part
> (the underlying MaskedArray), and repatching the dates at the end...
>
>
>>
>> Is there a time line for including the timeseries scikits in numpy/
>> scipy?
>> With code that is intended for incorporation in numpy/scipy, we are
>> restricted in our external dependencies.
>
> I can't tell, because the decision is not mine. For what I understood,
> there could be an inclusion in scipy if there's a need for it. For
> that, we need more users end more feedback.... If you catch my drift...
Thanks for the info, we will keep this in mind.
Personally, I still think of data just as an array or matrix of
numbers, when they still have dates and units attached to them, they
are usually a pain. And I'm only slowly getting used to the
possibility that it doesn't necessarily need to be so painful.
(I didn't know you moved the moving functions to C, I thought I saw
them in python.)
Josef
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