Would you like to put xirr in econpy until
it finds a home in SciPy? (Might as well
make it available.)
Cheers,
Alan Isaac
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I rewrote irr to use the iterative solver instead of polynomial roots
so that it can also handle large arrays. For 3000 values, I had to
kill the current np.irr since I didn't want to wait longer than 10
minutes
When writing the test, I found that npv is missing a when keyword,
for the case when
On May 25, 2009, at 10:59 PM, Joe Harrington wrote:
Let's keep this thread focussed on the original issue:
just add a floating array of times to irr or a new xirr
continuous interest
no more
Anyone can use the timeseries package to produce a floating array of
times from normal dates, if
On Sun, 24 May 2009 18:14:42 -0400 josef.p...@gmail.com wrote:
On Sun, May 24, 2009 at 4:33 PM, Joe Harrington j...@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
On Mon, May 25, 2009 at 11:50 AM, Joe Harrington j...@physics.ucf.edu wrote:
On Sun, 24 May 2009 18:14:42 -0400 josef.p...@gmail.com wrote:
On Sun, May 24, 2009 at 4:33 PM, Joe Harrington j...@physics.ucf.edu wrote:
I hate to ask for another function in numpy, but there's an obvious
one
On Mon, May 25, 2009 at 11:50 AM, Joe Harrington j...@physics.ucf.edu wrote:
On Sun, 24 May 2009 18:14:42 -0400 josef.p...@gmail.com wrote:
On Sun, May 24, 2009 at 4:33 PM, Joe Harrington j...@physics.ucf.edu wrote:
I hate to ask for another function in numpy, but there's an obvious
one
On Mon, 25 May 2009 13:51:38 -0400, josef.p...@gmail.com wrote:
On Mon, May 25, 2009 at 11:50 AM, Joe Harrington j...@physics.ucf.edu wrote:
On Sun, 24 May 2009 18:14:42 -0400 josef.p...@gmail.com wrote:
On Sun, May 24, 2009 at 4:33 PM, Joe Harrington j...@physics.ucf.edu
wrote:
I hate
On Mon, May 25, 2009 at 3:40 PM, Joe Harrington j...@physics.ucf.edu wrote:
On Mon, 25 May 2009 13:51:38 -0400, josef.p...@gmail.com wrote:
On Mon, May 25, 2009 at 11:50 AM, Joe Harrington j...@physics.ucf.edu
wrote:
On Sun, 24 May 2009 18:14:42 -0400 josef.p...@gmail.com wrote:
On Sun,
On Mon, May 25, 2009 at 4:27 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Mon, May 25, 2009 at 3:40 PM, Joe Harrington j...@physics.ucf.edu wrote:
On Mon, 25 May 2009 13:51:38 -0400, josef.p...@gmail.com wrote:
On Mon, May 25, 2009 at 11:50 AM, Joe Harrington j...@physics.ucf.edu
wrote:
Sorry to jump in a conversation I haven't followed too deep in
details, but I'm sure you're all aware of the scikits.timeseries
package by now. This should at least help you manage the dates
operations in a straightforward manner. I think that could be a nice
extension to the package:
On Mon, May 25, 2009 at 6:36 PM, Pierre GM pgmdevl...@gmail.com wrote:
Sorry to jump in a conversation I haven't followed too deep in
details, but I'm sure you're all aware of the scikits.timeseries
package by now. This should at least help you manage the dates
operations in a straightforward
The advantage of Skippers implementation using actual dates instead of
just an array of numbers is that it is possible to directly calculate
the annual irr, since the time units are well specified. The only
problem is the need for an equation solver in numpy. Just using a date
tuple would
On May 25, 2009, at 7:02 PM, josef.p...@gmail.com wrote:
On Mon, May 25, 2009 at 6:36 PM, Pierre GM pgmdevl...@gmail.com
wrote:
Sorry to jump in a conversation I haven't followed too deep in
details, but I'm sure you're all aware of the scikits.timeseries
package by now. This should at
On Mon, May 25, 2009 at 7:37 PM, Pierre GM pgmdevl...@gmail.com wrote:
On May 25, 2009, at 7:02 PM, josef.p...@gmail.com wrote:
On Mon, May 25, 2009 at 6:36 PM, Pierre GM pgmdevl...@gmail.com
wrote:
Sorry to jump in a conversation I haven't followed too deep in
details, but I'm sure you're
On May 25, 2009, at 8:06 PM, josef.p...@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
On Mon, May 25, 2009 at 7:27 PM, josef.p...@gmail.com wrote:
The advantage of Skippers implementation using actual dates instead of
just an array of numbers is that it is possible to directly calculate
the annual irr, since the time units are well specified. The only
problem is the need for
On Mon, May 25, 2009 at 8:30 PM, Pierre GM pgmdevl...@gmail.com wrote:
On May 25, 2009, at 8:06 PM, josef.p...@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
forgive me for jumping in on this thread and playing devil's advocate here, but
I am a natural pessimist so please bear with me :) ...
I think as this discussion has already demonstrated, it is *extremely*
difficult to build a solid general purpose API for financial functions (even
seemingly
On Mon, May 25, 2009 at 9:18 PM, Matt Knox mattknox...@gmail.com wrote:
forgive me for jumping in on this thread and playing devil's advocate here,
but
I am a natural pessimist so please bear with me :) ...
It's good to hear from a real finance person.
I think as this discussion has
josef.pktd at gmail.com writes:
So, while python won't get any industrial strength finance package,
a more modest designer package would be feasible, if there were any
interest in it (which I haven't seen).
...
The even more modest question is whether we would want to match open
office
I haven't read all the messages in detail, and I'm a consumer not a
producer, but I'll comment anyways.
I'd love to see additional financial functionality, but I'd like to
see them in a scikit, not in numpy. I think to be useful they are too
complicated to go into numpy. A couple of my
On May 25, 2009, at 9:15 PM, Matt Knox wrote:
josef.pktd at gmail.com writes:
So, while python won't get any industrial strength finance package,
a more modest designer package would be feasible, if there were any
interest in it (which I haven't seen).
...
The even more modest question
Let's keep this thread focussed on the original issue:
just add a floating array of times to irr or a new xirr
continuous interest
no more
Anyone can use the timeseries package to produce a floating array of
times from normal dates, if those are the dates they want. If they
want some
On Mon, May 25, 2009 at 23:59, Joe Harrington j...@physics.ucf.edu wrote:
Let's keep this thread focussed on the original issue:
just add a floating array of times to irr or a new xirr
continuous interest
no more
Anyone can use the timeseries package to produce a floating array of
times
On Tue, May 26, 2009 at 1:12 AM, Robert Kern robert.k...@gmail.com wrote:
On Mon, May 25, 2009 at 23:59, Joe Harrington j...@physics.ucf.edu wrote:
Let's keep this thread focussed on the original issue:
just add a floating array of times to irr or a new xirr
continuous interest
no more
On Tue, May 26, 2009 at 00:20, Skipper Seabold jsseab...@gmail.com wrote:
My only question then would be why have numpy.financials in the first
place?
You can go through the old threads for the arguments.
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless
On Mon, May 25, 2009 at 6:55 PM, josef.p...@gmail.com wrote:
On Mon, May 25, 2009 at 7:27 PM, josef.p...@gmail.com wrote:
The advantage of Skippers implementation using actual dates instead of
just an array of numbers is that it is possible to directly calculate
the annual irr, since the
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
On Sun, May 24, 2009 at 4:33 PM, Joe Harrington j...@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
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