[Numpy-discussion] Financial TS models
Sat Sep 18 09:33:44 CDT 2010
On Sat, Sep 18, 2010 at 10:13 AM, <firstname.lastname@example.org> wrote:
> On Sat, Sep 18, 2010 at 8:09 AM, Virgil Stokes <email@example.com> wrote:
>> I am considering the development of an all Python package (with numpy and
>> matplotlib) for the modeling and analysis of financial time series.
>> This is a rather ambitious and could take sometime before I can have something
>> that is useful. Thus, any suggestions, pointers, etc. to related work would be
I should have just asked you: What do you have in mind?
instead of writing my notes from the readings I just did into the email.
I think any open source contributions in this area will be very useful
with the increased popularity of python in Finance.
> Depends on what you want to do, but I would join or build on top of an
> existing package.
> I just got distracted with an extended internet search after finding
> (They use Redis as an in-memory and persistent storage. After reading
> up a bit, I think this might be useful if you have a web front end
> http://github.com/lsbardel/jflow in mind, but maybe not as good as
> hdf5 for desktop work. Just guessing since I used neither, and I
> always worry about installation problems on Windows.)
> They just started public development but all packages are in BSD from
> what I have seen.
> Otherwise, I would build on top of pandas, scikits.timeseries or larry
> or tabular if you want to handle your own time variable.
> For specific financial time series, e.g. stocks, exchange rates,
> options, I have seen only bits and pieces, or only partially
> implemented code (with a BSD compatible license), outside of quantlib
> and it's python bindings.
> Maybe someone knows more about what's available.
> For the econometrics/statistical analysis I haven't seen much outside
> of pandas and statsmodels in this area (besides isolated examples and
> recipes). I started to write on this in the statsmodels sandbox
> (including simulators).
> "modeling and analysis of financial time series" is a big project,
> and to get any results within a reasonable amount of time (unless you
> are part of a large team) is to specialize on some pieces.
> This is just my impression, since I thought of doing the same thing,
> but didn't see any way to get very far.
> (I just spend some weekends just to get the data from the Federal
> Reserve and wrap the API for the economics data base (Fred) of the
> Federal Reserve Saint Louis, the boring storage backend is zipped
>> Thank you,
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