[Numpy-discussion] Financial TS models
Sat Sep 18 09:13:34 CDT 2010
On Sat, Sep 18, 2010 at 8:09 AM, Virgil Stokes <firstname.lastname@example.org> 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
Depends on what you want to do, but I would join or build on top of an
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
"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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