On Sat, Sep 18, 2010 at 10:13 AM, <josef.p...@gmail.com> wrote: > On Sat, Sep 18, 2010 at 8:09 AM, Virgil Stokes <v...@it.uu.se> 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 >> appreciated.
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. Josef > > 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 > > http://github.com/quantmind/dynts > > (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 > csv-files) > > Josef > >> >> Thank you, >> --V >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
