Re: [Numpy-discussion] Numpy SVN frozen; move to Git
On Thu, Sep 16, 2010 at 1:58 AM, Pauli Virtanen p...@iki.fi wrote: Numpy SVN repository is now frozen, and does not accept new commits. Future development should end up in the Git repository: http://github.com/numpy/numpy Joining my voice to the collective thank you, I figured I'd pass along something I stumbled upon just now as I was reading on github's new (massively improved) pull requests; a nice description of branch management using git for a real-world scenario with release/development branches, etc: http://nvie.com/posts/a-successful-git-branching-model/ None of it is a new idea, but it's very nicely and concisely explained. Regards, f ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Financial TS models
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. Thank you, --V ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Financial TS models
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. 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
Re: [Numpy-discussion] Financial TS models
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
Re: [Numpy-discussion] Numpy SVN frozen; move to Git
On Sat, Sep 18, 2010 at 4:02 AM, Fernando Perez fperez@gmail.comwrote: On Thu, Sep 16, 2010 at 1:58 AM, Pauli Virtanen p...@iki.fi wrote: Numpy SVN repository is now frozen, and does not accept new commits. Future development should end up in the Git repository: http://github.com/numpy/numpy Joining my voice to the collective thank you, I figured I'd pass along something I stumbled upon just now as I was reading on github's new (massively improved) pull requests; a nice description of branch management using git for a real-world scenario with release/development branches, etc: http://nvie.com/posts/a-successful-git-branching-model/ None of it is a new idea, but it's very nicely and concisely explained. But the vim post was the best thing at the site ;) I've now got pathogen and snipmate in my .vim folder. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Financial TS models
On Sat, Sep 18, 2010 at 10:33 AM, josef.p...@gmail.com wrote: 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 I'm also interested as well what you might have in mind. For econometric models, the place to start (and perhaps contribute) would definitely be statsmodels. For other models it depends. Over the next year or so I plan to be working as often as I can on implementing time series models either inside statsmodels (with a pandas interface so you can carry around metadata) or inside pandas itself (which is not necessarily intended for financial applications, but that's what I've used it for). I'm interested in both standard econometric models (see e.g. Lütkepohl's 2006 time series book) and Bayesian time series models. - Wes ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
