[Numpy-discussion] Question on timeseries, for financial application
Sat Dec 12 21:03:09 CST 2009
On Sat, Dec 12, 2009 at 8:08 PM, THOMAS BROWNE <firstname.lastname@example.org> wrote:
> Hello all,
> Quite new to numpy / timeseries module, please forgive the elementary question.
> I wish to do quite to do a bunch of multivariate analysis on 1000 different financial markets series, each holding about 1800 data points (5 years of daily data).
> What's the best way to put this into a TimeSeries object? Should I use a structured data type (in which case I can reference each series by name), or should I put it into one big numpy array object (in which case I guess I'll have to keep track of the series name in an internal structure)? What are the advantages and disadvantages of each?
> Ideally I'd have liked the ease and simplicity of being able to reference each series by name, while maintaining the fast speed and clean structure of one big numpy array. Any way of getting both?
> Once I have a multivariate TimeSeries, how do I add another series to it?
I'm not sure if your TimeSeries object refers to the scikits or
writing your own application.
In the later case, I would recommend looking at the following for data
handling, the first two are written or co-written with finance
applications in mind, the last is a nice package for working with
structured arrays, but I'm don't know about how extensive time
http://code.google.com/p/pandas/ general (2d) panel data, arbitrary
axis labels possible, integrates scikits.statsmodels
http://scikits.appspot.com/timeseries based on masked arrays,
extensive time handling
If you need to do more serious data work and don't have a special
requirement on the data structure, I think, it would be better to use
( and contribute for adjustments/extensions/testing) to one of the
existing data structures than writing your own. All 3 are BSD or MIT
> Thanks for the help.
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