[Numpy-discussion] Question on timeseries, for financial application
Sat Dec 12 23:11:32 CST 2009
Have you considered creating a TimeSeries for each data series, and
then putting them all together in a dict, keyed by symbol?
One disadvantage of one big monster numpy array for all the series is
that not all series may have a full set of 1800 data points. So the
array isn't really nicely rectangular.
Not sure exactly what kind of analysis you want to do, but grabbing a
series from a dict is quite fast.
On Dec 12, 2009, at 6:08 PM, THOMAS BROWNE 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
> 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?
> Thanks for the help.
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