[Numpy-discussion] Question on timeseries, for financial application

[Robert Ferrell]

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.

-r

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  
> 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?
>
> Thanks for the help.
>
> Thomas.
>
>
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