[R-SIG-Finance] high frequency data analysis in R
comtech.usa at gmail.com
Thu May 21 17:21:38 CEST 2009
By high frequency I mean really the tick data. For example, during
peak time, the arrival of price events could be at about hundreds to
thousands within one second, irregularly spaced.
I've heard that forcing irregularly spaced data into regularly spaced
data(e.g. through interpolation) will lose information. How's that so?
On Thu, May 21, 2009 at 8:15 AM, Jeff Ryan <jeff.a.ryan at gmail.com> wrote:
> Not my domain, but you will more than likely have to aggregate to some
> sort of regular/homogenous type of series for most traditional tools
> to work.
> xts has to.period to aggregate up to a lower frequency from tick-level
> data. Coupled with something like na.locf you can make yourself some
> high frequency 'regular' data from 'irregular'
> Regular and irregular of course depend on what you are looking at
> (weekends missing in daily data can still be 'regular').
> I'd be interested in hearing thoughts from those who actually tread in
> the high-freq domain...
> A wealth of information can be found here:
> On Thu, May 21, 2009 at 10:04 AM, Michael <comtech.usa at gmail.com> wrote:
>> Hi all,
>> I am wondering if there are some special toolboxes to handle high
>> frequency data in R?
>> I have some high frequency data and was wondering what meaningful
>> experiments can I run on these high frequency data.
>> Not sure if normal (low frequency) financial time series textbook data
>> analysis tools will work for high frequency data?
>> Let's say I run a correlation between two stocks using the high
>> frequency data, or run an ARMA model on one stock, will the results be
>> Could anybody point me some classroom types of treatment or lab
>> tutorial type of document which show me what meaningful
>> experiments/tests I can run on high frequency data?
>> Thanks a lot!
>> R-SIG-Finance at stat.math.ethz.ch mailing list
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> Jeffrey Ryan
> jeffrey.ryan at insightalgo.com
> ia: insight algorithmics
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