Hi Geoffrey,

By cleaning up I assume you actually mean a variety of things such as
removing trades marked as corrected, delayed, or after-hours trades in
addition to the more difficult problem of identifying outliers in the raw
data.

While I am not aware of any R specific package to perform these goals, I can
point you in the direction of a few papers which might help get you started:

1. http://faculty.washington.edu/ezivot/research/hfanalysis.pdf

Eric Zivot's paper on high frequency data analysis, while somewhat dated is
still relevant.

2. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=886204

Christian Brownlees paper proposes a simple method to identify outliers in
the transaction data.

3. http://www.tickdata.com/FilteringWhitePaper.pdf

Discusses qualitatively some of the challenges in developing an algorithm to
identify outliers.

One important issue that I have with the first two references is that they
both describe outlier detection methods that are not suitable to use in a
real-time system: they introduce significant look ahead biases.  If you are
developing filtering algorithms with the intent to assess the performance of
real-time trading strategies neither of these approaches will be suitable.

Hope this helps,
Scott

On Tue, Jan 12, 2010 at 10:35 AM, Geoffrey Smith <gps@asu.edu> wrote:

> Hello, may I please ask if anyone is aware of any R package that could help
> with cleaning up and processing the high-frequency trade and quote data
> from
> the NYSE TAQ database?  Thank you.  Geoff
>
> --
> Geoffrey Smith
> Visiting Assistant Professor
> Department of Finance
> W. P. Carey School of Business
> Arizona State University
>
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