High Low Clustering on intraday high frequency sampled data

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Nothing unusually exciting on this post, but I happened to be engaged in some particle based methods recently and made some simple visual observations as I was setting up some of the sampling environment in R.  I am also using Rkward and Ubuntu to generate, so I’m gathering everything from the current environment (including graphics).

Fig 1. Parallel plot of half hr sample of High and Low intraday data points vs time (Max is purple dots, Min are red).

The plot illustrates sampled intraday data at half hour increments.
The highs and lows of each sample interval are overlaid using purple to denote an intraday high and red to denote an intraday low.
Interesting points of observation are–

1) The high and low samples tend to be clustered more often at concentrated intervals. Notice the majority of interval lows concentrated at the open, followed by highs at the first half hour interval, then back to lows.
2) Notice the sparsity of high and low events in the center of the sampled period.
3) Most importantly, high and low events do not appear to be uniformly and randomly distributed over time.

This kind of data processing is useful towards generating, exploring, and evaluating pattern based setups. The observed data matches well with some of the ideas promulgated by “The Logical Trader” author, Mark Fisher.

The study is by no means complete or conclusive, just stopping by to show more of the type of data processing and visual capabilities that R is capable of.   If anyone has done any more conclusive studies I’d be interested to hear.

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