From the July 01, 2010 issue of Futures Magazine • Subscribe!

Technical indicators: A statistical approach

Statistical implementation

The general implementation of a statistical indicator is:

  1. Calculate technical indicator values. This is identical to the computation used for the original technical indicator.
  2. Save the technical indicator value.
  3. At the close of the last bar, calculate the mean and standard deviation from the saved technical indicator values; then calculate the z-score value for the current bar.
  4. Plot the z-score value similarly to how the original indicator plotted its output.

There are two points to consider when carrying out a statistical implementation:

  1. When saving technical indicator values, make use of an efficient data structure to retain value history. Data arrays, which require shifting previous entries to make room for the next entry, are inefficient. A queue structure that provides efficient FIFO (first in, first out) insertion is preferred.
  2. Do not compute the technical indicator on intrabar updates. If lengthy price histories are used for mean and standard deviation calculation, then performing their computation intrabar can be compute intensive. Most platforms support the option of restricting indicator computation to the close of a bar, and this is adequate to avoid unnecessary computation. The minor restriction does not eliminate the use of tick charts and the statistical indicators continue to work with them. For example, see the TF tick chart used in “MACD gets some help.”

The technical analyst seeks to apply preferred indicators across markets and time frames. Moving technical indicators to a statistically valued format retains the properties of the original indicator, while making the new statistical indicators robust and portable. The statistical indicator often eliminates the need for time-consuming and error-prone modifications. This sort of simple statistical technique isn’t rocket science, but it is useful.

And the use of statistical indicators can standardize the discussion of trade decisions and may add discipline to a trading program. Trade entry and exit decisions begin to be based on a well-defined measure of general price action, as opposed to varied, and perhaps inaccurate, asset-specific values.

For 20 years, Michael Gutmann was a software engineer and manager at Intel Corp. He trades his system daily and is the author of “The Very Latest E-Mini Trading: Using Market Anticipation to Trade Electronic Futures.” Mike can be reached at

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