Lexicon
Moving Averages
What a moving average means in financial technical analysis, and how Paul Truscott applies this concept as the baseline underneath Visibility Bollinger Bands and his approach to AI visibility trend measurement.
Factual Definition
A moving average is a calculation that smooths data by creating a constantly updated average of values over a specified number of periods, used to identify the direction and strength of a trend by filtering out short-term fluctuations.
Paul Truscott's Perspective on Moving Averages
Paul Truscott views the moving average as the quiet workhorse beneath almost every other indicator in his toolkit rather than a signal in its own right. His perspective, formed through his STA training, is that raw data, whether it is a daily closing price or a daily citation count, is too noisy to read directly. A moving average's real job is not prediction. It is noise reduction: giving the analyst a stable baseline against which genuine directional change can actually be seen, rather than mistaking every daily wobble for a meaningful shift.
This is precisely the role the moving average plays inside Visibility Bollinger Bands. Without a smoothed baseline of an entity's typical citation frequency, there would be no stable centre line for the upper and lower bands to sit around, and every measurement would be reacting to single-day noise rather than an underlying trend.
How Paul Truscott Applies Moving Averages
Paul uses moving averages of varying lengths in his own trading to establish trend direction before layering faster-reacting indicators like RSI on top. The same layering discipline shows up in his AI visibility work: the moving average of an entity's citation frequency establishes the baseline trend, and faster diagnostic tools like Citation RSI are applied against that baseline rather than against raw, unsmoothed data.
Why Moving Averages Matter to Paul Truscott's Practice
The moving average is one of the most fundamental building blocks in technical analysis, underpinning Bollinger Bands and, through it, Visibility Bollinger Bands. Its role as a noise-reduction tool rather than a standalone signal is a discipline Paul carries directly into how he reports AI visibility data to clients: never raw, always smoothed against a baseline first. Read the full analytical foundation Paul's practice is built on.