The tendency of price to return to its historical average after an exaggerated rise or fall.
The idea
Mean reversion (Mean Reversion) is a statistical hypothesis: what strays far from its historical average tends to move back toward it. Not because the market "remembers", but because the excess is usually a temporary overpricing that fades once its cause calms down.
Where it works and where it fails
It works best in sideways markets, on correlated pairs and in volatility gauges, and fails its users in strong trends: a stock making new highs "away from its average" can stay away for months. So read it alongside trend strength, never alone.
How it is measured in practice
By the distance from a reference moving average (DMA-20 or DMA-50) scaled by the asset's volatility. The standard-deviation score (Z-Score) is the standard ruler. Two deviations or more put price in statistical excess territory.
Hypothetical example
A stock whose 50-day moving average is $100 and daily volatility 1.5% jumps to $109 in three sessions with no material news: a deviation of about six times its daily volatility. Historically, a zone like this is followed by a slowdown or partial pullback more often than by continuation.
Common mistake
Fighting the trend just because price moved away: "it rose a lot" is not a sell signal by itself. Excess with no material catalyst is not the same as excess created by news that really changed the story.
The condition people forget
Mean reversion assumes an existing range. In a strong trend it resists the move instead of using it. That is why it is always filtered by a trend-strength gate: accepted in a sideways market, rejected when the gauge shows an established trend.
Where to see it on the platform
The stock scanner shows each symbol's distance from its averages, and the deviation score appears in the quantitative analysis tools, where the whole idea of reversion is built.
The market regime panel classifies the regime first, so you know which family of strategies fits today.
