Standard Deviation (indicator)
Published Last updated
Key Takeaway
Standard deviation, as a technical indicator, measures how widely recent closing prices have dispersed from their moving average, providing a statistical quantification of current price volatility.
Learn These First
What Is Standard Deviation (indicator)?
Standard deviation, as a technical indicator, measures how widely recent closing prices have dispersed from their moving average, providing a statistical quantification of current price volatility.
How Standard Deviation (indicator) Works
Frequently Asked Questions
How does standard deviation determine Bollinger Band width?
Bollinger Bands place their upper and lower bands at a defined number of standard deviations — typically two — above and below a central moving average. The standard deviation is calculated over the same lookback period as the moving average, commonly 20 bars. When recent closes have been widely scattered around the average, the standard deviation value is large and the bands are wide. When closes have been tightly clustered near the average, the standard deviation is small and the bands are narrow. This direct relationship means the bands automatically expand during volatile periods and contract during quiet periods without requiring any manual adjustment.
What does a very low standard deviation reading on the indicator mean for a trader?
A very low standard deviation reading indicates that recent closing prices have been unusually tightly clustered around the moving average — a period of price compression. Markets historically alternate between compression and expansion phases, and unusually low standard deviation periods tend to precede significant directional price moves. When the standard deviation indicator reaches historically low levels for the asset and timeframe being traded, it alerts the trader to monitor for breakout setups and prepare for increased volatility. It does not indicate direction — only that the current tight price action is likely building toward a directional resolution with expanded volatility.
Why is standard deviation used instead of ATR for Bollinger Bands?
John Bollinger chose standard deviation for his band calculation because of its statistical property of measuring price dispersion around a mean — which directly answers the question of how widely price is ranging relative to its recent average. ATR measures bar-by-bar movement magnitude and responds to intrabar ranges and gaps rather than to how scattered closes are around a mean. For bands designed to capture the statistical range of typical price fluctuations around a moving average, standard deviation is the more mathematically appropriate measure. The result is bands that are self-calibrating to the precise statistical distribution of the asset's recent price behaviour.
Common Misconceptions About Standard Deviation (indicator)
Standard deviation and ATR measure the same volatility and can be used interchangeably
Standard deviation and ATR measure related but distinct aspects of volatility. Standard deviation measures how dispersed recent closing prices are around their moving average — a statistical distribution measure. ATR measures the average size of each bar's price movement including gaps from the previous close — a movement magnitude measure. A market can have low standard deviation but relatively high ATR if individual bars have wide intrabar ranges but consistently close near the moving average. These different properties make the two measures complementary, and their combined use in the Bollinger-Keltner Squeeze provides a more specific volatility compression signal than either measure alone.
Two standard deviations always contains 95% of prices as in a normal distribution
In classical statistics, two standard deviations contain approximately 95% of values in a perfectly normal distribution. Financial price data, however, does not follow a perfectly normal distribution — it exhibits fat tails, meaning extreme moves occur more frequently than a normal distribution would predict. John Bollinger himself stated that Bollinger Bands typically contain approximately 88 to 89% of price data rather than 95%, and this containment percentage changes with market conditions. Traders should treat the two-standard-deviation bands as dynamic reference zones for typical price behaviour rather than as statistically precise probability boundaries derived from a normal distribution assumption.
A high standard deviation reading means price will soon reverse toward the mean
High standard deviation indicates that price has been widely dispersed around its average — high recent volatility — but it does not confirm that a mean-reversion move is imminent. Strong trending markets can sustain high standard deviation readings for extended periods as price continues to advance or decline strongly. A high standard deviation may reflect an ongoing trend rather than an overextension about to reverse. Mean-reversion entries based on high standard deviation alone, without directional exhaustion signals from momentum indicators or price action, expose traders to counter-trend entries against moves that have sufficient momentum to continue for considerably longer.