Standard Deviation explained

What Standard Deviation measures

Standard deviation is the foundational statistical measure of how spread out price (or returns) has been around its own average over the lookback period — it's the same calculation that builds the width of Bollinger Bands. A higher standard deviation means price has been swinging more widely; a lower one means it's been tightly clustered near its average.

How Standard Deviation readings are interpreted

Rising standard deviation means volatility is expanding — larger price swings are becoming the norm. Falling standard deviation means volatility is contracting — price is settling into a tighter range, often a precursor to an eventual breakout once the compression resolves.

Conventional Standard Deviation levels

• Rising standard deviation: volatility expanding, larger expected price swings
• Falling standard deviation: volatility contracting, tighter expected range
• Extended periods of unusually low standard deviation have historically preceded sharp volatility expansions (the same logic underlying the Squeeze setup)
• Because it's expressed in raw price units, direct comparison across differently-priced securities requires normalizing (similar to how NATR normalizes ATR)

Where Standard Deviation works, and where it does not

A pure volatility measure with no directional component — useful in virtually any market condition as a read on how much price is moving, regardless of which way. Foundational to many other tools (Bollinger Bands, Z-Score) rather than typically used entirely on its own.

Indicators commonly read alongside Standard Deviation

• Bollinger Bands: Directly built from this exact calculation — Bollinger Band Width is essentially a normalized view of the same information
• Z-Score: Also built from standard deviation, measuring how many standard deviations price sits from its own mean
• NATR: A comparable volatility read from a different calculation base (true range rather than closing price dispersion)

Known limitations of Standard Deviation

• Expressed in raw price units, so it isn't directly comparable across different-priced securities without normalization
• Assumes a roughly normal distribution of price changes, which real markets don't always follow (see Kurtosis and Skew for exactly this limitation)
• Reacts to both up and down moves identically — unlike the Ulcer Index, it doesn't distinguish "good" volatility from "bad" (downside) volatility

Standard Deviation in practice

• Best used as a building block for other decisions (position sizing, band width) rather than as a standalone signal
• Compare a security's current standard deviation to its own historical range to judge whether volatility is currently elevated or subdued
• Pair with Kurtosis and Skew for a fuller picture, since standard deviation alone assumes a symmetric, normal-ish distribution that isn't always accurate
• A useful screening input for finding either "calm, low-volatility" or "currently expanding volatility" candidates depending on your strategy