Bollinger Band Width measures the distance between upper and lower Bollinger Bands, normalized by the middle band. When BBW is low, the bands are squeezing together, signaling compressed volatility and impending breakout. When BBW is high, the market is in an expanded volatility state. BBW transforms Bollinger Bands from a visual channel indicator into a quantifiable volatility oscillator, enabling algorithmic detection of "squeeze" conditions that often precede significant price moves.
## Historical Context
John Bollinger introduced Bollinger Bands in the 1980s as a self-adjusting volatility envelope. The bands expand and contract based on recent price volatility measured by standard deviation. While the bands themselves are useful for identifying overbought/oversold conditions, traders noticed that band contraction often preceded explosive moves.
BBW (Bollinger Band Width) was developed to quantify this contraction numerically. Rather than eyeballing chart patterns, BBW provides an objective measurement. The formula divides the band distance by the middle band (SMA), producing a percentage-based reading that allows comparison across different price levels and assets.
The "Bollinger Squeeze" became a popular trading setup: identify periods of historically low BBW, then trade the subsequent breakout. Some traders add a momentum filter (like Keltner Channels inside Bollinger Bands) to confirm the squeeze, but BBW alone captures the core volatility compression signal.
## Architecture & Physics
BBW is derived from Bollinger Bands components. It requires:
1.**SMA (Simple Moving Average)**: The middle band and normalizer
BBW is proportional to the Coefficient of Variation (CV):
$$
CV = \frac{\sigma}{\mu}
$$
$$
BBW = 2k \times CV
$$
With default $k=2$, BBW equals 4 times the coefficient of variation. This normalization allows BBW to be compared across assets with different price levels.
1.**Squeeze Detection Timing**: Low BBW signals *potential* breakout, not *immediate* breakout. Squeezes can persist for extended periods before resolution. Combine with momentum or volume confirmation.
2.**Directional Assumption**: BBW measures volatility magnitude, not direction. A squeeze can break upward or downward with equal probability from BBW alone. Use trend filters for directional bias.
3.**Period Sensitivity**: Shorter periods (10-15) produce more responsive but noisier BBW. Longer periods (25-50) are smoother but lag volatility changes. Match period to your trading timeframe.
4.**Multiplier Impact**: Changing the multiplier (k) scales BBW proportionally. BBW with k=3 will be 1.5× the value of BBW with k=2. Ensure consistent multiplier when comparing historical readings.
5.**Mean-Reverting Nature**: Unlike trending indicators, BBW tends to mean-revert. Extremely low BBW readings eventually return to average as volatility normalizes post-squeeze.
6.**Cross-Asset Comparison**: While BBW is percentage-normalized, different assets have different "normal" volatility ranges. A 0.10 BBW might be low for a volatile stock but high for a bond ETF.
7.**Zero Division Guard**: If SMA equals zero (theoretically impossible with positive prices), BBW would be undefined. Implementation guards against this edge case.
