What does a Bollinger Band squeeze mean?

A squeeze occurs when bands contract, indicating low volatility. This often precedes a significant price move, though direction is not predicted.

Key Takeaways

Difficulty:Beginner

Reliability:High

Category:volatility

Timeframes:15M, 1H, 4H

Bollinger Bands

volatility📊 15M, 1H, 4H, Daily

Consists of a middle band (SMA) and upper/lower bands set at standard deviations from the middle.

Formula

Code

Middle Band = 20-period SMA

Upper Band = Middle Band + (2 × Standard Deviation)

Lower Band = Middle Band - (2 × Standard Deviation)

Detailed Explanation

Bollinger Bands, developed by John Bollinger, create dynamic channels around price based on volatility. They consist of three lines:

Components:
- Middle Band: Simple Moving Average (typically 20)
- Upper Band: Middle + (2 × Std Dev)
- Lower Band: Middle - (2 × Std Dev)

Key Signals:
- Squeeze: Bands contract before breakouts
- Walking the Bands: Price hugging bands in trends
- Mean Reversion: Price returning to middle band

Trading Setup Example

Entry, stop loss, and take profit levels.

📈 Buy Setup

📉 Sell Setup

Always wait for signal confirmation before entering a trade.

Where This Indicator Works Best

Trend context and market position.

📈 Bullish Context

📉 Bearish Context

Measures market volatility and identifies breakout opportunities.

Live Chart — Bollinger Bands

See Bollinger Bands in action on a live chart. Try changing the settings and timeframe.

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Parameters

Period

Default: 20

SMA period

Std Dev

Default: 2

Standard deviation multiplier

📈 Bullish Signals

Price bounces off lower band, squeeze followed by upward breakout

📉 Bearish Signals

Price rejected at upper band, squeeze followed by downward breakout

Python Implementation

Bollinger Bands with pandas-ta

Python

import pandas_ta as ta

bb = ta.bbands(df['close'], length=20, std=2)

df['BB_Upper'] = bb['BBU_20_2.0']

df['BB_Middle'] = bb['BBM_20_2.0']

df['BB_Lower'] = bb['BBL_20_2.0']

df['BB_Width'] = bb['BBB_20_2.0']

TradingView Pine Script

Try on TradingView

JavaScript

//@version=5

indicator("Bollinger Bands", overlay=true)

[middle, upper, lower] = ta.bb(close, 20, 2)

plot(upper, "Upper", color=color.red)

plot(middle, "Middle", color=color.blue)

plot(lower, "Lower", color=color.green)

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MT5 MQL5 Code

C++

int bb_handle = iBands(_Symbol, _Period, 20, 0, 2, PRICE_CLOSE);

double upper[], middle[], lower[];

CopyBuffer(bb_handle, 0, 0, 3, middle);

CopyBuffer(bb_handle, 1, 0, 3, upper);

CopyBuffer(bb_handle, 2, 0, 3, lower);

Python Libraries

pandas-ta

Technical analysis

pip install pandas-ta

Common Mistakes

✗Selling just because price touches upper band

✗Ignoring the squeeze as a volatility warning

Confirmation Signals

RSI at extremes

Volume on breakout

Candlestick patterns

Best For

Volatility measurementOverbought/oversoldBreakout identification

💡 Pro Tips

•Band squeeze (narrow bands) often precedes strong moves
•Price can "walk the band" in strong trends
•Use with RSI for confirmation - RSI divergence at bands is powerful

Last updated: February 8, 2026

Educational Disclaimer

This content is for educational purposes only and does not constitute financial or investment advice. Trading involves significant risk and you may lose your capital. Always consult a licensed financial advisor before making investment decisions.

Learn More

Investopedia - Technical Indicators— Financial Education BabyPips - Technical Indicators— Trading School MetaTrader 5 Docs— Official Documentation

Frequently Asked Questions

Related Indicators

Relative Strength Index (RSI)

RSI measures the speed and magnitude of price changes, oscillating between 0-100 to identify overbought and oversold conditions.

Average True Range (ATR)

Measures market volatility by calculating the average range between high and low prices.

Keltner Channels

Volatility-based envelopes using EMA and ATR, similar to Bollinger Bands but smoother.

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