Bollinger Bands: volatility and reversion.

Bollinger Bands, created by John Bollinger in the 1980s, solve a problem that fixed indicators can't: they adapt to volatility. Instead of static levels, the bands breathe with the market — they open when it gets agitated, close when it calms down. That makes them excellent for measuring relative extremes and anticipating bursts of movement.

01What Bollinger Bands are

They're three lines:

• Central band: a simple moving average (usually 20 periods). It's the "fair value" reference.
• Upper band: the average + 2 standard deviations.
• Lower band: the average − 2 standard deviations.

The key concept is the standard deviation — a statistical measure of volatility. When the market is volatile, the deviation increases and the bands move apart; when it's calm, they move closer together. Statistically, about 95% of prices stay within 2 standard deviations, so touching a band means price is at a relative extreme.

02The main uses

1. Mean reversion (ranging market)

In a trendless market, price tends to oscillate between the bands. Touching the lower band can signal a buy (price "cheap"); the upper, a sell (price "expensive"). It's the basis of mean-reversion strategies.

2. The squeeze (the most powerful use)

When the bands narrow sharply, it means low volatility — the market is "compressed." And here's the insight: periods of low volatility tend to be followed by periods of high volatility. The squeeze doesn't tell you the direction, but it warns that a strong move is forming. Traders use it to prepare for a breakout.

3. "Walking the band" (strong trend)

Here's what trips up beginners: in a strong trend, price hugs the upper band (in an uptrend) and keeps rising. Anyone who reads this as "overbought, I'll sell" trades against the trend and loses. Walking the band is a sign of strength, not reversal.

03Coding Bollinger Bands

//@version=5
indicator("Bollinger Bands", overlay=true)

period = input.int(20, "Period")
devs   = input.float(2.0, "Standard deviations")

basis = ta.sma(close, period)
dev   = ta.stdev(close, period)
upper = basis + devs * dev
lower = basis - devs * dev

plot(basis, "Basis", color=color.blue)
p1 = plot(upper, "Upper", color=color.red)
p2 = plot(lower, "Lower", color=color.green)
fill(p1, p2, color=color.new(color.blue, 90))

// detect a squeeze: band width near its recent low
width = (upper - lower) / basis
squeeze = width < ta.lowest(width, 50) * 1.1
if squeeze
    alert("Squeeze — a strong move may be coming")

import pandas as pd

def bollinger(prices, period=20, devs=2.0):
    basis = prices.rolling(period).mean()
    dev   = prices.rolling(period).std()
    upper = basis + devs * dev
    lower = basis - devs * dev
    return basis, upper, lower

basis, upper, lower = bollinger(df["close"])

# relative band width (to detect a squeeze)
width = (upper - lower) / basis
df["squeeze"] = width < width.rolling(50).min() * 1.1

last = df["close"].iloc[-1]
if last <= lower.iloc[-1]:
    print("Price at the lower band — consider (ranging only!)")

04Building strategies

• Reversion (ranging): buy at the lower band + RSI < 30, sell at the upper + RSI > 70. Only in a trendless market.
• Squeeze breakout: detect the squeeze, wait for one of the bands to break, enter in the direction of the break. Captures the start of strong moves.
• Bollinger + trend: in an uptrend, use the lower band (or the central average) as a re-entry zone on pullbacks.

05Frequently asked questions