Bollinger BandWidth - Questdb

Bollinger BandWidth quantifies the width of Bollinger Bands as a percentage, helping traders identify low-volatility squeeze conditions that often precede significant price moves.

Problem

You have Bollinger Bands but want to objectively measure when volatility is unusually low. Visually spotting a "squeeze" is subjective. BandWidth provides a numeric value you can compare against historical levels to identify when bands are at their historical lows.

What is BandWidth?

BandWidth measures the percentage difference between the upper and lower Bollinger Bands:

BandWidth = ((Upper Band - Lower Band) / Middle Band) × 100

When BandWidth drops to historically low levels, the bands are in a "squeeze". Periods of low volatility are often followed by high volatility, so a squeeze suggests a significant price move may be coming. The squeeze does not indicate direction, only that a breakout is likely.

Solution

questdb-sql

DECLARE
  @symbol := 'BTC-USDT',
  @history := '$now - 6M..$now',
  @display := '$now - 1M..$now'

WITH daily_ohlc AS (
  SELECT
    timestamp,
    symbol,
    first(open) AS open,
    max(high) AS high,
    min(low) AS low,
    last(close) AS close
  FROM trades_ohlc_15m
  WHERE symbol = @symbol
    AND timestamp IN @history
  SAMPLE BY 1d
),
bands AS (
  SELECT
    timestamp,
    symbol,
    close,
    AVG(close) OVER w AS sma20,
    AVG(close * close) OVER w AS avg_close_sq
  FROM daily_ohlc
  WINDOW w AS (PARTITION BY symbol ORDER BY timestamp ROWS 19 PRECEDING)
),
bollinger AS (
  SELECT
    timestamp,
    symbol,
    close,
    sma20,
    sma20 + 2 * sqrt(avg_close_sq - (sma20 * sma20)) AS upper_band,
    sma20 - 2 * sqrt(avg_close_sq - (sma20 * sma20)) AS lower_band
  FROM bands
),
with_bandwidth AS (
  SELECT
    timestamp,
    symbol,
    close,
    sma20,
    upper_band,
    lower_band,
    (upper_band - lower_band) / sma20 * 100 AS bandwidth
  FROM bollinger
),
with_range AS (
  SELECT
    timestamp,
    symbol,
    close,
    sma20,
    upper_band,
    lower_band,
    bandwidth,
    min(bandwidth) OVER w AS min_bw,
    max(bandwidth) OVER w AS max_bw
  FROM with_bandwidth
  WINDOW w AS (PARTITION BY symbol)
)
SELECT
  timestamp,
  symbol,
  round(close, 2) AS close,
  round(sma20, 2) AS sma20,
  round(upper_band, 2) AS upper_band,
  round(lower_band, 2) AS lower_band,
  round(bandwidth, 4) AS bandwidth,
  round((bandwidth - min_bw) / (max_bw - min_bw) * 100, 1) AS range_position
FROM with_range
WHERE timestamp IN @display
ORDER BY timestamp;

The query first aggregates 15-minute candles into daily OHLC, then calculates standard 20-day Bollinger Bands. This matches the traditional approach where SMA20 represents roughly one month of trading. The 6-month lookback (@history) establishes the historical range, while @display limits output to the last month. Standard deviation uses the variance formula sqrt(avg(x²) - avg(x)²).

The range_position shows where current BandWidth falls within the 6-month range: 0% means at the historical minimum, 100% at the maximum. This works well for identifying squeeze conditions since you're comparing against historical extremes.

Interpreting results

Low range position (< 20%): BandWidth is near 6-month lows, indicating a squeeze
High range position (> 80%): BandWidth is near 6-month highs, volatility is elevated
Rising BandWidth: Volatility increasing, bands expanding
Falling BandWidth: Volatility decreasing, bands contracting

A squeeze signals that volatility expansion is likely, but not the direction. Use price action or other indicators to determine breakout direction.

:::info Related documentation