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Bollinger Bands

Classic volatility trend classic bands

A volatility indicator consisting of a middle SMA and two outer bands based on standard deviation.

Visual Example

Bollinger Bands — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-07-01 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

A volatility indicator consisting of a middle SMA and two outer bands based on standard deviation.

Use to identify overbought/oversold levels and volatility breakouts. Prices near the upper band suggest overbought conditions, while prices near the lower band suggest oversold conditions. Narrowing bands (The Squeeze) often precede large price moves.

Native Rust implementation with gold-standard or TA-Lib parity tests where applicable.

Developed by John Bollinger in the 1980s, Bollinger Bands adapt to volatility by using standard deviation. The middle band is typically a 20-period SMA, and the outer bands are set 2 standard deviations away. This ensures that 95% of price action typically stays within the bands, making escapes highly significant. — BollingerOnBollingerBands.com

Typical applications:

  • Size stops and position risk from band width or ATR expansion
  • Detect squeeze conditions (narrow bands) before breakout systems
  • Warm-up: first 20 bars build rolling volatility state
  • Combine with trend direction (SuperTrend, MACD) for breakout bias

QuantWave implements this via the universal Next<T> trait — bit-identical across Rust streaming, Python streaming, and Polars .ta() batch plugins.

Formula / Specification

Implementation (quantwave-core/src/indicators/overlap.rs):

\[ Middle = SMA(n) \\ Upper = Middle + (k \times \sigma) \\ Lower = Middle - (k \times \sigma) \]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/bbands.json.

Parameters

Parameter Default Description
timeperiod 20 SMA period
nbdevup 2.0 Upper deviation multiplier
nbdevdn 2.0 Lower deviation multiplier

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::BBANDS;
use quantwave_core::traits::Next;

let mut ind = BBANDS::new(20);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import BBANDS

ind = BBANDS(20)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_bollinger_bands(series: pl.Series) -> pl.Series:
    ind = qw.BBANDS(20)
    return pl.Series([ind.next(float(v)) for v in series.to_list()])

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").map_batches(apply_bollinger_bands, return_dtype=pl.Float64).alias("bollinger_bands")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Warm-up: first 20 bars may return NaN or partial state per implementation.
  • Parameter sensitivity: smaller periods increase noise; larger periods increase lag.
  • Sudden gaps or bad ticks can distort rolling windows — consider pre-filtering.
  • Single-series indicators ignore volume unless otherwise documented.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; streaming and Polars batch paths are bit-identical.

Boundary Behavior

Condition Behavior
Warm-up Leading bars return NaN until warmup_bars is satisfied.
period > len When period exceeds series length, output is all NaN.
NaN inputs NaN in input propagates to output (NaN out).
Invalid params Non-positive period or missing required params raise ValueError.
Empty data Empty input returns an empty result series.

Sources & References

Primary Source: https://www.investopedia.com/terms/b/bollingerbands.asp

Implementation: quantwave-core/src/indicators/overlap.rs (BBANDS / BBANDS_METADATA). Parity: quantwave-core/tests/gold_standard/bbands.json

Provenance: Standards bulk upgrade 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.