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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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The Bollinger Bands indicator is a technical analysis tool that a volatility indicator consisting of a middle sma and two outer bands based on standard deviation.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

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.

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

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.