Normalized Average True Range (NATR)

Classic volatility atr normalization classic

A normalized version of ATR that represents volatility as a percentage of price.

Visual Example

Normalized Average True Range (NATR) — 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 normalized version of ATR that represents volatility as a percentage of price.

Use to compare volatility across different securities with varying price levels. NATR allows for normalized risk assessment and position sizing.

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

Normalized ATR (NATR) was developed to allow traders to compare the volatility of high-priced stocks with low-priced stocks. By dividing the ATR by the closing price and multiplying by 100, the result is a percentage that can be used consistently across all assets. — TA-Lib Documentation

Typical applications:

Size stops and position risk from band width or ATR expansion
Detect squeeze conditions (narrow bands) before breakout systems
Warm-up: first 14 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/volatility.rs):

\[ NATR = \frac{ATR(n)}{Close} \times 100 \]

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

Parameters

Parameter	Default	Description
`timeperiod`	14	Smoothing period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::NATR;
use quantwave_core::traits::Next;

let mut ind = NATR::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import NATR

ind = NATR(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_normalized_average_true_range_natr(series: pl.Series) -> pl.Series:
    ind = qw.NATR(14)
    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_normalized_average_true_range_natr, return_dtype=pl.Float64).alias("normalized_average_true_range_natr")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 14 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.tradingtechnologies.com/help/x-study/technical-indicator-definitions/normalized-average-true-range-natr/

Implementation: quantwave-core/src/indicators/volatility.rs (NATR / NATR_METADATA). Parity: quantwave-core/tests/gold_standard/natr.json

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