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

Description

The Normalized Average True Range (NATR) indicator is a technical analysis tool that a normalized version of atr that represents volatility as a percentage of price.

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

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

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