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True Range

Classic volatility atr classic range

True Range measures daily volatility.

Visual Example

True Range — 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 True Range indicator is a technical analysis tool that true range measures daily volatility.

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 as the foundational volatility module providing ATR, True Range, and related volatility measures used by higher-level indicators such as SuperTrend and Keltner Channels.

Average True Range, developed by J. Welles Wilder in New Concepts in Technical Trading Systems (1978), measures the average of the true range over N bars. True Range accounts for overnight gaps by taking the maximum of: current high minus low, current high minus prior close, prior close minus current low. It remains the industry standard raw volatility measure.

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):

\[ TR = \max(H - L, |H - C_{t-1}|, |L - C_{t-1}|) \]

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

Parameters

Parameter Default Description
(none) — No tunable parameters for this detector.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::TRUE_RANGE;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import TRUE_RANGE

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_true_range(series: pl.Series) -> pl.Series:
    ind = qw.TRUE_RANGE(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_true_range, return_dtype=pl.Float64).alias("true_range")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first N 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/a/atr.asp

Implementation: quantwave-core/src/indicators/volatility.rs (TRUE_RANGE / TRUE_RANGE_METADATA). Parity: quantwave-core/tests/gold_standard/true_range.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.