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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 2026-07-01 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

True Range measures daily volatility.

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.

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

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.

Typical applications:

  • Size stops and position risk from band width or ATR expansion
  • Detect squeeze conditions (narrow bands) before breakout systems
  • Warm-up: first N 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):

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

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 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.