ATR Trailing Stop

Classic volatility trend stop-loss atr classic

A trailing stop based on Average True Range to keep trades in a trend.

Visual Example

ATR Trailing Stop — 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 ATR Trailing Stop indicator is a technical analysis tool that a trailing stop based on average true range to keep trades in a trend.

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 a dynamic trailing stop that widens in volatile markets and tightens in calm ones, automatically adjusting stop distance to current market conditions.

ATR Trailing Stop uses Average True Range to set a stop distance that scales with market volatility. During high-volatility regimes the stop moves further from price to avoid premature exit; during low-volatility regimes it tightens to lock in more profit. It is one of the most robust mechanical stop methods in systematic trading.

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/atr_ts.rs):

\[ Stop = P_{high} - (Multiplier \times ATR) \]

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

Parameters

Parameter	Default	Description
`period`	10	ATR period
`multiplier`	3.0	ATR Multiplier

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::ATR_TS;
use quantwave_core::traits::Next;

let mut ind = ATR_TS::new(10);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import ATR_TS

ind = ATR_TS(10)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_atr_trailing_stop(series: pl.Series) -> pl.Series:
    ind = qw.ATR_TS(10)
    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_atr_trailing_stop, return_dtype=pl.Float64).alias("atr_trailing_stop")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 10 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.tradingview.com/support/solutions/43000589105-average-true-range-atr/

Implementation: quantwave-core/src/indicators/atr_ts.rs (ATR_TS / ATR_TS_METADATA). Parity: quantwave-core/tests/gold_standard/atr_ts.json

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