True Range Adjusted Exponential Moving Average

Moving Averages moving-average adaptive true-range volatility

An exponential moving average that incorporates true range to measure volatility and adapt to price movements.

Visual Example

True Range Adjusted Exponential Moving Average — 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

An exponential moving average that incorporates true range to measure volatility and adapt to price movements.

Use to identify trend turning points and filter price movements. Comparing TRAdj EMA with a standard EMA of the same length provides insights into the overall trend.

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

Introduced by Vitali Apirine in TASC January 2023, TRAdj EMA modifies the standard exponential moving average by adjusting the smoothing factor using the True Range. The normalized true range modifies the rate, making the indicator more responsive during volatile periods while filtering out noise when volatility drops.

Typical applications:

Size stops and position risk from band width or ATR expansion
Detect squeeze conditions (narrow bands) before breakout systems
Warm-up: first 40 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/tradj_ema.rs):

\[ TRAdj = \frac{TR - TR_{min}}{TR_{max} - TR_{min}} \\ Rate = \frac{2}{P+1} \times (1 + TRAdj \times Multiplier) \]

Parameters

Parameter	Default	Description
`period`	40	Smoothing period
`pds`	40	Lookback period for True Range
`mltp`	10.0	Multiplier

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::TRADJ_EMA;
use quantwave_core::traits::Next;

let mut ind = TRADJ_EMA::new(40);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import TRADJ_EMA

ind = TRADJ_EMA(40)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_true_range_adjusted_exponential_moving_average(series: pl.Series) -> pl.Series:
    ind = qw.TRADJ_EMA(40)
    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_adjusted_exponential_moving_average, return_dtype=pl.Float64).alias("true_range_adjusted_exponential_moving_average")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 40 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: Technical Analysis of Stocks & Commodities, January 2023

Implementation: quantwave-core/src/indicators/tradj_ema.rs (TRADJ_EMA / TRADJ_EMA_METADATA).

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