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

Description

The True Range Adjusted Exponential Moving Average indicator is a technical analysis tool that an exponential moving average that incorporates true range to measure volatility and adapt to price movements.

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 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.

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.

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