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Average Directional Index (ADX)

Classic trend volatility classic wilder

An indicator used to quantify trend strength without regard to trend direction.

Visual Example

Average Directional Index (ADX) — 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 Average Directional Index (ADX) indicator is a technical analysis tool that an indicator used to quantify trend strength without regard to trend direction.

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 determine if the market is trending or ranging. ADX values above 25 indicate a strong trend, while values below 20 indicate a weak or non-trending market.

Developed by J. Welles Wilder, the ADX is derived from two other indicators, also developed by Wilder: the Positive Directional Indicator (+DI) and the Negative Directional Indicator (-DI). While +DI and -DI indicate trend direction, ADX measures the strength of that trend. — StockCharts ChartSchool

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/momentum.rs):

\[ ADX = 100 \times \frac{\text{EMA}(|(+DI) - (-DI)| / |(+DI) + (-DI)|, n)}{n} \]

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

Parameters

Parameter Default Description
timeperiod 14 Lookback period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::ADX;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import ADX

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_average_directional_index_adx(series: pl.Series) -> pl.Series:
    ind = qw.ADX(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_average_directional_index_adx, return_dtype=pl.Float64).alias("average_directional_index_adx")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first 14 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/adx.asp

Implementation: quantwave-core/src/indicators/momentum.rs (ADX / ADX_METADATA). Parity: quantwave-core/tests/gold_standard/adx.json

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