Harrington ADX Oscillator

Wilder adx dmi oscillator wilder momentum

An oscillator variant of the ADX where the sign reflects trend direction determined by DMI+ and DMI-.

Visual Example

Harrington ADX Oscillator — 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 Harrington ADX Oscillator indicator is a technical analysis tool that an oscillator variant of the adx where the sign reflects trend direction determined by dmi+ and dmi-.

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.

The oscillator is positive when DMI+ > DMI- and negative when DMI- > DMI+. The magnitude represents trend strength (ADX). Thresholds at 15 and 40 are often used to identify trend initiation and overextended states.

While originally created by Wilder, this revisualization by Harrington transforms the unipolar ADX into a bipolar oscillator. This allows for simultaneous identification of trend strength and direction in a single histogram display, simplifying the interpretation of complex directional movement data.

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/harrington_adx.rs):

[ TR = \max(H-L, |H-C_{t-1}|, |L-C_{t-1}|) ] [ +DM = (H-H_{t-1} > L_{t-1}-L) \text{ and } (H-H_{t-1} > 0) ? H-H_{t-1} : 0 ] [ -DM = (L_{t-1}-L > H-H_{t-1}) \text{ and } (L_{t-1}-L > 0) ? L_{t-1}-L : 0 ] [ +DI = 100 \cdot \frac{EMA(+DM, 1/L)}{EMA(TR, 1/L)} ] [ -DI = 100 \cdot \frac{EMA(-DM, 1/L)}{EMA(TR, 1/L)} ] [ DX = 100 \cdot \frac{|+DI - -DI|}{+DI + -DI} ] [ ADX = EMA(DX, 1/L) ] [ Result = (SMA(+DI, S) \ge SMA(-DI, S)) ? ADX : -ADX ]

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

Parameters

Parameter	Default	Description
`adx_length`	10	Wilder's ADX period
`adx_smooth_length`	1	SMA period for DMI components smoothing

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::HARRINGTON_ADX;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import HARRINGTON_ADX

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_harrington_adx_oscillator(series: pl.Series) -> pl.Series:
    ind = qw.HARRINGTON_ADX(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_harrington_adx_oscillator, return_dtype=pl.Float64).alias("harrington_adx_oscillator")
    )
    .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://github.com/lavs9/quantwave/blob/main/references/traderstipsreference/TRADERS%E2%80%99%20TIPS%20-%20DECEMBER%202024.html

Implementation: quantwave-core/src/indicators/harrington_adx.rs (HARRINGTON_ADX / HARRINGTON_ADX_METADATA). Parity: quantwave-core/tests/gold_standard/harrington_adx.json

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