DMH

Ehlers DSP momentum oscillator ehlers high-pass dsp

An improved Directional Movement indicator using Hann windowing for smoother signals and reduced lag.

Visual Example

DMH — 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 DMH indicator is a technical analysis tool that an improved directional movement indicator using hann windowing for smoother signals and reduced lag.

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 momentum oscillator with high-pass filtering to isolate cyclical momentum while removing the trend bias that corrupts standard momentum indicators.

Ehlers constructs the DMH by applying a high-pass filter to the momentum calculation, removing the low-frequency trend component that causes conventional momentum to drift. The result is a zero-centered momentum oscillator that oscillates cleanly around the cycle midpoint.

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/dmh.rs):

[ \text{PlusDM} = \text{High} - \text{High}{t-1} \text{ if } > (\text{Low} 0 ] [ \text{MinusDM} = \text{Low}} - \text{Low}) \text{ and } > 0, \text{ else {t-1} - \text{Low} \text{ if } > (\text{High} - \text{High} 0 ] [ \text{EMA} = \frac{1}{L}(\text{PlusDM} - \text{MinusDM}) + (1 - \frac{1}{L})\text{EMA}}) \text{ and } > 0, \text{ else {t-1} ] [ \text{DMH} = \frac{\sum}^{L} w_i \text{EMA{t-i+1}}{\sum\right) ]}^{L} w_i}, \text{ where } w_i = 1 - \cos\left(\frac{2\pi i}{L+1

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

Parameters

Parameter	Default	Description
`length`	14	Smoothing period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::DMH;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import DMH

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_dmh(series: pl.Series) -> pl.Series:
    ind = qw.DMH(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_dmh, return_dtype=pl.Float64).alias("dmh")
    )
    .collect()
)

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

Edge Cases & Limitations

Recursive DSP filters require a warm-up period; first N bars may be unstable or raw-pass-through.
Designed for cyclic/mean-reverting regimes; trending markets can produce lag or drift.
Parameter period (or equivalent) controls cutoff — too small adds noise, too large adds lag.
Prefer chaining with other Ehlers tools (Roofing Filter, SuperSmoother) on noisy inputs.
Validated via proptests against gold-standard vectors where available.
No look-ahead bias; suitable for live streaming and batch feature pipelines.

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/implemented/TRADERS%E2%80%99%20TIPS%20-%20DECEMBER%202021.html

Implementation: quantwave-core/src/indicators/dmh.rs (DMH / DMH_METADATA). Parity: quantwave-core/tests/gold_standard/dmh.json

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

DMH