Skip to content

DSMA

Ehlers DSP moving-average adaptive ehlers dsp dominant-cycle

Deviation Scaled Moving Average adapts to price variations using standard deviation scaled oscillators.

Visual Example

DSMA — 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 DSMA indicator is a technical analysis tool that deviation scaled moving average adapts to price variations using standard deviation scaled oscillators.

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 highly adaptive moving average that tracks price closely during trends and large moves but provides heavy filtering during consolidation. Ideal for trend-following entries and trailing stops.

In 'The Deviation-Scaled Moving Average' (2018), Ehlers introduces an adaptive EMA where the alpha (smoothing factor) is dynamically adjusted based on a deviation-scaled oscillator. By scaling the SuperSmoother-filtered momentum by its RMS, the indicator becomes reactive to significant price deviations while remaining smooth during low-volatility periods.

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/dsma.rs):

[ Zeros = Close - Close_{t-2} ] [ Filt = c_1 \frac{Zeros + Zeros_{t-1}}{2} + c_2 Filt_{t-1} + c_3 Filt_{t-2} ] [ RMS = \sqrt{\frac{1}{P} \sum_{i=0}^{P-1} Filt_{t-i}^2} ] [ \alpha = \min\left(1.0, \left| \frac{Filt}{RMS} \right| \frac{5}{P}\right) ] [ DSMA = \alpha \cdot Close + (1 - \alpha) \cdot DSMA_{t-1} ]

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

Parameters

Parameter Default Description
period 40 Critical period for smoothing and RMS calculation

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::DSMA;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import DSMA

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_dsma(series: pl.Series) -> pl.Series:
    ind = qw.DSMA(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_dsma, return_dtype=pl.Float64).alias("dsma")
    )
    .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/Ehlers%20Papers/DEVIATION%20SCALED%20MOVING%20AVERAGE.pdf

Implementation: quantwave-core/src/indicators/dsma.rs (DSMA / DSMA_METADATA). Parity: quantwave-core/tests/gold_standard/dsma.json

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