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Double Exponential Moving Average (DEMA)

Classic moving-average smoothing lag-reduction classic

A fast-acting moving average that reduces lag by using two exponential moving averages.

Visual Example

Double Exponential Moving Average (DEMA) — 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 Double Exponential Moving Average (DEMA) indicator is a technical analysis tool that a fast-acting moving average that reduces lag by using two exponential moving averages.

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 replacement for EMA when faster signal generation is required without excessive noise. DEMA reacts more quickly to price changes than a standard EMA.

Developed by Patrick Mulloy in 1994, DEMA provides a less-laggy alternative to traditional moving averages. It is calculated by taking a single EMA and then subtracting it from a double EMA of the same period. This effectively cancels out some of the lag inherent in the EMA calculation. — 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/overlap.rs):

\[ DEMA = 2 \times EMA - EMA(EMA) \]

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

Parameters

Parameter Default Description
timeperiod 30 Smoothing period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::DEMA;
use quantwave_core::traits::Next;

let mut ind = DEMA::new(30);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import DEMA

ind = DEMA(30)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").ta.dema(30).alias("double_exponential_moving_average_dema")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first 30 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/d/double-exponential-moving-average.asp

Implementation: quantwave-core/src/indicators/overlap.rs (DEMA / DEMA_METADATA). Parity: quantwave-core/tests/gold_standard/dema.json

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