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Reverse EMA

Ehlers DSP ema lag ehlers oscillator zero-lag

A causal forward and backward EMA indicator that minimizes lag using a series of alignment filters.

Visual Example

Reverse EMA — 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 Reverse EMA indicator is a technical analysis tool that a causal forward and backward ema indicator that minimizes lag using a series of alignment filters.

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 identify trends or cycles with minimal lag. Higher alpha values (e.g., 0.3) isolate cycles, while lower values (e.g., 0.05) isolate trends.

Ehlers' Reverse EMA approximates a non-causal zero-lag filter by using a product series of Z-transform components. It achieves double smoothing at high frequencies and mitigates spectral dilation at low frequencies, providing a unique balance of smoothness and responsiveness.

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/reverse_ema.rs):

[ EMA = \alpha \cdot Price + (1 - \alpha) \cdot EMA_{t-1} ] [ RE_1 = (1 - \alpha) \cdot EMA + EMA_{t-1} ] [ RE_i = (1 - \alpha)^{2^{i-1}} \cdot RE_{i-1} + RE_{i-1, t-1} \text{ for } i=2..8 ] [ Wave = EMA - \alpha \cdot RE_8 ]

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

Parameters

Parameter Default Description
alpha 0.1 Smoothing factor (0.0 to 1.0)

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::REVERSE_EMA;
use quantwave_core::traits::Next;

let mut ind = REVERSE_EMA::new(0.1);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import REVERSE_EMA

ind = REVERSE_EMA(0.1)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_reverse_ema(series: pl.Series) -> pl.Series:
    ind = qw.REVERSE_EMA(0.1)
    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_reverse_ema, return_dtype=pl.Float64).alias("reverse_ema")
    )
    .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/TRADERS%E2%80%99%20TIPS%20-%20SEPTEMBER%202017.html

Implementation: quantwave-core/src/indicators/reverse_ema.rs (REVERSE_EMA / REVERSE_EMA_METADATA). Parity: quantwave-core/tests/gold_standard/reverse_ema.json

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