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Adaptive Exponential Moving Average

Moving Averages moving-average adaptive volatility trend

An adaptive moving average that adjusts its smoothing factor based on volatility.

Visual Example

Adaptive Exponential Moving Average — 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 Adaptive Exponential Moving Average indicator is a technical analysis tool that an adaptive moving average that adjusts its smoothing factor based on volatility.

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 overall trends. AEMA reacts faster to large price movements by adapting the smoothing factor using the highest high and lowest low of a lookback period.

Introduced by Vitali Apirine in TASC April 2019, AEMA alters the EMA's alpha (smoothing factor) by comparing the distance of the close from the lowest low and highest high. This amplifies the smoothing factor during strong price moves while reducing it during sideways chop, yielding a moving average with less lag when it matters most.

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/adaptive_ema.rs):

\[ Rate = \frac{2}{P+1} \times \left(1 + \frac{|(C - L_{min}) - (H_{max} - C)|}{H_{max} - L_{min}}\right) \\ AEMA_t = AEMA_{t-1} + Rate \times (C - AEMA_{t-1}) \]

Parameters

Parameter Default Description
period 10 Smoothing period
pds 10 Lookback period for volatility

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::ADAPTIVE_EMA;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import ADAPTIVE_EMA

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_adaptive_exponential_moving_average(series: pl.Series) -> pl.Series:
    ind = qw.ADAPTIVE_EMA(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_adaptive_exponential_moving_average, return_dtype=pl.Float64).alias("adaptive_exponential_moving_average")
    )
    .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: Technical Analysis of Stocks & Commodities, April 2019

Implementation: quantwave-core/src/indicators/adaptive_ema.rs (ADAPTIVE_EMA / ADAPTIVE_EMA_METADATA).

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