Arnaud Legoux Moving Average

Classic moving-average smoothing low-latency adaptive

ALMA is designed to reduce lag while providing high smoothness.

Visual Example

Arnaud Legoux 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 Arnaud Legoux Moving Average indicator is a technical analysis tool that alma is designed to reduce lag while providing high smoothness.

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 low-latency moving average that reduces lag compared to EMA while controlling overshoot through the Gaussian offset parameter. Well-suited for momentum systems.

The Arnaud Legoux Moving Average applies a Gaussian-shaped weight distribution offset toward the recent end of the lookback window. The sigma parameter controls weight spread and the offset parameter controls how far the Gaussian peak is positioned from the current bar, enabling a lag-accuracy trade-off unavailable in standard MAs.

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/alma.rs):

\[ ALMA = \sum (W_i \times P_i) / \sum W_i \]

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

Parameters

Parameter	Default	Description
`period`	9	Period
`offset`	0.85	Offset
`sigma`	6.0	Sigma

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::ALMA;
use quantwave_core::traits::Next;

let mut ind = ALMA::new(9);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import ALMA

ind = ALMA(9)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_arnaud_legoux_moving_average(series: pl.Series) -> pl.Series:
    ind = qw.ALMA(9)
    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_arnaud_legoux_moving_average, return_dtype=pl.Float64).alias("arnaud_legoux_moving_average")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 9 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.prorealcode.com/prorealtime-indicators/arnaud-legoux-moving-average-alma/

Implementation: quantwave-core/src/indicators/alma.rs (ALMA / ALMA_METADATA). Parity: quantwave-core/tests/gold_standard/alma.json

Provenance: Standards bulk upgrade 2026-06-25 IST (DOCUMENTATION_STANDARDS.md).