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Moving AveragesPine Script v6

ta.alma()Arnaud Legoux Moving Average

Computes the Arnaud Legoux Moving Average (ALMA), a Gaussian-kernel weighted average over length bars. Parameters offset and sigma let you tune responsiveness versus smoothness; optional floor controls window flooring behavior.

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Syntax

Syntax
ta.alma(series, length, offset, sigma, floor) → series float

Arguments

ParameterTypeDescription
seriesseries int/floatSeries of values to process.
lengthseries intWindow size for the Gaussian kernel.
offsetsimple int/floatControls tradeoff between smoothness and responsiveness.
sigmasimple int/floatChanges smoothness of the Gaussian weights.
floorsimple boolOptional; default false.

Returns

Arnaud Legoux Moving Average.

Remarks

na values in the source series are included in calculations and will produce an na result.

Code Examples

Pine Script v6 Example
//@version=6
indicator("ta.alma", overlay=true)
plot(ta.alma(close, 9, 0.85, 6))

// same on pine, but much less efficient
pine_alma(series, windowsize, offset, sigma) =>
    m = offset * (windowsize - 1)
    s = windowsize / sigma
    norm = 0.0
    sum = 0.0
    for i = 0 to windowsize - 1
        weight = math.exp(-1 * math.pow(i - m, 2) / (2 * math.pow(s, 2)))
        norm := norm + weight
        sum := sum + series[windowsize - i - 1] * weight
    sum / norm
plot(pine_alma(close, 9, 0.85, 6))

Trading Applications

Use Gaussian distribution weighting for adaptive smoothing

Fine-tune responsiveness vs smoothness with offset parameter

Reduce lag while minimizing false signals

Apply in algorithmic trading systems requiring precise MA tuning

Related Functions

ta.smata.emata.rmata.wmata.vwmata.swma

Frequently Asked Questions

ta.alma() implements the Arnaud Legoux MA: a finite window average where weights follow a Gaussian curve. You can shift the peak of that curve (offset) and its width (sigma) to change how the line behaves.

Offset moves the effective center of the weighting within the window, trading lag against smoothness. Sigma scales the Gaussian width—higher sigma spreads weight more evenly (smoother); lower sigma concentrates weight (more localized).

EMA is a single recursive smoother with one length. ALMA uses an explicit Gaussian over a window with two shape parameters, giving finer control over the bias-variance tradeoff for a given horizon.

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