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Moving AveragesPine Script v6

ta.wma()Weighted Moving Average

Computes the weighted moving average (WMA) over the last length bars, assigning greater weight to more recent observations within the window. Its finite linear weights differ from both equal-weight SMA and recursively smoothed EMA; there is no universal responsiveness ordering.

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Syntax

Syntax
ta.wma(source, length) → series float

Arguments

ParameterTypeDescription
sourceseries int/floatSeries of values to process.
lengthseries intNumber of bars (length).

Returns

Weighted moving average of source for length bars back.

Remarks

The built-in ignores na source values. The manual loop illustrates linear weights for a complete window and does not reproduce missing-value handling.

Code Examples

Pine Script v6 Example
//@version=6
indicator("ta.wma")
plot(ta.wma(close, 15))

// same on pine, but much less efficient
pine_wma(x, y) =>
    norm = 0.0
    sum = 0.0
    for i = 0 to y - 1
        weight = (y - i) * y
        norm := norm + weight
        sum := sum + x[i] * weight
    sum / norm
plot(pine_wma(close, 15))

Trading Applications

Give more weight to recent prices for faster trend detection

Study the tradeoff between recent-value weighting and noise

Use in custom indicator calculations

Combine with volume-weighted approaches

Related Functions

ta.smata.emata.rmata.vwmata.swmata.alma

Frequently Asked Questions

ta.wma() calculates a weighted moving average: within the lookback window, newer bars receive higher linear weights and older bars lower weights, then the weighted sum is normalized.

SMA uses equal weights. WMA uses a fixed linear weight ramp over a finite window. EMA uses infinite recursive smoothing with exponential decay. WMA is often more responsive than SMA but behaves differently from EMA.

Consider WMA when you want explicit finite-window weighting toward recent prices without the full recursive nature of EMA, or when building indicators that specify WMA as part of their definition.

Generate ta.wma() Code with AI

Draft Pine Script using ta.wma() with Pineify's AI Coding Agent, then review the arguments and test the result on your chart. Usage depends on your plan and available credits.

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