tripleEMA() function

tripleEMA() returns the triple exponential moving average (TEMA) of values in the _value column.

tripleEMA uses n number of points to calculate the TEMA, giving more weight to recent data with less lag than exponentialMovingAverage() and doubleEMA().

Triple exponential moving average rules

• A triple exponential moving average is defined as tripleEMA = (3 * EMA_1) - (3 * EMA_2) + EMA_3.
  • EMA_1 is the exponential moving average of the original data.
  • EMA_2 is the exponential moving average of EMA_1.
  • EMA_3 is the exponential moving average of EMA_2.
• A true triple exponential moving average requires at least requires at least 3 * n - 2 values. If not enough values exist to calculate the TEMA, it returns a NaN value.
• tripleEMA() inherits all exponentialMovingAverage() rules.

Function type signature

(<-tables: stream[{A with _value: B}], n: int) => stream[C] where B: Numeric, C: Record

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Parameters

n

({{< req >}}) Number of points to use in the calculation.

tables

Input data. Default is piped-forward data (<-).

Examples

Calculate a three point triple exponential moving average

import "sampledata"

sampledata.int()
    |> tripleEMA(n: 3)

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Input data

Output data

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