Documentation

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
For more information, see Function type signatures.

Parameters

n

(Required) 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)

View example input and ouput


Was this page helpful?

Thank you for your feedback!


Upgrade to InfluxDB Cloud or InfluxDB 2.0!

InfluxDB Cloud and InfluxDB OSS 2.0 ready for production.