Documentation

experimental.kaufmansAMA() function

experimental.kaufmansAMA() is subject to change at any time. By using this function, you accept the risks of experimental functions.

experimental.kaufmansAMA() calculates the Kaufman’s Adaptive Moving Average (KAMA) of input tables using the _value column in each table.

Kaufman’s Adaptive Moving Average is a trend-following indicator designed to account for market noise or volatility.

Function type signature
(<-tables: stream[{A with _value: B}], n: int) => stream[{A with _value: float}] where B: Numeric
For more information, see Function type signatures.

Parameters

n

(Required) Period or number of points to use in the calculation.

tables

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

Examples

Calculate the KAMA of input tables

import "experimental"
import "sampledata"

sampledata.int()
    |> experimental.kaufmansAMA(n: 3)

View example input and ouput


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