experimental.kaufmansAMA() function

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

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)