docs-v2/content/flux/v0/stdlib/universe/kaufmansama.md

title	description	menu	weight	flux/v0/tags	introduced
kaufmansAMA() function	`kaufmansAMA()` calculates the Kaufman’s Adaptive Moving Average (KAMA) using values in input tables.	flux_v0_ref name parent identifier kaufmansAMA universe universe/kaufmansAMA	101	transformations	0.40.0

kaufmansAMA() calculates the Kaufman’s Adaptive Moving Average (KAMA) using values in input tables.

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

Function type signature

(<-tables: stream[A], n: int, ?column: string) => stream[B] where A: Record, B: Record

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Parameters

n

({{< req >}}) Period or number of points to use in the calculation.

column

Column to operate on. Default is _value.

tables

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

Examples

Calculate Kaufman's Adaptive Moving Average for input data

import "sampledata"

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

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Input data

_time	_value	*tag
2021-01-01T00:00:00Z	-2	t1
2021-01-01T00:00:10Z	10	t1
2021-01-01T00:00:20Z	7	t1
2021-01-01T00:00:30Z	17	t1
2021-01-01T00:00:40Z	15	t1
2021-01-01T00:00:50Z	4	t1

_time	_value	*tag
2021-01-01T00:00:00Z	19	t2
2021-01-01T00:00:10Z	4	t2
2021-01-01T00:00:20Z	-3	t2
2021-01-01T00:00:30Z	19	t2
2021-01-01T00:00:40Z	13	t2
2021-01-01T00:00:50Z	1	t2

Output data

_time	_value	*tag
2021-01-01T00:00:30Z	9.72641183951902	t1
2021-01-01T00:00:40Z	10.097401019601417	t1
2021-01-01T00:00:50Z	9.972614968115325	t1

_time	_value	*tag
2021-01-01T00:00:30Z	-2.9084287200832466	t2
2021-01-01T00:00:40Z	-2.142970089472789	t2
2021-01-01T00:00:50Z	-2.0940721758134693	t2

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