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

title	description	menu	weight	flux/v0/tags	introduced
experimental.kaufmansAMA() function	`experimental.kaufmansAMA()` calculates the Kaufman's Adaptive Moving Average (KAMA) of input tables using the `_value` column in each table.	flux_v0_ref name parent identifier experimental.kaufmansAMA experimental experimental/kaufmansAMA	101	transformations	0.107.0

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

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Parameters

n

({{< req >}}) 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)

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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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