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experimental.kaufmansAMA() function

content/flux/v0/stdlib/experimental/kaufmansama.md

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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
js
(<-tables: stream[{A with _value: B}], n: int) => stream[{A with _value: float}] where B: Numeric

{{% caption %}} For more information, see Function type signatures. {{% /caption %}}

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

js
import "experimental"
import "sampledata"

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

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time_value*tag
2021-01-01T00:00:00Z-2t1
2021-01-01T00:00:10Z10t1
2021-01-01T00:00:20Z7t1
2021-01-01T00:00:30Z17t1
2021-01-01T00:00:40Z15t1
2021-01-01T00:00:50Z4t1
_time_value*tag
2021-01-01T00:00:00Z19t2
2021-01-01T00:00:10Z4t2
2021-01-01T00:00:20Z-3t2
2021-01-01T00:00:30Z19t2
2021-01-01T00:00:40Z13t2
2021-01-01T00:00:50Z1t2

Output data

_time_value*tag
2021-01-01T00:00:30Z9.72641183951902t1
2021-01-01T00:00:40Z10.097401019601417t1
2021-01-01T00:00:50Z9.972614968115325t1
_time_value*tag
2021-01-01T00:00:30Z-2.9084287200832466t2
2021-01-01T00:00:40Z-2.142970089472789t2
2021-01-01T00:00:50Z-2.0940721758134693t2

{{% /expand %}} {{< /expand-wrapper >}}