Overlap studies
Kaufman Adaptive Moving Average
Adaptive moving average that responds to price efficiency and market noise.
- Engine
- TA-Lib
- Function
KAMA- Input series
- close
- Outputs
- 1
How it works
KAMA derives an efficiency ratio from directional change versus cumulative bar-to-bar movement, interpolates between fixed fast and slow smoothing constants equivalent to periods 2 and 30, squares that constant, and recursively updates the average.
Use case
Use KAMA as a trend feature that reacts faster to efficient directional movement and smooths more heavily during noisy or sideways movement.
Parameters
| Parameter | Type | Default | Bounds or options | Description |
|---|---|---|---|---|
| Time Period | integer | 30 | 1…100000 | Number of bars used to calculate the efficiency ratio that controls adaptive smoothing. |
Outputs
| Output | Type | Description |
|---|---|---|
| KAMA | number | Kaufman Adaptive Moving Average value for the bar. |
Reading the result
KAMA adapts its smoothing response to the efficiency of recent price movement. More directional movement allows faster adaptation, while noisy movement leads to stronger smoothing.
This makes the output useful for describing a changing noise/trend environment, but it does not guarantee that the adaptive response identifies a profitable regime.
Common mistakes
- Assuming adaptive automatically means low lag in every market state.
- Comparing KAMA with a fixed average using only one visual interval.
- Ignoring sensitivity to missing or revised path history.
- Treating the line itself as a complete regime classifier.
DataCat workflow notes
Useful downstream features can include distance from price, line slope, and changes in responsiveness. Keep those transformations separate from the persisted KAMA output so the original calculation remains auditable.