Kaufman’s Adaptive Moving Average (KAMA)

Created by Perry Kaufman, KAMA is an volatility adaptive moving average of price over configurable lookback periods. [Discuss] 💬

chart for Kaufman's Adaptive Moving Average (KAMA)

// C# usage syntax
IEnumerable<KamaResult> results =
  quotes.GetKama(erPeriods, fastPeriods, slowPeriods);


erPeriods int - Number of Efficiency Ratio (volatility) periods (E). Must be greater than 0. Default is 10.

fastPeriods int - Number of Fast EMA periods. Must be greater than 0. Default is 2.

slowPeriods int - Number of Slow EMA periods. Must be greater than fastPeriods. Default is 30.

Historical quotes requirements

You must have at least 6×E or E+100 periods of quotes, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least 10×E data points prior to the intended usage date for better precision.

quotes is a collection of generic TQuote historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.



Convergence warning: The first 10×E periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.


Date DateTime - Date from evaluated TQuote

ER double - Efficiency Ratio is the fractal efficiency of price changes

Kama double - Kaufman’s adaptive moving average

More about Efficiency Ratio: ER fluctuates between 0 and 1, but these extremes are the exception, not the norm. ER would be 1 if prices moved up or down consistently over the erPeriods window. ER would be zero if prices are unchanged over the erPeriods window.


See Utilities and helpers for more information.


This indicator may be generated from any chain-enabled indicator or method.

// example
var results = quotes

Results can be further processed on Kama with additional chain-enabled indicators.

// example
var results = quotes