Kaufman’s Adaptive Moving Average (KAMA)

An indicator that accounts for both the volatility and direction of of the market

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What is Kaufman’s Adaptive Moving Average (KAMA)?

Kaufman’s Adaptive Moving Average (KAMA) was developed by American quantitative financial theorist Perry J. Kaufman in 1998. The technique began in 1972 but Kaufman officially presented it to the public much later through his book, “Trading Systems and Methods.” Unlike other moving averages, Kaufman’s Adaptive Moving Average accounts not only for price action but also for market volatility.

Kaufman’s Adaptive Moving Average

The Kaufman Advantage

When market volatility is low, Kaufman’s Adaptive Moving Average remains near the current market price, but when volatility increases, it will lag behind. What the KAMA indicator aims to do is filter out “market noise” – insignificant, temporary surges in price action. One of the primary weaknesses of traditional moving averages is that when used for trading signals, they tend to generate many false signals. The KAMA indicator seeks to lessen this tendency – generate fewer false signals – by not responding to short-term, insignificant price movements.

Traders generally use the moving average indicator to identify market trends and reversals.

Calculating the Kaufman Adaptive Moving Average

When calculating Kaufman’s Adaptive Moving Average, the following standard settings are used:

  • 10 – Number of periods for the Efficiency Ratio
  • – Number of periods for the fastest exponential moving average
  • 30 – Number of periods for the slowest exponential moving average

To obtain the value of the KAMA, you must first calculate the value of the Efficiency Ratio and the Smoothing Constant.

Step 1: Efficiency Ratio (ER)

The efficiency ratio shows the efficiency of price changes. It fluctuates between 1 and 0. When the price remains unchanged over 10 periods, the ER is zero. However, if the price moves up or down 10 consecutive periods, the ER moves to 1. It is calculated by dividing the absolute difference between the current price and the price at the beginning of the period by the sum of the absolute difference between each pair of closes during the period. The formula for calculating ER is as follows:

ER = Change/volatility

Change = Absolute Value [Close – Close (past 10 periods)]

Volatility Sum = 10 periods (Close – Prior Close)

Step 2: Smoothing Constant (SC)

The smoothing constant is calculated for each period. It uses the value obtained for efficiency ratio and two smoothing constants as follows:

SC= [ER x (Fastest SC – Slowest SC) + Slowest SC]2

SC= [ER x (2/ (2+1) – 2/(30+1)) +2/ (30+1)]2

In the above equation, (2/30+1) is the smoothing constant for the recommended 30-period EMA. Also, the slowest smoothing constant is the SC for the slowest 30-period EMA, while the fastest smoothing constant is the SC for shorter 2-period EMA.

Step 3: KAMA

After getting the values of the efficiency function and smoothing constant, you can now calculate the Kaufman’s Adaptive Moving Average indicator values. The formula is as follows:

KAMA= KAMAi-1 + SC x (Price – KAMA i-1)


  • KAMAi  is the value of the current period
  • KAMAi-1 is the value of the period preceding the period being calculated.
  • Price is the source price for the period being calculated.

How the Adaptive Moving Average Works

When traders use Kaufman’s Adaptive Moving Average indicator, they get a clear picture of the market’s behavior, which they can use to make trading decisions. The indicator uses historical data to obtain the final values. Traders make a decision on the basis of the theory that future trends will continue to develop in the same direction as the past trends.

The KAMA indicator can easily be applied to a chart. The trader enjoys the option of customizing the indicator by specifying its parameters in the properties dialog box. The main parameters to be customized include the calculation periods and the appearance of the indicator. Traders can specify the number of periods to apply Kaufman’s Adaptive Moving Average to in the calculation parameter. The default number of periods is 14, but traders can select any value between 2 and 1000.

When the Kaufman’s Adaptive Moving Average indicator is represented on a chart, traders can use it to analyze the behavior of a market and predict future price movement. The KAMA indicator can be used to identify existing trends, indications of a possible impending trend change, and market reversal points that can be used for trade entries or exits.

Using the KAMA

One of the uses of Kaufman’s Adaptive Moving Average is to identify the general trend of current market price action. Basically, when the KAMA indicator line is moving lower, it indicates the existence of a downtrend. On the other hand, when the KAMA line is moving higher, it shows an uptrend. As compared to the Simple Moving Average, the KAMA indicator is less likely to generate false signals that may cause a trader to incur losses.

Kaufman’s Adaptive Moving Average can also be used to spot the beginning of new trends and pinpoint trend reversal points. One way to do this is by plotting two KAMA lines on a chart – one with a more short-term moving average and another with a longer-term moving average. When a faster KAMA line crosses above a slower KAMA line, this indicates a change from a downtrend to an uptrend.

The trader can take a long position and close the trade when the faster MA line crosses back to beneath the slower MA line. Trading signals can also be derived from the movement of market price in relation to Kaufman’s Adaptive Moving Average. If the price crosses from below to above the KAMA line, that is a bullish (buy) signal. Conversely, the price falling from above the KAMA line to below it is a bearish (sell) signal.

Related Readings

Thank you for reading CFI’s explanation of Kaufman’s Adaptive Moving Average. CFI offers the Capital Markets & Securities Analyst (CMSA)® certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following resources will be helpful:

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