Adaptive Moving Average

The Adaptive Moving Average (AMA) technical indicator is used to plot a moving average that has low noise sensitivity in price series and has a minimal delay in determining a trend. Perry Kaufman developed his Adaptive Moving Average, also known as Kaufman’s Adaptive Moving Average, in 1998.

One of the disadvantages of other moving average algorithms is too many signals are generated, which generates many false-signals when using it as a day trading indicator in small time frames. Also, the typical smoothing of moving averages leads to the inevitable delay of the stop signal or reversal of a trend. This indicator has been developed to overcome these two disadvantages.

Kaufman Adaptive Moving Average

Kaufman Adaptive Moving Average

The Kaufman Adaptive Moving Average, also known as “KAMA,” considers price action and market volatility. When market volatility is low, the Kaufman adaptive moving average remains close to the current market price, but it doesn’t match expectations when volatility increases.

The KAMA indicator tries to filter out “market noise” such as temporary price spikes. One of the main weaknesses of traditional moving averages is that trading signals tend to produce many false signals. The KAMA indicator tries to moderate this trend to trigger fewer false alerts – by not responding to short-term price movements.

Traders typically use the moving average indicator to identify market trends and reversals. The Kaufman adaptive moving average can be used as a potential addition or stand-alone method.

Kaufman adaptive moving average: Calculation

The following standard parameters are used when calculating the Kaufman Adaptive Moving Average.

  • Length: 10
  • EMA fast: 2
  • EMA slow: 30
  • Offset: 0

KAMA Settings

To obtain the KAMA value, first, calculate the efficiency value and the smoothing constant.

1. Efficiency Ratio (ER)

The efficiency index determines the effectiveness of price changes. The ratio generally fluctuates between 1 and 0. When the price remains constant for 10 periods, the efficiency ratio is considered zero. But if the price goes up or down for consecutive periods equal to 10, the ER fluctuates above 0.

The efficiency ratio calculation formula is as follows:

Efficiency Ratio = Change divided by volatility


  • Change = absolute value [closing price – closing price 10 periods ago of your chose time frame
  • Volatility = 10 * Close – Close[1] for 10 periods

If you change the standard setting for length, then the calculation will switch from using the 10 by the number chosen.

2. Smoothing Constant (SC)

For each period smoothing constant is calculated and it is used for the efficiency ratio and two further smooting constants namely “Fast” and “Slow”:

  • Smoothing Constant 1 = ([ER x (Fast SC – Slow SC) + Slow SC]) ^2
  • Smoothing Constant 2 = ([ER x (2 / (2 + 1) – 2 / (30 + 1)) + 2 / (30 + 1)]) ^ 2

The smoothing constant for the recommended EMA 30 is (2/30 + 1) in the above equation. The fastest smoothing constant is SC for the shortest EMA2. If you change the standard setting for slow EMA from 30 or fast EMA from 2 to any other number, the calculation will change accordingly.

3. KAMA Calculation

It is possible to calculate the values of the adaptive Kaufman moving average index. It is done after obtaining the values for the efficiency function and the smoothing constant. The formula looks like this:

  • KAMAi = KAMAi-1 + S x (Price – KAMA i-1)
  • KAMAi reflects the current period value.
  • KAMAi-1 reflects the value of the period before the calculation period.
  • Price: The original price for the period used.

How the Adaptive Moving Average Works

The final values may be calculated using the KAMA indicator. Seasoned traders often use the theory that future trends will continue to move in the same direction as before. This is why many trading decisions can be made using Kaufman’s Adaptive Average Movement Indicator.

Using the KAMA indicator in the charts is quite simple if your platform supports it. In this example, TrendSpider was used to create the chart. The standard settings should be used first, while any settings can be modified for testing. It is highly recommended to do backtesting for your favorite asset first and trade later.

The number of periods to use the Kaufman Adaptive Moving Average in the calculation parameter can be changed. With the Kaufman indicator, one can identify potential indications of a forthcoming trend. Changes in present trends and turning points in the market can also be identified to enter or exit a trade.

Fractal Adaptive Moving Average

FAMA averages the difference between the highest and lowest prices of the chosen period length. John Ehlers came up with Fractal Adaptive Moving Average, while I did not find a trading platform that supports it.

The Fractal Moving Average is a trading indicator often used with other studies to identify the most current market trend. A cross of prices with the average line does not represent trading signals by itself. The calculation is too complex to put it in a simple formula. A mid-point price, a period length, an exponential and logarithmic number is also used, while the current and previous periods are considered as well.

For indicators, I keep it simple. I use those who are used by most investors because market movement only occurs on indicator events like moving average crosses, if trading systems and algorithms understand it and if those signals lead to volatility and volume. In the case of FAMA, I think that other indicators like KAMA are more beneficial.


Kaufman’s Adaptive Moving Average can be used to determine trends in the current market price, and the indicator helps to detect the emergence of new trends. It can be used to identify their turning points as well.

When the price crosses the KAMA line to the upside, a trader can go long and close the trade when the price falls below KAMA. An alternative is to use the cross of prices from above to below the Kaufman moving average as a short signal and to stay short until the price reverses back above KAMA.

If the price moves from below to above the KAMA line, it is a bullish signal. Conversely, a price that drops from above the KAMA line below it is a bearish signal.

Anyway, it is essential to backtest the combination of the favorite asset with various settings before using it as a signal trigger. Instead, you can also use the indicator as an additional filter for your trading signals.

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Updated: November 15, 2020

About the author: Alexander is the founder of and has 20 years of experience in the financial markets. He aims to make trading and investing easy to understand for everybody, and has been quoted on Benzinga, Business Insider and GOBankingRates.