Adaptive Moving Average

The Kaufman adaptive moving average (KAMA) refers to a moving average that factors in the direction of the market and the market noise, which is also known as volatility. It aims to filter out the temporary, insignificant price action surges or 'market noise.'

This indicator enables individuals to minimize time lag in identifying trends, enabling one to react quickly to asset price movements. Moreover, it offers improved noise reduction and smoothing capabilities, enabling traders to spot more reliable signals and trends. This indicator is similar to the moving average as it minimizes outliers' influence without relinquishing the sensitivity.

• The adaptive moving average refers to an indicator that can account for the alterations in market volatility. It can improve the accuracy concerning signal generation and trend analysis, helping traders to make financial gains.
• A critical difference between EMA and KAMA is that the latter utilizes historical data while the former places more significance and weight on recent data points.
• Before calculating KAMA, one must calculate the smoothing constant and efficiency ratio.
• A key advantage of this indicator is that it can minimize the time lag involved in the identification of trends by adapting to the altering market conditions.

The adaptive moving average refers to an indicator in technical analysis that involves using multiple calculations to adjust to the changing market conditions and minimize noise. Also known as KAMA, its purpose is to offer individuals a more reliable and accurate signal for spotting the trend's direction and possible trading opportunities.

KAMA makes adjustments to its sensitivity to asset price movements on the basis of market volatility, which makes it more responsive to sudden alterations in market trends. The use of this indicator is common in futures, forex, and stocks.

This indicator utilizes adaptive smoothing to represent more accurate price trends. It works by adjusting its smoothing factor after factoring in the prevailing market conditions. This effectively filters out the market noise and allows individuals to capture asset price increases or decreases more effectively.

The adaptive moving average calculation combines volatility and price action to determine the ideal smoothing period. KAMA generates fewer false signals and is less susceptible to whipsaw signals when compared to any other moving average. This ensures more dependable trade signals, enabling traders to spot potential entry as well as exit points with more accuracy.

One must note that the adaptive moving average indicator utilizes historical data to get final values. Also, individuals can apply it to a chart. When represented on a chart of any financial instrument, for example, a stock, individuals can utilize it for analyzing market behavior and predicting future price movements.

This indicator allows one to spot existing trends and identify any indication of a potential impending trend change. Moreover, it can help identify market reversal points, enabling them to trade exits and entries.

The formula for adaptive moving average calculation is as follows:

KAMA_i = KAMA_i-1 + SC x (Price – KAMA_i-1)

Where:

• SC is the smoothing constant.
• Price represents the source price of the duration under consideration.
• KAMA_i is the current duration's value.
• KAMA_i-1 represents the value of the duration that precedes the timeframe under consideration.

As noted above, the calculation of the adaptive moving average indicator involves multiple calculations, which are detailed below. That said, to start, one must be aware of these standard settings:

• For the ER or efficiency ratio, the overall number of periods must be 10. 
• The overall number of periods for the fastest EMA or exponential moving average must be set at 2.
• For the slowest EMA, the total number of periods must be set at 30.

One must first calculate the values of ER and SC to find KAMA's value. So let us look at their calculation process.

Step #1 - Computing ER

This ratio shows the price changes' efficiency. It varies between 0 and 1. If the price remains constant over 10 periods in total, the efficiency ratio is 0. That said, when the price increases or decreases for 10 periods in a row, the efficiency ratio moves to 1. The formula to compute ER is as follows:

ER = Change ÷ Volatility

Where:

• Volatility Sum = 10 Periods(Close – Last Close)
• Change = Absolute Value {Close – Close(The last 10 periods)}

Step #2 - Computing SC

This step involves computing the SC. One must do this for every period. The calculation involves utilizing the values obtained for two smoothing periods and ER. Let us look at the formula.

SC = ²

One must note that the SC for the 30-period exponential moving average is (2 ÷30+1) in the above formula. Moreover, the slowest SC is the smoothing constant for the slowest 30-period exponential moving average. On the other hand, the fastest SC is the smoothing constant for the shorter 2-period exponential moving average.

Step #3 - Computing KAMA

Once individuals finish computing ER and SC, they can use the formula already mentioned above to calculate KAMA.

Before understanding when to use this indicator in technical analysis, one must remember these two things:

If the KAMA line shows a downward movement, especially with the asset's price lying mainly below it, one must see it as an indication of a downward trend. On the other hand, if the indicator line moves upward, it indicates a potential upward trend. 

Having said that, let us find out how to use this indicator in trading.

#1 - Utilizing Two KAMA Indicators

One can utilize them to spot the market's direction and pinpoint the trend reversal points. Individuals can do this by plotting two KAMA indicator lines on a chart. While one should have a faster MA or moving average, the other must have a slower MA. The fast indicator line that crosses above the long-term or slower-moving average line indicates an alteration from a downward trend to an upward trend has occurred. In this case, traders can enter a long position before exiting the trade when the short-term or faster-moving average line crosses back beneath the long-term moving average line. 

#2 - Utilizing The Price That Crosses The Indicator For Generating Signals

When an asset's price crosses the indicator line from below, one has a buy or bullish signal. That said, if the asset price drops below the indicator line from above it, individuals may have a sell or bearish signal.

#3 - Utilizing It With The Analysis Of Price Action

Individuals can utilize this indicator to spot the market's direction. Then, they may use reversal candlesticks at the resistance and support levels to determine the trade entry.

Here is a chart taken from TradingView where the KAMA indicator is displayed. In the chart there are lots of regions where the volatility is quite significant. But the indicator, which is denoted by the blue line, is smoothening out the fluctuations. Since it takes into account the historical data to calculate the points, it is a lagging indicator. Therefore, it will show the change after the price has already moved in a certain direction.

However, normal Exponential Moving Averages (EMA), is quite prone to price volatility. But this indicator is more stable and in case of a sudden movement in prices the KAMA will move much away from the candlesticks to indicate the volatility. In case of stable prices, the opposite will happen. The indicator will lie close to the candlesticks. This is clearly indicated in the chart below and can be identified by following the movement of the line as against the candlesticks. Therefore, is it definitely a good indicator to control the market noise or volatility of prices and get a smooth idea of the possible price movements. However, it is advisable to use it in combination with other suitable indicators to get clear confirmation.

Source

Adaptive and exponential moving averages can be confusing, especially for individuals learning about these topics for the first time. After all, they have some similarities. That said, people can fully understand their meaning and purpose and steer clear of any confusion if they understand their distinct features. Hence, let us look at their differences.

This article has been a guide to what is Kaufman Adaptive Moving Average (KAMA). Here we explain its formula, how to use it, vs exponential moving average, and how to calculate it. You may also find some useful articles here:

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