Kaufman's Adaptive Moving Average (KAMA): Formula & Strategies

Kaufman's Adaptive Moving Average (KAMA) is a moving average that adjusts its speed based on how efficiently price moves. KAMA is designed to follow price closely in strong trends and flatten out in sideways markets. As a result, it can help traders avoid many false signals.

Key Takeaways

• KAMA is a moving average that adapts to market conditions. It moves fast in a trending market and slows down in sideways markets. 
• It is calculated using a mix of the Efficiency Ratio, Smoothing Constant, and price data. 
• Common strategies are the price-KAMA crossover and the dual KAMA crossover. 
• KAMA reduces false signals in choppy markets, but it is still a lagging indicator based on historical prices.

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

Have you ever entered a trade on a moving average crossover, only to see the price reverse a few candles later? In choppy markets, SMA, EMA and WMA often whipsaw traders with false signals. Kaufman's Adaptive Moving Average (KAMA) was designed to solve this problem.

Most traders start with SMA. Then they start using EMA. And a few even use WMA. But there's another moving average called Kaufman's Adaptive Moving Average (KAMA). This strong trend-following indicator was developed by Perry J. Kaufman. Perry J. Kaufman introduced this moving average in his 1995 book Smarter Trading.

KAMA's core edge is that it adapts to changing market conditions. Market noise refers to small, random price movements that do not reflect the real trend. KAMA is designed to ignore this noise while still reacting quickly to genuine trends. 

KAMA is based on market volatility. So, in a strong trending market, KAMA adjusts quickly. On the other hand, if the market is sideways, KAMA slows down. This adaptive nature makes KAMA a popular choice among traders who want a moving average that balances responsiveness with noise reduction.

How Does KAMA Work

The basic idea behind KAMA is the Efficiency Ratio (ER). ER measures how efficiently prices are moving. Here is the general guideline:

• When prices trend smoothly in one direction, the Efficiency Ratio increases. This, in turn, causes KAMA to move closer to price. 
• If the market is range-bound, the Efficiency Ratio falls. This makes KAMA smoother and less responsive to random price fluctuations.

Components of KAMA

KAMA is built using three key components:

• Efficiency Ratio (ER): This measures the strength and efficiency of the price trend.
• Smoothing Constant (SC): This core component determines how quickly KAMA reacts to price changes.
• Price Data: As with other moving averages, it typically uses closing prices over the selected period.

The usual settings are (10, 2, 30), which means:

• 10: the period used to calculate the Efficiency Ratio
• 2: the period of the fast EMA, used when the market is trending strongly
• 30: the period of the slow EMA, used when the market is sideways

These defaults were chosen because a 10-period ER is short enough to spot changes in trend quality, while the 2 and 30 periods set the fastest and slowest limits KAMA can move between.

How to Calculate KAMA

Calculating KAMA has a few steps. Let us say that we are using a 10-period KAMA. And here are the closing prices of the last 10 periods:

Step 1: Calculate the Efficiency Ratio (ER)

To find the ER, we will first need to calculate the Absolute Change. This is the current price minus the previous day's price. Then the absolute value is taken. So here are the daily price movements.

The Total Price Movement = 10

Now, the ER can be calculated as net price change (108 – 100) / total price movement

= 8 ÷ 10 = 0.80

An ER of 0.80 indicates that the price has been moving in a relatively smooth trend with little market noise.

Step 2: Calculate the Smoothing Constant (SC)

Once you calculate the ER, use the standard KAMA settings (Fast = 2, Slow = 30) to calculate the smoothing constant. 

Here is the formula for the smoothing constant:

SC = [ER × (Fast SC - Slow SC) + Slow SC]²

Substituting the values:

• Fast SC = 2 / (2+1) = 0.6667
• Slow SC = 2 / (30 + 1) = 0.0645
• ER = 0.80

SC ≈ 0.298

Note that 2 / (N + 1) is the standard formula for an EMA smoothing constant, where N is the number of periods. KAMA uses it to set its fastest (2-period) and slowest (30-period) speeds. 

Step 3: Calculate the KAMA

The third step is to get today’s KAMA. Assume yesterday's KAMA value was ₹106, and today's closing price is ₹108. So, the value of today’s KAMA is:

KAMA = Previous KAMA + SC × (Current Price − Previous KAMA)

= 106 + 0.298 × (108 − 106)

= 106 + 0.596

= ₹106.60

Interpretation

Once we calculate the KAMA, we can see how far it is from the closing price. In our example, although the stock closed at ₹108, the KAMA value is ₹106.60 because moving averages smooth price movements.

Also, because the Efficiency Ratio is high (0.80), KAMA moves relatively quickly toward the current price. This clearly reflects a strong trending market. This is also shown in our example: prices trended quickly from 100 to 108.

If the market had been choppy, the Efficiency Ratio would have been much lower. This would have caused the KAMA to move more slowly and filter out short-term price fluctuations.

KAMA vs Traditional Moving Averages

Here are the major differences between different moving averages:

How to Read and Interpret KAMA

Interpret KAMA similarly to other moving averages. Here are the ground rules:

• Price above KAMA: Bullish trend.
• Price below KAMA: Bearish trend.
• Rising KAMA: Strengthening upward momentum.
• Falling KAMA: Increasing bearish momentum.

Some common strategies that are used by traders are:

1. Price-KAMA Crossover Strategy: Traders can buy when the price closes above KAMA and sell when it closes below. As with any trading strategy, this crossover strategy can also result in losses.

To reduce this, Kaufman suggested a filter: take the standard deviation of recent KAMA changes (for example, over 20 periods) and multiply it by a small fraction, such as 0.1. Take a trade only when KAMA moves by more than this filter value, confirming the breakout. 

1. Dual KAMA Strategy: This strategy uses a short-period KAMA and a long-period KAMA. When the short-period KAMA crosses above a long-period KAMA, it signals a bullish trend, while the opposite crossover suggests a bearish trend.

For example, traders can use a short KAMA (10, 2, 30) and a long KAMA (30, 2, 30) on a daily chart. A buy signal occurs when the short KAMA crosses above the long KAMA and a close above KAMA is commonly read as a bullish signal.

Benefits of Using KAMA

Many traders use KAMA for its many benefits. Here are a few reasons why traders prefer KAMA over other moving averages:

• It adapts automatically to changing market conditions.
• It can reduce false signals during sideways markets.
• It can work on different asset classes.
• It suits both swing and positional trading.

Limitations of KAMA

KAMA reduces market noise, but it is still a lagging indicator because it is based on historical prices. In highly volatile markets, it can still produce whipsaws and false breakouts. To manage this risk, traders can:

• Confirm signals with an oscillator like RSI, or with rising volume on breakouts
• Use a stop-loss on every trade
• Size positions so a single loss does not hurt the capital significantly

Conclusion

Kaufman's Adaptive Moving Average (KAMA) is an advanced moving average that automatically adjusts to market volatility. KAMA helps traders reduce false signals in choppy markets.

Disclaimer: This article is solely for educational purposes. The securities/investments quoted here are not recommendatory.