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

In this article, we will discuss

• What is Kaufman’s Adaptive Moving Average?
• Understanding the KAMA Indicator
• Calculating Kaufman’s Adaptive Moving Average
• Step 1: Finding the Efficiency Ratio
• Step 2: Finding the Smoothing Constant
• Step 3: Calculating KAMA<
• Interpreting and Using KAMA
• Pros and Cons of the Kaufman AMA
• Conclusion

Traditional moving averages can be extremely useful in their own right. They help you smooth out price action and get a broad overview of how the price of a stock or security is moving. If you are a beginner to short-term, you may rely on the simple moving average (SMA) or the exponential moving average (EMA) to better understand price trends. The SMA takes the simple average of the past price movements over a given period, while the EMA offers greater weightage to recent price movements, thus reducing price lag.

However, traditional moving averages have one major flaw — they tend to generate false signals often. This is mainly because simple and exponential moving averages are not designed to account for market volatility. They tend to react in the same way in volatile as well as stable markets. In other words, the degree of sensitivity in these conventional moving averages is constant. This proves to be a problem because it leads to uncharacteristic lags or overenthusiastic false signals.

This is why you need to rely on advanced indicators like Kaufman’s adaptive moving average (KAMA). In this article, we will discuss what the KAMA indicator is, how it is calculated and interpreted and what its advantages and limitations are.

What is Kaufman’s Adaptive Moving Average?

Also known as the KAMA indicator or simply KAMA, this is a statistical tool that smooths out price data over a given period. Unlike traditional moving averages, KAMA is an adaptive moving average that adjusts its sensitivity based on how volatile the prevailing market conditions are.

When the market is stable and the prices are not changing rapidly, KAMA reacts less and moves slowly. This helps you filter out small and random price changes, so you have a clear view of the overall trend. However, when the market is highly volatile and the prices are changing quickly, or if the prices are trending strongly, KAMA reacts swiftly as well and follows the price changes more closely.

Understanding the KAMA Indicator

Traditional moving averages use fixed periods like 1 week or 100 days to compute the average smoothed-out price. However, they do not factor in the volatility in the market. As a result, they are prone to the whiplash effect and may give false signals to traders.

To overcome this issue, Perry J. Kaufman, an American quantitative financial theorist, began working on developing a different statistical tool in 1972. His goal was to create a moving average that would adapt to market volatility more easily. In 1998, Kaufman’s adaptive moving average was finally ready to be officially presented for public use.

Ever since then, KAMA has been used by traders who want a clearer and truer picture of the prevailing market trends. Instead of using a fixed period to smooth data, this indicator adjusts its calculation based on current market conditions. This means it can react appropriately to different levels of market activity.

If the price is trending, i.e. if it is moving steadily in one direction, KAMA adjusts to follow the current price closely. Alternatively, if the price is bouncing around without any clear direction, KAMA smooths out these volatile price movements and shows you a clearer average. In short, it aims to follow the true price trend accurately.

Calculating Kaufman’s Adaptive Moving Average

To calculate Kaufman’s adaptive moving average, you need to find the smoothing constant (SC), which, in turn, requires you to know the efficiency ratio (ER). So, the steps involved in computing the KAMA value are as follows:

• Step 1: Calculate the efficiency ratio (ER)
• Step 2: Find the smoothing constant (SC)
• Step 3: Compute KAMA

Let us decode each of these steps one by one. The settings or criteria chosen for the computation of KAMA — as recommended by Perry Kaufman — are as follows:

• 10 periods for the efficiency ratio
• 2 periods for the fastest exponential moving average
• 30 periods for the slowest exponential moving average

Step 1: Finding the Efficiency Ratio

As the name indicates, this ratio tells you how efficiently the price is trending in a given direction. This makes it easy to measure how much the price has moved in a given period compared to how much it has fluctuated overall in that period — so you can determine whether the price is moving in a steady trend or just fluctuating randomly. The ER is high if the price is trending strongly and low if the market is volatile and the price is bouncing around randomly.

To calculate the ER, you need to take the total price change over a given period and divide it by the sum of the smaller, day-to-day (or period-to-period) changes. The formula for this ratio is as follows:

Here, the price change is the difference between the current price and the price from a specific number of days ago (generally, 10 days, as mentioned in the recommended settings). To find the price change, subtract the price from 10 days ago from today's price.

The volatility is the sum of the absolute differences between consecutive prices over the same period. To find the volatility, add up all the daily price changes over the last 10 days.

Step 2: Finding the Smoothing Constant

Using the efficiency ratio, you need to find the smoothing constant. This essentially adjusts the sensitivity of the adaptive moving average to price changes. When the market is trending strongly and the ER is high, the smoothing constant makes KAMA more responsive, so it can react quickly to price changes. If the market is moving sideways, the smoothing constant makes KAMA less responsive, so the unnecessary noise is filtered out.

To calculate the smoothing constant, you need to find the fastest SC (for 2 periods) and the slowest SC (for 30 periods). The formula for this is shown below:

Considering the recommended settings for the calculation, this effectively becomes:

Smoothing Constant = [ER x {2/(2+1) – 2/(30+1)} + 2/(30+1)]2

Step 3: Calculating KAMA

The final step is to calculate the KAMA indicator, which requires the previous day’s (or period’s) KAMA and the current price. With these details, you can find Kaufman’s adaptive moving average using the formula shown below:

Interpreting and Using KAMA

Just like other moving averages, the KAMA values are also plotted on a chart. Here is how you can interpret these values and lines to understand the market and plan your trades. 

• Trend Direction

An upward KAMA line means that the price is generally rising and it may be a good sign that the asset’s price might continue to increase. Conversely, when the KAMA line is falling, it means the price is generally declining and may continue to do so.

• Market Noise

Since KAMA uses the smoothing constant, minor price changes and fluctuations are evened out or eliminated. These changes have nothing to do with the real price trend. By eliminating this kind of market noise, KAMA helps you see the true direction of the price.

• Market Responsiveness

This adaptive indicator also reacts differently to market changes than other moving averages. When the market is stable and not changing much, KAMA moves slowly and doesn’t react to every little price change. However, when the market is volatile and prices are changing a lot, KAMA moves quickly to follow these changes, so it can adapt to the new conditions better.

• Buy and Sell Signals

If the price moves above the KAMA line, it might be a good time to buy because it suggests the price is trending upwards. Similarly, if the price moves below the KAMA line, it might be a good time to sell because it suggests the price is trending downwards.

• Crossovers for Trend Reversals

KAMA lines can also be used to predict potential price reversals. For instance, if the price has been below the KAMA line and then crosses above it, this could indicate a potential upward reversal. Conversely, if the price has been above the KAMA line and then crosses below it, this could indicate a potential downward reversal.

You can also use a combination of long-term and short-term KAMA indicators to understand if a trend is due to reverse. For instance, when a short-term KAMA crosses above a long-term KAMA, it could signal a potential uptrend (and vice versa).

Pros and Cons of the Kaufman AMA

This moving average has both advantages and disadvantages. Let us take a closer look at the two sides of this analytical tool.

Its benefits include the following:

• Adapts to market conditions easily
• Provides a more accurate representation of price trends
• Smoothes out noise in price data, making trends easier to identify
• Can be used for both short-term and long-term trend analysis
• Helps identify potential trend reversals.
• More responsive to recent price changes than traditional moving averages

Its limitations, however, include the following:

• Lagging indicator as it relies on past prices to calculate the moving average
• More complex calculations compared to simple moving averages
• Requires tuning of parameters (e.g., fast and slow constants) for optimal performance

Conclusion

This essentially sums up what the Kaufman adaptive MA is and how you can calculate, interpret and use it in your trading plan. While the calculation is tough, you need not manually calculate and plot the KAMA values for each day. Some platforms offer these charts as well as others to traders.

One such platform is the Samco trading app, which gives Samco users free access to updated and advanced TradingView charts. You can benefit from this feature as well as a suite of other analytical tools from Samco Securities that make trading effective and potentially more rewarding.

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