Adaptive moving average: how to adapt and overcome markets’ changes

There are several ways to calculate moving averages and each of them has its advantages and disadvantages.

Our main interest, however, is how to profitably use moving averages in trading.

Used extensively in the field of technical analysis, they are a foundational tool with a wide set of applications.

Moving average

Technical analysts use moving averages to determine trend, to determine levels of support and resistance, to spot price extremes, and for specific trading signals.


Generally, moving averages are used in four basic ways - to discover the support and resistance levels, to spot price extremes, to determine trend, and for specific trading signals.


Determining Trend

The most common usage of the moving averages is a measure of trend where the current price is compared with the moving average that represents the trader’s time frame.

Do Adaptive Moving Averages Lead To Better Results?

For example, 200-bar moving average is used widely. If the price is above the moving average, the trend is considered bullish.

Conversely, if the market is below the 200-bar moving average, the trend is considered downward.

So what’s the point?

The moving average itself can become a proxy for the trend line and used to decide when a trend is potentially changing direction, the same way a trend line can.


Determining Support and Resistance

The moving average often duplicates the trend line by acting as resistance or support.

Thus, it can be a convenient trailing stop mechanism to determine when a position should be closed or reduced.


Determining Price Extremes

The price extremes can be viewed more easily with help of moving average.

The reversion above is occasionally profitable when the current price has significantly deviated from the moving average. This deviation can become an opportunity to trade opposite to the trend direction.

While taking position against the trend is risky and demands close stops, the reversion itself allows to position with the trend when it happens.

In addition prices often signal that the trend is changing direction by continuing substantially away from the trend.

Therefore, by monitoring the distance between the price and the moving average, a position can be opened anticipating that the price will return to that mean.


The bottom line is this.

When these occurrences are in sync with the longer term trend, they combine reduced risk with excellent profit potential.



Giving Specific Signals

Moving averages are also used to provide specific signals.

These can develop when prices cross a moving average, or when a faster moving average crosses a slower moving average. Additional signals may exist when a third, even faster, moving average crosses two slower ones.

Using the crossover of two moving averages as a signal has been successful when price is trending, but with significant number of false signals in sideways markets.


The most popular technique in professional trading systems

Of the four approaches, the most practical application of moving averages is trend determination. The trend is where the profits are found, and the moving average can assist in discovering the direction of the trend.

Moving average signals are profitable only during a trending market, with sideways market being costly in almost always, and especially so moving average crossovers are used to generate signals.


Adaptive trend indicators

To remain consistent and effective the analysis must adapt to specific market conditions.

Therefore, some standards have to be adopted to classify the market into different conditions. Volatility represents a logical choice of such measures.


The adaptive indicator approach postulates that in order to establish a new trend, the counter movement must overcome the volatility displayed along the original trend.

The adaptive approach posits that in order to form a new trend, the counter movement must overcome the original trend’s volatility.


So let’s take a closer look.

Based on the observed variations in volatility, a market drop associated with a larger volatility on the way to the bottom requires a larger counter movement towards the upside to confirm.

The larger countermovement is necessary to avoid mistaking countertrend for the beginning of a new trend.

The recognition that different approaches are required for different market conditions has spurred innovative approaches based on the exponential moving average as the foundation, which build moving averages that automatically become slower with rising volatility.

On a broader scope, an adaptive technique is one that handles more changing market conditions without manual intervention.

Such as automatically adopting the calculation interval that determines the sensitivity of an indicator.

During sideways markets, it may be better to employ a slow trend or momentum indicator, and during violent volatility the same technique might work better if it responds quickly.

This change from slow to fast, associated with changing market conditions is resolved by using adaptive techniques that vary the length of the trend.

These methods are based on a common assumption that it is preferable to use a slower trend, one that is less responsive to price change, when the market is either exhibits low volatility, noisy or in a sideways pattern.

While it may seem like a plausible premise, identifying sideways conditions can be tough.

In many ways the adaptive techniques may boost the essential performance of standard trend calculations, but they are unable to resolve all of the issues.


Perry Kaufman’s Market Efficiency Model

Perry Kaufman wrote a book, “Smarter Trading”, where he offers some interesting adaptations of the trend-following approaches.

His idea is that trend following is a safe and conservative approach to the markets.

But one must be able to separate the trend from the random noise of the market at any given time. His argument is that while longer trends are the most dependable, they respond very slow to changing market circumstances.

For example, slower moving averages barely reflect a forceful, short term price move.

Furthermore, by the time they signal action, the price move usually has completed.

Therefore, Kaufman makes an argument in favor of an adaptive method for trend following.

A methodology is needed, one that speeds up entry when the markets are moving and does nothing when the markets are going sideways.

Kaufman’s solution to this is to develop an adaptive moving average.


Adaptive Moving Average


Kaufman’s adaptive moving average is based on the idea that a noisy market requires a longer trend than one with less noise.

Noise is the erratic up-and-down price movement that can be seen within a trend or during a sideways period.

And it turns out that market noise is not just volatility.


Let us explain

The basic assumption is that during a relatively noisy price jump, the trend line must lag further behind to avoid being violated by the erratic price movements, which would cause an unwanted signal.

When prices advance consistently in one direction with low noise, any trend speed may be used because there are no false triggers.

Adaptive moving average

During the periods of low noise, the trend line can be moved closer to the underlying price direction.

When noise is more profound, the trend has to lag farther behind to minimize violation of the trend line.

It all comes down to this.


The adaptive moving average is designed to use the fastest trend possible using the shortest calculation interval for the existing market conditions.

It changes the speed of the trend by using an exponential smoothing, varying the smoothing constant each period.

The smoothing constant was used because it allows for a full range of trends, represented as percentages, compared to the simple moving average, which is limited in its selection.


The adaptive moving average works like a slow moving average in that it reduces the influence of outliers, without sacrificing the sensitivity.

To successfully identify the beginning of a trend we want a moving average to be as short as possible.

At the same time it has to be as long as necessary to avoid whipsaw losses.

Put it another way.

Sometimes we need the moving average to contain a small number of bars, and other times we want a higher number.

We also don’t want to be forced to choose the value ourselves, because we have no way of knowing in advance which is the correct one.

What we need is automatic adjustment when variability changes, to adapt the moving average to the new market condition.

While we are not able to change the number of bars according to conditions, we can achieve the same objective by making the moving average adaptive.

An adaptive moving average uses a continuously variable exponential moving average smoothing constant that increases as the price trend slope approaches the 90 degrees angle and decreases as the price trend slope nears zero.

Here’s another way to think about it.

The exponential moving average period length grows shorter and therefore more responsive, when the trend is steep and accelerating.

During the price trend that flattens, the exponential moving average period length grows longer and less responsive.


The efficiency ratio that helps to avoid the outliers

Kaufman introduces the concept called an efficiency ratio, which is an indication of how straight the line prices follow as they move from one point to the next.

Think about it this way.

The efficiency rating of a straight line is 1.

Inefficient prices that follow the meandering path of a drunken sailor get an efficiency rating of zero, with most of the cases falling somewhere in between.

The rating is then converted to a smoothing constant that varies depending on the numerical values.


As it nears the value of 1, the moving average tracks the prices more closely.

When the smoothing constant is zero, the moving average value doesn’t change and is carried over unchanged from previous bar — in other words, the outlier spikes are simply ignored.



Trading the AMA

The adaptive moving average should be traded using the trend line to determine direction.

So let’s take a closer look.


A long position is initiated when the trend line turns up, and reversed to a short position when the trend line moves down.

Few other rules:

Keep the calculation period less than 14, somewhere between 7 to 11 bars is preferable.

There are few occasions when prices move in the same direction 14 bars in a row, therefore, any interval more than 14 will not change the numbers, only make them all smaller.

Leave the upper bound of 30 bars fixed.

To make the trend line less responsive, make the lower bound greater than 2.


Use a small limit value to filter the changes in trend direction.

Since the trend line is the result of netting all the price movements, it depicts the best measure of the trend.

Therefore, the sell and buy entries are based on the trend line direction, as opposed to the price violating the trend line.

With the use of exponential smoothing, the trend line always turns up and down simultaneously with the price violating the line.

The benefit of using the trend line for the adaptive moving average signal is that the formula limits the amount of change in the trend line, making it easy to increase reliability by using a small entry filter.


A basic trend-following system should not be confused with a complete trading strategy.

There are no subtleties in the selection of entry and exit timing, nor are there special techniques for entering multiple positions, taking profits, or using other risk controls.


Those features must be analyzed separately to maintain their integrity in a lateral solution.


Try It Yourself

After all the sides of the indicator were revealed, it is right the time for you to try either it will become your tool #1 for trading.

In order to try the indicator performance alone or in the combination with other ones, you can use Forex Tester with the historical data that comes along with the program.

Simply download Forex Tester for free. In addition, you will receive 19 years of free historical data (easily downloadable straight from the software).

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