Adaptive moving average crossover

Adaptive moving average crossover AMA

Adaptive Moving Average technical analysis indicator changes its sensitivity to price Simple Moving Average (SMA) · Moving Average Crossovers In the advanced representatives of the family comes the moving averages - as in the corresponding contributions read - a weighting factor is used, which should reduce the delay component of the concept. Fundamental problem with the indicator forms hitherto considered is the fact that the weighting factor regardless of the current market situation is steady, in phases so hectic market reacts the same way as in quiet.

This suggests an adaptive moving average crossover system that is virtually free of whipsaw trades. This disadvantage is trying to come forward with so-called adaptive indicators, which include going back to the P. Kaufman Adaptive Moving Average (AMA). Basic indicators of concern to all adaptive approach is an adaptation of calculating length or weight of the indicator to current market conditions with the aim to create responsive systems of indicators, their values ​​change understand the volatility of the underlying security.

Known examples of practice from the family of adaptive indicators are the Dynamic Momentum Index Dynamic and Variable Index Average. Based on the measured intensity fluctuation is a constant adjustment of length or weight adjustment calculation. Both mentioned approaches have in common the goal that the indicator responds more quickly during periods of hectic market, price movements rather then reproduces, however, in flat markets, a slower reaction and thus a slower succession over the base value profile desired.


The Adaptive Moving Average by Kaufman has exactly the opposite goal as described in the previous section Variable Index Dynamic Average. Common to both approaches, the use of an exponential average as a base, but Kaufman would create a trend filter in the form that the indicator during quiet market periods behaves slower than in strong trending markets. This is achieved by changing the weighting factor in calculating the exponential average.

Adaptive Moving Average - Buy Signal. Moving Average crossover. The mathematical derivation of the AMA is not easy and should not be deepened further at this point. It was in this context merely noted that the so-called efficiency ratio is responsible for scaling the weighting factor. This is about the relationship between net price movement (absolute price change in the viewing range) and the gross price movement (sum of all price changes in the viewing range). The efficiency ratio is a good measure of whether certain price movements are at a uniform level (ratio close to 1) or recorded a very volatile history was (ratio tends to 0).

The weighting factor, which told how to calculate concrete left out, is responsible for the sensitivity of the indicator, high efficiency trend is associated with a high weighting factor, low with correspondingly low. In summary, it can be said that the Adaptive Moving Average is in the family of the moving averages the most appropriate trend filter although this concept due to the delay component is included.

A particularly creative continuation represents the Fractal Adaptive Moving Average (FRAMA) dar. This indicator goes back to John Ehlers, who has the mathematical methods of signal filtering and analysis cycle prescribed. Based on an exponential moving average is calculated Ehlers Fraktakle the dimension N, with the aim of adjusting the weighting factor to the prevailing volatility and the price trend. The result is an indicator that reacts during the trend toward more stable phases very quickly to changes in volatile sideways markets, but behaves very sluggish.