The Battle for Trend or Moving Average vs. UniversalMA

From the point of view of calculations, UniversalMA differs from the usual moving average by additional averaging methods and an expanded list of data on which this averaging is carried out.

We will not go deep into issues that do not directly affect our trading profits. Therefore, we will briefly consider the main averaging methods added to UniversalMA.:

• Adaptive Smoothing JMA (JJMA) is a method that automatically adjusts smoothing parameters depending on market volatility. 
• JRSX Ultralinear Smoothing (JurX) is an advanced smoothing method that provides more accurate trend tracking while maintaining smoothness of the line.
• Parabolic approximation (ParMA) is a method of smoothing data using a parabolic curve, which makes it possible to better track the acceleration of price movement.
• The Tilson Algorithm (T3) is a method based on a combination of several exponential moving averages with different periods to improve the filtering of market noise.
• Variable Index Dynamic Average (VIDYA) is a method that uses the Average true range (ATR) to adapt to market volatility, making it particularly effective in a volatile market.
• Adaptive Moving Average (AMA) is a method that automatically adjusts the averaging period depending on market volatility, providing an optimal balance between sensitivity and smoothing.
• FATL (Fast Adaptive Trend Line) is a fast adaptive trend line. Each bar has its own unique weight assigned.

The expert is ready for the tests. It, as well as a set of settings, can be downloaded in the Downloads section on this page. When we tested the classic Moving Average, we found more profitable trend interpretation methods. It would be fair and correct to add the application of these methods to these tests too.

The number of parameters that need to be selected has become quite large. Therefore, we will have to resort to genetic optimization. Fortunately, the strategy tester built into MetaTrader 5 has such capabilities. It makes sense to repeat the optimization 3 times, this will be enough for the algorithm to determine the best parameters. Gold (XAUUSD), timeframe H1 and 13 years of real history - let's go.

Learn more about the RBTI calculation method

 

Let's select the parameters on the site for optimization and find IMP_opti.

The best parameters are: Period = 135, Mode = AMA, Price = Low, Phase = 50, Step = 6001, Shift = 0, Open BUY Strategy = ghost cross MA, Open SELL Strategy = ghost cross MA.

IMP_opti = 3.77. The balance line is fairly flat and the recovery factor is almost 5. If we had also tightened the profit factor (it is only 1.5), then we would have an almost ready-made trading system. I would like to draw your attention to the fact that this result was obtained by constantly being in the market. There will be no delay and we will move on to the most important and interesting thing – work on a site that was not involved in the selection of parameters.

 

And here, to be honest, a surprise awaited us. The parameters we found coped with an unfamiliar area with almost no loss of efficiency. The PC indicator (Prediction Coefficient) is close to 100%.

IMP_opti = 3,77

IMP_all = 3,37

PC =89%

RBTI = 3,5

Evaluate the difference in passing an unfamiliar flat section compared to the usual Moving Average. You can clearly see this in the attached picture.

 

 

When passing through a flat area, the usual Moving Average (MA) shows significant disadvantages compared to other types of moving averages. The main problem with MA in flat is its excessive smoothness and lag. The indicator tries to follow a trend that does not actually exist, which leads to a lot of false signals and a late reaction to real price changes. 

At the same time, the adaptive moving average (AMA) shows significantly better results in the flat due to its higher sensitivity to current price changes. It reacts faster to price fluctuations and provides more relevant trading signals.

1) In the trend determination competition, the Moving Average indicator was destroyed by its descendants UniversalMA. But to be honest, he had no chance of winning, at best a draw. Since all the functions from the classic Moving Average are fully present in UniversalMA.

 2) The best averaging method for trend identification tasks is AMA

3) The Step parameter proved to be in demand and fully justified its presence in the indicator.

4) The additional trend interpretations we proposed in the article about the classic version of the Moving Average turned out to be the best here.

5) The UniversalMA indicator has been assigned an RBTI rating of 3.57. The result will be recorded in the leaderboard.