MT5-TAMA Trend Adaptive Moving Averages

Short description : TAMA is, at its core, a novel method for correcting moving averages (MAs) during trends. | This usually also results in better smoothing and a significantly lower time delay (lag) of the MAs. | The indicator can additionally be used for price forecasts and band formation, also combined. | TAMA received an award from VTAD in 2022. |

Honored with the VTAD Award 2022 (VTAD = Association of Technical Analysts Germany, German branch of IFTA).

Core Features :

• Classic MAs are corrected in such a way that they then reflect the value without distortion during trends . (screenshots 1 and 2)
  In principle, this works for (almost) any MA; implemented in the indicator are the four main ones: SMA, WMA, EMA and HMA.
  
  
• The trend correction leads to better smoothing properties and usually significantly lower delays (lags) . (screenshots 2 and 3)


• Downstream indicators that are based on classic MAs receive trend-distortion-free input, which usually improves them . (screen shot 4)
  For example, in the fundamental work*, Bollinger bands, which were wrapped around a classic SMA as usual, contained approx. 82% of all next-day prices (instead of the often postulated 95%),
  while the corresponding value after the correction by TAMA was over 90% - with the same channel size !
  
  

Advanced features:

• Since TAMAs ​​are based on regressions, they can be used to forecast future price values . (screen shot 5)
  
  
• Because of the aggregation of multiple regressions, TAMAs ​​ can also be used as a quantile-based bands indicator . (screen shot 6)
  The bands generated this way are usually much narrower than Bollinger bands (in the fundamental work* factor 1:2.5), but still contain almost as many values ​​(in the fundamental work* approx. 75%)!
  
  
• By combining these approaches, possible trading signals can also be derived directly from TAMAs. (screen shot 7)
  
  

Input parameters:

Name	Symbol	Domain	Default value	Description
Period	n	Integer > 0	20	Period of the base MA
Base MA	w	Predefined value (enum)	HMA	Type of the base MA (SMA, WMA, EMA oder HMA)
Trend Adaption	TA	Boolean	true	Switch for the "TA" in TAMA; if 0, result like base MA
Prognosis	P	Integer >= 0	0	Whether or by how many days into the future a forecast should be made
Aggregation function	AF	-1 for mean or real number between 0 and 1 (incl.) for corresponding Q quantile (0=min, 1=max)	-1	Type of aggregation for the various regressions.

Caution: P>0 has an effect for each trading day, not just the latest!

Background:

The calculation of TAMAs ​​is based on multiple, dynamic linear regressions formed within an n-period window based on price, weighted and finally aggregated.

* Fundamental work: Patrick Winter (2022): Trend-Adaptive Moving Averages (TAMA). Contribution to the VTAD Award 2022; available online at the VTAD.

The author of the TAMA procedure, Dr. Patrick Winter, holds a doctorate in business informatics and has more than ten years of scientifically sound experience in the development of indicators. He is a multiple VTAD award winner and has been widely published in academic journals as well as the IFTA Journal.

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