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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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Elliott Wave Trend was designed for the scientific wave counting. This tool focuses to get rid of the vagueness of the classic Elliott Wave Counting using the guideline from the template and pattern approach. In doing so, firstly Elliott Wave Trend offers the template for your wave counting. Secondly, it offers Wave Structural Score to assist to identify accurate wave formation. It offers both impulse wave Structural Score and corrective wave Structure Score. Structural Score is the rating to sh
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