OPEN-SOURCE SCRIPT
Better DEMA

The Better DEMA is a new tool designed to recreate the classical moving average DEMA, into a smoother, more reliable tool. Combining many methodologies, this script offers users a unique insight into market behavior.
How does it work?
First, to get a smoother signal, we need to calculate the Gaussian filter. A Gaussian filter is a smoothing filter that reduces noise and detail by averaging data with weights following a Gaussian (bell-shaped) curve.
Now that we have the source, we will calculate the following:
n2 = n/2 (half of the user defined length)
a = 2/(1+n)
ns
Now that we have that out of the way, it is time to get into the core.
Now we calculate 2 EMAs:
slow EMA => EMA over n
fast EMA => EMA over n2 period
Rather then now doing this:
DEMA = fast EMA * 2 - slow EMA
I found this to be better:
DEMA = slow EMA * (1-a) + fast EMA * a
As a last touch I took a little something from the HMA, and used a EMA with period of √n to smooth the entire the thing.
The Trend condition at base is the following (but feel free to FAFO with it):
Long = dema > dema yesterday and dema < src
Short = dema < dema yesterday and dema > src
Methodology
While the DEMA is an amazing tool used in many great indicators, it can be far too noisy.
This made me test out many filters, out of which the Gaussian performed best.
Then I tried out the non subtractive approach and that worked too, as it made it smoother.
Compacting on all I learned and smoothing it bit by bit, I think I can say this is worth looking into :).
Use cases:
Following Trends => classic, effective :)
Smoothing sources for other indicators => if done well enough, could be useful :)
Easy trend visualization => Added extra options for that.
Strategy development => Yes
Another good thing is it does not a high lookback period, so it should be better and less overfit.
That is all for today Gs,
Have fun and enjoy!
How does it work?
First, to get a smoother signal, we need to calculate the Gaussian filter. A Gaussian filter is a smoothing filter that reduces noise and detail by averaging data with weights following a Gaussian (bell-shaped) curve.
Now that we have the source, we will calculate the following:
n2 = n/2 (half of the user defined length)
a = 2/(1+n)
ns
Now that we have that out of the way, it is time to get into the core.
Now we calculate 2 EMAs:
slow EMA => EMA over n
fast EMA => EMA over n2 period
Rather then now doing this:
DEMA = fast EMA * 2 - slow EMA
I found this to be better:
DEMA = slow EMA * (1-a) + fast EMA * a
As a last touch I took a little something from the HMA, and used a EMA with period of √n to smooth the entire the thing.
The Trend condition at base is the following (but feel free to FAFO with it):
Long = dema > dema yesterday and dema < src
Short = dema < dema yesterday and dema > src
Methodology
While the DEMA is an amazing tool used in many great indicators, it can be far too noisy.
This made me test out many filters, out of which the Gaussian performed best.
Then I tried out the non subtractive approach and that worked too, as it made it smoother.
Compacting on all I learned and smoothing it bit by bit, I think I can say this is worth looking into :).
Use cases:
Following Trends => classic, effective :)
Smoothing sources for other indicators => if done well enough, could be useful :)
Easy trend visualization => Added extra options for that.
Strategy development => Yes
Another good thing is it does not a high lookback period, so it should be better and less overfit.
That is all for today Gs,
Have fun and enjoy!
오픈 소스 스크립트
트레이딩뷰의 진정한 정신에 따라, 이 스크립트의 작성자는 이를 오픈소스로 공개하여 트레이더들이 기능을 검토하고 검증할 수 있도록 했습니다. 작성자에게 찬사를 보냅니다! 이 코드는 무료로 사용할 수 있지만, 코드를 재게시하는 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니다. 자세한 내용은 이용 약관을 참조하세요.
오픈 소스 스크립트
트레이딩뷰의 진정한 정신에 따라, 이 스크립트의 작성자는 이를 오픈소스로 공개하여 트레이더들이 기능을 검토하고 검증할 수 있도록 했습니다. 작성자에게 찬사를 보냅니다! 이 코드는 무료로 사용할 수 있지만, 코드를 재게시하는 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니다. 자세한 내용은 이용 약관을 참조하세요.