Adaptive Fisherized ROC

Introduction
Hello community, here I applied the Inverse Fisher Transform, Ehlers dominant cycle determination and smoothing methods on a simple Rate of Change (ROC) indicator
You have a lot of options to adjust the indicator.

Usage
The rate of change is most often used to measure the change in a security's price over time.
That's why it is a momentum indicator.

When it is positive, prices are accelerating upward; when negative, downward.

It is useable on every timeframe and could be a potential filter for you your trading system.

IMO it could help you to confirm entries or find exits (e.g. you have a long open, roc goes negative, you exit).
If you use a trend-following strategy, you could maybe look out for red zones in an in uptrend or green zones in a downtrend to confirm your entry on a pullback.

Signals
ROC above 0 => confirms bullish trend
ROC below 0 => confirms bearish trend
ROC hovers near 0 => price is consolidating

Enjoy! 🚀

הערות שחרור:

Updated default values
Fixed hilber transform calculation bug

הערות שחרור:

Updated default values
Added min length check
Fixed Inphase-Quadrature Transform calculation
Fixed adaptive mode selection bug

הערות שחרור:

Decreased lag of Hann Window smoothing by applying it to the price instead of the indicator itself.
Increased linewidth and decreased transparency on background colors

הערות שחרור:

Updated plot

הערות שחרור:

Just to update the chart

הערות שחרור:

• Added divergence detection
  
• Removed wrong Kalman filter
  
• Reworked smoothing system -> Now you can apply the smoothing methods on the source price (which is close) or on the indicator itself depending on what your goal is with the smoothing.
  

הערות שחרור:

Applied the IFT after the smoothing to stick to the scale of -1 to 1

הערות שחרור:

Removed 34 as max length of Homodyne Discriminator

הערות שחרור:

Fixed that T3 Normal wasn't selectale.