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! 🚀

Note di rilascio:

Updated default values
Fixed hilber transform calculation bug

Note di rilascio:

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

Note di rilascio:

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

Note di rilascio:

Updated plot

Note di rilascio:

Just to update the chart

Note di rilascio:

• 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.
  

Note di rilascio:

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

Note di rilascio:

Removed 34 as max length of Homodyne Discriminator

Note di rilascio:

Fixed that T3 Normal wasn't selectale.