Adaptive Fisherized ROC

2022年11月8日

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! 🚀

2022年11月8日

版本注释

Updated default values
Fixed hilber transform calculation bug

2022年11月9日

版本注释

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

2022年11月10日

版本注释

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

2022年11月25日

版本注释

Updated plot

2022年11月25日

版本注释

Just to update the chart

2023年9月18日

版本注释

• Added divergence detection
  
• Removed wrong Kalman filter
  
• Reworked smoothing system -> Now you can apply the smoothing methods on the source price (which is close[1]) or on the indicator itself depending on what your goal is with the smoothing.
  

2023年9月18日

版本注释

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

2月23日

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Removed 34 as max length of Homodyne Discriminator

3月4日

版本注释

Fixed that T3 Normal wasn't selectale.