Fisherized CCI

Introduction

This here is a non-repainting indicator where I use inverse Fisher transformation and smoothing on the well-known CCI (Commdity Channel Index) momentum indicator.

"The Inverse Fisher Transform" describes the calculation and use of the inverse Fisher transform by Dr . Ehlers in 2004. The transform is applied to any indicator with a known probability distribution function. It enables to transform an indicator signal into the range between +1 and -1. This can help to eliminate the noise of an indicator.
The CCI is an momentum indicator which describes the distance of the price to the average price.

For smoothing I used the Hann Window and NET (Noise Elimination Technique) methods.

Additional Features

Divergence Analysis
Trend-adaptive Histogram
Timeframe selection

Usage

It is usually used to spot potential trend reverals or mean-reversion (against the trend) trades on lower timeframes. IMO it can be even used to spot trend-following trades. It always depends on which settings you have, which timeframe do you use and which indicators you combine with it.
The suggested timeframe for this indicator is 15 min (with the length setting on 50).

The histogram with adaptive mode enabled could be used as filter applied on the buy and sell signals.
The divergence analysis can help to spot additional entries/exits or confirm the buy and sell signals.

Always try to find the best settings! This indicators has a lot of customization options you should take advantage of.

Signals

The indicator uses the following logic to generate the buy and sell signals:

Normal
Buy -> When CCI and MA go above the top band (usually +100) and cross
Sell -> When CCI and MA go below the the bottom band (usually -100) and cross

Fisherized
Buy -> When CCI and MA go above the the zero line and cross
Sell -> When CCI and MA go below the the zero line and cross

Have fun with the indicator! I am open for feedback and questions. :)

發行說明

Updated timeframe-realted bug
Removed MA price source options
Added more signal modes and updated the old ones (if you like the old signal methods more tell me, now fisherized version uses not anymore the zero line for overbought/oversold determination)
Added trend detection (used for the new auto signal mode!) and plot
Added Kalman filter
Added more MAs to select
Fixed IFT CCI calculation bug

發行說明

Fixed wrong Hilbert Transform calculation

發行說明

Renamed adaptive mode "Cycle Period" to "Median"
Included "Adaptive" in title
Added new adaptive mode "Homodyne Discriminator"

發行說明

Fixed Inphase-Quadrature Transform calculation

發行說明

Added long/short exit system
Updated default values
Added tooltip to trend sensitivity
Added option to disable divergence line plot
Fixed smoothing by applying it on the raw price (before the indicator calculation)

發行說明

Restructured settings input
Optimized divergence colors (light purple -> bullish, dark purple -> bearish)
Optimized scaling and linewidth
Added alerts for long and short exits
Fixed missing exits bugs

發行說明

Just to update the wrong chart picture

發行說明

Fixed a plot bug when NET is enabled
Fixed a bug in the auto signal mode when NET is enabled
Made bands transparent
Normalized adaptive histogram to fit to the indicator

發行說明

Added additional exit signals for auto signal mode
Now it exits long and short positions on switch to sidetrend too

發行說明

Updated trend follower signal mode

發行說明

Sorted MA types
Fixed wrong alert condition

發行說明

Removed COVWMA, because of very bad results
Updated default values
Fixed KAMA trend sensitivy determination
Added quicker exits when fisherization (IFT) is turned on

發行說明

Removed the MA types COVWMA and VIDYA
Fixed the bug: wrong volume used in VWMA calculation when another timeframe is selected
Renamed timeframe titles
Added new signal mode "Fast Overbought/Oversold" which instantly triggers when Overbought/Oversold area is reached

發行說明

Fixed bug where tr didn't use the custom timeframe

發行說明

Added something to the bug fix before

發行說明

Removed wrong Kalman filter

發行說明

Removed 34 as max length of Homodyne Discriminator