Leading T3Hello Fellas,
Here, I applied a special technique of John F. Ehlers to make lagging indicators leading. The T3 itself is usually not realling the classic lagging indicator, so it is not really needed, but I still publish this indicator to demonstrate this technique of Ehlers applied on a simple indicator.
The indicator does not repaint.
In the following picture you can see a comparison of normal T3 (purple) compared to a 2-bar "leading" T3 (gradient):
The range of the gradient is:
Bottom Value: the lowest slope of the last 100 bars -> green
Top Value: the highest slope of the last 100 bars -> purple
Ehlers Special Technique
John Ehlers did develop methods to make lagging indicators leading or predictive. One of these methods is the Predictive Moving Average, which he introduced in his book “Rocket Science for Traders”. The concept is to take a difference of a lagging line from the original function to produce a leading function.
The idea is to extend this concept to moving averages. If you take a 7-bar Weighted Moving Average (WMA) of prices, that average lags the prices by 2 bars. If you take a 7-bar WMA of the first average, this second average is delayed another 2 bars. If you take the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag.
T3
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
Thanks for checking this out and give a boost, if you enjoyed the content.
Best regards,
simwai
---
Credits to @loxx
Norepaint
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
---
Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
Adaptive Fisherized ROCIntroduction
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! 🚀
Multi Supertrend with no-repaint and HTF optionThis indicator has 2 Supertrends to filter the trend.
The Default one uses the same timeframe as chart.
The additional Supertrend is non-repaint type and can run on higher timeframes.
It has an auto-higher timeframe selection option, thanks to LonesomeTheBlue, the original author.
It is accurate on current timeframe also.
Peak/Valley EstimationEarly Signal
Estimating the Peaks and Valleys or extrema of the price is one of the best way to catch up early movements of a trend. Of course there is no perfect way to do so, if we want a perfect estimation of peaks and valleys then we must use a non causal indicator ( repainting ), if we want a causal indicator ( non repainting ) then we will need to tradeoff accuracy for allowing our indicator to be causal, its always a matter of tradeoff at the end when trying to have a desired effect (smoothness/lag for filters) .Our indicator is causal, it wont repaint but the accuracy will depend on various parameters.
In order to detect peaks and valleys in a certain period we must detrend the price, this mean subtracting it by its moving average. We take the absolute value of this result and we filter it with a local linear regression ( LSMA ) in order to eliminate noise, then we make the assumption that the highest of our result is or a peak or a valley of the price, so we divide our detrended calculation by its highest and we get a scaled result. Lets call this final result the peak index .
Parameters
There are 3 parameters in this indicator, a length parameter who control the period of the highest mentioned above, a smooth parameter who smooth our detrended price, and finally a mod parameter who select the trigger method for estimating a peak/valley.
Here are how mods work :
mod = 1 : when the peak index is equal to 1 and the previous value is not equal to 1 then we have a peak/valley. Its the fastest of the 3 mods but the one with less accuracy.
mod = 2 : when the peak index crossunder 0.8 then we have a peak/valley. This method is more robust but slower than the previous one.
mod = 3 : when the peak index is not equal to 1 and the previous peak index is equal to 1 then we have a peak/valley. Its an average of the precedents mod in term of speed and accuracy.
Lower length values tend to estimate the peak/valley of short periods of time but can also lead to the reverse desired effect ( breakouts signals ). Smoothing is important since it reduce the number of noise in our calculation and therefore help to get better results, its a parameter that should be high, sometimes higher than length if this one is low.
Estimation of medium terms peaks/valleys with length and smooth parameter both period 100 and mod = 3
Estimation peaks in palladium way to early, an example of bad accuracy. Such behaviour can be fixed with a change in the parameters.
Complementarity With Classics Indicators
As i said before its always a matter of tradeoff, here we get faster signals but we loose in accuracy, at the contrary classics indicators often have slower signals but with more accuracy. Mixing both of them can provide additional robustness in a strategy, lets take back our palladium case, using mod 3 could have been better, but its still not optimal, so lets use a classic indicator such as a moving average of period 200, our conditions are :
Long when our peak/valley estimator estimated a valley and the price crossover our moving average.
Short when our peak/valley estimator estimated a peak and the price crossunder our moving average.
here is an exemple of such signal :
We balanced our tradeoff in a way to fix both methods problems, of course its still not a perfect fix but it provide more robustness.
Other Uses
The indicator can also be used only as an order closing indicator, its safer than taking a position based on its estimation. The indicator can also give a use to the peak index used in the calculation as a trend strength indicator.
Values below 0.5 indicate a ranging market while values over 0.5 indicate a trending market.Since its a scaled measure you can use it a smoothing constant in a adaptive filter.
Conclusions
I showed how to estimate peaks and valleys and how to use such information in order to make better decision when using classical indicators, of course at the end nothing is perfect and considering the non stationarity of the markets the parameters efficiency could change drastically.
For any questions/demands feel free to pm me, i would be happy to help you
