Smooth Cloud [BigBeluga]This trend-following indicator, called Smooth Cloud, is built on top of a SuperSmoother Filter of John Ehlers with small modification.
It consists of three smoothed lines—Fast, Middle, and Slow—that together form a cloud. These lines are based on different periods, helping traders analyze market changes over different timeframes (fast, mid, and slow). The indicator offers a color-coded visual cloud to depict trend direction, along with a detailed dashboard that shows the positioning of the lines, whether they are rising or falling, and their price levels.
🔵 IDEA
The Smooth Cloud indicator is designed to help traders quickly assess the market trend by using three smoothed lines with varying periods. The lines represent fast, mid, and slow market changes, and their relative positioning provides a clear view of trend shifts. The dashboard gives a more granular view by showing if the lines are rising or falling individually, without comparing them to each other, providing insights into potential trend changes before they are fully formed. The color-coded cloud further enhances the visual experience by allowing traders to see trend direction at a glance, making it easier to spot major and minor shifts in the market.
🔵 KEY FEATURES & USAGE
◉ Three Smoothed Lines (Fast, Mid, Slow):
The indicator consists of three smoothed lines, each representing a different periods. The Fast line reacts more quickly to price changes, while the Slow line reacts more slowly, allowing traders to capture both short-term and long-term trend information. The lines are based on different lengths, and their positioning relative to each other helps determine market direction.
◉ Color-Coded Cloud:
The cloud formed between the lines is color-coded to indicate trend direction. When the Fast line is above the Slow line, it signals an upward trend, and the cloud is green. When the Fast line is below the Slow line, the cloud turns red, indicating a downward trend. This color coding makes it easy to spot the overall trend direction visually without having to analyze the lines in detail.
◉ Dashboard for Line Positioning and Trend Direction:
A dashboard in the top right corner of the chart shows the positioning of the Fast, Middle, and Slow lines relative to each other. It displays arrows for each line to indicate whether the line is above or below the other lines. For exae determines its trend direction based on its position to mid line — if it's above, an upward arrow is displayed, and if it's below mid line, a downward arrow is shown.mple, if the Fast line is above the Slow line, the dashboard shows an upward arrow for the Fast line. The Slow lin
Up trend:
Up trend shift:
Down trend shift:
Down Trend:
◉ Rising and Falling Detection:
The dashboard also tracks whether the lines are rising or falling based solely on their own values. If a line rises or falls consistently over three bars, the dashboard shows an upward or downward arrow under the "Rising or Falling" section. This feature provides additional insight into the market's momentum, allowing traders to spot potential trend reversals more quickly.
◉ Price Levels for Fast, Middle, and Slow Lines:
The dashboard includes the price levels for the Fast, Middle, and Slow lines, displayed at the bottom. These levels give traders a quick reference for where the lines are currently positioned relative to the price, adding further context to the trend information displayed.
◉ Fast Signals:
The fast signals are diplayed when fast line crosses slow line. Gree arrows up shows fast line crossed over slow and when arrow down fast line crossed under slow one.
🔵 CUSTOMIZATION
Length Input: You can adjust the length parameter, which affects the smoothing period for the lines. A shorter length makes the lines react more quickly to price changes, while a longer length provides a smoother, more gradual response.
Source Input: The indicator uses the hl2 source (the average of the high and low prices), but you can change this to another source to better suit your trading strategy.
Signals Type: Select between "Fast" and "Slow". Fast signals - is interaction of fast and slow lines. Slow signals is interaction of mid and slow lines
Related script:
Ehlersfilter
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
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Credits to @loxx
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
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Credits to:
@loxx
@Bjorgum
@Greeny
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
Ehlers Combo Strategy🚀 Presenting the Enhanced Ehlers Combo Strategy 🚀
Hello Traders! 👋 I'm thrilled to share the latest version of the Ehlers Combo Strategy v2.0. This powerful algorithm combines Ehlers Elegant Oscillator, Decycler, Instantaneous Trendline, Spearman Rank, and introduces the Signal to Noise Ratio for even more precise trading signals.
📊 Strategy Highlights:
Ehlers Elegant Oscillator: Captures market momentum and turning points.
Ehlers Decycler: Filters out market noise for clearer trend signals.
Instantaneous Trendline: Offers a dynamic view of the market trend.
Spearman Rank: Analyzes market rank correlations for enhanced insights.
Signal to Noise Ratio (SNR): Filters out noise for more accurate signals.
💡 Key Features & Customizations:
Adaptive Length: Enable adaptive length based on the market's current conditions.
SNR Threshold: Set your desired SNR threshold for filtering signals.
Exit Length: Define the length for exit signals.
📈 Trading Signals:
Long Entry: Elegant Oscillator and Decycler cross above 0, source crosses above Decycler, source is greater than an increasing Instantaneous Trendline, Spearman Rank is positive, and SNR exceeds the threshold.
Long Exit: Source crosses below the Instantaneous Trendline after entering a long position.
Short Entry: Elegant Oscillator and Decycler cross below 0, source crosses below Decycler, source is less than a decreasing Instantaneous Trendline, Spearman Rank is negative, and SNR exceeds the threshold.
Short Exit: Source crosses above the Instantaneous Trendline after entering a short position.
📊 Insights & Enhancements:
Dynamic Length: The strategy adapts its length dynamically based on market conditions.
Improved SNR: Signal to Noise Ratio ensures better filtering of signals.
Enhanced Visualization: The Elegant Oscillator now features improved color coding for a clearer interpretation.
🚨 Disclaimer:
Trading involves risk, and this script should be used judiciously. It's not a guaranteed profit machine, but with careful use, it can be a valuable addition to your toolkit.
Feel free to backtest, tweak, and make it your own! Let's conquer the markets together! 💪📈
🚀✨ Happy Trading! ✨🚀
---
🙌 Credits:
A big shoutout to the original contributors:
@blackcat1402
@cheatcountry
@DasanC
Ehlers DecyclerJohn F. Ehlers introuced Decycler in his book "Cycle Analytics for Traders", chapter 4.
The decycler is designed to remove the influence of shorter cycle fluctuations, resulting in an output that closely resembles a one-pole low-pass filter.
A standout feature of the decycler is its notably minimal lag. The most extended cycle elements experience a delay of less than five bars. When considering a frequency of 0.05 cycles per bar (equivalent to a 20-bar cycle period), the lag approximates 1.5 bars. Components with a higher frequency face even lesser delays. Consequently, any higher-frequency variations that pass the filter's attenuation align closely with the price fluctuations. This makes the decycler an optimal "immediate trend detector," giving a true depiction of the data's trend.
While the SuperSmoother filter can yield a comparably smoothed output, the decycler typically exhibits less lag when the two are juxtaposed. It's worth noting that the decycler operates as a one-pole filter, implying it doesn't have the best filtering capabilities. It's not advisable to use the decycler as a smoothing filter to eliminate aliasing disturbances. Instead, its application should focus on generating an immediate trend representation, especially when choosing a larger cutoff period. The broad cutoff period equips the decycler with the ability to reduce aliasing disturbances, given that it's significantly distant from the Nyquist frequency.
There are already several decycler indicators on Tradingview, but I like to structure the code and highlight the main components as functions rather than hiding them in the code. I hope this is useful for those who are starting to learn Pine Script.
Machine Learning : Dominant Cycle Elastic Volume KNNAbout the Script
Dominant Cycle Elastic Volume KNN ,
is a non-parametric algorithm, which means that, initially it makes no assumptions about the underlying distribution of the time-series price as well as volume.
This approach gives it flexibility so that it can be used on a wide variety of securities at variety of timeframes.(even on lower timeframes such as seconds)
The main purpose of this indicator is to predict the trend of the underlying, by converging price, volume and dominant cycle as dimensions and generate signals of action.
Key terms :
Dominant cycle is a time cycle that has a greater influence on the overall behaviour of a system than other cycles.
The system uses Ehlers method to calculate Dominant Cycle/ Period.
Dominant cycle is used to determine the influencing period for the underlying.
Once the dominant cycle/ period is identified, it is treated as a dynamic length for considering further calculations
Elastic Volume MA is a volume based moving average which is generally used to converge the volume with price, the dominant period is used here as the length parameter
KNN K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm. K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.
So, K-NN is used here to classify the trend of the Dominant Cycle Elastic Volume, and Generate Signals on top of it
How to Use the Indicator ?
The Buy Signal Candle
The Sell Signal Candle
The Buy Setup
The Sell Setup
Stop and Reverse Structure
What Timeframes and Symbols can this indicator be used on ?
The above indicator can be used on any liquid security which has volume information intact with ticker
and it can be used on any timeframe, but the best timeframes are
The indicator can also be used as a trend confirmatory indicators on lower time frames, like 30second
The Script has provision for alerts
Two alerts are there :
Alert 1= "LONG CONDITION : DCEV-ML"
Alert 2= "SHORT CONDITION : DCEV-ML"
How to request for access ?
Simply private message me !
Ehlers Detrending Filter [CC]The Detrending Filter was created by John Ehlers and this is a complementary indicator to one of my previous scripts:
This indicator builds upon his previous work by attempting to detrend the underlying source data that is used to calculate the final result. He was able to create a leading indicator by removing the trend data and by using his previous calculations to turn the source data into a leading indicator.
There are two ways to understand this indicator. First if the indicator is below the midline then it is in a mid to longterm downtrend and if it is above the midline then it is in a mid to longterm uptrend. Also this indicator shows great promise in predicting future trends so because of that aspect, it may give some false signals from time to time.
I have color coded everything to account for both strong signals and normal signals. Strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red.
Let me know if there are any other scripts you would like to see me publish!
Ehlers Reflex Indicator [CC]The Reflex Indicator was created by John Ehlers (Stocks and Commodities Feb 2020) and this is a zero lag indicator that works similar to an overbought/oversold indicator but with the current stock cycle data. I find that this indicator works well as a leading indicator as well as a divergence indicator. Generally speaking, this indicator indicates a medium to long term downtrend when the indicator is below the line and a medium to long term uptrend when the indicator is above the line. Ehlers has created a few complementary indicators that I will release in the next few days but just keep in mind that this indicator focuses on the underlying cycle component while removing as much noise with no lag. I have color coded the lines to show strong signals with the darker colors and normal signals with the lighter colors. Buy when the line turns green and sell when it turns red.
Let me know if there are any other scripts you would like to see me publish!
Ehlers Data Sampling Relative Strength Indicator [CC]The Data Sampling Indicator was created by John Ehlers (Stocks and Commodities Mar 2023) and this is a genius method to reduce noise in the market data but also doesn't introduce any lag while doing so. The way this works is because traditionally, people have always relied on the close price as the default input for many indicators such as the RSI or MACD as examples. Since the open is usually virtually identical to the previous close, it has been ignored by most people but Ehlers discovered that if you do a simple average of open and close for the input on any indicator, you can remove much of the noise without any added lag. I have used the RSI as he did in his example and plotted both to show the difference between the traditional RSI and using Ehlers' process as the new Data Sampling RSI. You can clearly see that this new RSI follows the price fluctuations much closer and is much smoother than the traditional RSI. As usual, I have included different colors to show the strength of the buy or sell signals so darker colors mean it is a very strong signal and lighter colors means it is a normal signal. Buy when the line turns green and sell when it turns red.
Feel free to try out this method to replace the input for any indicator and let me know how this works for you! And of course let me know if you would like me to publish any indicator script.
Adaptive Ehlers Deviation Scaled Moving Average (AEDSMA)AEDSMA INTRODUCTION
This indicator is a functional enhancement to “Ehlers Deviation Scaled Moving Average (EDSMA / DSMA)”. I’ve used Volume Breakout and Volatility for dynamic length adaption and further Slope too for trend evaluation.
EDSMA was originally developed by John F. Ehlers (Stocks & Commodities V. 36:8: The Deviation-Scaled Moving Average).
IDEA PLACEMENT
I’ve traded almost every kind of market with different volatility conditions using Moving Averages. It was too much of a hassle to select and use different MA length depending upon market trend. So, the journey started with adapting Moving Averages with another parameter and that’s how “MZ SAMA ” came into being where Slope was used to adapt Adaptive Moving Average with trend change. The problem was still pretty much the same as SAMA might not be effective on every market condition. Hence, I worked on Volume to adapt Moving Averages accordingly. I cane up with “MZ RVSI ” which I used in “MZ DVAMA ” to adapt dynamic length in Adaptive Moving Average and also used “MZ RVSI " alongside Slope as confirmation of trend changes.
Meanwhile, I started using DVAMA methodology on different types on Moving Averages that allow dynamic length for example Hull Moving Average, Linear Regression Curve, SMA, WMA, TMA and many more. All of my tested Mas showed too much flexibility because of volume based Adaptive length.
I came across a script of “Adaptive Hull Moving Average” which pretty much used the similar methodology as DVAMA but when I looked into its depth, its volume oscillator wasn’t working at all and only volatility based dynamic length was used. It was an interesting idea so, I decided to use Volume and Volatility alongside for better results but was nearly impossible to achieve what I wanted using only Hull Moving Average.
I had been using EDSMA in “MA MTF Cross Strategy” and “MZ SRSI Strategy V1.0” previously. It was the perfect choice when comparing to usage of slope on it. DSMA works perfectly as support and resistance as its Deviation Scaled. So, I tried using it to adapt dynamic length based on Volume and Volatility and I wasn’t disappointed. It worked like a charm when I adapted dynamic length between 50 and 255.
DYNAMIC LENGTH BENEFITS
Dynamic length adaption methodology works in a way of adapting Relatively Lower Length leading toward overfitting if trend is supported by Volume and Volatility . Similarly, adapting Relatively Higher Length leading toward underfitting if trend isn’t supported by Volume and Volatility .
Dynamic length adaption makes Moving Average to work better for both Bull and Bear-runs avoiding almost every fake break-in and breakouts. Hence, adaptive MA becomes more reliable for breakout trading.
MA would be more useful as it would adapt almost every chart based on its Volume and Volatility data.
DYNAMIC COLORS AND TREND CORRELATION
I’ve used dynamic coloring to identify trends with more detail which are as follows:
Lime Color: Strong Uptrend supported by Volume and Volatility or whatever you’ve chosen from both of them.
Fuchsia Color: Weak uptrend only supported by Slope or whatever you’ve selected.
Red Color: Strong Downtrend supported by Volume and Volatility or whatever you’ve chosen from both of them.
Grey Color: Weak Downtrend only supported by Slope or whatever you’ve selected.
Yellow Color: Possible reversal indication by Slope if enabled. Market is either sideways, consolidating or showing choppiness during that period.
SIGNALS
Green Circle: Market good for long with support of Volume and Volatility or whatever you’ve chosen from both of them.
Red Circle: Market good to short with support from Volume and Volatility or whatever you’ve chosen from both of them.
Yellow Cross: Market either touched top or bottom ATR band and can act as good TP or SL.
EDSMA EVELOPE/BANDS: I’ve included ATR based bands to the Adaptive EDSMA which act as good support/resistance despite from main Adaptive EDSMA Curve.
DEFAULT SETTINGS
I’ve set default Minimum length to 50 and Maximum length to 255 which I’ve found works best for almost all timeframes but you can change this delta to adapt your timeframe accordingly with more precision.
Dynamic length adoption is enabled based on both Volume and Volatility but only one or none of them can also be selected.
Trend signals are enabled based on Slope and Volume but Volatility can be enabled for more precise confirmations.
In “ RVSI ” settings TFS Volume Oscillator is set to default but others work good too especially Volume Zone Oscillator. For more details about Volume Breakout you can check “MZ RVSI Indicator".
ATR breakout is set to be positive if period 14 exceeds period 46 but can be changed if more adaption with volatility is required.
EDSMA super smoother filter length is set to 20 which can be increased to 50 or more for better smoothing but this will also change slope results accordingly.
EDSMA super smoother filter poles are set to 2 because found better results with 2 instead of 3.
FURTHER ENHANCEMENTS
So far, I’ve seen better results with Volume Breakout and Volatility but other parameters such as Linear Slope of Particular MA, MACD, “MZ SRSI ”, a Conditional Uptrend MA or simply KDJ can also be used for dynamic length adaption.
I haven't yet gotten used to pine script arrays so, defining and using conditional operators is pretty much lazy programming for me. Would be great redefining everything through truth matrix instead of using if-else conditions.
Ehlers_Super_SmootherThe 2 Pole and 3 Pole Super Smoother Filters were developed by John Ehlers and described in "Chapter 13: Super Smother" of his book Cybernetic Analysis for Stocks and Futures .
The 2 Pole Smoother is described as being a better approximation of price, whereas the 3 Pole Smoother has superior smoothing.
Library "Ehlers_Super_Smoother"
Provides the functions to calculate Double and Triple Exponentional Moving Averages (DEMA & TEMA)
twoPole(_source, _length) Calculates 2 Pole Ehlers Super Smoother Filter
Parameters:
_source : -> Open, Close, High, Low, etc ('close' is used if no argument is supplied)
_length : -> Ehlers Super Smoother length
Returns: 2 Pole Ehlers Super Smoothing to an input source at the specified input length
threePole(_source, _length) Calculates 3 Pole Ehlers Super Smoother Filter
Parameters:
_source : -> Open, Close, High, Low, etc ('close' is used if no argument is supplied)
_length : -> Ehlers Super Smoother length
Returns: 3 Pole Ehlers Super Smoothing to an input source at the specified input length
Ehlers 3 Pole Super Smoother Filter [CC]The 3 Pole Super Smoother Filter was created by John Ehlers and this is an oldie but a goodie. A great moving average that clearly shows a good trendline and so buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you want me to publish!
[blackcat] L2 The Distance Coefficient Ehlers FilterLevel: 2
Background
John F. Ehlers introuced the Distance Coefficient Ehlers Filter in his "Rocket Science for Traders" chapter 18 on 2001.
Function
Dr. Ehlers considered the gray shading levels as distances, he had away of computing filter coefficients in terms of sharpness of the edge. White is the maximum distance in one direction from the median gray, and black is the maximum distance in the other direction. In this sense, distance is a measure of departure from the edge, taking into account the edge sharpness. Transitioning to price charts, the difference in prices can be imagined as a distance. Recalling the Pythagorean Theorem (which the length of the hypotenuse of a triangle is equal to the sum of the squares of the lengths of the other two sides), Dr Ehlers applied it to the needs and say that a generalized length at any data sample is the square root of the sum of the squares of the price difference between that price and each of the prices back for the length of the filter window. The distances squared at each data point are the coefficients of the Ehlers filter.
Key Signal
Coef --> Ehlers filter coefficients array
Distance2 --> Distance array
Filt --> Ehlers filter output
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 15th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
[blackcat] L2 Ehlers FilterLevel: 2
Background
John F. Ehlers introuced Ehlers Filter in his "Rocket Science for Traders" chapter 18 on 2001.
Function
blackcat L2 Ehlers Filter is used to follow trend. The filters Dr. Ehlers have invented are nonlinear FIR filters. It turns out that they provide both extraordinary smoothing in sideways markets and aggressively follow major price movements with minimal lag. The development of Ehlers filters starts with a general
class of FIR filters called Order Statistic (OS) filters. These filters are well-known for speech and image processing, to sharpen edges, increase contrast, and for robust estimation. In contrast to linear filters, where temporal ordering of the samples is preserved, OS filters base their operation on the ranking of samples
within the filter window. The data are ranked by their summary statistics, such as their mean or variance, rather than by their temporal position.
Among OS filters, the Median filter is the best known. In a Median filter, the output is the median value of all the data values within the observation window. As opposed to an averaging filter, the Median filter simply discards all data except the median value. In this way, impulsive noise spikes and extreme price data are eliminated rather than included in the average. The median value can fall at the first sample in the data window, at the last sample, or anywhere in between. Thus, temporal characteristics are lost. The Median filter tends to smooth out short-term variations that lead to whipsaw trades with linear filters. However, the lag of a Median filter in response to a sharp and sustained price movement is substantial --- it necessarily is about half the filter window width.
Key Signal
Coef --> Ehlers filter coefficients array
Filt --> Ehlers filter output
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 14th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
