Institutional Composite Moving Average (ICMA) [Volume Vigilante]Institutional Composite Moving Average (ICMA)
The Next Evolution of Moving Averages — Built for Real Traders.
ICMA blends the strength of four powerful averages (SMA, EMA, WMA, HMA) into a single ultra-responsive, ultra-smooth signal.
It reacts faster than traditional MAs while filtering out noise, giving you clean trend direction with minimal lag.
🔹 Key Features:
• Faster reaction than SMA, EMA, or WMA individually
• Smoother and more stable than raw HMA
• Naturally adapts across trend, momentum, and consolidation conditions
• Zero gimmicks. Zero repainting. Full institutional quality.
🔹 Designed For:
• Scalping
• Swing trading
• Signal engines
• Algorithmic systems
📎 How to Use:
• Overlay it on any chart
• Fine-tune the length per timeframe
• Combine with your entries/exits for maximum edge
Created by Volume Vigilante 🧬 — Delivering Real-World Trading Tools.
Norepaint
SuperTrend MTF Pro [Cometreon]The SuperTrend MTF Pro takes the classic SuperTrend to a whole new level of customization and accuracy. Unlike the standard version, this indicator allows you to select different moving averages, apply it to various chart types, and fine-tune every key parameter.
If you're looking for an advanced, non-repainting, and highly configurable SuperTrend, this is the right choice for you.
🔷 New Features and Improvements
🟩 Multi-MA SuperTrend
Now you can customize the SuperTrend calculation by choosing from 15 different moving averages:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
RMA (Smoothed Moving Average)
HMA (Hull Moving Average)
JMA (Jurik Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
LSMA (Least Squares Moving Average)
VWMA (Volume-Weighted Moving Average)
SMMA (Smoothed Moving Average)
KAMA (Kaufman’s Adaptive Moving Average)
ALMA (Arnaud Legoux Moving Average)
FRAMA (Fractal Adaptive Moving Average)
VIDYA (Variable Index Dynamic Average)
🟩 Multiple Chart Types
You're no longer limited to candlestick charts! Now you can use SuperTrend with different chart formats, including:
Heikin Ashi
Renko
Kagi
Line Break
Point & Figure
🟩 Customizable Timeframe
Now you can adjust the SuperTrend timeframe without repainting issues, avoiding signal distortions.
🔷 Technical Details and Customizable Inputs
SuperTrend offers multiple customization options to fit any trading strategy:
1️⃣ ATR Period – Defines the ATR length, affecting the indicator’s sensitivity.
2️⃣ Source – Selects the price value used for calculations (Close, HL2, Open, etc.).
3️⃣ ATR Mult – Multiplies the ATR to determine band distance. Higher values reduce false signals, lower values make it more reactive.
4️⃣ Change ATR Calculation Method – When enabled, uses the default ATR method; when disabled, allows selecting another Moving Average with "Use Different Type".
5️⃣ Source Break – Defines the price source for trend changes (Close for more stability, High/Low for more reactivity).
6️⃣ Use Different Type – Allows selecting an alternative Moving Average for ATR calculation if "Change ATR Calculation Method" is disabled.
7️⃣ SuperTrend Type – Advanced options for specific MAs (JMA, ALMA, FRAMA, VIDYA), with dedicated parameters like Phase, Sigma, and Offset for optimized responsiveness.
8️⃣ Ticker Settings – Customize parameters for special chart types such as Renko, Heikin Ashi, Kagi, Line Break, and Point & Figure, adjusting reversal, number of lines, and ATR length.
9️⃣ Timeframe – Enables using SuperTrend on a higher timeframe.
🔟 Wait for Timeframe Closes -
✅ Enabled – Prevents multiple signals, useful for precise alerts.
❌ Disabled – Displays SuperTrend smoothly without interruptions.
🔷 How to Use SuperTrend MTF Pro
🔍 Identifying Trends
SuperTrend follows the ongoing trend and provides clear visual signals:
When the price is above the line, the trend is bullish.
When the price is below the line, the trend is bearish.
📈 Interpreting Signals
Line color and position change → Possible trend reversal
Bounce off the line → Potential trend continuation
Strong breakout of the line → Possible reversal
🛠 Integration with Other Tools
RSI or MACD to filter false signals
Moving Averages to confirm trend direction
Support and Resistance to improve entry points
☄️ If you find this indicator useful, leave a Boost to support its development!
Every feedback helps to continuously improve the tool, offering an even more effective trading experience. Share your thoughts in the comments! 🚀🔥
Xmaster Formula Indicator [TradingFinder] No Repaint Strategies🔵 Introduction
The Xmaster Formula Indicator is a powerful tool for forex trading, combining multiple technical indicators to provide insights into market trends, support and resistance levels, and price reversals. Developed in the early 2010s, it is widely valued for generating reliable buy and sell signals.
Key components include Exponential Moving Averages (EMA) for identifying trends and price momentum, and MACD (Moving Average Convergence Divergence) for analyzing trend strength and direction.
The Stochastic Oscillator and RSI (Relative Strength Index) enhance accuracy by signaling potential price reversals. Additionally, the Parabolic SAR assists in identifying trend reversals and managing risk.
By integrating these tools, the Xmaster Formula Indicator provides a comprehensive view of market conditions, empowering traders to make informed decisions.
🔵 How to Use
The Xmaster Formula Indicator offers two distinct methods for generating signals: Standard Mode and Advance Mode. Each method caters to different trading styles and strategies.
Standard Mode :
In Standard Mode, the indicator uses normalized moving average data to generate buy and sell signals. The difference between the short-term (10-period) and long-term (38-period) EMAs is calculated and normalized to a 0-100 scale.
Buy Signal : When the normalized value crosses above 55, accompanied by the trend line turning green, a buy signal is generated.
Sell Signal : When the normalized value crosses below 45, and the trend line turns red, a sell signal is issued.
This mode is simple, making it ideal for traders looking for straightforward signals without the need for additional confirmations.
Advance Mode :
Advance Mode combines multiple technical indicators to provide more detailed and robust signals.
This method analyzes trends by incorporating :
🟣 MACD
Buy Signal : When the MACD histogram bars are positive.
Sell Signal : When the MACD histogram bars are negative.
🟣 RSI
Buy Signal : When RSI is below 30, indicating oversold conditions.
Sell Signal : When RSI is above 70, suggesting overbought conditions.
🟣 Stochastic Oscillator
Buy Signal : When Stochastic is below 20.
Sell Signal : When Stochastic is above 80.
🟣 Parabolic SAR
Buy Signal : When SAR is below the price.
Sell Signal : When SAR is above the price.
A signal is generated in Advance Mode only when all these indicators align :
Buy Signal : All conditions point to a bullish trend.
Sell Signal : All conditions indicate a bearish trend.
This mode is more comprehensive and suitable for traders who prefer deeper analysis and stronger confirmations before executing trades.
🔵 Settings
Method :
Choose between "Standard" and "Advance" modes to determine how signals are generated. In Standard Mode, signals are based on normalized moving average data, while in Advance Mode, signals rely on the combination of MACD, RSI, Stochastic Oscillator, and Parabolic SAR.
Moving Average Settings :
Short Length : The period for the short-term EMA (default is 10).
Mid Length : The period for the medium-term EMA (default is 20).
Long Length : The period for the long-term EMA (default is 38).
MACD Settings :
Fast Length : The period for the fast EMA in the MACD calculation (default is 12).
Slow Length : The period for the slow EMA in the MACD calculation (default is 26).
Signal Line : The signal line period for MACD (default is 9).
Stochastic Settings :
Length : The period for the Stochastic Oscillator (default is 14).
RSI Settings :
Length : The period for the Relative Strength Index (default is 14).
🔵 Conclusion
The Xmaster Formula Indicator is a versatile and reliable tool for forex traders, offering both simplicity and advanced analysis through its Standard and Advance modes. In Standard Mode, traders benefit from straightforward signals based on normalized moving average data, making it ideal for quick decision-making.
Advance Mode, on the other hand, provides a more detailed analysis by combining multiple indicators like MACD, RSI, Stochastic Oscillator, and Parabolic SAR, delivering stronger confirmations for critical market decisions.
While the Xmaster Formula Indicator offers valuable insights and reliable signals, it is important to use it alongside proper risk management and other analytical methods. By leveraging its capabilities effectively, traders can enhance their trading strategies and achieve better outcomes in the dynamic forex market.
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
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
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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! ✨🚀
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🙌 Credits:
A big shoutout to the original contributors:
@blackcat1402
@cheatcountry
@DasanC
ATR_RSI_Strategy v2 with no repaint [liwei666]🎲 Overview
🎯 this is a optimized version based on ATR_RSI_Strategy with no-repaint.
Sharpe ratio: 1.4, trade times: 116 ,
trade symbol: BINANCE:BTCUSDTPERP 15M
you can get same backtesting result with the correct settings.
🎲 Strategy Logic
🎯 the core logic is quite simple, use ATR and RSI and SMA
1. when price is in high volatility ( atr_value > atr_ma);
2. wait for a break signal (rsi_value > rsi_buy or rsi_value < rsi_sell);
3. entry Long or Short,use trailing stop-loss to max security and percent TP to keep profit.
🎲 Settings
🎯 there are 7 input properties in script, but I only finetune 4 of them ( bold field below ),
you may change other parameter to get better result by yourself.
atr_length: length to get atr value
atr_ma_length : length of smoothing atr value
atr_ma_norm_min : atr_ma normalized min value, filter high volatility ranges
atr_ma_norm_max : atr_ma normalized max value, filter high volatility ranges
rsi_length : length to get rsi value
rsi_entry: 50 +/- rsi_entry to get entry threshold
trailing_percent: trailing stop-loss percent
🎲 Usage
🎯 the commission set to 0.05% , part of exchange the commission is less than 0.05% in reality,
but I will still use 0.05% in my next script.
🎯 this script use 50% of equity to size positions follow general script position,
you can adjust the value to fix size or 100% of equity to compare result with other strategy,
but I still suggest you use 5-10% of equity for each strategy in reality.
🎯any questions please comment below. if there are any words violate House Rule, please tell me below and i will revise immediately
don't want be hiddened again 😂😂
Additionally, I plan to publish 20 profitable strategies in 2023;
let‘s witness it together!
Hope this strategy will be usefull for you :)
enjoy! 🚀🚀🚀
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 HTF option strategyThis is the strategy version of the multi supertrend indicator.
The RoMDD looks good :)
Checked in ETHUSDT 1hour with auto HTF ON.
Have a look :)
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.
Stochastic + ATRStochastic oscillator with dynamic buy/sell levels. Levels calculate with volatility/averag true range. No repaint
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
