Orderblocks (Nephew_Sam_) - Open sourceHighlights orderblocks based on fractal market structure.
Whenever a new fractal high/low is created, it will search for the Orderblock and plot lines and labels
Options:
1. Select 3/5 bar fractal
2. Plot lines and labels on OB's
- Ability to filter OB only when a candle is fully engulfed
3. Change bar color of engulfed candles
4. Option to filter OB that follows with an FVG
View the published chart for more details on how this indicator works
Disclaimer: You have the permissions to use this code however make sure you give me the credits when you do and make it open source or grant me access to the code.
Cari dalam skrip untuk "Fractal"
CFB Adaptive MOGALEF Bands [Loxx]A Pine Script adaptation from MOGALEF Bands .
What are MOGALEF Bands?
Actual MOGALEF bands code is the final result of a lot of contributors.โ Syllables MO-GA-LEF are the initials of three of them.
The basic idea of bands: the markets are still in range, and trends that are moving ranges. The Mogalef bands try to estimate the current range and to project its on the future if prices move. This future estimation is often of great relevance and very useful, especialy for market profile users or pivot points users.
What is Composite Fractal Behavior ( CFB )?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Included:
-Color bars
-Fill levels
Moving Average Filters Add-on w/ Expanded Source Types [Loxx]Moving Average Filters Add-on w/ Expanded Source Types is a conglomeration of specialized and traditional moving averages that will be used in most of indicators that I publish moving forward. There are 39 moving averages included in this indicator as well as expanded source types including traditional Heiken Ashi and Better Heiken Ashi candles. You can read about the expanded source types clicking here . About half of these moving averages are closed source on other trading platforms. This indicator serves as a reference point for future public/private, open/closed source indicators that I publish to TradingView. Information about these moving averages was gleaned from various forex and trading forums and platforms as well as TASC publications and other assorted research publications.
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Included moving averages
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA, it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA.
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average (DEMA) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA. It's also considered a leading indicator compared to the EMA, and is best utilized whenever smoothness and speed of reaction to market changes are required.
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA (Simple Moving Average). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA.
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Hull Moving Average - HMA
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points.
IE/2 - Early T3 by Tim Tilson
The IE/2 is a Moving Average that uses Linear Regression slope in its calculation to help with smoothing. It's a worthy Moving Average on it's own, even though it is the precursor and very early version of the famous "T3 Indicator".
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA (Simple Moving Average) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA (Least Squares Moving Average)
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA. Although it's similar to the Simple Moving Average, the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track price better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average. The Linear Weighted Moving Average calculates the average by assigning different weight to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrows price.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA.
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average (SMA), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen a an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA (Smoothed Moving Average). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlersโs โSuper Smootherโ which consists of a a Two pole Butterworth filter combined with a 2-bar SMA (Simple Moving Average) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA. They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
The TMA and Sine Weighted Moving Average Filter are almost identical at times.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, it's signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers.
Volume Weighted EMA - VEMA
Utilizing tick volume in MT4 (or real volume in MT5), this EMA will use the Volume reading in its decision to plot its moves. The more Volume it detects on a move, the more authority (confirmation) it has. And this EMA uses those Volume readings to plot its movements.
Studies show that tick volume and real volume have a very strong correlation, so using this filter in MT4 or MT5 produces very similar results and readings.
Zero Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers, as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA, this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
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What are Heiken Ashi "better" candles?
The "better formula" was proposed in an article/memo by BNP-Paribas (In Warrants & Zertifikate, No. 8, August 2004 (a monthly German magazine published by BNP Paribas, Frankfurt), there is an article by Sebastian Schmidt about further development (smoothing) of Heikin-Ashi chart.)
They proposed to use the following:
(Open+Close)/2+(((Close-Open)/( High-Low ))*ABS((Close-Open)/2))
instead of using :
haClose = (O+H+L+C)/4
According to that document the HA representation using their proposed formula is better than the traditional formula.
What are traditional Heiken-Ashi candles?
The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, share some characteristics with standard candlestick charts but differ based on the values used to create each candle. Instead of using the open, high, low, and close like standard candlestick charts, the Heikin-Ashi technique uses a modified formula based on two-period averages. This gives the chart a smoother appearance, making it easier to spots trends and reversals, but also obscures gaps and some price data.
Expanded generic source types:
Close = close
Open = open
High = high
Low = low
Median = hl2
Typical = hlc3
Weighted = hlcc4
Average = ohlc4
Average Median Body = (open+close)/2
Trend Biased = (see code, too complex to explain here)
Trend Biased (extreme) = (see code, too complex to explain here)
Included:
-Toggle bar color on/off
-Toggle signal line on/off
Jurik CFB Adaptive, Elder Force Index w/ ATR Channels [Loxx]Jurik CFB Adaptive, Elder Force Index w/ ATR Channels is a variation of Elder Force Index that better adapts to trends by calculating dynamic lengths for the traditional Elder Force Index calculation. ATR channels are added to show levels of price extremes or exhaustion of price either up or down. Elder Force Index is typically used for spotting reversals on the weekly timeframe.
What is the Elder Force Index?
Dr. Alexander Elder is one of the contributors to a newer generation of technical indicators. His force index is an oscillator that measures the force, or power, of bulls behind particular market rallies and of bears behind every decline.1๏ปฟ
The three key components of the force index are the direction of price change, the extent of the price change, and the trading volume. When the force index is used in conjunction with a moving average, the resulting figure can accurately measure significant changes in the power of bulls and bears.1๏ปฟ In this way, Elder has taken an extremely useful solitary indicator, the moving average, and combined it with his force index for even greater predictive success.
What is Composite Fractal Behavior ( CFB )?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
[blackcat] L2 Ehlers Hurst Coefficient IndicatorLevel: 2
Background
John F. Ehlers introuced Hurst Coefficient Indicator in his "Cycle Analytics for Traders" chapter 6 on 2013.
Function
The Hurst coefficient is one way to attempt to get a handle on the slope of the power density of market data. The Hurst coefficient varies between 0 and 1, and is related to the ฮฑ power coefficient as H = 1 โ ฮฑ/2. The Hurst coefficient is more estimated than computed. Dr. Ehlers found the estimate using the fractal dimension was the most practical for shorter-term market data. The Hurst coefficient is related to the fractal dimension as H = 2 โ D. Dr. Ehlers would like to make it perfectly clear that the Hurst coefficient or the fractal dimension has no direct practical application to trading not only because it is an estimate, but also because it has no predictive value. These computations only reflect the general structure of the market, and the answer you get is dependent on your assumptions. For example, the Hurst coefficient changes dramatically with the length of data used in making the estimate.
The only user input is the length of data to be used. The number can be arbitrarily large if you have sufficient data. The results are critically dependent on the input data length selected. After declaring variables, the coefficients of a 20-bar SuperSmoother filter are computed. The computations of N1, N2, and N3 are as described in the previous section. The fractal dimension is then converted to the Hurst coefficient, which is subsequently smoothed in the SuperSmoother filter.
Key Signal
SmoothHurst --> Hurst Coefficient Indicator fast line
Trigger --> Hurst Coefficient Indicator slow line
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 40th 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.
FilthyIchiEMAXIchiEMAX has been backtested giving some great results, using Ichimoku and a mix of EMA'S/SMA .
I use this for both Cryptocurrency and Forex Trading.
Ema/SmaX system i have studied and have found it is giving me the results i want very similar to all the wave trend indicators being used currently. I have now adde buy/sell signals on crosses and williams fractal.
(JS) Ultimate RSISo my goal here was to combine all of my RSI ideas into a single indicator in order to make kind of a "Swiss Army Knife" version of the Relative Strength Index ...
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So, let's begin with the first RSI indicator I made, which is the RSIDVW (Divergence/Volume Weighted);
To rephrase my original post, the "divergence/volume weighted" portion is meant to expand upon the current RSI format by adding more variables into the equation.
The standard RSI is based off one value that you select (open, close, OHLC4, HLC3, etc.) while this version takes three variables into account.
The default setting is to have RSI normal without anything added to it (Divergence Weight = 0)
1st - it takes the standard variable that RSI normally uses.
2nd - it factors RSI divergence by taking the RSI change % and price change % to form a ratio. Using this ratio, I duplicated the RSI formula and created a divergence RS to be factored in with the standard price RS .
3rd - it takes Relative Volume and amplifies/weakens the move based upon volume confirmation. (So if Relative Volume for a price bar is 1.0, the RSI plot would be the same as it normally would)
So to explain the parameters
- Relative Volume Length: This uses the RV length you specify to determine spikes in volume (or lack of volume ), which then is added into the formula to influence the strength of the RSI move
- RV x Divergence: This is how I calculated the original formula, but you can leave this unchecked to turn Relative Volume off, or apply elsewhere.
- RV x RS: There's two sides, Divergence RS and Standard RS - these check marks allow you to select which part you prefer to be multiplied by Relative Volume .
Checking neither turns off Relative Volume , while checking both amplifies its effects by placing it on both sides of the equation.
-Divergence Weight: This controls how much the DVW portion of the formula influences the RSI plot. As I referred to earlier, default is 0 making RSI normal. The Scale is 0-2, so 1.0 would be the same as 50%.
When I do have DVW on, I generally set it to 0.5
-SMA Divergence: To smooth, or not to smooth, that is the question. UJsing an SMA here is much smoother in my opinon, but leaving it unchecked runs it through an RMA the same way standard RSI is calculated.
-Show Fractal Channel: This allows you to see the whole fractal channel around the RSI (This portion of the code, compliments of the original Ricardo Santos fractal script)
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The next portion of the script is adding a "Slow RSI"...
This is rather simple really, it allows you to add a second RSI plot so that you can watch for crossovers between fast and slow lines.
-Slow RSI: This turns on the second RSI Plot.
-Slow RSI Length: This determines the length of the second RSI Plot.
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Pivot Point RSI was something a friend of mine requested I make which turned out pretty cool, I thought... It is also available in this indicator.
-Pivot Points: Selecting this enables the rest of the pivot point related parts of the script
If Pivot Points isn't selected, none of the following things will work
-Plot Pivot: Plots the pivot point .
-Plot S1/R1: Plots S1/R1.
-Plot S2/R2: Plots S2/R2.
-Plot S3/R3: Plots S3/R3.
-Plot S4/R4: Plots S4/R4.
-Plot S5/R5: Plots S5/R5.
-Plot Halfway Points: Plots a line between each pivot .
-Show Pivot Labels: Shows the proper label for each pivot .
When using intraday charts, from a 15 minute interval or less the pivots are calculated based on a single days worth of price action, above that the distance expands.
Here are the current resolutions Pivot Points will work with:
Minutes - 1 , 2, 3, 5, 10, 13, 15, 20, 30, 39, 78, 130, 195
Hours - 1, 2, 3, 4, 5, 6
Daily
Weekly
Currently not available on seconds or monthly
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Background Colors
Background Colors: I have six color schemes I created for this which can be toggled here (they can be edited).
Gray Background for Dark Mode: Having this on looks much better when using dark mode on your charts.
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Now finally the last portion, Fibonacci Levels
-Fibonacci Levels: This is off, by default, which then uses the standard levels on RSI (30-50-70). When turned on, it removes these and marks fib levels from 0.146 through 0.886.
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So the quick rundown:
Ultimate RSI contains "divergence/volume weighted" modifications, a slow RSI plot, pivot points , and Fibonacci levels all while auto-plotting divergence and having the trend illustrated in the background colors.
RSI has always been my "go to" indicator, so I hope you all enjoy this as much as I do!
Double Stochastic DivergenceSame as my protected script but you can now see the code
This Study plots divergences and overlays a second %K as a fractal and changes the color of %D for the non fractal
Option to use Stochastic RSI for Fractal
Background Shading according to trend
Feel Free to change the indicator values to suit your style / system
The divergence script is thanks to @RicardoSantos, I've just adjusted it to suite my indicator
Remember that divergences work best when traded with the trend or very late in a trend when going against the trend
Common value for %K is 5, I have chosen 3 as it gives faster entries when using multiple time frames
If you are not using a momentum indicator as a trailing stop and using only cycle indicator
then I would recommended %K be 4 for exits
Patrones de entrada/salida V.1.0 -BETA-Este algoritmo intenta identificar patrones o fractales dentro de los movimientos de precios para dar seรฑales de compra o venta de activos.
Triple Exponental Moving Average (overlay)TRIX Overlay + TRIX change Histogramm = simplest tactic to trade.
Just use last counter trend fractal to place delayed order
A counter trend fractal is a fractal down on TRIX uptrend or fractal up on TRIX downtrend.
Use TRIX speed change histogramm to seek divergence
FRAMA (Ehlers true modified calculation)Credit goes to Shizaru for the original calculation. I made just a few fixes, so that the calculation is really that of Ehlers.
Fixed H2 and L2 period, fixed w natural logarithm
On-Balance Volume with Multiple MA TypesOn-Balance Volume with Multiple MA Types
English Description
Overview
This is the first version of the "On-Balance Volume with Multiple MA Types" indicator designed to overlay directly on the price chart, a significant evolution from its previous iterations, which functioned solely as an oscillator in a separate window. The indicator calculates On-Balance Volume (OBV) and applies various smoothing methods to provide a clear view of volume dynamics in relation to price movements. It is pinned to the price scale for seamless integration with the chart.
Interpretation Recommendations
Price Pushing the OBV Line from Below: When the price chart pushes the OBV line upward and remains below it, this indicates rising volume, suggesting strong buying pressure.
Price Above the OBV Line: When the price chart is above the OBV line, it signals falling volume, indicating weakening momentum or selling pressure.
OBV Line Crossings: When the price crosses the OBV line, it represents a balance point in volume dynamics. The price level at the current crossing can be compared to the previous crossing to assess changes in market sentiment or momentum.
Moving Average Types
The indicator offers eight smoothing options for the OBV line, each with unique characteristics:
EMA (Exponential Moving Average): A weighted average that prioritizes recent data, providing a smooth yet responsive line.
DEMA (Double Exponential Moving Average): Uses two EMAs to reduce lag, offering faster response to volume changes.
HMA (Hull Moving Average): Combines weighted moving averages to minimize lag while maintaining smoothness.
WMA (Weighted Moving Average): Assigns more weight to recent data, balancing responsiveness and noise reduction.
TMA (Triangular Moving Average): A double-smoothed simple moving average, emphasizing central data points for smoother output.
VIDYA (Variable Index Dynamic Average): Adapts smoothing based on market volatility, using a CMO (Chande Momentum Oscillator) for dynamic weighting. Controlled by the VIDYA Alpha parameter (default: 0.2, range: 0โ1), which adjusts sensitivity to volatility.
FRAMA (Fractal Adaptive Moving Average): Adjusts smoothing based on fractal dimensions of the OBV data, adapting to market conditions.
JMA (Jurik Moving Average): A proprietary adaptive average designed for minimal lag and high smoothness. Controlled by two parameters:
JMA Phase (default: 50, range: -100 to 100): Adjusts the balance between responsiveness and smoothness.
JMA Power (default: 1, range: 0.1+): Controls the strength of smoothing.
Input Parameters
OBV MA Length (default: 10): The lookback period for smoothing the OBV. Higher values produce smoother results but increase lag.
OBV MA Type (default: JMA): Selects the moving average type from the eight options listed above.
Line Width (default: 2): Thickness of the OBV line on the chart.
Bullish Color (default: Blue): Color of the OBV line when rising (indicating increasing volume).
Bearish Color (default: Red): Color of the OBV line when falling (indicating decreasing volume).
JMA Phase (default: 50): Adjusts the JMAโs responsiveness (used only when JMA is selected).
JMA Power (default: 1): Adjusts the JMAโs smoothing strength (used only when JMA is selected).
VIDYA Alpha (default: 0.2): Controls the sensitivity of VIDYA to market volatility (used only when VIDYA is selected).
How to Use
Add the indicator to your TradingView chart. It will overlay directly on the price chart, aligned with the price scale.
Adjust the OBV MA Type to select your preferred smoothing method based on your trading style (e.g., JMA for low lag, TMA for smoothness).
Modify the OBV MA Length to balance responsiveness and noise reduction. Shorter periods (e.g., 5โ10) are better for short-term trading, while longer periods (e.g., 20โ50) suit longer-term analysis.
Use the Bullish Color and Bearish Color to visually distinguish rising and falling volume trends.
For JMA or VIDYA, fine-tune the JMA Phase, JMA Power, or VIDYA Alpha to optimize the indicator for specific market conditions.
Interpret the OBV line in relation to price:
Watch for price pushing the OBV line upward (rising volume) or moving above it (falling volume).
Note crossings of the OBV line to identify balance points and compare with prior crossings to gauge momentum shifts.
Combine with other technical tools (e.g., support/resistance levels, trendlines) for a comprehensive trading strategy.
Notes
This indicator is designed to work on any timeframe and market, but its effectiveness depends on the chosen moving average type and parameters.
Experiment with different MA types and lengths to find the best fit for your trading approach.
The indicator is licensed under the Mozilla Public License 2.0 and copyrighted by TradingStrategyCourses ยฉ 2025.
Jimb0ws Strategy Trending Info PanelsJimb0ws Strategy โ Golden Candles + Bubble Zones
A price-action/EMA strategy built for FX scalping and intraday swings. It colors Golden Candles when strong bodies touch/skim EMA20/50 in trend (โbubbleโ) and optionally highlights Robin Candles (break of the prior golden body). Signals are throttled per bubble and filtered by multiple higher-timeframe conditions.
How it trades
Trend bubbles: Uses EMA20/50/100/200 alignment on the chart timeframe; also reads 1H & 4H bubbles for context.
Entries: BUY/SELL labels appear only when a golden setup aligns with fractal/structure checks and all active filters pass.
Stops/Targets (strategy mode):
โข Longs: SL = EMA100 if EMA200 > EMA100, else SL = EMA200.
โข Shorts: SL = EMA100 if EMA200 < EMA100, else SL = EMA200.
โข TP = RR ร risk (default 2R).
An on-chart SL/TP info label prints the exact prices at each signal.
Risk filter options: disable beyond 1H EMA50, proximity band around 1H EMA50, wick overdrive veto, session filter (toggle on/off), max signals per bubble.
Visuals & tools
Colored EMAs (20/50/100/200), bubble zone background.
4H info panel (state, start time, duration); Prev-Day ATR panel sits above it.
Optional 1H info panel and consolidation warning.
Fractal markers (size selectable).
Alerts
1H bubble state change (Long/Short/Consolidation).
BUY/SELL signals.
Inputs worth checking
Session & timezone, min body size, pip tolerances, proximity/WOD filters, max signals per bubble, RR, SL/TP label offset.
Notes
Best on FX pairs; pip = mintick ร 10. Backtest and adjust to your instrument and session. This is not financial advice.
3D Surface Modeling [PhenLabs]๐ 3D Surface Modeling
Version: PineScriptโข v6
๐ Description
The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics.
The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts.
๐ Points of Innovation
Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time
Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients
Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses
Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions
๐ง Core Components
Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics
3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping
Dynamic Scaling System: Implements custom normalization algorithms for different indicator types
Color Gradient Generator: Creates depth perception through programmatic color transitions
๐ฅ Key Features
Multi-Indicator Support: Comprehensive analysis across seven different technical indicators
Customizable Visualization: User-defined color schemes and line width parameters
Real-time Processing: Continuous calculation and rendering of 3D surfaces
Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods
๐จ Visualization
Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients
Depth Indicators: Color intensity variations showing indicator value magnitude
Pattern Recognition: Visual identification of market structures across multiple timeframes
๐ Usage Guidelines
Indicator Selection
Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension
Default: VWAP
Starting Length: Minimum 5 periods
Default: 10
Step Size: Interval between calculations
Range: 1-10
Visualization Parameters
Color Scheme: Green, Red, Blue options
Line Width: 1-5 pixels
Surface Resolution: Up to 500 lines
โ
Best Use Cases
Multi-timeframe market analysis
Pattern recognition across different technical indicators
Trend strength assessment through 3D visualization
Market behavior study across multiple periods
โ ๏ธ Limitations
High computational resource requirements
Maximum 500 line restriction
Requires substantial historical data
Complex visualization learning curve
๐ฌ How It Works
1. Data Processing:
Calculates selected indicator values across multiple timeframes
Stores results in multi-dimensional arrays
Applies custom scaling algorithms
2. Visualization Generation:
Creates line arrays for 3D surface representation
Applies color gradients based on value magnitude
Renders real-time updates to surface plot
3. Display Integration:
Synchronizes with chart timeframe
Updates surface plot dynamically
Maintains visual consistency across updates
๐ Credits:
Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings)
๐ก Note:
Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
Anomalous Holonomy Field Theory๐ Anomalous Holonomy Field Theory (AHFT) - Revolutionary Quantum Market Analysis
Where Theoretical Physics Meets Trading Reality
A Groundbreaking Synthesis of Differential Geometry, Quantum Field Theory, and Market Dynamics
๐ฌ THEORETICAL FOUNDATION - THE MATHEMATICS OF MARKET REALITY
The Anomalous Holonomy Field Theory represents an unprecedented fusion of advanced mathematical physics with practical market analysis. This isn't merely another indicator repackaging old concepts - it's a fundamentally new lens through which to view and understand market structure .
1. HOLONOMY GROUPS (Differential Geometry)
In differential geometry, holonomy measures how vectors change when parallel transported around closed loops in curved space. Applied to markets:
Mathematical Formula:
H = P exp(โฎ_C A_ฮผ dx^ฮผ)
Where:
P = Path ordering operator
A_ฮผ = Market connection (price-volume gauge field)
C = Closed price path
Market Implementation:
The holonomy calculation measures how price "remembers" its journey through market space. When price returns to a previous level, the holonomy captures what has changed in the market's internal geometry. This reveals:
Hidden curvature in the market manifold
Topological obstructions to arbitrage
Geometric phase accumulated during price cycles
2. ANOMALY DETECTION (Quantum Field Theory)
Drawing from the Adler-Bell-Jackiw anomaly in quantum field theory:
Mathematical Formula:
โ_ฮผ j^ฮผ = (eยฒ/16ฯยฒ)F_ฮผฮฝ Fฬ^ฮผฮฝ
Where:
j^ฮผ = Market current (order flow)
F_ฮผฮฝ = Field strength tensor (volatility structure)
Fฬ^ฮผฮฝ = Dual field strength
Market Application:
Anomalies represent symmetry breaking in market structure - moments when normal patterns fail and extraordinary opportunities arise. The system detects:
Spontaneous symmetry breaking (trend reversals)
Vacuum fluctuations (volatility clusters)
Non-perturbative effects (market crashes/melt-ups)
3. GAUGE THEORY (Theoretical Physics)
Markets exhibit gauge invariance - the fundamental physics remains unchanged under certain transformations:
Mathematical Formula:
A'_ฮผ = A_ฮผ + โ_ฮผฮ
This ensures our signals are gauge-invariant observables , immune to arbitrary market "coordinate changes" like gaps or reference point shifts.
4. TOPOLOGICAL DATA ANALYSIS
Using persistent homology and Morse theory:
Mathematical Formula:
ฮฒ_k = dim(H_k(X))
Where ฮฒ_k are the Betti numbers describing topological features that persist across scales.
๐ฏ REVOLUTIONARY SIGNAL CONFIGURATION
Signal Sensitivity (0.5-12.0, default 2.5)
Controls the responsiveness of holonomy field calculations to market conditions. This parameter directly affects the threshold for detecting quantum phase transitions in price action.
Optimization by Timeframe:
Scalping (1-5min): 1.5-3.0 for rapid signal generation
Day Trading (15min-1H): 2.5-5.0 for balanced sensitivity
Swing Trading (4H-1D): 5.0-8.0 for high-quality signals only
Score Amplifier (10-200, default 50)
Scales the raw holonomy field strength to produce meaningful signal values. Higher values amplify weak signals in low-volatility environments.
Signal Confirmation Toggle
When enabled, enforces additional technical filters (EMA and RSI alignment) to reduce false positives. Essential for conservative strategies.
Minimum Bars Between Signals (1-20, default 5)
Prevents overtrading by enforcing quantum decoherence time between signals. Higher values reduce whipsaws in choppy markets.
๐ ELITE EXECUTION SYSTEM
Execution Modes:
Conservative Mode:
Stricter signal criteria
Higher quality thresholds
Ideal for stable market conditions
Adaptive Mode:
Self-adjusting parameters
Balances signal frequency with quality
Recommended for most traders
Aggressive Mode:
Maximum signal sensitivity
Captures rapid market moves
Best for experienced traders in volatile conditions
Dynamic Position Sizing:
When enabled, the system scales position size based on:
Holonomy field strength
Current volatility regime
Recent performance metrics
Advanced Exit Management:
Implements trailing stops based on ATR and signal strength, with mode-specific multipliers for optimal profit capture.
๐ง ADAPTIVE INTELLIGENCE ENGINE
Self-Learning System:
The strategy analyzes recent trade outcomes and adjusts:
Risk multipliers based on win/loss ratios
Signal weights according to performance
Market regime detection for environmental adaptation
Learning Speed (0.05-0.3):
Controls adaptation rate. Higher values = faster learning but potentially unstable. Lower values = stable but slower adaptation.
Performance Window (20-100 trades):
Number of recent trades analyzed for adaptation. Longer windows provide stability, shorter windows increase responsiveness.
๐จ REVOLUTIONARY VISUAL SYSTEM
1. Holonomy Field Visualization
What it shows: Multi-layer quantum field bands representing market resonance zones
How to interpret:
Blue/Purple bands = Primary holonomy field (strongest resonance)
Band width = Field strength and volatility
Price within bands = Normal quantum state
Price breaking bands = Quantum phase transition
Trading application: Trade reversals at band extremes, breakouts on band violations with strong signals.
2. Quantum Portals
What they show: Entry signals with recursive depth patterns indicating momentum strength
How to interpret:
Upward triangles with portals = Long entry signals
Downward triangles with portals = Short entry signals
Portal depth = Signal strength and expected momentum
Color intensity = Probability of success
Trading application: Enter on portal appearance, with size proportional to portal depth.
3. Field Resonance Bands
What they show: Fibonacci-based harmonic price zones where quantum resonance occurs
How to interpret:
Dotted circles = Minor resonance levels
Solid circles = Major resonance levels
Color coding = Resonance strength
Trading application: Use as dynamic support/resistance, expect reactions at resonance zones.
4. Anomaly Detection Grid
What it shows: Fractal-based support/resistance with anomaly strength calculations
How to interpret:
Triple-layer lines = Major fractal levels with high anomaly probability
Labels show: Period (H8-H55), Price, and Anomaly strength (ฯ)
โก symbol = Extreme anomaly detected
โ symbol = Strong anomaly
โ symbol = Normal conditions
Trading application: Expect major moves when price approaches high anomaly levels. Use for precise entry/exit timing.
5. Phase Space Flow
What it shows: Background heatmap revealing market topology and energy
How to interpret:
Dark background = Low market energy, range-bound
Purple glow = Building energy, trend developing
Bright intensity = High energy, strong directional move
Trading application: Trade aggressively in bright phases, reduce activity in dark phases.
๐ PROFESSIONAL DASHBOARD METRICS
Holonomy Field Strength (-100 to +100)
What it measures: The Wilson loop integral around price paths
>70: Strong positive curvature (bullish vortex)
<-70: Strong negative curvature (bearish collapse)
Near 0: Flat connection (range-bound)
Anomaly Level (0-100%)
What it measures: Quantum vacuum expectation deviation
>70%: Major anomaly (phase transition imminent)
30-70%: Moderate anomaly (elevated volatility)
<30%: Normal quantum fluctuations
Quantum State (-1, 0, +1)
What it measures: Market wave function collapse
+1: Bullish eigenstate |โโฉ
0: Superposition (uncertain)
-1: Bearish eigenstate |โโฉ
Signal Quality Ratings
LEGENDARY: All quantum fields aligned, maximum probability
EXCEPTIONAL: Strong holonomy with anomaly confirmation
STRONG: Good field strength, moderate anomaly
MODERATE: Decent signals, some uncertainty
WEAK: Minimal edge, high quantum noise
Performance Metrics
Win Rate: Rolling performance with emoji indicators
Daily P&L: Real-time profit tracking
Adaptive Risk: Current risk multiplier status
Market Regime: Bull/Bear classification
๐ WHY THIS CHANGES EVERYTHING
Traditional technical analysis operates on 100-year-old principles - moving averages, support/resistance, and pattern recognition. These work because many traders use them, creating self-fulfilling prophecies.
AHFT transcends this limitation by analyzing markets through the lens of fundamental physics:
Markets have geometry - The holonomy calculations reveal this hidden structure
Price has memory - The geometric phase captures path-dependent effects
Anomalies are predictable - Quantum field theory identifies symmetry breaking
Everything is connected - Gauge theory unifies disparate market phenomena
This isn't just a new indicator - it's a new way of thinking about markets . Just as Einstein's relativity revolutionized physics beyond Newton's mechanics, AHFT revolutionizes technical analysis beyond traditional methods.
๐ง OPTIMAL SETTINGS FOR MNQ 10-MINUTE
For the Micro E-mini Nasdaq-100 on 10-minute timeframe:
Signal Sensitivity: 2.5-3.5
Score Amplifier: 50-70
Execution Mode: Adaptive
Min Bars Between: 3-5
Theme: Quantum Nebula or Dark Matter
๐ญ THE JOURNEY - FROM IMPOSSIBLE THEORY TO TRADING REALITY
Creating AHFT was a mathematical odyssey that pushed the boundaries of what's possible in Pine Script. The journey began with a seemingly impossible question: Could the profound mathematical structures of theoretical physics be translated into practical trading tools?
The Theoretical Challenge:
Months were spent diving deep into differential geometry textbooks, studying the works of Chern, Simons, and Witten. The mathematics of holonomy groups and gauge theory had never been applied to financial markets. Translating abstract mathematical concepts like parallel transport and fiber bundles into discrete price calculations required novel approaches and countless failed attempts.
The Computational Nightmare:
Pine Script wasn't designed for quantum field theory calculations. Implementing the Wilson loop integral, managing complex array structures for anomaly detection, and maintaining computational efficiency while calculating geometric phases pushed the language to its limits. There were moments when the entire project seemed impossible - the script would timeout, produce nonsensical results, or simply refuse to compile.
The Breakthrough Moments:
After countless sleepless nights and thousands of lines of code, breakthrough came through elegant simplifications. The realization that market anomalies follow patterns similar to quantum vacuum fluctuations led to the revolutionary anomaly detection system. The discovery that price paths exhibit holonomic memory unlocked the geometric phase calculations.
The Visual Revolution:
Creating visualizations that could represent 4-dimensional quantum fields on a 2D chart required innovative approaches. The multi-layer holonomy field, recursive quantum portals, and phase space flow representations went through dozens of iterations before achieving the perfect balance of beauty and functionality.
The Balancing Act:
Perhaps the greatest challenge was maintaining mathematical rigor while ensuring practical trading utility. Every formula had to be both theoretically sound and computationally efficient. Every visual had to be both aesthetically pleasing and information-rich.
The result is more than a strategy - it's a synthesis of pure mathematics and market reality that reveals the hidden order within apparent chaos.
๐ INTEGRATED DOCUMENTATION
Once applied to your chart, AHFT includes comprehensive tooltips on every input parameter. The source code contains detailed explanations of the mathematical theory, practical applications, and optimization guidelines. This published description provides the overview - the indicator itself is a complete educational resource.
โ ๏ธ RISK DISCLAIMER
While AHFT employs advanced mathematical models derived from theoretical physics, markets remain inherently unpredictable. No mathematical model, regardless of sophistication, can guarantee future results. This strategy uses realistic commission ($0.62 per contract) and slippage (1 tick) in all calculations. Past performance does not guarantee future results. Always use appropriate risk management and never risk more than you can afford to lose.
๐ CONCLUSION
The Anomalous Holonomy Field Theory represents a quantum leap in technical analysis - literally. By applying the profound insights of differential geometry, quantum field theory, and gauge theory to market analysis, AHFT reveals structure and opportunities invisible to traditional methods.
From the holonomy calculations that capture market memory to the anomaly detection that identifies phase transitions, from the adaptive intelligence that learns and evolves to the stunning visualizations that make the invisible visible, every component works in mathematical harmony.
This is more than a trading strategy. It's a new lens through which to view market reality.
Trade with the precision of physics. Trade with the power of mathematics. Trade with AHFT.
I hope this serves as a good replacement for Quantum Edge Pro - Adaptive AI until I'm able to fix it.
โ Dskyz, Trade with insight. Trade with anticipation.
OHLCVDataOHLCV Data Power Library
Multi-Timeframe Market Data with Mathematical Precision
๐ Overview
This Pine Script library provides structured OHLCV (Open, High, Low, Close, Volume) data across multiple timeframes using mathematically significant candle counts (powers of 3). Designed for technical analysts who work with fractal market patterns and need efficient access to higher timeframe data.
โจ Key Features
6 Timeframes: 5min, 1H, 4H, 6H, 1D, and 1W data
Power-of-3 Candle Counts: 3, 9, 27, 81, and 243 bars
Structured Data: Returns clean OHLCV objects with all price/volume components
Pine Script Optimized: Complies with all security() call restrictions
๐ Timeframe Functions
pinescript
f_get5M_3() // 3 candles of 5min data
f_get1H_27() // 27 candles of 1H data
f_get1D_81() // 81 candles of daily data
// ... and 27 other combinations
๐ Usage Example
pinescript
import YourName/OHLCVData/1 as OHLCV
weeklyData = OHLCV.f_get1W_27() // Get 27 weekly candles
latestHigh = array.get(weeklyData, 0).high
plot(latestHigh, "Weekly High")
๐ก Ideal For
Multi-timeframe analysis
Volume-profile studies
Fractal pattern detection
Higher timeframe confirmation
โ ๏ธ Note
Replace "YourName" with your publishing username
All functions return arrays of OHLCV objects
Maximum lookback = 243 candles
๐ Version History
1.0 - Initial release (2024)
Aesthetic RSI [AlchimistOfCrypto]๐ Aesthetic RSI โ Unveiling the Fractal Forces of Markets ๐
Category: Momentum Indicators ๐
"The RSI oscillator, formalized through an advanced mathematical prism, reveals the underlying fractal structures of price movements. This indicator draws inspiration from quantum principles of divergence-convergence where the probability of a return to equilibrium increases proportionally to the distance from the median point. Our implementation employs sophisticated algorithmic smoothing to filter out the stochastic noise inherent in financial markets, allowing visualization of the true momentum forces according to thermodynamic entropy principles applied to trading systems."
๐ Professional Trading Application
The Aesthetic RSI is a visually stunning and mathematically refined take on the classic Relative Strength Index. With customizable settings, advanced smoothing, and eight unique visual palettes, it empowers traders to detect momentum shifts and divergences with unparalleled clarity.
โ๏ธ Indicator Configuration
- Length ๐
The core parameter (default: 20) that determines the calculation period.
- Lower values (8-14): Increase sensitivity for short-term trading.
- Higher values (21-34): Provide stronger signals for position trading.
- OverBought/OverSold Thresholds ๐ฏ
Customizable boundaries (default: 75/25) to identify extreme market conditions.
- Calibrate based on asset volatility: Higher volatility assets may need wider thresholds (80/20) to reduce false signals.
- Style ๐จ
Eight meticulously crafted visual palettes optimized for pattern recognition:
- Miami Vice (default): High-contrast cyan/magenta scheme for spotting divergences.
- Cyberpunk: Yellow/purple combo to highlight momentum shifts.
- Classic: Traditional green/red for conventional analysis.
- High Contrast: Maximum visual separation for traders with visual impairments.
- Specialized palettes (Forest, Ocean, Fire, Monochrome): Tailored for diverse market conditions.
- Mode Selection ๐
- Full: Displays a complete gradient spectrum across the RSI range, emphasizing momentum transitions between 35-65.
- OverZone: Focuses on actionable extreme zones, reducing noise in ranging markets.
๐ How to Use
1. Adjust Length โฐ: Set the period based on your trading style (short-term or long-term).
2. Fine-Tune Thresholds ๐๏ธ: Customize overbought/oversold levels to match the assetโs volatility.
3. Select a Palette ๐: Choose a visual style that enhances your pattern recognition.
4. Choose Mode ๐: Use "Full" for detailed momentum analysis or "OverZone" for extreme zone focus.
5. Spot Divergences โ
: Look for price-RSI divergences to anticipate reversals.
6. Trade with Precision ๐ก๏ธ: Combine with other indicators for high-probability setups.
๐
Release Notes (April 2025)
Aesthetic RSI blends quantum-inspired mathematics with artistic visualization, redefining momentum analysis. Stay tuned for future enhancements! โจ
๐ท๏ธ Tags
#Trading #TechnicalAnalysis #RSI #Momentum #Divergence #MultiTimeframe #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #Bitcoin #AlgoTrading #DayTrading #SwingTrading #TheAlchimist #QuantumTrading #VisualTrading #PatternRecognition
Hurst-Based Trend Persistence w/Poisson Prediction
---
# **Hurst-Based Trend Persistence w/ Poisson Prediction**
## **Introduction**
The **Hurst-Based Trend Persistence with Poisson Prediction** is a **statistically-driven trend-following oscillator** that provides traders with **a structured approach to identifying trend strength, persistence, and potential reversals**.
This indicator combines:
- **Hurst Exponent Analysis** (to measure how persistent or mean-reverting price action is).
- **Color-Coded Trend Detection** (to highlight bullish and bearish conditions).
- **Poisson-Based Trend Reversal Probability Projection** (to anticipate when a trend is likely to end based on statistical models).
By integrating **fractal market theory (Hurst exponent)** with **Poisson probability distributions**, this indicator gives traders a **probability-weighted view of trend duration** while dynamically adapting to market volatility.
---
## **Simplified Explanation (How to Read the Indicator at a Glance)**
1. **If the oscillator line is going up โ The trend is strong.**
2. **If the oscillator line is going down โ The trend is weakening.**
3. **If the color shifts from red to green (or vice versa), a trend shift has occurred.**
- **Strong trends can change color without weakening** (meaning a bullish or bearish move can remain powerful even as the trend shifts).
4. **A weakening trend does NOT necessarily mean a reversal is coming.**
- The trend may slow down but continue in the same direction.
5. **A strong trend does NOT guarantee it will last.**
- Even a powerful move can **suddenly reverse**, which is why the **Poisson-based background shading** helps anticipate probabilities of change.
---
## **How to Use the Indicator**
### **1. Understanding the Rolling Hurst-Based Trend Oscillator (Main Line)**
The **oscillator line** is based on the **Hurst exponent (H)**, which quantifies whether price movements are:
- **Trending** (values above 0 โ momentum-driven, persistent trends).
- **Mean-reverting** (values below 0 โ price action is choppy, likely to revert to the mean).
- **Neutral (Random Walk)** (values around 0 โ price behaves like a purely stochastic process).
#### **Interpreting the Oscillator:**
- **H > 0.5 โ Persistent Trends:**
- Price moves tend to sustain in one direction for longer periods.
- Example: Strong uptrends in bull markets.
- **H < 0.5 โ Mean-Reverting Behavior:**
- Price has a tendency to revert back to its mean.
- Example: Sideways markets or fading momentum.
- **H โ 0.5 โ Random Walk:**
- No clear trend; price is unpredictable.
A **gray dashed horizontal line at 0** serves as a **baseline**, helping traders quickly assess whether the market is **favoring trends or mean reversion**.
---
### **2. Color-Coded Trend Signal (Visual Confirmation of Trend Shifts)**
The oscillator **changes color** based on **price slope** over the lookback period:
- **๐ข Green โ Uptrend (Price Increasing)**
- Price is rising relative to the selected lookback period.
- Suggests sustained bullish pressure.
- **๐ด Red โ Downtrend (Price Decreasing)**
- Price is falling relative to the selected lookback period.
- Suggests sustained bearish pressure.
#### **How to Use This in Trading**
โ **Stay in trends until a color change occurs.**
โ **Use color changes as confirmation for trend reversals.**
โ **Avoid counter-trend trades when the oscillator remains strongly colored.**
---
### **3. Poisson-Based Trend Reversal Projection (Anticipating Future Shifts)**
The **shaded orange background** represents a **Poisson-based probability estimation** of when the trend is likely to reverse.
- **Darker Orange = Higher Probability of Trend Reversal**
- **Lighter Orange / No Shade = Low Probability of Immediate Reversal**
๐ก **The idea behind this model:**
โ Trends **donโt last forever**, and their duration follows **statistical patterns**.
โ By calculating the **average historical trend duration**, the indicator predicts **how likely a trend shift is at any given time**.
โ The **Poisson probability function** is applied to determine the **expected likelihood of a reversal as time progresses**.
---
## **Mathematical Foundations of the Indicator**
This indicator is based on **two primary statistical models**:
### **1. Hurst Exponent & Trend Persistence (Fractal Market Theory)**
- The **Hurst exponent (H)** measures **autocorrelation** in price movements.
- If past trends **persist**, H will be **above 0.5** (meaning trend-following strategies are favorable).
- If past trends tend to **mean-revert**, H will be **below 0.5** (meaning reversal strategies are more effective).
- The **Rolling Hurst Oscillator** calculates this exponent over a moving window to track real-time trend conditions.
#### **Formula Breakdown (Simplified for Traders)**
The Hurst exponent (H) is derived using the **Rescaled Range (R/S) Analysis**:
\
Where:
- **R** = **Range** (difference between max cumulative deviation and min cumulative deviation).
- **S** = **Standard deviation** of price fluctuations.
- **Lookback** = The number of periods analyzed.
---
### **2. Poisson-Based Trend Reversal Probability (Stochastic Process Modeling)**
The **Poisson process** is a **probabilistic model used for estimating time-based events**, applied here to **predict trend reversals based on past trend durations**.
#### **How It Works**
- The indicator **tracks trend durations** (the time between color changes).
- A **Poisson rate parameter (ฮป)** is computed as:
\
- The **probability of a reversal at any given time (t)** is estimated using:
\
- **As t increases (trend continues), the probability of reversal rises**.
- The indicator **shades the background based on this probability**, visually displaying the likelihood of a **trend shift**.
---
## **Dynamic Adaptation to Market Conditions**
โ **Volatility-Adjusted Trend Shifts:**
- A **custom volatility calculation** dynamically adjusts the **minimum trend duration** required before a trend shift is recognized.
- **Higher volatility โ Requires longer confirmation before switching trend color.**
- **Lower volatility โ Allows faster trend shifts.**
โ **Adaptive Poisson Weighting:**
- **Recent trends are weighted more heavily** using an exponential decay function:
- **Decay Factor (0.618 by default)** prioritizes **recent intervals** while still considering historical trends.
- This ensures the model adapts to changing market conditions.
---
## **Key Takeaways for Traders**
โ
**Identify Persistent Trends vs. Mean Reversion:**
- Use the oscillator line to determine whether the market favors **trend-following or counter-trend strategies**.
โ
**Visual Trend Confirmation via Color Coding:**
- **Green = Uptrend**, **Red = Downtrend**.
- Trend changes help confirm **entry and exit points**.
โ
**Anticipate Trend Reversals Using Probability Models:**
- The **Poisson projection** provides a **statistical edge** in **timing exits before trends reverse**.
โ
**Adapt to Market Volatility Automatically:**
- Dynamic **volatility scaling** ensures the indicator remains effective in **both high and low volatility environments**.
Happy trading and enjoy!
Quarterly Theory ICT 02 [TradingFinder] True Open Session 90 Min๐ต Introduction
The Quarterly Theory ICT indicator is an advanced analytical system built on ICT (Inner Circle Trader) concepts and fractal time. It divides time into four quarters (Q1, Q2, Q3, Q4), and is designed based on the consistent repetition of these phases across all trading timeframes (annual, monthly, weekly, daily, and even shorter trading sessions).
Each cycle consists of four distinct phases: the first phase (Q1) is the Accumulation phase, characterized by price consolidation; the second phase (Q2), known as Manipulation or Judas Swing, is marked by initial false movements indicating a potential shift; the third phase (Q3) is Distribution, where price volatility peaks; and the fourth phase (Q4) is Continuation/Reversal, determining whether the previous trend continues or reverses.
๐ต How to Use
The central concept of this strategy is the "True Open," which refers to the actual starting point of each time cycle. The True Open is typically defined at the beginning of the second phase (Q2) of each cycle. Prices trading above or below the True Open serve as a benchmark for predicting the market's potential direction and guiding trading decisions.
The practical application of the Quarterly Theory strategy relies on accurately identifying True Open points across various timeframes.
True Open points are defined as follows :
Yearly Cycle :
Q1: January, February, March
Q2: April, May, June (True Open: April Monthly Open)
Q3: July, August, September
Q4: October, November, December
Monthly Cycle :
Q1: First Monday of the month
Q2: Second Monday of the month (True Open: Daily Candle Open price on the second Monday)
Q3: Third Monday of the month
Q4: Fourth Monday of the month
Weekly Cycle :
Q1: Monday
Q2: Tuesday (True Open: Daily Candle Open Price on Tuesday)
Q3: Wednesday
Q4: Thursday
Daily Cycle :
Q1: 18:00 - 00:00 (Asian session)
Q2: 00:00 - 06:00 (True Open: Start of London Session)
Q3: 06:00 - 12:00 (NY AM)
Q4: 12:00 - 18:00 (NY PM)
90 Min Asian Session :
Q1: 18:00 - 19:30
Q2: 19:30 - 21:00 (True Open at 19:30)
Q3: 21:00 - 22:30
Q4: 22:30 - 00:00
90 Min London Session :
Q1: 00:00 - 01:30
Q2: 01:30 - 03:00 (True Open at 01:30)
Q3: 03:00 - 04:30
Q4: 04:30 - 06:00
90 Min New York AM Session :
Q1: 06:00 - 07:30
Q2: 07:30 - 09:00 (True Open at 07:30)
Q3: 09:00 - 10:30
Q4: 10:30 - 12:00
90 Min New York PM Session :
Q1: 12:00 - 13:30
Q2: 13:30 - 15:00 (True Open at 13:30)
Q3: 15:00 - 16:30
Q4: 16:30 - 18:00
Micro Cycle (22.5-Minute Quarters) : Each 90-minute quarter is further divided into four 22.5-minute sub-segments (Micro Sessions).
True Opens in these sessions are defined as follows :
Asian Micro Session :
True Session Open : 19:30 - 19:52:30
London Micro Session :
T rue Session Open : 01:30 - 01:52:30
New York AM Micro Session :
True Session Open : 07:30 - 07:52:30
New York PM Micro Session :
True Session Open : 13:30 - 13:52:30
By accurately identifying these True Open points across various timeframes, traders can effectively forecast the market direction, analyze price movements in detail, and optimize their trading positions. Prices trading above or below these key levels serve as critical benchmarks for determining market direction and making informed trading decisions.
๐ต Setting
Show True Range : Enable or disable the display of the True Range on the chart, including the option to customize the color.
Extend True Range Line : Choose how to extend the True Range line on the chart, with the following options:
None: No line extension
Right: Extend the line to the right
Left: Extend the line to the left
Both: Extend the line in both directions (left and right)
Show Table : Determines whether the tableโwhich summarizes the phases (Q1 to Q4)โis displayed.
Show More Info : Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
๐ต Conclusion
The Quarterly Theory ICT, by dividing time into four distinct quarters (Q1, Q2, Q3, and Q4) and emphasizing the concept of the True Open, provides a structured and repeatable framework for analyzing price action across multiple time frames.
The consistent repetition of phasesโAccumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversalโallows traders to effectively identify recurring price patterns and critical market turning points. Utilizing the True Open as a benchmark, traders can more accurately determine potential directional bias, optimize trade entries and exits, and manage risk effectively.
By incorporating principles of ICT (Inner Circle Trader) and fractal time, this strategy enhances market forecasting accuracy across annual, monthly, weekly, daily, and shorter trading sessions. This systematic approach helps traders gain deeper insight into market structure and confidently execute informed trading decisions.
SYMPL Reversal BandsThis is an expansion of the Hybrid moving average. It uses the same hybrid moving code from the hybrid moving average script with an additional layer using the ta.hma function for some slight additional smoothing. Colors of the bands change dynamically based of the long and short hybrid moving averages running in the background. This can be really helpful in identifying periods to short bounces or long dips.
Below is the explanation of the hybrid moving average
Hybrid Moving Average Market Trend System - , designed to visualize market trends using a combination of three moving averages: FRAMA (Fractal Adaptive Moving Average), VIDYA (Variable Index Dynamic Average), and a Hamming windowed Volume-Weighted Moving Average (VWMA).
Key Features:
FRAMA Calculation:
FRAMA adapts to market volatility by dynamically adjusting its smoothing factor based on the fractal dimension of price movement. This allows it to be more responsive during trending periods while filtering out noise in sideways markets. The FRAMA is calculated for both short and long periods
VIDYA with CMO:
The VIDYA (Variable Index Dynamic Average) is based on a Chande Momentum Oscillator (CMO), which adjusts the smoothing factor dynamically depending on the momentum of the market. Higher momentum periods result in more responsive averages, while low momentum periods lead to smoother averages. Like FRAMA, VIDYA is calculated for both short and long periods.
Hamming Windowed VWMA:
This VWMA variation applies a Hamming window to smooth the weighting of volume across the calculation period. This method emphasizes central data points and reduces noise, making the VWMA more adaptive to volume fluctuations. The Hamming VWMA is calculated for short and long periods, offering another layer of adaptability to the hybrid moving average.
Hybrid Moving Averages:
Dynamic Coloring and Filling:
The script uses dynamic color transitions to visually distinguish between bullish and bearish conditions:
Hybrid Moving Average - Market TrendHybrid Moving Average Market Trend System - , designed to visualize market trends using a combination of three moving averages: FRAMA (Fractal Adaptive Moving Average), VIDYA (Variable Index Dynamic Average), and a Hamming windowed Volume-Weighted Moving Average (VWMA).
Key Features:
FRAMA Calculation:
FRAMA adapts to market volatility by dynamically adjusting its smoothing factor based on the fractal dimension of price movement. This allows it to be more responsive during trending periods while filtering out noise in sideways markets. The FRAMA is calculated for both short and long periods
VIDYA with CMO:
The VIDYA (Variable Index Dynamic Average) is based on a Chande Momentum Oscillator (CMO), which adjusts the smoothing factor dynamically depending on the momentum of the market. Higher momentum periods result in more responsive averages, while low momentum periods lead to smoother averages. Like FRAMA, VIDYA is calculated for both short and long periods.
Hamming Windowed VWMA:
This VWMA variation applies a Hamming window to smooth the weighting of volume across the calculation period. This method emphasizes central data points and reduces noise, making the VWMA more adaptive to volume fluctuations. The Hamming VWMA is calculated for short and long periods, offering another layer of adaptability to the hybrid moving average.
Hybrid Moving Averages:
Dynamic Coloring and Filling:
The script uses dynamic color transitions to visually distinguish between bullish and bearish conditions:
ka66: Bar Range BandsThis tool takes a bar's range, and reflects it above the high and below the low of that bar, drawing upper and lower bands around the bar. Repeated for each bar. There's an option to then multiply that range by some multiple. Use a value greater than 1 to get wider bands, and less than one to get narrower bands.
This tool stems out of my frustration from the use of dynamic bands (like Keltner Channels, or Bollinger Bands), in particular for estimating take profit points.
Dynamic bands work great for entries and stop loss, but their dynamism is less useful for a future event like taking profit, in my experience. We can use a smaller multiple, but then we can often lose out on a bigger chunk of gains unnecessarily.
The inspiration for this came from a friend explaining an ICT/SMC concept around estimating the magnitude of a trend, by calculating the Asian Session Range, and reflecting it above or below on to the New York and London sessions. He described this as standard deviation of the Asian Range, where the range can thus be multiplied by some multiple for a wider or narrower deviation.
This, in turn, also reminded me of the Measured Move concept in Technical Analysis. We then consider that the market is fractal in nature, and this is why patterns persist in most timeframes. Traders exist across the spectrum of timeframes. Thus, a single bar on a timeframe, is made up of multiple bars on a lower timeframe . In other words, when we reflect a bar's range above or below itself, in the event that in a lower timeframe, that bar fit a pattern whose take profit target could be estimated via a Measured Move , then the band's value becomes a more valid estimate of a take profit point .
Yet another way to think about it, by way of the fractal nature above, is that it is essentially a simplified dynamic support and resistance mechanism , even simpler than say the various Pivot calculations (e.g. Classical, Camarilla, etc.).
This tool in general, can also be used by those who manually backtest setups (and certainly can be used in an automated setting too!). It is a research tool in that regard, applicable to various setups.
One of the pitfalls of manual backtesting is that it requires more discipline to really determine an exit point, because it's easy to say "oh, I'll know more or less where to exit when I go live, I just want to see that the entry tends to work". From experience, this is a bad idea, because our mind subconsciously knows that we haven't got a trained reflex on where to exit. The setup may be decent, but without an exit point, we will never have truly embraced and internalised trading it. Again, I speak from experience!
Thus, to use this to research take profit/exit points:
Have a setup in mind, with all the entry rules.
Plot your setup's indicators, mark your signals.
Use this indicator to get an idea of where to exit after taking an entry based on your signal.
Credits:
@ICT_ID for providing the idea of using ranges to estimate how far a trend move might go, in particular he used the Asian Range projected on to the London and New York market sessions.
All the technicians who came up with the idea of the Measured Move.
Futures Weekly Open RangeThe weekly opening range ( high to low ) is calculated from the open of the market on Sunday (1800 EST) till the opening of the Bond Market on Monday morning (0800 EST). This is the first and most crucial range for the trading week. As ICT has taught, price is moving through an algorithm and as such is fractal; because price is fractal, the opening range can be calculated and projected to help determine if price is trending or consolidating. As well; this indicator can be used to incorporate his PO3 concept to enter above the weekly opening range for shorts if bearish, or entering below the opening range for longs if bullish.
This indicator takes the high and low of weekly opening range, plots those two levels, plots the opening price for the new week, and calculates the Standard Deviations of the range and plots them both above and below of the weekly opening range. These are all plotted through the week until the start of the new week.
The range is calculated by subtracting the high from the low during the specified time.
The mid-point is half of that range added to the low.
The Standard deviation is multiples of the range (up to 10) added to the high and subtracted
from the low.
At this time the indicator will only plot the Standard deviation lines on the minutes time frame below 1 hour.
Only the range and range lines will be plotted on the hourly chart.
