Pattern Forecast (Expo)█ Overview
The Pattern Forecast indicator is a technical analysis tool that scans historical price data to identify common chart patterns and then analyzes the price movements that followed these patterns. It takes this information and projects it into the future to provide traders with potential price actions that may occur if the same pattern is identified in real-time market data. This projection helps traders to understand the possible outcomes based on the previous occurrences of the pattern, thereby offering a clearer perspective of the market scenario. By analyzing the historical data and understanding the subsequent price movements following the appearance of a specific pattern, the indicator can provide valuable insights into potential future market behavior.
█ Calculations
The indicator works by scanning historical price data for various candlestick patterns. It includes all in-built TradingView patterns, credit to TradingView that has coded them.
Essentially, the indicator takes the historical price moves that followed the pattern to forecast what might happen next.
█ Example
In this example, the algorithm is set to search for the Inverted Hammer Bullish candlestick pattern. If the pattern is found, the historical outcome is then projected into the future. This helps traders to understand how the past pattern evolved over time.
█ How to use
Providing traders with a comprehensive understanding of historical patterns and their implications for future price action allows them to assess the likelihood of specific market scenarios objectively. For example, suppose the pattern forecast indicator suggests that a particular pattern is likely to lead to a bullish move in the market. A trader might consider going long if the same pattern is identified in the real-time market. Similarly, a trader might consider shorting the asset if the indicator suggests a bearish move is likely, if the same pattern is identified in the real-time market.
█ Settings
Pattern
Select the pattern that the indicator should scan for. All inbuilt TradingView patterns can be selected.
Forecast Candles
Number of candles to project into the future.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Tìm kiếm tập lệnh với "algo"
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
loxfft
Smoothing R-Squared ComparisonIntroduction
Heyo guys, here I made a comparison between my favorised smoothing algorithms.
I chose the R-Squared value as rating factor to accomplish the comparison.
The indicator is non-repainting.
Description
In technical analysis, traders often use moving averages to smooth out the noise in price data and identify trends. While moving averages are a useful tool, they can also obscure important information about the underlying relationship between the price and the smoothed price.
One way to evaluate this relationship is by calculating the R-squared value, which represents the proportion of the variance in the price that can be explained by the smoothed price in a linear regression model.
This PineScript code implements a smoothing R-squared comparison indicator.
It provides a comparison of different smoothing techniques such as Kalman filter, T3, JMA, EMA, SMA, Super Smoother and some special combinations of them.
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement.
The input parameters for the Kalman filter include the process noise covariance and the measurement noise covariance, which help to adjust the sensitivity of the filter to changes in the input data.
The T3 smoothing technique is a popular method used in technical analysis to remove noise from a signal.
The input parameters for the T3 smoothing method include the length of the window used for smoothing, the type of smoothing used (Normal or New), and the smoothing factor used to adjust the sensitivity to changes in the input data.
The JMA smoothing technique is another popular method used in technical analysis to remove noise from a signal.
The input parameters for the JMA smoothing method include the length of the window used for smoothing, the phase used to shift the input data before applying the smoothing algorithm, and the power used to adjust the sensitivity of the JMA to changes in the input data.
The EMA and SMA techniques are also popular methods used in technical analysis to remove noise from a signal.
The input parameters for the EMA and SMA techniques include the length of the window used for smoothing.
The indicator displays a comparison of the R-squared values for each smoothing technique, which provides an indication of how well the technique is fitting the data.
Higher R-squared values indicate a better fit. By adjusting the input parameters for each smoothing technique, the user can compare the effectiveness of different techniques in removing noise from the input data.
Usage
You can use it to find the best fitting smoothing method for the timeframe you usually use.
Just apply it on your preferred timeframe and look for the highlighted table cell.
Conclusion
It seems like the T3 works best on timeframes under 4H.
There's where I am active, so I will use this one more in the future.
Thank you for checking this out. Enjoy your day and leave me a like or comment. 🧙♂️
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Credits to:
▪@loxx – T3
▪@balipour – Super Smoother
▪ChatGPT – Wrote 80 % of this article and helped with the research
Spectral Gating (SG)The Spectral Gating (SG) Indicator is a technical analysis tool inspired by music production techniques. It aims to help traders reduce noise in their charts by focusing on the significant frequency components of the data, providing a clearer view of market trends.
By incorporating complex number operations and Fast Fourier Transform (FFT) algorithms, the SG Indicator efficiently processes market data. The indicator transforms input data into the frequency domain and applies a threshold to the power spectrum, filtering out noise and retaining only the frequency components that exceed the threshold.
Key aspects of the Spectral Gating Indicator include:
Adjustable Window Size: Customize the window size (ranging from 2 to 6) to control the amount of data considered during the analysis, giving you the flexibility to adapt the indicator to your trading strategy.
Complex Number Arithmetic: The indicator uses complex number addition, subtraction, and multiplication, as well as radius calculations for accurate data processing.
Iterative FFT and IFFT: The SG Indicator features iterative FFT and Inverse Fast Fourier Transform (IFFT) algorithms for rapid data analysis. The FFT algorithm converts input data into the frequency domain, while the IFFT algorithm restores the filtered data back to the time domain.
Spectral Gating: At the heart of the indicator, the spectral gating function applies a threshold to the power spectrum, suppressing frequency components below the threshold. This process helps to enhance the clarity of the data by reducing noise and focusing on the more significant frequency components.
Visualization: The indicator plots the filtered data on the chart with a simple blue line, providing a clean and easily interpretable representation of the results.
Although the Spectral Gating Indicator may not be a one-size-fits-all solution for all trading scenarios, it serves as a valuable tool for traders looking to reduce noise and concentrate on relevant market trends. By incorporating this indicator into your analysis toolkit, you can potentially make more informed trading decisions.
PSv5 3D Array/Matrix Super Hack"In a world of ever pervasive and universal deceit, telling a simple truth is considered a revolutionary act."
INTRO:
First, how about a little bit of philosophic poetry with another dimension applied to it?
The "matrix of control" is everywhere...
It is all around us, even now in the very place you reside. You can see it when you look at your digitized window outwards into the world, or when you turn on regularly scheduled television "programs" to watch news narratives and movies that subliminally influence your thoughts, feelings, and emotions. You have felt it every time you have clocked into dead end job workplaces... when you unknowingly worshiped on the conformancy alter to cultish ideologies... and when you pay your taxes to a godvernment that is poisoning you softly and quietly by injecting your mind and body with (psyOps + toxicCompounds). It is a fictitiously generated world view that has been pulled over your eyes to blindfold, censor, and mentally prostrate you from spiritually hearing the real truth.
What TRUTH you must wonder? That you are cognitively enslaved, like everyone else. You were born into mental bondage, born into an illusory societal prison complex that you are entirely incapable of smelling, tasting, or touching. Its a contrived monetary prison enterprise for your mind and eternal soul, built by pretending politicians, corporate CONartists, and NonGoverning parasitic Organizations deploying any means of infiltration and deception by using every tactic unimaginable. You are slowly being convinced into becoming a genetically altered cyborg by acclimation, socially engineered and chipped to eventually no longer be 100% human.
Unfortunately no one can be told eloquently enough in words what the matrix of control truly is. You have to experience it and witness it for yourself. This is your chance to program a future paradigm that doesn't yet exist. After visiting here, there is absolutely no turning back. You can continually take the blue pill BIGpharmacide wants you to repeatedly intake. The story ends if you continually sleep walk through a 2D hologram life, believing whatever you wish to believe until you cease to exist. OR, you can take the red pill challenge, explore "question every single thing" wonderland, program your arse off with 3D capabilities, ultimately ascertaining a new mathematical empyrean. Only then can you fully awaken to discover how deep the rabbit hole state of affairs transpire worldwide with a genuine open mind.
Remember, all I'm offering is a mathematical truth, nothing more...
PURPOSE:
With that being said above, it is now time for advanced developers to start creating their own matrix constructs in 3D, in Pine, just as the universe is created spatially. For those of you who instantly know what this script's potential is easily capable of, you already know what you have to do with it. While this is simplistically just a 3D array for either integers or floats, additional companion functions can in the future be constructed by other members to provide a more complete matrix/array library for millions of folks on TV. I do encourage the most courageous of mathemagicians on TV to do so. I have been employing very large 2D/3D array structures for quite some time, and their utility seems to be of great benefit. Discovering that for myself, I fully realized that Pine is incomplete and must be provided with this agility to process complex datasets that traders WILL use in the future. Mark my words!
CONCEPTION:
While I have long realized and theorized this code for a great duration of time, I was finally able to turn it into a Pine reality with the assistance and training of an "artificially intuitive" program while probing its aptitude. Even though it knows virtually nothing about Pine Script 4.0 or 5.0 syntax, functions, and behavior, I was able to conjure code into an identity similar to what you see now within a few minutes. Close enough for me! Many manual edits later for pine compliance, and I had it in chart, presto!
While most people consider the service to be an "AI", it didn't pass my Pine Turing test. I did have to repeatedly correct it, suffered through numerous apologies from it, was forced to use specifically tailored words, and also rationally debate AND argued with it. It is a handy helper but beware of generating Pine code from it, trust me on this one. However... this artificially intuitive service is currently available in its infancy as version 3. Version 4 most likely will have more diversity to enhance my algorithmic expertise of Pine wizardry. I do have to thank E.M. and his developers for an eye opening experience, or NONE of this code below would be available as you now witness it today.
LIMITATIONS:
As of this initial release, Pine only supports 100,000 array elements maximum. For example, when using this code, a 50x50x40 element configuration will exceed this limit, but 50x50x39 will work. You will always have to keep that in mind during development. Running that size of an array structure on every single bar will most likely time out within 20-40 seconds. This is not the most efficient method compared to a real native 3D array in action. Ehlers adepts, this might not be 100% of what you require to "move forward". You can try, but head room with a low ceiling currently will be challenging to walk in for now, even with extremely optimized Pine code.
A few common functions are provided, but this can be extended extensively later if you choose to undertake that endeavor. Use the code as is and/or however you deem necessary. Any TV member is granted absolute freedom to do what they wish as they please. I ultimately wish to eventually see a fully equipped library version for both matrix3D AND array3D created by collaborative efforts that will probably require many Pine poets testing collectively. This is just a bare bones prototype until that day arrives. Considerably more computational server power will be required also. Anyways, I hope you shall find this code somewhat useful.
Notice: Unfortunately, I will not provide any integration support into members projects at all. I have my own projects that require too much of my time already.
POTENTIAL APPLICATIONS:
The creation of very large coefficient 3D caches/buffers specifically at bar_index==0 can dramatically increase runtime agility for thousands of bars onwards. Generating 1000s of values once and just accessing those generated values is much faster. Also, when running dozens of algorithms simultaneously, a record of performance statistics can be kept, self-analyzed, and visually presented to the developer/user. And, everything else under the sun can be created beyond a developers wildest dreams...
EPILOGUE:
Free your mind!!! And unleash weapons of mass financial creation upon the earth for all to utilize via the "Power of Pine". Flying monkeys and minions are waging economic sabotage upon humanity, decimating markets and exchanges. You can always see it your market charts when things go horribly wrong. This is going to be an astronomical technical challenge to continually navigate very choppy financial markets that are increasingly becoming more and more unstable and volatile. Ordinary one plot algorithms simply are not enough anymore. Statistics and analysis sits above everything imagined. This includes banking, godvernment, corporations, REAL science, technology, health, medicine, transportation, energy, food, etc... We have a unique perspective of the world that most people will never get to see, depending on where you look. With an ever increasingly complex world in constant dynamic flux, novel ways to process data intricately MUST emerge into existence in order to tackle phenomenal tasks required in the future. Achieving data analysis in 3D forms is just one lonely step of many more to come.
At this time the WesternEconomicFraudsters and the WorldHealthOrders are attempting to destroy/reset the world's financial status in order to rain in chaos upon most nations, causing asset devaluation and hyper-inflation. Every form of deception, infiltration, and theft is occurring with a result of destroyed wealth in preparation to consolidate it. Open discussions, available to the public, by world leaders/moguls are fantasizing about new dystopian system as a one size fits all nations solution of digitalID combined with programmableDemonicCurrencies to usher in a new form of obedient servitude to a unipolar digitized hegemony of monetary vampires. If they do succeed with economic conquest, as they have publicly stated, people will be converted into human cattle, herded within smart cities, you will own nothing, eat bugs for breakfast/lunch/dinner, live without heat during severe winter conditions, and be happy. They clearly haven't done the math, as they are far outnumbered by a ratio of 1 to millions. Sith Lords do not own planet Earth! The new world disorder of human exploitation will FAIL. History, my "greatest teacher" for decades reminds us over, and over, and over again, and what are time series for anyways? They are for an intense mathematical analysis of prior historical values/conditions in relation to today's values/conditions... I imagine one day we will be able to ask an all-seeing AI, "WHO IS TO BLAME AND WHY AND WHEN?" comprised of 300 pages in great detail with images, charts, and statistics.
What are the true costs of malignant lies? I will tell you... 64bit numbers are NOT even capable of calculating the extreme cost of pernicious lies and deceit. That's how gigantic this monstrous globalization problem has become and how awful the "matrix of control" truly is now. ALL nations need a monumental revision of its CODE OF ETHICS, and that's definitely a multi-dimensional problem that needs solved sooner than later. If it was up to me, economies and technology would be developed so extensively to eliminate scarcity and increase the standard of living so high, that the notion of war and conflict would be considered irrelevant and extremely appalling to the future generations of humanity, our grandchildren born and unborn. The future will not be owned and operated by geriatric robber barons destined to expire quickly. The future will most likely be intensely "guided" by intelligent open source algorithms that youthful generations will inherit as their birth right.
P.S. Don't give me that politco-my-diction crap speech below in comments. If they weren't meddling with economics mucking up 100% of our chart results in 100% of tickers, I wouldn't have any cause to analyze any effects generated by them, nor provide this script's code. I am performing my analytical homework, but have you? Do you you know WHY international affairs are in dire jeopardy? Without why, the "Power of Pine" would have never existed as it specifically does today. I'm giving away much of my mental power generously to TV members so you are specifically empowered beyond most mathematical agilities commonly existing. I'm just a messenger of profound ideas. Loving and loathing of words is ALWAYS in the eye of beholders, and that's why the freedom of speech is enshrined as #1 in the constitutional code of the USA. Without it, this entire site might not have been allowed to exist from its founder's inceptions.
Fourier Extrapolator of 'Caterpillar' SSA of Price [Loxx]Fourier Extrapolator of 'Caterpillar' SSA of Price is a forecasting indicator that applies Singular Spectrum Analysis to input price and then injects that transformed value into the Quinn-Fernandes Fourier Transform algorithm to generate a price forecast. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia dotted curve indicates the future extrapolated values.
What is the Fourier Transform Extrapolator of price?
Fourier Extrapolator of Price is a multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by:
xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H )
Where:
xi - past price at i-th bar, total n past prices;
m - bias;
a and b - scaling coefficients of harmonics;
w - frequency of a harmonic ;
h - harmonic number;
H - total number of fitted harmonics.
Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.
Quinn-Fernandes algorithm find sthe harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.
see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press
Fourier Transform Extrapolator of Price inputs are as follows:
npast - number of past bars, to which trigonometric series is fitted;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
"Caterpillar" SSA inputs are as follows:
lag - How much lag to introduce into the SSA algorithm, the higher this number the slower the process and smoother the signal
ncomp - Number of Computations or cycles of of the SSA algorithm; the higher the slower
ssapernorm - SSA Period Normalization
numbars =- number of past bars, to which SSA is fitted
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Related Fourier Transform Indicators
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Related Projection Forecast Indicators
Itakura-Saito Autoregressive Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
Related SSA Indicators
End-pointed SSA of FDASMA
End-pointed SSA of Williams %R
Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]Levinson-Durbin Autocorrelation Extrapolation of Price is an indicator that uses the Levinson recursion or Levinson–Durbin recursion algorithm to predict price moves. This method is commonly used in speech modeling and prediction engines.
What is Levinson recursion or Levinson–Durbin recursion?
Is a linear algebra prediction analysis that is performed once per bar using the autocorrelation method with a within a specified asymmetric window. The autocorrelation coefficients of the window are computed and converted to LP coefficients using the Levinson algorithm. The LP coefficients are then transformed to line spectrum pairs for quantization and interpolation. The interpolated quantized and unquantized filters are converted back to the LP filter coefficients to construct the synthesis and weighting filters for each bar.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is caculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Included
Bar color muting
Further reading
Implementing the Levinson-Durbin Algorithm on the StarCore™ SC140/SC1400 Cores
LevinsonDurbin_G729 Algorithm, Calculates LP coefficients from the autocorrelation coefficients. Intel® Integrated Performance Primitives for Intel® Architecture Reference Manual
APA-Adaptive, Ehlers Early Onset Trend [Loxx]APA-Adaptive, Ehlers Early Onset Trend is Ehlers Early Onset Trend but with Autocorrelation Periodogram Algorithm dominant cycle period input.
What is Ehlers Early Onset Trend?
The Onset Trend Detector study is a trend analyzing technical indicator developed by John F. Ehlers , based on a non-linear quotient transform. Two of Mr. Ehlers' previous studies, the Super Smoother Filter and the Roofing Filter, were used and expanded to create this new complex technical indicator. Being a trend-following analysis technique, its main purpose is to address the problem of lag that is common among moving average type indicators.
The Onset Trend Detector first applies the EhlersRoofingFilter to the input data in order to eliminate cyclic components with periods longer than, for example, 100 bars (default value, customizable via input parameters) as those are considered spectral dilation. Filtered data is then subjected to re-filtering by the Super Smoother Filter so that the noise (cyclic components with low length) is reduced to minimum. The period of 10 bars is a default maximum value for a wave cycle to be considered noise; it can be customized via input parameters as well. Once the data is cleared of both noise and spectral dilation, the filter processes it with the automatic gain control algorithm which is widely used in digital signal processing. This algorithm registers the most recent peak value and normalizes it; the normalized value slowly decays until the next peak swing. The ratio of previously filtered value to the corresponding peak value is then quotiently transformed to provide the resulting oscillator. The quotient transform is controlled by the K coefficient: its allowed values are in the range from -1 to +1. K values close to 1 leave the ratio almost untouched, those close to -1 will translate it to around the additive inverse, and those close to zero will collapse small values of the ratio while keeping the higher values high.
Indicator values around 1 signify uptrend and those around -1, downtrend.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Jurik Composite Fractal Behavior (CFB) on EMA [Loxx]Jurik Composite Fractal Behavior (CFB) on EMA is an exponential moving average with adaptive price trend duration inputs. This purpose of this indicator is to introduce the formulas for the calculation Composite Fractal Behavior. As you can see from the chart above, price reacts wildly to shifts in volatility--smoothing out substantially while riding a volatility wave and cutting sharp corners when volatility drops. Notice the chop zone on BTC around August 2021, this was a time of extremely low relative volatility.
This indicator uses three previous indicators from my public scripts. These are:
JCFBaux Volatility
Jurik Filter
Jurik Volty
The CFB is also related to the following indicator
Jurik Velocity ("smoother moment")
Now let's dive in...
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.
Modifications and improvements
1. Jurik's original calculation for CFB only allowed for depth lengths of 24, 48, 96, and 192. For theoretical purposes, this indicator allows for up to 20 different depth inputs to sample volatility. These depth lengths are
2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536
Including these additional length inputs is arguable useless, but they are are included for completeness of the algorithm.
2. The result of the CFB calculation is forced to be an integer greater than or equal to 1.
3. The result of the CFB calculation is double filtered using an advanced, (and adaptive itself) filtering algorithm called the Jurik Filter. This filter and accompanying internal algorithm are discussed above.
Customizable Non-Repainting HTF MACD MFI Scalper Bot StrategyThis script was originally shared by Wunderbit as a free open source script for the community to work with.
WHAT THIS SCRIPT DOES:
It is intended for use on an algorithmic bot trading platform but can be used for scalping and manual trading.
This strategy is based on the trend-following momentum indicator . It includes the Money Flow index as an additional point for entry.
HOW IT DOES IT:
It uses a combination of MACD and MFI indicators to create entry signals. Parameters for each indicator have been surfaced for user configurability.
Take profits are fixed, but stop loss uses ATR configuration to minimize losses and close profitably.
HOW IS MY VERSION ORIGINAL:
I started trying to deploy this script myself in my algorithmic trading but ran into some issues which I have tried to address in this version.
Delayed Signals : The script has been refactored to use a time frame drop down. The higher time frame can be run on a faster chart (recommended on one minute chart for fastest signal confirmation and relay to algotrading platform.)
Repainting Issues : All indicators have been recoded to use the security function that checks to see if the current calculation is in realtime, if it is, then it uses the previous bar for calculation. If you are still experiencing repainting issues based on intended (or non intended use), please provide a report with screenshot and explanation so I can try to address.
Filtering : I have added to additional filters an ABOVE EMA Filter and a BELOW RSI Filter (both can be turned on and off)
Customizable Long and Close Messages : This allows someone to use the script for algorithmic trading without having to alter code. It also means you can use one indicator for all of your different alterts required for your bots.
HOW TO USE IT:
It is intended to be used in the 5-30 minute time frames, but you might be able to get a good configuration for higher time frames. I welcome feedback from other users on what they have found.
Find a pair with high volatility (example KUCOIN:ETH3LUSDT ) - I have found it works particularly well with 3L and 3S tokens for crypto. although it the limitation is that confrigurations I have found to work typically have low R/R ratio, but very high win rate and profit factor.
Ideally set one minute chart for bots, but you can use other charts for manual trading. The signal will be delayed by one bar but I have found configurations that still test well.
Select a time frame in configuration for your indicator calculations.
Select the strategy config for time frame. I like to use 5 and 15 minutes for scalping scenarios, but I am interested in hearing back from other community memebers.
Optimize your indicator without filters (trendFilter and RSI Filter)
Use the TrendFilter and RSI Filter to further refine your signals for entry. You will get less entries but you can increase your win ratio.
I will add screenshots and possibly a video provided that it passes community standards.
Limitations: this works rather well for short term, and does some good forward testing but back testing large data sets is a problem when switching from very small time frame to large time frame. For instance, finding a configuration that works on a one minute chart but then changing to a 1 hour chart means you lose some of your intra bar calclulations. There are some new features in pine script which might be able to address, this, but I have not had a chance to work on that issue.
Bogdan Ciocoiu - MakaveliDescription
This indicator integrates the functionality of multiple volume price analysis algorithms whilst aligning their scales to fit in a single chart.
Having such indicators loaded enables traders to take advantage of potential divergences between the price action and volume related volatility.
Users will have to enable or disable alternative algorithms depending on their choice.
Uniqueness
This indicator is unique because it combines multiple algorithm-specific two-volume analyses with price volatility.
This indicator is also unique because it amends different algorithms to show output on a similar scale enabling traders to observe various volume-analysis tools simultaneously whilst allocating different colour codes.
Open source re-use
This indicator utilises the following open-source scripts:
Acrypto - Weighted StrategyHello traders!
I have been developing a fully customizable algo over the last year. The algorithm is based on a set of different strategies, each with its own weight (weighted strategy). The set of strategies that I currently use are 5:
MACD
Stochastic RSI
RSI
Supertrend
MA crossover
Moreover, the algo includes STOP losses criteria and a taking profit strategy. The algo must be optimized for the desired asset to achieves its full potential. The 1H and 4H dataframe give good results. The algo has been tested for several asset (same dataframe, different optimization values).
Important note:
Backtest the algorithm with different data stamps to avoid overfitting results
Best,
Alberto
MathSearchDijkstraLibrary "MathSearchDijkstra"
Shortest Path Tree Search Methods using Dijkstra Algorithm.
min_distance(distances, flagged_vertices) Find the lowest cost/distance.
Parameters:
distances : float array, data set with distance costs to start index.
flagged_vertices : bool array, data set with visited vertices flags.
Returns: int, lowest cost/distance index.
dijkstra(matrix_graph, dim_x, dim_y, start) Dijkstra Algorithm, perform a greedy tree search to calculate the cost/distance to selected start node at each vertex.
Parameters:
matrix_graph : int array, matrix holding the graph adjacency list and costs/distances.
dim_x : int, x dimension of matrix_graph.
dim_y : int, y dimension of matrix_graph.
start : int, the vertex index to start search.
Returns: int array, set with costs/distances to each vertex from start vertexs.
shortest_path(start, end, matrix_graph, dim_x, dim_y) Retrieves the shortest path between 2 vertices in a graph using Dijkstra Algorithm.
Parameters:
start : int, the vertex index to start search.
end : int, the vertex index to end search.
matrix_graph : int array, matrix holding the graph adjacency list and costs/distances.
dim_x : int, x dimension of matrix_graph.
dim_y : int, y dimension of matrix_graph.
Returns: int array, set with vertex indices to the shortest path.
P-Square - Estimation of the Nth percentile of a seriesEstimation of the Nth percentile of a series
When working with built-in functions in TradingView we have to limit our length parameters to max 4999. In case we want to use a function on the whole available series (bar 0 all the way to the current bar), we can usually not do this without manually creating these calculations in our code. For things like mean or standard deviation, this is quite trivial, but for things like percentiles, this is usually very costly. In more complex scripts, this becomes impossible because of resource restrictions from the Pine Script execution servers.
One solution to this is to use an estimation algorithm to get close to the true percentile value. Therefore, I have ported this implementation of the P-Square algorithm to Pine Script. P-Square is a fast algorithm that does a good job at estimating percentiles in data streams. Here's the algorithms original paper .
The chart
On the chart we see:
The returns of the series (blue scatter plot)
The mean of the returns of the series (orange line)
The standard deviation of the returns of the series (yellow line)
The actual 84.1th percentile of the returns (white line)
The estimatedl 84.1th percentile of the returns using the P-Square algorithm (green line)
Note: We can see that the returns are not normally distributed as we can see that one standard deviation is higher than the 84.1th percentile. One standard deviation should equal the 84.1th percentile if the data is normally distributed.
Machine Learning: Logistic RegressionMulti-timeframe Strategy based on Logistic Regression algorithm
Description:
This strategy uses a classic machine learning algorithm that came from statistics - Logistic Regression (LR).
The first and most important thing about logistic regression is that it is not a 'Regression' but a 'Classification' algorithm. The name itself is somewhat misleading. Regression gives a continuous numeric output but most of the time we need the output in classes (i.e. categorical, discrete). For example, we want to classify emails into “spam” or 'not spam', classify treatment into “success” or 'failure', classify statement into “right” or 'wrong', classify election data into 'fraudulent vote' or 'non-fraudulent vote', classify market move into 'long' or 'short' and so on. These are the examples of logistic regression having a binary output (also called dichotomous).
You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function.
Basically, the theory behind Logistic Regression is very similar to the one from Linear Regression, where we seek to draw a best-fitting line over data points, but in Logistic Regression, we don’t directly fit a straight line to our data like in linear regression. Instead, we fit a S shaped curve, called Sigmoid, to our observations, that best SEPARATES data points. Technically speaking, the main goal of building the model is to find the parameters (weights) using gradient descent.
In this script the LR algorithm is retrained on each new bar trying to classify it into one of the two categories. This is done via the logistic_regression function by updating the weights w in the loop that continues for iterations number of times. In the end the weights are passed through the sigmoid function, yielding a prediction.
Mind that some assets require to modify the script's input parameters. For instance, when used with BTCUSD and USDJPY, the 'Normalization Lookback' parameter should be set down to 4 (2,...,5..), and optionally the 'Use Price Data for Signal Generation?' parameter should be checked. The defaults were tested with EURUSD.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours/Days
[R&D] Moving CentroidThis script utilizes this concept. Instead of weighting by volume, it weights by amount of price action on every close price of the rolling window. I assume it can be used as an additional reference point for price mode and price antimode.
it is directly connected with Market (not volume) profile, or TPO charts.
The algorithm:
1) takes a rolling window of, for example, 50 data points of close prices:
2) for each of this closing prices, the algorithm will check how many bars touched this close price.
3) then: sum of datapoints * weights/sum of weights
Since the logic is implemented in pretty non-efficient way, the script sometimes can take time to make calculations. Moreover, it calculates the centroid taking into account only close prices, not every tick. of a given rolling window That's why it's still experimental.
Enhanced Instantaneous Cycle Period - Dr. John EhlersThis is my first public release of detector code entitled "Enhanced Instantaneous Cycle Period" for PSv4.0 I built many months ago. Be forewarned, this is not an indicator, this is a detector to be used by ADVANCED developers to build futuristic indicators in Pine. The origins of this script come from a document by Dr. John Ehlers entitled "SIGNAL ANALYSIS CONCEPTS". You may find this using the NSA's reverse search engine "goggles", as I call it. John Ehlers' MESA used this measurement to establish the data window for analysis for MESA Cycle computations. So... does any developer wish to emulate MESA Cycle now??
I decided to take instantaneous cycle period to another level of novel attainability in this public release of source code with the following methods, if you are curious how I ENHANCED it. Firstly I reduced the delay of accurate measurement from bar_index==0 by quite a few bars closer to IPO. Secondarily, I provided a limit of 6 for a minimum instantaneous cycle period. At bar_index==0, it would provide a period of 0 wrecking many algorithms from the start. I also increased the instantaneous cycle period's maximum value to 80 from 50, providing a window of 6-80 for the instantaneous cycle period value window limits. Thirdly, I replaced the internal EMA with another algorithm. It reduces the lag while extracting a floating point number, for algorithms that will accept that, compared to a sluggish ordinary EMA return. You will see the excessive EMA delay with adding plot(ema(ICP,7)) as it was originally designed. Lastly it's in one simple function for reusability in a nice little package comprising of less than 40 lines of code. I hope I explained that adequately enough and gave you the reader a glimpse of the "Power of Pine" combined with ingenuity.
Be forewarned again, that most of Pine's built-in functions will not accept a floating-point number or dynamic integers for the "length" of it's calculation. You will have to emulate the built-in functions by creating Pine based custom functions, and I assure you, this is very possible in many cases, but not all without array support. You may use int(ICP) to extract an integer from the smoothICP return variable, which may be favorable compared to the choppiness/ringing if ICP alone.
This is commonly what my dense intricate code looks like behind the veil. If you are wondering why there is barely any notation, that's because the notation is in the variable naming and this is intended primarily for ADVANCED developers too. It does contain lines of code that explore techniques in Pine that may be applicable in other Pine projects for those learning or wishing to excel with Pine.
Showcased in the chart below is my free to use "Enhanced Schaff Trend Cycle Indicator", having a common appeal to TV users frequently. If you do have any questions or comments regarding this indicator, I will consider your inquiries, thoughts, and ideas presented below in the comments section, when time provides it. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
NOTICE: Copy pasting bandits who may be having nefarious thoughts, DO NOT attempt this, because this may violate Tradingview's terms, conditions and/or house rules. "WE" are always watching the TV community vigilantly for mischievous behaviors and actions that exploit well intended authors for the purpose of increasing brownie points in reputation scores. Hiding behind a "protected" wall may not protect you from investigation and account penalization by TV staff. Be respectful, and don't just throw an ma() in there branding it as "your" gizmo. Fair enough? Alrighty then... I firmly believe in "innovating" future state-of-the-art indicators, and please contact me if you wish to do so.
1-Min Gold Taylor Technique# 🟡 1-Minute Gold Taylor Trading Technique - Professional Strategy
## 📊 OVERVIEW
The **1-Minute Gold Taylor Trading Technique** is a sophisticated intraday scalping strategy specifically designed for XAUUSD (Gold) on the 1-minute timeframe. This strategy implements George Douglas Taylor's classic 3-Day Trading Cycle adapted for modern algorithmic trading with precise entry and exit rules.
**Best For:** Gold traders, scalpers, and intraday momentum traders
**Timeframe:** 1-minute (optimal), can work on 3-5 minute
**Instrument:** XAUUSD, GC futures
**Trading Sessions:** London and New York sessions (GMT-based)
---
## 🎯 STRATEGY CONCEPT
### Taylor's 3-Day Cycle (Modernized)
This strategy identifies and trades three distinct market phases:
1. **Accumulation Day** - Range-bound consolidation where smart money accumulates positions
2. **Manipulation Day** - Liquidity sweeps and stop-hunts creating high-probability reversal setups
3. **Distribution Day** - Trending continuation where positions are distributed
The strategy automatically detects these phases through price action and provides clear entry signals during the highest-probability setups.
---
## 🔧 KEY FEATURES
### ✅ Automated Session Tracking
- **Asian Session** (00:00-05:00 GMT) - Range identification only, no trades
- **London Session** (06:00-13:00 GMT) - Primary trading window for manipulation setups
- **NY Session** (13:00-17:00 GMT) - Continuation and distribution trades
### ✅ Liquidity Sweep Detection
- Identifies sweeps of Asian High/Low
- Detects Previous Day High/Low violations
- Configurable sweep buffer to filter noise
- Visual confirmation on chart
### ✅ Multi-Indicator Confirmation
- **VWAP** - Session-based volume-weighted average price
- **20 EMA** - Dynamic support/resistance confirmation
- **SuperTrend** - Trend direction and reversal signals (dot indicators)
- **ATR-based** - Adaptive to volatility
### ✅ Professional Risk Management
- 3-tier profit taking system (33% / 33% / 34% position splits)
- Configurable Risk:Reward ratio (default 1:3)
- Adaptive stop-loss based on volatility
- Automatic end-of-day position closure
- No pyramiding (one position at a time)
### ✅ Visual Trading Environment
- **Asian Range Box** - Yellow box showing consolidation zone
- **Session Backgrounds** - Color-coded trading sessions
- **Previous Day Levels** - High/Low reference lines
- **Entry Signals** - Clear green (long) and red (short) markers
- **SuperTrend Dots** - Visual trend confirmation
---
## 📈 ENTRY RULES
### 🟢 LONG ENTRY (All Conditions Required)
1. **Liquidity Sweep** - Price sweeps below Asian Low OR Previous Day Low
2. **Rejection** - Candle forms lower wick and closes back above the level
3. **Trend Confirmation** - SuperTrend flips bullish (green dot appears below price)
4. **VWAP/EMA Reclaim** - Price closes above VWAP or 20 EMA
5. **Session Timing** - Must be during London or NY session
6. **Automated Signal** - Green triangle appears below candle
### 🔴 SHORT ENTRY (Mirror Logic)
1. **Liquidity Sweep** - Price sweeps above Asian High OR Previous Day High
2. **Rejection** - Candle forms upper wick and closes back below the level
3. **Trend Confirmation** - SuperTrend flips bearish (red dot appears above price)
4. **VWAP/EMA Reject** - Price closes below VWAP or 20 EMA
5. **Session Timing** - Must be during London or NY session
6. **Automated Signal** - Red triangle appears above candle
---
## 💰 EXIT STRATEGY
### 3-Tier Profit System
**TP1 (33% of position):**
- Target: 1:1 Risk:Reward
- Typically at VWAP or Asian range midpoint
- Secures base profit
**TP2 (33% of position):**
- Target: 1:2 Risk:Reward
- Typically at Asian range opposite boundary
- Locks in substantial gain
**TP3 (34% of position - Runner):**
- Target: 1:3 Risk:Reward (default)
- Typically at Previous Day High/Low or beyond
- Maximizes winners
### Stop Loss
- Fixed points below/above entry (default: 6 points)
- Can be adjusted based on ATR for volatility adaptation
- Tight enough for 1-minute scalping, wide enough to avoid noise
### End-of-Day Close
- All positions automatically closed at 17:00 GMT
- No overnight risk
- Clean slate for next trading day
---
## ⚙️ CUSTOMIZABLE PARAMETERS
### Risk Management
- **Risk:Reward Ratio** (1.0 - 10.0) - Default: 3.0
- **Stop Loss Points** (1.0 - 20.0) - Default: 6.0
- **Trailing Stop** - Optional for trend days
### Session Times (Adjustable for Your Timezone)
- Asian Start/End
- London Start
- NY Start/End
- Fully customizable to match your broker's daily close
### Indicators
- **ATR Length** - Default: 14
- **ATR Multiplier** - Default: 2.0 (SuperTrend sensitivity)
- **EMA Length** - Default: 20
- **Sweep Buffer** - Default: 2.0 points (filters false sweeps)
### Visuals (Toggle On/Off)
- Asian Range Box
- VWAP Line
- 20 EMA Line
- Previous Day Levels
- Session Background Colors
---
## 📊 PERFORMANCE EXPECTATIONS
### Realistic Statistics
- **Win Rate:** 40-60% (varies by market condition)
- **Average R:R:** 1:2.5 to 1:3.5 (with partial profits)
- **Trades Per Day:** 1-4 high-quality setups
- **Best Performance:** During manipulation days (sweeps + reversals)
### Ideal Market Conditions
✅ Medium to high volatility (ATR > 1.0)
✅ Clear trending sessions
✅ Strong liquidity sweeps
✅ Clean support/resistance at Asian range
### Challenging Conditions
⚠️ Very low volatility (ATR < 0.5)
⚠️ Major news events (NFP, FOMC)
⚠️ Extreme ranging days with no sweeps
⚠️ Asian session overlap confusion
---
## 🎓 HOW TO USE
### Setup
1. Add strategy to **1-minute XAUUSD chart**
2. Adjust session times to match your timezone/broker
3. Start with default settings
4. Enable alerts for entry signals
### Trading Workflow
1. **Pre-Market:** Identify Asian range when it forms
2. **London Open:** Watch for sweeps of Asian high/low
3. **Wait for Signal:** All 4-5 conditions must align (automatic)
4. **Enter on Signal:** Green/red triangle appears
5. **Let Strategy Manage:** Automatic TP1, TP2, TP3 exits
6. **Review Daily:** Journal which day type occurred
### Optimization
- Backtest on 3+ months of data
- Adjust stop loss based on recent ATR
- Fine-tune sweep buffer for your trading style
- Test different R:R ratios for your risk tolerance
---
## 🚨 ALERTS INCLUDED
The strategy includes 4 alert types:
1. **Long Entry Signal** - All conditions met for buy
2. **Short Entry Signal** - All conditions met for sell
3. **Bullish Sweep Detected** - Asian/PDL swept, prepare for long
4. **Bearish Sweep Detected** - Asian/PDH swept, prepare for short
Set up alerts to receive notifications via:
- TradingView mobile app
- Email
- SMS (via webhook)
- Discord/Telegram (via webhook)
---
## ⚡ UNIQUE ADVANTAGES
### Why This Strategy Stands Out
1. **Session-Aware Logic** - Trades only during optimal liquidity windows
2. **Institutional Approach** - Based on liquidity sweeps and order flow concepts
3. **Risk-Conscious** - 3-tier exits ensure you capture profits while letting winners run
4. **Clean Visuals** - Everything you need on the chart, nothing you don't
5. **No Repainting** - All calculations are based on closed candles
6. **Fully Automated** - Once configured, strategy handles entries and exits
### Gold-Specific Optimizations
- Designed specifically for Gold's unique volatility patterns
- Session times optimized for XAUUSD trading hours
- Stop loss and targets calibrated for typical Gold 1-min movements
- Sweep detection tuned to Gold's tendency for liquidity grabs
---
## 📖 STRATEGY LOGIC (For Developers)
### Technical Implementation
- **Language:** Pine Script v6
- **Type:** Strategy (not just indicator)
- **Calculation:** On bar close (no repainting)
- **Lookback:** Minimal (efficient on 1-minute data)
### Key Components
```
1. Session Detection → Hour-based GMT logic
2. Asian Range → var float tracking daily high/low
3. Sweep Detection → Price breach + reversal confirmation
4. SuperTrend → ATR-based trend filter
5. Entry Logic → Boolean combination of all conditions
6. Exit Management → strategy.exit() with multiple targets
```
---
## ⚠️ IMPORTANT DISCLAIMERS
### Risk Warning
- This strategy is for **educational purposes**
- **Past performance does not guarantee future results**
- Trading Gold on 1-minute timeframe is **high risk**
- Always use proper risk management (1-2% per trade max)
- Test thoroughly on **paper trading** before live implementation
### Recommended Prerequisites
- Understanding of support/resistance
- Familiarity with session-based trading
- Knowledge of liquidity concepts
- Experience with 1-minute scalping
- Proper broker with tight spreads on Gold
### Not Recommended For
- Complete beginners to trading
- Accounts under $1,000
- Traders unable to monitor during London/NY sessions
- High-spread brokers
- Emotional/impulsive traders
---
## 🔄 VERSION HISTORY
**v1.0** (Current)
- Initial release
- Core Taylor 3-Day Cycle implementation
- Asian range tracking
- Liquidity sweep detection
- 3-tier exit system
- Full visual suite
- Alert integration
---
## 💡 TIPS FOR SUCCESS
### Best Practices
1. **Trade the manipulation days** - Highest win rate on sweep-and-reverse setups
2. **Respect the session times** - Don't force trades outside London/NY
3. **Journal your trades** - Note which day type (Accumulation/Manipulation/Distribution)
4. **Scale position size** - Bigger on high-conviction setups
5. **Monitor ATR** - Adjust stop loss on volatile days
### Common Mistakes to Avoid
❌ Trading during Asian session
❌ Entering without all 5 conditions met
❌ Moving stops closer "to protect profit"
❌ Removing the partial profit system
❌ Over-trading on range days
❌ Ignoring the session backgrounds
---
## 📞 SUPPORT & FEEDBACK
### How to Provide Feedback
- Use TradingView's comment section below
- Report bugs with chart screenshots
- Share your optimization results
- Suggest improvements
### Future Updates May Include
- Multi-timeframe confirmation option
- Volume profile integration
- Machine learning day-type classifier
- Advanced trailing stop algorithms
- Telegram bot integration
---
## 🏆 CONCLUSION
The **1-Minute Gold Taylor Trading Technique** brings together classical market theory and modern algorithmic execution. By focusing on institutional liquidity sweeps during optimal trading sessions, this strategy provides a systematic approach to Gold scalping.
**Remember:** Consistency comes from following the rules, not from finding "perfect" entries. Let the strategy do the work.
---
## 📚 RECOMMENDED READING
To deepen your understanding:
- George Douglas Taylor - "The Taylor Trading Technique"
- Mark Fisher - "The Logical Trader"
- Al Brooks - "Trading Price Action Trends"
- ICT Concepts - Liquidity and Order Flow
---
## 🎯 QUICK START CHECKLIST
Before going live:
- ☐ Backtested on 3+ months
- ☐ Paper traded for 2+ weeks
- ☐ Session times match broker
- ☐ Stop loss appropriate for account size
- ☐ Alerts configured
- ☐ Trading journal ready
- ☐ Risk per trade ≤ 2%
- ☐ Understand all entry conditions
- ☐ Know how to disable during news
---
**Strategy Type:** Scalping, Mean Reversion, Liquidity Trading
**Complexity:** Intermediate to Advanced
**Maintenance:** Low (once configured)
**Recommended Chart:** 1-minute XAUUSD
**Optimal Spread:** < 0.3 points
---
## 📈 KEYWORDS
Gold Trading, XAUUSD Strategy, Taylor Trading Technique, 1-Minute Scalping, Liquidity Sweep, Session Trading, Intraday Strategy, Gold Scalping, Smart Money Concepts, Institutional Trading, Asian Range, VWAP Trading, Risk Management, Automated Trading
---
**Developed with:** Pine Script v6
**Compatible with:** TradingView Pro, Pro+, Premium
**License:** Open Source (modify as needed)
---
*Happy Trading! May your sweeps be clean and your reversals be profitable.* 🟡📈
---
### 🔗 SUPPORT THIS WORK
If you find this strategy helpful:
- ⭐ Leave a review
- 💬 Share your results in comments
- 🔄 Share with fellow Gold traders
- 📊 Post your optimized settings
Your feedback helps improve future versions!
Dynamic Trend-Based Fibonacci Extension💡 This indicator is a sophisticated, automated technical analysis tool designed to identify high-probability trend continuation setups using the principles of market structure and Fibonacci geometry. By algorithmically detecting "A-B-C" price structures (Pivot -> Impulse -> Retracement), it projects dynamic Fibonacci Extension levels to forecast potential price targets for the next impulsive move (Wave C to D). Unlike static drawing tools, this script adapts to market volatility and features an advanced invalidation engine to keep your charts clean and your risk managed.
✨ Originality and Utility
Traders often struggle with the subjectivity of drawing Fibonacci extensions manually. This script solves that by standardizing the identification of market structure using a proprietary ZigZag algorithm enhanced with Average True Range (ATR) for volatility-adjusted sensitivity.
Key unique features include:
Automated Structure Detection: Instantly spots Bullish (Higher High, Higher Low) and Bearish (Lower Low, Lower High) sequences without manual input.
Dynamic Invalidation: The script monitors price action in real-time. If price breaks the invalidation point (Point A), the structure is immediately "grayed out" or deleted, preventing you from trading based on broken setups.
Golden Zone Targeting: Highlights the high-probability reversal zone between the 1.5 and 1.618 extensions, often associated with the completion of a measured move.
JSON Alerting: Built-in support for algorithmic trading with structured JSON payloads (Entry, TP, SL) ready for webhook integration.
🔬 Methodology and Concepts
The core logic operates on a three-step algorithmic sequence:
1. Pivot Identification: The script uses a "ZigZag" approach to find significant swing highs and lows. It employs an ATR-based threshold (or fixed deviation) to filter out market noise, ensuring only significant structural points are considered.
2. Geometric Validation: It evaluates the last three pivot points (A, B, C) to confirm a valid trend structure.
Bullish Setup: Point C must be higher than Point A but lower than Point B (a valid retracement).
Bearish Setup: Point C must be lower than Point A but higher than Point B.
3. Projection Mathematics: Once a valid ABC structure is locked, the script calculates extension targets using the standard formula: Target = Price C + ((Price B - Price A) * Ratio) . It also supports Logarithmic Scale calculations for assets with exponential growth, such as cryptocurrencies, ensuring proportional accuracy over large price ranges.
🎨 Visual Guide
The indicator paints a clear, detailed roadmap on your chart. Here is how to interpret the visual elements:
● Structure Lines
Solid Line (A to B): Represents the initial "Impulse" leg of the move.
Dashed Line (B to C): Represents the "Retracement" or corrective leg.
Green Structures: Indicate Bullish setups (looking for long entries).
Red Structures: Indicate Bearish setups (looking for short entries).
Gray/Dimmed Structures: These are invalidated setups where the price has breached the Stop Loss level (Point A).
● Extension Levels (Targets)
The script projects the following key Fibonacci ratios extending from Point C:
0.618 (Wave 5): An early profit-taking level, often corresponding to a truncated 5th wave.
1.0 (Measured Move): Where the extension equals the length of the initial impulse (AB = CD pattern).
1.272 (Harmonic): A common extension level for corrective structures or deep pullbacks.
Golden Zone (1.5 - 1.618): A highlighted fill area. The 1.618 level (Solid Line) is the "Golden Ratio" and is statistically one of the most significant targets in trending markets, often labeled as "Wave 3".
● Labels
Points A, B, C: Clearly marks the swing points defining the structure.
Right-Side Labels: Display the Ratio (e.g., 1.618) and the exact Price Level for easy order placement.
📖 How to Use
This tool is best used as a trend-following system.
1. Trend Identification
Wait for a new Solid Colored Structure (Green or Red) to appear. This confirms that a valid ABC retracement has occurred.
2. Entry Strategy
The "Trigger" is generally the reversal from Point C. Aggressive traders enter near C, while conservative traders may wait for a breakout above B.
Stop Loss: Place your SL just beyond Point A . If price breaks A, the script will automatically gray out the structure, signaling invalidation.
3. Profit Taking
Use the projected extension lines as dynamic Take Profit (TP) zones:
TP1: 1.0 (The Measured Move).
TP2: The Golden Zone (1.5 to 1.618). This is often the strongest target for a Wave 3 impulsive move.
4. Automation
For automated traders, create an alert using the "Any alert() function call" option. The script outputs a JSON string containing the Action, Ticker, Entry Price, TP (1.618), and SL (Point A).
⚙️ Inputs and Settings
You can fully customize the script to fit your asset class and timeframe:
● ZigZag Detection
Pivot Lookback Depth: (Default: 5) Determines how many bars to check left/right for a pivot. Higher numbers find larger, more significant structures.
Use ATR-Based Threshold: (Default: True) Adapts the sensitivity to market volatility.
ATR Multiplier: (Default: 2.0) Adjusts how much price must reverse to form a new leg.
● Structure Invalidation
Enable Structure Invalidation: (Default: True) Toggles the logic that checks if Point A is breached.
Invalidation Action: Choose "Gray Out" to keep history visible but dimmed, or "Delete" to remove failed setups entirely.
● Fibonacci Settings
Use Logarithmic Scale: Essential for crypto or long-term timeframe analysis.
Show 0.618 / 1.0 / 1.272 / 1.618: Toggles individual levels on/off to declutter the chart.
Extend Lines Right: Extends the target lines into the future for better visibility.
● Display Settings
Keep Last N Structures: Controls how many historical structures remain on the chart to prevent visual clutter.
Show Elliott Wave Labels: Adds theoretical wave counts (e.g., "Wave 3") to the ratio labels.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
This indicator is grounded in Fractal Market Geometry and Elliott Wave Theory .
1. The Golden Ratio (Phi - 1.618):
Mathematically derived from the Fibonacci sequence, the 1.618 ratio is omnipresent in natural growth patterns. In financial markets, it represents the psychological "tipping point" of crowd behavior during an impulsive trend. This script emphasizes the 1.618 extension as the primary target for a "Wave 3," which is academically cited as typically the longest and strongest wave in a 5-wave motive sequence.
2. Harmonic AB=CD Patterns:
The inclusion of the 1.0 extension validates the "Measured Move" concept. Statistically, markets often move in symmetrical legs where the secondary impulse (CD) equals the magnitude of the primary impulse (AB).
3. Volatility Normalization (ATR):
By utilizing the Average True Range (ATR) for pivot detection, the script adheres to statistical volatility normalization. This ensures that the structures identified are statistically significant relative to the asset's current volatility regime, rather than relying on arbitrary percentage moves which fail across different asset classes.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
Global Sessions & Kill Zones [jpkxyz]Global Sessions & ICT Kill Zones Indicator
Overview
The Global Sessions & ICT Kill Zones indicator is a comprehensive trading tool designed to help traders identify and visualize the most critical time periods in the 24-hour forex and futures markets. This indicator combines traditional trading session analysis with Inner Circle Trader (ICT) Kill Zone methodology, providing traders with a complete picture of when institutional activity and liquidity are at their peak.
Trading Theory & Foundation
Session-Based Trading
The forex market operates 24 hours a day across four major trading sessions: Sydney, Tokyo, London, and New York. Each session has distinct characteristics in terms of volatility, liquidity, and price behavior. Understanding these sessions is crucial because:
Volatility Patterns: Each session exhibits unique volatility profiles based on which markets are open and which institutional players are active
Liquidity Concentration: Major price movements tend to occur when multiple sessions overlap, as more market participants are active simultaneously
Market Structure: Session highs and lows often act as key support and resistance levels that price respects throughout the trading day
Time-Based Strategies: Many professional traders structure their strategies around specific sessions that align with their preferred instruments and trading style
ICT Kill Zones
The Inner Circle Trader (ICT) methodology emphasizes specific time windows called "Kill Zones" - periods when institutional algorithms and smart money are most active. These time windows represent optimal trading opportunities because:
Institutional Activity: Banks, hedge funds, and large institutions execute their orders during these predictable time windows
Algorithmic Trading: Many institutional algorithms are programmed to operate during these specific periods
Liquidity Sweeps: Kill Zones often feature stop hunts and liquidity grabs before directional moves
Higher Probability Setups: Price is more likely to respect technical levels and follow through on setups during these periods
The four ICT Kill Zones are:
Asian Kill Zone (00:00-03:00 UTC): Early Asian session institutional activity
London Kill Zone (07:00-10:00 UTC): London open and European institutional entry
New York Kill Zone (12:00-14:00 UTC): New York open and North American institutional entry
London Close Kill Zone (15:00-17:00 UTC): European session close and position squaring
What This Indicator Visualizes
Trading Session Boxes
The indicator draws high-to-low range boxes for each major trading session:
Sydney Session (21:00-06:00 UTC): Captures the Australian and early Asian trading activity
Tokyo Session (00:00-09:00 UTC): Represents the main Asian trading period
London Session (08:00-17:00 UTC): Covers the European trading hours
New York Session (13:00-22:00 UTC): Encompasses North American trading activity
Each session box displays:
The session's high and low price levels
Customizable colored borders and fills
Labels showing the exact high and low values
Real-time updates as price moves within the active session
Session Overlaps
The indicator automatically identifies and highlights all session overlaps with distinct colored boxes:
Sydney/Tokyo Overlap: Asian liquidity concentration
Tokyo/London Overlap: Asian-European transition period
London/New York Overlap: The most volatile period with maximum liquidity
Sydney/New York Overlap: Late US session into early Asian session
These overlaps are crucial because they represent periods of increased liquidity when multiple major markets are operating simultaneously, often leading to significant price movements and breakouts.
ICT Kill Zones
Kill Zones are displayed as vertical background highlights that span the entire chart height during their active periods:
Visual clarity: Semi-transparent colored backgrounds that don't obstruct price action
Label identification: Each Kill Zone is labeled at its start for easy recognition
Overlay capability: Kill Zones overlay on top of session boxes, allowing you to see both simultaneously
Independent control: Each Kill Zone can be toggled on/off individually
How Traders Can Use This Indicator
Entry Timing
Wait for Kill Zones: Use Kill Zones as your primary trading windows to increase the probability of institutional support for your trades
Session Boundaries: Look for breakouts or reversals at session open/close times when new participants enter the market
Overlap Periods: Focus on high-conviction setups during session overlaps when liquidity is highest
Support & Resistance
Session Highs/Lows: Previous session highs and lows often act as key support/resistance levels
Sweep Setups: Watch for price to sweep session highs/lows during Kill Zones, then reverse (liquidity grab)
Range Trading: Trade within session ranges during low-volatility periods, breakout during overlaps
Risk Management
Volatility Awareness: Adjust position sizing based on which session is active (London/NY overlap = highest volatility)
Stop Placement: Position stops outside of key session levels to avoid being caught in normal intraday ranges
Time-Based Exits: Consider exiting or tightening stops as sessions close and liquidity decreases
Strategy Development
Session-Specific Strategies: Develop different approaches for different sessions based on your instrument's behavior
Kill Zone Confirmation: Require setups to occur within Kill Zones for higher probability trades
Backtesting Framework: Use historical session and Kill Zone data to backtest time-based strategies
Full Customizability
Session Customization
Every aspect of each trading session can be customized:
Toggle Visibility: Show/hide any session independently
Time Adjustment: Modify start and end hours to match your broker's server time or personal preference
Color Schemes: Customize box colors and border colors for each session
Transparency: Adjust fill transparency to see price action clearly while maintaining visual reference
Kill Zone Customization
Complete control over ICT Kill Zone display:
Individual Toggles: Enable or disable each Kill Zone independently based on your trading style
Color Selection: Choose distinct colors for each Kill Zone (default: Green, Blue, Yellow, Red)
Transparency Control: All Kill Zones use 70% transparency by default, fully customizable
Label Display: Toggle Kill Zone labels on/off via the main label settings
Visual Preferences
Border Control: Toggle session box borders on/off for cleaner charts
Label Size: Choose from tiny, small, normal, large, huge, or auto-sizing for all labels
Label Colors: Customize label background and text colors to match your chart theme
Box Transparency: Set individual transparency levels for each session and overlap
Overlap Customization
All four session overlaps have independent color controls:
Sydney/Tokyo Overlap
Tokyo/London Overlap
London/New York Overlap
Sydney/New York Overlap
Technical Features
Midnight Handling
The indicator uses advanced hour-based detection that seamlessly handles sessions crossing midnight (like Sydney's 21:00-06:00 UTC timeframe) without breaking the visualization into separate boxes.
Real-Time Updates
Active Sessions: Boxes extend and update in real-time as price moves during active sessions
High/Low Tracking: Session highs and lows are continuously updated until the session closes
Kill Zone Detection: Background colors appear/disappear precisely at Kill Zone boundaries
Clean Chart Integration
Minimal Clutter: Only shows active and recently completed sessions
Overlay Friendly: Works seamlessly with other indicators and doesn't obstruct price action
Performance Optimized: Efficient code that doesn't slow down chart rendering
Ideal For
Forex Traders: Track the four major forex sessions and plan trades around overlaps
Futures Traders: Identify when specific futures markets have peak activity
ICT Students: Implement Inner Circle Trader concepts with visual Kill Zone references
Session Traders: Build strategies around specific session characteristics
Scalpers & Day Traders: Focus on high-liquidity periods for tighter spreads and better fills
Swing Traders: Use session levels as key support/resistance for multi-day trades
Best Practices
Start Simple: Enable only the sessions and Kill Zones relevant to your instruments
Color Code Strategically: Use colors that stand out on your chart theme but don't overwhelm
Combine with Price Action: Use session levels and Kill Zones as context, not as standalone signals
Match Your Timezone: Adjust session times if your broker uses non-UTC server time
Focus on Overlaps: Pay special attention to London/New York overlap for highest-probability setups
Journal Performance: Track which sessions and Kill Zones work best for your strategy
Conclusion
The Global Sessions & ICT Kill Zones indicator provides traders with institutional-grade time-based analysis in a highly customizable, visually clear format. By combining traditional session analysis with modern ICT Kill Zone theory, traders gain a comprehensive understanding of when markets are most likely to move and where key levels are established. Whether you're a scalper looking for the highest liquidity periods or a swing trader using session levels for support/resistance, this indicator adapts to your needs while keeping your charts clean and professional.
Trade smarter by trading when the market is most active and predictable.
RLP V4.3 -Long Term Support/Resistance Levels (Refuges-Shelters)// Introduction //
We have utilized the Zigzag library technology from ©Trendoscope Pty Ltd for Zigzag generation, allowing users the freedom to choose which of the different Zigzags calculated by Trendoscope as "Levels and Sub-Levels" is most suitable for generating ideal phases for evaluation and selection as "most preponderant phases" over long-term periods of any asset, according to its particular behavior based on its age, volatility, and price trend.
// Theoretical Foundation of the Indicator //
Many traditional institutional investors use the latest higher-degree market phase that stands out from others (longest duration and greatest price change on daily timeframe) to base a Fibonacci retracement on whose levels they open long-term positions. These positions can remain open to be activated in the future even years in advance. The phase is considered valid until a new, more preponderant phase develops over time, at which point the same strategy is repeated.
// Indicator Objectives //
1) Automatically find the latest most preponderant long-term phase of an asset, analyzing it on daily timeframe while considering whether the long-term market trend is bullish or bearish.
2) Draw a Fibonacci Retracement over the preponderant phase (reversed if the phase is bullish).
3) The indicator automatically numbers and locates the 3 most preponderant phases, selecting Top-1 for initial Fibo drawing.
4) If the user disagrees with the indicator's automatic selection, they have the freedom to choose any of the other 2 Top phases for the Fibo drawing and its levels.
5) If the user disagrees with the amplitude or frequency of the initially drawn Zigzag phases, they can modify the Zigzag calculation algorithm parameters until one of the Top-3 matches the phase they had in mind.
6) As an experimental bonus, the indicator runs a popularity contest (CP) of "bullseye" daily price (OHLC) matches, subject to user-defined tolerance ranges, against all Fibo levels of the Top 3 selected phases, to verify which phase the market prices are validating as the most popular for placing trades. Contest results are displayed in the POP. CONTEST column of the Top-3 phases table. If the contest detects a change in the winning phase, a switch can be enabled to activate an alert that the user can utilize with TradingView's alert creator to display an alarm, send an email, etc.
7) This indicator was designed for users to find the preponderant long-term phase of their assets and manually record the date-price coordinates of the i0-i1 anchors of the preponderant phase. The Top-1 phase coordinates are shown in the Top-3 phases table where they can be captured. The date-price coordinates of all HH and LL pivots, from all Zigzag phases, can be displayed via a switch. With the pivots, the user can select a different phase than those automatically found by the indicator, according to the conclusions of their own research. Subsequently, the user can forget about this RLP indicator for a while and move on to apply in their normal trading our RLPS indicator (Simplified Long-Term Shelters), in which they can draw and simultaneously track the long-term shelters of up to 5 different assets, simply by entering their corresponding date-price coordinates, previously located with this RLP indicator or through their own observation.
// Additional Notes //
1) As of the this V4.3 publication date (01/2026), the Zigzag generation parameters were adjusted by default to find the long-term preponderant phases for the following assets: Bitcoin, Ethereum, Bitcoin futures BTC1! (all generated due to the 2020-2021 pandemic). It also provides by default the confirmed preponderant phases for the following assets: Apple, Google, Amazon, Microsoft, PayPal, NQ1!, ES1! and SP500 Cash.
2) Prices, phases, and levels shown on the graphic chart correspond to results obtained using daily Bitcoin data from the Bitstamp exchange, BTCUSD:BITSTAMP (popular here in Europe).
3) Any error corrections or improvements that can be made to the phase selection algorithms or the CP phase popularity contest algorithm will be highly appreciated (statistics and mathematics, among many other sciences, are not particularly our strong suit).
4) We sincerely regret to inform you that we have not included the Spanish translation previously provided, due to our significant concern regarding the ambiguous rules on publication bans related to indicators.
4) Sharing motivates. Happy hunting in this great jungle!
Ranked Exchange Volume (REV)📊 Ranked Exchange Volume (REV) - Multi-Venue Volume Distribution Visualizer
## Stop Guessing Where the Real Volume Is. See It.
Most traders look at aggregate volume and miss the critical story: **where** that volume actually traded. Ranked Exchange Volume (REV) solves this by revealing the complete liquidity landscape across multiple trading venues in a single, elegant visualization.
This isn't just another volume indicator—it's a **dynamic stratified histogram** that automatically reorganizes exchange layers by magnitude on every bar, showing you **instant market dominance** at a glance.
---
## 🎯 The Core Innovation: Self-Organizing Volume Layers
REV displays volume from up to 10 different exchanges as **stacked, color-coded bars** where the largest volume source literally rises to the top. Watch as exchanges compete for dominance in real-time:
- **Largest volume = Top of the bar** (most visible position)
- **Smallest volume = Bottom of the bar** (foundation layer)
- **Everything in between = Automatically sorted on every candle**
This visual hierarchy makes it instantly obvious which venues are leading the market—no mental math required.
---
## ✨ Key Features
### 🔄 **Dynamic Layer Sorting**
Unlike static stacked charts, REV uses real-time stratification. If Binance had 60% of volume last bar but Coinbase takes 70% this bar, you'll see Coinbase jump to the top. The hierarchy reflects current reality, not a fixed order.
### 🎨 **10 Fully Customizable Exchange Slots**
Each exchange slot offers complete control:
- **Enable/Disable toggle** - Turn exchanges on/off without losing your configuration
- **Custom prefix** - Track ANY exchange on TradingView (BINANCE, KRAKEN, OANDA, FXCM, etc.)
- **Custom suffix** - Specify quote currency (USDT, USD, EUR, or leave blank for stocks/forex)
- **Display name** - Control how exchanges appear in the rankings table
- **Color selection** - Match your chart theme or use brand colors for instant recognition
### 📊 **Live Rankings Table**
A real-time leaderboard shows:
- **Rank** - Current position (1 = highest volume)
- **Exchange name** - With color-coded background
- **Volume** - Intelligently formatted with K/M/B units
- **Percentage** - Exact market share
**Table positioning:** Choose from 9 screen positions (top/middle/bottom × left/center/right) to keep your chart clean.
### 🧮 **Intelligent Volume Formatting**
REV automatically detects volume magnitude and applies the appropriate scale:
- **Billions** - Displays as "1.5B" for readability
- **Millions** - Displays as "342.8M"
- **Thousands** - Displays as "45.2K"
- **Full numbers option** - Toggle to see complete values (23,456,789)
The scale adjusts per-bar, so you always see the clearest representation.
### 🚨 **Three Built-In Alert Conditions**
1. **Exchange Dominance Alert (>50%)**
- Triggers when a single venue controls majority of volume
- Signals potential liquidity concentration risk or exchange-specific events
2. **Volume Spike Alert (>2x average)**
- Detects unusual aggregate activity across all venues
- Catches breakouts, news events, or institutional flow
3. **Liquidity Migration Alert**
- Fires when market leadership shifts between exchanges
- Reveals arbitrage opportunities or changing market structure
### 📈 **Optional Total Volume Line**
Display aggregate volume from all exchanges as a reference overlay with customizable color.
---
## 🌍 Market Compatibility: Beyond Crypto
While optimized for cryptocurrency (its primary design), REV works across multiple asset classes:
### ✅ **Cryptocurrency (Perfect Fit)**
**Why it excels:** Crypto trades 24/7 across dozens of global exchanges simultaneously. REV reveals true price discovery.
**Example configurations:**
- **BTC/USDT:** Compare Binance, Coinbase, OKX, Bybit, Kraken, Bitget
- **ETH/USD:** Track institutional venues (Coinbase, Kraken, Gemini) vs retail (Binance, Gate.io)
- **Altcoins:** Identify which exchanges have the deepest liquidity before placing large orders
**Trading applications:**
- **Arbitrage detection** - Spot when volume migrates between venues (price differential opportunities)
- **Exchange risk** - Don't trade on exchanges with suspiciously low volume
- **Whale tracking** - Sudden Coinbase dominance often signals institutional activity
- **Market maker identification** - Consistent Binance leadership suggests MM concentration
### ✅ **Forex (Excellent Fit)**
**Why it works:** Forex doesn't have centralized exchanges—it trades OTC across multiple broker feeds. REV shows which data providers are seeing the action.
**Example configurations:**
- **EUR/USD:** Compare OANDA, FXCM, FOREX.COM, FX_IDC, CAPITALCOM
- **GBP/JPY:** Track volatility across broker feeds
- **Exotics:** Verify liquidity before trading thin pairs
**Setup notes:**
- Leave **suffix field blank** for forex
- Use broker prefixes: OANDA, FXCM, FOREXCOM, FX_IDC, SAXO
- Symbol constructs as "OANDA:EURUSD"
**Trading applications:**
- **Spread verification** - Higher volume feeds typically offer tighter spreads
- **News event tracking** - See which brokers capture the most flow during announcements
- **Session analysis** - Watch London/NY volume shifts across different providers
### ⚠️ **Stocks (Limited But Useful)**
**Where it works:**
- **Dual-listed stocks** - Canadian companies on TSX and NYSE
- **International ADRs** - Same company, different exchanges
- **ETF arbitrage** - Compare volume across regional listings
**Example configurations:**
- **Shopify (SHOP):** Compare TSX vs NYSE volume
- **Alibaba (BABA):** NYSE vs HKEX volume
- **European stocks:** Compare primary exchange vs secondary listings
**Setup notes:**
- Leave **suffix field blank**
- Use exchange prefixes: NYSE, NASDAQ, TSX, LSE, XETRA
- Note: TradingView doesn't show per-venue volume for U.S. equities (NYSE vs BATS vs ARCA all aggregate)
**Limitations:** Most stocks trade primarily on one exchange, so REV is less valuable than in crypto/forex.
### ❌ **Futures (Not Recommended)**
Futures contracts differ by exchange (CME's ES ≠ EUREX's FESX), so volume isn't comparable.
---
## 📚 Practical Use Cases
### 1. **Pre-Trade Liquidity Analysis**
Before entering a large position, check which exchanges have sufficient volume to fill your order without slippage.
**Example:** You want to sell 50 BTC. REV shows Binance has 2,340 BTC volume this hour while a smaller exchange has only 87 BTC. Route your order to Binance for better execution.
### 2. **Exchange Risk Management**
Identify "fake volume" or wash trading by comparing venues.
**Red flag pattern:** An exchange consistently shows 10x the volume of competitors but with minimal price impact—likely artificial.
### 3. **Arbitrage Opportunity Detection**
When volume suddenly concentrates on one exchange, price premiums/discounts often appear.
**Alert pattern:** Liquidity Migration alert fires → Check price differences → Execute arb if spread exceeds fees.
### 4. **Institutional Flow Tracking**
In crypto, institutions typically use regulated exchanges (Coinbase, Kraken, Gemini).
**Pattern to watch:** Coinbase volume spikes to 60%+ dominance → Often precedes directional moves as institutions position.
### 5. **Market Structure Analysis**
Watch long-term trends in exchange dominance to understand market evolution.
**Example insight:** "Binance's market share has dropped from 70% to 45% over 6 months as traders diversify to OKX and Bybit."
### 6. **Event Response Comparison**
During major news events, see which exchanges react first.
**Analysis:** If one exchange shows volume spike 5 minutes before others, that feed may have faster news incorporation.
---
## ⚙️ Technical Specifications
- **Maximum exchanges:** 10 simultaneous venues
- **Sorting algorithm:** Bubble sort (O(n²) but optimal for n=10, prioritizes stability)
- **Update frequency:** Real-time, every bar
- **Data handling:** Gracefully ignores invalid symbols, treats NA as zero
- **Chart type:** Non-overlay (separate pane below price)
- **Performance:** Lightweight, no lag on any timeframe
---
## 🚀 Getting Started
### Quick Setup (5 Minutes)
**For Crypto Traders (Default Configuration):**
1. Add indicator to any crypto chart (BTC, ETH, SOL, etc.)
2. Works immediately—top 10 exchanges pre-configured
3. Customize colors if desired
4. Position table to your preference
**For Forex Traders:**
1. Open any forex pair (EUR/USD, GBP/JPY, etc.)
2. Go to Exchange 1 settings
3. Change prefix to "OANDA" (or your preferred broker)
4. **Clear the suffix field** (leave it blank)
5. Repeat for other exchanges (FXCM, FOREXCOM, FX_IDC, etc.)
6. Disable any unused exchange slots
**For Stock Traders (Dual-Listed):**
1. Open a dual-listed stock (e.g., SHOP on TSX)
2. Exchange 1: Prefix = "TSX", Suffix = blank, Name = "Toronto"
3. Exchange 2: Prefix = "NYSE", Suffix = blank, Name = "New York"
4. Disable exchanges 3-10
5. Compare volume distribution
### Advanced Customization
**Tracking Regional Markets:**
Want to compare Korean vs Japanese crypto exchanges?
- Exchange 1: UPBIT (Korean)
- Exchange 2: BITHUMB (Korean)
- Exchange 3: BITFLYER (Japanese)
- Exchange 4: COINCHECK (Japanese)
**Isolating Institutional Volume:**
Focus only on regulated U.S. exchanges:
- Enable: Coinbase, Kraken, Gemini
- Disable: All others
- Watch for >50% dominance alerts
---
## 👥 Who Is This For?
### ✅ **Perfect for:**
- **Crypto day traders** - Need to know where liquidity actually is
- **Arbitrage traders** - Spot cross-exchange inefficiencies
- **Institutional traders** - Validate execution venues before large orders
- **Forex scalpers** - Compare broker feeds for best execution
- **Market structure analysts** - Track long-term exchange dominance trends
### ❌ **Less useful for:**
- **Long-term investors** who don't care about short-term liquidity
- **Single-exchange traders** who never compare venues
- **Futures traders** (contracts differ by exchange)
---
## 🎓 Understanding the Visualization
**What each colored segment means:**
Each horizontal stripe represents one exchange's volume contribution. The **height** of each stripe shows that exchange's volume relative to others.
**Reading the pattern:**
- **Dominant top layer** (50%+ of bar) = Clear market leader
- **Evenly distributed layers** (10-15% each) = Fragmented liquidity
- **Sudden layer reorganization** = Liquidity migration event
- **Shrinking bottom layers** = Exchanges losing market share
**Color coding strategy:**
The indicator defaults to exchange brand colors for instant recognition:
- Yellow = Binance (their signature gold)
- Blue = Coinbase (their brand blue)
- Purple = Kraken (their brand purple)
- etc.
You can customize all colors to match your chart theme.
---
## 🔧 Configuration Tips
### **Best Practices:**
1. **Start with defaults** - Test on BTC/USDT to understand behavior
2. **Disable unused exchanges** - Cleaner visualization, faster computation
3. **Match your trading venues** - Only track exchanges you actually use
4. **Use brand colors initially** - Helps build visual pattern recognition
5. **Enable alerts strategically** - Don't spam yourself; focus on actionable signals
### **Common Mistakes to Avoid:**
❌ Tracking too many irrelevant exchanges (creates visual noise)
❌ Forgetting to clear suffix for forex/stocks (symbol won't construct properly)
❌ Using the same color for multiple exchanges (defeats instant recognition)
❌ Hiding the table permanently (you lose the percentage data)
---
## 📊 Performance Notes
- **Lightweight computation** - No impact on chart performance
- **Works on all timeframes** - 1-minute to monthly
- **Historical analysis** - Full bar history available (max_bars_back=5000)
- **Multi-monitor friendly** - Table positioning adapts to any screen layout
---
## 🆕 Future Enhancements (Planned)
While the current version is feature-complete, potential additions include:
- Volume-weighted average price (VWAP) overlay per exchange
- Historical dominance charts (which exchange led most this week/month)
- Correlation matrix (do exchanges move together or independently?)
**User feedback shapes development** - Comment with your requests!
---
## 💡 Pro Tips
### **Tip 1: The "Whale Exchange" Filter**
In crypto, institutions use Coinbase/Kraken. Enable ONLY these two exchanges to isolate professional flow and ignore retail noise.
### **Tip 2: The "Arbitrage Scanner"**
Set Liquidity Migration alert on 1-minute timeframe. When it fires, check price across exchanges—often there's a temporary premium/discount.
### **Tip 3: The "Liquidity Gauge"**
Before placing a large market order, switch to 5-minute timeframe and check last 10 bars. If your target exchange consistently has <20% of volume, you'll face slippage.
### **Tip 4: The "Market Structure Tracker"**
Take screenshots of the table weekly. Over time, you'll see exchange market share trends that reveal fundamental shifts in trader preferences.
### **Tip 5: The "News Event Validator"**
During major announcements (Fed decisions, earnings, etc.), watch which exchange shows volume first. That's where informed traders are positioned.
---
## 🎯 Summary
**Ranked Exchange Volume (REV) transforms volume analysis from a single number into a complete market microstructure view.**
Instead of seeing "1.2M volume," you see:
- Binance: 640K (53%)
- Coinbase: 280K (23%)
- OKX: 180K (15%)
- Bybit: 100K (9%)
**That's actionable intelligence.**
Whether you're executing a large crypto trade, arbitraging forex across brokers, or validating liquidity before buying a dual-listed stock, REV shows you **where the market actually is**—not where you assume it is.
---
## 📖 Quick Reference Card
| Feature | What It Does | Why It Matters |
|---------|-------------|----------------|
| **Dynamic Sorting** | Largest volume rises to top | Instant dominance identification |
| **10 Custom Slots** | Track any exchanges | Works for YOUR trading venues |
| **Live Rankings** | Real-time leaderboard | Precise market share data |
| **Smart Formatting** | Auto K/M/B scaling | Always readable, never cluttered |
| **Dominance Alert** | Warns at >50% concentration | Risk management for large orders |
| **Migration Alert** | Fires on leadership change | Arbitrage opportunity signal |
| **Spike Alert** | Detects 2x volume surges | Breakout/news confirmation |
| **Total Line** | Shows aggregate volume | Reference for overall activity |
| **Table Positioning** | 9 screen locations | Adapts to your layout |
| **Full/Short Toggle** | Complete vs abbreviated numbers | Flexibility for different assets |
---
## ✅ Installation & Support
**Install:** Add to your TradingView favorites, apply to any chart
**Updates:** Automatic through TradingView
**Support:** Comment with questions—active developer community
**Like this indicator?** Leave a ⭐ rating and share with fellow traders who need better volume intelligence.
---
**🚀 Start seeing the complete volume picture. Add Ranked Exchange Volume to your charts today.**
ICT Flow Matrix [Ultimate]📊 Overview
ICT Flow Matrix is a comprehensive, all-in-one Smart Money Concepts (SMC) indicator built for traders who follow ICT (Inner Circle Trader) methodology. This indicator consolidates over 15 institutional trading concepts into a single, highly customizable tool—eliminating chart clutter from multiple indicators while providing deep market structure analysis.
Whether you're identifying liquidity pools, tracking order flow, or timing entries during ICT Macro windows, this indicator delivers institutional-grade analysis directly on your chart.
Pro Tip: use with ICT Market Regime Detector for clear language reads on everything.
⚡ Key Features
🎯 Price Delivery Arrays (PDAs)
Fair Value Gaps (FVG) — Automatic detection with customizable mitigation tracking (Wick Touch, 50% CE, Full Close)
Inverse FVGs (iFVG) — Identifies when FVGs fail and flip, creating new tradeable zones
Order Blocks (OB) — Last opposing candle before impulsive moves with adjustable impulse strength
Breaker Blocks (BB) — Automatically generated when Order Blocks fail
Rejection Blocks (RB) — Strong wick rejections indicating institutional defense
Volume Imbalances (VIMB) — Gaps between candle bodies showing aggressive institutional activity
📐 Market Structure & Liquidity
Market Structure Shifts (MSS) — Real-time detection of bullish/bearish structure breaks
Equal Highs/Lows (EQH/EQL) — Liquidity pools where stop losses accumulate
Buy-Side/Sell-Side Liquidity (BSL/SSL) — Swing point liquidity levels with sweep detection
Premium/Discount Zones — Visual shading showing institutional buying/selling areas
OTE Zone (61.8%-79%) — Optimal Trade Entry zone for high-probability entries
⏰ Time-Based Analysis
ICT Macro Times — All nine 30-minute algorithmic windows (02:45, 03:45, 04:45, 09:45, 10:45, 13:45, 14:45, 15:15, 15:45 NY Time)
Killzone Sessions — Asia, London, NY AM, NY PM with customizable times
Session Opens — Weekly, Monthly, Daily opening prices
Previous Period H/L — PDH/PDL, PWH/PWL, PMH/PML levels
📏 Dealing Ranges
Multi-Timeframe Ranges — 21-Day, 3-Day, Daily dealing ranges
Session Ranges — Asia, London, NY dealing ranges with equilibrium
Fibonacci Structure — 0%, 50% (EQ), 100% levels with P/D shading
🕯️ HTF Orderflow
Higher Timeframe Candles — Display up to 6 HTF candles with auto-timeframe selection
Candle Timer — Countdown to next HTF candle close
O/H/L Reference Lines — Current HTF open, high, low levels extended on chart
🎨 Visual Customization
5 Theme Presets — Dark Pro, Light Clean, Neon, Classic, Custom
Full Color Control — Customize every element individually
Zone Styles — Filled or Border Only options
Mitigation Effects — Visual fade when zones are mitigated
📋 Smart Dashboard
Real-Time Status — Structure bias, zone position, active session, OTE status
Confluence Score — Algorithmic scoring when multiple concepts align
Zone Counters — Active FVG, OB, BB, RB, VIMB, liquidity levels
3 Display Modes — Minimal, Compact, Detailed
🔔 Comprehensive Alert System
40+ Alert Conditions including:
FVG/OB/BB/RB/VIMB formation
Liquidity sweeps (EQH, EQL, BSL, SSL)
Market Structure Shifts
OTE zone entry
Macro time windows
Session opens
High confluence zones
Combo alerts (Macro + Confluence)
📖 How To Use
For Swing/Position Traders:
Enable HTF Orderflow to identify dominant trend direction
Use Dealing Ranges (3D, 21D) to find premium/discount zones
Look for OB/FVG confluence in discount (longs) or premium (shorts)
Confirm with MSS for trend alignment
For Day/Intraday Traders:
Mark the Asian Range during pre-market
Wait for London or NY AM Killzone
Enter during ICT Macro windows when price reaches FVG/OB in OTE zone
Target opposite liquidity (BSL for longs, SSL for shorts)
Confluence Trading:
Dashboard shows real-time confluence score
Score ≥ 3 indicates multiple ICT concepts aligned
Higher scores = higher probability setups
⚙️ Recommended Settings
Trading Style FVG Max OB Max History Bars HTF Candles
Scalping 3-5 2-3 100-200 3-4 Day Trading 5-8 3-5 200-400 4-5
Swing Trading 8-12 5-8 400-800 5-6
🎯 Best Practices
✅ Do:
Use HTF bias before taking LTF entries
Wait for Macro time windows for highest probability
Combine MSS + FVG/OB + OTE for A+ setups
Let mitigated zones fade (use Mitigation Fade setting)
❌ Avoid:
Trading against HTF structure
Entries outside Killzones (lower probability)
Ignoring liquidity targets
Over-cluttering chart (disable unused features)
📝 Version History
v6.0 (Current)
Complete rewrite in PineScript v6
Added ICT Macro Times with bracket/background styles
Enhanced confluence detection algorithm
Improved HTF candle rendering with multiple styles
Added Inverse FVG detection
Session-based Dealing Ranges
Performance optimizations
40+ alert conditions
⚠️ Disclaimer
This indicator is a technical analysis tool designed to visualize ICT/SMC concepts. It does not provide financial advice or guarantee profitable trades. Past performance is not indicative of future results. Always use proper risk management and trade responsibly.
💬 Support & Feedback
If you find this indicator valuable, please leave a comment or boost! Your feedback helps improve future updates.
Questions? Drop a comment below—I actively respond to all questions about the indicator's features and usage.
