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Developing Market Profile / TPO [Honestcowboy]The Developing Market Profile Indicator aims to broaden the horizon of Market Profile / TPO research and trading. While standard Market Profiles aim is to show where PRICE is in relation to TIME on a previous session (usually a day). Developing Market Profile will change bar by bar and display PRICE in relation to TIME for a user specified number of past bars. What is a market profile? "Market Profile is an intra-day charting technique (price vertical, time/activity horizontal) devised by J. Peter Steidlmayer. Steidlmayer was seeking a way to determine and to evaluate market value as it developed in the day time frame. The concept was to display price on a vertical axis against time on the horizontal, and the ensuing graphic generally is a bell shape--fatter at the middle prices, with activity trailing off and volume diminished at the extreme higher and lower prices." For education on market profiles I recommend you search the net and study some profitable traders who use it. Key Differences Does not have a value area but distinguishes each column in relation to the biggest column in percentage terms. Updates bar by bar Does not take sessions into account Shows historical values for each bar While there is an entire education system build around Market Profiles they usually focus on a daily profile and in some cases how the value area develops during the day (there are indicators showing the developing value area). The idea of trading based on a developing value area is what inspired me to build the Developing Market Profile. 🟦 CALCULATION Think of this Developing Market Profile the same way as you would think of a moving average. On each bar it will lookback 200 bars (or as user specified) and calculate a Market Profile from those bars (range). 🔹Market Profile gets calculated using these steps: Get the highest high and lowest low of the price range. Separate that range into user specified amount of price zones (all spaced evenly) Loop through the ranges bars and on each bar check in which price zones price was, then add +1 to the zones price was in (we do this using the OccurenceArray) After it looped through all bars in the range it will draw columns for each price zone (using boxes) and make them as wide as the OccurenceArray dictates in number of bars 🔹Coloring each column: The script will find the biggest column in the Profile and use that as a reference for all other columns. It will then decide for each column individually how big it is in % compared to the biggest column. It will use that percentage to decide which color to give it, top 20% will be red, top 40% purple, top 60% blue, top 80% green and all the rest yellow. The user is able to adjust these numbers for further customisation. The historical display of the profiles uses plotchar() and will not only use the color of the column at that time but the % rating will also decide transparancy for further detail when analysing how the profiles developed over time. Each of those historical profiles is calculated using its own 200 past bars. This makes the script very heavy and that is why it includes optimisation settings, more info below. 🟦 USAGE My general idea of the markets is that they are ever changing and that in studying that changing behaviour a good trader is able to distinguish new behaviour from old behaviour and adapt his approach before losing traders "weak hands" do. A Market Profile can visually show a trader what kind of market environment we currently are in. In training this visual feedback helps traders remember past market environments and how the market behaved during these times. Use the history shown using plotchars in colors to get an idea of how the Market Profile looked at each bar of the chart. This history will help in studying how price moves at different stages of the Market Profile development. I'm in no way an expert in trading Market Profiles so take this information with a grain of salt. Below an idea of how I would trade using this indicator: 🟦 SETTINGS 🔹MARKET PROFILING Lookback: The amount of bars the Market Profile will look in the past to calculate where price has been the most in that range Resolution: This is the amount of columns the Market Profile will have. These columns are calculated using the highest and lowest point price has been for the lookback period Resolution is limited to a maximum of 32 because of pinescript plotting limits (64). Each plotchar() because of using variable colors takes up 2 of these slots 🔹VISUAL SETTINGS Profile Distance From Chart: The amount of bars the market profile will be offset from the current bar Border width (MP): The line thickness of the Market Profile column borders Character: This is the character the history will use to show past profiles, default is a square. Color theme: You can pick 5 colors from biggest column of the Profile to smallest column of the profile. Numbers: these are for % to decide column color. So on default top 20% will be red, top 40% purple... Always use these in descending order Show Market Profile: This setting will enable/disable the current Market Profile (columns on right side of current bar) Show Profile History: This setting will enable/disable the Profile History which are the colored characters you see on each bar 🔹OPTIMISATION AND DEBUGGING Calculate from here: The Market Profile will only start to calculate bar by bar from this point. Setting is needed to optimise loading time and quite frankly without it the script would probably exceed tradingview loading time limits. Min Size: This setting is there to avoid visual bugs in the script. Scaling the chart there can be issues where the Market Profile extends all the way to 0. To avoid this use a minimum size bigger than the bugged bottom box
Pine Script® Indikator
von Honestcowboy
20
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading. *** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed*** █ Brief Overview of the Goertzel Cycle Composite Wave The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions. The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes. The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here: Goertzel Browser The primary purpose of this indicator is to: 1. Detect and analyze the dominant cycles present in the price data. 2. Reconstruct and visualize the composite wave based on the detected cycles. To achieve this, the indicator performs several tasks: 1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components. 2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength. 3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength. 4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing. This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions. █ What is the Goertzel Algorithm? The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics. The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as: 1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed. 2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments. 3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear. 4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues. The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT: 1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases. 2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing. 3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest. 4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential. The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics. █ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis. Unveiling Hidden Market Cycles: Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies. Developing Quantitative Trading Strategies: The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets. For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction. Enhancing Risk Management: The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies. By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy. Expanding Quantitative Toolkits: Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately. Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals. The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies. █ Indicator Inputs src: This is the source data for the analysis, typically the closing price of the financial instrument. detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components. The available options are: hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average. zlagsmthdt: Detrend using zero-lag moving average centered moving average. logZlagRegression: Detrend using logarithmic zero-lag linear regression. hpsmth: Detrend using Hodrick-Prescott filter. zlagsmth: Detrend using zero-lag moving average. DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt. DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt. DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression. HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth. ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth. MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles. squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm. useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles. useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves. UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude. WindowSizePast: These inputs define the window size for the composite wave. FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles. BartNoCycles: This input sets the number of cycles to be used in Bartel's test. BartSmoothPer: This input sets the period for the moving average used in Bartel's test. BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant. SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results. StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles. UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input. SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles. █ Exploring Auxiliary Functions The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool. Zero-Lag Moving Average: The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator. The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator. Bartels Probability: The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets. The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies. Detrend Logarithmic Zero-Lag Regression: The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics. The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding. Bartels Cycle Significance Test: The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding. The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis. Hodrick-Prescott Filter: The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends. The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive. Detrending Options: Detrend Centered Moving Average: The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences. The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants. The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average, █ In-Depth Analysis of the Goertzel Cycle Composite Wave Code The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose. Function signature and input parameters: The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer). Initializing variables and arrays: The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm. Preprocessing input data: The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample. Iterative calculation of Goertzel coefficients: The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure. Cycle strength computation: The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values. Phase calculation: The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients. Peak detection and cycle extraction: The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively. Sorting cycles by amplitude or cycle strength: The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data. Bartels cycle significance test: If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values. Waveform calculation: The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted. Storing waveforms in a matrix: The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle. Returning the number of cycles: The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles. The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data. █ Generating and Visualizing Composite Waveform The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process: Updating WindowSizePast: The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period). Initializing matrices and arrays: The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information. Preparing the source data (srcVal) array: The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth). Goertzel function call: The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles. Initializing arrays for waveforms: The goertzel array is initialized to store the endpoint Goertzel. Calculating composite waveform (goertzel array): The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component. Drawing composite waveform (pvlines): The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward). To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines. █ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading. Enhancements for Financial Modeling Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results. Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data. Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models. Enhancements for General and Advanced Trading Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies. Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size. Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions. Enhancements for High-Frequency Finance Trading Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity. Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations. Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies. While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike. █ Understanding the Limitations of the Goertzel Algorithm While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are: Lagging nature: As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses. Parameter sensitivity: The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance. Complexity: The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions. Overfitting risk: As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance. Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity. While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions. █ Interpreting Results The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data. The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color. █ Conclusion The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development. █ Footnotes What is the Bartels Test for Cycle Significance? The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century. The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series. The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges. If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component. The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles. 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. 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.
Pine Script® Indikator
von loxx
3
TICK - Custom Tickers [Pt]Traditionally, the TICK index is a technical analysis indicator that shows the difference in the number of stocks that are trading on an uptick vs a downtick in a particular period of time. This indicator allows user to choose up to 40 tickers to calculate TICK. By default, it uses the SPY Top 40 stocks, but can be changed to any tickers. There are options to show: - Top 7 , ie. can be used for just showing TICK for FAANGMT => $FB + $AMZN + $AAPL + $NFLX + $GOOG + $MSFT + $TSLA - Top 10 - Top 20 - Top 30 - Top 40 Data can be displayed in candle bars, line, or both. Enjoy~
Pine Script® Indikator
von PtGambler
4
SessionInBoxesProLibrary "SessionInBoxesPro" get_time_by_bar(bar_count)   Parameters:      bar_count get_positions_func(sessiontime_, duration_)   Parameters:      sessiontime_      duration_ get_period(_session, _start, _lookback)   Parameters:      _session      _start      _lookback is_start(_session)   Parameters:      _session is_end(_session)   Parameters:      _session draw_progress(_show, _session, _is_started, _is_ended, _color, _bottom, _delete_history)   Parameters:      _show      _session      _is_started      _is_ended      _color      _bottom      _delete_history draw_label(_show, _session, _is_started, _color, _top, _bottom, _text, _delete_history, i_label_chg, i_label_size, i_label_position, i_tz, i_label_format_day)   Parameters:      _show      _session      _is_started      _color      _top      _bottom      _text      _delete_history      i_label_chg      i_label_size      i_label_position      i_tz      i_label_format_day draw_fib(_show, _session, _is_started, _color, _top, _bottom, _level, _width, _style, _is_extend, _delete_history)   Parameters:      _show      _session      _is_started      _color      _top      _bottom      _level      _width      _style      _is_extend      _delete_history get_op_stricts(_session, _is_started, top, bottom, i_o_minutes)   Parameters:      _session      _is_started      top      bottom      i_o_minutes draw_op(_show, _session, _is_started, _color, top, bottom, _is_extend, _delete_history, i_o_minutes, i_o_opacity)   Parameters:      _show      _session      _is_started      _color      top      bottom      _is_extend      _delete_history      i_o_minutes      i_o_opacity get_pm_stricts(_show, _show_pm, tf, ctf, _is_started)   Parameters:      _show      _show_pm      tf      ctf      _is_started draw_pm(_show, _show_pm, tf, ctf, _is_started, _is_ended, _delete_history, _color)   Parameters:      _show      _show_pm      tf      ctf      _is_started      _is_ended      _delete_history      _color draw_market(_show, _session, _is_started, _color, btr, _top, _bottom, _extend, _is_extend, _delete_history, i_sess_border_style, i_sess_border_width, i_sess_bgopacity)   Parameters:      _show      _session      _is_started      _color      btr      _top      _bottom      _extend      _is_extend      _delete_history      i_sess_border_style      i_sess_border_width      i_sess_bgopacity draw(_show, _show_pm, pm_tf, ctf, _session, _color, btr, _label, _extend, _show_fib, _show_op, i_label_chg, i_label_size, i_label_position, i_o_minutes, i_o_opacity, i_sess_border_style, i_sess_border_width, i_sess_bgopacity, i_show_history, i_show_closed, i_label_show, i_f_linewidth, i_f_linestyle, top, bottom, i_tz, i_label_format_day)   Parameters:      _show      _show_pm      pm_tf      ctf      _session      _color      btr      _label      _extend      _show_fib      _show_op      i_label_chg      i_label_size      i_label_position      i_o_minutes      i_o_opacity      i_sess_border_style      i_sess_border_width      i_sess_bgopacity      i_show_history      i_show_closed      i_label_show      i_f_linewidth      i_f_linestyle      top      bottom      i_tz      i_label_format_day
Pine Script® Bibliothek
von TheAlgoTradingResources
Daily Scalping Moving AveragesThis is a technical analysis study based on the most fit leading indicators for short timeframes like EMA and SMA. At the same time we have daily channel made from the last 2 weeks of ATR values, which will give us the daily top and bottom expected values(with 80%+ confidence) We have 3 groups of lengths for short length, medium length and a bigger length. At the same time we combine it with the daily vwap values . In the end we are going to have a total of 7 indicators telling us the direction. The way we can use it : The max ratings that we can have are +7 for long and -7 for short In general once we have at least 5 indicators(fast and medium ones) giving us a direction, there is a high chance that we can scalp that trend and then we can exit either when we will be at +7 or close to neutral point At the same time is very important to be aware of the current position inside of the TOP/BOTTOM channel that we have. For example lets assume we are at 40k on BTC and our top channel is around 41-42k while the bottom is around 38k. In this case the margin that we have for long is much smaller than for short, so we should be prepared to exit once we reach the top values and from there wait and see if there is a huge continuation or a reversal. If the top channel was hit and the market started the rebounce going downwards and the moving averages confirms it, then we have a huge advantage using the top points as a STOP LOSS and continue the short movements, giving us an amazing risk/reward ratio . If you have any questions let me know !
Pine Script® Indikator
von exlux
[blackcat] L2 Swing Oscillator Swing MeterLevel: 2 Background Swing trading is a type of trading aimed at making short to medium term profits from a trading pair over a period of a few days to several weeks. Swing traders mainly use technical analysis to look for trading opportunities. In addition to analyzing price trends and patterns, these traders can also use fundamental analysis. Function L2 Swing Oscillator Swing Meter is an oscillator based on breakouts. Another important feature of it is the swing meter, which confirms the top or bottom's confidence level with different color candles. The higher of the candles stack up, the higher confidence level is indicated. Key Signal absolutebot ---> absolute bottom with very high confidence level ltbot ---> long term bottom with high confidence level mtbot ---> middle term bottom with moderate confidence level stbot ---> short term bottom with low confidence level absolutetop ---> absolute top with very high confidence level lttop ---> long term top with high confidence level mttop ---> middle term top with moderate confidence level sttop ---> short term top with low confidence level fastline ---> oscillator fast line slowline ---> oscillator slow line Pros and Cons Pros: 1. reconfigurable swing oscillator based on breakouts 2. swing meter can confirm/validate the bottom and top signal Cons: 1. not appliable with trading pairs without volume information 2. small time frame may not trigger swing meter function Remarks This is a simple but very comprehensive technical indicator Readme In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts. The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works. Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element. Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level. Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated. Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop. Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Pine Script® Indikator
von blackcat1402
4
Golden RatioThis is inspired by Philip Swift's Golden Ratio Multiplier research however it uses the 300 DMA to predict the Macro Cycle Top's Price. It still uses the 350 DMA * 2 and 111 DMA to predict the top's date (the two cross). 111 DMA (Orange) crosses the 350 DMA * 2 (Green)= Macro Cycle Top Date 300 DMA * 3 (Red) predicts the Current Macro Cycle Top Price 300 DMA * 5 (Yellow) predicted the 2018 Macro Cycle Top Price 300 DMA * 8 (Blue) predicted the 2014 Macro Cycle Top Price
Pine Script® Indikator
von timgraham
Bearish Candlestick PatternsDoji Black Spinning Top White Spinning Top Bearish Abandoned Baby Bearish Advance Block Bearish Below The Stomach Bearish Belt Hold Bearish Breakaway Bearish Counter Attack Lines Bearish Dark Cloud Cover Bearish Deliberation Blok Bearish Descending Hawk Bearish Doji Star Bearish Downside Gap Three Methods Bearish Downside Tasuki Gap Bearish Dragonfly Doji Bearish Engulfing Bearish Evening Doji Star Bearish Evening Star Bearish Falling Three Methods Bearish Falling Window Bearish Gravestone Doji Bearish Hanging Man Bearish Harami Bearish Harami Cross Bearish Hook Reversal Bearish Identical Three Crows Bearish In Neck Bearish Island Reversal Bearish Kicking Bearish Ladder Top Bearish Last Engulfing Top Bearish Low Price Gapping Play Bearish Mat Hold Bearish Matching High Bearish Meeting Line Bearish On Neck Bearish One Black Crow Bearish Separating Lines Bearish Shooting Star Bearish Side by side White Lines Bearish Three Black Crows Bearish Three Gap Up Bearish Three Inside Down Bearish Three Line Strike Bearish Three Outside Down Bearish Three Stars in the North Bearish Thrusting Line During Dowtrend Bearish Tower Top Bearish Tristar Bearish Tweezers Top Bearish Two Black Gapping Bearish Two Crows Bearish Upside Gap Two Crows
Pine Script® Indikator
von ceyhun
MFI v1.0 Normal and Dinamic (Totals)The normal MFI script use an RSI in the formula so the quantity of movments are not visible, this script allows you to see how much volume is being trade at the moment, so you can detect unusual levels, but you will no be allowed to see the RSI (0-100)* so I suggest to use this script with a normal MFI Features: + Normal MFI length (14) + Green bars show the total of money trade of the bars that are going up + Red bars show the total of money trade when of the bars that are going down + Dinamic calculation (Optional)(Bellow) Normal MFI use hlc3 ((high+low+close)/3) * (volume) to calculate each bar The dinamic MFI: (This is an optional feature, if you dont active it you will use the normal MFI calculation) (The information bellow is experimental and theorical only, you can use it or not in the script with the Dinamic option) Dinamic MFI divides the bar and volume in three parts. Volume is corresponding on each part ex. If the bar has not a top or lower wick the 100% of volume is in the middle... ex 2 If the 50% of the bar is a top wick, the 50% of volume is in the top wick Top wick: Is calculated this way If the bar is red (high-open)*volume of top wick or If the bar is green (high-close)*volume of top wick Middle: Is calculated this way If the bar is green (close-open)*volumemiddle or If the bar is red (open-close)*volumemiddle Lower wick If the bar is red (close-low)*volume of lower wick or If the bar is green (open- low)*volume of lower wick
Pine Script® Indikator
von Oscar118
BTC - Satoshis Altcoin Graveyard OVERVIEW The Satoshi's Altcoin Graveyard (SAG) is a macro-statistical engine designed to solve the problem of Survivorship Bias . It is a well-known phenomenon in the crypto markets that the "Top 10" list is in a constant state of flux. If you look at historical data from CoinMarketCap (CMC) year by year, you will see a revolving door of projects that once seemed "too big to fail" disappearing into obscurity. Meanwhile, Bitcoin has remained the undisputed #1 since inception. While most traders have a "gut feeling" that Altcoins eventually depreciate against Bitcoin, I believe in measuring it and drawing it on a chart for better visibility. By locking in specific "Cohorts" of market leaders from the past, we can track their inevitable decay through the Satoshi Sieve . THE 13-COIN STATISTICAL BUCKET To ensure an objective, non-biased audit, each cohort (we look at 2018, 2020 and 2022) is constructed using a fixed market-cap methodology from the snapshot date (excluding stablecoins): • The Core: The Top 10 non-stablecoin assets at that time by Marketcap. • The Risk Alpha: Representative samples from the Top #25, #50, and #100 ranks. (By including lower-ranked "riskier" alts, we capture the full statistical decay of the market, not just the "Blue Chips.") TECHNICAL ARCHITECTURE This script is engineered to push the boundaries of the Pine Script engine. TradingView enforces a hard limit of 40 unique data requests . By tracking 3 cohorts of 13 assets plus the Bitcoin base, this indicator utilizes exactly 40/40 requests , providing the maximum possible data density in a single chart window. THE SPS CONCEPT (Survival Probability Score) The SPS measures the Breadth of Survival . It answers: "How many coins from this year (the year of the snapshot) are actually outperforming BTC?" We use a binary logic system to determine if a coin is "Winning" or "Losing" against the only benchmark that matters: Bitcoin. • The Status Formula: Status = Current_Alt_BTC_Ratio >= Entry_Alt_BTC_Ratio ? 1 : 0 . This means: Every single day, at the Daily Close , the script compares the current Alt/BTC ratio to the fixed ratio from the snapshot date. If the coin is worth more in Bitcoin today than it was back then, it is assigned a "1" (a Win). If it has lost value against Bitcoin, it gets a "0" (a Loss). • The SPS Line: SPS Line = (Sum of 'Wins' / 13) * 100 This means: We add up all the "Winners" for that specific day and turn it into a percentage. For example, if the Aqua line is at 7.69% on your chart, it confirms that on that day , exactly 1 out of the 13 coins was successfully beating Bitcoin, while the other 12 were underperforming. THE PERFORMANCE MATRIX In the top-right corner, we provide a Weighted Portfolio Simulation . This answers the financial question: "If I swapped 1 BTC into an equal-weight basket of these 13 coins on the snapshot day, what is my BTC value today?". • Value < 1.0 BTC: You lost purchasing power compared to holding Bitcoin. • Value > 1.0 BTC: You successfully achieved "Alpha" over the benchmark. HOW TO READ THE CHART • The Waterfall: Lines generally trend downward as the "Satoshi Sieve" filters out assets that cannot maintain their BTC-relative value. • Dynamic Winners: We dynamically print the names of the current survivors at the tip of each line. If a cohort shows "None," the graveyard is full. HOW TO READ THE MATRIX • The BTC Target: Any portfolio value in the matrix below 1.0 BTC represents a failed altcoin rotation. • Class of 2018: A portfolio value near 0.15 BTC at the current date, means a 85% loss rate. • Class of 2020: A portfolio value near 0.77 BTC at the current date, means an approx 20 % loss rate. • Class of 2022: A portfolio value near 0.31 BTC at the current date, means an approx 70% loss rate. DIFFERENCE FROM AN ALTCOIN INDEX Standard Altcoin Indexes (like my ALSI Index ) "rebalance" by removing losers and adding new winners. This is deceptive. The Altcoin Graveyard never rebalances . It forces you to watch the "losers" decay, providing a realistic look at the long-term opportunity cost of "Buy and Hold" for anything other than Bitcoin. CONCLUSION The data revealed by the Satoshi Sieve leads to a singular, sobering "Lesson Learned": Picking the right coin to outperform Bitcoin is not just difficult—it is statistically improbable over a long-term horizon. While the "Risk-Reward" of altcoins is often marketed as having higher upside, the Altcoin Graveyard proves that for the vast majority of assets, the reward does not justify the risk of total portfolio erosion in BTC terms. • The Mathematical Odds: If you picked a Top 10 coin in 2018, your chance of outperforming BTC today is effectively 0%. • The Rotation Trap: Most investors "HODL" these assets into the graveyard, hoping for a return to previous ATHs that never comes because the liquidity has already moved on to the next "Class" of winners. The final conclusion is clear: Diversification into altcoins is often just a slow-motion transfer of wealth back to Bitcoin. If you cannot identify the 1-out-of-13 that survives the Sieve, your best risk-adjusted move has historically been to simply hold the benchmark. DISCLAIMER This script is for educational purposes only. It does not constitute financial advice. It is a mathematical study of historical opportunity cost and survivorship bias. Tags bitcoin, btc, satoshis graveyard, altseason, dominance, total3, rotation, cycle, index, alsi, Rob Maths, robmaths
Pine Script® Indikator
von Rob_Maths
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.**
Pine Script® Indikator
von KevinSvenson_
Multi Cycles Slope-Fit System MLMulti Cycles Predictive System : A Slope-Adaptive Ensemble Executive Summary: The MCPS-Slope (Multi Cycles Slope-Fit System) represents a paradigm shift from static technical analysis to adaptive, probabilistic market modeling. Unlike traditional indicators that rely on a single algorithm with fixed settings, this system deploys a "Mixture of Experts" (MoE) ensemble comprising 13 distinct cycle and trend algorithms. Using a Gradient-Based Memory (GBM) learning engine, the system dynamically solves the "Cycle Mode" problem by real-time weighting. It aggressively curve-fits the Slope of component cycles to the Slope of the price action, rewarding algorithms that successfully predict direction while suppressing those that fail. This is a non-repainting, adaptive oscillator designed to identify market regimes, pinpoint high-probability reversals via OB/OS logic, and visualize the aggregate consensus of advanced signal processing mathematics. 1. The Core Philosophy: Why "Slope" Matters: In technical analysis, most traders focus on Levels (Price is above X) or Values (RSI is at 70). However, the primary driver of price action is Momentum, which is mathematically defined as the Rate of Change, or the Slope. This script introduces a novel approach: Slope Fitting. Instead of asking "Is the cycle high or low?", this system asks: "Is the trajectory (Slope) of this cycle matching the trajectory of the price?" The Dual-Functionality of the Normalized Oscillator The final output is a normalized oscillator bounded between -1.0 and +1.0. This structure serves two critical functions simultaneously: Directional Bias (The Slope): When the Combined Cycle line is rising (Positive Slope), the aggregate consensus of the 13 algorithms suggests bullish momentum. When falling (Negative Slope), it suggests bearish momentum. The script measures how well these slopes correlate with price action over a rolling lookback window to assign confidence weights. Overbought / Oversold (OB/OS) Identification: Because the output is mathematically clipped and normalized: Approaching +1.0 (Overbought): Indicates that the top-weighted algorithms have reached their theoretical maximum amplitude. This is a statistical extreme, often preceding a mean reversion or trend exhaustion. Approaching -1.0 (Oversold): Indicates the aggregate cycle has reached maximum bearish extension, signaling a potential accumulation zone. Zero Line (0.0): The equilibrium point. A cross of the Zero Line is the most traditional signal of a trend shift. 2. The "Mixture of Experts" (MoE) Architecture: Markets are dynamic. Sometimes they trend (Trend Following works), sometimes they chop (Mean Reversion works), and sometimes they cycle cleanly (Signal Processing works). No single indicator works in all regimes. This system solves that problem by running 13 Algorithms simultaneously and voting on the outcome. The 13 "Experts" Inside the Code: All algorithms have been engineered to be Non-Repainting. Ehlers Bandpass Filter: Extracts cycle components within a specific frequency bandwidth. Schaff Trend Cycle: A double-smoothed stochastic of the MACD, excellent for cycle turning points. Fisher Transform: Normalizes prices into a Gaussian distribution to pinpoint turning points. Zero-Lag EMA (ZLEMA): Reduces lag to track price changes faster than standard MAs. Coppock Curve: A momentum indicator originally designed for long-term market bottoms. Detrended Price Oscillator (DPO): Removes trend to isolate short-term cycles. MESA Adaptive (Sine Wave): Uses Phase accumulation to detect cycle turns. Goertzel Algorithm: Uses Digital Signal Processing (DSP) to detect the magnitude of specific frequencies. Hilbert Transform: Measures the instantaneous position of the cycle. Autocorrelation: measures the correlation of the current price series with a lagged version of itself. SSA (Simplified): Singular Spectrum Analysis approximation (Lag-compensated, non-repainting). Wavelet (Simplified): Decomposes price into approximation and detail coefficients. EMD (Simplified): Empirical Mode Decomposition approximation using envelope theory. 3. The Adaptive "GBM" Learning Engine This is the "Machine Learning" component of the script. It does not use pre-trained weights; it learns live on your chart. How it works: Fitting Window: On every bar, the system looks back 20 days (configurable). Slope Correlation: It calculates the correlation between the Slope of each of the 13 algorithms and the Slope of the Price. Directional Bonus: It checks if the algorithm is pointing in the same direction as the price. Weight Optimization: Algorithms that match the price direction and correlation receive a higher "Fit Score." Algorithms that diverge from price action are penalized. A "Softmax" style temperature function and memory decay allow the weights to shift smoothly but aggressively. The Result: If the market enters a clean sine-wave cycle, the Ehlers and Goertzel weights will spike. If the market explodes into a linear trend, ZLEMA and Schaff will take over, suppressing the cycle indicators that would otherwise call for a premature top. 4. How to Read the Interface: The visual interface is designed for maximum information density without clutter. The Dashboard (Bottom Left - GBM Stats) Combined Fit: A percentage score (0-100%). High values (>70%) mean the system is "Locked In" and tracking price accurately. Low values suggest market chaos/noise. Entropy: A measure of disorder. High entropy means the algorithms disagree (Neutral/Chop). Low entropy means the algorithms are unanimous (Strong Trend). Top 1 / Top 3 Weight: Shows how concentrated the decision is. If Top 1 Weight is 50%, one algorithm is dominating the decision. The Matrix (Bottom Right - Weight Table) This table lifts the hood on the engine. Fit Score: How well this specific algo is performing right now. Corr/Dir: Raw correlation and Direction Match stats. Weight: The actual percentage influence this algorithm has on the final line. Cycle: The current value of that specific algorithm. Regime: Identifies if the consensus is Bullish, Bearish, or Neutral. The Chart Overlay The Line: The Gradient-Colored line is the Weighted Ensemble Prediction. Green: Bullish Slope. Red: Bearish Slope. Triangles: Zero-Cross signals (Bullish/Bearish). "STRONG" Labels: Appears when the cycle sustains a value above +0.5 or below -0.5, indicating strong momentum. Background Color: Changes subtly to reflect the aggregate Regime (Strong Up, Bullish, Neutral, Bearish, Strong Down). 5. Trading Strategies: A. The Slope Reversal (OB/OS Fade) Concept: Catching tops and bottoms using the -1/+1 normalization. Signal: Wait for the Combined Cycle to reach extreme values (>0.8 or <-0.8). Trigger: The entry is taken not when it hits the level, but when the Slope flips. Short: Cycle hits +0.9, color turns from Green to Red (Slope becomes negative). Long: Cycle hits -0.9, color turns from Red to Green (Slope becomes positive). B. The Zero-Line Trend Join Concept: Joining an established trend after a correction. Signal: Price is trending, but the Cycle pulls back to the Zero line. Trigger: A "Triangle" signal appears as the cycle crosses Zero in the direction of the higher timeframe trend. C. Divergence Analysis Concept: Using the "Fit Score" to identify weak moves. Signal: Price makes a Higher High, but the Combined Cycle makes a Lower High. Confirmation: Check the GBM Stats table. If "Combined Fit" is dropping while price is rising, the trend is decoupling from the cycle logic. This is a high-probability reversal warning. 6. Technical Configuration: Fitting Window (Default: 20): The number of bars the ML engine looks back to judge algorithm performance. Lower (10-15) for scalping/quick adaptation. Higher (30-50) for swing trading and stability. GBM Learning Rate (Default: 0.25): Controls how fast weights change. High (>0.3): The system reacts instantly to new behaviors but may be "jumpy." Low (<0.15): The system is very smooth but may lag in regime changes. Max Single Weight (Default: 0.55): Prevents one single algorithm from completely hijacking the system, ensuring an ensemble effect remains. Slope Lookback: The period over which the slope (velocity) is calculated. 7. Disclaimer & Notes: Repainting: This indicator utilizes closed bar data for calculations and employs non-repainting approximations of SSA, EMD, and Wavelets. It does not repaint historical signals. Calculations: The "ML" label refers to the adaptive weighting algorithm (Gradient-based optimization), not a neural network black box. Risk: No indicator guarantees future performance. The "Fit Score" is a backward-looking metric of recent performance; market regimes can shift instantly. Always use proper risk management. Author's Note The MCPS-Slope was built to solve the frustration of "indicator shopping." Instead of switching between an RSI, a MACD, and a Stochastic depending on the day, this system mathematically determines which one is working best right now and presents you with a single, synthesized data stream. If you find this tool useful, please leave a Boost and a Comment below!
Pine Script® Indikator
von jaydesaigu
As Good As It Gets Pivot ArrowsAs Good As It Gets Pivot Arrows Description - As Good As It Gets Pivot Arrows is a clean, high-precision pivot detection indicator that plots bright green upward triangles for confirmed pivot lows (buy signals) and red downward triangles for confirmed pivot highs (sell signals), and comes with customizable pivot length. Additionally, it optionally displays white dots for double-top/double-bottom pivots within a user-defined percentage tolerance. Key Features - Exact replication of TOS pivot high/low triangles (12-arrow style) - Customizable pivot length (default 7) - Option to ignore the last unconfirmed bar - Toggle triangles and/or pivot dots independently - Double-top/bottom detection with adjustable % tolerance (0.1% default) - Clean visual signals with no repainting on confirmed pivots What Makes It Unique - This script delivers the pivot arrow behavior (including brighter lime-green buy triangles) that many traders love, with added flexibility: individual toggles for triangles/dots, double-top/bottom detection, and full customization. Unlike generic pivot indicators, it has precise confirmation logic while remaining fast and non-repainting on closed bars. How to Use and Trade With It - Adjust "Pivot Length" to suit your timeframe (7–14 common) - Enable/disable triangles or dots as preferred - Fine-tune "% Tolerance" for double-top/bottom sensitivity Trading Signals - Green upward triangle below bar: Confirmed pivot low → potential LONG entry or support - Red downward triangle above bar: Confirmed pivot high → potential SHORT entry or - resistance - White dots: Double-top (above) or double-bottom (below) within tolerance → higher-probability reversal zones Best Practice - Use triangles for primary swing entries/exits - Combine with volume, trend filters, or support/resistance for confirmation - Works on any timeframe; shorter lengths for intraday scalping, longer for positional trading
Pine Script® Indikator
von NPR21
1
SuperWaveTrendWaveTrend with Crosses + HyperWave + Confluence Zones + Thresholds SuperWaveTrend — Advanced Momentum System Integrating WaveTrend, HyperWave, Confluence Zones & Threshold Filters SuperWaveTrend is an enhanced momentum indicator built upon the classic WaveTrend (WT) framework. It integrates HyperWave extreme zones, top/bottom Confluence Zones, trend hesitation Threshold regions, WT crossover reversal signals, and more. This indicator is suitable for: • Trend following • Swing trading • Reversal spotting • Overbought/oversold structure analysis • Extreme market sentiment detection Whether you’re scalping or planning swing entries, SuperWaveTrend offers a more precise and visually intuitive momentum structure. Key Features 1. WaveTrend Core Structure (WT1 / WT2) • WT1: Primary momentum line • WT2: Signal line • Momentum Spread Area (WT1 − WT2) visualization highlights shifts in trend strength 2. HyperWave Extreme Momentum Zones Background highlight automatically appears during extreme momentum conditions: • Purple-red: Extreme bullish zone • Orange: Extreme bearish zone Helps identify: • Blow-off tops • Panic sell-offs • Extreme trend continuation phases 3. Confluence Zones (Top/Bottom Resonance) Combines overbought/oversold signals with momentum structure to mark: • Gold top zones → weakening bullish momentum • Blue bottom zones → weakening bearish momentum Useful for detecting: • Bearish divergence tops • Reversal bounces • High-level exhaustion / low-level capitulation 4. Threshold Hesitation Zone (Gray) When WT1 and WT2 converge tightly, a gray background highlights: • Unclear direction • Trend weakening • Higher risk of false signals Generally not recommended for new entries. 5. WT Crossover Signals (Cross Signals) WT1 and WT2 crossovers are marked with color-coded dots: • Green: Bullish cross • Red: Bearish cross A core signal for capturing reversal shifts. ⚠️ Creator’s Disclaimer & Usage Insights ***WARNING*** SuperWaveTrend is not designed for extremely strong one-sided trends. During highly impulsive markets, signals may become delayed or less reliable. Optimal Timeframes Based on extensive backtesting, In swing-trading environments, the indicator performs most effectively on the 1H–4H timeframes, where momentum cycles form cleanly and Confluence Zones provide high-probability setups. Trading Insights • In swing-trading environments, Confluence Zones often coincide with excellent long/short opportunities, especially when momentum exhaustion is confirmed. • When paired with a Bollinger Bands framework, the system exhibits significantly improved accuracy and structure clarity. Have fun, BigTrunks
Pine Script® Indikator
von BigTrunks
FVG Maxing - Fair Value Gaps, Equilibrium, and Candle Patterns What this script does This open-source indicator highlights 3-candle fair value gaps (FVGs) on the active chart timeframe, draws their midpoint ("equilibrium") line, tracks when each gap is mitigated, and optionally marks simple candle patterns (engulfing and doji) for confluence. It is intended as an educational tool to study how price interacts with imbalances. 3-candle bullish and bearish FVG zones drawn as forward-extending boxes. Equilibrium line at 50% of each gap. Different styling for mitigated vs unmitigated gaps. Compact statistics panel showing how many gaps are currently active and filled. Optional overlays for bullish/bearish engulfing patterns and doji candles. 1. FVG logic (3-candle gaps) The script focuses on a strict 3-candle definition of a fair value gap: Three consecutive candles with the same body direction. The wick of candle 3 is separated from the wick of candle 1 (no overlap). A bullish gap is created when price moves up fast enough to leave a gap between candle 1 and 3. A bearish gap is the mirror case to the downside. In Pine, the core detection looks like this: // Three candles with the same body direction bull_seq = close > open and close > open and close > open bear_seq = close < open and close < open and close < open // Wick gap between candle 1 and candle 3 bull_gap = bull_seq and low > high bear_gap = bear_seq and high < low // Final FVG flags is_bull_fvg = bull_gap is_bear_fvg = bear_gap For each detected FVG: Bullish FVG range: from high up to low (gap below current price). Bearish FVG range: from low down to high (gap above current price). Each zone is stored in a custom FVGData structure so it can be updated when price later trades back inside it. 2. Equilibrium line (0.5 of the gap) Every FVG box gets an optional equilibrium line plotted at the midpoint between its top and bottom: eq_level = (top + bottom) / 2.0 right_index = extend_boxes ? bar_index + extend_length_bars : bar_index bx = box.new(bar_index - 2, top, right_index, bottom) eq_ln = line.new(bar_index - 2, eq_level, right_index, eq_level) line.set_style(eq_ln, line.style_dashed) line.set_color(eq_ln, eq_color) You can use this line as a neutral “fair value” reference inside the zone, or as a simple way to think in terms of premium/discount within each gap. 3. Mitigation rules and styling Each FVG stays active until price trades back into the gap: Bullish FVG is considered mitigated when the low touches or moves below the top of the gap. Bearish FVG is considered mitigated when the high touches or moves above the bottom of the gap. When that happens, the script: Marks the internal FVGData entry as mitigated. Softens the box fill and border colors. Optionally updates the label text from "BULL EQ / BEAR EQ" to "BULL FILLED / BEAR FILLED". Can hide mitigated zones almost completely if you only want to see unfilled imbalances. This allows you to distinguish between current areas of interest and zones that have already been traded through. 4. Candle pattern overlays (engulfing and doji) For additional confluence, the script can mark simple candle patterns on top of the FVG view: Bullish engulfing — current candle body fully wraps the previous bearish body and is larger in size. Bearish engulfing — current candle body fully wraps the previous bullish body and is larger in size. Doji — candles where the real body is small relative to the full range (high–low). The detection is based on basic body and range geometry: curr_body = math.abs(close - open) prev_body = math.abs(close - open ) curr_range = high - low body_ratio = curr_range > 0 ? curr_body / curr_range : 1.0 bull_engulfing = close > open and close < open and open <= close and close >= open and curr_body > prev_body bear_engulfing = close < open and close > open and open >= close and close <= open and curr_body > prev_body is_doji = curr_range > 0 and body_ratio <= doji_body_ratio On the chart, they appear as: Small triangle markers below bullish engulfing candles. Small triangle markers above bearish engulfing candles. Small circles above doji candles. All three overlays are optional and can be turned on or off and recolored in the CANDLE PATTERNS group of inputs. 5. Inputs overview The script organizes settings into clear groups: DISPLAY SETTINGS : Show bullish/bearish FVGs, show/hide mitigated zones, box extension length, box border width, and maximum number of boxes. EQUILIBRIUM : Toggle equilibrium lines, color, and line width. LABELS : Enable labels, choose whether to label unmitigated and/or mitigated zones, and select label size. BULLISH COLORS / BEARISH COLORS : Separate fill and border colors for bullish and bearish gaps. MITIGATED STYLE : Opacity used when a gap is marked as mitigated. STATISTICS : Toggle the on-chart FVG statistics panel. CANDLE PATTERNS : Show engulfing patterns, show dojis, colors, and the body-to-range threshold that defines a doji. 6. Statistics panel An optional table in the corner of the chart summarizes the current state of all tracked gaps: Total number of FVGs still being tracked. Number of bullish vs bearish FVGs. Number of unfilled vs mitigated FVGs. Simple fill rate: percentage of tracked FVGs that have been marked as mitigated. This can help you study how a particular market tends to treat gaps over time. 7. How you might use it (examples) These are usage ideas only, not recommendations: Study how often your symbol mitigates gaps and where inside the zone price tends to react. Use higher-timeframe context and then refine entries near the equilibrium line on your trading timeframe. Combine FVG zones with basic candle patterns (engulfing/doji) as an extra visual anchor, if that fits your process. Hope you enjoy, give your feedback in the comments! - officialjackofalltrades
Pine Script® Indikator
von officialjackofalltrades
Open Interest RSI [BackQuant]Open Interest RSI A multi-venue open interest oscillator that aggregates OI across major derivatives exchanges, converts it to coin or USD terms, and runs an RSI-style engine on that aggregated OI so you can track positioning pressure, crowding, and mean reversion in leverage flows, not just in price. What this is This tool is an RSI built on top of aggregated open interest instead of price. It pulls futures OI from several major exchanges, converts it into a unified unit (COIN or USD), sums it into a single synthetic OI candle, then applies RSI and smoothing to that combined series. You can then render that Open Interest RSI in different visual modes: Clean line or colored line for classic oscillator-style reads. Column-style oscillator for impulse and compression views. Flag mode that fills between OI RSI and its EMA for trend/mean reversion blends. See: Heatmap mode that paints the panel based on OI RSI extremes, ideal for scanning. See: On top of that it includes: Aggregated OI source selection (Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit). Choice of OI units (COIN or USD). Reference lines and OB/OS zones. Extreme highlighting for either trend or mean reversion. A vertical OI RSI meter that acts as a quick strength gauge. Aggregated open interest source Under the hood, the indicator builds a synthetic open interest candle by: Looping over a list of supported exchanges: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit. Looping over multiple contract suffixes (such as USDT.P, USD.P, USDC.P, USD.PM) to capture different contract types on each venue. Requesting OI candles from each venue + contract combination for the same underlying symbol. Converting each OI stream into a common unit: In COIN mode, everything is normalized into coin-denominated OI. In USD mode, coin OI is multiplied by price to approximate notional OI. Summing up open, high, low and close of OI across venues into a single aggregated OI candle. If no valid OI is available for the current symbol across all sources, the script throws a clear runtime error so you know you are on an unsupported market. This gives you a single, exchange-agnostic open interest curve instead of being tied to one venue. That aggregated OI is then passed into the RSI logic. How the OI RSI is calculated The RSI side is straightforward, but it is applied to the aggregated OI close: Compute a base RSI of aggregated OI using the Calculation Period . Apply a simple moving average of length Smoothing Period (SMA) to reduce noise in the raw OI RSI. Optionally apply an EMA on top of the smoothed OI RSI as a moving average signal line. Key parameters: Calculation Period – base RSI length for OI. Smoothing Period (SMA) – extra smoothing on the RSI value. EMA Period – EMA length on the smoothed OI RSI. The result is: oi_rsi – raw RSI of aggregated OI. oi_rsi_s – SMA-smoothed OI RSI. ma – EMA of the smoothed OI RSI. Thresholds and extremes You control three core thresholds: Mid Point – central reference level, typically 50. Extreme Upper Threshold – high-level OI RSI edge (for example 80). Extreme Lower Threshold – low-level OI RSI edge (for example 20). These thresholds are used for: Reference lines or OB/OS zone fills. Heatmap gradient bounds. Background highlighting of extremes. The Extreme Highlighting mode controls how extremes are interpreted: None – do nothing special in extreme regions. Mean-Rev – background turns red on high OI RSI and green on low OI RSI, framing extremes as contrarian zones. Trend – background turns green on high OI RSI and red on low OI RSI, framing extremes as participation zones aligned with the prevailing move. Reference lines and OB/OS zones You can choose: None – clean plotting without guides. Basic Reference Lines – mid, upper and lower thresholds as simple gray horizontals. OB/OS Levels – filled zones between: Upper OB: from the upper threshold to 100, colored with the short/overbought color. Lower OS: from 0 to the lower threshold, colored with the long/oversold color. These guides help visually anchor the OI RSI within "normal" versus "extreme" regions. Plotting modes The Plotting Type input controls how OI RSI is drawn. All modes share the same underlying OI and RSI logic, but emphasise different aspects of the signal. 1) Line mode This is the classic oscillator representation: Plots the smoothed OI RSI as a simple line using RSI Line Color and RSI Line Width . Optionally plots the EMA overlay on the same panel. Works well when you want standard RSI-style signals on leverage flows: crosses of the midline, divergences versus price, and so on. 2) Colored Line mode In this mode: The OI RSI is plotted as a line, but its color is dynamic. If the smoothed OI RSI is above the mid point, it uses the Long/OB Color . If it is below the mid point, it uses the Short/OS Color . This creates an instant visual regime switch between "bullish positioning pressure" and "bearish positioning pressure", while retaining the feel of a traditional RSI line. 3) Oscillator mode Oscillator mode renders OI RSI as vertical columns around the mid level: The smoothed OI RSI is plotted as columns using plot.style_columns . The histogram base is fixed at 50, so bars extend above and below the mid line. Bar color is dynamic, using long or short colors depending on which side of the mid point the value sits. This representation makes impulse and compression in OI flows more obvious. It is especially useful when you want to focus on how quickly OI RSI is expanding or contracting around its neutral level. See: 4) Flag mode Flag mode turns OI RSI and its EMA into a two-line band with a filled area between them: The smoothed OI RSI and its EMA are both plotted. A fill is drawn between them. The fill color flips between the long color and the short color depending on whether OI RSI is above or below its EMA. Black outlines are added to both lines to make the band clear against any background. This creates a "flag" style region where: Green fills show OI RSI leading its EMA, suggesting positive positioning momentum. Red fills show OI RSI trailing below its EMA, suggesting negative positioning momentum. Crossovers of the two lines can be read as shifts in OI momentum regime. Flag mode is useful if you want a more structural view that combines both the level and slope behaviour of OI RSI. See: 5) Heatmap mode Heatmap mode recasts OI RSI as a single-row gradient instead of a line: A single row at level 1 is plotted using column style. The color is pulled from a gradient between the lower and upper thresholds: Near the lower threshold it approaches the short/oversold color and near the upper threshold it approaches the long/overbought color. The EMA overlay and reference lines are disabled in this mode to keep the panel clean. This is a very compact way to track OI RSI state at a glance, especially when stacking it alongside other indicators. See: OI RSI vertical meter Beyond the main plot, the script can draw a small "thermometer" table showing the current OI RSI position from 0 to 100: The meter is a two-column table with a configurable number of rows. Row colors form an inverted gradient: red at the top (100) and green at the bottom (0). The script clamps OI RSI between 0 and 100 and maps it to a row index. An arrow marker "▶" is drawn next to the row corresponding to the current OI RSI value. 0 and 100 labels are printed at the ends of the scale for orientation. You control: Show OI RSI Meter – turn the meter on or off. OI RSI Blocks – number of vertical blocks (granularity). OI RSI Meter Position – panel anchor (top/bottom, left/center/right). The meter is particularly helpful if you keep the main plot in a small panel but still want an intuitive strength gauge. How to read it as a market pressure gauge Because this is an RSI built on aggregated open interest, its extremes and regimes speak to positioning pressure rather than price alone: High OI RSI (near or above the upper threshold) indicates that open interest has been increasing aggressively relative to its recent history. This often coincides with crowded leverage and a buildup of directional pressure. Low OI RSI (near or below the lower threshold) indicates aggressive de-leveraging or closing of positions, often associated with flushes, forced unwinds or post-liquidation clean-ups. Values around the mid point indicate more balanced positioning flows. You can combine this with price action: Price up with rising OI RSI suggests fresh leverage joining the move, a more persistent trend. Price up with falling OI RSI suggests shorts covering or longs taking profit, more fragile upside. Price down with rising OI RSI suggests aggressive new shorts or levered selling. Price down with falling OI RSI suggests de-leveraging and potential exhaustion of the move. Trading applications Trend confirmation on leverage flows Use OI RSI to confirm or question a price trend: In an uptrend, rising OI RSI with values above the mid point indicates supportive leverage flows. In an uptrend, repeated failures to lift OI RSI above mid point or persistent weakness suggest less committed participation. In a downtrend, strong OI RSI on the downside points to aggressive shorting. Mean reversion in positioning Use thresholds and the Mean-Rev highlight mode: When OI RSI spends extended time above the upper threshold, the crowd is extended on one side. That can set up squeeze risk in the opposite direction. When OI RSI has been pinned low, it suggests heavy de-leveraging. Once price stabilises, a re-risking phase is often not far away. Background colours in Mean-Rev mode help visually identify these periods. Regime mapping with plotting modes Different plotting modes give different perspectives: Heatmap mode for dashboard-style use where you just need to know "hot", "neutral" or "cold" on OI flows at a glance. Oscillator mode for short term impulses and compression reads around the mid line. See: Flag mode for blending level and trend of OI RSI into a single banded visual. See: Settings overview RSI group Plotting Type – None, Line, Colored Line, Oscillator, Flag, Heatmap. Calculation Period – base RSI length for OI. Smoothing Period (SMA) – smoothing on RSI. Moving Average group Show EMA – toggle EMA overlay (not used in heatmap). EMA Period – length of EMA on OI RSI. EMA Color – colour of EMA line. Thresholds group Mid Point – central reference. Extreme Upper Threshold and Extreme Lower Threshold – OB/OS thresholds. Select Reference Lines – none, basic lines or OB/OS zone fills. Extreme Highlighting – None, Mean-Rev, Trend. Extra Plotting and UI RSI Line Color and RSI Line Width . Long/OB Color and Short/OS Color . Show OI RSI Meter , OI RSI Blocks , OI RSI Meter Position . Open Interest Source OI Units – COIN or USD. Exchange toggles: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit. Notes This is a positioning and pressure tool, not a complete system. It: Models aggregated futures open interest across multiple centralized exchanges. Transforms that OI into an RSI-style oscillator for better comparability across regimes. Offers several visual modes to match different workflows, from detailed analysis to compact dashboards. Use it to understand how leverage and positioning are evolving behind the price, to gauge when the crowd is stretched, and to decide whether to lean with or against that pressure. Attach it to your existing signals, not in place of them. Also, please check out @NoveltyTrade for the OI Aggregation logic & pulling the data source! Here is the original script:
Pine Script® Indikator
von BackQuant
Volume essential parameters overlayVolume EPO – Essential Volume Parameters Overlay 1. Motivation and design philosophy Volume EPO is designed as a conceptual overlay rather than a self contained trading system. The main idea behind this script is to take complex, foundational market concepts out of heavy, menu driven strategies and express them as lightweight, independent layers that sit on top of any chart or indicator. In many TradingView scripts, a single strategy tries to handle everything at once: signal logic, risk settings, visual cues, multi timeframe controls, and conceptual explanations. This usually leads to long input menus, performance issues, and difficult maintenance. The architectural approach behind Volume EPO is the opposite: keep the core strategy lean, and move the explanation and measurement of key concepts into dedicated overlays. In this framework, Volume EPO is the base layer for the concept of volume. It does not decide anything about entries or exits. Instead, it exposes and clarifies how different definitions of volume behave candle by candle. Other layers or strategies can then build on top of this understanding. 2. What Volume EPO does Volume EPO focuses on four essential volume parameters for each bar: - Buy volume - Sell volume - Total volume - Delta volume (the difference between buy and sell volume) The script presents these parameters in a compact heads up display (HUD) table that can be positioned anywhere on the chart. It is designed to be visually minimal, language aware, and usable on top of any other indicator or price action without cluttering the view. The indicator does not output signals, alerts, arrows, or strategy entries. It is a descriptive and educational tool that shows how volume is distributed, not a prescriptive tool that tells the trader what to do. 3. Two definitions of volume A central theme of this script is that there is more than one way to define and interpret “volume” inside a single candle. Volume EPO implements and clearly separates two different approaches: - A geometric, candle based approximation that uses only OHLC and volume of the current bar. - An intrabar, data driven definition that uses lower timeframe up and down volume when it is available. The user can switch between these modes via the calculation method input. The mode is prominently shown inside the on chart table so that the context is always explicit. 3.1 Geometry mode (Source File, approximate) In Geometry mode, Volume EPO works only with the current bar’s OHLC values and total volume. No lower timeframe data is required. The candle’s range is defined as high minus low. If the range is positive, the position of the close inside that range is used as a simple model for how volume might have been distributed between buyers and sellers: - The closer the close is to the high, the more of the total volume is attributed to the buying side. - The closer the close is to the low, the more of the total volume is attributed to the selling side. - In a rare case where the bar has no price range (for example a flat or doji bar), total volume is split evenly between buy and sell volume. From this model, the script derives: - Buy volume (approximated) - Sell volume (approximated) - Total volume (as reported by the bar) - Delta volume as the difference between buy and sell volume This approach is intentionally labeled as “Geometry (Approx)” in the HUD. It is a theoretical reconstruction based solely on the candle’s geometry and total volume, and it is always available on any market or timeframe that provides OHLCV data. 3.2 Intrabar mode (Precise) In Intrabar mode, Volume EPO uses the TradingView built in library for up and down volume on a user selected lower timeframe. Instead of inferring volume from the shape of the candle, it reads the underlying lower timeframe data when that data is accessible. The script requests up and down volume from a lower timeframe such as 15 seconds, using the official TA library functions. The results are then interpreted as follows: - Buy volume is taken as the absolute value of the up volume. - Sell volume is taken as the absolute value of the down volume. - Total volume is the sum of buy and sell volume. - Delta volume is provided directly by the library as the difference between up and down volume. If valid lower timeframe data exists for a bar, the bar is counted as covered by Intrabar data. If not, that bar is marked as invalid for this precise calculation and is excluded from the covered count. This mode is labeled “Precise” in the HUD, together with the selected lower timeframe, because it is anchored in actual intrabar data rather than in a geometric model. It provides a closer view of how buying and selling pressure unfolded inside the bar, at the cost of requiring more data and being dependent on the availability of that data. 4. Coverage, lookback, and what the numbers mean The top part of the HUD reports not only which volume definition is active, but also an additional line that describes the effective coverage of the data. In Intrabar (Precise) mode, the script displays: - “Scanned: N Bars” Here, N counts how many bars since the indicator was loaded have successfully received valid lower timeframe delta data. It is a measure of how much of the visible history has been truly covered by intrabar information, not a lookback window in the sense of a rolling calculation. In Geometry mode, the script displays: - “Lookback: L Bars” In this extracted layer, the lookback value L is purely descriptive. It does not change how the current bar’s volume is computed, and it is not used in any iterative or statistical calculation inside this script. It is meant as a conceptual label, for example to keep the volume layer consistent with a broader framework where lookback length is a structural parameter. Summarizing these two fields: - Scanned tells you how many bars have been processed using real intrabar data. - Lookback is a descriptive parameter in Geometry mode in this specific overlay, not a direct driver of the computations. 5. The HUD layout on the chart The on chart table is intentionally compact and structured to be read quickly: - Header: a title identifying the overlay as Volume EPO. - Mode line: explicitly states whether the script is in Precise or Geometry mode, and for Precise mode also shows the lower timeframe used. - Coverage line: - In Precise mode, it shows “Scanned: N Bars”. - In Geometry mode, it shows “Lookback: L Bars”. - Volume block: - A line for buy and sell volume, marked with clear directional symbols. - A line for total volume and the absolute delta, accompanied by the sign of the delta. - Numeric formatting uses human friendly suffixes (for example K, M, B) to keep the display readable. - Footer: the current symbol and a time stamp, adjusted by a user selectable timezone offset so that the HUD can be aligned with the trader’s local time reference. The table can be positioned anywhere on the chart and resized via inputs, and it supports multiple color themes and languages in order to integrate cleanly into different chart layouts. 6. How to use Volume EPO in practice Volume EPO is meant to be read together with price action and other tools, not in isolation. Typical uses include: - Studying how often a strong directional candle is actually supported by dominant buy or sell volume. - Comparing the behavior of delta volume between Geometry and Intrabar definitions. - Building a personal intuition for how intrabar data refines or contradicts the simple candle based approximation. - Feeding these insights into separate, lean strategy scripts that do not need to carry the full explanatory logic of volume inside them. Because it is an overlay layer, Volume EPO can be stacked with other custom indicators without adding new signals or complexity to their logic. It simply adds a clear and consistent view of volume behavior on top of whatever the trader is already watching. 7. Educational and non signalling nature Finally, it is important to stress that Volume EPO is not a trading system, not a signal generator, and not financial advice. The script does not tell the user when to enter or exit. It only reports how different definitions of volume describe the current bar. Deciding whether to trade, how to trade, and which risk parameters to use remains entirely with the user and with their own strategy. Volume EPO provides context and clarity around the concept of volume so that those decisions can be informed by a better understanding of how buying and selling pressure is structured inside each candle. Note: Even on lower timeframes, every reconstruction of volume remains an approximation, except at the true single tick level. However, the closer the chosen lower timeframe is to a one tick stream, the more accurately it can reflect the underlying order flow and balance between buying and selling pressure.
Pine Script® Indikator
von ata_sabanci
4
MA Strength Indicator EnhancedThe "MA Strength" is an indicator that measures market trend strength or (in the case of forex pairs) the relative strength of individual currencies based on up to five different moving averages (MA). It offers multiple calculation methods, such as simple summation, normalized value, or measuring ATR/percentage distance from the price. The results are summarized in a clear table, and it provides customizable alerts for trend changes or shifts in currency strength. The high level of configurability (e.g., MA weighting, "all MA alignment" requirement) allows for fine-tuning the strategy. 💬 Interpreting the Table (Top Rows) The top row of the table shows the final output of the indicator. This changes according to the set "Table Mode". Trend Mode: The top row shows the final, aggregated trend status (e.g., "BULLISH", "NEUTRAL") and the corresponding "Trend Value". This is the value the indicator compares to its thresholds. Forex Mode: (Only on 6-character pairs): The top two rows show the strength of the Base currency and the Quote currency separately. Calculation of the top rows: The indicator calculates the individual score of all active MAs (according to the chosen method). Trend Value: This is the final value calculated from the scores. If "Enable Averaging" is ON, this will be the average of the scores (e.g., MA1 score is 5.0, MA2 score is 7.0 -> Trend Value is 6.0). If averaging is OFF, this will be the sum of the scores (e.g., 5.0 + 7.0 = 12.0). Forex Calculation: "Forex Mode" uses this "Trend Value". If the Trend Value is +6.0 (on an EURUSD pair): The Base currency (EUR) value will be +6.0. The Quote currency (USD) value will be -6.0. The indicator compares these values to the thresholds to determine the "STRONG" status for EUR and "WEAK" status for USD. 📊 Calculation Methods The indicator can calculate trend strength using 5 methods. The final "Trend Value" is derived from the results of these calculations. Sum: Description: Simply adds up the individual scores of all enabled moving averages (MA). Formula: If the price is above an MA, it gets the "Score Above" value (e.g., +2.0); if below, it gets the "Score Below" value (e.g., -2.0). Example: Result = (MA1 score) + (MA2 score) + ... Normalized: Description: Takes the sum obtained by the "Sum" method and converts it to a scale between -100% (maximally bearish) and +100% (maximally bullish). It takes into account the maximum possible positive and negative scores. Formula: Result = (Total Score / Max Possible Score) * 100 Percentage Distance: Description: This method also considers distance. The further the price is from the MA in percentage terms, the higher the score. Formula: MA Score = (|Close Price - MA| / MA * 100) * Weight (The "Weight" is the "Score Above/Below" value set in settings). ATR Distance: Description: Similar to percentage distance, but normalizes the distance using volatility via ATR (Average True Range). Formula: MA Score = (|Close Price - MA| / ATR) * Weight Candle Count: Description: Counts how many consecutive candles have been above or below the MA. It multiplies this number by the set weight. Formula: MA Score = (Number of consecutive candles) * Weight ⚙️ Settings Options Moving Averages (MA 1-5) For each moving average, you can set: Enable MA: Turn the specific MA on or off. Type: The type of moving average (SMA, EMA, WMA, etc.). Period: The period of the MA (e.g., 50, 200). Score Above / Below: The most important setting. This defines the "weight" of the MA in the calculation. In "Sum" mode, this is a fixed score; in distance-based modes, this is a multiplier (weight). It is advisable to write a positive number for "Score Above" and a negative number for "Score Below". Calculation Settings Enable Averaging: If this is on, the indicator shows the average of the active MA scores, not the total score. Exception: This function is not available in "Normalized" mode. Require All MA Alignment: This is a strict filter. If enabled, the indicator only gives a "BULLISH" (or "STRONG") signal if the price is above all enabled moving averages. Similarly, a "BEARISH" signal only occurs if the price is below all moving averages. If the price is on the opposite side of even just one MA (e.g., above 4, below 1), the status becomes "NEUTRAL", regardless of the scores. Strength / Trend Thresholds Enable Extra Levels: If active, statuses are expanded: "EXT. BULLISH" / "EXT. BEARISH" (Trend mode) or "EXT. STRONG" / "EXT. WEAK" (Forex mode). This indicates stronger, overbought/oversold conditions. Threshold setting: The thresholds (e.g., "Strong Above - ATR") determine when the calculated value counts as a "STRONG" or "WEAK" status. 🔢 Setting Thresholds via Calculation If "Enable Averaging" is OFF, the "Trend Value" shown in the table will be the sum of the individual MA scores. Therefore, we must define the threshold by adding up the minimum expected performance from each moving average. This allows us to set different expectations for short, medium, and long-term averages. Step 1: Determine MA weights In our example, we use 3 active MAs with the following weights (Score Above values): MA1 (Short): Weight = +2 MA2 (Medium): Weight = +3 MA3 (Long): Weight = +4 Step 2: Determine the minimum expected distance Define a minimum distance expected from each MA to trigger a "Strong" signal. Step 3: Calculate target scores and the final threshold Note: If "Enable Averaging" is ON, the resulting value (sum of target scores) must be averaged to get the final threshold. Example 1: ATR Distance -Goal: I want a "Strong" signal if the price is... ...at least 1.0 ATR above MA1 (Short), ...at least 1.5 ATR above MA2 (Medium), ...and at least 2.0 ATR above MA3 (Long). -Calculation (Expected Distance * Weight): MA1 Target Score: 1.0 * 2 = 2.0 MA2 Target Score: 1.5 * 3 = 4.5 MA3 Target Score: 2.0 * 4 = 8.0 -Final Threshold (Sum of Target Scores): 2.0 + 4.5 + 8.0 = 14.5 -Setting: Set "Strong Above - ATR" threshold to 14.5. If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the threshold: 4.8 (14.5 / 3 = 4.83). Example 2: Percentage Distance -Goal: I want a "Strong" signal if the price is... ...at least 0.5% above MA1, ...at least 1.0% above MA2, ...and at least 1.5% above MA3. -Calculation (Expected Distance * Weight): MA1 Target Score: 0.5 * 2.0 = 1.0 MA2 Target Score: 1.0 * 3.0 = 3.0 MA3 Target Score: 1.5 * 4.0 = 6.0 -Final Threshold (Sum): 1.0 + 3.0 + 6.0 = 10.0 -Setting: Set "Strong Above - Percentage" threshold to 10.0. If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the threshold. Example 3: Candle Count -Goal: I want a "Strong" signal if... ...at least 3 consecutive candles are above MA1, ...at least 5 consecutive candles are above MA2, ...and at least 10 consecutive candles are above MA3. -Calculation (Expected Candle Count * Weight): MA1 Target Score: 3 * 2.0 = 6.0 MA2 Target Score: 5 * 3.0 = 15.0 MA3 Target Score: 10 * 4.0 = 40.0 -Final Threshold (Sum): 6.0 + 15.0 + 40.0 = 61.0 -Setting: Set "Strong Above - Candle" threshold to 61.0. If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the threshold. Example 4: Sum In this mode, distance does not matter, only whether the price is above or below the MA. -Goal: "Strong" signal if the price is above the long-term averages, but can be below the short-term (MA1). MA1 (Short): Can be below (Weight: -2.0) MA2 (Medium): Must be above (Weight: +3.0) MA3 (Long): Must be above (Weight: +4.0) -Calculation: -2.0 + 3.0 + 4.0 = 5.0 -Setting: Set "Strong Above - Sum" threshold to 5.0. If it must be above all three moving averages, the threshold would be 2.0 + 3.0 + 4.0 = 9.0. If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the threshold. Example 5: Normalized The basic logic is similar to the "Sum" method. -Goal: "Strong" signal if price is above MA2 and MA3, but potentially below MA1. -Calculation: Target Sum: 5.0. Max Possible Score (above all): 9.0. -Threshold: (5.0 / 9.0) * 100 = 55.5 In this calculation method, averaging cannot be set. The Usage of the "ATR %" Row The "ATR %" row shows the percentage movement of an average candle. How to use this with "Percentage Distance" mode: This number gives a baseline. It helps decide if the "Percentage Distance" threshold is realistic. Example: You see the "ATR %" value is hovering around 1.2%. This means a "normal" candle moves about 1.2%. If you set the Percentage threshold to 0.5%, it is too low. The indicator will constantly give a "Strong" signal because even average movement (noise) exceeds the threshold. Correct Usage: If "normal" movement is 1.2%, then a "strong" movement (trend) needs to be significantly larger. For example, set the threshold to double the ATR %: 2.4 (2 * 1.2). Thus, you only get a "Strong" signal if the movement is twice the average volatility. Supplementary Information Rounding Differences: The numbers displayed in the table and the precision of calculations in the background differ. Table Display: The indicator rounds numbers to two decimal places in the table. So, if the value is 0.996, the table shows 1.00 (rounded up). Internal Calculation: The background calculation uses much higher precision. When determining status (STRONG vs NEUTRAL), the program compares the precise, unrounded value to the threshold. Result: Due to rounding, it may happen that if the threshold is 1.00 and the table shows 1.00, the status flickers between Strong and Neutral. If this is bothersome, it is advisable to set a slightly lower threshold (e.g., 0.98). 🔔 Alert Settings The indicator can send alerts when the status changes. Alert Method: Trend: Alerts when the main trend status changes (e.g., from "NEUTRAL" to "BULLISH"). You can specify which direction to alert for (e.g., only "BULLISH"). Forex: Works only on 6-character forex pairs. You can set separate alerts for the Base or Quote currency. Forex Strength Level: You can specify at which status level to alert (e.g., "WEAK" or "EXT. STRONG"). 📈 Trading Tips Trend Confirmation: Use the "BULLISH" / "BEARISH" status to confirm your existing strategy (e.g., breakouts, bounces off support). Forex Pairing: In Forex mode, look for pairs where the Base currency is "STRONG" and the Quote currency is "WEAK" (or "EXT. STRONG" / "EXT. WEAK") for a long position. Short Position: Reverse the above (Base: WEAK, Quote: STRONG).
Pine Script® Indikator
von nzsolti18
3
Bollinger Bands Delta Matrix Analytics [BDMA] Bollinger Bands Delta Matrix Analytics (BDMA) v7.0 Deep Kinetic Engine – 5x8 Volatility & Delta Decision Matrix 1. Introduction & Concept Bollinger Bands Delta Matrix Analytics (BDMA) v7.0 is an analytical framework that merges: - Spatial analysis via Bollinger Bands (%B location), - with a 4-factor Deep Kinetic Engine based on: • Total Volume • Buy Volume • Sell Volume • Delta (Buy – Sell) Z-Scores and converts them into an expanded 5×8 decision matrix that continuously tracks where price is trading and how the underlying orderflow is behaving. BDMA is not a trading system or strategy. It does not generate entry/exit signals. Instead, it provides a structured contextual map of volatility, volume, and delta so traders can: - identify climactic extensions vs. fakeouts, - distinguish strong initiative moves vs. passive absorption, - and detect squeezes, traps, and liquidity voids with a unified visual dashboard. 2. Spatial Engine – Bollinger S-States (S1–S5) The spatial dimension of BDMA comes from classic Bollinger Bands. Price location is expressed as Percent B (%B) and mapped into 5 spatial states (S-States): S1 – Hyper Extension (Above Upper Band) Price has pushed beyond the upper Bollinger Band. Often associated with parabolic or blow-off behavior, late-stage momentum, and elevated reversal risk. S2 – Resistance Test (Upper Zone) Price trades in the upper Bollinger region but remains inside the bands. Represents a sustained test of resistance, typically within an established or emerging uptrend. S3 – Neutral Zone (Middle) Price hovers around the mid-band. This is the mean reversion gravity field where the market often consolidates or transitions between regimes. S4 – Support Test (Lower Zone) Price trades in the lower Bollinger region but inside the bands. Represents a sustained test of support within range or downtrend structures. S5 – Hyper Drop (Below Lower Band) Price extends below the lower Bollinger Band. Often aligned with panic, forced liquidations, or capitulation-type behavior, with increased snap-back risk. These 5 S-States define the vertical axis (rows) of the BDMA matrix. 3. Deep Kinetic Engine – 4-Factor Z-Score & D-States (D1–D8) The Deep Kinetic Engine transforms raw volume and delta into standardized Z-Scores to measure how abnormal current activity is relative to its recent history. For each bar: - Raw Buy Volume is estimated from the candle’s position within its range - Raw Sell Volume is complementary to buy volume - Raw Delta = Buy Volume – Sell Volume - Total Volume = Buy Volume + Sell Volume These 4 series are then normalized using a unified Z-Score lookback to produce: 1. Z_Vol_Total – overall activity and liquidity intensity 2. Z_Vol_Buy – aggression from buyers (attack) 3. Z_Vol_Sell – aggression from sellers (defense or attack) 4. Z_Delta – net victory of one side over the other Thresholds for Extreme, Significant, and Neutral Z-Score levels are fully configurable, allowing you to tune the sensitivity of the kinetic states. Using Z_Vol_Total and Z_Delta (plus threshold logic), BDMA assigns one of 8 Deep Kinetic states (D-States): D1 – Climax Buy Extreme Total Volume + Extreme Positive Delta → Buying climax or blow-off behavior. D2 – Strong Buy High Volume + High Positive Delta → Confirmed bullish initiative activity. D3 – Weak Buy / Fakeout Low Volume + High Positive Delta → Bullish delta without commitment, low-liquidity breakout risk. D4 – Absorption / Conflict High Volume + Neutral Delta → Aggressive two-way trade, strong absorption, war zone behavior. D5 – Neutral Low Volume + Neutral Delta → Low-energy environment with low conviction. D6 – Weak Sell / Fakeout Low Volume + High Negative Delta → Bearish delta without commitment, low-liquidity breakdown risk. D7 – Strong Sell High Volume + High Negative Delta → Confirmed bearish initiative activity. D8 – Capitulation Extreme Volume + Extreme Negative Delta → Panic selling or capitulation regime. These 8 D-States define the horizontal axis (columns) of the BDMA matrix. 4. The 5×8 BDMA Decision Matrix The core of BDMA is a 5×8 matrix where: - Rows (1–5) = Spatial S-States (S1…S5) - Columns (1–8) = Kinetic D-States (D1…D8) Each of the 40 possible combinations (SxDy) is pre-computed and mapped to: - a Status or Regime Title (for example: Climax Breakout, Bear Trap Spring, Capitulation Breakdown), - a Bias (Climactic Bull, Neutral, Strong Bear, Conflict or Reversal Risk, and similar labels), - and a Strategic Signal or Consideration (for example: High reversal risk, Wait for confirmation, Low probability zone – avoid). Internally, BDMA resolves all 40 regimes so the current state can be displayed on the dashboard without performance overhead. 5. Key Regime Families (How to Read the Matrix) 5.1. Breakouts and Breakdowns Climax Breakout (Top-side) Spatial S1 with Kinetic D1 or D2 Bias: Explosive or Extreme Bull Signal: - Strong or climactic upside extension with abnormal bullish orderflow. - Trend continuation is possible, but reversal risk is extremely high after blow-off phases. Low-Conviction Breakout (Fakeout Risk) S1 with D3 (Weak Buy, low liquidity) Bias: Weak Bull – Caution Signal: - Breakout not supported by volume. - Elevated risk of failed auction or bull trap. Capitulation Breakdown (Bottom-side) Spatial S5 with Kinetic D8 Bias: Climactic Bear (panic) Signal: - Capitulation-type selling or forced liquidations. - Trend can still proceed, but snap-back or violent short-covering risk is high. Initiative Breakdown vs. Weak Breakdown - Strong, high-volume breakdown typically corresponds to D7 (Strong Sell). - Low-volume breakdown often corresponds to D6 (Weak Sell or Fakeout) with potential for failure. 5.2. Absorption, Traps and Springs Absorption at Resistance (Top-side conflict) S1 or S2 with D4 (Absorption or Conflict) Bias: Conflict – Extreme Tension Signal: - Heavy two-way trade near resistance. - Potential distribution or reversal if sellers begin to dominate. Bull Trap or Failed Auction Typically S1 with D6 (Weak Sell breakdown behavior after a top-side attempt) Indicates a breakout attempt that fails and reverses, often after poor liquidity structure. Absorption at Support and Bear Trap (Spring) S4 or S5 with D4 or D3 Bias: Conflict or Weak Bear – Reversal Risk Signal: - Aggressive buying into lows (spring or shakeout behavior). - Potential bear trap if price reclaims lost territory. 5.3. Trend Phases Strong Uptrend Phases Typically seen when S2–S3 combine with strong bullish kinetic behavior. Bias: Strong or Extreme Bull Signal: - Pullbacks into S3 or S4 with supportive kinetic states often act as trend continuation zones. Strong Downtrend Phases Typically seen when S3–S4 combine with strong bearish kinetic behavior. Bias: Strong or Extreme Bear Signal: - Rallies into resistance with strong bearish kinetic backing may act as continuation sell zones. 5.4. Neutral, Exhaustion and Squeeze Exhaustion or Liquidity Void S1 or S5 with D5 (Neutral kinetics) Bias: Neutral or Exhaustion Signal: - Spatial extremes without kinetic confirmation. - Often marks the end of a move, with poor follow-through. Choppy, Low-Activity Range S3 with D5 Bias: Neutral Signal: - Low volume, low conviction market. - Typically a low-probability environment where standing aside can be logical. Squeeze or High-Tension Zone S3 with D4 or tightly clustered kinetic values Bias: Conflict or High Tension Signal: - Hidden battle inside a volatility contraction. - Often precedes large directionally-biased moves. 6. Dashboard Layout & Reading Guide When Show Dashboard is enabled, BDMA displays: 1. Title and Status Line Name of the current regime (for example: Climax Breakout, Bear Trap Spring, Mean Reversion). 2. Bias Line Plain-language summary of directional context such as Climactic Bull, Strong Bear, Neutral, or Conflict and Reversal Risk. 3. Signal or Strategic Notes Concise guidance focused on risk and context, not entries. For example: - High reversal risk – aggressive traders only - Wait for confirmation (break or rejection) - Low probability zone – avoid taking new positions 4. Kinetic Profile (4-Factor Z-Score) Shows the current Z-Scores for Total Volume (Activity), Buy Volume (Attack), Sell Volume (Defense), and Delta (Net Result). 5. Matrix Heatmap (5×8) Visual representation of S-State vs. D-State with color coding: - Bullish clusters in a green spectrum - Bearish clusters in a red spectrum - Conflict or exhaustion zones in yellow, amber, or neutral tones The dashboard can be repositioned (top right, middle right, or bottom right) and its size can be adjusted (Tiny, Small, Normal, or Large) to fit different layouts. 7. Inputs & Customization 7.1. Core Parameters (Bollinger and Z-Score) - Bollinger Length and Standard Deviation define the spatial engine. - Z-Score Lookback (All Factors) defines how many bars are used to normalize volume and delta. 7.2. Deep Kinetic Thresholds - Extreme Threshold defines what is considered climactic (D1 or D8). - Significant Threshold distinguishes strong initiative vs. weak or fakeout behavior. - Neutral Threshold is the band within which delta is treated as neutral. These thresholds allow you to tune the sensitivity of the kinetic classification to fit different timeframes or instruments. 7.3. Calculation Method (Volume Delta) Geometry (Approx) - Fast, non-repainting approach based on candle geometry. - Suitable for most users and real-time decision-making. Intrabar (Precise) - Uses lower-timeframe data for more precise volume delta estimation. - Intrabar mode can repaint and requires compatible data and plan support on the platform. - Best used for post-analysis or research, not blind automation. 7.4. Visuals and Interface - Toggle Bollinger Bands visibility on or off. - Switch between Dark and Light color themes. - Configure dashboard visibility, matrix heatmap display, position, and size. 8. Multi-Language Semantic Engine (Asia and Middle East Focus) BDMA v7.0 includes a fully integrated multi-language layer, targeting a wide geographic user base. Supported Languages: English, Türkçe, Русский, 简体中文, हिन्दी, العربية, فارسی, עברית All dashboard labels, regime titles, bias descriptions, and signal texts are dynamically translated via an internal dictionary, while semantic meaning is kept consistent across languages. This makes BDMA suitable for multi-language communities, study groups, and educational content across different regions. However, due to the heavy computational load of the Deep Kinetic Engine and TradingView’s strict Pine Script execution limits, it was not possible to expand support to additional languages. Adding more translation layers would significantly increase memory usage and exceed runtime constraints. For this reason, the current language set represents the maximum optimized configuration achievable without compromising performance or stability. 9. Practical Usage Notes BDMA is most powerful when used as a contextual overlay on top of market structure (HH, HL, LH, LL), higher-timeframe trend, key levels, and your own execution framework. Recommended usage: - Identify the current regime (Status and Bias). - Check whether price location (S-State) and kinetic behavior (D-State) agree with your trade idea. - Be especially cautious in climactic and absorption or conflict zones, where volatility and risk can be elevated. Avoid treating BDMA as an automatic green equals buy, red equals sell tool. The real edge comes from understanding where you are in the volatility or kinetic spectrum, not from forcing signals out of the matrix. 10. Limitations & Important Warnings BDMA does not predict the future. It organizes current and recent data into a structured context. Volume data quality depends on the underlying symbol, exchange, and broker feed. Forex, crypto, indices, and stocks may all behave differently. Intrabar mode can repaint and is sensitive to lower-timeframe data availability and your plan type. Use it with extra caution and primarily for research. No indicator can remove the need for clear trading rules, disciplined risk management, and psychological control. 11. Disclaimer This script is provided strictly for educational and analytical purposes. It is not a trading system, signal service, financial product, or investment advice. Nothing in this indicator or its description should be interpreted as a recommendation to buy or sell any asset. Past behavior of any indicator or market pattern does not guarantee future results. Trading and investing involve significant risk, including the risk of losing more than your initial capital in leveraged products. You are solely responsible for your own decisions, risk management, and results. By using this script, you acknowledge that you understand these risks and agree that the author or authors and publisher or publishers are not liable for any loss or damage arising from its use.
Pine Script® Indikator
von ata_sabanci
2
Support & Resistance Zone Hunter [BOSWaves]Support & Resistance Zone Hunter - Dynamic Structural Zones with Real-Time Breakout Intelligence Overview The Support & Resistance Zone Hunter is a professional-grade structural mapping framework designed to automatically detect high-probability support and resistance areas in real time. Unlike traditional static levels or manually drawn zones, this system leverages pivot detection, range thresholds, and optional volume validation to create dynamic zones that reflect the true structural architecture of the market. Zones evolve as price interacts with their boundaries. The first touch of a zone determines its bias - bullish, bearish, or neutral - and the system tracks the full lifecycle of each zone from formation, testing, and bias establishment to potential breakout events. Diamond-shaped breakout signals highlight structurally significant price expansions while filtering noise using a configurable cooldown period. By visualizing market structure in this way, traders gain a deeper understanding of price behavior, trend momentum, and areas where liquidity and reactive forces are concentrated. Theoretical Foundation The Support & Resistance Zone Hunter is built on the premise that meaningful structural zones arise from two core principles: Pivot-Based Turning Points : Only significant highs and lows that represent actual swings in price are considered. Contextual Validation : Zones must pass minimum range criteria and optional volume thresholds to ensure their relevance. Markets naturally generate numerous micro-pivots that do not carry predictive significance. By filtering out minor swings and validating zones against volume and range, the system isolates levels that are more likely to attract future price interaction or act as catalysts for breakout moves. This framework captures not only where price is likely to react but also the direction of potential pressure, providing a statistically grounded, visually intuitive representation of market structure. How It Works The Support & Resistance Zone Hunter constructs zones through a multi-layered process that blends pivot logic, range validation, and real-time bias determination: 1. Pivot Detection Core The indicator identifies pivot highs and pivot lows using a configurable lookback period. Zones are only considered valid when both a top and bottom pivot are present. 2. Zone Qualification Engine Prospective zones must satisfy two conditions: Range Threshold : The distance between pivot high and low must exceed the minimum percentage set by the user. Volume Requirement : If enabled, the current volume must exceed the 50-period moving average. Only zones meeting these criteria are drawn, reducing noise and emphasizing high-probability structural levels. 3. Zone Lifecycle Once a valid top and bottom pivot exist: The zone is created starting from the pivot formation bar. Zones remain active until both boundaries have been touched by price. The first boundary touched establishes bias: resistance first → bullish bias ,support first → bearish bias, neither → neutral. Inactive zones stop expanding but remain visible historically to maintain a clear structural context. 4. Visual Rendering Active zones are displayed as filled boxes with color corresponding to their bias. Top, bottom, and midpoint lines are drawn for reference. Once a zone becomes inactive, its lines are removed while the filled box remains as a historical footprint. 5. Breakout Detection Breakout signals occur when price closes above the top boundary or below the bottom boundary of an active zone. The system applies a cooldown period and requires price to return to the zone since the previous breakout to prevent signal spam. Bullish and bearish breakouts are visually represented by diamond-shaped markers with configurable colors. Interpretation The Support & Resistance Zone Hunter provides a structural view of market balance: Bullish Zones : Form when resistance is tested first, indicating upward pressure and potential continuation. Bearish Zones : Form when support is tested first, reflecting downward pressure and continuation risk. Neutral Zones : Fresh zones that have not yet been interacted with, representing undiscovered liquidity. Breakout Diamonds : Highlight significant structural price expansions, helping traders identify confirmed continuation moves while filtering noise. Zones do not simply indicate past levels; they dynamically reflect the evolving battle between buyers and sellers, providing actionable context for both trend continuation and reversion strategies. Strategy Integration The Support & Resistance Zone Hunter is versatile and can be applied across multiple trading approaches: Trend Continuation : Use bullish and bearish zones to confirm directional bias. Breakout diamonds indicate structural continuation opportunities. Reversion Entries : Neutral zones often act as magnets in ranging markets, allowing for high-probability mean-reversion setups. Breakout Trading : Diamonds mark true structural expansions, reducing false breakout risk and guiding stop placement or momentum entries. Liquidity Zone Alignment : Combining the indicator with order block, breaker, or volume-based tools helps validate zones against broader market participation. Technical Implementation Details Pivot Engine : Two-sided pivot detection based on configurable lookback. Zone Qualification : Minimum range requirement and optional volume filter. Bias Logic : Determined by the first boundary touched. Zone Lifecycle : Active until both boundaries are touched, historical visibility retained. Breakout Signals : Diamond markers with cooldown filtering and price-return validation. Visuals : Transparent filled zones with live top, bottom, and midpoint lines. Suggested Optimal Parameters Pivot Lookback : 10 - 30 for intraday, 20 - 50 for swing trading. Minimum Range % : 0.5 - 2% for crypto or indices, 1 - 3% for metals or forex. Volume Filter : Enable for assets with inconsistent liquidity; disable for consistently liquid markets. Breakout Cooldown : 5 - 20 bars depending on volatility. These suggested parameters should be used as a baseline; their effectiveness depends on the asset and timeframe, so fine-tuning is expected for optimal performance. Performance Characteristics High Effectiveness: Markets with clear pivot structure and reliable volume. Trending symbols with consistent retests. Assets where zones attract repeated price interaction. Reduced Effectiveness: Random walk markets lacking structural pivots. Low-volatility periods with minimal price reaction. Assets with irregular volume distribution or erratic price action. Integration Guidelines Use zone color as contextual bias rather than a standalone signal. Combine with structural tools, order blocks, or volume-based indicators for confluence. Validate zones on higher timeframes to refine lower timeframe entries. Treat breakout diamonds as confirmation of continuation rather than independent triggers. Disclaimer The Support & Resistance Zone Hunter provides structural zone mapping and breakout analytics. It does not predict price movement or guarantee profitability. Success requires disciplined risk management, proper parameter calibration, and integration into a comprehensive trading strategy.
Pine Script® Indikator
von BOSWaves
Time-Decay Liquidity Zones [BackQuant]Time-Decay Liquidity Zones A dynamic liquidity map that turns single-bar exhaustion events into fading, color-graded zones, so you can see where trapped traders and unfinished business still matter, and when those areas have finally stopped pulling price. What this is This indicator detects unusually strong impulsive moves into wicks, converts them into supply or demand “zones,” then lets those zones decay over time. Each zone carries a strength score that fades bar by bar. Zones that stop attracting or rejecting price are gradually de-emphasized and eventually removed, while the most relevant areas stay bright and obvious. Instead of static rectangles that live forever, you get a living liquidity map where: Zones are born from objective criteria: volatility, wick size, and optional volume spikes. Zones “age” using a configurable decay factor and maximum lifetime. Zone color and opacity reflect current relative strength on a unified clear → green → red gradient. Zones freeze when broken, so you can distinguish “active reaction areas” from “historical levels that have already given way”. Conceptual idea Large wicks with strong volatility often mark areas where aggressive orders met hidden liquidity and got absorbed. Price may revisit these areas to test leftover interest or to relieve trapped positions. However, not every wick matters for long. As time passes and more bars print, the market “forgets” some areas. Time-Decay Liquidity Zones turns that idea into a rule-based system: Find bars that likely reflect strong aggressive flows into liquidity. Mark a zone around the wick using ATR-based thickness. Assign a strength score of 1.0 at birth. Each bar, reduce that score by a decay factor and remove zones that fall below a threshold or live too long. Color all surviving zones from weak to strong using a single gradient scale and a visual legend. How events are detected Detection lives in the Event Detection group. The script combines range, wick size, and optional volume filters into simple rules. Volatility filter ATR Length — computes a rolling ATR over your chosen window. This is the volatility baseline. Min range in ATRs — bar range (High–Low) must exceed this multiple of ATR for an event to be considered. This avoids tiny bars triggering zones. Wick filters For each bar, the script splits the candle into body and wicks: Upper wick = High minus the max(Open, Close). Lower wick = min(Open, Close) minus Low. Then it tests: Upper wick condition — upper wick must be larger than Min wick size in ATRs × ATR. Lower wick condition — lower wick must be larger than Min wick size in ATRs × ATR. Only bars with a sufficiently long wick relative to volatility qualify as candidate “liquidity events”. Volume filter Optionally, the script requires a volume spike: Use volume filter — if enabled, volume must exceed a rolling volume SMA by a configurable multiplier. Volume SMA length — period for the volume average. Volume spike multiplier — how many times above the SMA current volume needs to be. This lets you focus only on “heavy” tests of liquidity and ignore quiet bars. Event types Putting it together: Upper event (potential supply / long liquidation, etc.) Occurs when: Upper wick is large in ATR terms. Full bar range is large in ATR terms. Volume is above the spike threshold (if enabled). Lower event (potential demand / short liquidation, etc.) Symmetric conditions using the lower wick. How zones are constructed Zone geometry lives in Zone Geometry . When an event is detected, the script builds a rectangular box that anchors to the wick and extends in the appropriate direction by an ATR-based thickness. For upper (supply-type) zones Bottom of the zone = event bar high. Top of the zone = event bar high + Zone thickness in ATRs × ATR. The zone initially spans only the event bar on the x-axis, but is extended to the right as new bars appear while the zone is active. For lower (demand-type) zones Top of the zone = event bar low. Bottom of the zone = event bar low − Zone thickness in ATRs × ATR. Same extension logic: box starts on the event bar and grows rightward while alive. The result is a band around the wick that scales with volatility. On high-ATR charts, zones are thicker. On calm charts, they are narrower and more precise. Zone lifecycle, decay, and removal All lifecycle logic is controlled by the Decay & Lifetime group. Each zone carries: Score — a floating-point “importance” measure, starting at 1.0 when created. Direction — +1 for upper zones, −1 for lower zones. Birth index — bar index at creation time. Active flag — whether the zone is still considered unbroken and extendable. 1) Active vs broken Each confirmed bar, the script checks: For an upper zone , the zone is counted as “broken” when the close moves above the top of the zone. For a lower zone , the zone is counted as “broken” when the close moves below the bottom of the zone. When a zone breaks: Its right edge is frozen at the previous bar (no further extension). The zone remains on the chart, but is no longer updated by price interaction. It still decays in score until removal. This lets you see where a major level was overrun, while naturally fading its influence over time. 2) Time decay At each confirmed bar: Score := Score × Score decay per bar . A decay value close to 1.0 means very slow decay and long-lived zones. Lower values (closer to 0.9) mean faster forgetting and more current-focused zones. You are controlling how quickly the market “forgets” past events. 3) Age and score-based removal Zones are removed when either: Age in bars exceeds Max bars a zone can live . This is a hard lifetime cap. Score falls below Minimum score before removal . This trims zones that have decayed into irrelevance even if their age is still within bounds. When a zone is removed, its box is deleted and all associated state is freed to keep performance and visuals clean. Unified gradient and color logic Color control lives in Gradient & Color . The indicator uses a single continuous gradient for all zones, above and below price, so you can read strength at a glance without guessing what palette means what. Base colors You set: Mid strength color (green) — used for mid-level strength zones and as the “anchor” in the gradient. High strength color (red) — used for the strongest zones. Max opacity — the maximum visual opacity for the solid part of the gradient. Lower values here mean more solid; higher values mean more transparent. The script then defines three internal points: Clear end — same as mid color, but with a high alpha (close to transparent). Mid end — mid color at the strongest allowed opacity. High end — high color at the strongest allowed opacity. Strength normalization Within each update: The script finds the maximum score among all existing zones. Each zone’s strength is computed as its score divided by this maximum. Strength is clamped into . This means a zone with strength 1.0 is currently the strongest zone on the chart. Other zones are colored relative to that. Piecewise gradient Color is assigned in two stages: For strength between 0.0 and 0.5: interpolate from “clear” green to solid green. Weak zones are barely visible, mid-strength zones appear as solid green. For strength between 0.5 and 1.0: interpolate from solid green to solid red. The strongest zones shift toward the red anchor, clearly separating them from everything else. Strength scale legend To make the gradient readable, the indicator draws a vertical legend on the right side of the chart: About 15 cells from top (Strong) to bottom (Weak). Each cell uses the same gradient function as the zones themselves. Top cell is labeled “Strong”; bottom cell is labeled “Weak”. This legend acts as a fixed reference so you can instantly map a zone’s color to its approximate strength rank. What it plots At a glance, the indicator produces: Upper liquidity zones above price, built from large upper wick events. Lower liquidity zones below price, built from large lower wick events. All zones colored by relative strength using the same gradient. Zones that freeze when price breaks them, then fade out via decay and removal. A strength scale legend on the right to interpret the gradient. There are no extra lines, labels, or clutter. The focus is the evolving structure of liquidity zones and their visual strength. How to read the zones Bright red / bright green zones These are your current “major” liquidity areas. They have high scores relative to other zones and have not yet decayed. Expect meaningful reactions, absorption attempts, or spillover moves when price interacts with them. Faded zones Pale, nearly transparent zones are either old, decayed, or minor. They can still matter, but priority is lower. If these are in the middle of a long consolidation, they often become background noise. Broken but still visible zones Zones whose extension has stopped have been overrun by closing price. They show where a key level gave way. You can use them as context for regime shifts or failed attempts. Absence of zones A chart with few or no zones means that, under your current thresholds, there have not been strong enough liquidity events recently. Either tighten the filters or accept that recent price action has been relatively balanced. Use cases 1) Intraday liquidity hunting Run the indicator on lower timeframes (e.g., 1–15 minute) with moderately fast decay. Use the upper zones as potential sell reaction areas, the lower zones as potential buy reaction areas. Combine with order flow, CVD, or footprint tools to see whether price is absorbing or rejecting at each zone. 2) Swing trading context Increase ATR length and range/wick multipliers to focus only on major spikes. Set slower decay and higher max lifetime so zones persist across multiple sessions. Use these zones as swing inflection areas for larger setups, for example anticipating re-tests after breakouts. 3) Stop placement and invalidation For longs, place invalidation beyond a decaying lower zone rather than in the middle of noise. For shorts, place invalidation beyond strong upper zones. If price closes through a strong zone and it freezes, treat that as additional evidence your prior bias may be wrong. 4) Identifying trapped flows Upper zones formed after violent spikes up that quickly fail can mark trapped longs. Lower zones formed after violent spikes down that quickly reverse can mark trapped shorts. Watching how price behaves on the next touch of those zones can hint at whether those participants are being rescued or squeezed. Settings overview Event Detection Use volume filter — enable or disable the volume spike requirement. Volume SMA length — rolling window for average volume. Volume spike multiplier — how aggressive the volume spike filter is. ATR length — period for ATR, used in all size comparisons. Min wick size in ATRs — minimum wick size threshold. Min range in ATRs — minimum bar range threshold. Zone Geometry Zone thickness in ATRs — vertical size of each liquidity zone, scaled by ATR. Decay & Lifetime Score decay per bar — multiplicative decay factor for each zone score per bar. Max bars a zone can live — hard cap on lifetime. Minimum score before removal — score cut-off at which zones are deleted. Gradient & Color Mid strength color (green) — base color for mid-level zones and the lower half of the gradient. High strength color (red) — target color for the strongest zones. Max opacity — controls the most solid end of the gradient (0 = fully solid, 100 = fully invisible). Tuning guidance Fast, session-only liquidity Shorter ATR length (e.g., 20–50). Higher wick and range multipliers to focus only on extreme events. Decay per bar closer to 0.95–0.98 and moderate max lifetime. Volume filter enabled with a decent multiplier (e.g., 1.5–2.0). Slow, structural zones Longer ATR length (e.g., 100+). Moderate wick and range thresholds. Decay per bar very close to 1.0 for slow fading. Higher max lifetime and slightly higher min score threshold so only very weak zones disappear. Noisy, high-volatility instruments Increase wick and range ATR multipliers to avoid over-triggering. Consider enabling the volume filter with stronger settings. Keep decay moderate to avoid the chart getting overloaded with old zones. Notes This is a structural and contextual tool, not a complete trading system. It does not account for transaction costs, execution slippage, or your specific strategy rules. Use it to: Highlight where liquidity has recently been tested hard. Rank these areas by decaying strength. Guide your attention when layering in separate entry signals, risk management, and higher-timeframe context. Time-Decay Liquidity Zones is designed to keep your chart focused on where the market has most recently “cared” about price, and to gradually forget what no longer matters. Adjust the detection, geometry, decay, and gradient to fit your product and timeframe, and let the zones show you which parts of the tape still have unfinished business.
Pine Script® Indikator
von BackQuant
O'Neil Market TimingBill O'Neil Market Timing Indicator - User Guide Overview This Pine Script indicator implements William O'Neil's market timing methodology, which assigns one of four distinct states to a market index (such as SPY or QQQ) to help traders identify optimal market conditions for investing. The indicator is designed to work exclusively on Daily timeframe charts. The Four Market States The indicator tracks the market through four distinct states, with specific transition rules between them: 1. Confirmed Uptrend (Green) - Meaning: The market is in a healthy uptrend with institutional support - Action: Favorable conditions for building positions in leading stocks - Can transition to: State 2 (Uptrend Under Pressure) 2. Uptrend Under Pressure (Yellow) - Meaning: The uptrend is showing signs of weakness with increasing distribution - Action: Be cautious, tighten stops, reduce position sizes - Can transition to: State 1 (Confirmed Uptrend) or State 3 (Downtrend) 3. Downtrend (Red) - Meaning: The market is in a confirmed downtrend - Action: Stay mostly in cash, avoid new purchases - Can transition to: State 4 (Rally Attempt) 4. Rally Attempt (Pink/Fuchsia) - Meaning: The market is attempting to bottom and reverse - Action: Watch for Follow-Through Day to confirm new uptrend - Can transition to: State 1 (Confirmed Uptrend) or State 3 (Downtrend) Key Concepts Distribution Day A distribution day occurs when: 1. The index closes down by more than the critical percentage (default 0.2%) 2. Volume is higher than the previous day's volume Distribution days indicate institutional selling and are marked with red triangles on the indicator. Follow-Through Day A follow-through day occurs during a Rally Attempt when: 1. The index closes up by more than the critical percentage (default 1.6%) 2. Volume is higher than the previous day's volume A Follow-Through Day confirms a new uptrend and triggers the transition from Rally Attempt to Confirmed Uptrend. State Transition Logic Valid Transitions The system only allows specific transitions: - 1 → 2: When distribution days reach the "pressure number" (default 5) within the lookback period (default 25 bars) - 2 → 1: When distribution days drop below the pressure number - 2 → 3: When distribution days reach "downtrend number" (default 7) AND price drops by "downtrend criterion" (default 6%) from the lookback high - 3 → 4: When the market doesn't make a new low for 3 consecutive days - 4 → 3: When a new low is made, undercutting the downtrend low - 4 → 1: When a Follow-Through Day occurs during the Rally Attempt Input Parameters Distribution Day Parameters - Distribution Day % Threshold (default 0.2%, range 0.1-2.0%) - Minimum percentage decline required to qualify as a distribution day. While 0.2% seems to be the canonical number I see in literature about this, I use a much higher threshold (at least 0.5%) Follow-Through Day Parameters - Follow-Through Day % Threshold (default 1.6%, range 1.0-2.0%) - Minimum percentage gain required to qualify as a follow-through day ### State Transition Parameters - Pressure Number (default 5, range 3-6) - Number of distribution days needed to transition from Confirmed Uptrend to Uptrend Under Pressure - Lookback Period (default 25 bars, range 20-30) - Number of days to count distribution days - Downtrend Number (default 7, range 4-10) - Number of distribution days needed (with price drop) to transition to Downtrend - Downtrend % Drop from High (default 6%, range 5-10%) - Percentage drop from lookback high required for downtrend confirmation Visual Settings - Color customization for each state - Table position selection (Top Left, Top Right, Bottom Left, Bottom Right) ## How to Use This Indicator ### Installation 1. Open TradingView and navigate to SPY or QQQ (or another major index) 2. **Important**: Switch to the Daily (1D) timeframe 3. Click on "Indicators" at the top of the chart 4. Click "Pine Editor" at the bottom of the screen 5. Copy and paste the Pine Script code 6. Click "Add to Chart" ### Interpretation **When the indicator shows:** - **Green (State 1)**: Market is healthy - consider adding quality positions - **Yellow (State 2)**: Exercise caution - tighten stops, be selective - **Red (State 3)**: Defensive mode - preserve capital, avoid new buys - **Pink (State 4)**: Watch closely - prepare for potential Follow-Through Day ### The Information Table The table displays: - **Current State**: The current market condition - **Distribution Days**: Number of distribution days in the lookback period - **Lookback Period**: Number of bars being analyzed - **Rally Attempt Day**: (Only in State 4) Days into the current rally attempt ### Visual Elements 1. **State Line**: A stepped line showing the current state (1-4) 2. **Red Triangles**: Mark each distribution day 3. **Horizontal Reference Lines**: Dotted lines marking each state level 4. **Color-Coded Display**: The state line changes color based on the current market condition ## Trading Strategy Guidelines ### In Confirmed Uptrend (State 1) - Build positions in stocks breaking out of proper bases - Use normal position sizing - Focus on stocks showing institutional accumulation - Hold winners as long as they act properly ### In Uptrend Under Pressure (State 2) - Take partial profits in extended positions - Tighten stop losses - Be more selective with new entries - Reduce overall exposure ### In Downtrend (State 3) - Move to cash or maintain very light exposure - Avoid new purchases - Focus on preservation of capital - Use the time for research and watchlist building ### In Rally Attempt (State 4) - Stay mostly in cash but prepare - Build a watchlist of strong stocks - On Day 4+ of the rally attempt, watch for Follow-Through Day - If FTD occurs, begin cautiously adding positions ## Best Practices 1. **Use with Major Indices**: This indicator works best with SPY, QQQ, or other broad market indices 2. **Daily Timeframe Only**: The indicator is designed for daily bars - do not use on intraday timeframes 3. **Combine with Stock Analysis**: Use the market state as a filter for individual stock decisions 4. **Respect the Signals**: When the market enters Downtrend, reduce exposure regardless of individual stock setups 5. **Monitor Distribution Days**: Pay attention when distribution days accumulate - it's a warning sign 6. **Wait for Follow-Through**: Don't jump back in too early during Rally Attempt - wait for confirmation ## Alert Conditions The indicator includes built-in alert conditions for: - State changes (entering any of the four states) - Distribution Day detection - Follow-Through Day detection during Rally Attempt To set up alerts: 1. Click the "Alert" button while the indicator is on your chart 2. Select "O'Neil Market Timing" 3. Choose your desired alert condition 4. Configure notification preferences ## Customization Tips ### For More Sensitive Detection - Lower the "Pressure Number" to 3-4 - Lower the "Distribution Day % Threshold" to 0.15% - Reduce the "Downtrend Number" to 5-6 ### For More Conservative Detection - Raise the "Pressure Number" to 6 - Raise the "Distribution Day % Threshold" to 0.3-0.5% - Increase the "Downtrend Number" to 8-9 ### For Different Market Conditions - **Bull Market**: Consider slightly higher thresholds - **Bear Market**: Consider slightly lower thresholds - **Volatile Market**: May need to increase percentage thresholds ## Limitations and Considerations 1. **Not a Crystal Ball**: The indicator identifies conditions but doesn't predict the future 2. **False Signals**: Follow-Through Days can fail - use proper risk management 3. **Whipsaws Possible**: In choppy markets, the indicator may switch states frequently 4. **Confirmation Lag**: By design, there's a lag as the system waits for confirmation 5. **Works Best with Price Action**: Combine with your analysis of individual stocks ## Historical Context This methodology is based on William J. O'Neil's decades of market research, documented in books like "How to Make Money in Stocks" and through Investor's Business Daily. O'Neil's research showed that: - Most major market tops are preceded by accumulation of distribution days - Most successful rallies begin with a Follow-Through Day on Day 4-7 of a rally attempt - Identifying market state helps prevent buying during unfavorable conditions ## Troubleshooting **Problem**: Indicator shows "Initializing" - **Solution**: Let the chart load at least 5 bars to establish the initial state **Problem**: No distribution day markers appear - **Solution**: Verify you're on daily timeframe and check if volume data is available **Problem**: Table not visible - **Solution**: Check the table position setting and ensure it's not off-screen **Problem**: State seems to change too frequently - **Solution**: Increase the lookback period or adjust threshold parameters ## Support and Further Learning For deeper understanding of this methodology: - Read "How to Make Money in Stocks" by William J. O'Neil - Study Investor's Business Daily's "Market Pulse" - Review historical market tops and bottoms to see the pattern - Practice identifying distribution days and follow-through days manually ## Version History **Version 1.0** (November 2025) - Initial implementation - Four-state system with proper transitions - Distribution day detection and marking - Follow-through day detection - Customizable parameters - Information table display - Alert conditions --- ## Quick Reference Card | State | Number | Color | Action | |-------|--------|-------|--------| | Confirmed Uptrend | 1 | Green | Buy quality setups | | Uptrend Under Pressure | 2 | Yellow | Tighten stops, be selective | | Downtrend | 3 | Red | Cash position, no new buys | | Rally Attempt | 4 | Pink | Watch for Follow-Through Day | **Distribution Day**: Down > 0.2% on higher volume (red triangle) **Follow-Through Day**: Up > 1.6% on higher volume during Rally Attempt (triggers State 4→1) --- *Remember: This indicator is a tool to help identify market conditions. It should be used as part of a comprehensive trading strategy that includes proper risk management, position sizing, and individual stock analysis.* Also, I created this with the help of an AI coding framework, and I didn't exhaustively test it. I don't actually use this for my own trading, so it's quite possible that it's materially wrong, and that following this will lead to poor investment decisions.. This is "copy left" software, so feel free to alter this to your own tastes, and claim authorship.
Pine Script® Indikator
von matthias
TraderDemircan Auto Fibonacci RetracementDescription: What This Indicator Does:This indicator automatically identifies significant swing high and swing low points within a customizable lookback period and draws comprehensive Fibonacci retracement and extension levels between them. Unlike the manual Fibonacci tool that requires you to constantly redraw levels as price action evolves, this automated version continuously updates the Fibonacci grid based on the most recent major swing points, ensuring you always have current and relevant support/resistance zones displayed on your chart.Key Features: Automatic Swing Detection: Continuously scans the specified lookback period to find the most significant high and low points, eliminating manual drawing errors Comprehensive Level Coverage: Plots 16 Fibonacci levels including 7 retracement levels (0.0 to 1.0) and 9 extension levels (1.115 to 3.618) Top-Down Methodology: Draws from swing high to swing low (right-to-left), following the traditional Fibonacci retracement convention where 100% is at the top Dual Labeling System: Shows both exact price values and Fibonacci percentages for easy reference Complete Customization: Individual toggle controls and color selection for each of the 16 levels Flexible Display Options: Adjust line thickness (1-5), style (solid/dashed/dotted), and extension direction (left/right/both) Visual Swing Markers: Red diamond at the swing high (starting point) and green diamond at the swing low (ending point) Optional Trend Line: Connects the two swing points to visualize the overall price movement direction How It Works:The indicator employs a sophisticated swing point detection algorithm that operates in two stages:Stage 1 - Find the Swing Low (Support Base): Scans the entire lookback period to identify the lowest low, which becomes the anchor point (0.0 level in traditional retracement terms, though displayed at the bottom of the grid).Stage 2 - Find the Swing High (Resistance Peak): After identifying the swing low, searches for the highest high that occurred after that low point, establishing the swing range. This creates a valid price movement range for Fibonacci analysis.Fibonacci Calculation Method: The indicator uses the top-down approach where: 1.0 Level = Swing High (100% retracement, the top) 0.0 Level = Swing Low (0% retracement, the bottom) Retracement Levels (0.236 to 0.786) = Potential support zones during pullbacks from the high Extension Levels (1.115 to 3.618) = Potential target zones below the swing low Formula: Price = SwingHigh - (SwingHigh - SwingLow) × FibonacciLevelThis ensures that 0.0 is at the bottom and extensions (>1.0) plot below the swing low, following standard Fibonacci retracement convention.Fibonacci Levels Explained:Retracement Levels (0.0 - 1.0): 0.0 (Gray): Swing low - the base support level 0.236 (Red): Shallow retracement, first minor support 0.382 (Orange): Moderate retracement, commonly watched support 0.5 (Purple): Psychological midpoint, significant support/resistance 0.618 (Blue - Golden Ratio): The most important retracement level, high-probability reversal zone 0.786 (Cyan): Deep retracement, last defense before full reversal 1.0 (Gray): Swing high - the initial resistance level Extension Levels (1.115 - 3.618): 1.115 (Green): First extension, minimal downside target 1.272 (Light Green): Minor extension, common profit target 1.414 (Yellow-Green): Square root of 2, mathematical significance 1.618 (Gold - Golden Extension): Primary downside target, most watched extension level 2.0 (Orange-Red): 200% extension, psychological round number 2.382 (Pink): Secondary extension target 2.618 (Purple): Deep extension, major target zone 3.272 (Deep Purple): Extreme extension level 3.618 (Blue): Maximum extension, rare but powerful target How to Use:For Retracement Trading (Buying Pullbacks in Uptrends): Wait for price to make a significant move up from swing low to swing high When price starts pulling back, watch for reactions at key Fibonacci levels Most common entry zones: 0.382, 0.5, and especially 0.618 (golden ratio) Enter long positions when price shows reversal signals (candlestick patterns, volume increase) at these levels Place stop loss below the next Fibonacci level Target: Return to swing high or higher extension levels For Extension Trading (Profit Targets): After price breaks below the swing low (0.0 level), use extensions as profit targets First target: 1.272 (conservative) Primary target: 1.618 (golden extension - most commonly reached) Extended target: 2.618 (for strong trends) Extreme target: 3.618 (only in powerful trending moves) For Counter-Trend Trading (Fading Extremes): When price reaches deep retracements (0.786 or below), look for exhaustion signals Watch for divergences between price and momentum indicators at these levels Enter reversal trades with tight stops below the swing low Target: 0.5 or 0.382 levels on the bounce For Trend Continuation: In strong uptrends, shallow retracements (0.236 to 0.382) often hold Use these as low-risk entry points to join the existing trend Failure to hold 0.5 suggests weakening momentum Breaking below 0.618 often indicates trend reversal, not just retracement Multi-Timeframe Strategy: Use daily timeframe Fibonacci for major support/resistance zones Use 4H or 1H Fibonacci for precise entry timing within those zones Confluence between multiple timeframe Fibonacci levels creates high-probability zones Example: Daily 0.618 level aligning with 4H 0.5 level = strong support Settings Guide:Lookback Period (10-500): Short (20-50): Captures recent swings, more frequent updates, suited for day trading Medium (50-150): Balanced approach, good for swing trading (default: 100) Long (150-500): Identifies major market structure, suited for position trading Higher values = more stable levels but slower to adapt to new trends Pivot Sensitivity (1-20): Controls how many candles are required to confirm a swing point Low (1-5): More sensitive, identifies minor swings (default: 5) High (10-20): Less sensitive, only major swings qualify Use higher sensitivity on lower timeframes to filter noise Individual Level Toggles: Enable only the levels you actively trade to reduce chart clutter Common minimalist setup: Show only 0.382, 0.5, 0.618, 1.0, 1.618, 2.618 Comprehensive setup: Enable all levels for maximum information Visual Customization: Line Thickness: Thicker lines (3-5) for presentation, thinner (1-2) for trading Line Style: Solid for primary levels (0.5, 0.618, 1.618), dashed/dotted for secondary Price Labels: Essential for knowing exact entry/exit prices Percent Labels: Helpful for quickly identifying which Fibonacci level you're looking at Extension Direction: Extend right for forward-looking analysis, left for historical context What Makes This Original:While Fibonacci indicators are common on TradingView, this script's originality comes from: Intelligent Two-Stage Detection: Unlike simple high/low finders, this uses a sequential approach (find low first, then find the high that occurred after it), ensuring logical price flow representation Comprehensive Level Set: Includes 16 levels spanning from retracement to extreme extensions, more than most Fibonacci tools Top-Down Methodology: Properly implements the traditional Fibonacci retracement convention (high to low) rather than the reverse Automatic Range Validation: Only draws Fibonacci when both swing points are valid and in the correct temporal order Dual Extension Options: Separate controls for extending lines left (historical context) and right (forward projection) Smart Label Positioning: Places percentage labels on the left and price labels on the right for clarity Visual Swing Confirmation: Diamond markers at swing points help users understand why levels are positioned where they are Important Considerations: Historical Nature: Fibonacci retracements are based on past price swings; they don't predict future moves, only suggest potential support/resistance Self-Fulfilling Prophecy: Fibonacci levels work partly because many traders watch them, creating actual support/resistance at those levels Not All Levels Hold: In strong trends, price may slice through multiple Fibonacci levels without pausing Context Matters: Fibonacci works best when aligned with other support/resistance (previous highs/lows, moving averages, trendlines) Volume Confirmation: The most reliable Fibonacci reversals occur with volume spikes at key levels Dynamic Updates: The levels will redraw as new swing highs/lows form, so don't rely solely on static screenshots Best Practices: Don't Trade Blindly: Fibonacci levels are zones, not exact prices. Look for confirmation (candlestick patterns, indicators, volume) Combine with Price Action: Watch for pin bars, engulfing candles, or doji at key Fibonacci levels Use Stop Losses: Place stops beyond the next Fibonacci level to give trades room but limit risk Scale In/Out: Consider entering partial positions at 0.5 and adding more at 0.618 rather than all-in at one level Check Multiple Timeframes: Daily Fibonacci + 4H Fibonacci convergence = high-probability zone Respect the 0.618: This golden ratio level is historically the most reliable for reversals Extensions Need Strong Trends: Don't expect extensions to be hit unless there's clear momentum beyond the swing low Optimal Timeframes: Scalping (1-5 minutes): Lookback 20-30, watch 0.382, 0.5, 0.618 only Day Trading (15m-1H): Lookback 50-100, all retracement levels important Swing Trading (4H-Daily): Lookback 100-200, focus on 0.5, 0.618, 0.786, and extensions Position Trading (Daily-Weekly): Lookback 200-500, all levels relevant for long-term planning Common Fibonacci Trading Mistakes to Avoid: Wrong Swing Selection: Choosing insignificant swings produces meaningless levels Premature Entry: Entering as soon as price touches a Fibonacci level without confirmation Ignoring Trend: Fighting the main trend by buying deep retracements in downtrends Over-Reliance: Using Fibonacci in isolation without confirming with other technical factors Static Analysis: Not updating your Fibonacci as market structure evolves Arbitrary Lookback: Using the same lookback period for all assets and timeframes Integration with Other Tools:Fibonacci + Moving Averages: When 0.618 level aligns with 50 or 200 EMA, confluence creates stronger support Price bouncing from both Fibonacci and MA simultaneously = high-probability trade Fibonacci + RSI/Stochastic: Oversold indicators at 0.618 or deeper retracements = strong buy signal Overbought indicators at swing high (1.0) = potential reversal warning Fibonacci + Volume Profile: High-volume nodes aligning with Fibonacci levels create robust support/resistance Low-volume areas near Fibonacci levels may see rapid price movement through them Fibonacci + Trendlines: Fibonacci retracement level + ascending trendline = double support Breaking both simultaneously confirms trend change Technical Notes: Uses ta.lowest() and ta.highest() for efficient swing detection across the lookback period Implements dynamic line and label arrays for clean redraws without memory leaks All calculations update in real-time as new bars form Extension options allow customization without modifying core code Format.mintick ensures price labels match the symbol's minimum price increment Tooltip on swing markers shows exact price values for precision
Pine Script® Indikator
von TraderDemircan_