LYGLibraryLibrary "LYGLibrary"
A collection of custom tools & utility functions commonly used with my scripts
getDecimals()
Calculates how many decimals are on the quote price of the current market
Returns: The current decimal places on the market quote price
truncate(number, decimalPlaces)
Truncates (cuts) excess decimal places
Parameters:
number (float)
decimalPlaces (simple float)
Returns: The given number truncated to the given decimalPlaces
toWhole(number)
Converts pips into whole numbers
Parameters:
number (float)
Returns: The converted number
toPips(number)
Converts whole numbers back into pips
Parameters:
number (float)
Returns: The converted number
getPctChange(value1, value2, lookback)
Gets the percentage change between 2 float values over a given lookback period
Parameters:
value1 (float)
value2 (float)
lookback (int)
av_getPositionSize(balance, risk, stopPoints, conversionRate)
Calculates OANDA forex position size for AutoView based on the given parameters
Parameters:
balance (float)
risk (float)
stopPoints (float)
conversionRate (float)
Returns: The calculated position size (in units - only compatible with OANDA)
bullFib(priceLow, priceHigh, fibRatio)
Calculates a bullish fibonacci value
Parameters:
priceLow (float) : The lowest price point
priceHigh (float) : The highest price point
fibRatio (float) : The fibonacci % ratio to calculate
Returns: The fibonacci value of the given ratio between the two price points
bearFib(priceLow, priceHigh, fibRatio)
Calculates a bearish fibonacci value
Parameters:
priceLow (float) : The lowest price point
priceHigh (float) : The highest price point
fibRatio (float) : The fibonacci % ratio to calculate
Returns: The fibonacci value of the given ratio between the two price points
getMA(length, maType)
Gets a Moving Average based on type (MUST BE CALLED ON EVERY CALCULATION)
Parameters:
length (simple int)
maType (string)
Returns: A moving average with the given parameters
getEAP(atr)
Performs EAP stop loss size calculation (eg. ATR >= 20.0 and ATR < 30, returns 20)
Parameters:
atr (float)
Returns: The EAP SL converted ATR size
getEAP2(atr)
Performs secondary EAP stop loss size calculation (eg. ATR < 40, add 5 pips, ATR between 40-50, add 10 pips etc)
Parameters:
atr (float)
Returns: The EAP SL converted ATR size
barsAboveMA(lookback, ma)
Counts how many candles are above the MA
Parameters:
lookback (int)
ma (float)
Returns: The bar count of how many recent bars are above the MA
barsBelowMA(lookback, ma)
Counts how many candles are below the MA
Parameters:
lookback (int)
ma (float)
Returns: The bar count of how many recent bars are below the EMA
barsCrossedMA(lookback, ma)
Counts how many times the EMA was crossed recently
Parameters:
lookback (int)
ma (float)
Returns: The bar count of how many times price recently crossed the EMA
getPullbackBarCount(lookback, direction)
Counts how many green & red bars have printed recently (ie. pullback count)
Parameters:
lookback (int)
direction (int)
Returns: The bar count of how many candles have retraced over the given lookback & direction
getBodySize()
Gets the current candle's body size (in POINTS, divide by 10 to get pips)
Returns: The current candle's body size in POINTS
getTopWickSize()
Gets the current candle's top wick size (in POINTS, divide by 10 to get pips)
Returns: The current candle's top wick size in POINTS
getBottomWickSize()
Gets the current candle's bottom wick size (in POINTS, divide by 10 to get pips)
Returns: The current candle's bottom wick size in POINTS
getBodyPercent()
Gets the current candle's body size as a percentage of its entire size including its wicks
Returns: The current candle's body size percentage
isHammer(fib, colorMatch)
Checks if the current bar is a hammer candle based on the given parameters
Parameters:
fib (float)
colorMatch (bool)
Returns: A boolean - true if the current bar matches the requirements of a hammer candle
isStar(fib, colorMatch)
Checks if the current bar is a shooting star candle based on the given parameters
Parameters:
fib (float)
colorMatch (bool)
Returns: A boolean - true if the current bar matches the requirements of a shooting star candle
isDoji(wickSize, bodySize)
Checks if the current bar is a doji candle based on the given parameters
Parameters:
wickSize (float)
bodySize (float)
Returns: A boolean - true if the current bar matches the requirements of a doji candle
isBullishEC(allowance, rejectionWickSize, engulfWick)
Checks if the current bar is a bullish engulfing candle
Parameters:
allowance (float)
rejectionWickSize (float)
engulfWick (bool)
Returns: A boolean - true if the current bar matches the requirements of a bullish engulfing candle
isBearishEC(allowance, rejectionWickSize, engulfWick)
Checks if the current bar is a bearish engulfing candle
Parameters:
allowance (float)
rejectionWickSize (float)
engulfWick (bool)
Returns: A boolean - true if the current bar matches the requirements of a bearish engulfing candle
isInsideBar()
Detects inside bars
Returns: Returns true if the current bar is an inside bar
isOutsideBar()
Detects outside bars
Returns: Returns true if the current bar is an outside bar
barInSession(sess, useFilter)
Determines if the current price bar falls inside the specified session
Parameters:
sess (simple string)
useFilter (bool)
Returns: A boolean - true if the current bar falls within the given time session
barOutSession(sess, useFilter)
Determines if the current price bar falls outside the specified session
Parameters:
sess (simple string)
useFilter (bool)
Returns: A boolean - true if the current bar falls outside the given time session
dateFilter(startTime, endTime)
Determines if this bar's time falls within date filter range
Parameters:
startTime (int)
endTime (int)
Returns: A boolean - true if the current bar falls within the given dates
dayFilter(monday, tuesday, wednesday, thursday, friday, saturday, sunday)
Checks if the current bar's day is in the list of given days to analyze
Parameters:
monday (bool)
tuesday (bool)
wednesday (bool)
thursday (bool)
friday (bool)
saturday (bool)
sunday (bool)
Returns: A boolean - true if the current bar's day is one of the given days
atrFilter(atrValue, maxSize)
Parameters:
atrValue (float)
maxSize (float)
fillCell(tableID, column, row, title, value, bgcolor, txtcolor)
This updates the given table's cell with the given values
Parameters:
tableID (table)
column (int)
row (int)
title (string)
value (string)
bgcolor (color)
txtcolor (color)
Returns: A boolean - true if the current bar falls within the given dates
Search in scripts for "Table"
Goertzel Browser [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 Browser 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.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser 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 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.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
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 past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
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 and their potential future trajectory, 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 and WindowSizeFuture: These inputs define the window size for past and future projections of 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.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
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 Browser Code
The Goertzel Browser 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 Browser 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 and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, 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 Browser 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 Browser 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 Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture 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 matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser 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 Browser 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 Browser'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 both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future 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 past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past 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.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past 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).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ 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 and potential future impact. 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.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
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 Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser 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 Browser 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 Browser 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:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
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.
LNL Smart TICKLNL Smart TICK
This study is mostly beneficial for intraday traders. It is basically a user-friendly "colorful" representation of the $TICK chart with highlighted $TICK extremes. This indicator also includes: a simple trend gauge that can visualize the bias for the day, cumulative tick cloud which is showing the cumulative strength of either longs & shorts on the day.
$TICK Trend Gauge
Although it is just a exponential moving average. This average (default set on 20) works quite well as an overall gauge for the day. Whenever the gauge is green (above zero), any negative $TICK values below -500 can offer great pullback opportunities. Same applies for the red gauge. 20 EMA is below zero ? Great time to fade any +500 or +1000 tick readings. Obviously the gauge can be ajdusted to any number based on personal style.
$TICK Extremes (little triangles)
These little triangles are triggered anytime $TICK jumps above or below the pre-set values of +1000 or -1000. By just simply observing the $TICK triangles during the day can tell you how much volaility or pressure there is. Sometimes there will be 20 green triangles and only 2 red ones. That obviously mean there is a strong bearish pressure. But there will be days when you are not going to see any triangles at all which can mean there is either a low volatility or the price is stuck in the indecisive market.
Cumulative $TICK Cloud
Cumulative $TICK by itself is a great study for day traders. It is basically running "counting" $TICK that is adding the previous $TICK values from previous bars. Cumulative $TICK can create a direct picture of the current market sentiment. It is not just a simple green / red line but a cloud that can really show you the depth on the $TICK. Some days, the cloud will be quite wide which is a good sign for the strength to one side, but sometimes the cloud will be so narrow it will practically disappear. This would be telling you the exact opposite - not much conviction to any side. Of course the depth as well as the color of the cloud can change during the day.
$TICK & Cumulative $TICK Tables
By just looking at these tables. You can immidiately tell the state of the current $TICK. They both can be red or green. It all depends whether the values are positive or negative. The tables are just a little visual addition to the whole $TICK study.
Hope it helps.
Nasdaq 100 ScreenerNasdaq 100 screener is comprehensive table displaying the following parameters :
Op = Open Price of the Day.
LaP = Last Price.
O-L = Open Price of the Day - Last Price.
ROC = Rate of Change .
SMA20 = Simple Moving Average 20 period.
S20d = Last Price - SMA 20.
SMA50 = Simple Moving Average 50 period.
S50d = Last Price - SMA 50.
SMA200 = Simple Moving Average 200 period.
S200d = Last Price - SMA 200.
ADX(14) = Average Directional Index.
RSI(14) = Relative Strength Index.
CCI(20) = Commodity Channel Index.
ATR(14) = Average True Range.
MOM(10) = Momentum.
AcDis(K) = Accumulation/Distribution.
CMF(20) = Chaikin Money Flow.
MACD = Moving Average Convergence Divergence.
Sig = MACD signal.
Nasdaq 100 stocks are divided into following alphabetical grouping for input access purpose under “Options” in “Settings” menu.
A to B 21 stocks “Input symbols” are listed under the “Options” in “Input A to B”
C to E 18 stocks “Input symbols” are listed under the head “Options” in “Input C to E”
F to L 19 stocks “Input symbols” are listed under the head “Options” in “Input F to L”
M to P 22 stocks “Input symbols” are listed under the head “Options” in “Input M to P”
R to Z 20 stocks “Input symbols” are listed under the head “Options” in “Input R to Z”
A to Z 100 stocks “Input symbols” are listed under the head “Options” in “Input A to Z”
User after visiting the “Settings” menu simply is required to select the “input symbol” from the stock listed under respective alphabetical Input lists to which the particular stock belongs. The resultant data is tabulated under respective row in Table .At a time User can see 5 different stocks i.e one each in different alphabetical lists in respective alphabetical order rows stated in the Table. User can scroll in each list to access and shift to any other stock in the list. In addition a Master list of all 100 stocks is given under “ Input A to Z “ at the last row of table.
Nasdaq 100 screener is a simple table , which facilitate to view 6 different stocks at a time (inclusive one from Master list of “Input A to Z” with a display of 19 parameters.
Tape [LucF]█ OVERVIEW
This script prints an ersatz of a trading console's "tape" section to the right of your chart. It displays the time, price and volume of each update of the chart's feed. It also calculates volume delta for the bar. As it calculates from realtime information, it will not display information on historical bars.
█ FEATURES
Calculations
Each new line in the tape displays the last price/volume update from the TradingView feed that's building your chart. These updates do not necessarily correspond to ticks from the originating broker/exchange's matching engine. Multiple broker/exchange ticks are often aggregated in one chart update.
The script first determines if price has moved up or down since the last update. The polarity of the price change, in turn, determines the polarity of the volume for that specific update. If price does not move between consecutive updates, then the last known polarity is used. Using this method, we can calculate a running volume delta accumulation for the bar, which becomes the bar's final volume delta value when the bar closes (you can inspect values of elapsed realtime bars in the Data Window or the indicator's values). Note that these values will all reset if the script re-executes because of a change in inputs or a chart refresh.
While this method of calculating volume delta is not perfect, it is currently the most precise way of calculating volume delta available on TradingView at the moment. Calculating more precise results would require scripts to have access to bid/ask levels from any chart timeframe. Charts at seconds timeframes do use exchange/broker ticks when the feeds you are using allow for it, and this indicator will run on them, but tick data is not yet available from higher timeframes, for now. Also note that the method used in this script is far superior to the intrabar inspection technique used on historical bars in my other "Delta Volume" indicators. This is because volume delta here is calculated from many more realtime updates than the available intrabars in history.
Inputs
You can use the script's inputs to configure:
• The number of lines displayed in the tape.
• If new lines appear at the top or bottom.
• If you want to hide lines with low volume.
• The precision of volume values.
• The size of the text and the colors used to highlight either the tape's text or background.
• The position where you want the tape on your chart.
• Conditions triggering three different markers.
Display
Deltas are shown at the bottom of the tape. They are reset on each bar. Time delta displays the time elapsed since the beginning of the bar, on intraday timeframes only. Contrary to the price change display by TradingView at the top left of charts, which is calculated from the close of the previous bar, the price delta in the tape is calculated from the bar's open, because that's the information used in the calculation of volume delta. The time will become orange when volume delta's polarity diverges from that of the bar. The volume delta value represents the current, cumulative value for the bar. Its color reflects its polarity.
When new realtime bars appear on the chart, a ↻ symbol will appear before the volume value in tape lines.
Markers
There are three types of markers you can choose to display:
• Marker 1 on volume bumps. A bump is defined as two consecutive and increasing/decreasing plus/minus delta volume values,
when no divergence between the polarity of delta volume and the bar occurs on the second bar.
• Marker 2 on volume delta for the bar exceeding a limit of your choice when there is no divergence between the polarity of delta volume and the bar. These trigger at the bar's close.
• Marker 3 on tape lines with volume exceeding a threshold. These trigger in realtime. Be sure to set a threshold high enough so that it doesn't generate too many alerts.
These markers will only display briefly under the bar, but another marker appears next to the relevant line in the tape.
The marker conditions are used to trigger alerts configured on the script. Alert messages will mention the marker(s) that triggered the specific alert event, along with the relevant volume value that triggered the marker. If more than one marker triggers a single alert, they will overprint under the bar, which can make it difficult to distinguish them.
For more detailed on-chart analysis of realtime volume delta, see my Delta Volume Realtime Action .
█ NOTES FOR CODERS
This script showcases two new Pine features:
• Tables, which allow Pine programmers to display tabular information in fixed locations of the chart. The tape uses this feature.
See the Pine User Manual's page on Tables for more information.
• varip -type variables which we can use to save values between realtime updates.
See the " Using `varip` variables " publication by PineCoders for more information.
SPX Iron Fly Session TrackerOverview
This indicator provides visual tracking for iron fly option structures designed for SPX 0-day-to-expiration (0DTE) intraday trading. It implements a two-phase position management system that adapts to different market conditions throughout the trading day.
This is a visualization and tracking tool only. It does not execute trades, access real options data, or calculate actual profit and loss. All displayed positions are theoretical representations based on underlying price movement.
Strategy Goal and Context
The Core Objective:
The strategy aims to have SPX price expire within your iron fly positions at end of day. When price expires inside a fly's profit zone (between the wings), that position captures maximum premium. The challenge is that price moves throughout the day, so static positioning rarely succeeds.
The Solution: Active Management
Rather than setting positions and hoping price cooperates, this approach continuously manages and repositions flies to keep price centered within your profit zones. As SPX drifts during the trading session, you add new flies at current price levels and close flies that price has moved away from.
The Goal: Multiple Profitable Expirations
By session end, you want as many flies as possible to have price expire within their center zones. This requires:
Adding new flies as price moves away from existing positions
Closing flies when price crosses beyond their optimal range
Building layered coverage in the afternoon to increase probability of capture
Adapting wing widths to time of day and volatility
The Reality: Capital and Time Intensive
This is not a passive strategy. Successful implementation requires:
Substantial capital (each fly requires margin, multiple flies compound this)
Active monitoring throughout trading sessions
Quick decision-making as positions trigger
Multiple position adjustments per session
Disciplined adherence to management rules
How This Indicator Helps:
For backtesting:
Use replay mode to study how positions would have managed on historical sessions
Test different parameter combinations to find optimal settings
Observe position behavior during various market conditions
Understand timing and frequency of position adds and closes
Validate whether your capital can support the required position count
For live session support:
Real-time visual tracking shows current position coverage
Alerts notify you immediately when new positions should be added
Position closure alerts help you manage exits promptly
Reference strike tracking shows where you're measuring movement from
History table provides audit trail of all position activity
The indicator handles the complex tracking and rule application, allowing you to focus on execution and risk management.
Key Use Cases
1. Replay Mode - Backtest and Study
Use TradingView's replay feature to validate the strategy on historical sessions:
Step through past SPX sessions bar-by-bar
See exactly when positions would have opened and closed
Count how many flies would have expired profitably
Analyze different parameter settings on the same historical data
Study position behavior during trending vs ranging conditions
Calculate approximate capital requirements for your setup
Refine your parameters before risking real capital
2. Live Session Alerts
Set up real-time notifications for active trading sessions:
Get alerted immediately when new positions trigger
Receive notifications when positions close
Alerts include strike level, wing width, and closure reason
Works on mobile, desktop, email, or webhook
Never miss a position signal during active trading
Maintain awareness even when away from screens briefly
3. Fully Customizable Parameters
Adapt every aspect to your risk tolerance and capital:
Adjust trigger distances for more or fewer position adds
Modify wing widths for different volatility environments
Change session timing to match your trading schedule
Set maximum concurrent positions to your capital limits
Fine-tune spacing to match available strike increments
Iron Fly Structure
An iron fly is a neutral options strategy with four legs:
- Short 1 ATM Call
- Short 1 ATM Put
- Long 1 OTM Call (upper wing protection)
- Long 1 OTM Put (lower wing protection)
The structure creates a defined risk zone. Maximum profit occurs when price expires at the center strike. Loss increases as price moves toward the wings (breakeven points). Maximum loss is defined and occurs beyond the wings.
Expiration Goal:
You want SPX to close inside the fly's wings. If SPX expires at the strike, you capture maximum premium. If SPX expires between the strike and either wing, you still profit (reduced). If SPX expires beyond the wings, you realize a loss (but it's defined and limited by the wings).
Two-Phase Management System
The indicator tracks positions across two distinct trading phases with different management rules:
Phase 1: TWO_GLASS - Morning Session (Default 10am-1pm ET)
Conservative positioning with active repositioning:
- Trigger new positions when price moves 7.5 points from reference strike (configurable)
- Maintain maximum 2 concurrent positions (configurable)
- 10-point spacing between position strikes (configurable)
- 40-point wing width (configurable)
- Exit rule: When two positions are active and price crosses to one strike level, close the OTHER position
This phase uses a "follow the price" approach. You're not trying to stack multiple positions yet - you're maintaining one or two flies centered on wherever price currently is. As price drifts, you add a new fly at the current level and close the old one when price moves too far away.
Phase 2: THREE_GLASS - Afternoon Session (Default 1pm-4pm ET)
Accumulation mode with layered coverage:
- Trigger new positions every 2.5 points of price movement (configurable)
- Maintain maximum 6 concurrent positions (configurable)
- 5-point spacing between strikes (configurable)
- 20-point wings early, reducing to 10 points after 3pm (configurable)
- Exit rule: Positions only close when price reaches wing extremes
This phase builds a stacked profit zone. Instead of swapping positions, you accumulate multiple flies as price moves. The goal is to have several flies active at expiration, creating a wider net to capture price. Tighter spacing and more frequent triggers create this layered coverage.
Why Two Different Phases?
Morning (Phase 1):
Earlier in the day, price has more time to move substantially. Maintaining many concurrent positions is riskier because price could trend and hit multiple wings. The strategy uses selective positioning with wider wings and active replacement.
Afternoon (Phase 2):
Closer to expiration, price movements typically compress. Time for large moves decreases. The strategy shifts to accumulation, building a net of positions to increase probability that final expiration price falls within at least one (ideally several) of your flies. Tighter wings and more positions become appropriate.
Exit Mechanisms
Strike Cross Exit (Phase 1 Only)
When two positions are active, if price moves to or beyond one position's strike level, the OTHER position closes. This keeps your coverage centered on current price action rather than maintaining positions price has moved away from.
Example: Flies at 5900 and 5910 are open. Price moves to 5910. The fly at 5900 closes because price has moved to the 5910 level. You're now positioned at current price (5910) rather than maintaining coverage at old price (5900).
Wing Extreme Exit (Both Phases)
Any position closes immediately when price touches its upper or lower wing boundary. This represents the breakeven/maximum loss point, so the position is closed to prevent further deterioration.
Dynamic Wing Adjustment
Wing widths automatically adjust based on time of day:
- Phase 1 (Morning): 40 points (customizable)
- Phase 2 Early (1pm-3pm): 20 points (customizable)
- Phase 2 Late (3pm-4pm): 10 points (customizable)
This progressive tightening reflects decreasing price movement potential as expiration approaches. Wider wings earlier provide more protection when price could move substantially. Tighter wings later allow more precise positioning when price movements typically compress.
All values are fully adjustable to match your risk parameters and observed market volatility.
Customization Guide
Every parameter can be modified to suit your trading style, risk tolerance, and capital:
Session Timing
- TWO_GLASS Start Hour: When Phase 1 begins (default: 10am ET)
- THREE_GLASS Start Hour: When Phase 2 begins (default: 1pm ET)
- Wing Width Change Hour: When wings tighten (default: 3pm ET)
- Session End Hour: When tracking stops (default: 4pm ET)
Phase 1 Parameters (Fully Adjustable)
- Trigger Distance: How far price must move from reference strike to add new position (default: 7.5, range: 0.1+)
- Fly Spacing: Distance between position strikes (default: 10, range: 1.0+)
- Wing Width: Distance from strike to wings (default: 40, range: 5.0+)
- Max Flies: Maximum concurrent positions (default: 2, range: 1-10)
Phase 2 Early Parameters (Fully Adjustable)
- Trigger Distance: Movement needed to add new position (default: 2.5, range: 0.1+)
- Fly Spacing: Distance between strikes (default: 5, range: 1.0+)
- Wing Width: Strike to wing distance (default: 20, range: 5.0+)
- Max Flies: Maximum concurrent positions (default: 6, range: 1-20)
Phase 2 Late Parameters
- Wing Width: Reduced width after 3pm (default: 10, range: 5.0+)
General Settings
- Strike Rounding: Round strikes to nearest multiple (default: 5.0, range: 1.0+)
- Bars Before Check: Bars to wait before allowing closure (default: 2, prevents premature exits)
Display Options
- Show History Table: Toggle detailed position log (default: on)
- History Table Rows: Number of positions displayed (default: 15, range: 5-30)
Alert Settings
- Enable Alerts: Toggle notifications for opens/closes (default: on)
How to Use
For Backtesting in Replay Mode:
Select a historical SPX trading session
Apply indicator to 1-5 minute timeframe
Configure your preferred parameters
Activate TradingView's replay feature
Play through the session (step-by-step or continuous)
Observe when positions open (green boxes appear)
Watch position closures (boxes turn gray)
Count how many flies would have expired with price inside (green at session end)
Note total number of position adds throughout session
Calculate approximate capital needed (positions × margin per fly)
Test different parameter combinations on same historical data
Study position behavior during trending vs ranging sessions
For Live Trading Sessions:
Apply indicator to SPX on 1-5 minute timeframe
Configure parameters based on your backtest results
Create alerts for "Iron Fly Opened" and "Iron Fly Closed"
Set alert frequency to "Once Per Bar Close"
Choose notification method (popup, mobile app, email, webhook)
Monitor the status table (top-right) for current session and reference strike
Review history table (bottom-right) for position log with timestamps
When alert triggers, use visual cues to manually place actual option orders
Execute position adds and closes as indicated by the tracker
Visual Interpretation:
Green boxes = Active positions (theoretical profit zones)
White lines (Phase 1) / Aqua lines (Phase 2) = Strike levels
Red/Blue dotted lines = Wing boundaries (breakeven/risk limits)
Gray boxes = Closed positions (historical reference)
Current SPX price line = Shows where price is relative to positions
Top-right table = Current session status, reference strike, open/closed counts
Bottom-right table = Complete position history with open/close timestamps
Alert System Details
The indicator generates detailed alert messages for position management:
Position Opened:
- Strike level where fly should be placed
- Wing width (±points from strike)
- Session phase (Phase 1 or Phase 2)
- Alert format example: "Iron Fly OPENED | Strike: 5900 | Wings: ±40 | Session: TWO_GLASS"
Position Closed:
- Strike level of fly being closed
- Closure reason (strike cross, wing extreme, etc.)
- Session phase
- Alert format example: "Iron Fly CLOSED | Strike: 5900 | Reason: Price crossed to lower fly | Session: TWO_GLASS"
Configure alerts once before market open, then receive automatic notifications as positions trigger throughout the trading session.
Parameter Optimization Suggestions
For Higher Volatility Environments:
- Increase trigger distances (e.g., Phase 1: 10-15 points, Phase 2: 3-5 points)
- Widen wing widths (e.g., Phase 1: 50-60 points, Phase 2: 25-30 points early, 15-20 late)
- Increase strike spacing to reduce position frequency
For Lower Volatility Environments:
- Decrease trigger distances (e.g., Phase 1: 5-7 points, Phase 2: 1.5-2 points)
- Tighten wing widths (e.g., Phase 1: 30-35 points, Phase 2: 15-18 points early, 8-10 late)
- Reduce strike spacing for more granular coverage
For Conservative Risk Management:
- Reduce maximum concurrent positions (Phase 1: 1, Phase 2: 3-4)
- Widen wing widths for more breathing room
- Increase bars before check to avoid whipsaws
- Use wider trigger distances to reduce position frequency
For Aggressive Positioning:
- Increase maximum concurrent positions (Phase 2: 8-10)
- Tighten trigger distances for more frequent adds
- Reduce bars before check for faster responses
- Use tighter spacing to create denser coverage
Capital Considerations:
Remember that each fly requires margin. If Phase 2 allows 6 concurrent flies and each requires $10,000 margin, you need $60,000 in available capital just for position requirements, plus additional cushion for adverse movement.
Use replay mode to count maximum concurrent positions that would have occurred on historical sessions with your parameters, then calculate total capital needed.
Practical Application
This tool provides visual guidance and management support. To implement the strategy:
Backtest thoroughly in replay mode first
Validate capital requirements for your parameter settings
Confirm you can actively monitor positions during trading hours
Use displayed positions as reference for manual order placement
Match indicator parameters to your actual option contracts
Account for real-world factors: commissions, slippage, bid-ask spreads, option availability
Implement proper position sizing based on available capital
Set up alerts before market open to catch all signals
Execute actual trades manually in your brokerage platform
Track actual results versus indicator expectations
Important Limitations
Theoretical tracking only - not an automated trading system
No access to real option prices, Greeks, or implied volatility
No profit/loss calculations or risk metrics
Does not account for time decay (theta), delta, gamma, vega changes
Assumes continuous price action - gaps or halts not handled
Designed for 0DTE SPX options - not suitable for other timeframes or instruments
Assumes option availability at all strike levels - may not reflect reality
Does not model actual option bid/ask spreads or liquidity
Assumes instant execution at desired strikes - slippage not considered
Historical replay shows theoretical behavior only - actual market conditions may differ
Does not adjust for changing implied volatility throughout session
Position count and timing may not match what's executable in real markets
Capital and Time Requirements
This strategy is resource-intensive:
Capital Requirements:
Each iron fly requires margin (varies by broker and strike width)
Multiple concurrent positions multiply capital needs
Example: 6 flies at $10,000 each = $60,000 minimum
Additional cushion needed for adverse movement
Pattern Day Trader rules may apply (requires $25,000 minimum)
Time Requirements:
Active monitoring during trading hours (typically 10am-4pm ET)
Quick response to position add/close signals
Multiple position adjustments per session possible
Cannot be passive or set-and-forget
Requires ability to place orders promptly when alerted
Use replay mode to understand the commitment level before attempting live implementation.
Risk Considerations
Iron fly trading involves substantial risk. This indicator provides visualization and management support only - it does not constitute financial advice or trading recommendations.
Options trading can result in total loss of capital. The indicator's theoretical positions do not reflect actual trading results. Backtest analysis and historical visualization do not guarantee similar future outcomes. Multiple concurrent positions multiply both profit potential and loss risk.
Always conduct independent research, understand all risks, validate capital requirements, and never trade with funds you cannot afford to lose. Consider starting with paper trading to validate execution capability before risking real capital.
Technical Notes
The indicator uses price-based triggers only. It does not:
Connect to options data feeds
Calculate theoretical option values or Greeks
Execute trades automatically
Provide specific trading signals or recommendations
Account for option-specific factors (implied volatility, time decay, bid/ask spreads)
All displayed information represents theoretical position placement based solely on underlying SPX price movement and user-configured parameters. The tool helps visualize the management framework but requires the trader to handle all actual execution and risk management decisions.
This is an educational and analytical tool for understanding iron fly position management concepts. It requires active interpretation, backtesting validation, and manual implementation by the user.
Ultimate MACD [captainua]Ultimate MACD - Comprehensive MACD Trading System
Overview
This indicator combines traditional MACD calculations with advanced features including divergence detection, volume analysis, histogram analysis tools, regression forecasting, strong top/bottom detection, and multi-timeframe confirmation to provide a comprehensive MACD-based trading system. The script calculates MACD using configurable moving average types (EMA, SMA, RMA, WMA) and applies various smoothing methods to reduce noise while maintaining responsiveness. The combination of these features creates a multi-layered confirmation system that reduces false signals by requiring alignment across multiple indicators and timeframes.
Core Calculations
MACD Calculation:
The script calculates MACD using the standard formula: MACD Line = Fast MA - Slow MA, Signal Line = Moving Average of MACD Line, Histogram = MACD Line - Signal Line. The default parameters are Fast=12, Slow=26, Signal=9, matching the traditional MACD settings. The script supports four moving average types:
- EMA (Exponential Moving Average): Standard and most responsive, default choice
- SMA (Simple Moving Average): Equal weight to all periods
- RMA (Wilder's Moving Average): Smoother, less responsive
- WMA (Weighted Moving Average): Recent prices weighted more heavily
The price source can be configured as Close (standard), Open, High, Low, HL2, HLC3, or OHLC4. Alternative sources provide different sensitivity characteristics for various trading strategies.
Configuration Presets:
The script includes trading style presets that automatically configure MACD parameters:
- Scalping: Fast/Responsive settings (8,18,6 with minimal smoothing)
- Day Trading: Balanced settings (10,22,7 with minimal smoothing)
- Swing Trading: Standard settings (12,26,9 with moderate smoothing)
- Position Trading: Smooth/Conservative settings (15,35,12 with higher smoothing)
- Custom: Full manual control over all parameters
Histogram Smoothing:
The histogram can be smoothed using EMA to reduce noise and filter minor fluctuations. Smoothing length of 1 = raw histogram (no smoothing), higher values (3-5) = smoother histogram. Increased smoothing reduces noise but may delay signals slightly.
Percentage Mode:
MACD values can be converted to percentage of price (MACD/Close*100) for cross-instrument comparison. This is useful when comparing MACD signals across instruments with different price levels (e.g., BTC vs ETH). The percentage mode normalizes MACD values, making them comparable regardless of instrument price.
MACD Scale Factor:
A scale factor multiplier (default 1.0) allows adjusting MACD display size for better visibility. Use 0.3-0.5 if MACD appears too compressed, or 2.0-3.0 if too small.
Dynamic Overbought/Oversold Levels:
Overbought and oversold levels are calculated dynamically based on MACD's mean and standard deviation over a lookback period. The formula: OB = MACD Mean + (StdDev × OB Multiplier), OS = MACD Mean - (StdDev × OS Multiplier). This adapts to current market conditions, widening in volatile markets and narrowing in calm markets. The lookback period (default 20) controls how quickly the levels adapt: longer periods (30-50) = more stable levels, shorter (10-15) = more responsive.
OB/OS Background Coloring:
Optional background coloring can highlight the entire panel when MACD enters overbought or oversold territory, providing prominent visual indication of extreme conditions. The background colors are drawn on top of the main background to ensure visibility.
Divergence Detection
Regular Divergence:
The script uses the MACD line (not histogram) for divergence detection, which provides more reliable signals. Bullish divergence: Price makes a lower low while MACD line makes a higher low. Bearish divergence: Price makes a higher high while MACD line makes a lower high. Divergences often precede reversals and are powerful reversal signals.
Pivot-Based Divergence:
The divergence detection uses actual pivot points (pivotlow/pivothigh) instead of simple lowest/highest comparisons. This provides more accurate divergence detection by identifying significant pivot lows/highs in both price and MACD line. The pivot-based method compares two recent pivot points: for bullish divergence, price makes a lower low while MACD makes a higher low at the pivot points. This method reduces false divergences by requiring actual pivot points rather than just any low/high within a period.
The pivot lookback parameters (left and right) control how many bars on each side of a pivot are required for confirmation. Higher values = more conservative pivot detection.
Hidden Divergence:
Continuation patterns that signal trend continuation rather than reversal. Bullish hidden divergence: Price makes a higher low but MACD makes a lower low. Bearish hidden divergence: Price makes a lower high but MACD makes a higher high. These patterns indicate the trend is likely to continue in the current direction.
Zero-Line Filter:
The "Don't Touch Zero Line" option ensures divergences occur in proper context: for bullish divergence, MACD must stay below zero; for bearish divergence, MACD must stay above zero. This filters out divergences that occur in neutral zones.
Range Filtering:
Minimum and maximum lookback ranges control the time window between pivots to consider for divergence. This helps filter out divergences that are too close together (noise) or too far apart (less relevant).
Volume Confirmation System
Volume threshold filtering requires current volume to exceed the volume SMA multiplied by the threshold factor. The formula: Volume Confirmed = Volume > (Volume SMA × Threshold). If the threshold is set to 1.0 or lower, volume confirmation is effectively disabled (always returns true). This allows you to use the indicator without volume filtering if desired. Volume confirmation significantly increases divergence and signal reliability.
Volume Climax and Dry-Up Detection:
The script can mark bars with extremely high volume (volume climax) or extremely low volume (volume dry-up). Volume climax indicates potential reversal points or strong momentum continuation. Volume dry-up indicates low participation and may produce unreliable signals. These markers use standard deviation multipliers to identify extreme volume conditions.
Zero-Line Cross Detection
MACD zero-line crosses indicate momentum shifts: above zero = bullish momentum, below zero = bearish momentum. The script includes alert conditions for zero-line crosses with cooldown protection to prevent alert spam. Zero-line crosses can provide early warning signals before MACD crosses the signal line.
Histogram Analysis Tools
Histogram Moving Average:
A moving average applied to the histogram itself helps identify histogram trend direction and acts as a signal line for histogram movements. Supports EMA, SMA, RMA, and WMA types. Useful for identifying when histogram momentum is strengthening or weakening.
Histogram Bollinger Bands:
Bollinger Bands are applied to the MACD histogram instead of price. The calculation: Basis = SMA(Histogram, Period), StdDev = stdev(Histogram, Period), Upper = Basis + (StdDev × Deviation Multiplier), Lower = Basis - (StdDev × Deviation Multiplier). This creates dynamic zones around the histogram that adapt to histogram volatility. When the histogram touches or exceeds the bands, it indicates extreme conditions relative to recent histogram behavior.
Stochastic MACD (StochMACD):
Stochastic MACD applies the Stochastic oscillator formula to the MACD histogram instead of price. This normalizes the histogram to a 0-100 scale, making it easier to identify overbought/oversold conditions on the histogram itself. The calculation: %K = ((Histogram - Lowest Histogram) / (Highest Histogram - Lowest Histogram)) × 100. %K is smoothed, and %D is calculated as the moving average of smoothed %K. Standard thresholds are 80 (overbought) and 20 (oversold).
Regression Forecasting
The script includes advanced regression forecasting that predicts future MACD values using mathematical models. This helps anticipate potential MACD movements and provides forward-looking context for trading decisions.
Regression Types:
- Linear: Simple trend line (y = mx + b) - fastest, works well for steady trends
- Polynomial: Quadratic curve (y = ax² + bx + c) - captures curvature in MACD movement
- Exponential Smoothing: Weighted average with more weight on recent values - responsive to recent changes
- Moving Average: Uses difference between short and long MA to estimate trend - stable and smooth
Forecast Horizon:
Number of bars to forecast ahead (default 5, max 50 for linear/MA, max 20 for polynomial due to performance). Longer horizons predict further ahead but may be less accurate.
Confidence Bands:
Optional upper/lower bands around forecast show prediction uncertainty based on forecast error (standard deviation of prediction vs actual). Wider bands = higher uncertainty. The confidence level multiplier (default 1.5) controls band width.
Forecast Display:
Forecast appears as dotted lines extending forward from current bar, with optional confidence bands. All forecast values respect percentage mode and scale factor settings.
Strong Top/Bottom Signals
The script detects strong recovery from extreme MACD levels, generating "sBottom" and "sTop" signals. These identify significant reversal potential when MACD recovers substantially from overbought/oversold extremes.
Strong Bottom (sBottom):
Triggered when:
1. MACD was at or near its lowest point in the bottom period (default 10 bars)
2. MACD was in or near the oversold zone
3. MACD has recovered by at least the threshold amount (default 0.5) from the lowest point
4. Recovery persists for confirmation bars (default 2 consecutive bars)
5. MACD has moved out of the oversold zone
6. Volume is above average
7. All enabled filters pass
8. Minimum bars have passed since last signal (reset period, default 5 bars)
Strong Top (sTop):
Triggered when:
1. MACD was at or near its highest point in the top period (default 7 bars)
2. MACD was in or near the overbought zone
3. MACD has declined by at least the threshold amount (default 0.5) from the highest point
4. Decline persists for confirmation bars (default 2 consecutive bars)
5. MACD has moved out of the overbought zone
6. Volume is above average
7. All enabled filters pass
8. Minimum bars have passed since last signal (reset period, default 5 bars)
Label Placement:
sTop/sBottom labels appear on the historical bar where the actual extreme occurred (not on current bar), showing the exact MACD value at that extreme. Labels respect the unified distance checking system to prevent overlaps with Buy/Sell Strength labels.
Signal Strength Calculation
The script calculates a composite signal strength score (0-100) based on multiple factors:
- MACD distance from signal line (0-50 points): Larger separation indicates stronger signal
- Volume confirmation (0-15 points): Volume above average adds points
- Secondary timeframe alignment (0-15 points): Higher timeframe agreement adds points
- Distance from zero line (0-20 points): Closer to zero can indicate stronger reversal potential
Higher scores (70+) indicate stronger, more reliable signals. The signal strength is displayed in the statistics table and can be used as a filter to only accept signals above a threshold.
Smart Label Placement System
The script includes an advanced label placement system that tracks MACD extremes and places Buy/Sell Strength labels at optimal locations:
Label Placement Algorithm:
- Labels appear on the current bar at confirmation (not on historical extreme bars), ensuring they're visible when the signal is confirmed
- The system tracks pending signals when MACD enters OB/OS zones or crosses the signal line
- During tracking, the system continuously searches for the true extreme (lowest MACD for buys, highest MACD for sells) within a configurable historical lookback period
- Labels are only finalized when: (1) MACD exits the OB/OS zone, (2) sufficient bars have passed (2x minimum distance), (3) MACD has recovered/declined by a configurable percentage from the extreme (default 15%), and (4) tracking has stopped (no better extreme found)
Label Spacing and Overlap Prevention:
- Minimum Bars Between Labels: Base distance requirement (default 5 bars)
- Label Spacing Multiplier: Scales the base distance (default 1.5x) for better distribution. Higher values = more spacing between labels
- Effective distance = Base Distance × Spacing Multiplier (e.g., 5 × 1.5 = 7.5 bars minimum)
- Unified distance checking prevents overlaps between all label types (Buy Strength, Sell Strength, sTop, sBottom)
Strength-Based Filtering:
- Label Strength Minimum (%): Only labels with strength at or above this threshold are displayed (default 75%)
- When multiple potential labels are close together, the system automatically compares strengths and keeps only the strongest one
- This ensures only the most significant signals are displayed, reducing chart clutter
Zero Line Polarity Enforcement:
- Enforce Zero Line Polarity (default enabled): Ensures labels follow traditional MACD interpretation
- Buy Strength labels only appear when the tracked extreme MACD value was below zero (negative territory)
- Sell Strength labels only appear when the tracked extreme MACD value was above zero (positive territory)
- This prevents counter-intuitive labels (e.g., Buy labels above zero line) and aligns with standard MACD trading principles
Recovery/Decline Confirmation:
- Recovery/Decline Confirm (%): Percent move away from the extreme required before finalizing (default 15%)
- For Buy labels: MACD must recover by at least this percentage from the tracked bottom
- For Sell labels: MACD must decline by at least this percentage from the tracked top
- Higher values = more confirmation required, fewer but more reliable labels
Historical Lookback:
- Historical Lookback for Label Placement: Number of bars to search for true extremes (default 20)
- The system searches within this period to find the actual lowest/highest MACD value
- Higher values analyze more history but may be slower; lower values are faster but may miss some extremes
Cross Quality Score
The script calculates a MACD cross quality score (0-100) that rates crossover quality based on:
- Cross angle (0-50 points): Steeper crosses = stronger signals
- Volume confirmation (0-25 points): Volume above average adds points
- Distance from zero line (0-25 points): Crosses near zero line are stronger
This score helps identify high-quality crossovers and can be used as a filter to only accept signals meeting minimum quality threshold.
Filtering System
Histogram Filter:
Requires histogram to be above zero for buy signals, below zero for sell signals. Ensures momentum alignment before generating signals.
Signal Strength Filter:
Requires minimum signal strength score for signals. Higher threshold = only strongest signals pass. This combines multiple confirmation factors into a single filter.
Cross Quality Filter:
Requires minimum cross quality score for signals. Rates crossover quality based on angle, volume, momentum, and distance from zero. Only signals meeting minimum quality threshold will be generated.
All filters use the pattern: filterResult = not filterEnabled OR conditionMet. This means if a filter is disabled, it always passes (returns true). Filters can be combined, and all must pass for a signal to fire.
Multi-Timeframe Analysis
The script can display MACD from a secondary (higher) timeframe and use it for confirmation. When secondary timeframe confirmation is enabled, signals require the higher timeframe MACD to align (bullish/bearish) with the signal direction. This ensures signals align with the larger trend context, reducing counter-trend trades.
Secondary Timeframe MACD:
The secondary timeframe MACD uses the same calculation parameters (fast, slow, signal, MA type) as the main MACD but from a higher timeframe. This provides context for the current timeframe's MACD position relative to the larger trend. The secondary MACD lines are displayed on the chart when enabled.
Noise Filtering
Noise filtering hides small histogram movements below a threshold. This helps focus on significant moves and reduces chart clutter. When enabled, only histogram movements above the threshold are displayed. Typical threshold values are 0.1-0.5 for most instruments, depending on the instrument's price range and volatility.
Signal Debounce
Signal debounce prevents duplicate MACD cross signals within a short time period. Useful when MACD crosses back and forth quickly, creating multiple signals. Debounce ensures only one signal per period, reducing signal spam during choppy markets. This is separate from alert cooldown, which applies to all alert types.
Background Color Modes
The script offers three background color modes:
- Dynamic: Full MACD heatmap based on OB/OS conditions, confidence, and momentum. Provides rich visual feedback.
- Monotone: Soft neutral background but still allows overlays (OB/OS zones). Keeps the chart clean without overpowering candles.
- Off: No MACD background (only overlays and plots). Maximum chart cleanliness.
When OB/OS background colors are enabled, they are drawn on top of the main background to ensure visibility.
Statistics Table
A real-time statistics table displays current MACD values, signal strength, distance from zero line, secondary timeframe alignment, volume confirmation status, and all active filter statuses. The table dynamically adjusts to show only enabled features, keeping it clean and relevant. The table position can be configured (Top Left, Top Right, Bottom Left, Bottom Right).
Performance Statistics Table
An optional performance statistics table shows comprehensive filter diagnostics:
- Total buy/sell signals (raw crossover count before filters)
- Filtered buy/sell signals (signals that passed all filters)
- Overall pass rates (percentage of signals that passed filters)
- Rejected signals count
- Filter-by-filter rejection diagnostics showing which filters rejected how many signals
This table helps optimize filter settings by showing which filters are most restrictive and how they impact signal frequency. The diagnostics format shows rejections as "X B / Y S" (X buy signals rejected, Y sell signals rejected) or "Disabled" if the filter is not active.
Alert System
The script includes separate alert conditions for each signal type:
- MACD Cross: MACD line crosses above/below Signal line (with or without secondary confirmation)
- Zero-Line Cross: MACD crosses above/below zero
- Divergence: Regular and hidden divergence detections
- Secondary Timeframe: Higher timeframe MACD crosses
- Histogram MA Cross: Histogram crosses above/below its moving average
- Histogram Zero Cross: Histogram crosses above/below zero
- StochMACD: StochMACD overbought/oversold entries and %K/%D crosses
- Histogram BB: Histogram touches/breaks Bollinger Bands
- Volume Events: Volume climax and dry-up detections
- OB/OS: MACD entry/exit from overbought/oversold zones
- Strong Top/Bottom: sTop and sBottom signal detections
Each alert type has its own cooldown system to prevent alert spam. The cooldown requires a minimum number of bars between alerts of the same type, reducing duplicate alerts during volatile periods. Alert types can be filtered to only evaluate specific alert types (All, MACD Cross, Zero Line, Divergence, Secondary Timeframe, Histogram MA, Histogram Zero, StochMACD, Histogram BB, Volume Events, OB/OS, Strong Top/Bottom).
How Components Work Together
MACD crossovers provide the primary signal when the MACD line crosses the Signal line. Zero-line crosses indicate momentum shifts and can provide early warning signals. Divergences identify potential reversals before they occur.
Volume confirmation ensures signals occur with sufficient market participation, filtering out low-volume false breakouts. Histogram analysis tools (MA, Bollinger Bands, StochMACD) provide additional context for signal reliability and identify significant histogram zones.
Signal strength combines multiple confirmation factors into a single score, making it easy to filter for only the strongest signals. Cross quality score rates crossover quality to identify high-quality setups. Multi-timeframe confirmation ensures signals align with higher timeframe trends, reducing counter-trend trades.
Usage Instructions
Getting Started:
The default configuration shows MACD(12,26,9) with standard EMA calculations. Start with default settings and observe behavior, then customize settings to match your trading style. You can use configuration presets for quick setup based on your trading style.
Customizing MACD Parameters:
Adjust Fast Length (default 12), Slow Length (default 26), and Signal Length (default 9) based on your trading timeframe. Shorter periods (8,17,7) for faster signals, longer (15,30,12) for smoother signals. You can change the moving average type: EMA for responsiveness, RMA for smoothness, WMA for recent price emphasis.
Price Source Selection:
Choose Close (standard), or alternative sources (HL2, HLC3, OHLC4) for different sensitivity. HL2 uses the midpoint of the high-low range, HLC3 and OHLC4 incorporate more price information.
Histogram Smoothing:
Set smoothing to 1 for raw histogram (no smoothing), or increase (3-5) for smoother histogram that reduces noise. Higher smoothing reduces false signals but may delay signals slightly.
Percentage Mode:
Enable percentage mode when comparing MACD across instruments with different price levels. This normalizes MACD values, making them directly comparable.
Dynamic OB/OS Levels:
The dynamic thresholds automatically adapt to volatility. Adjust the multipliers (default 1.5) to fine-tune sensitivity: higher values (2.0-3.0) = more extreme thresholds (fewer signals), lower (1.0-1.5) = more frequent signals. Adjust the lookback period to control how quickly levels adapt. Enable OB/OS background colors for visual indication of extreme conditions.
Volume Confirmation:
Set volume threshold to 1.0 (default, effectively disabled) or higher (1.2-1.5) for standard confirmation. Higher values require more volume for confirmation. Set to 0.1 to completely disable volume filtering.
Filters:
Enable filters gradually to find your preferred balance. Start with histogram filter for basic momentum alignment, then add signal strength filter (threshold 50+) for moderate signals, then cross quality filter (threshold 50+) for high-quality crossovers. Combine filters for highest-quality signals but expect fewer signals.
Divergence:
Enable divergence detection and adjust pivot lookback parameters. Pivot-based divergence provides more accurate detection using actual pivot points. Hidden divergence is useful for trend-following strategies. Adjust range parameters to filter divergences by time window.
Zero-Line Crosses:
Zero-line cross alerts are automatically available when alerts are enabled. These provide early warning signals for momentum shifts.
Histogram Analysis Tools:
Enable Histogram Moving Average to see histogram trend direction. Enable Histogram Bollinger Bands to identify extreme histogram zones. Enable Stochastic MACD to normalize histogram to 0-100 scale for overbought/oversold identification.
Multi-Timeframe:
Enable secondary timeframe MACD to see higher timeframe context. Enable secondary confirmation to require higher timeframe alignment for signals.
Signal Strength:
Signal strength is automatically calculated and displayed in the statistics table. Use signal strength filter to only accept signals above a threshold (e.g., 50 for moderate, 70+ for strong signals only).
Smart Label Placement:
Configure label placement settings to control label appearance and quality:
- Label Strength Minimum (%): Set threshold (default 75%) to show only strong signals. Higher = fewer, stronger labels
- Label Spacing Multiplier: Adjust spacing (default 1.5x) for better distribution. Higher = more spacing between labels
- Recovery/Decline Confirm (%): Set confirmation requirement (default 15%). Higher = more confirmation, fewer labels
- Enforce Zero Line Polarity: Enable (default) to ensure Buy labels only appear when tracked extreme was below zero, Sell labels only when above zero
- Historical Lookback: Adjust search period (default 20 bars) for finding true extremes. Higher = more history analyzed
Cross Quality:
Cross quality score is automatically calculated for crossovers. Use cross quality filter to only accept high-quality crossovers (threshold 50+ for moderate, 70+ for high quality).
Alerts:
Set up alerts for your preferred signal types. Enable alert cooldown (default enabled, 5 bars) to prevent alert spam. Use alert type filter to only evaluate specific alert types (All, MACD Cross, Zero Line, Divergence, Secondary Timeframe, Histogram MA, Histogram Zero, StochMACD, Histogram BB, Volume Events, OB/OS, Strong Top/Bottom). Each signal type has its own alert condition, so you can be selective about which signals trigger alerts.
Visual Elements and Signal Markers
The script uses various visual markers to indicate signals and conditions:
- MACD Line: Green when above signal (bullish), red when below (bearish) if dynamic colors enabled. Optional black outline for enhanced visibility
- Signal Line: Orange line with optional black outline for enhanced visibility
- Histogram: Color-coded based on direction and momentum (green for bullish rising, lime for bullish falling, red for bearish falling, orange for bearish rising)
- Zero Line: Horizontal reference line at MACD = 0
- Fill to Zero: Green/red semi-transparent fill between MACD line and zero line showing bullish/bearish territory
- Fill Between OB/OS: Blue semi-transparent fill between overbought/oversold thresholds highlighting neutral zone
- OB/OS Background Colors: Background coloring when MACD enters overbought/oversold zones
- Background Colors: Dynamic or monotone backgrounds indicating MACD state, or custom chart background
- Divergence Labels: "🐂" for bullish, "🐻" for bearish, "H Bull" for hidden bullish, "H Bear" for hidden bearish
- Divergence Lines: Colored lines connecting pivot points when divergences are detected
- Volume Climax Markers: ⚡ symbol for extremely high volume
- Volume Dry-Up Markers: 💧 symbol for extremely low volume
- Buy/Sell Strength Labels: Show signal strength percentage (e.g., "Buy Strength: 75%")
- Strong Top/Bottom Labels: "sTop" and "sBottom" for extreme level recoveries
- Secondary MACD Lines: Purple lines showing higher timeframe MACD
- Histogram MA: Orange line showing histogram moving average
- Histogram BB: Blue bands around histogram showing extreme zones
- StochMACD Lines: %K and %D lines with overbought/oversold thresholds
- Regression Forecast: Dotted blue lines extending forward with optional confidence bands
Signal Priority and Interpretation
Signals are generated independently and can occur simultaneously. Higher-priority signals generally indicate stronger setups:
1. MACD Cross with Multiple Filters - Highest priority: Requires MACD crossover plus all enabled filters (histogram, signal strength, cross quality) and secondary timeframe confirmation if enabled. These are the most reliable signals.
2. Zero-Line Cross - High priority: Indicates momentum shift. Can provide early warning signals before MACD crosses the signal line.
3. Divergence Signals - Medium-High priority: Pivot-based divergence is more reliable than simple divergence. Hidden divergence indicates continuation rather than reversal.
4. MACD Cross with Basic Filters - Medium priority: MACD crosses signal line with basic histogram filter. Less reliable alone but useful when combined with other confirmations.
Best practice: Wait for multiple confirmations. For example, a MACD crossover combined with divergence, volume confirmation, and secondary timeframe alignment provides the strongest setup.
Chart Requirements
For proper script functionality and compliance with TradingView requirements, ensure your chart displays:
- Symbol name: The trading pair or instrument name should be visible
- Timeframe: The chart timeframe should be clearly displayed
- Script name: "Ultimate MACD " should be visible in the indicator title
These elements help traders understand what they're viewing and ensure proper script identification. The script automatically includes this information in the indicator title and chart labels.
Performance Considerations
The script is optimized for performance:
- Calculations use efficient Pine Script functions (ta.ema, ta.sma, etc.) which are optimized by TradingView
- Conditional execution: Features only calculate when enabled
- Label management: Old labels are automatically deleted to prevent accumulation
- Array management: Divergence label arrays are limited to prevent memory accumulation
The script should perform well on all timeframes. On very long historical data with many enabled features, performance may be slightly slower, but it remains usable.
Known Limitations and Considerations
- Dynamic OB/OS levels can vary significantly based on recent MACD volatility. In very volatile markets, levels may be wider; in calm markets, they may be narrower.
- Volume confirmation requires sufficient historical volume data. On new instruments or very short timeframes, volume calculations may be less reliable.
- Higher timeframe MACD uses request.security() which may have slight delays on some data feeds.
- Stochastic MACD requires the histogram to have sufficient history. Very short periods on new charts may produce less reliable StochMACD values initially.
- Divergence detection requires sufficient historical data to identify pivot points. Very short lookback periods may produce false positives.
Practical Use Cases
The indicator can be configured for different trading styles and timeframes:
Swing Trading:
Use MACD(12,26,9) with secondary timeframe confirmation. Enable divergence detection. Use signal strength filter (threshold 50+) and cross quality filter (threshold 50+) for higher-quality signals. Enable histogram analysis tools for additional context.
Day Trading:
Use MACD(8,17,7) or use "Day Trading" preset with minimal histogram smoothing for faster signals. Enable zero-line cross alerts for early signals. Use volume confirmation with threshold 1.2-1.5. Enable histogram MA for momentum tracking.
Trend Following:
Use MACD(12,26,9) or longer periods (15,30,12) for smoother signals. Enable secondary timeframe confirmation for trend alignment. Hidden divergence signals are useful for trend continuation entries. Use cross quality filter to identify high-quality crossovers.
Reversal Trading:
Focus on divergence detection (pivot-based for accuracy) combined with zero-line crosses. Enable volume confirmation. Use histogram Bollinger Bands to identify extreme histogram zones. Enable StochMACD for overbought/oversold identification.
Multi-Timeframe Analysis:
Enable secondary timeframe MACD to see context from larger timeframes. For example, use daily MACD on hourly charts to understand the larger trend context. Enable secondary confirmation to require higher timeframe alignment for signals.
Practical Tips and Best Practices
Getting Started:
Start with default settings and observe MACD behavior. The default configuration (MACD 12,26,9 with EMA) is balanced and works well across different markets. After observing behavior, customize settings to match your trading style. Consider using configuration presets for quick setup.
Reducing Repainting:
All signals are based on confirmed bars, minimizing repainting. The script uses confirmed bar data for all calculations to ensure backtesting accuracy.
Signal Quality:
MACD crosses with multiple filters provide the highest-quality signals because they require alignment across multiple indicators. These signals have lower frequency but higher reliability. Use signal strength scores to identify the strongest signals (70+). Use cross quality scores to identify high-quality crossovers (70+).
Filter Combinations:
Start with histogram filter for basic momentum alignment, then add signal strength filter for moderate signals, then cross quality filter for high-quality crossovers. Combining all filters significantly reduces false signals but also reduces signal frequency. Find your balance based on your risk tolerance.
Volume Filtering:
Set volume threshold to 1.0 (default, effectively disabled) or lower to effectively disable volume filtering if you trade instruments with unreliable volume data or want to test without volume confirmation. Standard confirmation uses 1.2-1.5 threshold.
MACD Period Selection:
Standard MACD(12,26,9) provides balanced signals suitable for most trading. Shorter periods (8,17,7) for faster signals, longer (15,30,12) for smoother signals. Adjust based on your timeframe and trading style. Consider using configuration presets for optimized settings.
Moving Average Type:
EMA provides balanced responsiveness with smoothness. RMA is smoother and less responsive. WMA gives more weight to recent prices. SMA gives equal weight to all periods. Choose based on your preference for responsiveness vs. smoothness.
Divergence:
Pivot-based divergence is more reliable than simple divergence because it uses actual pivot points. Hidden divergence indicates continuation rather than reversal, useful for trend-following strategies. Adjust pivot lookback parameters to control sensitivity.
Dynamic Thresholds:
Dynamic OB/OS thresholds automatically adapt to volatility. In volatile markets, thresholds widen; in calm markets, they narrow. Adjust the multipliers to fine-tune sensitivity. Enable OB/OS background colors for visual indication.
Zero-Line Crosses:
Zero-line crosses indicate momentum shifts and can provide early warning signals before MACD crosses the signal line. Enable alerts for zero-line crosses to catch these early signals.
Alert Management:
Enable alert cooldown (default enabled, 5 bars) to prevent alert spam. Use alert type filter to only evaluate specific alert types. Signal debounce (default enabled, 3 bars) prevents duplicate MACD cross signals during choppy markets.
Technical Specifications
- Pine Script Version: v6
- Indicator Type: Non-overlay (displays in separate panel below price chart)
- Repainting Behavior: Minimal - all signals are based on confirmed bars, ensuring accurate backtesting results
- Performance: Optimized with conditional execution. Features only calculate when enabled.
- Compatibility: Works on all timeframes (1 minute to 1 month) and all instruments (stocks, forex, crypto, futures, etc.)
- Edge Case Handling: All calculations include safety checks for division by zero, NA values, and boundary conditions. Alert cooldowns and signal debounce handle edge cases where conditions never occurred or values are NA.
Technical Notes
- All MACD values respect percentage mode conversion when enabled
- Volume confirmation uses cached volume SMA for performance
- Label arrays (divergence) are automatically limited to prevent memory accumulation
- Background coloring: OB/OS backgrounds are drawn on top of main background to ensure visibility
- All calculations are optimized with conditional execution - features only calculate when enabled (performance optimization)
- Signal strength calculation combines multiple factors into a single score for easy filtering
- Cross quality calculation rates crossover quality based on angle, volume, and distance from zero
- Secondary timeframe MACD uses request.security() for higher timeframe data access
- Histogram analysis features (Bollinger Bands, MA, StochMACD) provide additional context beyond basic MACD signals
- Statistics table dynamically adjusts to show only enabled features, keeping it clean and relevant
- Divergence detection uses MACD line (not histogram) for more reliable signals
- Configuration presets automatically optimize MACD parameters for different trading styles
- Smart label placement: Labels appear on current bar at confirmation, using strength from tracked extreme point
- Label spacing uses effective distance (base distance × spacing multiplier) for better distribution
- Zero line polarity enforcement ensures Buy labels only appear when tracked extreme MACD < 0, Sell labels only when tracked extreme MACD > 0
- Label finalization requires MACD exit from OB/OS zone, sufficient bars passed, and recovery/decline percentage confirmation
- Strength-based filtering automatically compares and keeps only the strongest label when multiple signals are close together
- Enhanced visualization: Line outlines drawn behind main lines for superior visibility (black default, configurable)
- Enhanced visualization: Fill between MACD and zero line provides instant visual feedback (green above, red below)
- Enhanced visualization: Fill between OB/OS thresholds highlights neutral zone when dynamic levels are active
- Custom chart background overrides background mode when enabled, allowing theme-consistent indicator panels
SMC Post-Analysis Lab [PhenLabs]📊 SMC Post-Analysis Lab
Version: PineScript™ v6
📌 Description
The SMC Post-Analysis Lab is a dedicated hindsight analysis tool built for traders who want to understand what really happened during any historical trading period. Unlike forward-looking indicators, this tool lets you scroll back through time and instantly receive algorithmic classification of market states using Smart Money Concepts methodology.
Whether you’re reviewing a losing trade, studying a successful session, or building your pattern recognition skills, this indicator provides immediate context. The expansion-aware algorithm processes price action within your selected window and outputs clear, actionable classifications ranging from Parabolic Expansion to Consolidation Inducements.
Stop relying on subjective post-trade analysis. Let the algorithm objectively tell you whether institutional players were accumulating, distributing, or running inducements during your trades.
🚀 Points of Innovation
First indicator specifically designed for SMC-based post-trade review rather than live signal generation
Dual-mode analysis system allowing both dynamic scrollback and precise date selection
Expansion-aware classification algorithm that weighs range position against net displacement
Real-time efficiency metrics calculating directional quality of price movement
Integrated visual FVG detection within the analysis window only
Interactive table with clickable date range adjustment via chart interface
🔧 Core Components
Pivot Detection Engine: Uses configurable pivot length to identify significant swing highs and lows for structure break detection
Window Calculator: Determines active analysis zone based on either bar offset or timestamp boundaries
Data Aggregator: Tracks window open, high, low, close and counts bullish/bearish structure break events
State Classification Algorithm: Applies hierarchical logic to determine market state from six possible classifications
Visual Renderer: Draws structure breaks, FVG boxes, and window highlighting within the active zone
🔥 Key Features
Sliding Window Mode: Use the Scroll Back slider to dynamically move your analysis zone backwards through history bar-by-bar
Date Range Mode: Select specific start and end timestamps for precise session or trade review
Six Market State Classifications: Parabolic Expansion (Bull/Bear), Bullish/Bearish Order Flow, Accumulation/Distribution Reversal, and Consolidation/Inducement
Range Position Percentile: See exactly where price closed relative to the window’s high-low range as a percentage
Bull/Bear Event Counter: Quantified count of structure breaks in each direction during the analysis period
Efficiency Calculation: Net move divided by total range reveals trending quality versus chop
🎨 Visualization
Blue Window Highlight: Active analysis zone is clearly marked with blue background shading on the chart
Structure Break Lines: Dashed lines appear at each bullish or bearish structure break within the window
FVG Boxes: Fair Value Gaps automatically render as semi-transparent boxes in bullish or bearish colors
Dashboard Table: Top-right positioned table displays State, Analysis description, and Metrics in real-time
Color-Coded States: Each classification uses distinct coloring for immediate visual recognition
Interactive Tip Row: Optional help text guides users on clicking the table to adjust date range
📖 Usage Guidelines
General Configuration
Analysis Mode: Default is Sliding Window. Choose Date Range for specific timestamp analysis.
Sliding Window Settings
Scroll Back (Bars): Default 0. Increase to move window backwards into history.
Window Width (Bars): Default 100. Range 20-50 for scalping, 100+ for swing analysis.
Date Range Settings
Start Date: Select the beginning timestamp for your analysis period.
End Date: Select the ending timestamp for your analysis period.
Visual Settings
Show Help Tip: Default true. Toggle to hide instructional row in dashboard.
Bullish Color: Default teal. Customize for bullish elements.
Bearish Color: Default red. Customize for bearish elements.
SMC Parameters
Pivot Length: Default 5. Lower values (3-5) catch minor breaks. Higher values (10+) focus on major swings.
✅ Best Use Cases
Post-trade review to understand why entries succeeded or failed
Session analysis to identify institutional activity patterns
Trade journaling with objective algorithmic classifications
Pattern recognition training through historical scrollback
Identifying whether stop hunts were inducements or legitimate breaks
Comparing your real-time read versus what the algorithm detected
⚠️ Limitations
Designed for historical analysis only, not live trade signals
Classification accuracy depends on appropriate pivot length for the timeframe
FVG detection uses simple gap logic without mitigation tracking
State classification is based on window data only, not broader context
Requires manual scrolling or date input to review different periods
💡 What Makes This Unique
Purpose-Built for Review: Unlike most indicators focused on live signals, this is designed specifically for post-trade analysis
Expansion-Aware Logic: Algorithm weighs both position in range AND directional efficiency for accurate state detection
Interactive Date Control: Click the dashboard table to reveal draggable anchors for window adjustment directly on chart
🔬 How It Works
1. Window Definition:
User selects either Sliding Window or Date Range mode
System calculates which bars fall within the active analysis zone
Active zone receives blue background highlighting
2. Data Collection:
Algorithm captures window open, running high, running low, and current close
Structure breaks are detected when price crosses above last pivot high or below last pivot low
Bullish and bearish events are counted separately
3. State Classification:
Range Position calculates where close sits as percentage of high-low range
Efficiency calculates net move divided by total range
Hierarchical logic applies priority rules from Parabolic states down to Consolidation
4. Output Rendering:
Dashboard table updates with State title, Analysis description, and Metrics
Visual elements render within window only to keep chart clean
Colors reflect bullish, bearish, or neutral classification
💡 Note:
This indicator is intended for educational and review purposes. Use it to develop your understanding of Smart Money Concepts by analyzing what institutional order flow looked like during historical periods. Combine insights with your own analysis methodology for best results.
ShooterViz Lazy Trader EMA SystemShooterViz Lazy Trader EMA System - Complete User Guide
What This Script Does
This is a position scaling indicator that tells you exactly when to enter, add to, and exit trades using a simplified 5-EMA system. It removes the guesswork and decision fatigue from trading by giving you clear visual signals.
The Core Concept
3 entry signals that build your position from 20% → 50% → 100%
2 exit signals that scale you out at 50% → 50% (complete exit)
1 higher timeframe filter that keeps you on the right side of the trend
No Fibonacci calculations, no RSI divergence, no multi-indicator confusion. Just EMAs and price action.
What You'll See On Your Chart
1. Colored EMA Lines
Blue Lines (Entry Zone):
3 EMA (lightest blue) - Early reversal detector
5 EMA (darker blue) - Confirmation line
Green Lines (Add Zone):
21 EMA (bright green) - First add location
34 EMA (lighter green) - Final add location
Red Lines (Exit Zone):
89 EMA (lighter red) - First exit trigger
144 EMA (darker red) - Final exit trigger
Orange Lines (Hyper Frame - optional):
Hyper 21 EMA (from higher timeframe) - Trend direction
Hyper 34 EMA (from higher timeframe) - Bias confirmation
2. Triangle Signals
Green Triangles (Below Price) = BUY/ADD:
Lime triangle with "20%" = Entry 1: Price reclaimed 3→5 EMA (starter position)
Green triangle with "30%" = Entry 2: Price bounced off 21 EMA (first add)
Teal triangle with "50%" = Entry 3: Price broke out from 34 EMA compression (final add)
Red Triangles (Above Price) = SELL:
Orange triangle with "50% OFF" = Exit 1: Price broke below 89 EMA (take half off)
Red triangle with "EXIT ALL" = Exit 2: Price broke below 144 EMA (close remaining position)
3. Background Color (Trend Bias)
Light green background = Hyper frame EMAs trending up (bias LONG)
Light red background = Hyper frame EMAs trending down (bias SHORT)
Gray background = Neutral/choppy (be cautious)
4. Info Table (Top Right Corner)
A live status dashboard showing:
Which entry signals are currently active (✓ or —)
Which exit signals are currently active (⚠ or ⛔)
Current hyper frame bias (🟢 LONG / 🔴 SHORT / ⚪ NEUTRAL)
Which timeframe you're using for hyper frame filtering
How to Install and Set Up
Step 1: Add the Script to TradingView
Open TradingView
Click "Pine Editor" at the bottom of the screen
Copy the entire script code
Paste it into the Pine Editor
Click "Add to Chart"
Step 2: Configure Your Settings
Click the gear icon ⚙️ next to "LazyEMA" in your indicators list.
Critical Settings to Configure:
Hyper Frame Selection (Most Important!)
Location: "Hyper Frame (Pick ONE)" section
Setting: "Timeframe"
What to choose:
Trading 15min or 1H charts? → Use "240" (4-hour)
Trading 4H or Daily charts? → Use "D" (Daily)
Trading Daily or Weekly charts? → Use "W" (Weekly)
Why this matters: This filter keeps you aligned with the bigger trend. Only take longs when this timeframe is green, shorts when it's red.
MA Type (Optional, default is fine)
Location: "MA Config" section
Default: EMA (recommended)
Options: EMA, SMA, WMA, HMA, RMA, VWMA
Most traders should stick with EMA
Visual Toggles (Customize your view)
Entry Zone: Turn individual EMAs on/off (3, 5, 21, 34)
Exit Zone: Turn individual EMAs on/off (89, 144)
Hyper Frame: Toggle the higher timeframe EMAs on/off
Step 3: Clean Up Your Chart
Turn OFF these if visible:
Volume bars (they clutter the view)
Any other indicators you have loaded
Grid lines (optional, but cleaner)
Keep ONLY:
Price candles
Your ShooterViz Lazy Trader EMA System
Maybe support/resistance levels if you manually draw them
How to Trade With This Script
The Basic Workflow
Before the Market Opens:
Check the background color and info table bias
Green background? Look for LONG setups only
Red background? Look for SHORT setups only
Gray background? Stay flat or trade small
During the Trading Session:
LONGS (When hyper frame is bullish):
Wait for Entry 1 signal:
Lime triangle appears with "20%"
Price has reclaimed the 5 EMA after dipping to 3 EMA
Action: Enter 20% of your intended position
Stop loss: Place below the 5 EMA or recent swing low
Wait for Entry 2 signal:
Green triangle appears with "30%"
Price pulled back to 21 EMA and bounced
Action: Add 30% more (you're now at 50% total)
Move stop: Trail it up to below 21 EMA
Wait for Entry 3 signal:
Teal triangle appears with "50%"
Price compressed at 34 EMA and broke out
Action: Add final 50% (you're now 100% loaded)
Move stop: Trail it up to below 34 EMA
Wait for Exit 1 signal:
Orange triangle appears with "50% OFF"
Price broke below 89 EMA
Action: Exit 50% of your position immediately
Move stop on rest: Trail to 89 EMA or lock in profits
Wait for Exit 2 signal:
Red triangle appears with "EXIT ALL"
Price broke below 144 EMA
Action: Exit remaining 50% (you're now flat)
Or: Stop gets hit at 89 EMA (same result)
SHORTS (When hyper frame is bearish):
Same process, but inverted
Triangles appear above price instead of below
Look for breakdowns below EMAs instead of bounces off them
Exit when price reclaims 89 and 144 EMAs
Real-World Example Walkthrough
Setup: Trading ES (S&P 500 Futures) on 1H Chart
Chart Configuration:
Timeframe: 1 Hour
Hyper Frame: 240 (4-hour)
Ticker: ES
Pre-Market Check:
Background is light green
Info table shows "🟢 LONG" for Hyper Bias
Decision: Only look for long entries today
9:30 AM - Market Opens
Price dips and touches 3 EMA
Watch for: Reclaim of 5 EMA
9:45 AM - Entry 1 Triggers
Lime triangle appears below bar
Price closed above 5 EMA at $4,550
Action taken:
Enter long 20% position (2 contracts if targeting 10 total)
Stop loss at $4,545 (below 5 EMA)
Risk: $10 per contract × 2 = $20 risk
10:30 AM - Entry 2 Triggers
Price rallied to $4,565, pulls back
Green triangle appears at 21 EMA ($4,555)
Action taken:
Add 30% (3 more contracts, now have 5 total)
Move stop to $4,550 (below 21 EMA)
Current P/L: +$25 ($5 gain on original 2 contracts, break-even on new 3)
11:15 AM - Entry 3 Triggers
Price consolidates at 34 EMA around $4,560
Teal triangle appears as price breaks to $4,568
Action taken:
Add final 50% (5 more contracts, now have 10 total)
Move stop to $4,555 (below 34 EMA)
Current P/L: +$70
1:00 PM - Price Extends
Price rallies to $4,595 (on track)
89 EMA is at $4,575
No action yet, let it run
2:15 PM - Exit 1 Triggers
Price pulls back from $4,600
Orange triangle appears as price breaks below 89 EMA at $4,580
Action taken:
Exit 50% (5 contracts closed at $4,580)
Keep 5 contracts with stop at 89 EMA ($4,575)
Banked: +$150 average gain on closed 5 contracts
2:45 PM - Exit 2 Triggers
Price continues down
Red triangle appears as price breaks 144 EMA at $4,570
Action taken:
Exit remaining 5 contracts at $4,570
Banked: +$100 on remaining 5 contracts
Final Results:
Total gain: $250 on the trade
Initial risk: $50 (if stopped out at Entry 1)
Risk/Reward: 5:1
Time in trade: ~5 hours
Common Questions
"What if I miss Entry 1? Can I still take Entry 2?"
Yes! Each entry is independent. If you miss the 3→5 reclaim, wait for the 21 EMA bounce. You'll start with a 30% position instead of 20%, but that's fine.
Rule: Never chase. Wait for the next EMA setup.
"What if multiple entry signals trigger at the same bar?"
Rare, but possible. If you see both Entry 1 and Entry 2 trigger together:
Take Entry 1 first (20%)
If the next bar confirms Entry 2 is still valid, add 30%
When in doubt, scale in gradually
"The hyper frame is green but I'm seeing short signals?"
Don't take them. The hyper frame is your bias filter. If it says "go long," ignore short setups. They're usually lower probability and will get stopped out.
"Can I use this for swing trading overnight?"
Absolutely. Just switch your hyper frame:
If you're on Daily charts, use Weekly hyper frame
If you're on 4H charts, use Daily hyper frame
Adjust position sizes for overnight risk
"What if the signal appears right at market close?"
Don't chase it. Wait for the next bar (next day) to confirm. Signals that appear in the last 5 minutes are often noise.
"How do I set up alerts?"
Right-click on the chart
Select "Add Alert"
Choose "LazyEMA" from the condition dropdown
Select which signal you want alerts for:
Entry 1: 3→5 Reclaim
Entry 2: 21 EMA Add
Entry 3: 34 EMA Breakout
Exit 1: 89 EMA Break
Exit 2: 144 EMA Break
Click "Create"
Pro tip: Set up all 5 alerts so you never miss a signal.
Position Sizing Guide see
swingtradenotes.substack.com
Critical Rule: Know your total risk BEFORE you take Entry 1. Don't wing it.
Customization Tips
For Day Traders (Scalpers)
Use 5min or 15min charts
Hyper frame: 1H or 4H
Expect 2-4 setups per day
Tighter stops (0.5% risk per entry)
For Swing Traders
Use 4H or Daily charts
Hyper frame: Daily or Weekly
Expect 1-2 setups per week
Wider stops (1-2% risk per entry)
For Position Traders
Use Daily or Weekly charts
Hyper frame: Weekly or Monthly
Expect 1-2 setups per month
Widest stops (2-3% risk per entry)
The "Don't Be Stupid" Checklist
Before taking ANY signal from this script, ask:
✅ Is the hyper frame bias pointing in my direction?
✅ Is the signal clean (not at a weird time or during news)?
✅ Do I know my stop loss level?
✅ Do I know my position size?
✅ Can I afford to lose if this trade fails?
If you answered "no" to ANY of these, skip the trade.
Troubleshooting
"I'm not seeing any signals"
Possible causes:
The "Show Lazy Trader System" toggle is off (turn it on)
Your chart timeframe is too high (try 1H or 4H)
Market is in a tight range (EMAs are compressed)
You need to refresh the chart
"Too many signals, getting whipsawed"
Fixes:
Increase your chart timeframe (go from 15m to 1H)
Switch to a less volatile ticker
Only trade when hyper frame bias is STRONG (not neutral)
Add a minimum bar count between signals
"The info table is covering my price action"
Fix:
Edit the script
Find the line: table.new(position.top_right, ...
Change position.top_right to position.bottom_right or position.top_left
"Signals appear then disappear"
This is normal (repainting). Some signals (especially compression breakouts) can disappear if the next bar reverses. This is why you:
Wait for bar close before acting
Use alerts that only fire on confirmed bars
Don't chase signals mid-bar
Final Thoughts
This script is a decision-making tool, not a crystal ball. It shows you high-probability setups based on EMA dynamics and trend structure. You still need to:
Manage your risk
Choose your position size
Stick to the rules
Accept losses when they happen
The system works when YOU work the system.
Print this guide, tape it next to your monitor, and follow it religiously for 20 trades before making ANY changes.
Good luck, and stay lazy (the smart way).
RVol based Support & Resistance ZonesDescription:
This indicator is designed to help traders identify significant price levels based on institutional volume. It monitors two higher timeframes (defined by the user) simultaneously. When a candle on these higher timeframes exhibits unusually high volume—known as high Relative Volume (RVol)—the indicator automatically draws a "Zone of Interest" box on your current chart.
These zones are defined by:
Up candle : from candle open to low of candle
Down candle : from candle open to high of candle
Key Features:
Multi-Timeframe Monitoring: You can trade on a lower timeframe (e.g., 5-minute) while the indicator monitors the 30-minute and 1-hour charts for volume spikes.
RVol Boxes: Automatically draws boxes extending from high-volume candles.
Up Candles: Box covers Low to Open.
Down Candles: Box covers High to Open.
Live Dashboard: A neat, color-coded table displays the current Volume, Average Volume, and RVol percentage for your watched timeframes.
Real-Time vs. Confirmed: Choose whether to see boxes appear immediately as volume spikes (Live) or only after the candle has closed and confirmed the volume (Candle Close).
Settings Guide:
1. General Settings
Relative Volume Length: The number of past candles used to calculate the "Average Volume." (Default is 20).
Max Days Back to Draw: To keep your chart clean, this limits how far back in history the script looks for high-volume zones. (e.g., set to 5 to only see zones created in the last 5 days).
Draw Mode:
- Live (Real-time): Draws the box immediately if the current developing candle hits the volume threshold. (Note: The box may disappear if the volume average shifts before the candle closes).
- Candle Close: The box only appears once the candle has finished and permanently confirmed the volume spike.
2. Table Settings
Show Info Table: Toggles the dashboard on or off.
Text Size & Position: Customise where the table appears on your screen and how large the text is.
Colours: Fully customisable colours for the Table Header (Top row) and Data Rows (Bottom rows).
3. Timeframe 1 & 2 Settings
You have two identical sections to configure two different timeframes (e.g., 30m and 1H).
Timeframe: The chart interval to monitor (e.g., "30" for 30 minutes, "60" for 1 Hour, "240" for 4 Hours).
Threshold %: The "Trigger" for drawing a box based on relative candle volume in that timeframe.
Example:
100% = Candle Volume is equal to the average volume for the specified timeframe.
200% = Candle Volume is 2x the average volume for the specified timeframe.
300% = Candle Volume is 3x the average volume for the specified timeframe.
Box & Edge Colour: Distinct colours for each timeframe so you can easily tell which timeframe created the zone.
Trading Sessions [QuantAlgo]🟢 Overview
The Trading Sessions indicator tracks and displays the four major global trading sessions: Sydney, Tokyo, London, and New York. It provides session-based background highlighting, real-time price change tracking from session open, and a data table with session status. The script works across all markets (forex, equities, commodities, crypto) and helps traders identify when specific geographic markets are active, which directly correlates with changes in liquidity and volatility patterns. Default session times are set to major financial center hours in UTC but are fully adjustable to match your trading methodology.
🟢 Key Features
→ Session Background Color Coding
Each trading session gets a distinct background color on your chart:
1. Sydney Session - Default orange, 22:00-07:00 UTC
2. Tokyo Session - Default red, 00:00-09:00 UTC
3. London Session - Default green, 08:00-16:00 UTC
4. New York Session - Default blue, 13:00-22:00 UTC
When sessions overlap, the color priority is New York > London > Tokyo > Sydney. This means if London and New York are both active, the background shows New York's color. The priority matches typical liquidity and volatility patterns where later sessions generally show higher volume.
→ Color Customization
All session colors are configurable in the Color Settings panel:
1. Click any session color input to open the color picker
2. Select your preferred color for that session
3. Use the "Background Transparency" slider (0-100) to adjust opacity. Lower values = more visible, higher values = more subtle
4. Enable "Color Price Bars" to color candlesticks themselves according to the active session instead of just the background
The Color column in the info table shows a block (█) in each session's assigned color, matching what you see on the chart background.
→ Information Table Breakdown
→ Timeframe Warning
If you're viewing a timeframe of 12 hours or higher, a red warning label appears center-screen. Session boundaries don't render accurately on high timeframes because the time() function in Pine Script can't detect intra-bar session changes when each bar spans multiple sessions. The warning tells you to switch to sub-12H timeframes (e.g., 4H, 1H, 30m, 15m, etc.) for proper session detection. You can disable this warning in Color Settings if needed, but session highlighting can be unreliable on 12H+ charts regardless.
→ Time Range Configuration
Every session's time range is editable in Session Settings:
1. Click the time input field next to each session
2. Enter time as HHMM-HHMM in 24-hour format
3. All times are interpreted as UTC
4. Modify these to account for daylight saving shifts or to define custom session periods based on your backtested optimal trading windows
For example, if your strategy performs best during London/NY overlap specifically, you could set London to 08:00-17:00 and New York to 13:00-22:00 to ensure you see the full overlap highlighted.
→ Weekdays Filter
The "Weekdays Only (Mon-Fri)" toggle controls whether sessions display on weekends:
Enabled: Sessions only show Monday-Friday and hide on Saturday-Sunday. Use this for markets that close on weekends (most equities, forex).
Disabled: Sessions display 24/7 including weekends. Use this for markets that trade continuously (crypto).
→ Table Display Options
The info table has several configuration options in Table Settings:
Visibility: Toggle "Show Info Table" on/off to display or hide the entire table.
Position: Nine position options (Top/Middle/Bottom + Left/Center/Right) let you place the table wherever it doesn't block your price action or other indicators.
Text Size: Four size options (Tiny, Small, Normal, Large) to match your screen resolution and visual preferences.
→ Color Schemes:
Mono: Black background, gray header, white text
Light: White background, light gray header, black text
Blue: Dark blue background, medium blue header, white text
Custom: Manual selection of all five color components (table background, header background, header text, data text, borders)
→ Alert Functionality
The indicator includes ten alert conditions you can access via TradingView's alert system:
Session Opens:
1. Sydney Session Started
2. Tokyo Session Started
3. London Session Started
4. New York Session Started
5. Any Session Started
Session Closes:
6. Sydney Session Ended
7. Tokyo Session Ended
8. London Session Ended
9. New York Session Ended
10. Any Session Ended
These alerts fire when sessions transition based on your configured time ranges, letting you automate monitoring of session changes without watching the chart continuously. Useful for strategies that trade specific session opens/closes or need to adjust position sizing when volatility regime shifts between sessions.
Scientific Correlation Testing FrameworkScientific Correlation Testing Framework - Comprehensive Guide
Introduction to Correlation Analysis
What is Correlation?
Correlation is a statistical measure that describes the degree to which two assets move in relation to each other. Think of it like measuring how closely two dancers move together on a dance floor.
Perfect Positive Correlation (+1.0): Both dancers move in perfect sync, same direction, same speed
Perfect Negative Correlation (-1.0): Both dancers move in perfect sync but in opposite directions
Zero Correlation (0): The dancers move completely independently of each other
In financial markets, correlation helps us understand relationships between different assets, which is crucial for:
Portfolio diversification
Risk management
Pairs trading strategies
Hedging positions
Market analysis
Why This Script is Special
This script goes beyond simple correlation calculations by providing:
Two different correlation methods (Pearson and Spearman)
Statistical significance testing to ensure results are meaningful
Rolling correlation analysis to track how relationships change over time
Visual representation for easy interpretation
Comprehensive statistics table with detailed metrics
Deep Dive into the Script's Components
1. Input Parameters Explained-
Symbol Selection:
This allows you to select the second asset to compare with the chart's primary asset
Default is Apple (NASDAQ:AAPL), but you can change this to any symbol
Example: If you're viewing a Bitcoin chart, you might set this to "NASDAQ:TSLA" to see if Bitcoin and Tesla are correlated
Correlation Window (60): This is the number of periods used to calculate the main correlation
Larger values (e.g., 100-500) provide more stable, long-term correlation measures
Smaller values (e.g., 10-50) are more responsive to recent price movements
60 is a good balance for most daily charts (about 3 months of trading days)
Rolling Correlation Window (20): A shorter window to detect recent changes in correlation
This helps identify when the relationship between assets is strengthening or weakening
Default of 20 is roughly one month of trading days
Return Type: This determines how price changes are calculated
Simple Returns: (Today's Price - Yesterday's Price) / Yesterday's Price
Easy to understand: "The asset went up 2% today"
Log Returns: Natural logarithm of (Today's Price / Yesterday's Price)
More mathematically elegant for statistical analysis
Better for time-additive properties (returns over multiple periods)
Less sensitive to extreme values.
Confidence Level (95%): This determines how certain we want to be about our results
95% confidence means we accept a 5% chance of being wrong (false positive)
Higher confidence (e.g., 99%) makes the test more strict
Lower confidence (e.g., 90%) makes the test more lenient
95% is the standard in most scientific research
Show Statistical Significance: When enabled, the script will test if the correlation is statistically significant or just due to random chance.
Display options control what you see on the chart:
Show Pearson/Spearman/Rolling Correlation: Toggle each correlation type on/off
Show Scatter Plot: Displays a scatter plot of returns (limited to recent points to avoid performance issues)
Show Statistical Tests: Enables the detailed statistics table
Table Text Size: Adjusts the size of text in the statistics table
2.Functions explained-
calcReturns():
This function calculates price returns based on your selected method:
Log Returns:
Formula: ln(Price_t / Price_t-1)
Example: If a stock goes from $100 to $101, the log return is ln(101/100) = ln(1.01) ≈ 0.00995 or 0.995%
Benefits: More symmetric, time-additive, and better for statistical modeling
Simple Returns:
Formula: (Price_t - Price_t-1) / Price_t-1
Example: If a stock goes from $100 to $101, the simple return is (101-100)/100 = 0.01 or 1%
Benefits: More intuitive and easier to understand
rankArray():
This function calculates the rank of each value in an array, which is used for Spearman correlation:
How ranking works:
The smallest value gets rank 1
The second smallest gets rank 2, and so on
For ties (equal values), they get the average of their ranks
Example: For values
Sorted:
Ranks: (the two 2s tie for ranks 1 and 2, so they both get 1.5)
Why this matters: Spearman correlation uses ranks instead of actual values, making it less sensitive to outliers and non-linear relationships.
pearsonCorr():
This function calculates the Pearson correlation coefficient:
Mathematical Formula:
r = (nΣxy - ΣxΣy) / √
Where x and y are the two variables, and n is the sample size
What it measures:
The strength and direction of the linear relationship between two variables
Values range from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship)
0 indicates no linear relationship
Example:
If two stocks have a Pearson correlation of 0.8, they have a strong positive linear relationship
When one stock goes up, the other tends to go up in a fairly consistent proportion
spearmanCorr():
This function calculates the Spearman rank correlation:
How it works:
Convert each value in both datasets to its rank
Calculate the Pearson correlation on the ranks instead of the original values
What it measures:
The strength and direction of the monotonic relationship between two variables
A monotonic relationship is one where as one variable increases, the other either consistently increases or decreases
It doesn't require the relationship to be linear
When to use it instead of Pearson:
When the relationship is monotonic but not linear
When there are significant outliers in the data
When the data is ordinal (ranked) rather than interval/ratio
Example:
If two stocks have a Spearman correlation of 0.7, they have a strong positive monotonic relationship
When one stock goes up, the other tends to go up, but not necessarily in a straight-line relationship
tStatistic():
This function calculates the t-statistic for correlation:
Mathematical Formula: t = r × √((n-2)/(1-r²))
Where r is the correlation coefficient and n is the sample size
What it measures:
How many standard errors the correlation is away from zero
Used to test the null hypothesis that the true correlation is zero
Interpretation:
Larger absolute t-values indicate stronger evidence against the null hypothesis
Generally, a t-value greater than 2 (in absolute terms) is considered statistically significant at the 95% confidence level
criticalT() and pValue():
These functions provide approximations for statistical significance testing:
criticalT():
Returns the critical t-value for a given degrees of freedom (df) and significance level
The critical value is the threshold that the t-statistic must exceed to be considered statistically significant
Uses approximations since Pine Script doesn't have built-in statistical distribution functions
pValue():
Estimates the p-value for a given t-statistic and degrees of freedom
The p-value is the probability of observing a correlation as strong as the one calculated, assuming the true correlation is zero
Smaller p-values indicate stronger evidence against the null hypothesis
Standard interpretation:
p < 0.01: Very strong evidence (marked with **)
p < 0.05: Strong evidence (marked with *)
p ≥ 0.05: Weak evidence, not statistically significant
stdev():
This function calculates the standard deviation of a dataset:
Mathematical Formula: σ = √(Σ(x-μ)²/(n-1))
Where x is each value, μ is the mean, and n is the sample size
What it measures:
The amount of variation or dispersion in a set of values
A low standard deviation indicates that the values tend to be close to the mean
A high standard deviation indicates that the values are spread out over a wider range
Why it matters for correlation:
Standard deviation is used in calculating the correlation coefficient
It also provides information about the volatility of each asset's returns
Comparing standard deviations helps understand the relative riskiness of the two assets.
3.Getting Price Data-
price1: The closing price of the primary asset (the chart you're viewing)
price2: The closing price of the secondary asset (the one you selected in the input parameters)
Returns are used instead of raw prices because:
Returns are typically stationary (mean and variance stay constant over time)
Returns normalize for price levels, allowing comparison between assets of different values
Returns represent what investors actually care about: percentage changes in value
4.Information Table-
Creates a table to display statistics
Only shows on the last bar to avoid performance issues
Positioned in the top right of the chart
Has 2 columns and 15 rows
Populating the Table
The script then populates the table with various statistics:
Header Row: "Metric" and "Value"
Sample Information: Sample size and return type
Pearson Correlation: Value, t-statistic, p-value, and significance
Spearman Correlation: Value, t-statistic, p-value, and significance
Rolling Correlation: Current value
Standard Deviations: For both assets
Interpretation: Text description of the correlation strength
The table uses color coding to highlight important information:
Green for significant positive results
Red for significant negative results
Yellow for borderline significance
Color-coded headers for each section
=> Practical Applications and Interpretation
How to Interpret the Results
Correlation Strength
0.0 to 0.3 (or 0.0 to -0.3): Weak or no correlation
The assets move mostly independently of each other
Good for diversification purposes
0.3 to 0.7 (or -0.3 to -0.7): Moderate correlation
The assets show some tendency to move together (or in opposite directions)
May be useful for certain trading strategies but not extremely reliable
0.7 to 1.0 (or -0.7 to -1.0): Strong correlation
The assets show a strong tendency to move together (or in opposite directions)
Can be useful for pairs trading, hedging, or as a market indicator
Statistical Significance
p < 0.01: Very strong evidence that the correlation is real
Marked with ** in the table
Very unlikely to be due to random chance
p < 0.05: Strong evidence that the correlation is real
Marked with * in the table
Unlikely to be due to random chance
p ≥ 0.05: Weak evidence that the correlation is real
Not marked in the table
Could easily be due to random chance
Rolling Correlation
The rolling correlation shows how the relationship between assets changes over time
If the rolling correlation is much different from the long-term correlation, it suggests the relationship is changing
This can indicate:
A shift in market regime
Changing fundamentals of one or both assets
Temporary market dislocations that might present trading opportunities
Trading Applications
1. Portfolio Diversification
Goal: Reduce overall portfolio risk by combining assets that don't move together
Strategy: Look for assets with low or negative correlations
Example: If you hold tech stocks, you might add some utilities or bonds that have low correlation with tech
2. Pairs Trading
Goal: Profit from the relative price movements of two correlated assets
Strategy:
Find two assets with strong historical correlation
When their prices diverge (one goes up while the other goes down)
Buy the underperforming asset and short the outperforming asset
Close the positions when they converge back to their normal relationship
Example: If Coca-Cola and Pepsi are highly correlated but Coca-Cola drops while Pepsi rises, you might buy Coca-Cola and short Pepsi
3. Hedging
Goal: Reduce risk by taking an offsetting position in a negatively correlated asset
Strategy: Find assets that tend to move in opposite directions
Example: If you hold a portfolio of stocks, you might buy some gold or government bonds that tend to rise when stocks fall
4. Market Analysis
Goal: Understand market dynamics and interrelationships
Strategy: Analyze correlations between different sectors or asset classes
Example:
If tech stocks and semiconductor stocks are highly correlated, movements in one might predict movements in the other
If the correlation between stocks and bonds changes, it might signal a shift in market expectations
5. Risk Management
Goal: Understand and manage portfolio risk
Strategy: Monitor correlations to identify when diversification benefits might be breaking down
Example: During market crises, many assets that normally have low correlations can become highly correlated (correlation convergence), reducing diversification benefits
Advanced Interpretation and Caveats
Correlation vs. Causation
Important Note: Correlation does not imply causation
Example: Ice cream sales and drowning incidents are correlated (both increase in summer), but one doesn't cause the other
Implication: Just because two assets move together doesn't mean one causes the other to move
Solution: Look for fundamental economic reasons why assets might be correlated
Non-Stationary Correlations
Problem: Correlations between assets can change over time
Causes:
Changing market conditions
Shifts in monetary policy
Structural changes in the economy
Changes in the underlying businesses
Solution: Use rolling correlations to monitor how relationships change over time
Outliers and Extreme Events
Problem: Extreme market events can distort correlation measurements
Example: During a market crash, many assets may move in the same direction regardless of their normal relationship
Solution:
Use Spearman correlation, which is less sensitive to outliers
Be cautious when interpreting correlations during extreme market conditions
Sample Size Considerations
Problem: Small sample sizes can produce unreliable correlation estimates
Rule of Thumb: Use at least 30 data points for a rough estimate, 60+ for more reliable results
Solution:
Use the default correlation length of 60 or higher
Be skeptical of correlations calculated with small samples
Timeframe Considerations
Problem: Correlations can vary across different timeframes
Example: Two assets might be positively correlated on a daily basis but negatively correlated on a weekly basis
Solution:
Test correlations on multiple timeframes
Use the timeframe that matches your trading horizon
Look-Ahead Bias
Problem: Using information that wouldn't have been available at the time of trading
Example: Calculating correlation using future data
Solution: This script avoids look-ahead bias by using only historical data
Best Practices for Using This Script
1. Appropriate Parameter Selection
Correlation Window:
For short-term trading: 20-50 periods
For medium-term analysis: 50-100 periods
For long-term analysis: 100-500 periods
Rolling Window:
Should be shorter than the main correlation window
Typically 1/3 to 1/2 of the main window
Return Type:
For most applications: Log Returns (better statistical properties)
For simplicity: Simple Returns (easier to interpret)
2. Validation and Testing
Out-of-Sample Testing:
Calculate correlations on one time period
Test if they hold in a different time period
Multiple Timeframes:
Check if correlations are consistent across different timeframes
Economic Rationale:
Ensure there's a logical reason why assets should be correlated
3. Monitoring and Maintenance
Regular Review:
Correlations can change, so review them regularly
Alerts:
Set up alerts for significant correlation changes
Documentation:
Keep notes on why certain assets are correlated and what might change that relationship
4. Integration with Other Analysis
Fundamental Analysis:
Combine correlation analysis with fundamental factors
Technical Analysis:
Use correlation analysis alongside technical indicators
Market Context:
Consider how market conditions might affect correlations
Conclusion
This Scientific Correlation Testing Framework provides a comprehensive tool for analyzing relationships between financial assets. By offering both Pearson and Spearman correlation methods, statistical significance testing, and rolling correlation analysis, it goes beyond simple correlation measures to provide deeper insights.
For beginners, this script might seem complex, but it's built on fundamental statistical concepts that become clearer with use. Start with the default settings and focus on interpreting the main correlation lines and the statistics table. As you become more comfortable, you can adjust the parameters and explore more advanced applications.
Remember that correlation analysis is just one tool in a trader's toolkit. It should be used in conjunction with other forms of analysis and with a clear understanding of its limitations. When used properly, it can provide valuable insights for portfolio construction, risk management, and pair trading strategy development.
ema200 plus Description:
This advanced indicator displays Exponential Moving Averages (EMA) across multiple timeframes to help traders identify trend direction and strength across different market perspectives.
Key Features:
Multi-Timeframe EMA Analysis:
Plots 200-period EMA on four different timeframes: 30-minute, 1-hour, 4-hour, and Daily
Each timeframe is displayed with distinct colors for easy visual identification
Visual Elements:
Chart Lines: Four colored EMA lines plotted directly on the price chart
Price Labels: Clear labels showing each EMA's current value at the latest bar
Color-coded Table: Comprehensive data table showing price position relative to each EMA
Trend Identification:
Bullish Signal: When price closes above an EMA (green background in table)
Bearish Signal: When price closes below an EMA (dark background in table)
Helps identify confluence when multiple timeframes align in direction
Customizable Settings:
Adjustable EMA length (default: 200 periods)
Customizable line width and offset
Flexible table positioning (top/middle/bottom, left/center/right)
Configurable table cell size and text appearance
Swing traders analyzing multiple timeframes
Position traders looking for trend confirmation
Technical analysts seeking confluence across time horizons
This indicator provides a comprehensive view of market trends across different time perspectives, helping traders make more informed decisions based on multi-timeframe analysis.
This indicator does not provide trading advice. It is for educational and informational purposes only.
**指标名称：多时间框架200 EMA**
**描述：**
这款高级指标在多个时间框架上显示指数移动平均线（EMA），帮助交易者识别不同市场视角下的趋势方向和强度。
**主要特点：**
1. **多时间框架EMA分析：**
- 在四个不同时间框架上绘制200周期EMA：30分钟、1小时、4小时和日线
- 每个时间框架使用独特颜色显示，便于视觉识别
2. **视觉元素：**
- **图表线：** 在价格图表上直接绘制四条彩色EMA线
- **价格标签：** 清晰显示最新K线处各EMA的当前值
- **颜色编码表格：** 综合数据表格显示价格相对于各EMA的位置
3. **趋势识别：**
- **看涨信号：** 当价格收于EMA上方时（表格中显示绿色背景）
- **看跌信号：** 当价格收于EMA下方时（表格中显示深色背景）
- 帮助识别多个时间框架方向一致时的共振信号
4. **可自定义设置：**
- 可调整EMA长度（默认：200周期）
- 可自定义线宽和偏移量
- 灵活的表格定位（上/中/下，左/中/右）
- 可配置表格单元格大小和文本外观
**适合人群：**
- 分析多时间框架的摆动交易者
- 寻求趋势确认的头寸交易者
- 寻找不同时间维度共振信号的技术分析师
Custom Bollinger Band Squeeze Screener [Pineify]Custom Bollinger Band Squeeze Screener
Key Features
Multi-symbol scanning: Analyze up to 6 tickers simultaneously.
Multi-timeframe flexibility: Screen across four selectable timeframes for each symbol.
Bollinger Band Squeeze algorithm: Detect volatility contraction and imminent breakouts.
Advanced ATR integration: Measure expansion and squeeze states with custom multipliers.
Customizable indicator parameters: Fine-tune Bollinger and ATR settings for tailored detection.
Visual table interface: Rapidly compare squeeze and expansion signals across all instruments.
How It Works
At the core, this screener leverages a unique blend of Bollinger Bands and Average True Range (ATR) to quantify volatility states for multiple assets and timeframes at once. For each symbol and every selected timeframe, the indicator calculates Bollinger Band width and compares it against ATR levels, offering real-time squeeze (consolidation) and expansion (breakout) signals.
Bollinger Band width is computed using standard deviations around a SMA basis.
ATR is calculated to gauge market volatility independent of price direction.
Squeeze: Triggered when BB width contracts below a multiple of ATR, forecasting lower volatility and set-up for a move.
Expansion: Triggered when BB width expands above a higher ATR multiple, signaling a high-volatility breakout.
Display: Results shown in an intuitive table, marking each status per ticker and TF.
Trading Ideas and Insights
Spot assets poised for volatility-driven breakouts.
Compare squeeze presence across timeframes for optimal entry timing.
Integrate screener results with price action or volume for high-confidence setups.
Use squeeze signals to avoid choppy or non-trending conditions.
Expand and diversify watchlists with multi-symbol coverage.
How Multiple Indicators Work Together
This script seamlessly merges Bollinger Bands and ATR with customized multipliers:
Bollinger Bands identify price consolidation and volatility squeeze zones.
ATR tailors the definition of squeeze and expansion, making signals adaptive to volatility regime changes.
By layering these with multi-symbol/multi-timeframe data, traders access a high-precision view of market readiness for trend acceleration or reversal.
The real synergy is in the screener's ability to visualize volatility states for a diverse asset selection, transforming traditional single-chart analysis into a broad market view.
Unique Aspects
Original implementation: Not a simple trend or scalping indicator; utilizes advanced volatility logic.
Fully multi-symbol and multi-timeframe support uncommon in most screeners.
Custom ATR multipliers for both squeeze and expansion allow traders to match their risk profile and market dynamics.
Visual clarity: Table structure promotes actionable insights and reduces decision fatigue.
How to Use
Add the indicator to your TradingView chart (supports any asset class including crypto, forex, stocks).
Select up to six symbols (tickers) and set your preferred timeframes.
Adjust Bollinger Band Length/Deviation and ATR multipliers to refine squeeze/expansion criteria.
Review the screener table: Look for "SQZ" (squeeze) or "EXP" (expansion) cells for entry/exit ideas.
Combine screener information with other technical or fundamental signals for trade confirmation.
Customization
Symbols: Choose any tickers for scanning.
Timeframes: Select short- to long-term intervals to match your trading style.
Bollinger Band parameters: Modify length and deviation for sensitivity.
ATR multipliers: Set low or high values to adjust squeeze/expansion triggers.
Table size and layout: Adapt display for optimal workflow.
Conclusion
The Bollinger Band Squeeze Screener Pineify delivers an innovative, SEO-friendly multi-asset solution for volatility and trend detection. Harness its original algorithmic design to uncover powerful breakout opportunities and optimize your portfolio. Whether you trade crypto with dynamic volatility or scan stocks for momentum, this tool supercharges your TradingView workflow.
RSI Divergence Screener [Pineify]RSI Divergence Screener
Key Features
Multi-symbol and multi-timeframe support for advanced market screening.
Real-time detection and visualization of bullish and bearish RSI divergences.
Seamless integration with core technical indicators and custom divergences.
Highly customizable parameters for precise adaptation to personal trading strategies.
Comprehensive screener table for swift asset comparison and analysis.
How It Works
The RSI Divergence Screener leverages the power of Relative Strength Index (RSI) to systematically track momentum shifts across cryptocurrencies and their respective timeframes. By monitoring both fast and slow RSI calculations, the screener isolates divergence signals—key reversal points that often precede major price moves.
The indicator calculates two RSI values for each selected asset: one with a short lookback (Fast RSI) and another with a longer period (Slow RSI).
It runs a comparative algorithm to find divergences—whenever Fast RSI deviates significantly from Slow RSI, it flags the signal as bullish or bearish.
All detected divergences are dynamically presented in a table view, allowing traders to scan symbols and timeframes for optimal trading setups.
Trading Ideas and Insights
Spot early momentum reversals and preempt major price swings via divergence signals.
Combine multiple symbols and timeframes for cross-market trending opportunities.
Identify high-probability scalping and swing trading setups informed by RSI divergence logic.
Quickly compare crypto asset strength and trend exhaustion across short and long-term horizons.
How Multiple Indicators Work Together
This screener’s edge lies in its synergistic use of multi-setting RSI calculations and customizable input groups.
The dual-RSI approach (Fast vs. Slow) isolates subtle trend shifts missed by traditional single-period RSI.
Safe and reliable divergences arise only when the mathematical difference between Fast RSI and Slow RSI meets predefined thresholds, minimizing false positives.
Divergences are contextualized using tailored color codes and backgrounds, rendering insights immediately actionable.
You can expand analysis with additional moving average filters or overlays for further confirmation.
Unique Aspects
First-of-its-kind screener dedicated solely to RSI divergence, designed especially for crypto volatility.
Efficient screening of up to eight assets and multiple timeframes in one compact dashboard.
Intuitive iconography, color logic, and table layouts optimized for rapid decision-making.
Advanced input group design for fine-tuning indicator settings per symbol, timeframe, and source.
How to Use
Select up to eight cryptocurrency symbols to screen for divergence signals.
Assign individual timeframes and source prices for each asset to customize analysis.
Set Fast RSI and Slow RSI lengths according to your preferred strategy (e.g., scalping, swing, or trend following).
Review the screener table: colored cells highlight actionable bullish (green) and bearish (red) divergences.
Confirm trade setups with additional indicators or price action for robust risk management.
Customization
Symbols: Choose any crypto pair or ticker for dynamic divergence tracking.
Timeframes: Scan across 1m, 5m, 10m, 30m, and more for full market coverage.
RSI lengths: Configure Fast and Slow RSI periods based on volatility and trading style.
Visuals: Tailor table colors, fonts, and alert backgrounds per your preference.
Conclusion
The RSI Divergence Screener is a versatile, original TradingView indicator that empowers traders to scan, compare, and act on divergence signals with speed and precision. Its multi-symbol design, robust logic, and extensive customization options set a new standard for market screening tools. Integrate it into your crypto trading process to capture actionable opportunities ahead of the crowd and optimize your technical analysis workflow.
Squeeze Hour Frequency [CHE]Squeeze Hour Frequency (ATR-PR) — Standalone — Tracks daily squeeze occurrences by hour to reveal time-based volatility patterns
Summary
This indicator identifies periods of unusually low volatility, defined as squeezes, and tallies their frequency across each hour of the day over historical trading sessions. By aggregating counts into a sortable table, it helps users spot hours prone to these conditions, enabling better scheduling of trading activity to avoid or target specific intraday regimes. Signals gain robustness through percentile-based detection that adapts to recent volatility history, differing from fixed-threshold methods by focusing on relative lowness rather than absolute levels, which reduces false positives in varying market environments.
Motivation: Why this design?
Traders often face uneven intraday volatility, with certain hours showing clustered low-activity phases that precede or follow breakouts, leading to mistimed entries or overlooked calm periods. The core idea of hourly squeeze frequency addresses this by binning low-volatility events into 24 hourly slots and counting distinct daily occurrences, providing a historical profile of when squeezes cluster. This reveals time-of-day biases without relying on real-time alerts, allowing proactive adjustments to session focus.
What’s different vs. standard approaches?
- Reference baseline: Classical volatility tools like simple moving average crossovers or fixed ATR thresholds, which flag squeezes uniformly across the day.
- Architecture differences:
- Uses persistent arrays to track one squeeze per hour per day, preventing overcounting within sessions.
- Employs custom sorting on ratio arrays for dynamic table display, prioritizing top or bottom performers.
- Handles timezones explicitly to ensure consistent binning across global assets.
- Practical effect: Charts show a persistent table ranking hours by squeeze share, making intraday patterns immediately visible—such as a top hour capturing over 20 percent of total events—unlike static overlays that ignore temporal distribution, which matters for avoiding low-liquidity traps in crypto or forex.
How it works (technical)
The indicator first computes a rolling volatility measure over a specified lookback period. It then derives a relative ranking of the current value against recent history within a window of bars. A squeeze is flagged when this ranking falls below a user-defined cutoff, indicating the value is among the lowest in the recent sample.
On each bar, the local hour is extracted using the selected timezone. If a squeeze occurs and the bar has price data, the count for that hour increments only if no prior mark exists for the current day, using a persistent array to store the last marked day per hour. This ensures one tally per unique trading day per slot.
At the final bar, arrays compile counts and ratios for all 24 hours, where the ratio represents each hour's share of total squeezes observed. These are sorted ascending or descending based on display mode, and the top or bottom subset populates the table. Background shading highlights live squeezes in red for visual confirmation. Initialization uses zero-filled arrays for counts and negative seeds for day tracking, with state persisting across bars via variable declarations.
No higher timeframe data is pulled, so there is no repaint risk from external fetches; all logic runs on confirmed bars.
Parameter Guide
ATR Length — Controls the lookback for the volatility measure, influencing sensitivity to short-term fluctuations; shorter values increase responsiveness but add noise, longer ones smooth for stability — Default: 14 — Trade-offs/Tips: Use 10-20 for intraday charts to balance quick detection with fewer false squeezes; test on historical data to avoid over-smoothing in trending markets.
Percentile Window (bars) — Sets the history depth for ranking the current volatility value, affecting how "low" is defined relative to past; wider windows emphasize long-term norms — Default: 252 — Trade-offs/Tips: 100-300 bars suit daily cycles; narrower for fast assets like crypto to catch recent regimes, but risks instability in sparse data.
Squeeze threshold (PR < x) — Defines the cutoff for flagging low relative volatility, where values below this mark a squeeze; lower thresholds tighten detection for rarer events — Default: 10.0 — Trade-offs/Tips: 5-15 percent for conservative signals reducing false positives; raise to 20 for more frequent highlights in high-vol environments, monitoring for increased noise.
Timezone — Specifies the reference for hourly binning, ensuring alignment with market sessions — Default: Exchange — Trade-offs/Tips: Set to "America/New_York" for US assets; mismatches can skew counts, so verify against chart timezone.
Show Table — Toggles the results display, essential for reviewing frequencies — Default: true — Trade-offs/Tips: Disable on mobile for performance; pair with position tweaks for clean overlays.
Pos — Places the table on the chart pane — Default: Top Right — Trade-offs/Tips: Bottom Left avoids candle occlusion on volatile charts.
Font — Adjusts text readability in the table — Default: normal — Trade-offs/Tips: Tiny for dense views, large for emphasis on key hours.
Dark — Applies high-contrast colors for visibility — Default: true — Trade-offs/Tips: Toggle false in light themes to prevent washout.
Display — Filters table rows to focus on extremes or full list — Default: All — Trade-offs/Tips: Top 3 for quick scans of risky hours; Bottom 3 highlights safe low-squeeze periods.
Reading & Interpretation
Red background shading appears on bars meeting the squeeze condition, signaling current low relative volatility. The table lists hours as "H0" to "H23", with columns for daily squeeze counts, percentage share of total squeezes (summing to 100 percent across hours), and an arrow marker on the top hour. A summary row above details the peak count, its share, and the leading hour. A label at the last bar recaps total days observed, data-valid days, and top hour stats. Rising shares indicate clustering, suggesting regime persistence in that slot.
Practical Workflows & Combinations
- Trend following: Scan for hours with low squeeze shares to enter during stable regimes; confirm with higher highs or lower lows on the 15-minute chart, avoiding top-share hours post-news like tariff announcements.
- Exits/Stops: Tighten stops in high-share hours to guard against sudden vol spikes; use the table to shift to conservative sizing outside peak squeeze times.
- Multi-asset/Multi-TF: Defaults work across crypto pairs on 5-60 minute timeframes; for stocks, widen percentile window to 500 bars. Combine with volume oscillators—enter only if squeeze count is below average for the asset.
Behavior, Constraints & Performance
Logic executes on closed bars, with live bars updating counts provisionally but finalizing on confirmation; table refreshes only at the last bar, avoiding intrabar flicker. No security calls or higher timeframes, so no repaint from external data. Resources include a 5000-bar history limit, loops up to 24 iterations for sorting and totals, and arrays sized to 24 elements; labels and table are capped at 500 each for efficiency. Known limits: Skips hours without bars (e.g., weekends), assumes uniform data availability, and may undercount in sparse sessions; timezone shifts can alter profiles without warning.
Sensible Defaults & Quick Tuning
Start with ATR Length at 14, Percentile Window at 252, and threshold at 10.0 for broad crypto use. If too many squeezes flag (noisy table), raise threshold to 15.0 and narrow window to 100 for stricter relative lowness. For sluggish detection in calm markets, drop ATR Length to 10 and threshold to 5.0 to capture subtler dips. In high-vol assets, widen window to 500 and threshold to 20.0 for stability.
What this indicator is—and isn’t
This is a historical frequency tracker and visualization layer for intraday volatility patterns, best as a filter in multi-tool setups. It is not a standalone signal generator, predictive model, or risk manager—pair it with price action, news filters, and position sizing rules.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Thanks to Duyck
for the ma sorter
VWMA True Range | Lyro RSVWMA True Range | Lyro RS
This script is a hybrid technical analysis tool designed to identify trends and spot potential reversals. It employs a consensus-based system that uses multiple smoothed, Volume-Weighted Moving Averages (VWMA) to generate both trend-following and counter-trend signals.
Understanding the Indicator's Components
The indicator plots a main line on a separate pane and provides visual alerts directly on the chart.
The Main Line: This line represents a smoothed average of momentum scores derived from multiple VWMAs. Its direction and value are the foundation of the analysis.
Signal Generation: The tool provides two distinct types of signals:
Trend Signals: These trend-following signals ("⬆️Long" / "⬇️Short") activate when the indicator's consensus reaches a pre-set strength threshold, indicating sustained momentum in one direction.
Reversal Signals: These counter-trend alerts ("📈Oversold" / "📉Overbought") trigger when the main line breaks a previous period's level, hinting at exhaustion and a potential short-term reversal.
Visual Alerts:
Colored Background: The indicator's background highlights during strong trend signals for added visual emphasis.
Chart Shapes: Small circles appear on the main chart to mark where potential reversals are detected.
Colored Candles: You can choose to color the price candles to reflect the current trend signal.
Information Table: A compact table provides an at-a-glance summary of all currently active signals.
Suggested Use and Interpretation
Here are a few ways to incorporate this indicator into your analysis:
Following the Trend: Use the "Long" or "Short" trend signals to align your trades with the prevailing market momentum.
Spotting Reversals: Watch for "Oversold" or "Overbought" reversal signals, often accompanied by chart shapes, to identify potential market turning points.
Combining Signals: Use the primary trend signal for context and look for reversal signals that may indicate a pullback within the larger trend, potentially offering favorable entry points.
Customization Options:
You can tailor the indicator's behavior and appearance through several settings:
Core Settings: Adjust the Calculation Period and Smooth Length to make the main line more or less responsive to price movements.
Signal Thresholds: Fine-tune the Long threshold and Short threshold to control how easily trend signals are triggered.
Visual Settings: Toggle various visual elements like the indicator band, candle coloring, and the information table on or off.
Table Settings: Customize where the information table appears and its size to suit your chart layout.
⚠️Disclaimer
This indicator is a tool for technical analysis and does not guarantee future results. It should be used as part of a comprehensive trading strategy that includes other analysis techniques and strict risk management. The creators are not responsible for any financial decisions made based on its signals.
ATR Regime Study [CHE] ATR Regime Study — ATR percentile regimes with clear bands, table and live label
Summary
This study classifies volatility into five regimes by converting ATR into a percentile rank over a rolling window, plotted on a standardized scale between zero and one hundred. Colored bands mark regime thresholds, while a compact table and an optional label report the current percentile and regime. The standardized scale makes symbols and timeframes easier to compare than raw ATR values. Implemented in Pine v6 as a separate pane (overlay set to false), it is a context tool to adapt tactics and risk handling to the prevailing volatility environment.
Motivation: Why this design?
Raw ATR varies with price scale and asset characteristics, which makes regime comparison inconsistent and leads to poor transfer of settings across symbols and timeframes. The core idea is to transform ATR into a percentile rank within a user-defined lookback, then map it into discrete regimes. This yields a stable, interpretable context signal that shifts slower than raw ATR while still responding to genuine volatility changes.
What’s different vs. standard approaches?
Reference baseline: Traditional ATR plots or ATR bands using fixed multipliers.
Architecture differences:
Percentile ranking of ATR within a rolling window.
Five discrete regimes with fixed thresholds at ninety, seventy, thirty, and ten.
Visual fills between thresholds plus a live table and a last-bar label.
Practical effect: You read a single normalized line between zero and one hundred with consistent thresholds. This improves cross-asset comparison and makes regime shifts obvious at a glance.
How it works (technical)
The script computes ATR over a configurable length, then converts that series to a percentile rank over a configurable number of bars. The percentile is naturally scaled and limited between zero and one hundred. That value is mapped to one of five regimes: above ninety (Extreme), between seventy and ninety (Elevated), between thirty and seventy (Normal), between ten and thirty (Calm), and below ten (Squeeze). Horizontal guide lines mark the thresholds, and fills shade the regions. A table is created once and updated on each bar to show regime definitions and highlight the current row. An optional label on the last bar displays the current percentile and regime. No higher-timeframe requests are used, so repaint risk is limited to normal live-bar fluctuation until the bar closes.
Parameter Guide
ATR length — Effect: Controls how fast ATR reacts to new ranges. Default: fourteen. Trade-offs/Tips: Increase to reduce noise in choppy markets; decrease to react faster during regime changes.
Percentile window (bars) — Effect: Number of bars used for the percentile ranking. Default: two hundred fifty-two. Trade-offs/Tips: Larger windows stabilize the percentile but slow adaptation after structural regime shifts; smaller windows adapt faster but may flip more often.
Table › Show — Effect: Toggles the regime overview table. Default: enabled. Trade-offs/Tips: Disable on constrained layouts to reduce visual clutter.
Table › Position — Effect: Anchors the table in a chart corner. Default: Top Right. Trade-offs/Tips: Choose a corner that avoids overlapping other panels or drawings.
Label › Show — Effect: Toggles a last-bar label with current percentile and regime. Default: enabled. Trade-offs/Tips: Useful for quick reads; disable if it obscures other annotations.
Reading & Interpretation
The white line shows ATR percentile between zero and one hundred. Crossing above seventy signals an elevated volatility environment; above ninety indicates event-driven extremes. Between thirty and seventy represents typical conditions. Between ten and thirty indicates calm conditions that often suit mean reversion. Below ten reflects compression, where breakout probability often increases. The colored bands visually reinforce these ranges. The table summarizes regime definitions and highlights the current state. The last-bar label mirrors the current percentile and regime for quick inspection.
Practical Workflows & Combinations
Trend following: Prefer continuation tactics when the percentile holds in the Normal or Elevated bands and structure confirms higher highs and higher lows. Consider wider stops and partial position sizing as percentile rises.
Mean reversion: Favor fades in Calm regimes within defined ranges; use structure filters and time-of-day constraints to avoid low-liquidity whipsaws.
Breakout preparation: Track compressions below ten; plan entries only with structure confirmation and risk caps, since compressions can persist.
Multi-asset/Multi-TF: Defaults travel well on daily charts. For intraday, reduce the percentile window to align with session dynamics. Combine with trend or market structure tools for confirmation.
Behavior, Constraints & Performance
Repaint/confirmation: The percentile updates during live bars and stabilizes on close; closed bars do not repaint.
security/HTF: Not used. If you add higher-timeframe aggregation externally, account for standard repaint caveats.
Resources: Declared maximum bars back is two thousand; limits for lines and labels are five hundred each. A short loop updates the table rows; arrays are used for table content only.
Known limits: Regime boundaries are fixed; assets with persistent volatility shifts may require window retuning. Low-liquidity periods and gaps can produce abrupt percentile changes. ATR is direction-agnostic and should be paired with trend or structure context.
Sensible Defaults & Quick Tuning
Start with ATR length fourteen and percentile window two hundred fifty-two on daily charts.
Too many flips: Increase ATR length or increase the percentile window.
Too sluggish: Decrease the percentile window or reduce ATR length.
Intraday noise: Keep ATR length moderate and reduce the window to a session-appropriate size; optionally hide the label to declutter.
Compressed markets: Maintain defaults but rely more on structure and volume filters before acting.
What this indicator is—and isn’t
This is a volatility regime context layer that standardizes ATR into interpretable regimes. It is not a complete trading system, not predictive, and not a stand-alone entry signal. Use it alongside structure analysis, confirmation tools, and disciplined risk management.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Best regards and happy trading
Chervolino
RenKagi Fusion: Aura & SMA Clash IndicatorRenKagi Fusion: Aura & SMA Clash Indicator
Welcome to the RenKagi Fusion Indicator – a powerful, customizable tool that blends the strengths of Renko and Kagi charts to provide noise-filtered trend insights, enhanced with visual Aura effects and SMA (Simple Moving Average) crossover signals. Designed for traders seeking a unique edge in trend detection and reversal identification, this indicator combines traditional charting techniques with modern visualizations to help you navigate markets more effectively. Whether you're trading stocks, forex, or crypto, RenKagi Fusion offers a clean, actionable overview of market dynamics.
Key Features
RenKagi Line (Weighted Fusion of Renko and Kagi): The core of the indicator is the RenKagi line, a weighted average of Renko (brick-based trend filtering) and Kagi (reversal-focused line charts). Users can adjust the weight (default: 60% Renko, 40% Kagi) to prioritize stability or sensitivity. This fusion reduces market noise while highlighting key price movements.
Trend Scoring System: Calculates strength scores for Renko, Kagi, and RenKagi (capped at 20 points, converted to percentages). Scores increase with trend continuation and reset on reversals, giving a quantitative measure of momentum.
Aura Effects (Optional): Visual "glow" around lines based on score percentage – higher scores mean more opaque and thicker auras, adding a dynamic layer to trend visualization.
SMA Clash (Crossover Detection): Monitors daily SMA50, SMA100, and SMA200 for golden/death crosses (SMA50 crossing above/below longer SMAs) and RenKagi-SMA crossovers. These are displayed in a persistent info table for quick reference.
Customizable Visuals: Toggle lines, boxes, shapes, auras, and labels. Background coloring based on selected source (Renko, Kagi, or RenKagi) for intuitive trend bias.
Info Table: A configurable table (position and colors adjustable) summarizing scores, directions, cross states, brick size (with type), Kagi reversal (with type), and weights. No clutter – all in one place.
Alert Conditions: Built-in alerts for direction changes (Renko, Kagi, RenKagi), SMA crossovers, and golden/death crosses – perfect for real-time notifications.
How It Works
Renko Logic: Builds bricks based on user-selected type (Traditional fixed size, ATR dynamic, or Percentage). Scores build as trends persist, resetting on reversals.
Kagi Logic: Line reverses on thresholds (Traditional, ATR, or Percentage), scoring continuous moves.
RenKagi Calculation: Weighted average: (renkoPrice * renkoWeight + kagiLine * (100 - renkoWeight)) / 100. Score is a blend of individual scores.
SMA Integration: Daily timeframe SMAs for reliable long-term signals. Crossovers trigger alerts and update table states persistently until reversed.
Advantages for Traders
Noise Reduction: By fusing Renko's block structure with Kagi's reversal focus, it filters out minor fluctuations, helping identify strong trends early.
Versatility: Fully customizable – adjust weights, types, and visuals to fit any market or timeframe. Ideal for swing trading, trend following, or scalping.
Visual Clarity: Aura and background coloring provide at-a-glance insights, while the table consolidates data without overwhelming the chart.
Actionable Signals: Golden/Death crosses and direction changes offer clear entry/exit points, backed by alerts for timely execution.
Performance Optimization: Limits on lines/labels/boxes (500 each) ensure smooth operation on large datasets.
Usage Tips
Start with default settings for balanced performance.
Use in higher timeframes for trend confirmation or lower for intraday signals.
Combine with your favorite strategies – e.g., buy on RenKagi upward cross with SMA50 and golden cross confirmation.
Test on historical data to optimize weights and thresholds.
Note: This indicator is for educational and informational purposes only. Past performance is not indicative of future results. Always conduct your own analysis and use risk management. No financial advice is provided.
If you find this useful, please like, comment, or share your feedback!
Cumulative Returns by Session [BackQuant]Cumulative Returns by Session
What this is
This tool breaks the trading day into three user-defined sessions and tracks how much each session contributes to return, volatility, and volume. It then aggregates results over a rolling window so you can see which session has been pulling its weight, how streaky each session has been, and how sessions relate to one another through a compact correlation heatmap.
We’ve also given the functionality for the user to use a simplified table, just by switching off all settings they are not interested in.
How it works
1) Session segmentation
You define APAC, EU, and US sessions with explicit hours and time zones. The script detects when each session starts and ends on every intraday bar and records its open, intraday high and low, close, and summed volume.
2) Per-session math
At each session end the script computes:
Return — either Percent: (Close−Open)÷Open×100(Close − Open) ÷ Open × 100(Close−Open)÷Open×100 or Points: (Close−Open)(Close − Open)(Close−Open), based on your selection.
Volatility — either Range: (High−Low)÷Open×100(High − Low) ÷ Open × 100(High−Low)÷Open×100 or ATR scaled by price: ATR÷Open×100ATR ÷ Open × 100ATR÷Open×100.
Volume — total volume transacted during that session.
3) Storage and lookback
Each day’s three session stats are stored as a row. You choose how many recent sessions to keep in memory. The script then:
Builds cumulative returns for APAC, EU, US across the lookback.
Computes averages, win rates, and a Sharpe-like ratio avgreturn÷avgvolatilityavg return ÷ avg volatilityavgreturn÷avgvolatility per session.
Tracks streaks of positive or negative sessions to show momentum.
Tracks drawdowns on cumulative returns to show worst runs from peak.
Computes rolling means over a short window for short-term drift.
4) Correlation heatmap
Using the stored arrays of session returns, the script calculates Pearson correlations between APAC–EU, APAC–US, and EU–US, and colors the matrix by strength and sign so you can spot coupling or decoupling at a glance.
What it plots
Three lines: cumulative return for APAC, EU, US over the chosen lookback.
Zero reference line for orientation.
A statistics table with cumulative %, average %, positive session rate, and optional columns for volatility, average volume, max drawdown, current streak, return-to-vol ratio, and rolling average.
A small correlation heatmap table showing APAC, EU, US cross-session correlations.
How to use it
Pick the asset — leave Custom Instrument empty to use the chart symbol, or point to another symbol for cross-asset studies.
Set your sessions and time zones — defaults approximate APAC, EU, and US hours, but you can align them to exchange times or your workflow.
Choose calculation modes — Percent vs Points for return, Range vs ATR for volatility. Points are convenient for futures and fixed-tick assets, Percent is comparable across symbols.
Decide the lookback — more sessions smooths lines and stats; fewer sessions makes the tool more reactive.
Toggle analytics — add volatility, volume, drawdown, streaks, Sharpe-like ratio, rolling averages, and the correlation table as needed.
Why session attribution helps
Different sessions are driven by different flows. Asia often sets the overnight tone, Europe adds liquidity and direction changes, and the US session can dominate range expansion. Separating contributions by session helps you:
Identify which session has been the main driver of net trend.
Measure whether volatility or volume is concentrated in a specific window.
See if one session’s gains are consistently given back in another.
Adapt tactics: fade during a mean-reverting session, press during a trending session.
Reading the tables
Cumulative % — sum of session returns over the lookback. The sign and slope tell you who is carrying the move.
Avg Return % and Positive Sessions % — direction and hit rate. A low average but high hit rate implies many small moves; the reverse implies occasional big swings.
Avg Volatility % — typical intrabars range for that session. Compare with Avg Return to judge efficiency.
Return/Vol Ratio — return per unit of volatility. Higher is better for stability.
Max Drawdown % — worst cumulative give-back within the lookback. A quick way to spot riskiness by session.
Current Streak — consecutive up or down sessions. Useful for mean-reversion or regime awareness.
Rolling Avg % — short-window drift indicator to catch recent turnarounds.
Correlation matrix — green clusters indicate sessions tending to move together; red indicates offsetting behavior.
Settings overview
Basic
Number of Sessions — how many recent days to include.
Custom Instrument — analyze another ticker while staying on your current chart.
Session Configuration and Times
Enable or hide APAC, EU, US rows.
Set hours per session and the specific time zone for each.
Calculation Methods
Return Calculation — Percent or Points.
Volatility Calculation — Range or ATR; ATR Length when applicable.
Advanced Analytics
Correlation, Drawdown, Momentum, Sharpe-like ratio, Rolling Statistics, Rolling Period.
Display Options and Colors
Show Statistics Table and its position.
Toggle columns for Volatility and Volume.
Pick individual colors for each session line and row accents.
Common applications
Session bias mapping — find which window tends to trend in your market and plan exposure accordingly.
Strategy scheduling — allocate attention or risk to the session with the best return-to-vol ratio.
News and macro awareness — see if correlation rises around central bank cycles or major data releases.
Cross-asset monitoring — set the Custom Instrument to a driver (index future, DXY, yields) to see if your symbol reacts in a particular session.
Notes
This indicator works on intraday charts, since sessions are defined within a day. If you change session clocks or time zones, give the script a few bars to accumulate fresh rows. Percent vs Points and Range vs ATR choices affect comparability across assets, so be consistent when comparing symbols.
Session context is one of the simplest ways to explain a messy tape. By separating the day into three windows and scoring each one on return, volatility, and consistency, this tool shows not just where price ended up but when and how it got there. Use the cumulative lines to spot the steady driver, read the table to judge quality and risk, and glance at the heatmap to learn whether the sessions are amplifying or canceling one another. Adjust the hours to your market and let the data tell you which session deserves your focus.
Simplified Market ForecastSimplified Market Forecast Indicator
This indicator pairs nicely with the Contrarian 100 MA and can be located here:
Overview
The "Simplified Market Forecast" (SMF) indicator is a streamlined technical analysis tool designed for traders to identify potential buy and sell opportunities based on a momentum-based oscillator. By analyzing price movements relative to a defined lookback period, SMF generates clear buy and sell signals when the oscillator crosses customizable threshold levels. This indicator is versatile, suitable for various markets (e.g., forex, stocks, cryptocurrencies), and optimized for daily timeframes, though it can be adapted to other timeframes with proper testing. Its intuitive design and visual cues make it accessible for both novice and experienced traders.
How It Works
The SMF indicator calculates a momentum oscillator based on the price’s position within a specified range over a user-defined lookback period. It then smooths this value to reduce noise and plots the result as a line in a separate lower pane. Buy and sell signals are generated when the smoothed oscillator crosses above a user-defined buy level or below a user-defined sell level, respectively. These signals are visualized as triangles either on the main chart or in the lower pane, with a table displaying the current ticker and oscillator value for quick reference.
Key Components
Momentum Oscillator: The indicator measures the price’s position relative to the highest high and lowest low over a specified period, normalized to a 0–100 scale.
Signal Generation: Buy signals occur when the oscillator crosses above the buy level (default: 15), indicating potential oversold conditions. Sell signals occur when the oscillator crosses below the sell level (default: 85), suggesting potential overbought conditions.
Visual Aids: The indicator includes customizable horizontal lines for buy and sell levels, shaded zones for clarity, and a table showing the ticker and current oscillator value.
Mathematical Concepts
Oscillator Calculation: The indicator uses the following formula to compute the raw oscillator value:
c1I = close - lowest(low, medLen)
c2I = highest(high, medLen) - lowest(low, medLen)
fastK_I = (c1I / c2I) * 100
The result is smoothed using a 5-period Simple Moving Average (SMA) to produce the final oscillator value (inter).
Signal Logic:
A buy signal is triggered when the smoothed oscillator crosses above the buy level (ta.crossover(inter, buyLevel)).
A sell signal is triggered when the smoothed oscillator crosses below the sell level (ta.crossunder(inter, sellLevel)).
Entry and Exit Rules
Buy Signal (Blue Triangle): Triggered when the oscillator crosses above the buy level (default: 15), indicating a potential oversold condition and a buying opportunity. The signal appears as a blue triangle either below the price bar (if plotted on the main chart) or at the bottom of the lower pane.
Sell Signal (White Triangle): Triggered when the oscillator crosses below the sell level (default: 85), indicating a potential overbought condition and a selling opportunity. The signal appears as a white triangle either above the price bar (if plotted on the main chart) or at the top of the lower pane.
Exit Rules: Traders can exit positions when an opposite signal occurs (e.g., exit a buy on a sell signal) or based on additional technical analysis tools (e.g., support/resistance, trendlines). Always apply proper risk management.
Recommended Usage
The SMF indicator is optimized for the daily timeframe but can be adapted to other timeframes (e.g., 1H, 4H) with careful testing. It performs best in markets with clear momentum shifts, such as trending or range-bound conditions. Traders should:
Backtest the indicator on their chosen asset and timeframe to validate signal reliability.
Combine with other indicators (e.g., moving averages, support/resistance) or price action for confirmation.
Adjust the lookback period and buy/sell levels to suit market volatility and trading style.
Customization Options
Intermediate Length: Adjust the lookback period for the oscillator calculation (default: 31 bars).
Buy/Sell Levels: Customize the threshold levels for buy (default: 15) and sell (default: 85) signals.
Colors: Modify the colors of the oscillator line, buy/sell signals, and threshold lines.
Signal Display: Toggle whether signals appear on the main chart or in the lower pane.
Visual Aids: The indicator includes dotted horizontal lines at the buy (green) and sell (red) levels, with shaded zones between 0–buy level (green) and sell level–100 (red) for clarity.
Ticker Table: A table in the top-right corner displays the current ticker and oscillator value (in percentage), with customizable colors.
Why Use This Indicator?
The "Simplified Market Forecast" indicator provides a straightforward, momentum-based approach to identifying potential reversals in overbought or oversold markets. Its clear signals, customizable settings, and visual aids make it easy to integrate into various trading strategies. Whether you’re a swing trader or a day trader, SMF offers a reliable tool to enhance decision-making and improve market timing.
Tips for Users
Test the indicator thoroughly on your chosen asset and timeframe to optimize settings.
Use in conjunction with other technical tools for stronger trade confirmation.
Adjust the buy and sell levels based on market conditions (e.g., lower levels for less volatile markets).
Monitor the ticker table for real-time oscillator values to gauge market momentum.
Happy trading with the Simplified Market Forecast indicator!
Calculator - AOC📊 Calculator - AOC Indicator 🚀
The Calculator - AOC indicator is a powerful and user-friendly tool designed for TradingView to help traders plan and visualize trades with precision. It calculates key trade metrics, displays entry, take-profit (TP), stop-loss (SL), and liquidation levels, and provides a clear overview of risk management and potential profits. Perfect for both novice and experienced traders! 💡
✨ Features
📈 Trade Planning: Input your Entry Price, Take Profit (TP), Stop Loss (SL), and Trade Direction (Long/Short) to visualize your trade setup on the chart.
💰 Risk Management: Set your Initial Capital and Risk per Trade (%) to calculate the optimal Position Size and Risk Amount for each trade.
⚖️ Leverage Support: Define your Leverage to compute the Required Margin and Liquidation Price, ensuring you stay aware of potential risks.
📊 Risk/Reward Ratio: Automatically calculates the Risk-to-Reward Ratio to evaluate trade profitability.
🎨 Visuals: Displays Entry, TP, SL, and Liquidation levels as lines and boxes on the chart, with customizable Line Width, Line Style, and Label Size.
✅ Trade Validation: Checks if your trade setup is valid (e.g., correct TP/SL placement) and highlights issues like potential liquidation risks with color-coded statuses (Correct ✅, Incorrect ❌, or Liquidation ⚠️).
📋 Summary Table: A clean, top-right table summarizes key metrics: Capital, Risk %, Risk Amount, Position Size, Potential Profit, Risk/Reward, Margin, Liquidation Price, Trade Status, and % to TP/SL.
🖌️ Customization: Adjust Line Extension (Bars) for how far lines extend, and choose from Solid, Dashed, or Dotted line styles for a personalized chart experience.
🛠️ How to Use
Add to Chart: Apply the indicator to your TradingView chart.
Configure Inputs:
Accountability: Set your Initial Capital and Risk per Trade (%).
Target: Enter Entry Price, TP, and SL prices.
Leverage: Specify your leverage (e.g., 10x).
Direction: Choose Long or Short.
Display Settings: Customize Line Width, Line Style, Label Size, and Line Extension.
Analyze: The indicator plots Entry, TP, SL, and Liquidation levels on the chart and displays a table with all trade metrics.
Validate: Check the Trade Status in the table to ensure your setup is valid or if adjustments are needed.
🎯 Why Use It?
Plan Smarter: Visualize your trade setup and understand your risk/reward profile instantly.
Stay Disciplined: Precise position sizing and risk calculations help you stick to your trading plan.
Avoid Mistakes: Clear validation warnings prevent costly errors like incorrect TP/SL placement or liquidation risks.
User-Friendly: Intuitive visuals and a summary table make trade analysis quick and easy.
📝 Notes
Ensure Entry, TP, and SL prices align with your trade direction to avoid "Incorrect" or "Liquidation" statuses.
The indicator updates dynamically on the latest bar, ensuring real-time visuals.
Best used with proper risk management to maximize trading success! 💪
Happy trading! 🚀📈
Beta Zones [MMT]Beta Zones
Overview
The Beta Zones indicator is a multi-timeframe analysis tool designed to identify and visualize price ranges (zones) across different timeframes on a TradingView chart. It draws boxes to represent high and low price levels for each enabled timeframe, helping traders spot key support and resistance zones, track price movements, and assess market signals relative to these zones. The indicator is highly customizable, allowing users to toggle timeframes, adjust colors, and control historical visibility.
Features
Multi-Timeframe Support : Tracks up to five user-defined timeframes (default: 15m, 1H, 4H, 1D, 1W) to display price zones.
Dynamic Price Boxes : Draws boxes on the chart to represent the high and low prices for each timeframe, updating dynamically as new bars form.
Signal Indicators : Provides directional signals (▲, ▼, →) based on the previous close relative to the current box's top and bottom.
Customizable Display : Includes options to show or hide historical boxes, adjust box colors, and configure a summary table.
Summary Table : Displays a table with timeframe status, price range, and signal information for quick reference.
Settings
Timeframes
Enable/Disable : Toggle each timeframe (e.g., 15m, 1H, 4H, 1D, 1W) to display or hide its respective zones.
Timeframe Selection : Choose custom timeframes for each of the five slots.
Color Customization : Set unique fill and border colors for each timeframe's boxes (default colors: green, blue, orange, purple, red).
Display
Max Historical Boxes : Limit the number of historical boxes per timeframe (default: 1, max: 50).
Show History : Toggle visibility of historical boxes (default: false, showing only the latest box).
Min Box Height : Ensures boxes have a minimum height in ticks (default: 1.0, currently hardcoded).
Table
Show Table : Enable or disable the summary table (default: true).
Background Color : Customize the table's background color.
Header Color : Set the color for the table's header row.
Text Color : Adjust the text color for table content.
Table Columns
Timeframe : Displays the selected timeframe (e.g., 15m, 1H).
Color : Shows the color associated with the timeframe's boxes.
Status : Indicates if the timeframe is "Active" (valid and lower than the chart's timeframe), "Invalid" (enabled but not lower), or "Disabled".
Range : Shows the price range (high - low) of the current box.
Signal : Displays ▲ (price above box), ▼ (price below box), or → (price within box) based on the previous close.
How to Use
Add to Chart : Apply the indicator to your TradingView chart.
Configure Timeframes : Enable desired timeframes and adjust their settings (e.g., 15m, 1H) to match your trading strategy.
Analyze Zones : Use the boxes to identify key price levels for support, resistance, or breakout opportunities.
Monitor Signals : Check the table's "Signal" column to gauge price direction relative to each timeframe's zone.
Customize Appearance : Adjust colors and historical box visibility to suit your preferences.
Ideal For
Swing Traders : Identify key price zones across multiple timeframes for entry/exit points.
Day Traders : Monitor short-term price movements relative to higher timeframe zones.
Technical Analysts : Combine with other indicators to confirm support/resistance levels.
