Dividend Calendar (Zeiierman)█ Overview
The Dividend Calendar is a financial tool designed for investors and analysts in the stock market. Its primary function is to provide a schedule of expected dividend payouts from various companies.
Dividends, which are portions of a company's earnings distributed to shareholders, represent a return on their investment. This calendar is particularly crucial for investors who prioritize dividend income, as it enables them to plan and manage their investment strategies with greater effectiveness. By offering a comprehensive overview of when dividends are due, the Dividend Calendar aids in informed decision-making, allowing investors to time their purchases and sales of stocks to optimize their dividend income. Additionally, it can be a valuable tool for forecasting cash flow and assessing the financial health and dividend-paying consistency of different companies.
█ How to Use
Dividend Yield Analysis:
By tracking dividend growth and payouts, traders can identify stocks with attractive dividend yields. This is particularly useful for income-focused investors who prioritize steady cash flow from their investments.
Income Planning:
For those relying on dividends as a source of income, the calendar helps in forecasting income.
Trend Identification:
Analyzing the growth rates of dividends helps in identifying long-term trends in a company's financial health. Consistently increasing dividends can be a sign of a company's strong financial position, while decreasing dividends might signal potential issues.
Portfolio Diversification:
The tool can assist in diversifying a portfolio by identifying a range of dividend-paying stocks across different sectors. This can help mitigate risk as different sectors may react differently to market conditions.
Timing Investments:
For those who follow a dividend capture strategy, this indicator can be invaluable. It can help in timing the buying and selling of stocks around their ex-dividend dates to maximize dividend income.
█ How it Works
This script is a comprehensive tool for tracking and analyzing stock dividend data. It calculates growth rates, monthly and yearly totals, and allows for custom date handling. Structured to be visually informative, it provides tables and alerts for the easy monitoring of dividend-paying stocks.
Data Retrieval and Estimation: It fetches dividend payout times and amounts for a list of stocks. The script also estimates future values based on historical data.
Growth Analysis: It calculates the average growth rate of dividend payments for each stock, providing insights into dividend consistency and growth over time.
Summation and Aggregation: The script sums up dividends on a monthly and yearly basis, allowing for a clear view of total payouts.
Customization and Alerts: Users can input custom months for dividend tracking. The script also generates alerts for upcoming or current dividend payouts.
Visualization: It produces various tables and visual representations, including full calendar views and income tables, to display the dividend data in an easily understandable format.
█ Settings
Overview:
Currency:
Description: This setting allows the user to specify the currency in which dividend values are displayed. By default, it's set to USD, but users can change it to their local currency.
Impact: Changing this value alters the currency denomination for all dividend values displayed by the script.
Ex-Date or Pay-Date:
Description: Users can select whether to show the Ex-dividend day or the Actual Payout day.
Impact: This changes the reference date for dividend data, affecting the timing of when dividends are shown as due or paid.
Estimate Forward:
Description: Enables traders to predict future dividends based on historical data.
Impact: When enabled, the script estimates future dividend payments, providing a forward-looking view of potential income.
Dividend Table Design:
Description: Choose between viewing the full dividend calendar, just the cumulative monthly dividend, or a summary view.
Impact: This alters the format and extent of the dividend data displayed, catering to different levels of detail a user might require.
Show Dividend Growth:
Description: Users can enable dividend growth tracking over a specified number of years.
Impact: When enabled, the script displays the growth rate of dividends over the selected number of years, providing insight into dividend trends.
Customize Stocks & User Inputs:
This setting allows users to customize the stocks they track, the number of shares they hold, the dividend payout amount, and the payout months.
Impact: Users can tailor the script to their specific portfolio, making the dividend data more relevant and personalized to their investments.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Tìm kiếm tập lệnh với " TABLE "
Buy/Sell EMA CandleThis indicator is designed to display various technical indicators, candle patterns, and trend directions on a price chart. Let's break down the code and explain its different sections:
Exponential Moving Averages (EMA):
The code calculates and plots five EMAs of different lengths (13, 21, 55, 90, and 200) on the price chart. These EMAs are used to identify trends and potential crossovers.
Engulfing Candle Patterns:
The code identifies and highlights potential bullish and bearish engulfing candle patterns. It checks if the current candle's body size is larger than the combined body sizes of the previous and subsequent four candles. If this condition is met, it marks the pattern on the chart.
s3.tradingview.com
EMA Crossovers:
The code identifies and highlights points where the shorter EMA (ema1) crosses above or below the longer EMA (ema2). It plots circles to indicate these crossover points.
Candle Direction and RSI Trend:
The code determines the trend direction of the last candle based on whether it closed higher or lower than its open price. It also calculates the RSI (Relative Strength Index) and determines its trend direction (overbought, oversold, or neutral) based on predefined thresholds.
s3.tradingview.com
Table Display:
The code creates a table displaying trend directions for different timeframes (monthly, weekly, daily, 4-hour, and 1-hour) for candle direction and RSI trends. The trends are labeled with "L" for long, "S" for short, and "N/A" for not applicable.
High Volume Bars (HVB):
The code identifies and colors bars with above-average volume as either bullish or bearish based on whether the price closed higher or lower than it opened. The color and conditions for high volume bars can be customized.
s3.tradingview.com
Doji Candle Pattern:
The code identifies and marks doji candle patterns, where the open and close prices are very close to each other within a certain percentage of the candle's high-low range.
RSI-Based Candle Coloring:
The code adjusts the color of the candles based on the RSI value. If the RSI value is above the overbought threshold or below the oversold threshold, the candles are colored yellow.
Usage and Interpretation:
Traders can use this indicator to identify potential trend changes based on EMA crossovers and candle patterns like engulfing and doji.
The RSI trend direction can provide additional insight into potential overbought or oversold conditions.
High volume bars can indicate potential price reversals or continuation patterns.
The table provides an overview of trend directions on different timeframes for both candle direction and RSI trends.
Keep in mind that this is a complex indicator with multiple features. Users should carefully evaluate its performance and consider combining it with other indicators and analysis methods for more accurate trading decisions.
The table is designed to provide a consolidated view of trend directions and other indicators across multiple timeframes. It is displayed on the chart and organized into rows and columns. Each row corresponds to a specific aspect of analysis, and each column corresponds to a different timeframe.
Here's a breakdown of the components of the table:
Row 1: Separation.
Row 2 (Header Row): This row contains the headers for the columns. The headers represent the different timeframes being analyzed, such as Monthly (M), Weekly (W), Daily (D), 4-hour (4h), and 1-hour (1h).
Row 3 (Content Row): This row contains labels indicating the types of information being displayed in the columns. The labels include "T" for Trend, "C" for Current Candle, and "R" for RSI Trend.
Row 4 and Onwards: These rows display the actual data for each aspect of analysis across different timeframes.
For each aspect of analysis (Trend, Current Candle, RSI Trend), the corresponding rows display the following information:
Monthly (M): The trend direction for the given aspect on the monthly timeframe.
Weekly (W): The trend direction for the given aspect on the weekly timeframe.
Daily (D): The trend direction for the given aspect on the daily timeframe.
4-hour (4h): The trend direction for the given aspect on the 4-hour timeframe.
1-hour (1h): The trend direction for the given aspect on the 1-hour timeframe.
The trend directions are represented by labels such as "L" for Long, "S" for Short, or "N/A" for Not Applicable.
The table's purpose is to provide a quick overview of trend directions and related information across multiple timeframes, aiding traders in making informed decisions based on the analysis of trend changes and other indicators.
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
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.
Market Structure & Liquidity: CHoCHs+Nested Pivots+FVGs+Sweeps//Purpose:
This indicator combines several tools to help traders track and interpret price action/market structure; It can be divided into 4 parts;
1. CHoCHs, 2. Nested Pivot highs & lows, 3. Grade sweeps, 4. FVGs.
This gives the trader a toolkit for determining market structure and shifts in market structure to help determine a bull or bear bias, whether it be short-term, med-term or long-term.
This indicator also helps traders in determining liquidity targets: wether they be voids/gaps (FVGS) or old highs/lows+ typical sweep distances.
Finally, the incorporation of HTF CHoCH levels printing on your LTF chart helps keep the bigger picture in mind and tells traders at a glance if they're above of below Custom HTF CHoCH up or CHoCH down (these HTF CHoCHs can be anything from Hourly up to Monthly).
//Nomenclature:
CHoCH = Change of Character
STH/STL = short-term high or low
MTH/MTL = medium-term high or low
LTH/LTL = long-term high or low
FVG = Fair value gap
CE = consequent encroachement (the midline of a FVG)
~~~ The Four components of this indicator ~~~
1. CHoCHs:
•Best demonstrated in the below charts. This was a method taught to me by @Icecold_crypto. Once a 3 bar fractal pivot gets broken, we count backwards the consecutive higher lows or lower highs, then identify the CHoCH as the opposite end of the candle which ended the consecutive backwards count. This CHoCH (UP or DOWN) then becomes a level to watch, if price passes through it in earnest a trader would consider shifting their bias as market structure is deemed to have shifted.
•HTF CHoCHs: Option to print Higher time frame chochs (default on) of user input HTF. This prints only the last UP choch and only the last DOWN choch from the input HTF. Solid line by default so as to distinguish from local/chart-time CHoCHs. Can be any Higher timeframe you like.
•Show on table: toggle on show table(above/below) option to show in table cells (top right): is price above the latest HTF UP choch, or is price below HTF DOWN choch (or is it sat between the two, in a state of 'uncertainty').
•Most recent CHoCHs which have not been met by price will extend 10 bars into the future.
• USER INPUTS: overall setting: SHOW CHOCHS | Set bars lookback number to limit historical Chochs. Set Live CHoCHs number to control the number of active recent chochs unmet by price. Toggle shrink chochs once hit to declutter chart and minimize old chochs to their origin bars. Set Multi-timeframe color override : to make Color choices auto-set to your preference color for each of 1m, 5m, 15m, H, 4H, D, W, M (where up and down are same color, but 'up' icon for up chochs and down icon for down chochs remain printing as normal)
2. Nested Pivot Highs & Lows; aka 'Pivot Highs & Lows (ST/MT/LT)'
•Based on a seperate, longer lookback/lookforward pivot calculation. Identifies Pivot highs and lows with a 'spikeyness' filter (filtering out weak/rounded/unimpressive Pivot highs/lows)
•by 'nested' I mean that the pivot highs are graded based on whether a pivot high sits between two lower pivot highs or vice versa.
--for example: STH = normal pivot. MTH is pivot high with a lower STH on either side. LTH is a pivot high with a lower MTH on either side. Same applies to pivot lows (STL/MTL/LTL)
•This is a useful way to measure the significance of a high or low. Both in terms of how much it might be typically swept by (see later) and what it would imply for HTF bias were we to break through it in earnest (more than just a sweep).
• USER INPUTS: overall setting: show pivot highs & lows | Bars lookback (historical pivots to show) | Pivots: lookback/lookforward length (determines the scale of your pivot highs/lows) | toggle on/off Apply 'Spikeyness' filter (filters out smooth/unimpressive pivot highs/lows). Set Spikeyness index (determines the strength of this filter if turned on) | Individually toggle on each of STH, MTH, LTH, STL, MTL, LTL along with their label text type , and size . Toggle on/off line for each of these Pivot highs/lows. | Set label spacer (atr multiples above / below) | set line style and line width
3. Grade Sweeps:
•These are directly related to the nested pivots described above. Most assets will have a typical sweep distance. I've added some of my expected sweeps for various assets in the indicator tooltips.
--i.e. Eur/Usd 10-20-30 pips is a typical 'grade' sweep. S&P HKEX:5 - HKEX:10 is a typical grade sweep.
•Each of the ST/MT/LT pivot highs and lows have optional user defined grade sweep boxes which paint above until filled (or user option for historical filled boxes to remain).
•Numbers entered into sweep input boxes are auto converted into appropriate units (i.e. pips for FX, $ or 'handles' for indices, $ for Crypto. Very low $ units can be input for low unit value crypto altcoins.
• USER INPUTS: overall setting: Show sweep boxes | individually select colors of each of STH, MTH, LTH, STL, MTL, LTL sweep boxes. | Set Grade sweep ($/pips) number for each of ST, MT, LT. This auto converts between pips and $ (i.e. FX vs Indices/Crypto). Can be a float as small or large as you like ($0.000001 to HKEX:1000 ). | Set box text position (horizontal & vertical) and size , and color . | Set Box width (bars) (for non extended/ non-auto-terminating at price boxes). | toggle on/off Extend boxes/lines right . | Toggle on/off Shrink Grade sweeps on fill (they will disappear in realtime when filled/passed through)
4. FVGs:
•Fair Value gaps. Represent 'naked' candle bodies where the wicks to either side do not meet, forming a 'gap' of sorts which has a tendency to fill, or at least to fill to midline (CE).
•These are ICT concepts. 'UP' FVGS are known as BISIs (Buyside imbalance, sellside inefficiency); 'DOWN' FVGs are known as SIBIs (Sellside imbalance, buyside inefficiency).
• USER INPUTS: overall setting: show FVGs | Bars lookback (history). | Choose to display: 'UP' FVGs (BISI) and/or 'DOWN FVGs (SIBI) . Choose to display the midline: CE , the color and the line style . Choose threshold: use CE (as opposed to Full Fill) |toggle on/off Shrink FVG on fill (CE hit or Full fill) (declutter chart/see backtesting history)
////••Alerts (general notes & cautionary notes)::
•Alerts are optional for most of the levels printed by this indicator. Set them via the three dots on indicator status line.
•Due to dynamic repainting of levels, alerts should be used with caution. Best use these alerts either for Higher time frame levels, or when closely monitoring price.
--E.g. You may set an alert for down-fill of the latest FVG below; but price will keep marching up; form a newer/higher FVG, and the alert will trigger on THAT FVG being down-filled (not the original)
•Available Alerts:
-FVG(BISI) cross above threshold(CE or full-fill; user choice). Same with FVG(SIBI).
-HTF last CHoCH down, cross below | HTF last CHoCH up, cross above.
-last CHoCH down, cross below | last CHoCH up, cross above.
-LTH cross above, MTH cross above, STH cross above | LTL cross below, MTL cross below, STL cross below.
////••Formatting (general)::
•all table text color is set from the 'Pivot highs & Lows (ST, MT, LT)' section (for those of you who prefer black backgrounds).
•User choice of Line-style, line color, line width. Same with Boxes. Icon choice for chochs. Char or label text choices for ST/MT/LT pivot highs & lows.
////••User Inputs (general):
•Each of the 4 components of this indicator can be easily toggled on/off independently.
•Quite a lot of options and toggle boxes, as described in full above. Please take your time and read through all the tooltips (hover over '!' icon) to get an idea of formatting options.
•Several Lookback periods defined in bars to control how much history is shown for each of the 4 components of this indicator.
•'Shrink on fill' settings on FVGs and CHoCHs: Basically a way to declutter chart; toggle on/off depending on if you're backtesting or reading live price action.
•Table Display: applies to ST/MT/LT pivot highs and to HTF CHoCHs; Toggle table on or off (in part or in full)
////••Credits:
•Credit to ICT (Inner Circle Trader) for some of the concepts used in this indicator (FVGS & CEs; Grade sweeps).
•Credit to @Icecold_crypto for the specific and novel concept of identifying CHoCHs in a simple, objective and effective manner (as demonstrated in the 1st chart below).
CHoCH demo page 1: shifting tweak; arrow diagrams to demonstrate how CHoCHs are defined:
CHoCH demo page 2: Simplified view; short lookback history; few CHoCHs, demo of 'latest' choch being extended into the future by 10 bars:
USAGE: Bitcoin Hourly using HTF daily CHoCHs:
USAGE-2: Cotton Futures (CT1!) 2hr. Painting a rather bullish picture. Above HTF UP CHoCH, Local CHoCHs show bullish order flow, Nice targets above (MTH/LTH + grade sweeps):
Full Demo; 5min chart; CHoCHs, Short term pivot highs/lows, grade sweeps, FVGs:
Full Demo, Eur/Usd 15m: STH, MTH, LTH grade sweeps, CHoCHs, Usage for finding bias (part A):
Full Demo, Eur/Usd 15m: STH, MTH, LTH grade sweeps, CHoCHs, Usage for finding bias, 3hrs later (part B):
Realtime Vs Backtesting(A): btc/usd 15m; FVGs and CHoCHs: shrink on fill, once filled they repaint discreetly on their origin bar only. Realtime (Shrink on fill, declutter chart):
Realtime Vs Backtesting(B): btc/usd 15m; FVGs and CHoCHs: DON'T shrink on fill; they extend to the point where price crosses them, and fix/paint there. Backtesting (seeing historical behaviour):
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.
Zendog V2 backtest DCA bot 3commasHi everyone,
After a few iterations and additional implemented features this version of the Backtester is now open source.
The Strategy is a Backtester for 3commas DCA bots. The main usage scenario is to plugin your external indicator, and backtest it using different DCA settings.
Before using this script please make sure you read these explanations and make sure you understand how it works.
Features:
- Because of Tradingview limitations on how orders are grouped into Trades, this Strategy statistics are calculated by the script, so please ignore the Strategy Tester statistics completely
Statistics Table explained:
- Status: either all deals are closed or there is a deal still running, in which case additional info
is provided below, as when the deal started, current PnL, current SO
- Finished deals: Total number of closed deals both Winning and Losing.
A deal is comprised as the Base Order (BO) + all Safety Orders (SO) related to that deal, so this number
will be different than the Strategy Tester List of Trades
- Winning Deals: Deal ended in profit
- Losing deals: Deals ended with loss due to Stop Loss. In the future I might add a Deal Stop condition to
the script, so that will count towards this number as well.
- Total days ( Max / Avg days in Deal ):
Total Days in the Backtest given by either Tradingview limitation on the number of candles or by the
config of the script regarding "Limit Date Range".
Max Days spent in a deal + which period this happened.
Avg days spent in a deal.
- Required capital: This is the total capital required to run the Backtester and it is automatically calculated by
the script taking into consideration BO size, SO size, SO volume scale. This should be the same as 3commas.
This number overwrites strategy.initial_capital and is used to calculate Profit and other stats, so you don't need
to update strategy.initial_capital every time you change BO/SO settings
- Profit after commission
- Buy and Hold return: The PnL that could have been obtained by buying at the close of the first candle of the
backtester and selling at the last.
- Covered deviation: The % of price move from initial BO order covered by SO settings
- Max Deviation: Biggest market % price move vs BO price, in the other direction (for long
is down, for short it is up)
- Max Drawdown: Biggest market % price move vs Avg price of the whole Trade (BO + any SO), in the other
direction (for long price goes down, for short it goes up)
This is calculated for the whole Trade so it is different than List of Trades
- Max / Avg bars in deal
- Total volume / Commission calculated by the strategy. For correct commission please set Commission in the
Inputs Tab and you may ignore Properties Tab
- Close stats for deals: This is a list of how many Trades were closed at each step, including Stop Loss (if
configured), together with covered deviation for that step, the number of deals, and the percentage of this
number from all the deals
TODO: Might add deal avg value for each step
- Settings Table that can be enabled / disabled just to have an overview of your configs on the chart, this is a
drawn on bottom left
- Steps Table similar to 3commas, this is also drawn on bottom left, so please disable Settings table if you want
to see this one
TODO: Might add extra stats here
- Deal start condition: built in RSI-7 or plugin any external indicator and compare with any value the indicator plots
(main purpose of this strategy is to connect your own studies, so using external indicator is recommended)
- Base order and safety orders configs similar to 3commas (order size, percent deviation, safety orders,
percent scale and volume scale)
- Long and Short
- Stop Loss
- Support for Take profit from base order or from Total volume of the deal
- Configs help (besides self explanatory):
- Chart theme: Adjust according to the theme you run on. There is no way to detect theme at the moment.
This adjust different colors
- Deal Start Type: Either a builtin RSI7 or "External indicator"
- Indicator Source an value: If using External Indicator then select source, comparison and value.
For example you could start a deal when Volume is greater than xxxx, or code a custom indicator that plots
different values based on your conditions and test those values
- Visuals / Decimals for display: Adjust according to your symbol
- BO Entry Price for steps table: This is the BO start deal price used to calculate the steps in the table
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.
Seasonality Heatmap [QuantAlgo]🟢 Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
🟢 How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price × 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) × 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close × 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) × 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
→ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
→ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
→ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
→ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
🟢 Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
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.
Market Structure ICT Screener [TradingFinder] BoS ChoCh🔵 Introduction
Market Structure is the foundation of every Smart Money and ICT based trading model. It describes how price moves through a sequence of highs and lows, forming clear phases of expansion, retracement and reversal. Understanding this structure allows traders to read institutional order flow and align their positions with the true direction of liquidity.
Two of the most critical components in Market Structure are the Break of Structure (BOS) and Change of Character (CHOCH). A BOS represents trend continuation, confirming strength within the current direction. In contrast, CHOCH also known as a Market Structure Shift (MSS) signals the first sign of a trend reversal or liquidity shift where order flow begins to change from bullish to bearish or vice versa.
Because the market is fractal, structure can exist at multiple levels known as Major (External) and Minor (Internal). Major structure defines the overall trend on higher timeframes while minor or internal structure reveals short term swings and early reversals within that larger move.
🔵 How to Use
Understanding Market Structure starts with identifying how price interacts with previous swing highs and swing lows. Every trend in the market, whether bullish or bearish, is built from a sequence of impulsive and corrective moves. Impulsive legs show strong displacement in the direction of liquidity flow, while corrective legs represent temporary pullbacks as the market rebalances before the next expansion. Recognizing these sequences is essential for reading the story of price and anticipating what may happen next.
A Break of Structure (BOS) occurs when price decisively moves beyond a previous structural point by breaking above the last high in an uptrend or falling below the last low in a downtrend. This event confirms that the current trend remains intact and that liquidity has been successfully taken from one side of the market. A BOS acts as confirmation of continuation and reflects strength within the existing directional bias.
A Change of Character (CHOCH) appears when price violates structure in the opposite direction of the prevailing trend. This is the first signal that market sentiment and order flow may be shifting. For example, during a downtrend if price breaks above a previous high, it indicates that sellers are losing control and a potential bullish reversal may be developing. In an uptrend, when price drops below a recent low, it suggests a possible bearish transition.
Because the market is fractal, structure exists across multiple layers. Major structure reflects the dominant movement visible on higher timeframes and defines the broader directional bias. Minor or internal structure represents smaller swings within that move and helps identify early transitions before they appear on the higher timeframe. When internal and external structures align, they offer a high probability signal for trend continuation or reversal.
By observing BOS and CHOCH across both internal and external structures, traders can clearly visualize when the market is expanding, contracting or preparing to shift direction. This structured understanding of price movement forms the foundation for precise trend analysis and high quality decision making in any Smart Money or ICT based trading approach.
🔵 Settings
🟣 Display Settings
Table on Chart : Allows users to choose the position of the signal dashboard either directly on the chart or below it, depending on their layout preference.
Number of Symbols : Enables users to control how many symbols are displayed in the screener table, from 10 to 20, adjustable in increments of 2 symbols for flexible screening depth.
Table Mode : This setting offers two layout styles for the signal table :
Basic : Mode displays symbols in a single column, using more vertical space.
Extended : Mode arranges symbols in pairs side-by-side, optimizing screen space with a more compact view.
Table Size : Lets you adjust the table’s visual size with options such as: auto, tiny, small, normal, large, huge.
Table Position : Sets the screen location of the table. Choose from 9 possible positions, combining vertical (top, middle, bottom) and horizontal (left, center, right) alignments.
🟣 Symbol Settings
Each of the 20 symbol slots comes with a full set of customizable parameters :
Symbol : Define or select the asset (e.g., XAUUSD, BTCUSD, EURUSD, etc.).
Timeframe : Set your desired timeframe for each symbol (e.g., 15, 60, 240, 1D).
Pivot Period : Set the length used to detect swing highs and lows. Shorter values increase sensitivity, longer ones focus on major structures.
🔵 Conclusion
Mastering Market Structure and understanding the relationship between BOS and CHOCH allows traders to see the market with greater clarity and confidence. These two elements reveal how liquidity moves through different phases of expansion and retracement and how institutional order flow shifts between accumulation and distribution.
By analyzing both internal and external structures, traders can align short term and long term perspectives and anticipate where price is most likely to react. The ability to read these structural shifts helps identify continuation points, reversals and areas where liquidity is engineered or collected.
Incorporating Market Structure into a consistent trading process transforms the way a trader views the chart. Instead of reacting to random movements, each swing, break and shift becomes part of a logical framework that reflects the true behavior of the market. Understanding BOS and CHOCH is not just a concept but a complete language of price that guides every professional decision in Smart Money and ICT based trading.
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!
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
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! 🚀📈
Yelober - Market Internal direction+ Key levelsYelober – Market Internals + Key Levels is a focused intraday trading tool that helps you spot high-probability price direction by anchoring decisions to structure that matters: yesterday’s RTH High/Low, today’s pre-market High/Low, and a fast Value Area/POC from the prior session. Paired with a compact market internals dashboard (NYSE/NASDAQ UVOL vs. DVOL ratios, VOLD slopes, TICK/TICKQ momentum, and optional VIX trend), it gives you a real-time read on breadth so you can choose which direction to trade, when to enter (breaks, retests, or fades at PMH/PML/VAH/VAL/POC), and how to plan exits as internals confirm or deteriorate. On top of these intraday decision benefits, it also allows traders—in a very subtle but powerful way—to keep an eye on the VIX and immediately recognize significant spikes or sharp decreases that should be factored in before entering a trade, or used as a quick signal to modify an existing position. In short: clear levels for the chart, live internals for the context, and a smarter, rules-based path to execution.
# Yelober – Market Internals + Key Levels
*A TradingView indicator for session key levels + real‑time market internals (NYSE/NASDAQ TICK, UVOL/DVOL/VOLD, and VIX).*
**Script name in Pine:** `Yelober - Market Internal direction+ Key levels` (Pine v6)
---
## 1) What this indicator does
**Purpose:** Help intraday traders quickly find high‑probability reaction zones and read market internals momentum without switching charts. It overlays yesterday/today’s **automatic price levels** on your active chart and shows a **market breadth table** that summarizes NYSE/NASDAQ buying pressure and TICK direction, with an optional VIX trend read.
### Key features at a glance
* **Automatic Price Levels (overlay on chart)**
* Yesterday’s High/Low of Day (**yHoD**, **yLoD**)
* Extended Hours High/Low (**yEHH**, **yEHL**) across yesterday AH + today pre‑market
* Today’s Pre‑Market High/Low (**PMH**, **PML**)
* Yesterday’s **Value Area High/Low** (**VAH/VAL**) and **Point of Control (POC)** computed from a volume profile of yesterday’s **regular session**
* Smart de‑duplication:
* Shows **only the higher** of (yEHH vs PMH) and **only the lower** of (yEHL vs PML) to avoid redundant bands
* **Market Breadth Table (on‑chart table)**
* **NYSE ratio** = UVOL/DVOL (signed) with **VOLD slope** from session open
* **NASDAQ ratio** = UVOLQ/DVOLQ (signed) with **VOLDQ slope** from session open
* **TICK** and **TICKQ**: live cumulative ratio and short‑term slope
* **VIX** (optional): current value + slope over a configurable lookback/timeframe
* Color‑coded trends with sensible thresholds and optional normalization
---
## 2) How to use it (trader workflow)
1. **Mark your reaction zones**
* Watch **yHoD/yLoD**, **PMH/PML**, and **VAH/VAL/POC** for first touches, break/retest, and failure tests.
* Expect increased responsiveness when multiple levels cluster (e.g., PMH ≈ VAH ≈ daily pivot).
2. **Read the breadth panel for context**
* **NYSE/NASDAQ ratio** (>1 = more up‑volume than down‑volume; <−1 = down‑dominant). Strong green across both favors long setups; red favors short setups.
* **VOLD slopes** (NYSE & NASDAQ): positive and accelerating → broadening participation; negative → persistent pressure.
* **TICK/TICKQ**: cumulative ratio and **slope arrows** (↗ / ↘ / →). Use the slope to gauge **near‑term thrust or fade**.
* **VIX slope**: rising VIX (red) often coincides with risk‑off; falling VIX (green) with risk‑on.
3. **Confluence = higher confidence**
* Example: Price reclaims **PMH** while **NYSE/NASDAQ ratios** print green and **TICK slopes** point ↗ — consider break‑and‑go; if VIX slope is ↘, that adds risk‑on confidence.
* Example: Price rejects **VAH** while **VOLD slopes** roll negative and VIX ↗ — consider fade/reversal.
4. **Risk management**
* Place stops just beyond key levels tested; if breadth flips, tighten or exit.
> **Timeframes:** Works best on 1–15m charts for intraday. Value Area is computed from **yesterday’s RTH**; choose a smaller calculation timeframe (e.g., 5–15m) for stable profiles.
---
## 3) Inputs & settings (what each option controls)
### Global Style
* **Enable all automatic price levels**: master toggle for yHoD/yLoD, yEHH/yEHL, PMH/PML, VAH/VAL/POC.
* **Line style/width**: applies to all drawn levels.
* **Label size/style** and **label color linking**: use the same color as the line or override with a global label color.
* **Maximum bars lookback**: how far the script scans to build yesterday metrics (performance‑sensitive).
### Value Area / Volume Profile
* **Enable Value Area calculations** *(on by default)*: computes yesterday’s **POC**, **VAH**, **VAL** from a simplified intraday volume profile built from yesterday’s **regular session bars**.
* **Max Volume Profile Points** *(default 50)*: lower values = faster; higher = more precise.
* **Value Area Calculation Timeframe** *(default 15)*: the security timeframe used when collecting yesterday’s highs/lows/volumes.
### Individual Level Toggles & Colors
* **yHoD / yLoD** (yesterday high/low)
* **yEHH / yEHL** (yesterday AH + today pre‑market extremes)
* **PMH / PML** (today pre‑market extremes)
* **VAH / VAL / POC** (yesterday RTH value area + point of control)
### Market Breadth Panel
* **Show NYSE / NASDAQ / VIX**: choose which series to display in the table.
* **Table Position / Size / Background Color**: UI placement and legibility.
* **Slope Averaging Periods** *(default 5)*: number of recent TICK/TICKQ ratio points used in slope calculation.
* **Candles for Rate** *(default 10)* & **Normalize Rate**: VIX slope calculation as % change between `now` and `n` candles ago; normalize divides by `n`.
* **VIX Timeframe**: optionally compute VIX on a higher TF (e.g., 15, 30, 60) for a smoother regime read.
* **Volume Normalization** (NYSE & NASDAQ): display VOLD slopes scaled to `tens/thousands/millions/10th millions` for readable magnitudes; color thresholds adapt to your choice.
---
## 4) Data sources & definitions
* **UVOL/VOLD (NYSE)** and **UVOLQ/DVOLQ/VOLDQ (NASDAQ)** via `request.security()`
* **Ratio** = `UVOL/DVOL` (signed; negative when down‑volume dominates)
* **VOLD slope** ≈ `(VOLD_now − VOLD_open) / bars_since_open`, then normalized per your setting
* **TICK/TICKQ**: cumulative sum of prints this session with **positives vs negatives ratio**, plus a simple linear regression **slope** of the last `N` ratio values
* **VIX**: value and slope across a user‑selected timeframe and lookback
* **Sessions (EST/EDT)**
* **Regular:** 09:30–16:00
* **Pre‑Market:** 04:00–09:30
* **After Hours:** 16:00–20:00
* **Extended‑hours extremes** combine **yesterday AH** + **today PM**
> **Note:** All session checks are done with TradingView’s `time(…,"America/New_York")` context. If your broker’s RTH differs (e.g., futures), adjust expectations accordingly.
---
## 5) How the algorithms work (plain English)
### A) Key Levels
* **Yesterday’s RTH High/Low**: scans yesterday’s bars within 09:30–16:00 and records the extremes + bar indices.
* **Extended Hours**: scans yesterday AH and today PM to get **yEHH/yEHL**. Script shows **either yEHH or PMH** (whichever is **higher**) and **either yEHL or PML** (whichever is **lower**) to avoid duplicate bands stacked together.
* **Value Area & POC (RTH only)**
* Build a coarse volume profile with `Max Volume Profile Points` buckets across the price range formed by yesterday’s RTH bars.
* Distribute each bar’s volume uniformly across the buckets it spans (fast approximation to keep Pine within execution limits).
* **POC** = bucket with max volume. **VA** expands from POC outward until **70%** of cumulative volume is enclosed → yields **VAH/VAL**.
### B) Market Breadth Table
* **NYSE/NASDAQ Ratio**: signed UVOL/DVOL with basic coloring.
* **VOLD Slopes**: from session open to current, normalized to human‑readable units; colors flip green/red based on thresholds that map to your normalization setting (e.g., ±2M for NYSE, ±3.5×10M for NASDAQ).
* **TICK/TICKQ Slope**: linear regression over the last `N` ratio points → **↗ / → / ↘** with the rounded slope value.
* **VIX Slope**: % change between now and `n` candles ago (optionally divided by `n`). Red when rising beyond threshold; green when falling.
---
## 6) Recommended presets
* **Stocks (liquid, intraday)**
* Value Area **ON**, `Max Volume Points` = **40–60**, **Timeframe** = **5–15**
* Breadth: show **NYSE & NASDAQ & VIX**, `Slope periods` = **5–8**, `Candles for rate` = **10–20**, **Normalize VIX** = **ON**
* **Index futures / very high‑volume symbols**
* If you see Pine timeouts, set `Max Volume Points` = **20–40** or temporarily **disable Value Area**.
* Keep breadth panel **ON** (it’s light). Consider **VIX timeframe = 15/30** for regime clarity.
---
## 7) Tips, edge cases & performance
* **Performance:** The volume profile is capped (`maxBarsToProcess ≤ 500` and bucketed) to keep it responsive. If you experience slowdowns, reduce `Max Volume Points`, `Maximum bars lookback`, or disable Value Area.
* **Redundant lines:** The script **intentionally suppresses** PMH/PML when yEHH/yEHL are more extreme, and vice‑versa.
* **Label visibility:** Use `Label style = none` if you only want clean lines and read values from the right‑end labels.
* **Futures/RTH differences:** Value Area is from **yesterday’s RTH** only; for 24h instruments the RTH period may not reflect overnight structure.
* **Session transitions:** PMH/PML tracking stops as soon as RTH starts; values persist as static levels for the session.
---
## 8) Known limitations
* Uses public TradingView symbols: `UVOL`, `VOLD`, `UVOLQ`, `DVOLQ`, `VOLDQ`, `TICK`, `TICKQ`, `VIX`. If your data plan or region limits any symbol, the corresponding table rows may show `na`.
* The VA/POC approximation assumes uniform distribution of each bar’s volume across its high–low. That’s fast but not a tick‑level profile.
* Works best on US equities with standard NY session; alternative sessions may need code changes.
---
## 9) Troubleshooting
* **“Script is too slow / timed out”** → Lower `Max Volume Points`, lower `Maximum bars lookback`, or toggle **OFF** `Enable Value Area calculations` for that instrument.
* **Missing breadth values** → Ensure the symbols above load on your account; try reloading chart or switching timeframes once.
* **Overlapping labels** → Set `Label style = none` or reduce label size.
---
## 10) Version / license / contribution
* **Version:** Initial public release (Pine v6).
* **Author:** © yelober
* **License:** Free for community use and enhancement. Please keep author credit.
* **Contributing:** Open PRs/ideas: presets, alert conditions, multi‑day VA composites, optional mid‑value (`(VAH+VAL)/2`), session filter for futures, and alertable state machine for breadth regime transitions.
---
## 11) Quick start (TL;DR)
1. Add the indicator and **keep default settings**.
2. Trade **reactions** at yHoD/yLoD/PMH/PML/VAH/VAL/POC.
3. Use the **breadth table**: look for **green ratios + ↗ slopes** (risk‑on) or **red ratios + ↘ slopes** (risk‑off). Check **VIX** slope for confirmation.
4. Manage risk around levels; when breadth flips against you, tighten or exit.
---
### Changelog (public)
* **v1.0:** First community release with automatic RTH levels, VA/POC approximation, breadth dashboard (NYSE/NASDAQ/TICK/TICKQ/VIX) with normalization and adaptive color thresholds.
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.
Weekly pecentage tracker by PRIVATE
Settings Picture below this link: 👇
i.ibb.co
What it is
A lightweight “Weekly % Tracker” overlay that lets you manually enter weekly performance (in percent) for XAUUSD + up to 10 FX pairs, then shows:
a small table panel with each enabled symbol and its % result
one TOTAL row (Sum / Average / Compounded across all enabled symbols)
an optional mini badge showing the % for a single selected symbol
Nothing is auto-calculated from price—you type the % yourself.
Key settings
Panel: show/hide, position, number of decimals, colors (background, text, green/red).
Total mode:
Sum – adds percentages
Average – mean of enabled rows
Compounded –
(
∏
(
1
+
𝑝
/
100
)
−
1
)
×
100
(∏(1+p/100)−1)×100
Symbols:
XAUUSD (toggle + label + % input)
10 FX pairs (each has On/Off, label text, % input). You can rename labels to any symbol text you want.
Mini badge: show/hide, position, and symbol to display.
How it works
Overlay indicator: overlay=true; just draws UI on the chart (no plots).
Arrays (syms, vals, ons) collect the row data in order: XAU first, then FX1…FX10.
Helpers:
posFrom() converts a position string (e.g., “Top Right”) into a position.* constant.
wp_col() picks green/red/neutral based on the sign of the %.
wp_round() rounds values to the selected decimals.
calc_total() computes the TOTAL with the chosen mode over enabled rows only.
Table creation logic:
Counts how many rows are enabled.
If none enabled or panel is off: the panel table is deleted, so no box/background is visible.
If enabled and on: the panel is (re)created at the chosen position.
On each last bar (barstate.islast), it clears the table to transparent (bgcolor=na) and then fills one row per enabled symbol, followed by a single TOTAL row.
Mini badge:
Always (re)created on position change.
Shows selected symbol’s % (or “-” if that symbol isn’t enabled or has no value).
Colors text green/red by sign.
Notes & limits
It’s manual input—the script doesn’t read trades or P/L from price.
You can rename each row’s label to match any symbol name you want.
When no rows are enabled, the panel disappears entirely (no empty background).
Designed to be light: only draws tables; no heavy plotting.
If you want the TOTAL row to be optional, or different color thresholds, or CSV-style export/import of the values, say the word and I’ll add it.
Mikula's Master 360° Square of 12Mikula’s Master 360° Square of 12
An educational W. D. Gann study indicator for price and time. Anchor a compact Square of 12 table to a start point you choose. Begin from a bar’s High or Low (or set a manual start price). From that anchor you can progress or regress the table to study how price steps through cycles in either direction.
What you’re looking at :
Zodiac rail (far left): the twelve signs.
Degree rail: 24 rows in 15° steps from 15° up to 360°/0°.
Transit rail and Natal rail: track one planet per rail. Each planet is placed at its current row (℞ shown when retrograde). As longitude advances, the planet climbs bottom → top, then wraps to the bottom at the next sign; during retrograde it steps downward.
Hover a planet’s cell to see a tooltip with its exact longitude and sign (e.g., 152.4° ♌︎). The linked price cell in the grid moves with the planet’s row so you can follow a planet’s path through the zodiac as a path through price.
Price grid (right): the 12×24 Square of 12. Each column is a cycle; cells are stepped price levels from your start price using your increment.
Bottom rail: shows the current square number and labels the twelve columns in that square.
How the square is read
The square always begins at the bottom left. Read each column bottom → top. At the top, return to the bottom of the next column and read up again. One square contains twelve cycles. Because the anchor can be a High or a Low, you can progress the table upward from the anchor or regress it downward while keeping the same bottom-to-top reading order.
Iterate Square (shifting)
Iterate Square shifts the entire 12×24 grid to the next set of twelve cycles.
Square 1 shows cycles 1–12; Square 2 shows 13–24; Square 3 shows 25–36, etc.
Visibility rules
Pivot cells are table-bound. If you shift the square beyond those prices, their highlights won’t appear in the table.
A/B levels and Transit/Natal planetary lines are chart overlays and can remain visible on the table as you shift the square.
Quick use
Choose an anchor (date/time + High/Low) or enable a manual start price .
Set the increment. If you anchored with a Low and want the table to step downward from there, use a negative value.
Optional: pick Transit and Natal planets (one per rail), toggle their plots, and hover their cells for longitude/sign.
Optional: turn on A/B levels to display repeating bands from the start price.
Optional: enable swing pivots to tint matching cells after the anchor.
Use Iterate Square to shift to later squares of twelve cycles.
Examples
These are exploratory examples to spark ideas:
Overview layout (zodiac & degree rails, Transit/Natal rails, price grid)
A-levels plotted, pivots tinted on the table, real-time price highlighted
Drawing angles from the anchor using price & time read from the table
Using a TradingView Gann box along the A-levels to study reactions
Attribution & originality
This script is an original implementation (no external code copied). Conceptual credit to Patrick Mikula, whose discussion of the Master 360° Square of 12 inspired this study’s presentation.
Further reading (neutral pointers)
Patrick Mikula, Gann’s Scientific Methods Unveiled, Vol. 2, “W. D. Gann’s Use of the Circle Chart.”
W. D. Gann’s Original Commodity Course (as provided by WDGAN.com).
No affiliation implied.
License CC BY-NC-SA 4.0 (non-commercial; please attribute @Javonnii and link the original).
Dependency AstroLib by @BarefootJoey
Disclaimer Educational use only; not financial advice.
Lot Size + Margin InfoThis indicator is designed to give Futures & Options traders instant access to lot size and estimated margin requirements for the instrument they are viewing — directly on their TradingView chart. It combines real-time symbol detection with a built-in, regularly updated margin lookup table (sourced from Kotak Securities’ published margin requirements), while also handling fallback logic for unknown or unsupported symbols.
---
### What It Does
* Automatically Detects the Instrument Type
Identifies whether the current chart’s symbol is a futures contract, option, or a cash/spot instrument.
* Shows Accurate Lot Size
For supported F\&O symbols, it fetches the correct lot size directly from exchange data.
For options, it retrieves the lot size from the option’s point value.
For cash/spot symbols with linked futures, it uses the futures lot size.
* Calculates Estimated Margin
* For futures: `Lot Size × Current Price × Margin%` (Margin% sourced from the internal lookup table).
* For options: `Lot Size × Current Price` (simple multiplication, as options margin ≈ premium cost).
* For unsupported or non-FnO symbols: Displays "No FnO".
* Fallback Margin Logic
If a symbol is missing from the margin lookup table, the script applies a user-defined default margin percentage and highlights the data in orange to indicate it’s using fallback values.
* Debug Mode for Transparency
A toggle to display the exact symbol string used for fetching lot size and margin, so traders can verify the data source.
---
### How It Works
1. Symbol Normalization
The script standardizes symbol names to match the margin table format (e.g., converting `"NIFTY1!"` to `"NIFTY"`).
2. Type-Based Handling
* Futures – Uses point value for lot size, applies specific margin % from the table.
* Options – Uses option point value for lot size, margin is simply premium × lot size.
* Cash Symbols with Linked Futures – Attempts to find and use the associated futures contract for lot/margin data.
* Unsupported Symbols – Displays `"No FnO"`.
3. Margin Table Integration
The margin % table is manually updated from a reliable broker’s margin sheet (Kotak Securities) — ensuring alignment with real trading conditions.
4. Customizable Display
* Position (Top Right / Bottom Left / Bottom Right)
* Table background color, text color, font size, border width
* Editable label text for lot size and margin display
* Toggleable lot size and margin sections
---
### How to Use
1. Add the Indicator to Your Chart – Works on any NSE Futures, Options, or Cash symbol with linked F\&O.
2. Configure Display Settings – Choose whether to show lot size, margin, or both, and place the info table where you prefer.
3. Adjust Fallback Margin % – If you trade less common contracts, set your default margin % to reflect your broker’s requirement.
4. Enable Debug Mode (Optional) – To see the exact symbol source the script is using.
---
### Best For
* Intraday & Positional F\&O Traders who need instant clarity on lot size and margin before entering trades.
* Options Sellers & Buyers who want quick cost estimates.
* Traders Switching Symbols Quickly — saves time by removing the need to check the broker’s margin sheet manually.
---
💡 Pro Tip: Since margin requirements can change, keep the script updated whenever your broker revises margin data. This version’s margin table is updated as of 13-08-2025.
Volume Delta Pressure Tracker by GSK-VIZAG-AP-INDIA📢 Title:
Volume Delta Pressure Tracker by GSK-VIZAG-AP-INDIA
📝 Short Description (for script title box):
Real-time volume pressure tracker with estimated Buy/Sell volumes and Delta visualization in an Indian-friendly format (K, L, Cr).
📃 Full Description
🔍 Overview:
This indicator estimates buy and sell volumes using candle structure (OHLC) and displays a real-time delta table for the last N candles. It provides traders with a quick view of volume imbalance (pressure) — often indicating strength behind price moves.
📊 Features:
📈 Buy/Sell Volume Estimation using the candle’s OHLC and Volume.
⚖️ Delta Calculation (Buy Vol - Sell Vol) to detect pressure zones.
📅 Time-stamped Table displaying:
Time (HH:MM)
Buy Volume (Green)
Sell Volume (Red)
Delta (Color-coded)
🔢 Indian Number Format (K = Thousands, L = Lakhs, Cr = Crores).
🧠 Fully auto-calculated — no need for tick-by-tick bid/ask feed.
📍 Neatly placed bottom-right table, customizable number of rows.
🛠️ Inputs:
Show Table: Toggle the table on/off
Number of Bars to Show: Choose how many recent candles to include (5–50)
🎯 Use Cases:
Identify hidden buyer/seller strength
Detect volume absorption or exhaustion
✅ Compatibility:
Works on any timeframe
Ideal for intraday instruments like NIFTY, BANKNIFTY, etc.
Ideal for volume-based strategy confirmation.
🖋️ Developed by:
GSK-VIZAG-AP-INDIA






















