MestreDoFOMO Future Projection BoxMestreDoFOMO Future Projection Box - Description & How to Use
Description
The "MestreDoFOMO Future Projection Box" is a TradingView indicator tailored for crypto traders (e.g., BTC/USDT on 1H, 4H, or 1D timeframes). It visualizes current price ranges, projects future levels, and confirms trends using semi-transparent boxes. With labeled price levels and built-in alerts, it’s a simple yet powerful tool for identifying support, resistance, and potential price targets.
How It Works
Blue Box (Current Channel): Shows the recent price range over the last 10 bars (adjustable). The top is the highest high plus an ATR buffer, and the bottom is the lowest low minus the buffer. Labels display exact levels (e.g., "Top: 114000", "Bottom: 102600").
Green Box (Future Projection): Projects the price range 10 bars ahead (adjustable) based on the trend slope of the moving average. Labels show "Proj Top" and "Proj Bottom" for future targets.
Orange Box (Moving Average): Traces a 50-period EMA (adjustable) to confirm the trend. An upward slope signals a bullish trend; a downward slope signals a bearish trend. A label shows the current MA value (e.g., "MA: 105000").
Alerts: Triggers when the price nears the projected top or bottom, helping you catch breakouts or retracements.
How to Use
Add the Indicator: Apply "MestreDoFOMO Future Projection Box" to your chart in TradingView.
Interpret the Trend: Check the orange box’s slope—upward for bullish, downward for bearish.
Identify Key Levels: Use the blue box’s top as resistance and bottom as support. On a 4H chart, if the top is 114,000, expect resistance; if the bottom is 102,600, expect support.
Plan Targets: Use the green box for future targets—top for profit-taking (e.g., 114,000), bottom for stop-loss or buying (e.g., 102,600).
Set Alerts: Enable alerts for "Near Upper Projection" or "Near Lower Projection" to get notified when the price hits key levels.
Trade Examples:
Bullish: If the price breaks above the blue box top (e.g., 114,000), buy with a target at the green box top. Set a stop-loss below the green box bottom.
Bearish: If the price rejects at the blue box top and drops below the orange MA, short with a target at the blue box bottom.
Customize: Adjust the lookback period, projection bars, ATR multiplier, and MA length in the settings to fit your trading style.
Tips
Use on 1H for short-term trades, 4H for swing trades, or 1D for long-term trends.
Combine with volume or RSI to confirm signals.
Validate levels with market structure (e.g., candlestick patterns).
Komut dosyalarını "top" için ara
MVRVZ BTCMVRVZ BTC (Market Value to Realized Value Z-Score)
Description:
The MVRVZ BTC indicator provides insights into the relationship between the market value and realized value of Bitcoin, using the Market Value to Realized Value (MVRV) ratio, which is then adjusted using a Z-Score. This indicator highlights potential market extremes and helps in identifying overbought or oversold conditions, offering a unique perspective on Bitcoin's valuation.
How It Works:
MVRVZ is calculated by taking the difference between Bitcoin's Market Capitalization (MC) and Realized Capitalization (MCR), then dividing that by the Standard Deviation (Stdev) of the price over a specified period (usually 104 weeks).
The resulting value is plotted as the MVRVZ line, representing how far the market price deviates from its realized value.
Z-Score is then applied to the MVRVZ line, with the Z-Score bounded between +2 and -2, which allows it to be used within a consistent evaluation framework, regardless of how high or low the MVRVZ line goes. The Z-Score will reflect overbought or oversold conditions:
A Z-Score above +2 indicates the market is likely overbought (possible market top).
A Z-Score below -2 indicates the market is likely oversold (possible market bottom).
Values between -2 and +2 indicate more neutral market conditions.
How to Read the Indicator:
MVRVZ Line:
The MVRVZ line shows the relationship between market cap and realized cap. A higher value indicates the market is overvalued relative to the actual capital realized by holders.
The MVRVZ line can move above or below the top and bottom lines you define, which are adjustable according to your preferences. These lines act as trigger levels.
Top and Bottom Trigger Lines:
You can customize the Top Line and Bottom Line values to your preference.
When the MVRVZ line crosses the Top Line, the market might be considered overbought.
When the MVRVZ line crosses the Bottom Line, the market might be considered oversold.
SCDA Z-Score:
The Z-Score is displayed alongside the MVRVZ line and is bounded between -2 and +2. It scales proportionally based on the MVRVZ line's position relative to the top and bottom trigger lines.
The Z-Score ensures that even if the MVRVZ line moves beyond the trigger lines, the Z-Score will stay within the limits of -2 to +2, making it ideal for your custom evaluation system (SCDA).
Background Highlighting:
The background color changes when the MVRVZ line crosses key levels:
When the MVRVZ line exceeds the Top Trigger, the background turns red, indicating overbought conditions.
When the MVRVZ line falls below the Bottom Trigger, the background turns green, indicating oversold conditions.
Data Sources:
The data for the MVRVZ indicator is sourced from Glassnode and Coinmetrics, which provide the necessary values for:
BTC Market Cap (MC) – The total market capitalization of Bitcoin.
BTC Realized Market Cap (MCR) – The capitalization based on the price at which Bitcoin was last moved on the blockchain (realized value).
How to Use the Indicator:
Market Extremes:
Use the MVRVZ and Z-Score to spot potential market tops or bottoms.
A high Z-Score (above +2) suggests the market is overbought, while a low Z-Score (below -2) suggests the market is oversold.
Adjusting the Triggers:
Customize the Top and Bottom Trigger Lines to suit your trading strategy. These lines can act as dynamic reference points for when to take action based on the Z-Score or MVRVZ line crossing these levels.
Market Evaluation (SCDA Framework):
The bounded Z-Score (from -2 to +2) is tailored for your SCDA evaluation system, allowing you to assess market conditions based on consistent criteria, no matter how volatile the MVRVZ line becomes.
Conclusion:
The MVRVZ BTC indicator is a powerful tool for assessing the relative valuation of Bitcoin based on its market and realized capitalization. By combining it with the Z-Score, you get an easy-to-read, bounded evaluation system that highlights potential market extremes and helps you make informed decisions about Bitcoin's price behavior.
Drummond Geometry - Pldot and EnvelopeThis Pine Script will:
1.Calculate and display the PL Dot (Price Level Dot), a moving average that reflects short-term market trends.
2.Plot the Envelope Top and Bottom lines based on averages of previous highs and lows, which represent key areas of resistance and support.
Drummond Geometry Overview
Drummond Geometry is a method of market analysis focused on:
PL Dot : Captures market energy and trend direction. It reacts to price deviations and serves as a magnet for price returns, often referred to as a "PL Dot Refresh."
Envelope Theory : Considers price movements as cycles oscillating between the Envelope Top and Bottom. Prices breaking these boundaries often indicate trends, retracements, or exhaustion.
The geometry helps traders visualize energy flows in the market and anticipate directional changes using established support and resistance zones.
Understanding PL Dot and Envelope Top/Bottom
PL Dot:
Formula: Average(Average(H, L, C) of last three bars)
Usage: Indicates short-term trends:
Trend: PL Dot slopes upward or downward.
Congestion: PL Dot moves horizontally.
Envelope Top and Bottom:
Formula:
Top: (11 H1 + 11 H2 + 11 H3) / 3
Bottom: (11 L1 + 11 L2 + 11 L3) / 3
Usage: Acts as dynamic resistance and support:
Price above the top: Indicates strong bullish momentum.
Price below the bottom: Indicates strong bearish momentum.
Advantages of Drummond Geometry
Clarity of Market Flow: Highlights the relationship between price and key levels (PL Dot, Envelope Top/Bottom).
Predictive Power: Suggests possible reversals or continuation based on energy distribution.
Adaptability: Works across multiple time frames and market types (trending, congestion).
Trading Strategy
PL Dot Trades:
Buy: When price returns to the PL Dot in an uptrend.
Sell: When price returns to the PL Dot in a downtrend.
Envelope Trades:
Reversal: Trade counter to price if it breaks and retreats from the Envelope Top/Bottom.
Continuation: Trade in the direction of price if it sustains movement beyond the Envelope Top/Bottom.
Bitcoin Cycle Master [InvestorUnknown]The "Bitcoin Cycle Master" indicator is designed for in-depth, long-term analysis of Bitcoin's price cycles, using several key metrics to track market behavior and forecast potential price tops and bottoms. The indicator integrates multiple moving averages and on-chain metrics, offering a comprehensive view of Bitcoin’s historical and projected performance. Each of its components plays a crucial role in identifying critical cycle points:
Top Cap: This is a multiple of the Average Cap, which is calculated as the cumulative sum of Bitcoin’s price (price has a longer history than Market Cap) divided by its age in days. Top Cap serves as an upper boundary for speculative price peaks, multiplied by a factor of 35.
Time_dif() =>
date = ta.valuewhen(bar_index == 0, time, 0)
sec_r = math.floor(date / 1000)
min_r = math.floor(sec_r / 60)
h_r = math.floor(min_r / 60)
d_r = math.floor(h_r / 24)
// Launch of BTC
start = timestamp(2009, 1, 3, 00, 00)
sec_rb = math.floor(start / 1000)
min_rb = math.floor(sec_rb / 60)
h_rb = math.floor(min_rb / 60)
d_rb = math.floor(h_rb / 24)
difference = d_r - d_rb
AverageCap() =>
ta.cum(btc_price) / (Time_dif() + btc_age)
TopCap() =>
// To calculate Top Cap, it is first necessary to calculate Average Cap, which is the cumulative sum of Market Cap divided by the age of the market in days.
// This creates a constant time-based moving average of market cap.
// Once Average cap is calculated, those values are multiplied by 35. The result is Top Cap.
// For AverageCap the BTC price was used instead of the MC because it has more history
// (the result should have minimal if any deviation since MC would have to be divided by Supply)
AverageCap() * 35
Delta Top: Defined as the difference between the Realized Cap and the Average Cap, this metric is further multiplied by a factor of 7. Delta Top provides a historically reliable signal for Bitcoin market cycle tops.
DeltaTop() =>
// Delta Cap = Realized Cap - Average Cap
// Average Cap is explained in the Top Cap section above.
// Once Delta Cap is calculated, its values over time are then multiplied by 7. The result is Delta Top.
(RealizedPrice() - AverageCap()) * 7
Terminal Price: Derived from Coin Days Destroyed, Terminal Price normalizes Bitcoin’s historical price behavior by its finite supply (21 million bitcoins), offering an adjusted price forecast as all bitcoins approach being mined. The original formula for Terminal Price didn’t produce expected results, hence the calculation was adjusted slightly.
CVDD() =>
// CVDD stands for Cumulative Value Coin Days Destroyed.
// Coin Days Destroyed is a term used for bitcoin to identify a value of sorts to UTXO’s (unspent transaction outputs). They can be thought of as coins moving between wallets.
(MCR - TV) / 21000000
TerminalPrice() =>
// Theory:
// Before Terminal price is calculated, it is first necessary to calculate Transferred Price.
// Transferred price takes the sum of > Coin Days Destroyed and divides it by the existing supply of bitcoin and the time it has been in circulation.
// The value of Transferred Price is then multiplied by 21. Remember that there can only ever be 21 million bitcoin mined.
// This creates a 'terminal' value as the supply is all mined, a kind of reverse supply adjustment.
// Instead of heavily weighting later behavior, it normalizes historical behavior to today. By normalizing by 21, a terminal value is created
// Unfortunately the theoretical calculation didn't produce results it should, in pinescript.
// Therefore the calculation was slightly adjusted/improvised
TransferredPrice = CVDD() / (Supply * math.log(btc_age))
tp = TransferredPrice * 210000000 * 3
Realized Price: Calculated as the Market Cap Realized divided by the current supply of Bitcoin, this metric shows the average value of Bitcoin based on the price at which coins last moved, giving a market consensus price for long-term holders.
CVDD (Cumulative Value Coin Days Destroyed): This on-chain metric analyzes Bitcoin’s UTXOs (unspent transaction outputs) and the velocity of coins moving between wallets. It highlights key market dynamics during prolonged accumulation or distribution phases.
Balanced Price: The Balanced Price is the difference between the Realized Price and the Terminal Price, adjusted by Bitcoin's supply constraints. This metric provides a useful signal for identifying oversold market conditions during bear markets.
BalancedPrice() =>
// It is calculated by subtracting Transferred Price from Realized Price
RealizedPrice() - (TerminalPrice() / (21 * 3))
Each component can be toggled individually, allowing users to focus on specific aspects of Bitcoin’s price cycle and derive meaningful insights from its long-term behavior. The combination of these models provides a well-rounded view of both speculative peaks and long-term value trends.
Important consideration:
Top Cap did historically provide reliable signals for cycle peaks, however it may not be a relevant indication of peaks in the future.
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.
Williams Vix Fix BB + RVI + LinReg & Squeeze (Keltner) BBW + %BLegend:
Entery signal: When line color turns to lime (lighter green) after a blue dot appears
Exit signal: When line color turns to red (darker red) after a red dot appears
Note: it is more affective as an entry signal (Bottom is stronger)
- When line touches or crosses red band it is Top signal (Williams Vix Fix)
- When line touches or crosses blue band it is Bottom signal (Williams Vix Fix)
- Red dot at the top of indicator is a Top signal (Relative Volatility Index)
- Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index)
- Gray dot at the bottom of indicator is a Keltner Squeeze signal (filtered by either BBW or %B)
- Silver dot at the bottom of indicator is a weaker Keltner Squeeze signal (Doesn't meet either BBW or %B filter)
- Purple is a 'Half Squeeze' only 1 Bollinger Band crossed the Keltner Channel
This is an attempt to make use of the main features of all 6 of these Volatility tools:
- Williams Vix Fix + Bollinger Bands
- Relative Volatility Index (RVI)
- Linear Regression (detects Vix Fix starts to rise or fall to a certain degree in order to help validate bottom/top)
Note : There is also added precision on Linear Regression entry by dividing WVF by square roots of basis.
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
Conditions to Help Filter Keltner Squeeze:
- When the Bollinger Bands Width (BBW) value is lower than the lowest value within a period plus a margin of error (percentage)
- When the %B value goes up or down by the impulse value (threshold value in setting) detailed in LazyBears indicator. (www.tradingview.com)
If it meets one of these 2 filters and there is a Keltner Channel Squeeze than gray color or else if the squeeze doesn’t meet one of the 2 filters than silver color (weaker Squeeze).
The goal is to find the best tool to find bottoms and top relative to volatility and filter squeeze.
Note: You can also change the threshold for RVI top and bottom.
And this work builds on my last indicators:
- Williams Vix Fix + BB & RVI (Top/Bottom) & Squeeze ()
- Williams Vix Fix BB + RVI & Squeeze (Keltner) filtered BBW + %B ()
If you have ideas on this work or have ideas on potential combinations please message me, I always want to learn or get perspective on how it can be improved.
Sharing is how we get better (Parameter tuning, ideas, discussion)
I don’t reinvent the wheel, just trying to make the wheel better.
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores
BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance.
By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements.
Key Features
1. Cross-Sectional Z-Score Calculation:
The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance.
2. Asset Ranking and Allocation:
The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets.
3. Rolling Window for Mean and Standard Deviation Calculations:
The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data.
4. Mean and Standard Deviation Visualization:
The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions.
5. Top & Bottom Performer Tables:
The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio.
6. Customizable Parameters:
The strategy allows traders to customize several key parameters, including:
Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations.
Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to.
Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference.
7. Forward Test & Out-of-Sample Testing:
The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions.
8. Visual Feedback and Alerts:
The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments.
9. Risk Management via Z-Scores and Std Dev:
The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction.
10. Note on Mean Reversion Strategy:
If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy.
References
www.uts.edu.au
onlinelibrary.wiley.com
www.cmegroup.com
Final Thoughts
The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
Half Cup [LuxAlgo]The Half Cup indicator detects and displays patterns with the shape of a Half Cup , initiating a channel. From this channel, breakouts are detected and highlighted with dots.
Users can control the shape of the Half Cup and the channel length through various settings.
Do note that the displayed half cups are displayed retrospectively, making them subject to backpainting.
🔶 USAGE
The idea behind the indicator is derived from the Cup & Handle pattern, which requires waiting for the pattern full completion.
Our Half Cup publication aims to find opportunities when the potential cup is only formed halfway.
In this example, a green dot shows the first breakout of the upper channel extremity. A few bars later, the price went under it, after which it returned above, triggering a second green dot. Both triggers were good opportunities in this case, and the price rose afterward.
The Half Cup pattern can be the start of a potential complete Cup & Handle (As in the example above, a complete Cup pattern (without the Handle ) is shown, manually drawn with dashed lines).
Every green/red dot, whether on a bullish or bearish pattern, points to a breakout respectively above/below the channel.
Besides drawing patterns and the corresponding breakouts, the Half Cup indicator can also provide insights into trends and potential opportunities in the long run.
🔶 DETAILS
🔹 Validation
Several criteria must be fulfilled before a visible pattern on the chart is drawn.
Calculations are done beforehand to know where the Half Cup pattern would be positioned.
The pattern's bottom and top edges are checked for the number of bars whose closing price is outside the half-cup area. When the number of breakouts above/below is equal to or lower than the user-defined settings ( Max % Breaks Top/Bottom ), the pattern is drawn together with a brighter-colored channel next to it.
Dots highlighting the channel's breakout can be drawn from that moment until the end of both channel lines.
🔹 Positioning
Users can adjust the following settings to fit their needs:
% Broadness: Moves the Top/Bottom line (bullish or bearish) diagonally upwards/downwards.
Vertical Shift: Shifts the entire pattern up/down.
Channel Length: Sets the line length of the channel.
Note that adjusting the position of the pattern will change the validation; the script will be rerun to check if patterns are still valid or if new patterns can be drawn. Some patterns may disappear, while new ones may appear.
Before adjusting the position, the user can set Max % Breaks Top/Bottom at 100%. When the positioning is set, Max % Breaks Top/Bottom can be set as desired.
🔹 Updated Drawings
The Half Cup pattern is always drawn retrospectively (that is it is subject to backpainting), the channel is drawn from the bar from where the pattern is detected. Every breakout of the channel will remain visible as dots.
When a new swing high/low is found while the previous swing low/high remains the same, the pattern is updated to minimize clutter. The dots of earlier drawings will remain visible (to ensure no repainting occurs), but the color becomes faded, as such bright dots are associated with patterns that are visible on the chart, while faded dots are from removed/updated patterns.
🔶 SETTINGS
Swing Length: Period used for the swing detection, with higher values returning longer-term Swing Levels.
🔹 Validation
Max % Breaks Bottom: Allowed maximum amount of bars where the closing price is below the bottom of the Half Cup pattern against the total width of the pattern (bars).
Max % Breaks Top: Allowed maximum amount of bars where the closing price is above the top of the Half Cup pattern against the total width of the pattern (bars).
🔹 Positioning
% Broadness: Moves the Top/Bottom line (bullish or bearish) diagonally upwards/downwards.
Vertical Shift: Shifts the entire pattern up/down.
Channel Length: Sets the line length of the channel.
Weighted Moving Range with Trend Signals (WMR-TS)Weighted Moving Range with Trend Signals (WMR-TS)
Technical analysis involves analyzing statistical trends from trading activity , such as price movement and volume, to make trading decisions. Technical indicators are mathematical calculations based on the price, volume, or open interest of a security or contract. They are used by traders to analyze price movements and predict future market behavior. The WMR-TS indicator combines weighted moving averages and range calculations to identify key trading levels and generate buy/sell signals. It dynamically adjusts to market conditions, offering traders insights into potential support, resistance, and trend reversal points. Key levels are color-coded for quick interpretation. It utilizes weighted moving averages (WMA) and range calculations to determine these levels, making it a robust tool for both trending and ranging markets.
SUMMARY
Parameters :
WMA Length : Determines the length for the primary weighted moving average.
Highest High Length : Sets the period for calculating the highest high.
Lowest Low Length : Sets the period for calculating the lowest low.
Range Corrector : Adjusts the range calculation slightly for fine-tuning.
Top Level : Multiplier for determining the top level from the calculated range.
Bottom Level : Multiplier for determining the bottom level from the calculated range.
Levels Visibility : Sets how many recent bars will display the levels.
Trading Zones :
Short Area : Highlighted zone indicating potential shorting opportunities.
Long Area : Highlighted zone indicating potential buying opportunities.
The Levels :
Wave (Yellow): Midpoint of the calculated range, adjusted by WMA.
Top Level (Red): Calculated upper boundary of the trading range.
Sell Level (Pink): Intermediate sell level.
Resistance Level (Magenta): Immediate resistance level.
Support Level (Cyan): Immediate support level.
Buy Level (Light Green): Intermediate buy level.
Bottom Level (Dark Green): Calculated lower boundary of the trading range.
Interpreting the Signals :
Hammer Signal : Red circles above bars indicate potential sell signals.
Rocket Signal : Green circles below bars indicate potential buy signals.
KEY CONCEPTS
Highest High and Lowest Low :
These values represent the highest high ( HH ) and lowest low ( LL ) over a specified number of periods.
Support Level :
This is the lower boundary of the trading range. It is a price level where demand is strong enough to prevent the price from falling further. As the price approaches the support level, it is likely to bounce back up.
Resistance Level :
This is the upper boundary of the trading range. It is a price level where supply is strong enough to prevent the price from rising further. As the price approaches the resistance level, it is likely to pull back down.
THE USE OF MULTIPLIERS :
The script uses several multipliers to adjust and fine-tune the calculated support and resistance levels, as well as to control the range and sensitivity of these levels. Here is a detailed explanation of these multipliers and their purpose:
Range Corrector : This multiplier adjusts the calculated high ( H ) and low ( L ) levels, adding flexibility to how these levels are positioned relative to the highest high and lowest low. It ranges from -1 to 1 , with a default value of 0 . The use of positive values increase the range, making the calculated levels further apart. Thus, using negative values decrease the range, bringing the calculated levels closer together.
Top Level : This multiplier adjusts the distance of the top level from the calculated high H ) level. It fluctuates from 0 to 2 , with a default value of 0.382 . Higher values will push the top level further above the high level, while lower values will bring it closer.
Bottom Level : This multiplier adjusts the distance of the bottom support level from the calculated low support level. Ranging from 0 to 2, with a default value of 0.214, the higher values will push the bottom level further below the low level, while lower values will bring it closer.
The script plots the support and resistance levels on the chart, allowing traders to visualize the trading range. Color-coded zones are used to indicate areas where buying or selling opportunities may arise based on the current price relative to the trading range. A trading range refers to the area between a price's support and resistance levels over a specific period of time. Within this range, the price of the security fluctuates up and down but does not break out above the resistance or below the support. Support and resistance levels to make trading decisions. Buying near the support level and selling near the resistance level is a common strategy. When the price moves above the resistance level, it is called a breakout . A breakout often indicates that the price may start a new upward trend . Conversely, when the price moves below the support level, it is called a breakdown . A breakdown often indicates that the price may start a new downward trend . By understanding and utilizing trading ranges, traders can make more informed decisions, optimize their trading strategies, and manage risk more effectively.
Understanding Moving Averages
A moving average (MA) is a widely used technical indicator that helps smooth out price data by creating a constantly updated average price. The main purpose of using a moving average is to identify the direction of the trend and to reduce the "noise" of random price fluctuations. The Weighted Moving Average ( WMA ) assigns different weights to each period, with more recent periods typically given more weight. A 10-day WMA might give the most recent day a weight of 10, the second most recent day a weight of 9, and so on. It is useful for traders who want to emphasize recent price data more than older data. When the price is above the moving average, it suggests an Bullish trend . A Bearish Trend is expected to take place when the price is below the moving average. Understanding the price reactions around these levels can be used to make trading decisions.
APPLYING CONCEPTS
Support and Resistance Calculations in the Script :
The script calculates dynamic support and resistance levels using weighted moving averages ( WMA s) and the highest high and lowest low over specified periods. Buy ( Rocket ) and sell ( Hammer ) signals are generated based on the crossing of the price with calculated top and bottom levels.These signals help traders identify potential entry and exit points within the trading range .
Weighted Moving Average (WMA) Application in the Script
This script calculates a special trendWMA using the close price that helps in creating a more dynamic moving average that considers both high and low price actions. This modified WMA is used in conjunction with highest high and lowest low values over specified periods to calculate dynamic support and resistance levels.
Explanation of the Levels in the Script
By understanding these levels, traders can make more informed decisions about where to enter and exit trades, manage risk, and anticipate potential market movements. The script incorporates several key levels levels that traders can use to better anticipate price movements and make more informed trading decisions. Leveraging the principles of Fibonacci retracement ratios ( 23.6%, 38.2%, 50%, 61.8%, and 100% ) to identify key support and resistance zones can also serve for gauging the overall market sentiment.
Top Level and Sell Leve l: Used to identify potential resistance zones where the price may reverse or pause.
Support Level and Buy Level : Used to identify potential support zones where the price may bounce.
Upper and Lower Pivot Values : Serve as intermediate levels for possible price retracements or extensions within the trading range.
Wave Level : Indicates the central trend direction, which can be useful for gauging the overall market sentiment.
Alerts are a crucial part of the script as they notify traders of potential buy and sell signals based on predefined conditions. There are two main alerts: one for a " Hammer " signal (sell condition) and one for a " Rocket " signal (buy condition).
Adjust the input parameters to fit your trading style and the specific asset being analyzed. Shorter lengths may be more responsive to price changes but can produce more false signals , while longer lengths provide smoother signals but may lag . Always backtest the indicator on historical data to understand its behavior and performance. Also remember that different markets may require different parameter settings for optimal performance.
Keep in mind that by nature like all moving averages, WMAs lag behind price action. This means that signals may be delayed. The indicator performs differently in various market conditions. Always consider the overall market context when interpreting signals.
Adjusting parameters like the range corrector and visibility can help tailor the indicator to specific market conditions or trading strategies, improving its effectiveness. The script uses the calculated levels to plot lines and fill zones on the chart, helping traders visualize potential support, resistance, and trend reversal points. The use of multipliers allows for dynamic adjustment of these levels, making the indicator flexible and adaptable to different market conditions.
I think traders can make more informed decisions about where to enter and exit trades, manage risk, and anticipate potential market movements following this code. Stay safe and always remember that market is always changing. Use this tool if you want, please stay informed and plan safe trades,
D.
Multi Time Frame Normalized PriceEnhance Your Trading Experience with the Multi Time Frame Normalized Price Indicator
Introduction
As a trader, having a clear and informative chart is crucial for making informed decisions. In this post, we will introduce the Multi Time Frame Normalized Price (MTFNP) Indicator, an innovative trading tool that offers an insightful perspective on price action. The script creates a symmetric chart, with the time axis going from top to bottom, making it easier to identify potential tops and bottoms in various ranges. Let's dive deeper into this powerful tool to understand how it works and how it can improve your trading experience.
The Multi Time Frame Normalized Price Indicator
The MTFNP Indicator is designed to provide a comprehensive view of price action across multiple time frames. By plotting the normalized price levels for each time frame, traders can easily identify areas of support and resistance, as well as potential tops and bottoms in various ranges.
One of the key features of this indicator is the symmetry of the chart. Instead of the traditional horizontal time axis, the MTFNP Indicator plots the time axis vertically from top to bottom. This innovative approach makes it easier for traders to visualize the price action across different time frames, enabling them to make more informed decisions.
Benefits of a Symmetric Chart
There are several advantages to using a symmetric chart with a vertical time axis, such as:
Easier to read: The unique layout of the chart makes it easier to analyze price action across multiple time frames. The clear separation between each time frame helps traders avoid confusion and identify important price levels more effectively.
Identifying tops and bottoms: The symmetric presentation of price action enables traders to quickly spot potential tops and bottoms in various ranges. This can be particularly useful for identifying potential reversal points or areas of support and resistance.
Improved decision-making: By offering a comprehensive view of price action, the MTFNP Indicator helps traders make better-informed decisions. This can lead to improved trading strategies and ultimately, better results.
The MTFNP Indicator Script
The MTFNP Indicator script leverages several custom functions, including the Chebyshev Type I Moving Average, to provide a smooth and responsive signal. Additionally, the indicator uses the Spider Plot function to create a symmetric chart with the time axis going from top to bottom.
To customize the MTFNP Indicator to your preferences, you can adjust the input parameters, such as the standard deviation length, multiplier, axes color, bottom color, and top color. You can also change the scale to fit your desired chart size.
Exploring the Relationship between Min, Max Values and Time Frames
In the Multi Time Frame Normalized Price (MTFNP) script, it is crucial to understand the relationship between the min and max values across different time frames. By analyzing how these values relate to each other, traders can make more informed decisions about market trends and potential reversals. In this section, we will dive deep into the relationship between the current time frame's min and max values and those of the further-out time frames.
Interpreting Min and Max Values Across Time Frames
When analyzing the min and max values of the current time frame in relation to the further-out time frames, it is essential to keep in mind the following points:
All min values: If the current time frame and all further-out time frames have min values, this is a strong indication that the current price level is not just a local minimum. Instead, it is likely a more significant support level. In such cases, there is a higher probability that the price will bounce back upwards, making it a potentially favorable entry point for a long position.
All max values: Conversely, if the current time frame and all further-out time frames have max values, this suggests that the current price level is not just a local maximum. Instead, it is likely a more significant resistance level. In these situations, there is a higher probability that the price will reverse downwards, making it a potentially favorable entry point for a short position.
Neutral values with high current time frame: If the current time frame has a high value while the further-out time frames are more neutral, it could indicate that the trend may continue. This is because the high value in the current time frame may signify momentum in the market, whereas the neutral values in the further-out time frames suggest that the trend has not yet reached an extreme level. In this case, traders might consider following the trend and entering a position in the direction of the current movement.
Neutral values with low current time frame: If the current time frame has a low value while the further-out time frames are more neutral, it could indicate that the trend may reverse. This is because the low value in the current time frame may suggest a potential reversal point, whereas the neutral values in the further-out time frames imply that the trend has not yet reached an extreme level. In this case, traders might consider entering a counter-trend position, anticipating a potential reversal.
Balancing Different Time Frames for Optimal Decision Making
It is essential to remember that relying solely on min and max values across different time frames can lead to potential pitfalls. The market is influenced by a wide array of factors, and no single indicator or data point can provide a complete picture. To make the most informed decisions, traders should consider incorporating additional technical analysis tools and evaluating the overall market context.
Moreover, it is crucial to maintain a balance between the current time frame and the further-out time frames. While the current time frame provides information about the most recent market movements, the further-out time frames offer a broader perspective on the market's historical behavior. By combining insights from both types of time frames, traders can make more comprehensive assessments of potential opportunities and risks.
Conclusion
In conclusion, the Multi Time Frame Normalized Price (MTFNP) script offers traders valuable insights by analyzing the relationship between the current time frame and further-out time frames. By identifying potential trend reversals and continuations, traders can make better-informed decisions about market entry and exit points.
Understanding the relationship between min and max values across different time frames is an essential component of using the MTFNP script effectively. By carefully analyzing these relationships and incorporating additional technical analysis tools, traders can improve their decision-making process and enhance their overall trading strategy.
However, it is important to remember that relying solely on the MTFNP script or any single indicator can lead to potential pitfalls. The market is influenced by a wide array of factors, and no single indicator or data point can provide a complete picture. To make the most informed decisions, traders should consider using a combination of technical analysis tools, evaluating the overall market context, and maintaining a balance between the current time frame and the further-out time frames for a comprehensive understanding of the market's behavior. By doing so, they can increase their chances of success in the ever-changing and complex world of trading.
Sniffer
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Overview
A vast majority of modern data analysis & modelling techniques rely upon the idea of hidden patterns, wether it is some type of visualisation tool or some form of a complex machine learning algorithm, the one thing that they have in common is the belief, that patterns tell us what’s hidden behind plain numbers. The same philosophy has been adopted by many traders & investors worldwide, there’s an entire school of thought that operates purely based on chart patterns. This is where Sniffer comes in, it is a tool designed to simplify & quantify the job of pattern recognition on any given price chart, by combining various factors & techniques that generate high-quality results.
This tool analyses bars selected by the user, and highlights bar clusters on the chart that exhibit similar behaviour across multiple dimensions. It can detect a single candle pattern like hammers or dojis, or it can handle multiple candles like morning/evening stars or double tops/bottoms, and many more. In fact, the tool is completely independent of such specific candle formations, instead, it works on the idea of vector similarity and generates a degree of similarity for every single combination of candles. Only the top-n matches are highlighted, users get to choose which patterns they want to analyse and to what degree, by customising the feature-space.
Background
In the world of trading, a common use-case is to scan a price chart for some specific candlestick formations & price structures, and then the chart is further analysed in reference to these events. Traders are often trying to answer questions like, when was the last time price showed similar behaviour, what are the instances similar to what price is doing right now, what happens when price forms a pattern like this, what were some of other indicators doing when this happened last(RSI, CCI, ADX etc), and many other abstract ideas to have a stronger confluence or to confirm a bias.Having such a context can be vital in making better informed decisions, but doing this manually on a chart that has thousands of candles can have many disadvantages. It’s tedious, human errors are rather likely, and even if it’s done with pin-point accuracy, chances are that we’ll miss out on many pieces of information. This is the thought that gave birth to Sniffer .
Sniffer tries to provide a general solution for pattern-based analysis by deploying vector-similarity computation techniques, that cover the full-breadth of a price chart and generate a list of top-n matches based on the criteria selected by the user. Most of these techniques come from the data science space, where vector similarity is often implemented to solve classification & clustering problems. Sniffer uses same principles of vector comparison, and computes a degree of similarity for every single candle formation within the selected range, and as a result generates a similarity matrix that captures how similar or dissimilar a set of candles is to the input set selected by the user.
How It Works
A brief overview of how the tool is implemented:
- Every bar is processed, and a set of features are mapped to it.
- Bars selected by the user are captured, and saved for later use.
- Once the all the bars have been processed, candles are back-tracked and degree of similarity is computed for every single bar(max-limit is 5000 bars).
- Degree of similarity is computed by comparing attributes like price range, candle breadth & volume etc.
- Similarity matrix is sorted and top-n results are highlighted on the chart through boxes of different colors.
A brief overview of the features space for bars:
- Range: Difference between high & low
- Body: Difference between close & open
- Volume: Traded volume for that candle
- Head: Upper wick for green candles & lower wick for red candles
- Tail: Lower wick for green candles & upper wick for red candles
- BTR: Body to Range ratio
- HTR: Head to Range ratio
- TTR: Tail to Range ratio
- HTB: Head to Body ratio
- TTB: Tail to Body ratio
- ROC: Rate of change for HL2 for four different periods
- RSI: Relative Strength Index
- CCI: Commodity Channel Index
- Stochastic: Stochastic Index
- ADX: DMI+, DMI- & ADX
A brief overview of how degree of similarity is calculated:
- Each bar set is compared to the inout bar set within the selected feature space
- Features are represented as vectors, and distance between the vectors is calculated
- Shorter the distance, greater the similarity
- Different distance calculation methods are available to choose from, such as Cosine, Euclidean, Lorentzian, Manhattan, & Pearson
- Each method is likely to generate slightly different results, users are expected to select the method & the feature space that best fits their use-case
How To Use It
- Usage of this tool is relatively straightforward, users can add this indicator to their chart and similar clusters will be highlighted automatically
- Users need to select a time range that will be treated as input, and bars within that range become the input formation for similarity calculations
- Boxes will be draw around the clusters that fit the matching criteria
- Boxes are color-coded, green color boxes represent the top one-third of the top-n matches, yellow boxes represent the middle third, red boxes are for bottom third, and white box represents user-input
- Boxes colors will be adjusted as you adjust input parameters, such as number of matches or look-back period
User Settings
Users can configure the following options:
- Select the time-range to set input bars
- Select the look-back period, number of candles to backtrack for similarity search
- Select the number of top-n matches to show on the chart
- Select the method for similarity calculation
- Adjust the feature space, this enables addition of custom features, such as pattern recognition, technical indicators, rate of change etc
- Toggle verbosity, shows degree of similarity as a percentage value inside the box
Top Features
- Pattern Agnostic: Designed to work with variable number of candles & complex patterns
- Customisable Feature Space: Users get to add custom features to each bar
- Comprehensive Comparison: Generates a degree of similarity for all possible combinations
Final Note
- Similarity matches will be shown only within last 4500 bars.
- In theory, it is possible to compute similarity for any size candle formations, indicator has been tested with formations of 50+ candles, but it is recommended to select smaller range for faster & cleaner results.
- As you move to smaller time frames, selected time range will provide a larger number of candles as input, which can produce undesired results, it is advised to adjust your selection when you change time frames. Seeking suggestions on how to directly receive bars as user input, instead of time range.
- At times, users may see array index out of bound error when setting up this indicator, this generally happens when the input range is not properly configured. So, it should disappear after you select the input range, still trying to figure out where it is coming from, suggestions are welcome.
Credits
- @HeWhoMustNotBeNamed for publishing such a handy PineScript Logger, it certainly made the job a lot easier.
Relative Strength Index (OSC)Hello everyone, I'm sorry that the previous open-source version was hidden due to the house rules, I've re-edited the description and re-posted it
(1) Indicator introduction
This is RSI indicator with original divergence algorithm
This indicator is plotted on the RSI and can display the divergence locations and corresponding divergence intensity
The tolerance of N Klines at the top or bottom positions for price and indicator is supported, which is set by the "Tolerant Kline Number"
Support the display of divergence intensity, that is, the REG/HID value displayed on the label, which is less than 0. The smaller the intensity value, the more obvious divergence
Support the filtering of divergence intensity, which is set by "Cov Threshold". The divergence that REG/HID divergence intensity greater than this value will be ignored
In the label, REG indicates regular top/bottom divergence while HID indicates hidden top/bottom divergence
In the label, SRC(x-y) indicates a divergence occurred from the x-th kline to the y-th kline
In the label, OSC(x-y) indicates a divergence occurred from the indicator corresponding to the x-th kline to the y-th kline
(2) Parameter introduction
- RSI Settings
Source: The source to calculate RSI, close by default
RSI Length: The length of RSI, 14 by default
- RSI Divergence
Pivot Lookback Right: Number of K-line bars recalling the pivot top/bottom point to the right
Pivot Lookback Left: Number of K-line bars recalling the pivot top/bottom point to the left
Max of Lookback Range: Maximum number of retracing K-line bars to find the pivot top/bottom point
Min of Lookback Range: Minimum number of retracing K-line bars to find the pivot top/bottom point
Tolerant Kline Number: Maximum tolerance in indexing top/bottom points of Klines and indicators
Cov Threshold: Divergence intensity, which is less than 0. The smaller the intensity value, the more obvious divergence
Plot Bullish: Whether to draw regular bullish divergence label
Plot Hidden Bullish: Whether to draw hidden bullish divergence label
Plot Bearish: Whether to draw regular bearish divergence label
Plot Hidden Bearish: Whether to draw hidden bearish divergence label
Happy trading and enjoy your life!
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各位朋友大家好，很抱歉之前的开源版本因为规则原因被隐藏，我已经重新编辑了说明并重新发布
(1) 指标说明
该指标绘制于 RSI 上，并在对应位置显示背离点以及背离程度
支持顶底位置 N 根K线的容差，由 Tolerant Kline Number 参数设置
支持背离强度的显示，即标签上显示的 REG/HID 值，该值小于 0，且越小说明背离程度越大
支持背离强度的过滤，由 Cov Threshold 参数设置， REG/HID 值大于这个值的背离会被忽略
标签中，REG 表示常规顶/低背离，而 HID 表示隐藏顶/底背离
标签中，SRC(x-y) 表示从当前第 x 根 bar 开始到第 y 跟 bar 出现背离
标签中，OSC(x-y) 表示从当前第 x 根 bar 所对应的指标开始到第 y 跟 bar 所对应的指标出现背离
(2) 参数说明
- RSI Settings
Source: 计算 RSI 指标的 source，默认为 close
RSI Length: 计算 RSI 指标的长度，默认为 14
- RSI Divergence
Pivot Lookback Right: 枢纽顶/底点往右回顾的 K线 bar 数量
Pivot Lookback Left: 枢纽顶/底点往左回顾的 K线 bar 数量
Max of Lookback Range: 回寻找枢纽顶/底点的最大回溯 K线 bar 数量
Min of Lookback Range: 回寻找枢纽顶/底点的最小回溯 K线 bar 数量
Tolerant Kline Number: K线和指标的顶/底点索引的最大误差
Cov Threshold: 背离程度，该值小于 0，且越小说明背离程度越大
Plot Bullish: 是否绘制常规底背离提示
Plot Hidden Bullish: 是否绘制隐藏底背离提示
Plot Bearish: 是否绘制常规顶背离提示
Plot Hidden Bearish: 是否绘制隐藏顶背离提示
祝大家交易愉快
Relative Strength Index (SRC)Hello everyone, I'm sorry that the previous open-source version was hidden due to the house rules, I've re-edited the description and re-posted it
(1) Indicator introduction
This is RSI indicator with original divergence algorithm
This indicator is plotted on the klines and can display the divergence locations and corresponding divergence intensity
The tolerance of N Klines at the top or bottom positions for price and indicator is supported, which is set by the "Tolerant Kline Number"
Support the display of divergence intensity, that is, the REG/HID value displayed on the label, which is less than 0. The smaller the intensity value, the more obvious divergence
Support the filtering of divergence intensity, which is set by "Cov Threshold". The divergence that REG/HID divergence intensity greater than this value will be ignored
In the label, REG indicates regular top/bottom divergence while HID indicates hidden top/bottom divergence
In the label, SRC(x-y) indicates a divergence occurred from the x-th kline to the y-th kline
In the label, OSC(x-y) indicates a divergence occurred from the indicator corresponding to the x-th kline to the y-th kline
(2) Parameter introduction
- RSI Settings
Source: The source to calculate RSI, close by default
RSI Length: The length of RSI, 14 by default
- RSI Divergence
Pivot Lookback Right: Number of K-line bars recalling the pivot top/bottom point to the right
Pivot Lookback Left: Number of K-line bars recalling the pivot top/bottom point to the left
Max of Lookback Range: Maximum number of retracing K-line bars to find the pivot top/bottom point
Min of Lookback Range: Minimum number of retracing K-line bars to find the pivot top/bottom point
Tolerant Kline Number: Maximum tolerance in indexing top/bottom points of Klines and indicators
Cov Threshold: Divergence intensity, which is less than 0. The smaller the intensity value, the more obvious divergence
Plot Bullish: Whether to draw regular bullish divergence label
Plot Hidden Bullish: Whether to draw hidden bullish divergence label
Plot Bearish: Whether to draw regular bearish divergence label
Plot Hidden Bearish: Whether to draw hidden bearish divergence label
Happy trading and enjoy your life!
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各位朋友大家好，很抱歉之前的开源版本因为规则原因被隐藏，我已经重新编辑了说明并重新发布
(1) 指标说明
该指标绘制于 K线 上，并在对应位置显示背离点以及背离程度
支持顶底位置 N 根K线的容差，由 Tolerant Kline Number 参数设置
支持背离强度的显示，即标签上显示的 REG/HID 值，该值小于 0，且越小说明背离程度越大
支持背离强度的过滤，由 Cov Threshold 参数设置， REG/HID 值大于这个值的背离会被忽略
标签中，REG 表示常规顶/低背离，而 HID 表示隐藏顶/底背离
标签中，SRC(x-y) 表示从当前第 x 根 bar 开始到第 y 跟 bar 出现背离
标签中，OSC(x-y) 表示从当前第 x 根 bar 所对应的指标开始到第 y 跟 bar 所对应的指标出现背离
(2) 参数说明
- RSI Settings
Source: 计算 RSI 指标的 source，默认为 close
RSI Length: 计算 RSI 指标的长度，默认为 14
- RSI Divergence
Pivot Lookback Right: 枢纽顶/底点往右回顾的 K线 bar 数量
Pivot Lookback Left: 枢纽顶/底点往左回顾的 K线 bar 数量
Max of Lookback Range: 回寻找枢纽顶/底点的最大回溯 K线 bar 数量
Min of Lookback Range: 回寻找枢纽顶/底点的最小回溯 K线 bar 数量
Tolerant Kline Number: K线和指标的顶/底点索引的最大误差
Cov Threshold: 背离程度，该值小于 0，且越小说明背离程度越大
Plot Bullish: 是否绘制常规底背离提示
Plot Hidden Bullish: 是否绘制隐藏底背离提示
Plot Bearish: 是否绘制常规顶背离提示
Plot Hidden Bearish: 是否绘制隐藏顶背离提示
祝大家交易愉快
On Balance Volume wi Normalization (OSC)Hello everyone, I'm sorry that the previous open-source version was hidden due to the house rules, I've re-edited the description and re-posted it
(1) Indicator introduction
This indicator is a normalized OBV that never dulls and has a better divergence accuracy than RSI
This indicator is plotted on the Normalized OBV and can display the divergence locations and corresponding divergence intensity
The tolerance of N Klines at the top or bottom positions for price and indicator is supported, which is set by the "Tolerant Kline Number"
Support the display of divergence intensity, that is, the REG/HID value displayed on the label, which is less than 0. The smaller the intensity value, the more obvious divergence
Support the filtering of divergence intensity, which is set by "Cov Threshold". The divergence that REG/HID divergence intensity greater than this value will be ignored
In the label, REG indicates regular top/bottom divergence while HID indicates hidden top/bottom divergence
In the label, SRC(x-y) indicates a divergence occurred from the x-th kline to the y-th kline
In the label, OSC(x-y) indicates a divergence occurred from the indicator corresponding to the x-th kline to the y-th kline
(2) Parameter introduction
- Normalized On Balance Volume
MA Type: Type of moving average for calculating the normalized OBV, default is SMA
MA Period: Period of moving average of normalized OBV, which is SMA14 by default
NOBV Sigma: Upper and lower range of normalized OBV
- Normalized On Balance Volume Divergence
Pivot Lookback Right: Number of K-line bars recalling the pivot top/bottom point to the right
Pivot Lookback Left: Number of K-line bars recalling the pivot top/bottom point to the left
Max of Lookback Range: Maximum number of retracing K-line bars to find the pivot top/bottom point
Min of Lookback Range: Minimum number of retracing K-line bars to find the pivot top/bottom point
Tolerant Kline Number: Maximum tolerance in indexing top/bottom points of Klines and indicators
Cov Threshold: Divergence intensity, which is less than 0. The smaller the intensity value, the more obvious divergence
Plot Bullish: Whether to draw regular bullish divergence label
Plot Hidden Bullish: Whether to draw hidden bullish divergence label
Plot Bearish: Whether to draw regular bearish divergence label
Plot Hidden Bearish: Whether to draw hidden bearish divergence label
Happy trading and enjoy your life!
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各位朋友大家好，很抱歉之前的开源版本因为规则原因被隐藏，我已经重新编辑了说明并重新发布
(1) 指标说明
该指标是 OBV 的归一化版本，永不钝化，背离准确率高于 RSI
该指标绘制于 归一化OBV 上，并在对应位置显示背离点以及背离程度
支持顶底位置 N 根K线的容差，由 Tolerant Kline Number 参数设置
支持背离强度的显示，即标签上显示的 REG/HID 值，该值小于 0，且越小说明背离程度越大
支持背离强度的过滤，由 Cov Threshold 参数设置， REG/HID 值大于这个值的背离会被忽略
标签中，REG 表示常规顶/低背离，而 HID 表示隐藏顶/底背离
标签中，SRC(x-y) 表示从当前第 x 根 bar 开始到第 y 跟 bar 出现背离
标签中，OSC(x-y) 表示从当前第 x 根 bar 所对应的指标开始到第 y 跟 bar 所对应的指标出现背离
(2) 参数说明
- Normalized On Balance Volume
MA Type: 计算归一化 OBV 的移动平均的类型，默认为 SMA
MA Period: 计算归一化 OBV 的移动平均的周期，默认为 SMA14
NOBV Sigma: 归一化 OBV 的过滤区间
- Normalized On Balance Volume Divergence
Pivot Lookback Right: 枢纽顶/底点往右回顾的 K线 bar 数量
Pivot Lookback Left: 枢纽顶/底点往左回顾的 K线 bar 数量
Max of Lookback Range: 回寻找枢纽顶/底点的最大回溯 K线 bar 数量
Min of Lookback Range: 回寻找枢纽顶/底点的最小回溯 K线 bar 数量
Tolerant Kline Number: K线和指标的顶/底点索引的最大误差
Cov Threshold: 背离程度，该值小于 0，且越小说明背离程度越大
Plot Bullish: 是否绘制常规底背离提示
Plot Hidden Bullish: 是否绘制隐藏底背离提示
Plot Bearish: 是否绘制常规顶背离提示
Plot Hidden Bearish: 是否绘制隐藏顶背离提示
祝大家交易愉快
On Balance Volume wi Normalization (SRC)Hello everyone, I'm sorry that the previous open-source version was hidden due to the house rules, I've re-edited the description and re-posted it
(1) Indicator introduction
This indicator is a normalized OBV that never dulls and has a better divergence accuracy than RSI
This indicator is plotted on the klines and can display the divergence locations and corresponding divergence intensity
The tolerance of N Klines at the top or bottom positions for price and indicator is supported, which is set by the "Tolerant Kline Number"
Support the display of divergence intensity, that is, the REG/HID value displayed on the label, which is less than 0. The smaller the intensity value, the more obvious divergence
Support the filtering of divergence intensity, which is set by "Cov Threshold". The divergence that REG/HID divergence intensity greater than this value will be ignored
In the label, REG indicates regular top/bottom divergence while HID indicates hidden top/bottom divergence
In the label, SRC(x-y) indicates a divergence occurred from the x-th kline to the y-th kline
In the label, OSC(x-y) indicates a divergence occurred from the indicator corresponding to the x-th kline to the y-th kline
(2) Parameter introduction
- Normalized On Balance Volume
MA Type: Type of moving average for calculating the normalized OBV, default is SMA
MA Period: Period of moving average of normalized OBV, which is SMA14 by default
NOBV Sigma: Upper and lower range of normalized OBV, but the function is reserved
- Normalized On Balance Volume Divergence
Pivot Lookback Right: Number of K-line bars recalling the pivot top/bottom point to the right
Pivot Lookback Left: Number of K-line bars recalling the pivot top/bottom point to the left
Max of Lookback Range: Maximum number of retracing K-line bars to find the pivot top/bottom point
Min of Lookback Range: Minimum number of retracing K-line bars to find the pivot top/bottom point
Tolerant Kline Number: Maximum tolerance in indexing top/bottom points of Klines and indicators
Cov Threshold: Divergence intensity, which is less than 0. The smaller the intensity value, the more obvious divergence
Plot Bullish: Whether to draw regular bullish divergence label
Plot Hidden Bullish: Whether to draw hidden bullish divergence label
Plot Bearish: Whether to draw regular bearish divergence label
Plot Hidden Bearish: Whether to draw hidden bearish divergence label
Happy trading and enjoy your life!
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各位朋友大家好，很抱歉之前的开源版本因为规则原因被隐藏，我已经重新编辑了说明并重新发布
(1) 指标说明
该指标是 OBV 的归一化版本，永不钝化，背离准确率高于 RSI
该指标绘制于 K线 上，并在对应位置显示背离点以及背离程度
支持顶底位置 N 根K线的容差，由 Tolerant Kline Number 参数设置
支持背离强度的显示，即标签上显示的 REG/HID 值，该值小于 0，且越小说明背离程度越大
支持背离强度的过滤，由 Cov Threshold 参数设置， REG/HID 值大于这个值的背离会被忽略
标签中，REG 表示常规顶/低背离，而 HID 表示隐藏顶/底背离
标签中，SRC(x-y) 表示从当前第 x 根 bar 开始到第 y 跟 bar 出现背离
标签中，OSC(x-y) 表示从当前第 x 根 bar 所对应的指标开始到第 y 跟 bar 所对应的指标出现背离
(2) 参数说明
- Normalized On Balance Volume
MA Type: 计算归一化 OBV 的移动平均的类型，默认为 SMA
MA Period: 计算归一化 OBV 的移动平均的周期，默认为 SMA14
NOBV Sigma: 归一化 OBV 的过滤区间，其功能暂时保留
- Normalized On Balance Volume Divergence
Pivot Lookback Right: 枢纽顶/底点往右回顾的 K线 bar 数量
Pivot Lookback Left: 枢纽顶/底点往左回顾的 K线 bar 数量
Max of Lookback Range: 回寻找枢纽顶/底点的最大回溯 K线 bar 数量
Min of Lookback Range: 回寻找枢纽顶/底点的最小回溯 K线 bar 数量
Tolerant Kline Number: K线和指标的顶/底点索引的最大误差
Cov Threshold: 背离程度，该值小于 0，且越小说明背离程度越大
Plot Bullish: 是否绘制常规底背离提示
Plot Hidden Bullish: 是否绘制隐藏底背离提示
Plot Bearish: 是否绘制常规顶背离提示
Plot Hidden Bearish: 是否绘制隐藏顶背离提示
祝大家交易愉快
Thrax - Pullback based short side scalping⯁ This indicator is built for short trades only.
⤞ Pullback based scalping is a strategy where a trader anticipates a pullback and makes a quick scalp in this pullback. This strategy usually works in a ranging market as probability of pullbacks occurrence in ranging market is quite high.
⤞ The strategy is built by first determining a possible candidate price levels having high chance of pullbacks. This is determined by finding out multiple rejection point and creating a zone around this price. A rejection is considered to be valid only if it comes to this zone after going down by a minimum pullback percentage. Once the price has gone down by this minimum pullback percentage multiple times and reaches the zone again chances of pullback goes high and an indication on chart for the same is given.
⯁ Inputs
⤞ Zone-Top : This input parameter determines the upper range for the price zone.
⤞ Zone bottom : This input parameter determines the lower range for price zone.
⤞ Minimum Pullback : This input parameter determines the minimum pullback percentage required for valid rejection. Below is the recommended settings
⤞ Lookback : lookback period before resetting all the variables
⬦Below is the recommended settings across timeframes
⤞ 15-min : lookback – 24, Pullback – 2, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ 5-min : lookback – 50, pullback – 1% - 1.5%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ 1-min : lookback – 100, pullback – 1%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ Anything > 30-min : lookback – 11, pullback – 3%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
✵ This indicator gives early pullback detection which can be used in below ways
1. To take short trades in the pullback.
2. To use this to exit an existing position in the next few candles as pullback may be incoming.
📌 Kindly note, it’s not necessary that pullback will happen at the exact point given on the chart. Instead, the indictor gives you early signals for the pullback
⯁ Trade Steup
1. Wait for pullback signal to occur on the chart.
2. Once the pullback warning has been displayed on the chart, you can either straight away enter the short position or wait for next 2-4 candles for initial sign of actual pullback to occurrence.
3. Once you have initiated short trade, since this is pullback-based strategy, a quick scalp should be made and closed as price may resume it’s original direction. If you have risk appetite you can stay in the trade longer and trial the stops if price keeps pulling back.
4. You can zone top as your stop, usually zone top + some% should be used as stop where ‘some %’ is based on your risk appetite.
5. It’s important to note that this indicator gives early sings of pullback so you may actually wait for 2-3 candles post ‘Pullback warning’ occurs on the chart before entering short trade.
Engulfing Patterns & Inside Bar at NWOGEngulfing Patterns & Inside Bar at NWOG:
This indicator is designed to detect and display specific candlestick patterns (Bearish Engulfing, Bullish Engulfing, and Inside Bar) when they occur at the New Week Open Gap (NWOG). The indicator provides tiny dots plotted at the top of the candle for each detected pattern, keeping the chart clean and minimal. Below is a detailed description of the logic and components:
Candlestick Patterns Detected:
Bearish Engulfing:
A Bearish Engulfing pattern occurs when:
The current candle’s high is above the previous candle’s high.
The current candle’s close is below the previous candle’s low.
This pattern signals a potential downtrend and is marked by a red dot at the top of the candle.
Bullish Engulfing:
A Bullish Engulfing pattern occurs when:
The current candle’s low is below the previous candle’s low.
The current candle’s close is above the previous candle’s high.
This pattern signals a potential uptrend and is marked by a green dot at the top of the candle.
Inside Bar:
An Inside Bar pattern occurs when:
The current candle’s high is lower than the previous candle’s high.
The current candle’s low is higher than the previous candle’s low.
This pattern indicates a period of consolidation and possible breakout or breakdown, and is marked by a blue dot at the top of the candle.
New Week Open Gap (NWOG) Condition:
The patterns (Bearish Engulfing, Bullish Engulfing, and Inside Bar) are only considered valid if the candles occur within or touch the range of the New Week Open Gap (NWOG).
The NWOG is defined as the gap between:
The Friday close (previous week’s closing price).
The Monday open (current week’s opening price).
If the signal patterns (Bullish Engulfing, Bearish Engulfing, Inside Bar) align with the NWOG, a tiny dot is plotted at the top of the candle where the pattern occurs.
Visual Representation:
Red Dots: Indicate Bearish Engulfing signals that occur at the NWOG.
Green Dots: Indicate Bullish Engulfing signals that occur at the NWOG.
Blue Dots: Indicate Inside Bar Breakdown signals that occur at the NWOG.
Each dot is plotted as a tiny circle at the top of the candle, ensuring the chart remains minimal and clean without cluttering the view.
Key Features:
Minimal and Clean: The indicator only plots tiny dots at the top of the candles for the detected signals. No additional lines, labels, or other visual elements clutter the chart.
Customizable Signal Colors: Users can customize the colors for each signal type (Bearish Engulfing, Bullish Engulfing, and Inside Bar).
Alerts: Alerts are included for all detected patterns (Bullish Engulfing, Bearish Engulfing, Inside Bar) at the NWOG.
Alerts:
Bearish Engulfing Detected: Alerts when a Bearish Engulfing pattern occurs at the NWOG.
Bullish Engulfing Detected: Alerts when a Bullish Engulfing pattern occurs at the NWOG.
Inside Bar Breakdown Detected: Alerts when an Inside Bar Breakdown pattern occurs at the NWOG.
This indicator is helpful for traders who want to focus on clean, easy-to-spot patterns and trade based on market conditions near the New Week Open Gap (NWOG). The tiny dots ensure that only relevant signals are displayed without any distractions.
Trading IQ - ICT LibraryLibrary "ICTlibrary"
Used to calculate various ICT related price levels and strategies. An ongoing project.
Hello Coders!
This library is meant for sourcing ICT related concepts. While some functions might generate more output than you require, you can specify "Lite Mode" as "true" in applicable functions to slim down necessary inputs.
isLastBar(userTF)
Identifies the last bar on the chart before a timeframe change
Parameters:
userTF (simple int) : the timeframe you wish to calculate the last bar for, must be converted to integer using 'timeframe.in_seconds()'
Returns: bool true if bar on chart is last bar of higher TF, dalse if bar on chart is not last bar of higher TF
necessaryData(atrTF)
returns necessaryData UDT for historical data access
Parameters:
atrTF (float) : user-selected timeframe ATR value.
Returns: logZ. log return Z score, used for calculating order blocks.
method gradBoxes(gradientBoxes, idColor, timeStart, bottom, top, rightCoordinate)
creates neon like effect for box drawings
Namespace types: array
Parameters:
gradientBoxes (array) : an array.new() to store the gradient boxes
idColor (color)
timeStart (int) : left point of box
bottom (float) : bottom of box price point
top (float) : top of box price point
rightCoordinate (int) : right point of box
Returns: void
checkIfTraded(tradeName)
checks if recent trade is of specific name
Parameters:
tradeName (string)
Returns: bool true if recent trade id matches target name, false otherwise
checkIfClosed(tradeName)
checks if recent closed trade is of specific name
Parameters:
tradeName (string)
Returns: bool true if recent closed trade id matches target name, false otherwise
IQZZ(atrMult, finalTF)
custom ZZ to quickly determine market direction.
Parameters:
atrMult (float) : an atr multiplier used to determine the required price move for a ZZ direction change
finalTF (string) : the timeframe used for the atr calcuation
Returns: dir market direction. Up => 1, down => -1
method drawBos(id, startPoint, getKeyPointTime, getKeyPointPrice, col, showBOS, isUp)
calculates and draws Break Of Structure
Namespace types: array
Parameters:
id (array)
startPoint (chart.point)
getKeyPointTime (int) : the actual time of startPoint, simplystartPoint.time
getKeyPointPrice (float) : the actual time of startPoint, simplystartPoint.price
col (color) : color of the BoS line / label
showBOS (bool) : whether to show label/line. This function still calculates internally for other ICT related concepts even if not drawn.
isUp (bool) : whether BoS happened during price increase or price decrease.
Returns: void
method drawMSS(id, startPoint, getKeyPointTime, getKeyPointPrice, col, showMSS, isUp, upRejections, dnRejections, highArr, lowArr, timeArr, closeArr, openArr, atrTFarr, upRejectionsPrices, dnRejectionsPrices)
calculates and draws Market Structure Shift. This data is also used to calculate Rejection Blocks.
Namespace types: array
Parameters:
id (array)
startPoint (chart.point)
getKeyPointTime (int) : the actual time of startPoint, simplystartPoint.time
getKeyPointPrice (float) : the actual time of startPoint, simplystartPoint.price
col (color) : color of the MSS line / label
showMSS (bool) : whether to show label/line. This function still calculates internally for other ICT related concepts even if not drawn.
isUp (bool) : whether MSS happened during price increase or price decrease.
upRejections (array)
dnRejections (array)
highArr (array) : array containing historical highs, should be taken from the UDT "necessaryData" defined above
lowArr (array) : array containing historical lows, should be taken from the UDT "necessaryData" defined above
timeArr (array) : array containing historical times, should be taken from the UDT "necessaryData" defined above
closeArr (array) : array containing historical closes, should be taken from the UDT "necessaryData" defined above
openArr (array) : array containing historical opens, should be taken from the UDT "necessaryData" defined above
atrTFarr (array) : array containing historical atr values (of user-selected TF), should be taken from the UDT "necessaryData" defined above
upRejectionsPrices (array) : array containing up rejections prices. Is sorted and used to determine selective looping for invalidations.
dnRejectionsPrices (array) : array containing down rejections prices. Is sorted and used to determine selective looping for invalidations.
Returns: void
method getTime(id, compare, timeArr)
gets time of inputted price (compare) in an array of data
this is useful when the user-selected timeframe for ICT concepts is greater than the chart's timeframe
Namespace types: array
Parameters:
id (array) : the array of data to search through, to find which index has the same value as "compare"
compare (float) : the target data point to find in the array
timeArr (array) : array of historical times
Returns: the time that the data point in the array was recorded
method OB(id, highArr, signArr, lowArr, timeArr, sign)
store bullish orderblock data
Namespace types: array
Parameters:
id (array)
highArr (array) : array of historical highs
signArr (array) : array of historical price direction "math.sign(close - open)"
lowArr (array) : array of historical lows
timeArr (array) : array of historical times
sign (int) : orderblock direction, -1 => bullish, 1 => bearish
Returns: void
OTEstrat(OTEstart, future, closeArr, highArr, lowArr, timeArr, longOTEPT, longOTESL, longOTElevel, shortOTEPT, shortOTESL, shortOTElevel, structureDirection, oteLongs, atrTF, oteShorts)
executes the OTE strategy
Parameters:
OTEstart (chart.point)
future (int) : future time point for drawings
closeArr (array) : array of historical closes
highArr (array) : array of historical highs
lowArr (array) : array of historical lows
timeArr (array) : array of historical times
longOTEPT (string) : user-selected long OTE profit target, please create an input.string() for this using the example below
longOTESL (int) : user-selected long OTE stop loss, please create an input.string() for this using the example below
longOTElevel (float) : long entry price of selected retracement ratio for OTE
shortOTEPT (string) : user-selected short OTE profit target, please create an input.string() for this using the example below
shortOTESL (int) : user-selected short OTE stop loss, please create an input.string() for this using the example below
shortOTElevel (float) : short entry price of selected retracement ratio for OTE
structureDirection (string) : current market structure direction, this should be "Up" or "Down". This is used to cancel pending orders if market structure changes
oteLongs (bool) : input.bool() for whether OTE longs can be executed
atrTF (float) : atr of the user-seleceted TF
oteShorts (bool) : input.bool() for whether OTE shorts can be executed
@exampleInputs
oteLongs = input.bool(defval = false, title = "OTE Longs", group = "Optimal Trade Entry")
longOTElevel = input.float(defval = 0.79, title = "Long Entry Retracement Level", options = , group = "Optimal Trade Entry")
longOTEPT = input.string(defval = "-0.5", title = "Long TP", options = , group = "Optimal Trade Entry")
longOTESL = input.int(defval = 0, title = "How Many Ticks Below Swing Low For Stop Loss", group = "Optimal Trade Entry")
oteShorts = input.bool(defval = false, title = "OTE Shorts", group = "Optimal Trade Entry")
shortOTElevel = input.float(defval = 0.79, title = "Short Entry Retracement Level", options = , group = "Optimal Trade Entry")
shortOTEPT = input.string(defval = "-0.5", title = "Short TP", options = , group = "Optimal Trade Entry")
shortOTESL = input.int(defval = 0, title = "How Many Ticks Above Swing Low For Stop Loss", group = "Optimal Trade Entry")
Returns: void (0)
displacement(logZ, atrTFreg, highArr, timeArr, lowArr, upDispShow, dnDispShow, masterCoords, labelLevels, dispUpcol, rightCoordinate, dispDncol, noBorders)
calculates and draws dispacements
Parameters:
logZ (float) : log return of current price, used to determine a "significant price move" for a displacement
atrTFreg (float) : atr of user-seleceted timeframe
highArr (array) : array of historical highs
timeArr (array) : array of historical times
lowArr (array) : array of historical lows
upDispShow (int) : amount of historical upside displacements to show
dnDispShow (int) : amount of historical downside displacements to show
masterCoords (map) : a map to push the most recent displacement prices into, useful for having key levels in one data structure
labelLevels (string) : used to determine label placement for the displacement, can be inside box, outside box, or none, example below
dispUpcol (color) : upside displacement color
rightCoordinate (int) : future time for displacement drawing, best is "last_bar_time"
dispDncol (color) : downside displacement color
noBorders (bool) : input.bool() to remove box borders, example below
@exampleInputs
labelLevels = input.string(defval = "Inside" , title = "Box Label Placement", options = )
noBorders = input.bool(defval = false, title = "No Borders On Levels")
Returns: void
method getStrongLow(id, startIndex, timeArr, lowArr, strongLowPoints)
unshift strong low data to array id
Namespace types: array
Parameters:
id (array)
startIndex (int) : the starting index for the timeArr array of the UDT "necessaryData".
this point should start from at least 1 pivot prior to find the low before an upside BoS
timeArr (array) : array of historical times
lowArr (array) : array of historical lows
strongLowPoints (array) : array of strong low prices. Used to retrieve highest strong low price and see if need for
removal of invalidated strong lows
Returns: void
method getStrongHigh(id, startIndex, timeArr, highArr, strongHighPoints)
unshift strong high data to array id
Namespace types: array
Parameters:
id (array)
startIndex (int) : the starting index for the timeArr array of the UDT "necessaryData".
this point should start from at least 1 pivot prior to find the high before a downside BoS
timeArr (array) : array of historical times
highArr (array) : array of historical highs
strongHighPoints (array)
Returns: void
equalLevels(highArr, lowArr, timeArr, rightCoordinate, equalHighsCol, equalLowsCol, liteMode)
used to calculate recent equal highs or equal lows
Parameters:
highArr (array) : array of historical highs
lowArr (array) : array of historical lows
timeArr (array) : array of historical times
rightCoordinate (int) : a future time (right for boxes, x2 for lines)
equalHighsCol (color) : user-selected color for equal highs drawings
equalLowsCol (color) : user-selected color for equal lows drawings
liteMode (bool) : optional for a lite mode version of an ICT strategy. For more control over drawings leave as "True", "False" will apply neon effects
Returns: void
quickTime(timeString)
used to quickly determine if a user-inputted time range is currently active in NYT time
Parameters:
timeString (string) : a time range
Returns: true if session is active, false if session is inactive
macros(showMacros, noBorders)
used to calculate and draw session macros
Parameters:
showMacros (bool) : an input.bool() or simple bool to determine whether to activate the function
noBorders (bool) : an input.bool() to determine whether the box anchored to the session should have borders
Returns: void
po3(tf, left, right, show)
use to calculate HTF po3 candle
@tip only call this function on "barstate.islast"
Parameters:
tf (simple string)
left (int) : the left point of the candle, calculated as bar_index + left,
right (int) : :the right point of the candle, calculated as bar_index + right,
show (bool) : input.bool() whether to show the po3 candle or not
Returns: void
silverBullet(silverBulletStratLong, silverBulletStratShort, future, userTF, H, L, H2, L2, noBorders, silverBulletLongTP, historicalPoints, historicalData, silverBulletLongSL, silverBulletShortTP, silverBulletShortSL)
used to execute the Silver Bullet Strategy
Parameters:
silverBulletStratLong (simple bool)
silverBulletStratShort (simple bool)
future (int) : a future time, used for drawings, example "last_bar_time"
userTF (simple int)
H (float) : the high price of the user-selected TF
L (float) : the low price of the user-selected TF
H2 (float) : the high price of the user-selected TF
L2 (float) : the low price of the user-selected TF
noBorders (bool) : an input.bool() used to remove the borders from box drawings
silverBulletLongTP (series silverBulletLevels)
historicalPoints (array)
historicalData (necessaryData)
silverBulletLongSL (series silverBulletLevels)
silverBulletShortTP (series silverBulletLevels)
silverBulletShortSL (series silverBulletLevels)
Returns: void
method invalidFVGcheck(FVGarr, upFVGpricesSorted, dnFVGpricesSorted)
check if existing FVGs are still valid
Namespace types: array
Parameters:
FVGarr (array)
upFVGpricesSorted (array) : an array of bullish FVG prices, used to selective search through FVG array to remove invalidated levels
dnFVGpricesSorted (array) : an array of bearish FVG prices, used to selective search through FVG array to remove invalidated levels
Returns: void (0)
method drawFVG(counter, FVGshow, FVGname, FVGcol, data, masterCoords, labelLevels, borderTransp, liteMode, rightCoordinate)
draws FVGs on last bar
Namespace types: map
Parameters:
counter (map) : a counter, as map, keeping count of the number of FVGs drawn, makes sure that there aren't more FVGs drawn
than int FVGshow
FVGshow (int) : the number of FVGs to show. There should be a bullish FVG show and bearish FVG show. This function "drawFVG" is used separately
for bearish FVG and bullish FVG.
FVGname (string) : the name of the FVG, "FVG Up" or "FVG Down"
FVGcol (color) : desired FVG color
data (FVG)
masterCoords (map) : a map containing the names and price points of key levels. Used to define price ranges.
labelLevels (string) : an input.string with options "Inside", "Outside", "Remove". Determines whether FVG labels should be inside box, outside,
or na.
borderTransp (int)
liteMode (bool)
rightCoordinate (int) : the right coordinate of any drawings. Must be a time point.
Returns: void
invalidBlockCheck(bullishOBbox, bearishOBbox, userTF)
check if existing order blocks are still valid
Parameters:
bullishOBbox (array) : an array declared using the UDT orderBlock that contains bullish order block related data
bearishOBbox (array) : an array declared using the UDT orderBlock that contains bearish order block related data
userTF (simple int)
Returns: void (0)
method lastBarRejections(id, rejectionColor, idShow, rejectionString, labelLevels, borderTransp, liteMode, rightCoordinate, masterCoords)
draws rejectionBlocks on last bar
Namespace types: array
Parameters:
id (array) : the array, an array of rejection block data declared using the UDT rejection block
rejectionColor (color) : the desired color of the rejection box
idShow (int)
rejectionString (string) : the desired name of the rejection blocks
labelLevels (string) : an input.string() to determine if labels for the block should be inside the box, outside, or none.
borderTransp (int)
liteMode (bool) : an input.bool(). True = neon effect, false = no neon.
rightCoordinate (int) : atime for the right coordinate of the box
masterCoords (map) : a map that stores the price of key levels and assigns them a name, used to determine price ranges
Returns: void
method OBdraw(id, OBshow, BBshow, OBcol, BBcol, bullishString, bearishString, isBullish, labelLevels, borderTransp, liteMode, rightCoordinate, masterCoords)
draws orderblocks and breaker blocks for data stored in UDT array()
Namespace types: array
Parameters:
id (array) : the array, an array of order block data declared using the UDT orderblock
OBshow (int) : the number of order blocks to show
BBshow (int) : the number of breaker blocks to show
OBcol (color) : color of order blocks
BBcol (color) : color of breaker blocks
bullishString (string) : the title of bullish blocks, which is a regular bullish orderblock or a bearish orderblock that's converted to breakerblock
bearishString (string) : the title of bearish blocks, which is a regular bearish orderblock or a bullish orderblock that's converted to breakerblock
isBullish (bool) : whether the array contains bullish orderblocks or bearish orderblocks. If bullish orderblocks,
the array will naturally contain bearish BB, and if bearish OB, the array will naturally contain bullish BB
labelLevels (string) : an input.string() to determine if labels for the block should be inside the box, outside, or none.
borderTransp (int)
liteMode (bool) : an input.bool(). True = neon effect, false = no neon.
rightCoordinate (int) : atime for the right coordinate of the box
masterCoords (map) : a map that stores the price of key levels and assigns them a name, used to determine price ranges
Returns: void
FVG
UDT for FVG calcualtions
Fields:
H (series float) : high price of user-selected timeframe
L (series float) : low price of user-selected timeframe
direction (series string) : FVG direction => "Up" or "Down"
T (series int) : => time of bar on user-selected timeframe where FVG was created
fvgLabel (series label) : optional label for FVG
fvgLineTop (series line) : optional line for top of FVG
fvgLineBot (series line) : optional line for bottom of FVG
fvgBox (series box) : optional box for FVG
labelLine
quickly pair a line and label together as UDT
Fields:
lin (series line) : Line you wish to pair with label
lab (series label) : Label you wish to pair with line
orderBlock
UDT for order block calculations
Fields:
orderBlockData (array) : array containing order block x and y points
orderBlockBox (series box) : optional order block box
vioCount (series int) : = 0 violation count of the order block. 0 = Order Block, 1 = Breaker Block
traded (series bool)
status (series string) : = "OB" status == "OB" => Level is order block. status == "BB" => Level is breaker block.
orderBlockLab (series label) : options label for the order block / breaker block.
strongPoints
UDT for strong highs and strong lows
Fields:
price (series float) : price of the strong high or strong low
timeAtprice (series int) : time of the strong high or strong low
strongPointLabel (series label) : optional label for strong point
strongPointLine (series line) : optional line for strong point
overlayLine (series line) : optional lines for strong point to enhance visibility
overlayLine2 (series line) : optional lines for strong point to enhance visibility
displacement
UDT for dispacements
Fields:
highPrice (series float) : high price of displacement
lowPrice (series float) : low price of displacement
timeAtPrice (series int) : time of bar where displacement occurred
displacementBox (series box) : optional box to draw displacement
displacementLab (series label) : optional label for displacement
po3data
UDT for po3 calculations
Fields:
dHigh (series float) : higher timeframe high price
dLow (series float) : higher timeframe low price
dOpen (series float) : higher timeframe open price
dClose (series float) : higher timeframe close price
po3box (series box) : box to draw po3 candle body
po3line (array) : line array to draw po3 wicks
po3Labels (array) : label array to label price points of po3 candle
macros
UDT for session macros
Fields:
sessions (array) : Array of sessions, you can populate this array using the "quickTime" function located above "export macros".
prices (matrix) : Matrix of session data -> open, high, low, close, time
sessionTimes (array) : Array of session names. Pairs with array sessions.
sessionLines (matrix) : Optional array for sesion drawings.
OTEtimes
UDT for data storage and drawings associated with OTE strategy
Fields:
upTimes (array) : time of highest point before trade is taken
dnTimes (array) : time of lowest point before trade is taken
tpLineLong (series line) : line to mark tp level long
tpLabelLong (series label) : label to mark tp level long
slLineLong (series line) : line to mark sl level long
slLabelLong (series label) : label to mark sl level long
tpLineShort (series line) : line to mark tp level short
tpLabelShort (series label) : label to mark tp level short
slLineShort (series line) : line to mark sl level short
slLabelShort (series label) : label to mark sl level short
sweeps
UDT for data storage and drawings associated with liquidity sweeps
Fields:
upSweeps (matrix) : matrix containing liquidity sweep price points and time points for up sweeps
dnSweeps (matrix) : matrix containing liquidity sweep price points and time points for down sweeps
upSweepDrawings (array) : optional up sweep box array. Pair the size of this array with the rows or columns,
dnSweepDrawings (array) : optional up sweep box array. Pair the size of this array with the rows or columns,
raidExitDrawings
UDT for drawings associated with the Liquidity Raid Strategy
Fields:
tpLine (series line) : tp line for the liquidity raid entry
tpLabel (series label) : tp label for the liquidity raid entry
slLine (series line) : sl line for the liquidity raid entry
slLabel (series label) : sl label for the liquidity raid entry
m2022
UDT for data storage and drawings associated with the Model 2022 Strategy
Fields:
mTime (series int) : time of the FVG where entry limit order is placed
mIndex (series int) : array index of FVG where entry limit order is placed. This requires an array of FVG data, which is defined above.
mEntryDistance (series float) : the distance of the FVG to the 50% range. M2022 looks for the fvg closest to 50% mark of range.
mEntry (series float) : the entry price for the most eligible fvg
fvgHigh (series float) : the high point of the eligible fvg
fvgLow (series float) : the low point of the eligible fvg
longFVGentryBox (series box) : long FVG box, used to draw the eligible FVG
shortFVGentryBox (series box) : short FVG box, used to draw the eligible FVG
line50P (series line) : line used to mark 50% of the range
line100P (series line) : line used to mark 100% (top) of the range
line0P (series line) : line used to mark 0% (bottom) of the range
label50P (series label) : label used to mark 50% of the range
label100P (series label) : label used to mark 100% (top) of the range
label0P (series label) : label used to mark 0% (bottom) of the range
sweepData (array)
silverBullet
UDT for data storage and drawings associated with the Silver Bullet Strategy
Fields:
session (series bool)
sessionStr (series string) : name of the session for silver bullet
sessionBias (series string)
sessionHigh (series float) : = high high of session // use math.max(silverBullet.sessionHigh, high)
sessionLow (series float) : = low low of session // use math.min(silverBullet.sessionLow, low)
sessionFVG (series float) : if applicable, the FVG created during the session
sessionFVGdraw (series box) : if applicable, draw the FVG created during the session
traded (series bool)
tp (series float) : tp of trade entered at the session FVG
sl (series float) : sl of trade entered at the session FVG
sessionDraw (series box) : optional draw session with box
sessionDrawLabel (series label) : optional label session with label
silverBulletDrawings
UDT for trade exit drawings associated with the Silver Bullet Strategy
Fields:
tpLine (series line) : tp line drawing for strategy
tpLabel (series label) : tp label drawing for strategy
slLine (series line) : sl line drawing for strategy
slLabel (series label) : sl label drawing for strategy
unicornModel
UDT for data storage and drawings associated with the Unicorn Model Strategy
Fields:
hPoint (chart.point)
hPoint2 (chart.point)
hPoint3 (chart.point)
breakerBlock (series box) : used to draw the breaker block required for the Unicorn Model
FVG (series box) : used to draw the FVG required for the Unicorn model
topBlock (series float) : price of top of breaker block, can be used to detail trade entry
botBlock (series float) : price of bottom of breaker block, can be used to detail trade entry
startBlock (series int) : start time of the breaker block, used to set the "left = " param for the box
includes (array) : used to store the time of the breaker block, or FVG, or the chart point sequence that setup the Unicorn Model.
entry (series float) : // eligible entry price, for longs"math.max(topBlock, FVG.get_top())",
tpLine (series line) : optional line to mark PT
tpLabel (series label) : optional label to mark PT
slLine (series line) : optional line to mark SL
slLabel (series label) : optional label to mark SL
rejectionBlocks
UDT for data storage and drawings associated with rejection blocks
Fields:
rejectionPoint (chart.point)
bodyPrice (series float) : candle body price closest to the rejection point, for "Up" rejections => math.max(open, close),
rejectionBox (series box) : optional box drawing of the rejection block
rejectionLabel (series label) : optional label for the rejection block
equalLevelsDraw
UDT for data storage and drawings associated with equal highs / equal lows
Fields:
connector (series line) : single line placed at the first high or low, y = avgerage of distinguished equal highs/lows
connectorLab (series label) : optional label to be placed at the highs or lows
levels (array) : array containing the equal highs or lows prices
times (array) : array containing the equal highs or lows individual times
startTime (series int) : the time of the first high or low that forms a sequence of equal highs or lows
radiate (array) : options label to "radiate" the label in connector lab. Can be used for anything
necessaryData
UDT for data storage of historical price points.
Fields:
highArr (array) : array containing historical high points
lowArr (array) : array containing historical low points
timeArr (array) : array containing historical time points
logArr (array) : array containing historical log returns
signArr (array) : array containing historical price directions
closeArr (array) : array containing historical close points
binaryTimeArr (array) : array containing historical time points, uses "push" instead of "unshift" to allow for binary search
binaryCloseArr (array) : array containing historical close points, uses "push" instead of "unshift" to allow the correct
binaryOpenArr (array) : array containing historical optn points, uses "push" instead of "unshift" to allow the correct
atrTFarr (array) : array containing historical user-selected TF atr points
openArr (array) : array containing historical open points
True Trend Average BandsThis is the indicator I am most proud of. After reading Glenn Neely's book "Mastering Eliott Waves" / "Neowave" and chatting with @timwest who got acknowledged by Neely, we came up with the idea of an moving average which does calculate the real average price since a trend started. Addionally I adapted a method from Neely Neowave and Tim Wests TimeAtMode to not force a timeframe on a chart but instead let the charts data decide which timeframe to use, to then calculate the real average price since the trend started.
It took me a while to get this right and coded, so take a moment and dive deeper and you might learn something new.
We assume that the price is in multiple trends on multiple timeframes, this is caused by short term traders, long term traders and investors who trade on different timeframes. To find out in which timeframe the important trends are, we have to look out for significant lows and highs. Then we change the timeframe in the chart to a value so that we have 10 to 20 bars since the significant low/high. While new bars are printed, and we reach more than 20 bars, we have to switch to a higher timeframe so we have 10 to 20 bars again. In the chart you see two significant trends: a downtrend on the 3 week timeframe and an uptrend from the 2 month timeframe. Based on the logic I have described, these are the two important timeframes to watch right now for the spx (there is another uptrend in the yearly chart, which is not shown here).
Now that we understand how to find the important timeframes, let's look what the magic in this script is that tells us the real average price since a trend started.
I developed a new type of moving average, which includes only the prices since a trend started. The difference to the regular sma is that it will not include prices which happened before the significant low or high happened. For example, if a top happened in a market 10 days ago, the regular sma20 would be calculated by 10 bars which happened before the top and 10 bars which happened after the top. If we want to know the average price of the last 10 bars we manually have to change the ma20 to the ma10 which is annoying manual work, additionally even if we use the ma10 in this case, and we look at yesterday's bar the ma10 will include 9 bars from after the top and one bar before the top, so the ma10 would only show the real average price for the current bar which is not what we want.
To come up with a solution to this problem, the True Trend Average searches for the lowest/highest bar in a given period (20 bars). Then starts to calculate the average value since the low/high. For example: if the price reaches a new 20 day high and then trades below it, the day of the high will be the sma1, the day after it's the sma2, ... up to the maximum look back length.
This way, we always know what the average price would have been if someone sold/bought a little bit every bar of his investment since the high/low.
Why is this even important? Let's assume we missed selling the top or buying the low, and think it would have been at least better to buy/sell a little bit since the new trend started. Once the price reaches the true trend average again, we can buy/sell, and it would be as good as selling/buying a little bit every day. We find prices to buy the dip and sell the bounce, which are as good as scaling in/out.
There is a lot more we can learn from these price levels but I think it is better to let you figure out yourself what you can learn from the information given by this indicator. Think about how market participants who accumulate or distribute feel when prices are above or below certain levels.
Now that we understand this new type of moving average, let's look into the lines we see in the chart:
The upper red band line shows the true trend average high price since the last significant top within 20 bars.
The lower red band line shows the true trend average hl2 price since the last significant top within 20 bars.
The lower green band line shows the true trend average low price since the last significant low within 20 bars.
The upper green band line shows the true trend average hl2 price since the last significant low within 20 bars.
The centerline is the average between the upper red band and the lower green band.
The teal lines show 1 standard deviation from the outer bands.
Before today only a few people had access to this indicator, now that it is public and open source, I am curious if you will find it useful and what you will do with it. Please share your findings.
/edit: The chart only shows the 3week timeframe so here are the other two trends from the 2month and 1year timeframe
USD Liquidity Conditions Index Swing Stock Strategy Original credits goes to @ElDoggo22 www.tradingview.com
I looked in the post created by him, of USD liquidity and I have noticed that if you are going to apply a percentile top and bottom to it, can become an interesting swing strategy for US Stocks.
So in this case I decided to create a 99th percentile for top and 4th percentile for bot with a big length, preferably 100+ candles, for this example i took 150.
Rules for entry :
Long : either bot or top lines are ascending
We exit long either the top line is descending, or we have sudden cross of the moving average with both top and bot within the same candle
Short: we enter short when we have a sudden cross down of the moving average with both top and bot within the same candle
We exit short when we have a cross over of the moving average with both top and bot within the same candle ( or we have a long entry condition)
If there are qny questions, please let me know !
Fractal Breakout Strategy (by ChartArt)This long only strategy determines the price of the last fractal top and enters a trade when the price breaks above the last fractal top. The strategy also calculates the average price of the last fractal tops to get the trend direction. The strategy exits the long trade, when the average of the fractal tops is falling (when the trend is lower highs as measured by fractals). And the user can manually set a time delay of this exit condition. The default setting is a long strategy exit always 3 bars after the long entry condition appeared.
In addition as gimmicks the fractals tops can be highlighted (the default is blue) and a line can be drawn based on the fractal tops.This fractal top line is colored by the fractal top average trend in combination with the fractal breakout condition.
This strategy works better on higher time-frames (weekly and monthly), but it also works on the daily and some other time-frames. This strategy does not repaint, no repainting.
P.S. I thank Tradingview user barracuda who helped me with the time based exit condition code. And user RicardoSantos for coding the definition of the fractal top, which he uses in his " Fractals" scripts.
All trading involves high risk; past performance is not necessarily indicative of future results. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
