Oscillator Toolkit (Expo)█ Overview
The Oscillators Toolkit stands at the forefront of technical trading tools, offering a comprehensive suite of sophisticated, adaptive, and unique oscillators. This toolkit has been thoughtfully designed to cater to all trading styles, ensuring versatility and utility for every trader. The toolkit features our flagship oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and Bellcurves. Furthermore, it offers many great features such as trend recognition, market impulses, and trend changes; all consolidated into a single, easy-to-use indicator.
Access to these high-quality oscillators and tools can elevate your trading strategy, providing you with insightful market analysis and potential trading opportunities. In addition, these tools help traders and investors to identify and interpret various market trends, momentum, and volatility patterns more efficiently.
The Oscillator toolkit works in any market and timeframe for discretionary analysis and includes many oscillators and features:
█ Oscillators
WaveTrend Momentum
The WaveTrend Momentum oscillator is a significant component of the toolkit. It factors in both the direction and the momentum of market trends. The waves within this system are both quick and responsive, operating independently to offer the most pertinent insights at the most opportune moments. Their rapid response time ensures that traders receive timely information, which is essential in the fast-paced, dynamic world of trading.
Example of how to use the WaveTrend Momentum Oscialltor
The WaveTrend Momentum is proficient at identifying trend reversals and pullbacks, allowing traders to enter or exit trades at optimal moments.
Leading RSI
The Leading Relative Strength Index (RSI) is a type of momentum oscillator that is commonly used in technical analysis to predict price movements. As the name suggests, it is an advanced form of the traditional Relative Strength Index (RSI), and it provides traders with more timely signals for market entries and exits.
The Leading RSI works on similar principles but is designed to provide signals ahead of the traditional RSI. This is achieved through more advanced mathematical modeling and calculations, which aim to identify shifts in market momentum before they happen. It takes into account not only the current price action but also considers historical data in a way that can foresee changes in trend directions.
Example of how to use the Leading RSI
The Leading RSI is an enhanced version of the traditional Relative Strength Index, offering more timely indications of divergences and overbought or oversold market conditions.
Momentum Oscillator
This oscillator measures the amount that a security's price has changed over a given time span. It is an excellent tool for understanding the strength of a trend and its potential endurance. When the momentum oscillator rises, it suggests that the price is moving upwards and vice versa.
The Momentum Oscillator is an advanced technical analysis tool that helps traders identify the rate of change or the momentum of the market. It is typically used to determine the strength or speed at which the price of an asset increases or decreases for a set of returns. This oscillator is considered 'fast-moving' and 'sensitive' because it responds quickly to changes in price momentum. The fast-moving nature of this oscillator helps traders to get early signals for potential market entry or exit points.
The Momentum Oscillator analyzes the current price compared to the previous price and adds two additional layers of analysis: 'Buy & Sell moves' and 'Extremes.'
Buy & Sell Moves: This layer of the oscillator helps identify the buying and selling pressure in the market. This can provide traders with valuable information about the possible direction of future price moves. When there is high buying pressure (demand), the price tends to rise, and when there is high selling pressure (supply), the price tends to fall.
Extremes: This layer helps to identify extreme overbought or oversold conditions. When the oscillator enters the overbought territory, it could indicate that the price is at a high and could potentially reverse. Conversely, if the oscillator enters the oversold territory, it could suggest that the price is at a low and could potentially rebound.
Example of how to use the Momentum Oscillator
The Momentum Oscillator is a sensitive and fast-moving oscillator that adapts quickly to price changes while keeping track of the long-term momentum, making it easier to spot buying or selling opportunities in trends.
Bellcurves
The Bellcurves indicator is a powerful tool for traders that uses statistical analysis to help identify potential market reversals and key support and resistance levels by leveraging the principles of statistical analysis to measure market impulses. The concept behind this tool is the normal distribution, also known as the bell curve, which is a fundamental statistical concept signifying that data tends to cluster around the average or mean value. The "impulses" in the market context refer to significant price movements driven by a high volume of trading activity. These are typically sharp and swift moves either upwards (bullish impulse) or downwards (bearish impulse). These impulses often signify a strong sentiment in the market and can result at the beginning of a new trend or the continuation of an existing one.
In effect, the Bellcurve indicator is designed to filter out minor price fluctuations or 'noise,' allowing traders to focus solely on significant market impulses. This makes it easier for traders to identify key market movements.
Example of how to use the Bellcurve
The Bellcurves uses the principles of statistical analysis to identify significant market impulses and potential market reversals.
█ Why is this Oscillator Toolkit Needed?
The Oscillator Toolkit is a vital asset for traders for several reasons:
Insight into Market Trends: The Oscillator Toolkit provides valuable insight into current market trends. This includes understanding whether the market is bullish (rising) or bearish (falling), as well as identifying potential future price movements.
Identification of Overbought or Oversold Conditions: Oscillators like those in the toolkit can help traders identify when an asset is overbought (potentially overvalued) or oversold (potentially undervalued). This can signal potential market reversals.
Confirmation of Price Patterns: The oscillators in the toolkit can confirm price patterns and trends. For example, if a price pattern suggests a bullish trend, an oscillator can help confirm this by showing rising momentum.
Versatility Across Markets and Timeframes: The Oscillator Toolkit is designed to work across a variety of markets, including stocks, forex, commodities, and cryptocurrencies. It's also effective across different timeframes, from short-term day trading to longer-term investment strategies.
Timely Trade Signals: By providing real-time insights into market conditions and price momentum, the Oscillator Toolkit offers timely signals for trade entries and exits.
Enhancing Trading Strategy: Every trader has a unique approach to the market. The Oscillator Toolkit, with its suite of different oscillators, provides a robust set of tools that can be customized to enhance any trading strategy, whether it's a trend following, swing trading, scalping, or any other approach.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ How are the Oscillators calculated? - Overview
The Toolkit combines many of our existing premium indicators and new technical analysis algorithms to analyze the market. This overview covers how the main features are calculated.
WaveTrend Momentum
The WaveTrend Momentum oscillator operates at its core by comparing the current price to previous prices. If the current price is higher than the previous price, the oscillator value will rise, indicating an uptrend. Conversely, if the current price is lower than the previous price, the oscillator value will fall, indicating a downtrend. To make it unique and useful normalized weighting functions are added.
Leading RSI
The Leading RSI is based on the traditional Relative Strength Index, with an added exploration function that takes into account historical price movements.
Momentum Oscillator
The Momentum oscillator measures how quickly the price is changing, on average, over a certain period, relative to the variability of the price over that same period. It gives higher values when the price is changing rapidly in one direction and lower values when the price is fluctuating or changing more slowly. In addition, other functions, such as market extremes and buying/selling pressure, are factored in.
Bellcurves
The Bellcurves assume that some common historical price data is normally distributed, and once these patterns or moves are found the in the price data, a Bellcurve is formed.
█ In conclusion , the Oscillator Toolkit is an advanced, versatile, and indispensable asset for traders across various markets and timeframes. This innovative collection includes different oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and the Bellcurves Indicator, each serving a unique function in providing valuable insights into the market's behavior.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
在腳本中搜尋"algo"
MTF Fusion - PSAR [TradingIndicators]MTF Fusion PSAR intelligently adapts to whatever timeframe you're trading - dynamically calculating Parabolic SAR (Stop and Reverse) levels combined from four appropriate higher timeframes to give you a much broader view of the market and an edge in your trading decisions. It is the third indicator in our MTF Fusion series, and leverages our MTF Fusion algorithm - only this time to visualize J. Welles Wilder Jr.'s famous Parabolic SAR indicator.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates PSAR levels, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of PSAR levels are calculated by determining the value of the PSAR indicator for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a PSAR level from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_PSAR_Level_1 + HigherTF_PSAR_Level_2 + HigherTF_PSAR_Level_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the PSAR calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
What is the PSAR indicator?
The Parabolic SAR (Stop and Reverse) indicator is a technical analysis tool that helps identify potential trend reversals in price movements. It was developed by J. Welles Wilder Jr. and is widely used by traders to determine entry and exit points in the market. It consists of levels that are plotted above or below current price. The position of these plots relative to the price provides valuable information about the prevailing trend and potential reversal points.
Here's how the original PSAR indicator works:
Upward Trend: When the Parabolic SAR level is plotted below the price, it indicates an upward trend in the market. The level generally moves closer to the price as the trend progresses. This creates a parabolic curve that rises with time. Traders typically interpret this as a bullish signal, suggesting that it may be a good time to buy or hold positions.
Downward Trend: Conversely, when the Parabolic SAR level is plotted above the price, it indicates a downward trend in the market. The plot generally moves closer to the price as the trend continues, forming a parabolic curve that declines with time. This is considered a bearish signal, suggesting that it may be a suitable time to sell or avoid taking long positions.
Reversal Points: The primary purpose of the Parabolic SAR indicator is to identify potential trend reversals. When the price crosses above or below the Parabolic SAR level, it indicates a possible reversal in the trend.
The Parabolic SAR indicator is versatile and can be used in various market conditions and timeframes. It is particularly useful in trending markets, where it helps traders ride the trend and capture potential profits. However, it's important to note that the Parabolic SAR may generate false signals or provide delayed indications in sideways or choppy markets.
Included Features
Fusion PSAR levels
Filled zones to highlight trends
Full customization of PSAR parameters
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion PSAR levels calculated from multiple higher timeframes
Fill Trending Zones: Show/hide the fill for 'trending zones' between price and the Fusion PSAR levels
Start: Defines the rate at which the PSAR levels move closer to the price during the initial stages of a trend (higher = faster convergence, lower = slower convergence)
Increment: Controls the rate at which the acceleration factor increases or decreases as the trend continues (higher = faster convergence, lower = slower convergence)
Max: Sets a limit on the maximum value that the acceleration factor can reach
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
MTF Fusion - S/R Levels [TradingIndicators]MTF Fusion S/R Levels intelligently adapt to whatever timeframe you're trading - dynamically calculating pivot-based support and resistance levels combined from four appropriate higher timeframes to give you a much broader view of the market and an edge in your trading decisions. It is the second indicator in our MTF Fusion series, and leverages our MTF Fusion algorithm - only this time to visualize pivot-based S/R levels and zones.
These levels are not programmed to repaint - so you can use them in real-time just as they appeared historically.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates S/R Levels based on pivot points, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of support and resistance levels are calculated by determining pivot points for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a support level from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_Support_Level_1 + HigherTF_Support_Level_2 + HigherTF_Support_Level_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the lookbacks used for pivot and S/R level calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Included Features
Fusion Support and Resistance Levels
Dynamic Multi-Timeframe S/R Levels
Breakaway Zone fills to highlight breakouts and breakdowns from the Fusion S/R Levels
Customizable lookback approach
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion S/R Levels calculated from multiple higher timeframes
MTF View: Show/hide the S?R levels from multiple higher timeframes used to calculate the Fusion S/R Levels
Breakaway Zones: Show/hide the fill for zones where price breaks away from the Fusion S/R Levels
Lookback: Select how you want your S/R Levels to be calculated (longer = long-term levels, shorter = short-term levels)
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Complete Discrete Fourier Transform ToolkitThis is an expansion from my Discrete Fourier Transform Overlay indicator which offers various features that may be useful for traders wishing to apply frequency analysis or integral transform to their trading. For those unfamiliar with the concept, the discrete Fourier transform decomposes wave or wave-like data into functions depending on frequency. This can be helpful in demonstrating or interpreting trends and periodic frequencies in time-series price data, or oscillating indicators.
This toolkit has the following features:
Fourier bands (deviation cloud): The deviation cloud expresses the uncertainty in the DFT algorithm, as well as the relative change in frequency of the curve.
Fourier supertrend: The supertrend is applied as a product of the DFT algorithm, instead of onto the price data itself. This filters the supertrend from infrequent periodicities. For trading, this means that the supertrend will not be affected by false breakouts or breakdowns. See the image below for an example:
Future updates may include:
Projection of the probabilistic uncertainty principle. In a nutshell, the concept can be used to project uncertainties forwards through price data to forecast the path of least resistance, or, the most probable frequency.
Machine learning capabilities. Justin Doherty has done the Pine Script community a great service in introducing kNN algorithms with Lorentzian distance calculations; however, this is only the start of relativistic mechanics that can be applied to time series data. The DFT algorithm essentially filters data into its periodicities; this data can be inserted into a relativistic kNN algorithm - Lorenz or otherwise - to possibly improve accuracy.
Pattern Forecast (Expo)█ Overview
The Pattern Forecast indicator is a technical analysis tool that scans historical price data to identify common chart patterns and then analyzes the price movements that followed these patterns. It takes this information and projects it into the future to provide traders with potential price actions that may occur if the same pattern is identified in real-time market data. This projection helps traders to understand the possible outcomes based on the previous occurrences of the pattern, thereby offering a clearer perspective of the market scenario. By analyzing the historical data and understanding the subsequent price movements following the appearance of a specific pattern, the indicator can provide valuable insights into potential future market behavior.
█ Calculations
The indicator works by scanning historical price data for various candlestick patterns. It includes all in-built TradingView patterns, credit to TradingView that has coded them.
Essentially, the indicator takes the historical price moves that followed the pattern to forecast what might happen next.
█ Example
In this example, the algorithm is set to search for the Inverted Hammer Bullish candlestick pattern. If the pattern is found, the historical outcome is then projected into the future. This helps traders to understand how the past pattern evolved over time.
█ How to use
Providing traders with a comprehensive understanding of historical patterns and their implications for future price action allows them to assess the likelihood of specific market scenarios objectively. For example, suppose the pattern forecast indicator suggests that a particular pattern is likely to lead to a bullish move in the market. A trader might consider going long if the same pattern is identified in the real-time market. Similarly, a trader might consider shorting the asset if the indicator suggests a bearish move is likely, if the same pattern is identified in the real-time market.
█ Settings
Pattern
Select the pattern that the indicator should scan for. All inbuilt TradingView patterns can be selected.
Forecast Candles
Number of candles to project into the future.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
loxfft
XPrecisionSwing (XPS)* XPrecisionSwing (XPS) Indicator *
Is a visual representation of the Forces of Supply / Demand in the markets in the form of UP and DOWN waves. The Supply / Demand (denoted by a number on top or below the wave line) is computed using the *MBox Precision Supply / Demand* algorithm. These numbers diligently show the forces of Supply and Demand moving price in the markets. The algorithm for computing the numbers on the top and bottom of the wave lines measures the strength of the Supply / Demand. It is this algorithm that makes this indicator unique as it gives an accurate representation of the forces pulling the market up and down. When forces oppose each other, meaning when the direction of price does not agree with the direction of the Supply or Demand it creates a divergence and an opportunity in the markets. These situations are called BUY / SELL Imbalances. Explanation about this below.
* WHAT THE SCRIPT DOES *
The XPrecisionSwing indicator draws swing waves lines going up and down. These waves lines are representative of Supply and Demand. Waves going up are Demand, while waves going down are Supply. The strength of the Supply / Demand corresponds to the number drawn either on top of the wave line or below it. The numbers drawn on the chart are powered by the *MBox Precision Supply / Demand* algorithm, which are representative of the Forces of Supply / Demand in the markets. This is not just volume added up like in a regular zig zag indicator, since volume alone does not show Supply / Demand, and regular volume will not show BUY / SELL Imbalances as depicted by XPrecisionSwing. Volume summated will not show both positive and negative numbers on the chart. Having Supply / Demand split into both positive and negative numbers allows us to see BUY / SELL Imbalances, which can be a very powerful divergence. Information on how these numbers are computed are in the "HOW IT WORKS" section.
The numbers drawn on the chart can be either negative or positive. Positive relates to Demand, while negative relates to Supply. In this manner the strength of Supply and Demand can be gauged in each wave. If the price goes up but the number is negative (More Supply) it is a divergence and called a SELL Imbalance. This means there was more Supply even though price went up. It is important to pay attention to these scenarios, as often it can be indicative of NO DEMAND. Conversely. if the price goes down but the number is positive (No Demand) it is a divergence and is called a BUY Imbalance. This means there was more Demand even though price went down. This is indicative of NO SUPPLY. As such, it now becomes possible to know when there is a sign of Supply, Demand, No Supply, No Demand, Supply Exhaustion, and Demand exhaustion. Supply occurs when the negative numbers on the charts begin to increase (more negatively). Demand occurs when the positive numbers on the chart begin to increase (more positively). A Supply Exhaustion pattern happens when the price is starting to move down more slowly, while Supply is decreasing, and Demand is increasing. This means that the behavior of the market is changing and also a signal to look to reverse positions. A Demand Exhaustion pattern happens when the price is starting to move up more slowly, while Demand is decreasing, and Supply is increasing. The behavior of the market here is also changing.
* HOW IT WORKS *
- Technical Details for the Numbers on the Swing -
The numbers on the chart represent Supply / Demand. Supply or Demand is determined by analyzing the movement of price and quantity of volume.
When price goes up and is combined with an increase in volume it is Expansion of Demand.
(Positive Numbers get larger)
However if price goes up and is combined with a decrease in volume it is Contraction of Demand.
(Positive Numbers get smaller)
When price goes down and is combined with an increase in volume it is Expansion of Supply.
(Negative Numbers get larger)
However if price goes down and is combined with a decrease in volume it is Contraction of Supply.
(Negative Numbers get smaller)
- Technical Details for the Swing -
The way XPrecisionSwing draws the swings is fractal in nature, which make it very convenient and easier to use over the traditional zig zag indicator. The traditional zig zag indicator uses a tick reversal which needs to be adjusted every time you change time frames. However, with XPrecisionSwing you do not have to change any settings every time you load a different time frame since it will adjust to any time frame you are loading. How the swing is drawn is explained below.
XPrecisionSwing uses 3 bars (by default) to define a swing
This parameter can be adjusted. Can be 1, 2, 4 bars, etc...
Swings are always drawn using High / Low of the bar
- Rules -
To start upswing, bar high needs to be higher than previous 3 candle highs
To start downswing, bar low needs to be lower than previous 3 candle lows
If in upswing, a higher high will continue the upswing
if in downswing, a lower low will continue the downswing
- Exceptions -
If outside bar (both high and low exceeds previous 3 bars) swing will continue in current direction
- Swing Confirmation -
Swing wave line in progress (unconfirmed) is denoted by a brown box around the swing number
Once the brown box disappears, that swing wave and number is confirmed
* HOW TO USE IT *
As the numbers on the down waves increase (negatively), this shows that the bears have taken control of the markets. Conversely, as the numbers on the up waves increase (positively), this shows the bulls have taken control of the markets. Whoever is in control is the direction you generally want to place your trades in. When you see an increase in Supply (numbers on down wave) accompanied with a decrease in Demand (numbers on up wave) this shows a Supply + Demand Exhaustion Pattern. This is stronger than if you only see an increase in Supply without a decrease in Demand.
- The Buy / Sell Imbalances -
If you see a positive blue number on the bottom of a DOWN Wave, this means that there was more buying than selling even though price moved down.
If you see a negative red number on the top of an UP Wave, this means that there was more selling than buying even though price moved up.
Both of these cases signify and imbalance and a divergence.
* EXAMPLE AND USE CASES *
- Sell Imbalance Example -
If you see a large negative number with a lower low on a down wave, and then the next up wave is a lower high also with a negative number it shows that there is only Supply flooding the market and no sign of Demand. This is a very powerful combo.
- Buy Imbalance Example -
If you see a large positive number with a higher high on an up wave, and then the next down wave is a higher low also with a positive number it shows that there is only Demand flooding the market and no sign of Supply. This is a very powerful combo.
- Supply Exhaustion example -
If you see price movement struggling to make newer lows and the Supply numbers on the down waves are decreasing, while the Demand numbers on the up waves are increasing this is indicative of a *Change of Behavior*, and that the market is showing signs of reversal.
- Break out on Demand example -
If you see price has been ranging and now the numbers on the UP waves begin to increase while breaking out of a previous area of resistance, it is a good sign that the movement is backed by the strength coming from the Demand.
* BUY / SELL IMBALANCE ALERTS *
The Green / Red crosses on the chart show exactly where the Buy / Sell Imbalance Alerts trigger.
These will NEVER repaint! The crosses can be hidden in Styles if you wish to.
Alerts can be set very easily with the instructions below.
1. Right Click Chart -> Add Alert...
(Ignore Caution Warning. These alerts will *ONLY* trigger on Confirmed BUY / SELL Imbalances and will NOT repaint)
2. Select Condition to be "XPrecisionSwing"
3. Select "Buy Imbalance" or "Sell Imbalance"
4. Select "Greater Than" with Value = 0
5. Options set "Once Per Bar"
6. Customize Any other Alert Options you want
* WHAT MAKES IT ORIGINAL *
XPrecisionSwing gives an inside look into the markets by showing price movements as a series of waves going up and down with their corresponding Supply / Demand numbers associated with each wave. Reading the numbers shows the strength of Supply / Demand. The bigger the number the stronger the Supply / Demand is. The smaller the number the weaker the Supply / Demand is. It becomes possible to see where Supply / Demand comes in, along with Exhaustion of Supply / Demand to spot opportunities to place trades. The Buy / Sell Imbalances show imbalances where price movement and the direction of the Supply / Demand diverge to create potential opportunities as well.
* AUTHOR *
This script is published by MBoxWave LLC
Smoothing R-Squared ComparisonIntroduction
Heyo guys, here I made a comparison between my favorised smoothing algorithms.
I chose the R-Squared value as rating factor to accomplish the comparison.
The indicator is non-repainting.
Description
In technical analysis, traders often use moving averages to smooth out the noise in price data and identify trends. While moving averages are a useful tool, they can also obscure important information about the underlying relationship between the price and the smoothed price.
One way to evaluate this relationship is by calculating the R-squared value, which represents the proportion of the variance in the price that can be explained by the smoothed price in a linear regression model.
This PineScript code implements a smoothing R-squared comparison indicator.
It provides a comparison of different smoothing techniques such as Kalman filter, T3, JMA, EMA, SMA, Super Smoother and some special combinations of them.
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement.
The input parameters for the Kalman filter include the process noise covariance and the measurement noise covariance, which help to adjust the sensitivity of the filter to changes in the input data.
The T3 smoothing technique is a popular method used in technical analysis to remove noise from a signal.
The input parameters for the T3 smoothing method include the length of the window used for smoothing, the type of smoothing used (Normal or New), and the smoothing factor used to adjust the sensitivity to changes in the input data.
The JMA smoothing technique is another popular method used in technical analysis to remove noise from a signal.
The input parameters for the JMA smoothing method include the length of the window used for smoothing, the phase used to shift the input data before applying the smoothing algorithm, and the power used to adjust the sensitivity of the JMA to changes in the input data.
The EMA and SMA techniques are also popular methods used in technical analysis to remove noise from a signal.
The input parameters for the EMA and SMA techniques include the length of the window used for smoothing.
The indicator displays a comparison of the R-squared values for each smoothing technique, which provides an indication of how well the technique is fitting the data.
Higher R-squared values indicate a better fit. By adjusting the input parameters for each smoothing technique, the user can compare the effectiveness of different techniques in removing noise from the input data.
Usage
You can use it to find the best fitting smoothing method for the timeframe you usually use.
Just apply it on your preferred timeframe and look for the highlighted table cell.
Conclusion
It seems like the T3 works best on timeframes under 4H.
There's where I am active, so I will use this one more in the future.
Thank you for checking this out. Enjoy your day and leave me a like or comment. 🧙♂️
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Credits to:
▪@loxx – T3
▪@balipour – Super Smoother
▪ChatGPT – Wrote 80 % of this article and helped with the research
Spectral Gating (SG)The Spectral Gating (SG) Indicator is a technical analysis tool inspired by music production techniques. It aims to help traders reduce noise in their charts by focusing on the significant frequency components of the data, providing a clearer view of market trends.
By incorporating complex number operations and Fast Fourier Transform (FFT) algorithms, the SG Indicator efficiently processes market data. The indicator transforms input data into the frequency domain and applies a threshold to the power spectrum, filtering out noise and retaining only the frequency components that exceed the threshold.
Key aspects of the Spectral Gating Indicator include:
Adjustable Window Size: Customize the window size (ranging from 2 to 6) to control the amount of data considered during the analysis, giving you the flexibility to adapt the indicator to your trading strategy.
Complex Number Arithmetic: The indicator uses complex number addition, subtraction, and multiplication, as well as radius calculations for accurate data processing.
Iterative FFT and IFFT: The SG Indicator features iterative FFT and Inverse Fast Fourier Transform (IFFT) algorithms for rapid data analysis. The FFT algorithm converts input data into the frequency domain, while the IFFT algorithm restores the filtered data back to the time domain.
Spectral Gating: At the heart of the indicator, the spectral gating function applies a threshold to the power spectrum, suppressing frequency components below the threshold. This process helps to enhance the clarity of the data by reducing noise and focusing on the more significant frequency components.
Visualization: The indicator plots the filtered data on the chart with a simple blue line, providing a clean and easily interpretable representation of the results.
Although the Spectral Gating Indicator may not be a one-size-fits-all solution for all trading scenarios, it serves as a valuable tool for traders looking to reduce noise and concentrate on relevant market trends. By incorporating this indicator into your analysis toolkit, you can potentially make more informed trading decisions.
PSv5 3D Array/Matrix Super Hack"In a world of ever pervasive and universal deceit, telling a simple truth is considered a revolutionary act."
INTRO:
First, how about a little bit of philosophic poetry with another dimension applied to it?
The "matrix of control" is everywhere...
It is all around us, even now in the very place you reside. You can see it when you look at your digitized window outwards into the world, or when you turn on regularly scheduled television "programs" to watch news narratives and movies that subliminally influence your thoughts, feelings, and emotions. You have felt it every time you have clocked into dead end job workplaces... when you unknowingly worshiped on the conformancy alter to cultish ideologies... and when you pay your taxes to a godvernment that is poisoning you softly and quietly by injecting your mind and body with (psyOps + toxicCompounds). It is a fictitiously generated world view that has been pulled over your eyes to blindfold, censor, and mentally prostrate you from spiritually hearing the real truth.
What TRUTH you must wonder? That you are cognitively enslaved, like everyone else. You were born into mental bondage, born into an illusory societal prison complex that you are entirely incapable of smelling, tasting, or touching. Its a contrived monetary prison enterprise for your mind and eternal soul, built by pretending politicians, corporate CONartists, and NonGoverning parasitic Organizations deploying any means of infiltration and deception by using every tactic unimaginable. You are slowly being convinced into becoming a genetically altered cyborg by acclimation, socially engineered and chipped to eventually no longer be 100% human.
Unfortunately no one can be told eloquently enough in words what the matrix of control truly is. You have to experience it and witness it for yourself. This is your chance to program a future paradigm that doesn't yet exist. After visiting here, there is absolutely no turning back. You can continually take the blue pill BIGpharmacide wants you to repeatedly intake. The story ends if you continually sleep walk through a 2D hologram life, believing whatever you wish to believe until you cease to exist. OR, you can take the red pill challenge, explore "question every single thing" wonderland, program your arse off with 3D capabilities, ultimately ascertaining a new mathematical empyrean. Only then can you fully awaken to discover how deep the rabbit hole state of affairs transpire worldwide with a genuine open mind.
Remember, all I'm offering is a mathematical truth, nothing more...
PURPOSE:
With that being said above, it is now time for advanced developers to start creating their own matrix constructs in 3D, in Pine, just as the universe is created spatially. For those of you who instantly know what this script's potential is easily capable of, you already know what you have to do with it. While this is simplistically just a 3D array for either integers or floats, additional companion functions can in the future be constructed by other members to provide a more complete matrix/array library for millions of folks on TV. I do encourage the most courageous of mathemagicians on TV to do so. I have been employing very large 2D/3D array structures for quite some time, and their utility seems to be of great benefit. Discovering that for myself, I fully realized that Pine is incomplete and must be provided with this agility to process complex datasets that traders WILL use in the future. Mark my words!
CONCEPTION:
While I have long realized and theorized this code for a great duration of time, I was finally able to turn it into a Pine reality with the assistance and training of an "artificially intuitive" program while probing its aptitude. Even though it knows virtually nothing about Pine Script 4.0 or 5.0 syntax, functions, and behavior, I was able to conjure code into an identity similar to what you see now within a few minutes. Close enough for me! Many manual edits later for pine compliance, and I had it in chart, presto!
While most people consider the service to be an "AI", it didn't pass my Pine Turing test. I did have to repeatedly correct it, suffered through numerous apologies from it, was forced to use specifically tailored words, and also rationally debate AND argued with it. It is a handy helper but beware of generating Pine code from it, trust me on this one. However... this artificially intuitive service is currently available in its infancy as version 3. Version 4 most likely will have more diversity to enhance my algorithmic expertise of Pine wizardry. I do have to thank E.M. and his developers for an eye opening experience, or NONE of this code below would be available as you now witness it today.
LIMITATIONS:
As of this initial release, Pine only supports 100,000 array elements maximum. For example, when using this code, a 50x50x40 element configuration will exceed this limit, but 50x50x39 will work. You will always have to keep that in mind during development. Running that size of an array structure on every single bar will most likely time out within 20-40 seconds. This is not the most efficient method compared to a real native 3D array in action. Ehlers adepts, this might not be 100% of what you require to "move forward". You can try, but head room with a low ceiling currently will be challenging to walk in for now, even with extremely optimized Pine code.
A few common functions are provided, but this can be extended extensively later if you choose to undertake that endeavor. Use the code as is and/or however you deem necessary. Any TV member is granted absolute freedom to do what they wish as they please. I ultimately wish to eventually see a fully equipped library version for both matrix3D AND array3D created by collaborative efforts that will probably require many Pine poets testing collectively. This is just a bare bones prototype until that day arrives. Considerably more computational server power will be required also. Anyways, I hope you shall find this code somewhat useful.
Notice: Unfortunately, I will not provide any integration support into members projects at all. I have my own projects that require too much of my time already.
POTENTIAL APPLICATIONS:
The creation of very large coefficient 3D caches/buffers specifically at bar_index==0 can dramatically increase runtime agility for thousands of bars onwards. Generating 1000s of values once and just accessing those generated values is much faster. Also, when running dozens of algorithms simultaneously, a record of performance statistics can be kept, self-analyzed, and visually presented to the developer/user. And, everything else under the sun can be created beyond a developers wildest dreams...
EPILOGUE:
Free your mind!!! And unleash weapons of mass financial creation upon the earth for all to utilize via the "Power of Pine". Flying monkeys and minions are waging economic sabotage upon humanity, decimating markets and exchanges. You can always see it your market charts when things go horribly wrong. This is going to be an astronomical technical challenge to continually navigate very choppy financial markets that are increasingly becoming more and more unstable and volatile. Ordinary one plot algorithms simply are not enough anymore. Statistics and analysis sits above everything imagined. This includes banking, godvernment, corporations, REAL science, technology, health, medicine, transportation, energy, food, etc... We have a unique perspective of the world that most people will never get to see, depending on where you look. With an ever increasingly complex world in constant dynamic flux, novel ways to process data intricately MUST emerge into existence in order to tackle phenomenal tasks required in the future. Achieving data analysis in 3D forms is just one lonely step of many more to come.
At this time the WesternEconomicFraudsters and the WorldHealthOrders are attempting to destroy/reset the world's financial status in order to rain in chaos upon most nations, causing asset devaluation and hyper-inflation. Every form of deception, infiltration, and theft is occurring with a result of destroyed wealth in preparation to consolidate it. Open discussions, available to the public, by world leaders/moguls are fantasizing about new dystopian system as a one size fits all nations solution of digitalID combined with programmableDemonicCurrencies to usher in a new form of obedient servitude to a unipolar digitized hegemony of monetary vampires. If they do succeed with economic conquest, as they have publicly stated, people will be converted into human cattle, herded within smart cities, you will own nothing, eat bugs for breakfast/lunch/dinner, live without heat during severe winter conditions, and be happy. They clearly haven't done the math, as they are far outnumbered by a ratio of 1 to millions. Sith Lords do not own planet Earth! The new world disorder of human exploitation will FAIL. History, my "greatest teacher" for decades reminds us over, and over, and over again, and what are time series for anyways? They are for an intense mathematical analysis of prior historical values/conditions in relation to today's values/conditions... I imagine one day we will be able to ask an all-seeing AI, "WHO IS TO BLAME AND WHY AND WHEN?" comprised of 300 pages in great detail with images, charts, and statistics.
What are the true costs of malignant lies? I will tell you... 64bit numbers are NOT even capable of calculating the extreme cost of pernicious lies and deceit. That's how gigantic this monstrous globalization problem has become and how awful the "matrix of control" truly is now. ALL nations need a monumental revision of its CODE OF ETHICS, and that's definitely a multi-dimensional problem that needs solved sooner than later. If it was up to me, economies and technology would be developed so extensively to eliminate scarcity and increase the standard of living so high, that the notion of war and conflict would be considered irrelevant and extremely appalling to the future generations of humanity, our grandchildren born and unborn. The future will not be owned and operated by geriatric robber barons destined to expire quickly. The future will most likely be intensely "guided" by intelligent open source algorithms that youthful generations will inherit as their birth right.
P.S. Don't give me that politco-my-diction crap speech below in comments. If they weren't meddling with economics mucking up 100% of our chart results in 100% of tickers, I wouldn't have any cause to analyze any effects generated by them, nor provide this script's code. I am performing my analytical homework, but have you? Do you you know WHY international affairs are in dire jeopardy? Without why, the "Power of Pine" would have never existed as it specifically does today. I'm giving away much of my mental power generously to TV members so you are specifically empowered beyond most mathematical agilities commonly existing. I'm just a messenger of profound ideas. Loving and loathing of words is ALWAYS in the eye of beholders, and that's why the freedom of speech is enshrined as #1 in the constitutional code of the USA. Without it, this entire site might not have been allowed to exist from its founder's inceptions.
Curved Stop Loss (Expo)█ Overview
Curved Stop Loss (Expo) automatically calculates the best stop-loss distance based on real-time momentum and volatility. Once the algorithm has analyzed the current market characteristics, a curved stop loss is placed on the chart. As a result, the trader can be confident that the stop loss is based on data insights. One of the key elements of a curved stop loss is that it ensures that the trade can either be stopped with a profit or only with a minor loss without compromising the profit potential. Hence, using the Curved Stop Loss makes a massive difference in the overall results.
█ Why is this tool needed?
Risk management is a key concept to grasp and use in your trading, and it's one of the most critical aspects that will determine your long-term success in this industry. The market is uncertain, and it's impossible to know what the future holds. The only way to take control of the unknown is to have a proper risk management system that ensures you don't blow your account in one trade. Therefore, all traders need to understand the importance of using a risk- and money-management tool that calculates and provides stop-loss and take-profit levels in real-time. This way, you will always know where to take your stop loss and secure profit.
█ How to use
This Curved Stop Loss helps traders set a stop loss based on current momentum and volatility. It can be used to minimize your risk and maximize your profit potential.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Fourier Extrapolator of 'Caterpillar' SSA of Price [Loxx]Fourier Extrapolator of 'Caterpillar' SSA of Price is a forecasting indicator that applies Singular Spectrum Analysis to input price and then injects that transformed value into the Quinn-Fernandes Fourier Transform algorithm to generate a price forecast. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia dotted curve indicates the future extrapolated values.
What is the Fourier Transform Extrapolator of price?
Fourier Extrapolator of Price is a multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by:
xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H )
Where:
xi - past price at i-th bar, total n past prices;
m - bias;
a and b - scaling coefficients of harmonics;
w - frequency of a harmonic ;
h - harmonic number;
H - total number of fitted harmonics.
Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.
Quinn-Fernandes algorithm find sthe harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.
see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press
Fourier Transform Extrapolator of Price inputs are as follows:
npast - number of past bars, to which trigonometric series is fitted;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
"Caterpillar" SSA inputs are as follows:
lag - How much lag to introduce into the SSA algorithm, the higher this number the slower the process and smoother the signal
ncomp - Number of Computations or cycles of of the SSA algorithm; the higher the slower
ssapernorm - SSA Period Normalization
numbars =- number of past bars, to which SSA is fitted
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Related Fourier Transform Indicators
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Related Projection Forecast Indicators
Itakura-Saito Autoregressive Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
Related SSA Indicators
End-pointed SSA of FDASMA
End-pointed SSA of Williams %R
Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]Levinson-Durbin Autocorrelation Extrapolation of Price is an indicator that uses the Levinson recursion or Levinson–Durbin recursion algorithm to predict price moves. This method is commonly used in speech modeling and prediction engines.
What is Levinson recursion or Levinson–Durbin recursion?
Is a linear algebra prediction analysis that is performed once per bar using the autocorrelation method with a within a specified asymmetric window. The autocorrelation coefficients of the window are computed and converted to LP coefficients using the Levinson algorithm. The LP coefficients are then transformed to line spectrum pairs for quantization and interpolation. The interpolated quantized and unquantized filters are converted back to the LP filter coefficients to construct the synthesis and weighting filters for each bar.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is caculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Included
Bar color muting
Further reading
Implementing the Levinson-Durbin Algorithm on the StarCore™ SC140/SC1400 Cores
LevinsonDurbin_G729 Algorithm, Calculates LP coefficients from the autocorrelation coefficients. Intel® Integrated Performance Primitives for Intel® Architecture Reference Manual
APA-Adaptive, Ehlers Early Onset Trend [Loxx]APA-Adaptive, Ehlers Early Onset Trend is Ehlers Early Onset Trend but with Autocorrelation Periodogram Algorithm dominant cycle period input.
What is Ehlers Early Onset Trend?
The Onset Trend Detector study is a trend analyzing technical indicator developed by John F. Ehlers , based on a non-linear quotient transform. Two of Mr. Ehlers' previous studies, the Super Smoother Filter and the Roofing Filter, were used and expanded to create this new complex technical indicator. Being a trend-following analysis technique, its main purpose is to address the problem of lag that is common among moving average type indicators.
The Onset Trend Detector first applies the EhlersRoofingFilter to the input data in order to eliminate cyclic components with periods longer than, for example, 100 bars (default value, customizable via input parameters) as those are considered spectral dilation. Filtered data is then subjected to re-filtering by the Super Smoother Filter so that the noise (cyclic components with low length) is reduced to minimum. The period of 10 bars is a default maximum value for a wave cycle to be considered noise; it can be customized via input parameters as well. Once the data is cleared of both noise and spectral dilation, the filter processes it with the automatic gain control algorithm which is widely used in digital signal processing. This algorithm registers the most recent peak value and normalizes it; the normalized value slowly decays until the next peak swing. The ratio of previously filtered value to the corresponding peak value is then quotiently transformed to provide the resulting oscillator. The quotient transform is controlled by the K coefficient: its allowed values are in the range from -1 to +1. K values close to 1 leave the ratio almost untouched, those close to -1 will translate it to around the additive inverse, and those close to zero will collapse small values of the ratio while keeping the higher values high.
Indicator values around 1 signify uptrend and those around -1, downtrend.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Jurik Composite Fractal Behavior (CFB) on EMA [Loxx]Jurik Composite Fractal Behavior (CFB) on EMA is an exponential moving average with adaptive price trend duration inputs. This purpose of this indicator is to introduce the formulas for the calculation Composite Fractal Behavior. As you can see from the chart above, price reacts wildly to shifts in volatility--smoothing out substantially while riding a volatility wave and cutting sharp corners when volatility drops. Notice the chop zone on BTC around August 2021, this was a time of extremely low relative volatility.
This indicator uses three previous indicators from my public scripts. These are:
JCFBaux Volatility
Jurik Filter
Jurik Volty
The CFB is also related to the following indicator
Jurik Velocity ("smoother moment")
Now let's dive in...
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Modifications and improvements
1. Jurik's original calculation for CFB only allowed for depth lengths of 24, 48, 96, and 192. For theoretical purposes, this indicator allows for up to 20 different depth inputs to sample volatility. These depth lengths are
2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536
Including these additional length inputs is arguable useless, but they are are included for completeness of the algorithm.
2. The result of the CFB calculation is forced to be an integer greater than or equal to 1.
3. The result of the CFB calculation is double filtered using an advanced, (and adaptive itself) filtering algorithm called the Jurik Filter. This filter and accompanying internal algorithm are discussed above.
Relative Strength Super Smoother by lastguruA better version of Apirine's RS EMA by using a superior MA: Ehlers Super Smoother.
In January 2022 edition of TASC Vitaly Apirine introduced his Relative Strength Exponential Moving Average. A concept not entirely new, as Tushar Chande used a similar calculation for his VIDYA moving average. Both are based on the idea to change EMA length depending on the absolute RSI value, so the moving average would speed up then RSI is going up or down from the center value (when there is a significant directional price movement), and slow down when RSI returns to the center value (when there is a neutral or sideways movement). That way EMA responsiveness would increase where it matters most, but decrease where there is a high probability of whipsaw.
There are only two main differences between VIDYA and RS EMA:
RSI internal smoothing - VIDYA uses SMA, as Chande's CMO is an RSI with SMA; RS EMA uses EMA
Change direction - VIDYA sets the fastest length; RS EMA sets the slowest length
Both algorithms use EMA as the base of their calculation. As John F. Ehlers has shown in his article "Predictive and Successful Indicators" (January 2014 issue of TASC), EMA is not a very efficient filter, as it introduces a significant lag if sufficient smoothing is required. He describes a new smoothing filter called SuperSmoother, "that sharply attenuates aliasing noise while minimizing filtering lag." In other words, it provides better smoothing with lower lag than EMA.
In this script, I try to get the best of all these approaches and present to you Relative Strength Super Smoother. It uses RS EMA algorithm to calculate the SuperSmoother length. Unlike the original RS EMA algorithm, that has an abstract "multiplier" setting to scale the period variance (without this parameter, RSI would only allow it to speed up twice; Vitaly Apirine sets the multiplier to 10 by default), my implementation has explicit lower bound setting, so you can specify the exact range of calculated length.
Settings:
Lower Bound - fastest SuperSmoother length (when RSI is +100 or -100)
Upper Bound - slowest SuperSmoother length (when RSI is 0)
RSI Length - underlying RSI length. Unlike the original RSI that uses RMA as an internal smoothing algorithm, Vitaly Apirine uses EMA, which is approximately twice as fast (that is needed because he uses a generally long RSI length and RMA would be too slow for this). It is the same as the Upper Bound by default (0), as in the original implementation
The original RS EMA is also shown on the chart for comparison. The default multiplier of 10 for RS EMA means that the fastest EMA period is around 4. I use the fastest period of 8 by default. It does not introduce too much of a lag in comparison, but the curve is much smoother.
This script is just an interface for my public libraries. Check them out for more information.
Bogdan Ciocoiu - MakaveliDescription
This indicator integrates the functionality of multiple volume price analysis algorithms whilst aligning their scales to fit in a single chart.
Having such indicators loaded enables traders to take advantage of potential divergences between the price action and volume related volatility.
Users will have to enable or disable alternative algorithms depending on their choice.
Uniqueness
This indicator is unique because it combines multiple algorithm-specific two-volume analyses with price volatility.
This indicator is also unique because it amends different algorithms to show output on a similar scale enabling traders to observe various volume-analysis tools simultaneously whilst allocating different colour codes.
Open source re-use
This indicator utilises the following open-source scripts:
Bogdan Ciocoiu - LitigatorDescription
The Litigator is an indicator that encapsulates the value delivered by the Relative Strength Index, Ultimate Oscillator, Stochastic and Money Flow Index algorithms to produce signals enabling users to enter positions in ideal market conditions. The Litigator integrates the value delivered by the above four algorithms into one script.
This indicator is handy when trading continuation/reversal divergence strategies in conjunction with price action.
Uniqueness
The Litigator's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for short term scalping (1-5 minutes).
In addition, the Litigator allows configuring the above four algorithms in such a way to coordinate signals by colour-coding or shape thickness to aid the user with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same, and in doing so, enabling users to plug them in/out as needed. This also includes ensuring the ratios of the shapes are similar (applicable to the same scale).
Open-source
The indicator uses the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
Bogdan Ciocoiu - MoonshotDescription
Moonshot is an indicator that encapsulates the value delivered by the TSI, MACD, Awesome Oscillator and CCI algorithms to produce signals to enable users to enter positions in ideal market conditions. Moonshot integrates the value delivered by the above four algorithms into one script.
This indicator is particularly useful when trading continuation/reversal divergence strategies.
Uniqueness
The Moonshot's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for 1-3 minute scalping techniques.
In addition, Moonshot allows swapping or furthermore configuring the above four algorithms in such a way to align signals by colour-coding or shape thickness to aid the users with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same (including the scale at which the shapes are shown) and, in doing so, enables users to plug them in/out as needed.
Open-source
The indicator leverages the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
Bogdan Ciocoiu - Sniper EntryWhat is Sniper Entry
Sniper Entry is a set indicator that encapsulates a collection of pre-configured scripts using specific variables that enable users to extract signals by interpreting market behaviour quickly, suitable for 1-3min scalping. This instrument is a tool that acts as a confluence for traders to make decisions concerning current market conditions. This indicator does not apply solely to an asset.
What Sniper Entry is not
Sniper Entry is not interpreting fundamental analysis and will also not be providing out of box market signals. Instead, it will provide a collection of integrated and significantly improved open-source subscripts designed to help traders speculate on market trends. Traders must apply their strategies and configure Sniper Entry accordingly to maximise the script's output.
Originality and usefulness
The collection of subscripts encapsulated in this tool makes it unique in the Trading View ecosystem. This indicator enables traders to consider entry positions or exit positions by comparing similar algorithms at once.
Its usefulness also emerges from the unique configurations embedded in the indicator's settings, which are different from those of the original scripts.
This indicator's originality is also reflected in how its modules are integrated, including the integration of the settings.
Open-source reuse
I used the following open-source resources, which I simplified significantly and pre-configured for short term scalping. The source codes for the below are already in the public domain, including the following links listed below.
www.tradingview.com (open source)
(open source and generic algorithm)
www.tradingview.com (open source)
(open source)
(open source)
www.tradingview.com (generic MA algorithm and open source)
(generic VWAP algorithm and open source)
Financial Intelligent Eval [Fundamental] (MYTRIC)█ OVERVIEW
Financial Algorithm is a system to quickly understanding company fundamental, and judge the company type based on their financial condition.
All evaluation from the system is the result of combination with Balancing Calculation and Company Historical Financial Data(Financial Report) by using over than 30 financial ratios.
This indicator are classified into 5 level (Very Weak, Weak, Moderate, Good, Excellent)
Advantages of Financial Algorithm
• By combining and calculating company's latest 4 quarterly report, provide rating to help investor quickly know about company's fundamentals and financial performance.
• Able to identify company have what kind of strength, weakness, chance and threat. For instance, according to current economic situation, is it an advantages or a threat for a company, investor can identify it via Financial Algorithm.
• Able to identify which company have better business management by keep following the company rating, observe the improvement level of company's.
Application
*When notice there are not improvement on a company's fundamentals or financial performance which is profitable without further developing, it usually reveals the lack of management capability to generate more value, company unable to fully utilise its profit, reinvest and expanding its business to become more competitive. Sometimes this kind of company may be suspected accounting fraud.
█ BENEFITS
• Avoid investing in companies suspected of financial fraud.
• To quickly understanding company's fundamental and financial structure.
• Able to analyze whether the company build profit after it is used to optimize the company's internal
█ FEATURES
You can configure the following attributes of the display:
• Table position on your chart.
• The size and colour of text.
• Language between English and Chinese.
• Rating bar chart colour.
• On / Off Statement Review Helper Function
• On / Off 3 Years Evaluation Function
• On / Off Basic Information
• Full descriptions of each evaluation and content are included in the settings
█ LIMITATIONS
• When changing the indicator's inputs, allow around 20 seconds calculation for the change to be reflected in the display.
• This system only able to evaluate non-financial industry.
• This system is based on company's historical financial report data to generate the results and rating, it does not includes prediction from any external factor.
(External Factor: Business Model, Business Distribution & Geography, Corporate Structure, Competitor and Peer company's, Prospect, Costing Breakdown, Disaster and etc)
• Any results calculated by this system all is based on data provided by Tradingview, Data may have some tolerance, we recommend that users pay attention to the official quarterly/annual report.
█ FINANCIALS INTELLIGENT ALGORITHM FUNCTION
This lists all combination calculate financials.
01. Total Revenue
02. Earnings before interest and tax
03. Net Income
04. Property, Plant, and Equipment
05. Total Receivables
06. Cash and short-term Investments
07. Cash & Cash equivalents
08. Total Liability
09. Working Capital
10. Total Debt
11. Total Equity
12. Retained Earnings
13. Total Asset
14. Cash From Operating Activities
15. Income before extraordinary items
16. Total depreciation and amortization
17. Free Cash Flow
18. Altman Z-score
19. Cash to Debt Ratio
20. Current Ratio
21. Debt to Assets Ratio
22. Debt to Equity ratio
23. EBITDA Margin
24. Free Cash Flow Margin
25. Grahams Number
26. Net Margin
27. Price Book Ratio
28. Piotroski-F Score
29. Quick Ratio
30. Return on Assets
31. Return on Equity
32. Return on Invested Capital
33. Float Shares Outstanding
34. Total Common Shares Outstanding
35. Cash to Revenue
36. Cash to Market Capital
37. Cash to Debt
38. Receivable Turnover
39. Quality of Earning
40. Market Capital
8 financial evaluation :
3 years financial evaluation tracking :
Statement Review Helper :
█ HOW TO MAKE THE RIGHT INVESMENT OR TREND TRADING DECISION BASED ON OUR EVALUATION
Avoid mid/long term invest in companies with poor financial evaluation, only suite for trend trading. The below following assessments need to be focused.
• Comprehensive rating is poor or below.
• Quality of Earning is very poor or below.
• Receivability is very poor or below (Total Receivable is too high)
• Before : Poor Financial Strength with revenue growth
• After : The price dropped by about -80% within 2 months
███████████████████████████████████████████████████████████████████████████████████████████
• Before : Poor Financial Strength with revenue growth
• After : The price dropped by about -90% within 1 year
███████████████████████████████████████████████████████████████████████████████████████████
• Before : Excellent Financial Strength
• After : Steady growth
Steady growth
• Conclusion :
Do not judge it as a good company just because it has continuous income.
When we analyze the company's financial report, we should not only look at the company's revenue,
we should pay more attention to the company's finances and weaknesses.
Only companies with strong financial strength that can expand their business in a stable manner.
Disclaimer :
*The following conclusion are purely based on my personal opinions and views, it’s only for study and research, without any trading and investment advice.
CyberBot {LM.Alerts} [BETA]This is the "LONGS-MANAGEMENT ALERTS" {LM.Alerts} of my Risk Management Engine to enable auto-trading via alerts signaling, which can then be fed into APIs of various exchanges which users can themselves setup on their own .
Only the long-signals, generated from the underlying Trading Edge algorithm, is used in this strategy-alerts script, with my latest risk-exit (collect gains) and stop-limit algorithms, as well as a bear-market filter, implemented.
The user is able to define the %gain for {LM.Alerts} to signal to collect or Lock-in Gains (CG) as well the %stop-loss for {LM.Alerts} to stopped-out (SL) the trade. Alternatively, the CG and SL %levels can also be set to be modulated by ATR. Exchange fees can be inputted into the {LM.Alerts} as well for the total gain to be calculated. These needs to be optimize for a specific chart and timeframe for optimal result which could be back tested to a maximum of 3000 candle bars.
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Since {LM.Alerts} engine only focuses on trading and managing longs (entries and exits), a bear-market filter is implemented base on the FUSIONGAPS indicator.
The FUSIONGAPS algorithm signals local bull or bear market phases, and then disables trades conditionally to reduce the chances of having to take losses during a local bear market phase (since the short-signals are not traded).
Enabling the different (Fastest >> Slowest) FUSIONGAPS levels (e.g. 50/15, 100/50, 200/50, 200/100, etc) activates the use of each of these levels to decide the local bull/bear market phases.
So in summary, the {LM.Alerts} algorithm trades up a bullish-hill, taking profits along the way; but stops all trading activity when the market is rolling down a bearish-hill; and then once a local bull-phase is detected again, it resumes trading, etc.
Note: To trade on both bullish and bearish phases, {LM.Alerts} scripts can be applied on an inverse-chart (i.e. 0-BTCUSD) for shorts.
The {LM.Alerts} engine will be ported to my other more powerful trade-signaling scripts in the future.
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In this BETA version, this script generates Buy/Sell signals simply by the crossing of two MA-lines, which parameters can be set by the user, before feeding into a Risk Management Engine for Longs-only (entries and exit) trades.
I'm working on this casually for now due to family commitments. However, do feel free to leave a comment or PM me to report on any bugs or suggestions, and I will consider them in my own limited free time, and may or may not take them on board eventually.
Use at your own risk.
Comprehensive Market AnalyzerVERSION 2.0:
Notice to users: To better reflect its extensive features, this indicator has been renamed from "Tsūrubokkusu (Toolbox) 🧰" to "Comprehensive Market Analyzer". Thank you for your understanding and adaptation to this change.
Purpose and Usage:
The Comprehensive Market Analyzer is designed to provide traders with a holistic view of market conditions by integrating various technical indicators into a single,
cohesive tool. Each indicator has been carefully selected and improved to work together, offering enhanced customization and advanced market insights.
This combination allows for more comprehensive market analysis, improved decision-making, and efficient trading strategies.
📘 Machine Learning Integration
Purpose : Utilizes machine learning algorithms to analyze past market data and provides predictive insights based on historical data.
Usage : Activate machine learning features, set lookback windows, influence weighting, and start bar for improved trend predictions.
Activate Machine Learning :
Description : Enables advanced machine learning features that analyze past market data.
Details : This feature allows the algorithm to use historical data to forecast market movements, providing traders with enhanced predictive insights on historical data.
Kernel Lookback Window :
Description : Sets the number of previous bars that the algorithm will analyze.
Details : A higher number provides a broader view of market trends, while a lower number makes the model more sensitive to recent changes.
Kernel Influence Weighting :
Description : Adjusts the emphasis on recent versus older data.
Details : Increasing this value gives more importance to recent data, potentially making predictions more responsive to new trends.
Kernel Calculation Start Bar :
Description : Specifies the bar number from which to start the machine learning calculations.
Details : Avoids early data which may contain excessive noise and less reliable market signals.
Kernel Functions :
Gaussian Kernel :
Description : Uses a Gaussian distribution to weight historical data, focusing on more recent data points for trend analysis.
Details : Calculates weights based on the Gaussian distribution, emphasizing data points closer to the present.
Laplacian Kernel :
Description : Applies Laplacian distribution, emphasizing data points closer to the current time more heavily.
Details : Uses the Laplacian function to provide a different perspective on data weighting.
RBF Kernel :
Description : Utilizes a Radial Basis Function for smoothing and analyzing data, providing a different approach to trend prediction.
Details : Applies the RBF function to smooth data and enhance the accuracy of trend predictions.
Wavelet Kernel :
Description : Applies wavelet transform for analyzing frequency components, helping to detect patterns in the price movements.
Details : Uses wavelet-based calculations to focus on specific frequency components within the data, aiding in pattern recognition.
📘 Enhanced Ichimoku Kinkō Hyō Integration
Purpose : Provides a comprehensive overview of market trends and momentum using the Ichimoku Kinkō Hyō indicator.
Usage : Display various components of the Ichimoku Kinkō Hyō, customize their appearance, provides additional calculations for trend analysis.
Display Ichimoku Kinkō Hyō :
Description : Toggle to show or hide the Ichimoku Kinkō hyō indicator.
Details : This indicator helps traders see support and resistance levels, trend direction, and potential future movements.
Activate Heikin-Ashi Source :
Description : Switches between regular price data and Heikin-Ashi candles for analysis.
Details : Heikin-Ashi candles smooth price data, making trends easier to spot.
Display Tenkan-Sen Line :
Description : Shows the Tenkan-Sen line, a key short-term trend indicator.
Color Customization : Set the color of the Tenkan-Sen line for better visibility.
Minimum Length : Determine the shortest period for calculating the Tenkan-Sen line.
Maximum Length : Determine the longest period for calculating the Tenkan-Sen line.
Dynamic Length Adjustment : Automatically adjusts the length of the Tenkan-Sen based on market conditions.
Display Kijun-Sen Line :
Description : Shows the Kijun-Sen line, a key medium-term trend indicator.
Color Customization : Set the color of the Kijun-Sen line for better visibility.
Minimum Length : Determine the shortest period for calculating the Kijun-Sen line.
Maximum Length : Determine the longest period for calculating the Kijun-Sen line.
Dynamic Length Adjustment : Automatically adjusts the length of the Kijun-Sen based on market conditions.
Kijun-Sen Divider Tool : Adjust the sensitivity of the Kijun-Sen calculation.
Display Chikou Span :
Description : Shows the Chikou Span, which lags behind the current price to help confirm trends.
Bear Phase Color : Set the color for bearish periods.
Bull Phase Color : Set the color for bullish periods.
Consolidation Color : Set the color for consolidation periods.
Minimum Length : Determine the shortest lag period for the Chikou Span.
Maximum Length : Determine the longest lag period for the Chikou Span.
Dynamic Length Adjustment : Automatically adjusts the length of the Chikou Span based on market conditions.
Display Senkou Span A and B :
Description : Shows the Senkou Span A and B, which form the Ichimoku Cloud indicating future support and resistance levels.
Bear Color : Set the color for bearish clouds.
Bull Color : Set the color for bullish clouds.
Neutral Color : Set the color for neutral periods.
Minimum Length : Determine the shortest period for calculating the Senkou Span.
Maximum Length : Determine the longest period for calculating the Senkou Span.
Dynamic Length Adjustment : Automatically adjusts the length of the Senkou Span based on market conditions.
Projection Offset : Set how far ahead the Senkou Span is projected.
Kumo Cloud Settings :
Enable Kumo Cloud Fill : Toggle to fill the space between Senkou Span A and B with color.
Cloud Fill Transparency : Adjust the transparency of the cloud fill.
Apply WMA Smoothing :
Description : Smooths the indicator lines using a Weighted Moving Average to clarify trends.
Bar Coloring Based on Ichimoku Signals :
Description : Colors the bars based on Ichimoku signals to provide a quick visual indication of market sentiment.
Bearish Signal Bar Color : Set the color for bars during bearish signals.
Bullish Signal Bar Color : Set the color for bars during bullish signals.
Consolidation Signal Bar Color : Set the color for bars during consolidation periods.
Neutral Bar Color : Set the color for bars during neutral conditions.
Enhanced Calculations :
Heikin Ashi Values : Smooths price movements to make trends more visible.
Alternative Source Calculation : Uses a different method for calculating the indicator based on user settings.
Volume Calculations : Enhanced functions for calculating volume based on different candlestick patterns.
Dynamic Length Adjustment : Automatically adjusts the length of Ichimoku components based on market volatility.
Gaussian Kernel Calculations : Uses advanced calculations for smoother and more accurate trend analysis.
Chikou Span Adaptation : Improved calculation for the Chikou Span using dynamic lengths and advanced methods.
Visual Enhancements : Adds color gradients to the Senkou Span and dynamic coloring for the Chikou Span to improve trend visibility.
Plotting Ichimoku Components :
Tenkan-Sen : Plots the Tenkan-Sen line with dynamic adjustments.
Kijun-Sen : Plots the Kijun-Sen line with dynamic adjustments.
Senkou Span A and B : Plots these lines with dynamic projections and advanced smoothing.
Chikou Span : Plots the Chikou Span with dynamic offsets and coloring.
📘 Enhanced Candlestick Patterns Integration
Purpose : Identifies and displays various candlestick patterns to help traders spot key market movements and potential reversals.
Usage : Toggle the display of patterns, select specific pattern types, and customize pattern labels for improved visual analysis.
Display Patterns :
Description : Toggle to enable or disable the display of all candlestick patterns.
Details : When enabled, all selected candlestick patterns will be displayed on the chart, aiding traders in identifying key market movements and potential reversals.
Select Pattern Type :
Description : Select the type of candlestick patterns to detect.
Details : Options include Bullish (indicating potential upward trends), Bearish (indicating potential downward trends), or Both.
Trend Filter Method :
Description : Select the method to filter trends.
Details : Options include True Range (based on price range), Fractals, Volume, Combined, or None (no filtering).
Pattern Label Colors :
Bullish Pattern Color : Choose the color for labeling Bullish patterns, indicating potential upward trends.
Bearish Pattern Color : Choose the color for labeling Bearish patterns, indicating potential downward trends.
Indecision Pattern Color : Choose the color for labeling Indecision patterns, indicating no clear trend direction.
Base Line and Patterns Display Options :
Show Base Line in Place of Labels : Toggle to display a base line instead of labels for detected patterns. This helps visualize the general trend.
Show Counterattack Lines : Toggle to display Counterattack Lines patterns, indicating potential reversal points.
Show Dark Cloud Cover : Toggle to display Dark Cloud Cover patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Engulfing Patterns : Toggle to display Engulfing patterns. Bullish Engulfing patterns suggest a potential upward reversal, while Bearish Engulfing patterns suggest a potential downward reversal.
Show Hammer Patterns : Toggle to display Hammer patterns, a bullish pattern indicating a potential reversal from a downtrend to an uptrend.
Show Hanging Man Patterns : Toggle to display Hanging Man patterns, a bearish pattern indicating a potential reversal from an uptrend to a downtrend.
Show Harami Patterns : Toggle to display Harami patterns. Bullish Harami patterns suggest a potential upward reversal, while Bearish Harami patterns suggest a potential downward reversal.
Show In-Neck Patterns : Toggle to display In-Neck patterns, indicating a potential continuation of the current trend.
Show On-Neck Patterns : Toggle to display On-Neck patterns, indicating a potential continuation of the current trend.
Show Piercing Patterns : Toggle to display Piercing patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Three Black Crows : Toggle to display Three Black Crows patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Thrusting Patterns : Toggle to display Thrusting patterns, a bearish pattern suggesting a potential continuation of the downtrend.
Show Upside Gap Two Crows : Toggle to display Upside Gap Two Crows patterns, a bearish pattern suggesting a potential downward reversal after an upward gap.
Show Evening Star : Toggle to display Evening Star patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Inverted Hammer : Toggle to display Inverted Hammer patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Morning Star : Toggle to display Morning Star patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Shooting Star : Toggle to display Shooting Star patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Doji Patterns : Toggle to display Doji patterns, indicating market indecision and potential reversals.
Show Dragonfly Doji : Toggle to display Dragonfly Doji patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Evening Doji Star : Toggle to display Evening Doji Star patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Gravestone Doji : Toggle to display Gravestone Doji patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Long-Legged Doji : Toggle to display Long-Legged Doji patterns, indicating high market indecision and potential reversals.
Show Morning Doji Star : Toggle to display Morning Doji Star patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Rising Three Methods : Toggle to display Rising Three Methods patterns, a bullish pattern suggesting a continuation of the uptrend.
Show Falling Three Methods : Toggle to display Falling Three Methods patterns, a bearish pattern suggesting a continuation of the downtrend.
Show Tasuki Patterns : Toggle to display Tasuki patterns, indicating potential trend continuation after a gap.
Show Marubozo : Toggle to display Marubozo patterns, indicating strong trend continuation, either bullish or bearish.
Show Long Lower Shadow : Toggle to display Long Lower Shadow patterns, indicating strong buying pressure and potential upward movement.
Show Long Upper Shadow : Toggle to display Long Upper Shadow patterns, indicating strong selling pressure and potential downward movement.
Show Three Inside Up/Down : Toggle to display Three Inside Up/Down patterns, indicating potential bullish or bearish reversals.
Show Kicker Pattern : Toggle to display Kicker patterns, indicating significant potential reversals.
Show Tweezer Tops/Bottoms : Toggle to display Tweezer Tops/Bottoms patterns, indicating potential reversals at the tops or bottoms.
Show Mat Hold Pattern : Toggle to display Mat Hold patterns, a bullish pattern suggesting a continuation of the uptrend.
Candle Body/Shadow Comparison Options :
Candle Body/Shadow Comparison : Choose the criteria to compare candle sizes: Shadows (larger shadows), Body (larger body), Both (larger shadows and body), Either (larger shadows or body), or None (no comparison).
Look-back Period for Candle Comparison : Specify the number of periods to look back when comparing the current candle size to determine if it is significant.
Period for Body Length Average : Specify the period for calculating the average body length of candles to help identify significant patterns.
Period for Candle Length Average : Specify the period for calculating the average length of candles to help identify significant patterns.
Specific Pattern Thresholds :
Doji Body Percentage Threshold : Set the percentage threshold for identifying Doji patterns based on the candle body size compared to its range.
Upper Shadow Percentage Limit : Set the maximum allowed upper shadow percentage of the candle’s range for identifying specific Doji patterns.
Lower Shadow Percentage Limit : Set the maximum allowed lower shadow percentage of the candle’s range for identifying specific Doji patterns.
Price Deviation Tolerance : Specify the price deviation tolerance for pattern recognition, which helps in identifying patterns within a certain price range.
Thrusting Neck Percentage : Set the percentage threshold for identifying Thrusting Neck patterns, indicating a potential continuation of the current trend.
Base Line Settings :
Base Line EMA Length : Specify the length of the EMA for the Base Line, helping to visualize the general trend.
Enhanced Calculations :
Wavelet Transform : If machine learning is enabled, calculates the wavelet transform for smoother and more accurate pattern detection.
Candle Body and Shadows Calculation : Detailed calculations for candle body and shadow lengths to improve pattern detection.
Average Calculations : Calculate averages for body and candle sizes to help identify significant patterns.
Fractals Calculation : Identify fractal highs and lows to aid in trend detection.
Trend Filters : Apply user-selected trend filters based on True Range, Fractals, Volume, or a combination.
Pattern Detection and Labeling : Detects and labels various candlestick patterns, including Doji, Engulfing, Hammer, and more, with options for displaying labels or base lines.
Alerts and Notifications : Set alerts for detected patterns and base line colors to notify traders of significant market events.
Plotting Candlestick Patterns :
Pattern Detection : Automatically detects and labels various candlestick patterns based on user settings.
Label Customization : Customize the labels for different patterns, including color and text.
Base Line Plotting : Option to plot a base line instead of labels for detected patterns, enhancing trend visualization.
Alerts for Patterns : Set alerts for detected patterns to keep traders informed of significant market changes.
📘 Enhanced Fibonacci Retracement Integration
Purpose : Provides a tool for identifying potential support and resistance levels using Fibonacci retracement.
Usage : Toggle the display of Fibonacci levels, adjust the lookback period, and customize the appearance of Fibonacci levels for better market analysis.
Auto Mode :
Description : Toggle to enable or disable automatic detection of price points.
Details : When enabled, the highest and lowest price points within a specified period will be automatically detected to set Fibonacci levels. Disable to manually set the top and bottom prices.
Period :
Description : Set the lookback period for detecting price points.
Details : Defines the number of bars to look back when detecting the highest and lowest prices in Auto Mode, used for calculating Fibonacci levels.
Manual Top :
Description : Manually set the top price level.
Details : Adjust this setting to reflect the peak price of interest when Auto Mode is disabled.
Manual Bottom :
Description : Manually set the bottom price level.
Details : Adjust this setting to reflect the low price of interest when Auto Mode is disabled.
Display Fibonacci :
Description : Toggle to show or hide Fibonacci retracement levels.
Details : When enabled, the calculated Fibonacci levels will be displayed on the chart, overlaying the price data.
Baseline Levels :
Description : Select Fibonacci levels to highlight as baselines.
Details : Choose specific levels to be visually distinct, emphasizing their significance in the analysis.
Fibonacci Levels Colors :
Upper Levels Color : Set the color for Fibonacci levels above the baseline, indicating potential resistance levels.
Lower Levels Color : Set the color for Fibonacci levels below the baseline, indicating potential support levels.
Baseline Levels Color : Set the color for highlighted baseline Fibonacci levels, making them stand out from other levels.
Display Individual Fibonacci Levels :
Show Level : Toggle to enable or disable the display of specific Fibonacci levels.
Level Value : Set the multiplier used to calculate each specific Fibonacci level relative to the price range.
Reverse Levels :
Description : Toggle to switch the calculation direction of Fibonacci levels.
Details : When enabled, levels are calculated in reverse, useful for analyzing downtrends.
Line Extension :
Description : Choose how Fibonacci level lines are extended on the chart.
Details : Options include extending lines to the left, right, or both, affecting their visual presentation.
Text Size :
Description : Adjust the font size of the labels for Fibonacci levels.
Details : Options range from large to tiny, allowing for readability adjustments according to user preference.
Line Style :
Description : Select the line style for Fibonacci levels.
Details : Options include solid, dotted, and dashed, providing visual distinction.
Line Width :
Description : Set the thickness of the Fibonacci level lines.
Details : A higher value makes the lines more prominent on the chart.
Baseline Line Style :
Description : Choose the line style specifically for the baseline levels.
Details : This can differ from other Fibonacci levels to emphasize their importance.
Baseline Line Width :
Description : Adjust the thickness of the baseline level lines.
Details : Can be set differently from other levels for visual emphasis.
Enhanced Calculations :
Automatic and Manual Top/Bottom Setup : Detect or manually set the highest and lowest price points.
Price Range Calculation : Determine the range between the highest and lowest prices.
Fibonacci Level Values : Calculate the values for each Fibonacci level.
Visual and Label Configuration : Configure visual aspects and labels for each level.
Plotting and Labeling :
Level Plotting :
Description : Plot each Fibonacci level on the chart.
Details : Draw lines representing each calculated level.
Label Customization :
Description : Customize the labels for Fibonacci levels.
Details : Include text, colors, and positioning for clarity.
📘 Supports and Resistances Integration
Purpose : Identifies key support and resistance levels to aid in market analysis.
Usage : Toggle the display of support and resistance lines, customize their appearance, and use Bollinger Bands for additional insights.
Display Supports and Resistances :
Description : Toggle to enable or disable the display of support and resistance lines.
Details : When enabled, support and resistance lines will be shown on the chart, providing key levels for market analysis.
Swing Period :
Description : Set the retrospective period for identifying swing points.
Details : A longer period captures more significant trends but may reduce sensitivity. The default value is 10.
Support Line Color :
Description : Set the color for support lines.
Details : Choose a color that enhances chart readability. Default is green.
Resistance Line Color :
Description : Set the color for resistance lines.
Details : Choose a color that makes resistance lines easily distinguishable. Default is red.
Trend-Based Line Color :
Description : Toggle to enable dynamic coloring based on trend direction.
Details : When enabled, the color of the lines will change according to the trend, aiding visual analysis.
Line Thickness :
Description : Adjust the thickness of the support and resistance lines.
Details : Choose a thickness value between 1 and 5 for better visibility.
Line Style :
Description : Select the style of the lines.
Details : Options include Solid, Dotted, or Dashed lines for visual distinction.
Number of Lines to Display :
Description : Set the maximum number of support/resistance lines to display.
Details : Adjust the number of lines to avoid clutter or to show more levels.
Display Bollinger Bands :
Description : Toggle to show or hide Bollinger Bands on the chart.
Details : Bollinger Bands provide a visual representation of volatility and potential price ranges.
Bollinger Bands Integration :
Description : Enable the integration of Bollinger Bands for S/R calculation.
Details : This feature adjusts the placement of S/R lines based on the market volatility captured by the Bollinger Bands.
Bollinger Bands Color Settings :
Description : Set colors for different Bollinger Band conditions.
Details :
Green: Prices above the median but below the upper band (potential overbought area).
Dark green: Prices above the upper band (strong upward momentum).
Light red: Prices below the median but above the lower band (potential oversold area).
Dark red: Prices below the lower band (strong downward momentum).
Fill Opacity Adjustment :
Description : Adjust the fill opacity between Bollinger Bands.
Details : Set the opacity level to balance visibility with other chart elements.
BB Sensitivity Level :
Description : Adjust the sensitivity for determining S/R levels near Bollinger Bands.
Details : A higher value increases the consideration of levels near the bands.
Band Width Multiplier :
Description : Control the width of the Bollinger Bands.
Details : Adjust the multiplier to expand or contract the bands based on market volatility.
Uniform BB Coloring :
Description : Apply a consistent color to Bollinger Bands.
Details : Simplify visual interpretation with a uniform color.
Plotting and Alerts :
Plotting Bollinger Bands :
Description : Plot the Bollinger Bands on the chart.
Details : The bands are colored based on the conditions set for market volatility and price ranges.
Alerts and Notifications :
Description : Set alerts for support/resistance breaks and Bollinger Band breakouts.
Details : Notify traders of significant market events related to these levels.
📘 Enhanced Trend Lines Integration
Purpose : Identifies and plots trend lines based on market structure to help traders understand market direction and potential buy/sell points.
Usage : Toggle the display of trend lines, customize their appearance, and use enhanced calculations for trend analysis.
Display Trend Lines :
Description : Enable or disable the display of trend lines on the chart.
Details : These trend lines are calculated based on market structure, specifically through the detection of Breaks of Structure (BOS). If enabled, the trend lines will help in identifying the market overall trend and potential buy and sell points.
Trend Line Colors :
Upper Line Color : Set the color for the upper trend lines to enhance visual distinction.
Lower Line Color : Set the color for the lower trend lines, aiding in easy identification of support levels.
Pivot Labels :
Show Pivots Labels : Control the display of pivot labels on the chart.
Pivot Label Size : Select the size of the pivot labels displayed on the chart. Options include Tiny, Small, Normal, Large, and Huge.
Trend Line Calculations :
Pivot Depth : Adjust the depth for pivot calculation based on the selected timeframe to capture significant price movements.
Pivot Deviation : Set the deviation for pivot calculation to identify key turning points.
Pivot Backstep : Define the backstep for pivot calculation to ensure accurate detection of pivot points.
Enhanced Calculations :
Market Structure Detection : Utilize advanced algorithms to identify key market structures, improving trend line accuracy.
Adaptive Parameters : Automatically adjust pivot depth, deviation, and backstep based on the selected timeframe for better relevance.
Zigzag Calculation : Implement zigzag patterns to dynamically adjust trend lines, ensuring they reflect current market conditions.
Slope and Intercept Calculation : Compute the slope and intercept for trend lines to enhance precision in trend detection.
Dynamic Updates : Continuously update trend lines as new data becomes available, ensuring real-time accuracy.
Alerts and Notifications : Set alerts for new high and low pivots, as well as for when the price crosses upper or lower trend lines, keeping traders informed of significant market changes.
Plotting Trend Lines :
Trend Line Plotting : Automatically draw trend lines based on detected BOS, helping traders visualize the market trend.
Diagonal Support/Resistance Lines : Plot diagonal lines to indicate support and resistance levels, enhancing the understanding of market dynamics.
Pivot Label Customization : Customize pivot labels for clear identification of high and low points in the trend.
Alerts for Trend Lines : Set alerts for when price crosses trend lines, ensuring timely notifications of potential trading opportunities.
📘 Enhanced Linear Regression Integration
Purpose : Uses linear regression to analyze price movements and identify trends.
Usage : Display the linear regression projection line, customize its appearance, and use enhanced calculations for better trend analysis.
Display Projection Line :
Description : Toggle to display or hide the linear regression projection line on the chart.
Details : This line represents the best fit line that predicts future prices based on historical data.
Data Source :
Description : Select the data source for the linear regression projection.
Details : This is typically the closing price but can be any price point such as open, high, or low. The selected source will be used to calculate the linear regression projection line.
Trend-Based Line Color :
Enable Trend-Based Line Color : Toggle to automatically color the projection line based on the trend direction. When enabled, the line will be red for a downward trend and green for an upward trend, providing a visual indication of market direction.
Uptrend Line Color : Select the color for the projection line when the trend is upward. This color will be used when "Enable Trend-Based Line Color" is active.
Downtrend Line Color : Select the color for the projection line when the trend is downward. This color will be used when "Enable Trend-Based Line Color" is active.
Enhanced Calculations :
Standard Deviation Calculation : Calculate the standard deviation for a given length to understand the volatility around the linear regression line.
Pearson's Correlation Calculation : Compute Pearson's R to measure the strength of the linear relationship between the price points and the linear regression line.
Slope and Intercept Calculation : Calculate the slope and intercept for the regression line, providing the basis for the projection.
Kernel Application : Optionally apply the RBF Kernel to the selected source data for smoothing and enhancing the regression calculations.
Dynamic Length Selection : Automatically select the optimal regression period based on the highest Pearson's R value, ensuring the most accurate trend representation.
Real-Time Updates : Continuously update the regression line and related calculations as new data becomes available, maintaining accuracy in real-time.
Alerts and Notifications : Set alerts for when the price crosses the linear regression projection line, notifying traders of significant market events.
Plotting Linear Regression Components :
Projection Line Plotting : Automatically draw the linear regression projection line based on historical data and the selected data source.
Label Customization : Customize the label for the projection line, including color and text, for clear identification on the chart.
Alerts for Projection Line : Set alerts for when the price crosses the projection line, ensuring timely notifications of potential trading opportunities.
📘 POC Analysis Integration
Purpose : Identifies the Point of Control (POC) to highlight price levels with the highest trading volume.
Usage : Toggle the display of the POC, customize its appearance, and use enhanced calculations for better market analysis.
Display POC :
Description : Toggle to display or hide the Point of Control (POC) on the chart.
Details : The POC is the price level at which the highest volume of trading occurred, indicating a focal point of market activity.
Data Source :
Description : Select the price source for POC analysis.
Details : This is typically the closing price but can be any price point such as open, high, or low. The selected source will be used to calculate the POC.
POC Line Colors :
Color Above POC : Set the line color when the closing price is above the POC.
Color Below POC : Set the line color when the closing price is below the POC.
Width Multiplier :
Description : Adjust the width around the price for POC analysis.
Details : A higher value broadens the calculation range.
POC Calculation and Visualization :
Price Level Initialization : Calculate the initial spacing between price levels based on the first candlestick and user settings.
Volume Data Accumulation : Accumulate volume data at specified price levels for each candlestick to determine the POC.
Dynamic Array Expansion : Expand price levels array to accommodate new price data outside the current range.
POC Determination : Determine and visualize the POC at the last candlestick if enabled by the user.
Alerts and Notifications : Set alerts for when the price crosses the POC, notifying traders of significant market events.
Plotting POC Components :
POC Line Plotting : Automatically draw the POC line based on historical data and the selected data source.
Label Customization : Customize the label for the POC line, including color and text, for clear identification on the chart.
Alerts for POC : Set alerts for when the price crosses the POC, ensuring timely notifications of potential trading opportunities.
📘 Enhanced Divergences Integration
Purpose : Detects and displays divergences between price movements and indicators to identify potential reversal points.
Usage : Toggle the display of divergences, select data sources, customize divergence colors, and use enhanced calculations for better trend analysis.
Display Divergences :
Description : Toggle to display or hide the detected divergences on the chart.
Details : Divergences occur when the price movement of an asset and a related indicator (e.g., volume or momentum) move in opposite directions. They are used to identify potential reversal points in the market. Regular divergences signal possible reversals, while hidden divergences can indicate continuation.
Data Source :
Description : Defines the timeframe from which to fetch data for analysis.
Details : Typically lower than the chart current timeframe for multi-timeframe analysis.
Divergence Colors :
Bearish Divergence Color : Sets the color for bearish divergence lines. Bearish divergences typically suggest potential downward price movement.
Bullish Divergence Color : Sets the color for bullish divergence lines. Bullish divergences typically indicate potential upward price movement.
Pivot Bars :
Left Bars : Number of bars to the left of the pivot point to consider. Helps in identifying the pivot high or low by looking back these many bars.
Right Bars : Number of bars to the right of the pivot point to consider. Assists in confirming a pivot point by ensuring no higher high or lower low is present within this range.
Display Hidden Divergences :
Description : When enabled, this setting reveals hidden divergences on the chart.
Details : Hidden divergences are a subtler form of divergence that often signal continuation rather than reversal. A hidden bullish divergence occurs when price makes a higher low while the indicator makes a lower low, suggesting the continuation of an uptrend. Conversely, a hidden bearish divergence occurs when price makes a lower high while the indicator makes a higher high, indicating the continuation of a downtrend. These divergences are particularly useful for identifying the strength of the current trend.
Dynamic Line Width Based on Divergence Count :
Description : When enabled, adjusts the width of the divergence line dynamically based on the count of divergences detected.
Details : This provides visual emphasis on stronger signals.
Enhanced Calculations :
Standard Deviation Calculation : Calculate the standard deviation for a given length to understand the volatility around the linear regression line.
Pearson's Correlation Calculation : Compute Pearson's R to measure the strength of the linear relationship between the price points and the linear regression line.
Slope and Intercept Calculation : Calculate the slope and intercept for the regression line, providing the basis for the projection.
Kernel Application : Optionally apply the RBF Kernel to the selected source data for smoothing and enhancing the regression calculations.
Dynamic Length Selection : Automatically select the optimal regression period based on the highest Pearson's R value, ensuring the most accurate trend representation.
Real-Time Updates : Continuously update the regression line and related calculations as new data becomes available, maintaining accuracy in real-time.
Alerts and Notifications : Set alerts for when the price crosses the linear regression projection line, notifying traders of significant market events.
Plotting Divergence Components :
Divergence Line Plotting : Automatically draw divergence lines based on historical data and the selected data source.
Label Customization : Customize the label for the divergence lines, including color and text, for clear identification on the chart.
Alerts for Divergences : Set alerts for when a divergence is detected, ensuring timely notifications of potential trading opportunities.
📘 Enhanced Average True Range Integration
Purpose : Measures market volatility using the Average True Range (ATR) to assist in identifying potential buy and sell points.
Usage : Set the ATR period, minimum tick filter, upper and lower coefficients, and customize ATR colors for better market analysis.
Show Labels :
Description : Enable or disable the display of labels for the Average True Range (ATR) indicator.
Details : This option controls whether the ATR signals (buy and sell) are shown on the chart with respective labels.
ATR Period :
Description : Sets the period for calculating the Average True Range (ATR).
Details : The ATR measures market volatility by calculating the average range of price movement over a specified period. A shorter period makes the ATR more sensitive to recent price movements, while a longer period smooths out short-term volatility.
Minimum Tick Filter :
Description : Sets the minimum tick filter for buy and sell signals.
Details : This filter ensures that the price movement is significant enough to be considered a valid signal. For example, a value of 20 means that the price must move at least 20 ticks from the open to the close to generate a signal.
Upper Coefficient :
Description : Sets the upper coefficient for band calculation.
Details : This value adjusts the sensitivity of the upper band used to detect high points. A higher coefficient makes the band wider, capturing more significant price movements, while a lower coefficient makes the band narrower, making it more sensitive to smaller price changes.
Lower Coefficient :
Description : Sets the lower coefficient for band calculation.
Details : This value adjusts the sensitivity of the lower band used to detect low points. A higher coefficient makes the band wider, capturing more significant price movements, while a lower coefficient makes the band narrower, making it more sensitive to smaller price changes.
ATR Colors :
Bullish Color : Sets the color for the bullish signal, helping to visually distinguish bullish trends.
Bearish Color : Sets the color for the bearish signal, helping to visually distinguish bearish trends.
Enhanced Calculations :
Dynamic Coefficient Calculation : Calculates dynamic coefficients based on market volatility, adjusting the sensitivity of ATR bands accordingly.
Band Calculation : Computes high and low bands using dynamic coefficients to detect significant price movements.
High/Low Point Detection : Identifies potential high and low points based on ATR band calculations and price thresholds.
Real-Time Updates : Continuously updates ATR calculations and signals as new data becomes available, ensuring accuracy in real-time.
Plotting ATR Components :
Signal Plotting : Plots bullish and bearish ATR signals on the chart based on calculated conditions.
Label Customization : Customize the labels for ATR signals, including color and text, for clear identification on the chart.
Alerts for Signals : Set alerts for detected bullish and bearish signals, ensuring timely notifications of potential trading opportunities.
📘 Enhanced ATR Visualization Parameters
Purpose : Provides a visual representation of market volatility using the ATR Strength Meter.
Usage : Toggle the display of the ATR Strength Meter, set thresholds, and customize its appearance for better market analysis.
Display ATR Strength Meter :
Description : Toggle to display or hide the ATR Strength Meter, a visual representation of market volatility.
Details : The meter is based on the Average True Range (ATR) and helps identify volatility trends.
High ATR Threshold :
Description : Set the threshold for high volatility.
Details : ATR values above this threshold indicate increased market volatility.
Low ATR Threshold :
Description : Set the threshold for low volatility.
Details : ATR values below this threshold indicate decreased market volatility.
Progression Bar Position :
Description : Select the position of the ATR Strength Meter on the chart.
Details : Options are "Top" or "Bottom", affecting where the volatility meter is displayed relative to price action.
Progress Bar Length :
Description : Set the horizontal length of the ATR Strength progression bar.
Details : Adjust to increase or decrease the bar's width, accommodating different chart sizes and user preferences.
Enhanced Calculations :
ATR Strength Calculation : Calculate the ATR strength to measure market volatility.
Dynamic Coefficients : Use dynamic coefficients based on volatility for more accurate calculations.
Progress Bar Calculation : Determine the position and color of the progression bar based on ATR strength.
Label Positioning : Dynamically position labels for minimum and maximum values to avoid overlap.
Plotting ATR Strength Meter :
Progression Bar Plotting : Plot the progression bar to represent the ATR strength.
Label Customization : Customize labels for the ATR strength, minimum, and maximum values.
📘 Enhanced Relative Strength Index Integration
(A special thanks to RumpyPumpyDumpy for allowing the private reuse of his script.)
Purpose : Measures market momentum using the Relative Strength Index (RSI) and Stochastic RSI to assist in identifying potential buy and sell points.
Usage : Set the RSI and StochRSI parameters, toggle the display of the RSI Meter, and customize its appearance for better market analysis.
RSI Calculation Parameters :
RSI Length : Defines the length of the RSI calculation.
Details : A longer period captures more data points but may reduce sensitivity.
RSI Overbought Level : Sets the overbought level for RSI.
Details : Values above this level indicate overbought conditions.
RSI Oversold Level : Sets the oversold level for RSI.
Details : Values below this level indicate oversold conditions.
StochRSI Length : Defines the length of the StochRSI calculation.
Details : A longer period captures more data points but may reduce sensitivity.
StochRSI %K Length : Defines the length of the %K line of the StochRSI.
StochRSI %D Length : Defines the length of the %D line (SMA of %K) of the StochRSI.
RSI Visualization Parameters :
Display RSI Meter : Toggle the display of the RSI Meter on the chart.
RSI Meter Size : Adjust the size of the RSI Meter displayed on the chart.
Details : Measured as the diameter of the meter. Increase the value for larger display size, enhancing visibility and making it easier to read the RSI trend at a glance.
Horizontal Offset : Move the RSI Meter horizontally across the chart.
Details : Positive values shift the meter to the left, allowing for placement adjustments relative to the chart's current view or specific visual preferences.
RSI Meter Components :
Sectors and Ticks : Draw sector arcs and tick marks around the RSI Meter to represent different RSI levels and thresholds.
Needle : Draw the needle on the RSI Meter to indicate the current RSI value.
Sector Labels : Label each sector of the RSI Meter to indicate market conditions like "Strong Buy," "Buy," "Neutral," "Sell," and "Strong Sell."
Title Label : Draw the title label for the RSI Meter displaying the RSI value and its period.
Enhanced Calculations :
RSI Calculation : Calculate the RSI using the built-in function with the specified length and source.
StochRSI Calculation : Calculate StochRSI values using the specified lengths for RSI, %K, and %D.
Dynamic Line Management : Efficiently manage and update dynamically created line objects to prevent potential memory leaks.
Optimized Sector and Needle Drawing : Enhanced the drawing functions for sectors, needles, and ticks to improve visual clarity and performance.
Plotting RSI Meter :
Sector Plotting : Draw the sectors on the RSI Meter using specified colors and widths to represent different RSI levels and thresholds.
Needle Plotting : Plot the needle on the RSI Meter based on the calculated RSI value to visually indicate the current RSI level.
Tick Plotting : Plot tick marks around the RSI Meter to denote key RSI levels and thresholds for better readability.
Label Plotting : Draw sector labels and a title label on the RSI Meter to provide context and information about the RSI levels and their corresponding market conditions.
📘 Market Sentiment Integration
Purpose : Analyzes market sentiment using various indicators to provide an overall sentiment score.
Usage : Enable or disable individual sentiment indicators, set account type, and customize sentiment calculations for better market analysis.
Volatility Index (IV) :
Description : Enable or disable the use of the Volatility Index in sentiment calculation.
Details : When enabled, the Volatility Index (IV) provides insight into market sentiment by measuring market volatility. The selected Volatility Index varies based on your TradingView account type.
Account Type :
Description : Select your TradingView account type.
Details : Free accounts use SPX, while Premium accounts use VIX.
Put/Call Ratio (PCR) :
Description : Enable or disable the use of the Put/Call ratio in sentiment calculation.
Details : The Put/Call ratio is a sentiment indicator that measures the volume of put options traded relative to call options, indicating market sentiment towards bearish or bullish expectations.
Fear and Greed Index :
Description : Enable or disable the use of the Fear and Greed Index in sentiment calculation.
Details : The Fear and Greed Index gauges the prevailing emotions in the market, indicating whether investors are inclined towards fear (bearish sentiment) or greed (bullish sentiment).
Momentum Indicators :
Description : Enable or disable the use of momentum indicators like MACD and RoC in sentiment calculation.
Details : Momentum indicators help identify the strength and direction of price movements, assisting in sentiment analysis.
Adaptive Periods for Shorter Timeframes :
Description : Toggle this option to use shorter periods for sentiment indicators when analyzing lower timeframes.
Details : Enabling this option allows for more responsive and sensitive analysis when working with shorter timeframes.
Calculation Details :
Normalization Function : Normalize the values of the indicators over a 252-period range.
Set Periods Function : Set periods based on user preference for faster or slower periods, adjusting the analysis sensitivity.
IV Calculation : Calculate the IV value based on the selected Volatility Index (SPX for Free accounts, VIX for Premium accounts).
Put/Call Ratio Calculation : Calculate the Put/Call ratio using volume data, where put volume is proportional to the trading range, and call volume is proportional to the price change.
RoC Calculation : Calculate the Rate of Change (RoC) as a momentum indicator, measuring the percentage change in closing prices over a specified period.
Dynamic Thresholds : Define dynamic thresholds based on historical data, calculating mean and standard deviation to determine upper and lower thresholds for IV, PCR, and RoC.
📘 Enhanced Market Trend Dashboard Integration
Purpose : Provides a summary of key market indicators and signals in a single dashboard for quick and easy reference.
Usage : Customize the dashboard settings to display relevant market information, including Ichimoku components, Linear Regression, Support/Resistance levels, MACD, RSI, and Market Sentiment.
Market Trend Dashboard Parameters :
Display Market Trend Dashboard : Toggle to show or hide the market trend dashboard, providing a summary of key indicators and signals.
Panel Position : Select the position of the dashboard on the chart for optimal viewing.
Panel Text Size : Choose the text size for the information displayed in the dashboard, ensuring readability.
Panel Background Color : Set the background color of the market trend dashboard, enhancing contrast with the chart.
Ichimoku Dashboard Parameters :
Display Ichimoku Dashboard : Toggle to show or hide the Ichimoku section in the dashboard.
Display Tenkan-Sen Price Cross : Indicate when the price crosses the Tenkan-Sen line, signaling potential trade opportunities.
Display Kijun-Sen Price Cross : Indicate when the price crosses the Kijun-Sen line, often considered a stronger signal than Tenkan-Sen crosses.
Display Chikou Span Price Cross : Indicate Chikou Span price crosses, providing insight into potential trend reversals.
Display Kumo Breakout : Indicate Kumo (cloud) breakouts, which can signify major trend shifts.
Display Kumo Twist : Indicate Kumo twists, suggesting changing market dynamics and potential reversals.
Linear Regression Projection Dashboard Parameters :
Display LR Projection Dashboard : Toggle to show or hide the Linear Regression Projection section in the dashboard.
Display Linear Regression Period : Indicate the period used for Linear Regression Projection analysis.
Display Pearson R Details : Show the Pearson R value in the dashboard, indicating the strength and direction of the correlation in the Linear Regression Projection.
Supports and Resistances Dashboard Parameters :
Display S/R Dashboard : Toggle to show or hide the Support and Resistance section in the dashboard.
Display S/R Break Prices : Show the latest break prices of support and resistance levels in the dashboard.
MACD Dashboard Parameters :
Display MACD Dashboard : Toggle to show or hide the MACD section in the dashboard.
RSI Dashboard Parameters :
Display RSI Dashboard : Toggle to show or hide the Relative Strength Index section in the dashboard.
Display RSI Details : Show the RSI value and status in the dashboard.
Display StochRSI Details : Show the StochRSI %K, %D values and status in the dashboard.
Market Sentiment Dashboard Parameters :
Display Market Sentiment Dashboard : Enable or disable the display of the Market Sentiment Dashboard, which summarizes key market sentiment indicators like Implied Volatility, Put/Call Ratio, and Fear and Greed Index.
Display Implied Volatility Details : Show or hide the Implied Volatility details in the Market Sentiment Dashboard.
Display Put/Call Ratio Details : Show or hide the Put/Call Ratio details in the Market Sentiment Dashboard.
Display Fear and Greed Index Details : Show or hide the Fear and Greed Index details in the Market Sentiment Dashboard.
Enhanced Calculations :
Ichimoku Cloud Trend Calculation : Calculates trend based on the relationship between Ichimoku Cloud components, identifying bullish or bearish trends.
Support and Resistance Break Detection : Detects breaks in support and resistance levels and updates the dashboard accordingly.
Linear Regression Projection Calculation : Calculates Linear Regression Projection and Pearson R value for trend analysis.
MACD Signal Calculation : Determines MACD status based on histogram values.
RSI and StochRSI Calculation : Calculates RSI and StochRSI values and updates their statuses in the dashboard.
Market Sentiment Score Calculation : Calculates overall market sentiment score based on individual sentiment indicators.
Dynamic Alert Management : Manages alerts for various dashboard signals to prevent repeated alerts.
Real-Time Data Integration : Continuously updates the dashboard with real-time data for accurate and current trend analysis.
Plotting Market Trend Dashboard Components :
Ichimoku Components Plotting : Plots Tenkan-Sen, Kijun-Sen, Chikou Span, and Kumo cloud with dynamic adjustments.
Support and Resistance Levels Plotting : Plots support and resistance levels and updates them dynamically based on market data.
Linear Regression Projection Plotting : Plots the Linear Regression Projection line and labels with trend-based colors.
MACD and RSI Plotting : Plots MACD and RSI signals on the dashboard, including status updates.
Market Sentiment Indicators Plotting : Plots Market Sentiment indicators like IV, PCR, and Fear and Greed Index with dynamic updates.
Alert Notifications Plotting : Plots alert notifications for significant market changes based on dashboard signals.
Summary
This comprehensive market analyzer integrates multiple technical indicators, including machine learning, Ichimoku Kinkō Hyō, candlestick patterns, Fibonacci retracement, support and resistance levels, trend lines, linear regression, POC analysis, divergences, ATR, RSI, and market sentiment. Each section includes detailed descriptions and usage instructions to help traders understand how to effectively utilize the indicator in their trading strategies.
