Rainbow EMA Areas with Volatility HighlightThe indicator provides traders with an enhanced visual tool to observe price movements, trend strength, and market volatility on their charts. It combines multiple EMAs (Exponential Moving Averages) with color-coded areas to indicate the market’s directional bias and a high-volatility highlight for detecting times of increased market activity.
Explanation of Key Components
Multiple EMAs (Exponential Moving Averages):
Six different EMAs are calculated for various periods (15, 45, 100, 150, 200, 300).
Each EMA period represents a different timeframe, from short-term to long-term trends, providing a well-rounded view of price behavior across different market cycles.
The EMAs are color-coded for easy differentiation:
Green shades indicate bullish trends when prices are above the EMAs.
Red shades indicate bearish trends when prices are below the EMAs.
The space between each EMA is filled with a gradient color, creating a "wave" effect that helps identify the market’s overall direction.
ATR-Based Volatility Detection:
The ATR (Average True Range), a measure of market volatility, is used to assess how much the price is fluctuating. When volatility is high, price movements are typically more significant, indicating potential trading opportunities or times to exercise caution.
The indicator calculates ATR and uses a customizable multiplier to set a high-volatility threshold.
When the ATR exceeds this threshold, it signals that the market is experiencing high volatility.
Visual High Volatility Highlight:
A yellow background appears on the chart during periods of high volatility, giving a subtle but clear visual indication that the market is active.
This highlight helps traders spot potential breakout areas or increased activity zones without obstructing the EMA areas.
Volatility Signal Markers:
Small, red triangular markers are plotted above price bars when high volatility is detected, marking these areas for additional emphasis.
These signals serve as alerts to help traders quickly recognize high volatility moments where price moves may be stronger.
How to Use This Indicator
Identify Trends Using EMA Areas:
Bullish Trend: When the price is above most or all EMAs, and the EMA areas are colored in shades of green, it indicates a strong bullish trend. Traders might look for buy opportunities in this scenario.
Bearish Trend: When the price is below most or all EMAs, and the EMA areas are colored in shades of red, it signals a bearish trend. This condition can suggest potential sell opportunities.
Consolidation or Neutral Trend: If the price is moving within the EMA bands without a clear green or red dominance, the market may be in a consolidation phase. This period often precedes a breakout in either direction.
Volatility-Based Entries and Exits:
High Volatility Areas: The yellow background and red triangular markers signal high-volatility areas. This information can be valuable for identifying potential breakout points or strong moves.
Trading in High Volatility: During high-volatility phases, the market may experience rapid price changes, which can be ideal for breakout trades. However, high volatility also involves higher risk, so traders may adjust their strategies accordingly (e.g., setting wider stops or adjusting position sizes).
Trading in Low Volatility: When the yellow background and markers are absent, volatility is lower, indicating a calmer market. In these times, traders may choose to look for range-bound trading opportunities or wait for the next trend to develop.
Combining with Other Indicators:
This indicator works well in combination with momentum or oscillating indicators like RSI or MACD, providing a well-rounded view of the market.
For example, if the indicator shows a bullish EMA area with high volatility, and an RSI is trending up, it could be a stronger buy signal. Conversely, if the indicator shows a bearish EMA area with high volatility and RSI is trending down, this could be a stronger sell signal.
Practical Trading Examples
Bullish Trend in High Volatility:
Price is above the EMAs, showing green EMA areas, and the high volatility background is active.
This indicates a strong bullish trend with significant price movement potential.
A trader could look for breakout or continuation entries in the direction of the trend.
Bearish Reversal Signal:
Price crosses below the EMAs, showing red EMA areas, while high volatility is also detected.
This suggests that the market may be reversing to a bearish trend with increased price movement.
Traders could consider taking short positions or setting stops on existing long trades.
This indicator is designed to provide a rich visual experience, making it easy to spot trends, consolidations, and volatility zones at a glance. It is best used by traders who benefit from visual cues and who seek a quick understanding of both trend direction and market activity. Let me know if you'd like further customization or additional functionalities!
Volatilityindicator
[BRAIN] Absolute Volatility of Price
Hello traders!
Today I want to share with you a series of scripts and strategies that I developed a few years ago. This is one of my first works, born from the curiosity of seeing a candlestick representation in a different way, without considering the price movement along the y-axis.
Imagine observing the price movement in dollars and percentages, always starting from the same reference point: the 0 axis. This approach can offer new insights and ideas on how and how much prices move.
To explain it better, the open of each candle does not start from the previous close negotiations but always starts from the 0 axis . In this way, it is possible to clearly compare the bodies of the candles with each other.
Script Visualization Methods and Input
- Study Normal: Simply reports the prices, including the negative ones of the red candles, on the same scale in absolute terms (ABS), as shown in the first indicator above.
- Study Normal Neg: In this version, the red candles vary negatively below zero, instead of in absolute terms above zero, as shown in the second indicator above.
- Study Perc: Similar to "Study Normal" but uses percentage values instead of dollars, useful for very low timeframes and low variations with many decimals, such as 1 minute on EUR/USD.
- Study Perc Neg: Similar to "Study Normal Neg" but uses percentage values.
Additionally, I have added the possibility to display or not, through two buttons, an average of the candle bodies adjustable in length via input and the range of each candle, always correlated in dollars or percentages, as per the main study setting.
I hope this work can be useful to many of you. I invite you to like if you appreciate my scripts and want to see more like these. Do not hesitate to comment or contact me for any doubts or questions.
PS: If you notice that in the script the sum of the percentage values between the shadow and the body of the candle does not correspond to the range, it is only a rounding issue. Change the precision setting to a lower value and you will see that the rounding disappears.
PS: In the script, to better visualize the percentage growth and decline of the instrument on very high timeframes, I decided to represent it as follows:
- If close ≥ open: (high - low) / low * 100
- If close < open: (high - low) / high * 100
The same method is also applied for calculating the percentage variations of the shadows relative to themselves.
I hope you like this version! If you need any further modifications or adjustments, let me know. Good luck with your project!
(In the photos below I show 3 versions of the indicator open on 3 different tickers as an example: from top to bottom in the 3 indicators are set these Study: Study Normal, Study Perc and Study Perc Neg)
Machine Learning Adaptive SuperTrend [AlgoAlpha]📈🤖 Machine Learning Adaptive SuperTrend - Take Your Trading to the Next Level! 🚀✨
Introducing the Machine Learning Adaptive SuperTrend , an advanced trading indicator designed to adapt to market volatility dynamically using machine learning techniques. This indicator employs k-means clustering to categorize market volatility into high, medium, and low levels, enhancing the traditional SuperTrend strategy. Perfect for traders who want an edge in identifying trend shifts and market conditions.
What is K-Means Clustering and How It Works
K-means clustering is a machine learning algorithm that partitions data into distinct groups based on similarity. In this indicator, the algorithm analyzes ATR (Average True Range) values to classify volatility into three clusters: high, medium, and low. The algorithm iterates to optimize the centroids of these clusters, ensuring accurate volatility classification.
Key Features
🎨 Customizable Appearance: Adjust colors for bullish and bearish trends.
🔧 Flexible Settings: Configure ATR length, SuperTrend factor, and initial volatility guesses.
📊 Volatility Classification: Uses k-means clustering to adapt to market conditions.
📈 Dynamic SuperTrend Calculation: Applies the classified volatility level to the SuperTrend calculation.
🔔 Alerts: Set alerts for trend shifts and volatility changes.
📋 Data Table Display: View cluster details and current volatility on the chart.
Quick Guide to Using the Machine Learning Adaptive SuperTrend Indicator
🛠 Add the Indicator: Add the indicator to favorites by pressing the star icon. Customize settings like ATR length, SuperTrend factor, and volatility percentiles to fit your trading style.
📊 Market Analysis: Observe the color changes and SuperTrend line for trend reversals. Use the data table to monitor volatility clusters.
🔔 Alerts: Enable notifications for trend shifts and volatility changes to seize trading opportunities without constant chart monitoring.
How It Works
The indicator begins by calculating the ATR values over a specified training period to assess market volatility. Initial guesses for high, medium, and low volatility percentiles are inputted. The k-means clustering algorithm then iterates to classify the ATR values into three clusters. This classification helps in determining the appropriate volatility level to apply to the SuperTrend calculation. As the market evolves, the indicator dynamically adjusts, providing real-time trend and volatility insights. The indicator also incorporates a data table displaying cluster centroids, sizes, and the current volatility level, aiding traders in making informed decisions.
Add the Machine Learning Adaptive SuperTrend to your TradingView charts today and experience a smarter way to trade! 🌟📊
ATR GerchikAverage True Range ( ATR ) is a technical analysis indicator that measures market volatility. It is a moving average of the true range over a period of time. Originally developed by a market technician J. Welles Wilder Jr. in the 1970s, ATR was utilized to measure the average volatility of an asset over a given time period. Wilder realized that measuring volatility using only closing prices would not yield accurate results, necessitating a more complex system. To calculate the Average True Range, one must first determine the True Range (TR).
ATR calculation procedure:
1. Determine the true maximum - this is the highest of the current maximum and yesterday's closing price of the day.
2. Determine the true minimum - this is the smallest of the current minimum and yesterday's closing price.
3. Determine the true range - this is the distance between the true maximum and minimum.
4. Exclude extremely large candles and extremely small ones from the obtained true ranges.
5. Calculate the average for the selected period based on the remaining range.
6. Calculate the percentage of the current True Range relative to the average ATR value for the previous period.
Description:
If you analyze market movements, you will find that 75-80% of the time, an instrument moves only 1 ATR per day. Understanding this is crucial; for example, if an instrument has already moved 80% of its daily range, it is not advisable to enter a new position. This concept is similar to a car's fuel tank; if the tank is nearly empty, the car won’t go far. Many indicators include anomalous candles in their ATR calculations, which can yield unreliable results and lead to incorrect decisions. This is why many traders prefer to calculate ATR manually.
However, the Gerchik ATR indicator accounts for anomalous candles by filtering out extremely large and small candles. Users can set the coefficient for the upper and lower filtering thresholds. Experiment with these settings to find your criteria for filtering out abnormal candles. Personally, I filter out candles larger than 2x ATR and smaller than 0.5x ATR. Additionally, this indicator displays the consumed “fuel” of the instrument for the entire day and the current percentages, so you don’t have to calculate the distance traveled manually. The indicator also visually displays the boundaries of the average true range on the chart, enabling quick and informed decisions. When building any strategy, relying on the average true range movement is essential.
This extended version of the indicator includes a NATP indicator (Normalized ATR), a variation of the ATR that measures volatility as a percentage of the current price. It helps gauge market volatility levels and assists traders in making informed decisions.
Procedure for calculating NATR (Normalized ATR):
1. Determine the true maximum - the higher of the current high and the previous close.
2. Determine the true minimum - the lower of the current low and the previous close.
3. Determine the true range - the distance between the true maximum and minimum.
4. Filter out extremely large and small values from the obtained true ranges.
5. Calculate the average for n candles based on the remaining ranges.
Additionally in this version:
- Change table position
- Added NATP indicator
- Option to turn off the table description
- Option to turn off some indicators in the table
- Indication of the selected period in the table
- Changing coefficients for filtering abnormal candles
- Display of the number of invalid candles in the selected period
- Inclusion of labels with full ATR, NATR, candle range, and validity information
- Color-coding labels based on validity
- Selection of colors for valid and invalid candles
- Adjustable label size
- ATR graph display on the chart
- Customizable graph style, line thickness, and fill color
Detailed description:
Displays colored labels with detailed information. Labels can be color-coded based on validity and selected color. The text color will automatically adjust if a lighter color is chosen.
Panel of available settings
Graphic styles:
Line ATR graph style
Cross line ATR graph style
Step line ATR graph style
Step line diamond ATR graph style
Cross ATR graph style
Columns ATR graph style
Circles ATR graph style
Area ATR graph style
Cross area ATR graph style
Key Features:
- Anomalous Candle Filtering: Excludes extremely large and small candles for more reliable ATR values. Set filtering thresholds independently as coefficients.
- Consumed Fuel Indicator: Shows the percentage of the ATR consumed, aiding quick assessment of remaining movement potential.
- Daily Timeframe Focus: Designed for daily charts for accurate long-term analysis. The indicator is displayed on the daily timeframe if enabled, hiding it on lower timeframes.
- Visual Indicator Boundaries: Displays indicator boundaries on the chart with customizable styles and settings.
Practical Applications:
ATR helps traders predict potential future price movements, aiding in setting Stop Loss and Take Profit targets. Using ATR for SL/TP placement helps avoid market noise. ATR can also form an exit strategy by placing Trailing Stop Losses.
- Entry and Exit Points: Determine optimal entry and exit points by assessing market volatility and potential price movement.
- Stop-Loss Placement: Calculate stop-loss levels based on ATR to ensure appropriate placement, accounting for current market volatility.
- Trend Confirmation: Use ATR percentage consumption to confirm trend strength and decide on trade entries or exits.
Examples of Use:
- Trend Following: During strong trends, ATR identifies increased volatility periods, signaling potential breakouts or reversals.
- Range Trading: In ranging markets, ATR highlights low volatility periods, indicating consolidation and potential breakout zones.
[Pandora] Vast Volatility Treasure TroveINTRODUCTION:
Volatility enthusiasts, prepare for VICTORY on this day of July 4th, 2024! This is my "Vast Volatility Treasure Trove," intended mostly for educational purposes, yet these functions will also exhibit versatility when combined with other algorithms to garner statistical excellence. Once again, I am now ripping the lid off of Pandora's box... of volatility. Inside this script is a 'vast' collection of volatility estimators, reflecting the indicators name. Whether you are a seasoned trader destined to navigate financial strife or an eagerly curious learner, this script offers a comprehensive toolkit for a broad spectrum of volatility analysis. Enjoy your journey through the realm of market volatility with this code!
WHAT IS MARKET VOLATILITY?:
Market volatility refers to various fluctuations in the value of a financial market or asset over a period of time, often characterized by occasional rapid and significant deviations in price. During periods of greater market volatility, evolving conditions of prices can move rapidly in either direction, creating uncertainty for investors with results of sharp declines as well as rapid gains. However, market volatility is a typical aspect expected in financial markets that can also present opportunities for informed decision-making and potential benefits from the price flux.
SCRIPT INTENTION:
Volatility is assuredly omnipresent, waxing and waning in magnitude, and some readers have every intention of studying and/or measuring it. This script serves as an all-in-one armada of volatility estimators for TradingView members. I set out to provide a diverse set of tools to analyze and interpret market volatility, offering volatile insights, and aid with the development of robust trading indicators and strategies.
In today's fast-paced financial markets, understanding and quantifying volatility is informative for both seasoned traders and novice investors. This script is designed to empower users by equipping them with a comprehensive suite of volatility estimators. Each function within this script has been meticulously crafted to address various aspects of volatility, from traditional methods like Garman-Klass and Parkinson to more advanced techniques like Yang-Zhang and my custom experimental algorithms.
Ultimately, this script is more than just a collection of functions. It is a gateway to a deeper understanding of market volatility and a valuable resource for anyone committed to mastering the complexities of financial markets.
SCRIPT CONTENTS:
This script includes a variety of functions designed to measure and analyze market volatility. Where applicable, an input checkbox option provides an unbiased/biased estimate. Below is a brief description of each function in the original order they appear as code upon first publish:
Parkinson Volatility - Estimates volatility emphasizing the high and low range movements.
Alternate Parkinson Volatility - Simpler version of the original Parkinson Volatility that I realized.
Garman-Klass Volatility - Estimates volatility based on high, low, open, and close prices using a formula that adjusts for biases in price dynamics.
Rogers-Satchell-Yoon Volatility #1 - Estimates volatility based on logarithmic differences between high, low, open, and close values.
Rogers-Satchell-Yoon Volatility #2 - Similar estimate to Rogers-Satchell with the same result via an alternate formulation of volatility.
Yang-Zhang Volatility - An advanced volatility estimate combining both strengths of the Garman-Klass and Rogers-Satchell estimators, with weights determined by an alpha parameter.
Yang-Zhang (Modified) Volatility - My experimental modification slightly different from the Yang-Zhang formula with improved computational efficiency.
Selectable Volatility - Basic customizable volatility calculation based on the logarithmic difference between selected numerator and denominator prices (e.g., open, high, low, close).
Close-to-Close Volatility - Estimates volatility using the logarithmic difference between consecutive closing prices. Specifically applicable to data sources without open, high, and low prices.
Open-to-Close Volatility - (Overnight Volatility): Estimates volatility based on the logarithmic difference between the opening price and the last closing price emphasizing overnight gaps.
Hilo Volatility - Estimates volatility using a method similar to Parkinson's method, which considers the logarithm of the high and low prices.
Vantage Volatility - My experimental custom 'vantage' method to estimate volatility similar to Yang-Zhang, which incorporates various factors (Alpha, Beta, Gamma) to generate a weighted logarithmic calculation. This may be a volatility advantage or disadvantage, hence it's name.
Schwert Volatility - Estimates volatility based on arithmetic returns.
Historical Volatility - Estimates volatility considering logarithmic returns.
Annualized Historical Volatility - Estimates annualized volatility using logarithmic returns, adjusted for the number of trading days in a year.
If I omitted any other known varieties, detailed requests for future consideration can be made below for their inclusion into this script within future versions...
BONUS ALGORITHMS:
This script also includes several experimental and bonus functions that push the boundaries of volatility analysis as I understand it. These functions are designed to provide additional insights and also are my ideal notions for traders looking to explore other methods of volatility measurement.
VOLATILITY APPLICATIONS:
Volatility estimators serve a common role across various facets of trading and financial analysis, offering insights into market behavior. These tools are already in instrumental with enhancing risk management practices by providing a deeper understanding of market dynamics and the inherent uncertainty in asset prices. With volatility estimators, traders can effectively quantifying market risk and adjust their strategies accordingly, optimizing portfolio performance and mitigating potential losses. Additionally, volatility estimations may serve as indication for detecting overbought or oversold market conditions, offering probabilistic insights that could inform strategic decisions at turning points. This script
distinctly offers a variety of volatility estimators to navigate intricate financial terrains with informed judgment to address challenges of strategic planning.
CODE REUSE:
You don't have to ask for my permission to use/reuse these functions in your published scripts, simply because I have better things to do than answer requests for the reuse of these functions.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already.
Liquidity Dependent Price Movement AlgorithmLiquidity-Dependent Price Movement (LDPM) is a metric designed to directly measure liquidity on a equity in real time, and to translate those measurements into signals to provide insights into where the anticipate price-direction is headed.
Liquidity can be characterized as a way of measuring how smoothly things are running in the market. When things are running smoothly – such as when there is good agreement as to the price of an asset, then things are considered liquid. Conversely, when things are not running smoothly, just as when the bid or the ask do not agree with each other, then things are considered not liquid. These different states have different outcome liklihoods.
In a liquid environment, a stock can trade a lot of shares without moving the price. On the other hand, when a stock is not liquid, even small volumes can move the price substantially.
It is therefore helpful to know when a stock is liquid to the upside or to the downside, or even, when a stock is not liquid to the upside or the downside. These data have statistical associations with future price movement and volatility.
The use of LDPM is straightforward:
If the price is above LDPM: bullish outlooks.
If the price is below LDPM: bearish outlooks.
There are a few key differences about LDPM as compared to other indicators, namely that timeframe matters . That means, LDPM will tailor its output to the timeframe selected. The advantage of this is that it allows LDPM to be "tailored" to the specific timeframe as desired, without having to do any conversions or adaptations mentally.
Key Settings and Configurations:
Setting - Smoothing Type of LDPM :
Default: KF.
LDPM can be smoothened if desired. There are 5 different types of smoothing available:
EMA : Exponential Smoothing
SMA : Simple Smoothing
WMA : Weighted Smoothing
RMA : Modified Smoothing
KF : Kellman Smoothing
The default is "KF" for Kellman Smoothing.
Setting - Include LDPM-Granular :
Default: Off.
LDPM-Granular is the more "raw" form of LDPM that displays the candle-specific result, rather than the smoothened result. This can be toggled on or off, if desired. LDPM granular is helpful for looking at candle-specific
Setting - Place LDPM Standard :
Default: Off.
An additional, single, LDPM line can be placed via this toggle. Settings for this LDPM can be configured directly below toggle.
Setting - Place LDPM-Fib :
Default: On.
LDPM-Fib is a default setting for displaying 5 LDPMs (LDPM-13, LDPM-21, LDPM-34, LDPM-55, and LDPM-89) whose lookbacks are spaced via the Fib sequence. Useful for those who enjoy a static relationship between the different "layers" of LDPM.
Setting - Place LDPM-Reference :
Default: Off.
Since LDPM is time-interval dependent, there may be times when a higher-order timeframe is desired to act as a reference. For instance, suppose you want to go long if the 1-Hour LDPM experiences a bullish crossover, but you want to scalp shorts on the 15-minute timeframe until then. Then you could place the chart on the 15-minute interval for your scalping, and then place a 1-Hour reference LDPM that will show you when the 1-Hour LDPM and price experience a crossover.
Note: The reference must be a higher-order timeframe. So if your chart is on the 15-minute, you can only reference timeframes greater than 15.
Setting - LDPM Box Creation :
Default: On.
Instead of implementing a reference LDPM, it is possible to display the other timeframes in a data table with conditional coloring for if the overall LDPM-Price relationship is bullish or bearish.
Why Chose LDPM
There are no other Liquidity-measuring indicators available to the retail investor. Measuring liquidity often requires the use of expensive data and high-throughput computing to be used in real-time. Neither of these requirements apply to utilizing LDPM.
Additionally, the data are supportive that LDPM provides statistically significant, price-direction-correct outlooks.
Oster's Vola Sentiment (OVS)Overview:
Oster's Vola Sentiment (OVS) is an indicator that reflects market sentiment dynamics based on volatility , employing Oster's Volatility Method for calculation. Inspired by traditional volatility analysis, this indicator provides a versatile tool for traders to interpret market sentiments and identify potential trading opportunities, including potential reversal points . By adjusting the period length in the settings, users can fine-tune OVS sensitivity to capture buy or sell signals, achieving different signal qualities.
Sophisticated Calculation Methodology:
The OVS derives insights from Oster's Volatility Method, utilizing metrics related to price range and movement to assess market dynamics. It calculates the relative movement index, providing traders with a quantifiable measure of market sentiment. Additionally, OVS incorporates the Average True Range (ATR) to further refine its analysis, ensuring comprehensive insights into market volatility dynamics.
Interpretation:
Oster's Vola Sentiment (OVS) , represented on the chart, offers traders insights into market sentiment dynamics and potential reversal points . Values above 0 indicate a buy tendency, suggesting favorable conditions for buying opportunities, while values below 0 suggest a sell tendency, signaling potential selling pressure. The probability of a significant market move increases as OVS values approach the predefined buy or sell thresholds. Values exceeding the buy threshold indicate stronger buying signals, while values below the sell threshold signify stronger selling signals. By aligning these interpretations with the trader's investment strategy, OVS aids in decision-making processes, offering nuanced perspectives on market movements.
Dynamic Color Coding for Visual Clarity:
To enhance user experience and facilitate quick decision-making, OVS incorporates dynamic color coding . Market conditions favoring selling are denoted by red hues, while those conducive to buying are highlighted in green. Neutral conditions, indicative of balanced market sentiment, are represented in neutral colors. This intuitive visual feedback enables traders to swiftly identify market opportunities and risks, empowering them to make informed trading decisions.
Customizable Parameters for Tailored Analysis:
Acknowledging the diverse trading preferences and strategies of its users, OVS offers customizable parameters. Traders can adjust the period length to fine-tune the indicator's sensitivity to their desired level, balancing the frequency and quality of signals according to their trading objectives. Additionally, OVSs alert functionalities allow traders to set personalized thresholds, aligning with their risk tolerance and market outlook.
Conclusion:
In conclusion, Oster's Vola Sentiment (OVS) emerges as a valuable addition to the trader's toolkit, offering a versatile and accessible approach to market analysis. Built upon Oster's Volatility Method and sophisticated calculation methodologies, OVS provides traders with actionable insights into market sentiment across various timeframes and asset classes , including potential reversal points. Its intuitive visualizations, coupled with customizable parameters and alert functionalities, empower traders to navigate dynamic market conditions with confidence. Whether you're a seasoned investor or a novice trader, OVS equips you with the tools needed to stay ahead in today's competitive markets.
Volatility Filter v2VF v2 is a new iteration of my tool designed for traders who wish to gain a deeper understanding of market dynamics, specifically to distinguish periods of high volatility, which often correspond to strong market trends. By identifying these periods, traders can make more informed decisions, potentially leading to better trading outcomes.
Understanding Market Volatility:
At the heart of this script lies the concept of market volatility, a statistical measure reflecting the degree of variation in trading prices. Volatility is pivotal for traders; it provides insights into the market's emotional state, indicating periods of uncertainty or confidence. High volatility often correlates with strong trends, making it a critical indicator for trend-followers. By identifying when volatility crosses a certain threshold, traders can discern whether the market is likely to be in a trending phase or a more subdued, range-bound state.
How the Script Works:
The core functionality of the script revolves around a signal line that oscillates around a zero threshold. When the signal line is above zero, it indicates increased market volatility, suggesting the presence of a trend. The farther the oscillator deviates from zero, the stronger the implied trend. This mechanism enables traders to visually gauge market conditions and adjust their strategies accordingly.
Controlling the Indicator:
To cater to diverse trading styles and preferences, the script is equipped with several customizable settings:
Filter Threshold: This 'zero line' acts as the baseline for distinguishing between different volatility regimes. Crossing this threshold is a primary signal for changes in market volatility.
Moving Average Type: With over 30 types of moving averages to choose from, traders can select the one that best fits their analysis style. Each type offers a different perspective on price data, allowing for a tailored approach to trend identification.
Colorize Indicator: This feature enhances the visual representation of the indicator, making it easier to interpret. When enabled, the oscillator's color intensity varies with its proximity to the extremes, providing a quick visual cue about trend strength.
Advanced Settings – Length and Multiplier:
The script introduces an innovative approach to time frame analysis through its length and multiplier settings:
Length: This parameter sets the base period for all metrics within the script, similar to traditional indicators.
Multiplier: This unique feature differentiates the script by incorporating three distinct timeframes into the analysis: a lower timeframe, the main (current) timeframe, and a higher timeframe. The multiplier adjusts these timeframes relative to the main one. For instance, with a daily main timeframe and a multiplier of 2, the lower timeframe would be 12 hours, and the higher timeframe would be 2 days. This tri-timeframe approach aims to provide a more comprehensive volatility assessment.
Volatility Filter Indicators Section:
The script utilizes nine different, undisclosed metrics within its volatility filter. Traders have the flexibility to enable or disable these metrics based on their preferences, allowing for a customizable trading experience. Additionally, the script offers alert functionality for when the indicator crosses the threshold, either upwards or downwards, facilitating timely decision-making.
P.S
With better understanding of markets over time, I designed a new iteration of my volatility filter indicator. The second version provides faster, more precise way to analyze markets, but I also wanted to keep my first version untouched in case if some people find it better for their purposes. As I mentioned above, this version is calculated in a very different way from a previous one, so if you never tried it you can do it here
Realized volatility differentialAbout
This is a simple indicator that takes into account two types of realized volatility: Close-Close and High-Low (the latter is more useful for intraday trading).
The output of the indicator is two values / plots:
an average of High-Low volatility minus Close-Close volatility (10day period is used as a default)
the current value of the indicator
When the current value is:
lower / below the average, then it means that High-Low volatility should increase.
higher / above then obviously the opposite is true.
How to use it
It might be used as a timing tool for mean reversion strategies = when your primary strategy says a market is in mean reversion mode, you could use it as a signal for opening a position.
For example: let's say a security is in uptrend and approaching an important level (important to you).
If the current value is:
above the average, a short position can be opened, as High-Low volatility should decrease;
below the average, a trend should continue.
Intended securities
Futures contracts
RWEDT Weighted Moving Average Overview:
The RWEDT MA, which is short for rolling, weighted, exponential, double exponential, and triple exponential, is a group of moving averages that were subjected to a log transformation to deal with the skewness of price, and the weight of each of these moving averages was also used for calculating the standard deviations from the mean.
Clearing a misunderstanding on Standard Deviation Bands and Moving Averages
Bands, such as standard deviation bands, are frequently misinterpreted as indicators of support and resistance levels or as "mean-reverting" indicators." However, this is not their intended purpose. Bands are statistical tools that provide ranges within which price (in this case) movements are expected to occur based on historical data. Deviations beyond these bands suggest a decrease in confidence in the model rather than a reversal back to a moving average or a "support/resistance level."
Example : Assuming you correctly applied a log transformation to your standard deviation bands to remove the right skew, and assuming your data closely resembles a normal distribution or some other type of symmetrical distribution, then the probability of a value being in the 2 standard deviation range is around 95%. This does not mean it will reject or go up, or mean revert. The price won't bounce from -2 STDEV 95% of the time; that is incorrect. It just tells you that around 95% of the values will be within the 2 SD range.
Moving averages, including the ones in this indicator, are often misinterpreted as signals of trend reversals or levels of "bouncing." What moving averages actually tell you is what the expected value is. It does not show where you expect the price to be in the future; it tells you that based on the lookback, the expected value is in the center, and the confidence you have in the estimate is the confidence interval or the standard deviation range.
Example: Let's say you enter a trade with a positive expected value (expecting the price to drift up), and we have the limits set at 95%. What it tells you is that as long as the price stays within the limits, you can be 95% certain the model isn't completely random. As the price moves further away from the average, or expected value, it tells you that the model is less likely to be correct.
RWEDT MA
This indicator comes with 5 moving averages, each log transformed to reduce the skewness and asymmetry of price as much as possible
Rolling
Weighted
Exponential
Double Exponential
Triple Exponential
The band standard deviation can be adjusted, and the standard deviations have the weight of all of the moving averages that are present in the indicator. The weight is not customizable.
Why this indicator is useful:
This indicator can tell you what the expected value is. Above the moving average signifies a positive expected value, and below the moving average signifies a negative expected value. As previously stated above, the price moving further from the expected value lets you know that you should have less confidence that the model is "correct," and you could see this as taking profits as the price deviates further from the expected value.
The importance of log-transforming prices for standard deviations and moving averages.
Symmetry: Logarithmic transformations can help achieve symmetry in the distribution of price data. Stock prices, for example, exhibit some type of right-skewed distribution, where large positive price movements are more common than large negative movements. Price also can't go below 0 but can go towards positive infinity, so having a right-skew makes sense; all the outliers will be towards infinity, while all the average occurrences are "near" 0.
Stabilizing Variance: Price data typically exhibit heteroscedasticity, meaning that the variance of price movements changes over time. Log transformations can stabilize the variance and make it more consistent across different price levels. This is important for ensuring that the variability in price moves is not disproportionately influenced by extreme values.
Statistical Assumptions: Many retail indicators like Bollinger Bands use the standard deviation and moving average models of a normal distribution to attempt to model price, whose distribution more closely resembles some type of right-skew distribution. Even with the log-transformation, it still won't always resemble a perfect symmetrical distribution, and you still should not use it for mean reversion. You can still use it to understand the expected value and whether or not you should have confidence in your model.
Squeeze Momentum DeluxeThe Squeeze Momentum Deluxe is a comprehensive trading toolkit built with features of momentum, volatility, and price action. This script offers a suite for both mean reversion and trend-following analysis. Developed based on the original TTM Squeeze implementation by @LazyBear, this indicator introduces several innovative components to enhance your trading insights.
🔲 Components and Features
Momentum Oscillator - as rooted in the TTM Squeeze, quantifies the relationship between price and its extremes over a defined period. By normalizing the calculation, the values become comparable throughout time and across securities, allowing for a nuanced assessment of Bullish and Bearish momentum. Furthermore, by presenting it as a ribbon with a signal line we gain additional information about the direction of price swings.
Squeeze Bars - The original squeeze concept is based on the relationship between the Bollinger Bands and Keltner Channel , once the BB resides inside the KC a squeeze occurs. By understanding their fundamentals a new form of calculation can be inferred.
method bb(float src, simple int len, simple float mult) => method kc(float src, simple int len, simple float mult) =>
float basis = ta.sma (src, len) float basis = ta.sma (src, len)
float dev = ta.stdev(src, len) float rng = ta.atr ( len)
float upper = basis + dev * mult float upper = basis + rng * mult
float lower = basis - dev * mult float lower = basis - rng * mult
Both BB and KC are constructed upon a moving average with the addition of Standard Deviation and Average True Range respectively. Therefore, the calculation can be transformed to when the Stdev is lower than the ATR a squeeze occurs.
method sqz(float src, simple int len) =>
float dev = ta.stdev(src, len)
float atr = ta.atr ( len)
dev < atr ? true : false
This indicator uses three different thresholds for the ATR to gain three levels of price "Squeeze" for further analysis.
Directional Flux- This component measures the overall direction of price volatility, offering insights into trend sentiment. Presented as waves in the background, it includes an OverFlux feature to signal extreme market bias in a particular direction which can signal either exhaustion or vital continuation. Additionally, the user can choose if to base the calculation on Heikin-Ashi Candles to bias the tool toward trend assessment.
Confluence Gauges - Placed at the top and bottom of the indicator, these gauges measure confluence in the relationship between the Momentum Oscillator and Directional Flux. They provide traders with an easily interpretable visual aid for detecting market sentiment. Reversal doritos displayed alongside them contribute to mean reversion analysis.
Divergences (Real-Time) - Equipped with a custom algorithm, the indicator detects real-time divergences between price and the oscillator. This dynamic feature enhances your ability to spot potential trend reversals as they occur.
🔲 Settings
Directional Flux Length - Adjusts the period of which the background volatility waves operate on.
Trend Bias - Bases the calculation of the Flux to HA candles to bias its behavior toward the trend of price action.
Squeeze Momentum Length - Calibrates the length of the main oscillator ribbon as well as the period for the squeeze algorithm.
Signal - Controls the width of the ribbon. Lower values result in faster responsiveness at the cost of premature positives.
Divergence Sensitivity - Adjusts a threshold to limit the amount of divergences detected based on strength. Higher values result in less detections, stronger structure.
🔲 Alerts
Sell Signal
Buy Signal
Bullish Momentum
Bearish Momentum
Bullish Flux
Bearish Flux
Bullish Swing
Bearish Swing
Strong Bull Gauge
Strong Bear Gauge
Weak Bull Gauge
Weak Bear Gauge
High Squeeze
Normal Squeeze
Low Squeeze
Bullish Divergence
Bearish Divergence
As well as the option to trigger 'any alert' call.
The Squeeze Momentum Deluxe is a comprehensive tool that goes beyond traditional momentum indicators, offering a rich set of features to elevate your trading strategy. I recommend using toolkit alongside other indicators to have a wide variety of confluence to therefore gain higher probabilistic and better informed decisions.
Weighted Average Volume Depth [QuantraSystems]Weighted Average Volume Depth
Introduction
The Weighted Average Volume Depth (𝓦𝓐𝓥𝓓) indicator is calibrated to provide extensive insights, calculated using volumetric price action and volume depth, and provides dynamic adjustments based upon historical volatility.
This indicator is a valuable asset for traders and investors, aiming to capture trends, measure dynamic volatility, and provide market reversion analysis in a systematic way.
Legend
Volumetric Top Cap: Plotted at y = 0, this line represents the probabilistic maximum value, or ‘cap’ for the signal line. It is colored using a binary color scheme, and indicates the dominant trend direction - green for an uptrend and purple for a downtrend.
Base Line: Calculated using a volume-weighted volatility measurement, this line is used as the benchmark to calculate momentum in the 𝓦𝓐𝓥𝓓 indicator.
Signal Line: The signal line represents the volume and volatility weighted measurements, and oscillates between the Base Line and Top Cap. Its position between these levels provides the depth of insights available in this script.
When the signal line is remaining in close proximity to the base line, this is indicative of a low volatility market environment. These periods are also reflected as muted bar coloring when the ‘Trend Intensity’ setting is enabled.
Conversely, when the signal line approaches, or even breaks above the Top Cap, this is characteristic of an unsustainable trending action - and probabilistically speaking, a reversion or consolation is likely to occur at these levels.
Highlighting: When this setting is enabled, background coloring is applied when the Signal Line breaks above the Top Cap. This highlights green as an oversold zone, and purple as an overbought zone.
Reversal Signals: When price begins to reverse from a zone of overextension, a signal is plotted when this reversion occurs from a high probability zone.
Circle - Shows a possible bullish reversal.
Cross - Shows a possible bearish reversal.
Case Study
In the above image, we showcase three distinct trades in short succession, showcasing the 𝓦𝓐𝓥𝓓’s speed and accuracy under the right conditions.
The first long trade was initiated upon receiving a bullish reversal signal. The trade was then closed after the price experienced a sharp upwards movement - and an overbought signal was indicated by the purple shading.
The second, short trade was entered on the next bar, after a bearish reversal signal was printed by the indicator (a white cross). Similarly, this trade was closed upon the oversold signal.
Once again, a reversal signal was indicated by the 𝓦𝓐𝓥𝓓 indicator. This time a bullish signal (a white circle), and hence a long position was opened. However, this trade was held until a negative trend confirmation (signaled by the Top Cap’s shift in color). This makes apparent the indicator’s flexible nature, and showcases the multiple signaling types available for traders to use.
Recommended Settings
The optimal settings for the 𝓦𝓐𝓥𝓓 indicator will vary upon the chosen asset’s average level volatility, as well as the timeframe it is applied to.
Due to increased volatility levels on lower timeframes, it is recommended to increase the 'Top Cap Multiplier' to take into account the increased frequency of false signals found in these trading environments. The same can be said when used on highly volatile assets - a trader will likely benefit from using a higher 'Top Cap Multiplier.'
On more price-stable assets, as well as any asset on higher timeframes, there is merit to tightening the length of the 'Top Cap Multiplier,' due to the slower nature of price action.
Methodology
The 𝓦𝓐𝓥𝓓 starts with calculating the volume weighted average price and the volume weighted variance - which is the expectation of the squared deviation of a variable from its mean, giving insights into the distribution of trading volume.
Using the volume weighted variance, a standard deviation value is calculated based on user input. This value acts as the ‘Volumetric Top Cap’ - seen in the 𝓦𝓐𝓥𝓓 indicator window as the zero line.
The signal line is calculated as the difference between the current price and the theoretical upper or lower VWAP deviation bands. This line acts as the trigger for identifying prevailing trends and high probability reversal points.
The base line serves as a reference point for historical momentum. It is calculated using an exponential moving average of the lowest signal line values over a defined lookback period. This baseline helps in assessing whether the current momentum is high or low relative to historical norms.
Notes
Bar coloring can be turned off - especially useful when stacking multiple indicators as recommended, or set to 'Trend Intensity,' or 'Binary Trend' (which reflects the top cap coloring).
It is always recommended to never rely on a single indicator - and instead build and test multiple strategies utilizing more than one indicator as confirmation.
Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
Bandwidth Bands - Silverman's rule of thumbWhat are Bandwidth Bands?
This indicator uses Silverman Rule of Thumb Bandwidth to estimate the width of bands around the rolling moving average which takes in the log transformation of price to remove most of price skewness for the rest of the volatility calculations and then a exp() function is performed to convert it back to a right skewed distribution. These bandwidths bands could offer insights into price volatility and trading extremes.
Silverman rule of thumb bandwidth:
The Silverman Rule of Thumb Bandwidth is a heuristic method used to estimate the optimal bandwidth for kernel density estimation, a statistical technique for estimating the probability density function of a random variable. In the context of financial analysis, such as in this indicator, it helps determine the width of bands around a moving average, providing insights into the level of volatility in the market. This method is particularly useful because it offers a quick and straightforward way to estimate bandwidth without requiring extensive computational resources or complex mathematical calculation
The bandwidth estimator automatically adjust to the characteristics of the data, providing a flexible and dynamic measure of dispersion that can capture variations in volatility over time. Standard deviations alone may not be as adaptive to changes in data distributions. The Bandwidth considers the overall shape and structure of the data distribution rather than just focusing on the spread of data points.
Settings
Source
Sample length
1-4 SD options to disable or enable each band
Conditional Volatility PercentileSimple Description: This indicator can basically help you find when a big move might happen ( This indicator can't determine the direction but when a big move could happen. ) Basically, a low-extreme value like 0 means that it only has room for upside, so volatility can only expand from that point on, and the fact that volatility mean reverts supports this.
Conditional Volatility Percentile Indicator
This indicator is a tool designed to view current market volatility relative to historical levels. It uses a statistical approach to assess the percentile rank of the calculated conditional volatility.
The Volatility Calculation
This indicator calculates conditional variance with user-defined parameters, which are Omega, Alpha, Beta, and Sigma, and then takes the square root of the variance to calculate the standard deviation. The script then calculates the percentile rank of the conditional variance over a specified lookback.
What this indicator tells you:
Volatility Assessment: Higher percentile values indicate heightened conditional volatility, suggesting increased market activity or potential stress. Meanwhile, lower percentiles suggest relatively lower conditional volatility.
Extreme Values: Volatility is a mean-reverting process. If the volatility percentile value is at a low value for an extended period of time, you can eventually bet on the volatility percentile value increasing with high confidence.
In financial markets, volatility itself exhibits mean-reverting properties. This means that periods of high volatility are likely to be followed by periods of lower volatility, and vice versa.
1. High Volatility Periods: High volatility levels may be followed by a subsequent decrease in volatility as the market returns to a more typical state.
2. Low Volatility Periods: Periods of low volatility may be followed by an uptick in volatility as the market experiences new information or changes in sentiment.
GARCH Volatility Estimation - The Quant ScienceThe GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events.
The general formula of the GARCH model is:
σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1)
where:
σ²(t) is the conditional variance at time t (i.e., squared volatility)
ω is the constant term (intercept) representing the baseline level of volatility
α is the coefficient representing the impact of the squared lagged error term on the conditional variance
ε²(t-1) is the squared lagged error term at the previous time period
β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance
In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset.
HOW TO USE
This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red).
USER INTERFACE
Alpha: select the starting value of Alpha (default value is 0.10).
Beta: select the starting value of Beta (default value is 0.80).
Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20).
Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30).
Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.
Squeeze & Release [AlgoAlpha]Introduction:
💡The Squeeze & Release by AlgoAlpha is an innovative tool designed to capture price volatility dynamics using a combination of EMA-based calculations and ATR principles. This script aims to provide traders with clear visual cues to spot potential market squeezes and release scenarios. Hence it is important to note that this indicator shows information on volatility, not direction.
Core Logic and Components:
🔶EMA Calculations: The script utilizes the Exponential Moving Average (EMA) in multiple ways to smooth out the data and provide indicator direction. There are specific lengths for the EMAs that users can modify as per their preference.
🔶ATR Dynamics: Average True Range (ATR) is a core component of the script. The differential between the smoothed ATR and its EMA is used to plot the main line. This differential, when represented as a percentage of the high-low range, provides insights into volatility.
🔶Squeeze and Release Detection: The script identifies and highlights squeeze and release scenarios based on the crossover and cross-under events between our main line and its smoothed version. Squeezes are potential setups where the market may be consolidating, and releases indicate a potential breakout or breakdown.
🔶Hyper Squeeze Detection: A unique feature that detects instances when the main line is rising consistently over a user-defined period. Hyper squeeze marks areas of extremely low volatility.
Visual Components:
The main line (ATR-based) changes color depending on its position relative to its EMA.
A middle line plotted at zero level which provides a quick visual cue about the main line's position. If the main line is above the zero level, it indicates that the price is squeezing on a longer time horizon, even if the indicator indicates a shorter-term release.
"𝓢" and "𝓡" characters are plotted to represent 'Squeeze' and 'Release' scenarios respectively.
Standard Deviation Bands are plotted to help users gauge the extremity and significance of the signal from the indicator, if the indicator is closer to either the upper or lower deviation bands, this means that statistically, the current value is considered to be more extreme and as it is further away from the mean where the indicator is oscillating at for the majority of the time. Thus indicating that the price has experienced an unusual amount or squeeze or release depending on the value of the indicator.
Usage Guidelines:
☝️Traders can use the script to:
Identify potential consolidation (squeeze) zones.
Gauge potential breakout or breakdown scenarios (release).
Fine-tune their entries and exits based on volatility.
Adjust the various lengths provided in the input for better customization based on individual trading styles and the asset being traded.
SuperTrend ToolkitThe SuperTrend Toolkit (Super Kit) introduces a versatile approach to trend analysis by extending the application of the SuperTrend indicator to a wide array of @TradingView's built-in or Community Scripts . This tool facilitates the integration of the SuperTrend algorithm with various indicators, including oscillators, moving averages, overlays, and channels.
Methodology:
The SuperTrend, at its core, calculates a trend-following indicator based on the Average-True-Range (ATR) and price action. It creates dynamic support and resistance levels, adjusting to changing market conditions, and aiding in trend identification.
pine_st(simple float factor = 3., simple int length = 10) =>
float atr = ta.atr(length)
float up = hl2 + factor * atr
up := up < nz(up ) or close > nz(up ) ? up : nz(up )
float lo = hl2 - factor * atr
lo := lo > nz(lo ) or close < nz(lo ) ? lo : nz(lo )
int dir = na
float st = na
if na(atr )
dir := 1
else if st == nz(up )
dir := close > up ? -1 : 1
else
dir := close < lo ? 1 : -1
st := dir == -1 ? lo : up
@TradingView's native SuperTrend lacks the flexibility to incorporate different price sources into its calculation.
Community scripts, addressed the limitation by implementing the option to input different price sources, for example, one of the most popular publications, @KivancOzbilgic's SuperTrend script.
In May 2023, @TradingView introduced an update allowing the passing of another indicator's plot as a source value via the input.source() function. However, the built-in ta.atr function still relied on the chart's price data, limiting the formerly mentioned scripts to the chart's price data alone.
Unique Approach -
This script addresses the aforementioned limitations by processing the data differently.
Firstly we create a User-Defined-Type (UDT) replicating a bar's open, high, low, close (OHLC) values.
type bar
float o = open
float h = high
float l = low
float c = close
We then use this type to store the external input data.
src = input.source(close, "External Source")
bar b = bar.new(
nz(src ) , open 𝘷𝘢𝘭𝘶𝘦
math.max(nz(src ), src), high 𝘷𝘢𝘭𝘶𝘦
math.min(nz(src ), src), low 𝘷𝘢𝘭𝘶𝘦
src ) close 𝘷𝘢𝘭𝘶𝘦
Finally, we pass the data into our custom built SuperTrend with ATR functions to derive the external source's version of the SuperTrend indicator.
supertrend st = b.st(mlt, len)
- Setup Guide -
Utility and Use Cases:
Universal Compatibility - Apply SuperTrend to any built-in indicator or script, expanding its use beyond traditional price data.
- A simple example on one of my own public scripts -
Trend Analysis - Gain additional trend insights into otherwise mainly mean reverting or volume indicators.
- Alerts Setup Guide -
The Super Kit empowers traders and analysts with a tool that adapts the robust SuperTrend algorithm to a myriad of indicators, allowing comprehensive trend analysis and strategy development.
Williams Vix Fix [CC]The Vix Fix indicator was created by Larry Williams and is one of my giant backlog of unpublished scripts which I'm going to start publishing more of. This indicator is a great synthetic version of the classic Volatility Index and can be useful in combination with other indicators to determine when to enter or exit a trade due to the current volatility. The indicator creates this synthetic version of the Volatility Index by a fairly simple formula that subtracts the current low from the highest close over the last 22 days and then divides that result by the same highest close and multiplies by 100 to turn it into a percentage. The 22-day length is used by default since there is a max of 22 trading days in a month but this formula works well for any other timeframe. By itself, this indicator doesn't generate buy or sell signals but generally speaking, you will want to enter or exit a trade when the Vix fix indicator amount spikes and you get an entry or exit signal from another indicator of your choice. Keep in mind that the colors I'm using for this indicator are only a general idea of when volatility is high enough to enter or exit a trade so green colors mean higher volatility and red colors mean low volatility. This is one of the few indicators I have written that don't recommend to buy or sell when the colors change.
This was a custom request from one of my followers so please let me know if you guys have any other script requests you want to see!
Standardized SuperTrend Oscillator
The Standardized SuperTrend Oscillator (SSO) is a versatile tool that transforms the SuperTrend indicator into an oscillator, offering both trend-following and mean reversion capabilities. It provides deeper insights into trends by standardizing the SuperTrend with respect to its upper and lower bounds, allowing traders to identify potential reversals and contrarian signals.
Methodology:
Lets begin with describing the SuperTrend indicator, which is the fundamental tool this script is based on.
SuperTrend:
The SuperTrend is calculated based on the average true range (ATR) and multiplier. It identifies the trend direction by placing a line above or below the price. In an uptrend, the line is below the price; in a downtrend, it's above the price.
pine_st(float src = hl2, float factor = 3., simple int len = 10) =>
float atr = ta.atr(len)
float up = src + factor * atr
up := up < nz(up ) or close > nz(up ) ? up : nz(up )
float lo = src - factor * atr
lo := lo > nz(lo ) or close < nz(lo ) ? lo : nz(lo )
int dir = na
float st = na
if na(atr )
dir := 1
else if st == nz(up )
dir := close > up ? -1 : 1
else
dir := close < lo ? 1 : -1
st := dir == -1 ? lo : up
SSO Oscillator:
The SSO is derived from the SuperTrend and the source price. It calculates the standardized difference between the SuperTrend and the source price. The standardization is achieved by dividing this difference by the distance between the upper and lower bounds of the SuperTrend.
float sso = (src - st) / (up - lo)
Components and Features:
SuperTrend of Oscillator - An additional SuperTrend based on the direction and volatility of the oscillator, behaving as the SuperTrend OF the SuperTrend. This provides further trend analysis of the underlying broad trend regime.
Reversion Tracer - The RSI of the direction of the original SuperTrend, providing a dynamic threshold for premium and discount price areas.
float rvt = ta.rsi(dir, len)
Heikin Ashi Transform - An option to apply the Heikin Ashi transform to the source price of the oscillator, providing a smoother visual representation of trends.
Display Modes - Choose between Line mode for a standard oscillator view or Candle mode, displaying the oscillator as Heikin Ashi candles for more in-depth trend analysis.
Contrarian and Reversion Signals:
Contrarian Signals - Based on the SuperTrend of the oscillator, these signals can act as potential buy or sell indications, highlighting potential trend exhaustion or premature reversals.
Reversion Signals - Generated when the oscillator crosses above or below the Reversion Tracer, signaling potential mean reversion opportunities or trend breakouts.
Utility and Use Cases:
Trend Analysis - Utilize the SSO as a trend-following tool with the added benefits of the oscillator's SuperTrend and Heikin Ashi transform.
Valuation Analysis - Leverage the oscillator's reversion signals for identifying potential mean reversion opportunities in the market.
The Standardized SuperTrend Oscillator enhances the capabilities of the SuperTrend indicator, offering a balanced approach to both trend-following and mean reversion strategies. Its customizable options and contrarian signals make it a valuable instrument for traders seeking comprehensive trend analysis and potential reversal signals.
Volume-Price DiffScript is designed to analize volatility in real-time.
Once added to chart, script starting to collect 2 things:
Ticks count (tc)
Price changing ticks count (pctc)
The pctc/tc ratio may be interpret as a volatility measure.
Label above real-time bar shows:
Ticks count
Price changing ticks count
Ratio between (2) and (1) in percents
Using this indicator trader may detect volatility spikes.
More the "Diff" - less the volatility and vice versa.
Expected Move BandsExpected Moves
The Expected Move of a security shows the amount that a stock is expected to rise or fall from its current market price based on its level of volatility or implied volatility. The expected move of a stock is usually measured with standard deviations.
An Expected Move Range of 1 SD shows that price will be near the 1 SD range 68% of the time given enough samples.
Expected Move Bands
This indicator gets the Expected Move for 1-4 Standard Deviation Ranges using Historical Volatility. Then it displays it on price as bands.
The Expected Move indicator also allows you to see MTF Expected Moves if you want to.
This indicator calculates the expected price movements by analyzing the historical volatility of an asset. Volatility is the measure of fluctuation.
This script uses log returns for the historical volatility calculation which can be modelled as a normal distribution most of the time meaning it is symmetrical and stationary unlike other scripts that use bands to find "reversals". They are fundamentally incorrect.
What these ranges tell you is basically the odds of the price movement being between these levels.
If you take enough samples, 95.5% of the them will be near the 2nd Standard Deviation. And so on. (The 3rd Standard deviation is 99.7%)
For higher timeframes you might need a smaller sample size.
Features
MTF Option
Parameter customization
Advanced Market Opening Gap DetectorThe Advanced Market Opening Gap Detector (AMOGD) is a Pine Script indicator designed to help you identify market gaps at the opening of a new trading day. Gaps are areas on a chart where the price of a security moves sharply up or down with little or no trading in between. They are significant as they may indicate a change in market sentiment. This indicator highlights the size and direction of the opening gap, allowing you to potentially adjust your strategies accordingly.
By setting a minimum gap size, you can filter out smaller, less significant gaps, focusing only on larger gaps which may have more substantial implications. You can define the minimum gap size in points or pips, providing flexibility based on your trading preferences and the asset being traded.
How-to Use:
Apply the AMOGD indicator to your TradingView chart.
Configure the minimum gap size and unit (points or pips) based on your preference using the settings panel.
At the opening of each new trading day, the indicator will check for a gap between the previous close and the opening price.
If a valid gap is detected (i.e., the gap size meets or exceeds the minimum gap size specified), the indicator will:
Draw lines to indicate the opening price and previous close.
Display a label indicating the size of the gap.
Highlight the gap on the chart for better visibility.
Importance:
Market gaps can be pivotal points indicating a possible new trend or a continuation of the current trend. Being able to identify and analyze these gaps is crucial for making informed trading decisions. The AMOGD indicator automates the process of identifying and visualizing opening market gaps, saving traders time and allowing for quick assessment of market conditions at the start of each trading day. By setting a minimum gap size, traders can also filter out less significant price movements, allowing them to focus on potentially trend-changing gaps. This tool can be a valuable addition to a trader's toolkit, aiding in the analysis and interpretation of market behavior at the open, which is often a very volatile and crucial period in the trading day.
DISCLAIMER! RISK WARNING!
PAST PERFORMANCE IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS. TRADERS SHOULD NOT BASE THEIR DECISION ON INVESTING IN ANY TRADING PROGRAM SOLELY ON THE PAST PERFORMANCE PRESENTED, ADDITIONALLY, IN MAKING AN INVESTMENT DECISION, TRADERS MUST ALSO RELY ON THEIR OWN EXAMINATION OF THE PERSON OR ENTITY MAKING THE TRADING DECISIONS.
