חפש סקריפטים עבור "Volatility"
Exponential weighted volatilityEstimator of current annualized volatility that works for daily, weekly, monthly timeframes.
Lambda should be choosen inside the 0 to 1 range, with a lower lambda giving more weight to the movement in the most recent candlesticks. The literature default is 0.97, I'm setting a default value of 0.94 instead.
Relative Volatility Index + EMA + HTF RVI// this Script is based on
// added EMA of RVI
// added HTF RVI
// for HTF RVI i use at least 3xcurrent TF
// if RVI goes below EMA and HTF RVI -> weakness
// if RVI goes above EMA and HTF RVI -> strength
Smoothed Exponential Volatility IndexSame as the Exponential Volatility Index but with a smoothening factor included.
Smoothed Exponential Volatility IndexSimilar to Exponential Volatility Index but with a exponential smoothening incorporated.
Volatility Impulse [VI] (Expo)█ Overview
The Volatility Impulse Indicator is a trading tool that measures the rate of change in an asset's price volatility. It helps identify potential market entry or exit points by signaling high or low volatility periods, which could suggest increased price momentum or consolidation. The Volatility Impulse Indicator will spike when the market is highly volatile, indicating a potential trend reversal or breakout. Conversely, when the market is less volatile, the indicator will be more stable, indicating a possible continuation of the current trend.
█ Trend Feature
Adding a Trend feature to the volatility line makes the indicator a complete trading tool that can be used in many strategies. This trend feature capitalizes on the historical price momentum to determine the current trend direction, providing additional context and insight for traders. The historical price momentum essentially encapsulates the speed and strength of price changes over a certain period. By integrating this information into the volatility indicator, traders gain a clearer picture of not only the magnitude of price fluctuations but also the prevailing trend in the market.
█ How is the Volatility Impulse calculated?
The Volatility Impulse Indicator is based on the principle that volatility precedes price action. Therefore, they are useful in predicting future price movements.
In this calculation, we're determining volatility by looking at the greatest absolute difference in price. This is done by comparing two separate things:
The highest price and a previous highest price: The code is essentially looking back at a specific number of bars ('Length') and finding the highest price during that period. It then compares that highest price to the previous highest price (found during the previous 'Length' period). The difference between these two gives a measure of how much the highest price is changing.
The lowest price and a previous lowest price: Similar to the highest price, the code looks back at a specific number of bars and finds the lowest price. It then compares that to the lowest price of the previous period. The difference gives a measure of how much the lowest price is changing.
The 'greatest absolute difference' means it's considering the magnitude of the change, not the direction. So whether the price is increasing or decreasing doesn't matter here - it's the size of the change that counts.
This way of calculating volatility is looking at how much the extreme values (the highest and lowest prices) are changing. If these values are changing a lot, it suggests that price movements are quite volatile. Conversely, if the highest and lowest prices aren't changing much, it suggests lower volatility.
█ How to use
Using the Volatility Impulse Indicator is relatively simple.
Identify potential trend reversals: When the Volatility Impulse Indicator shows a spike, indicating high volatility, traders can look for potential trend reversals.
Volatility Retracement: Volatility retracement takes place in the direction of the ongoing trend and can be interpreted as a sign that the retracement phase is over or exhausted. This typically indicates that enough retail stop losses have been triggered or that sufficient profit-taking has been completed. Both of these factors can contribute to a pause or a reversal in the trend's direction, leading to a temporary spike in volatility.
Volatility Breakout: Sudden and rapid price movement beyond a certain level may indicate a potential breakout. This event suggests that the price has enough momentum to continue its direction, marking the breakout as valid.
Trend Confirmation: When the volatility line reaches its upper or lower band, it indicates an increase in volatility, suggesting a strengthening trend. When the volatility line oscillates around the midline, it may indicate decreasing volatility and a weakening trend or consolidation.
Overbought/Oversold Conditions: If the volatility line is above the upper line, it could indicate an overbought situation, suggesting a potential reversal or pullback, a perfect place to take partial profit. Conversely, a volatility line below the lower band may signal an oversold market, suggesting a possible upward movement or reversal, a perfect place to take partial profit.
Manage risk: Traders can use the Volatility Impulse Indicator to manage risk. When the market is highly volatile, traders can place stop-loss orders at strategic levels, thereby limiting their risk.
█ Any Alert Function Call
Any alert function call allows traders to combine predefined alerts. For example, they can pair 'trend is positive' with 'volatility line spikes below the lower band,' and so on.
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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!
Volatility Trend (Zeiierman)█ Overview
The Volatility Trend (Zeiierman) is an indicator designed to help traders identify and analyze market trends based on price volatility. By calculating a dynamic trend line and volatility-adjusted bands, the indicator provides visual cues to understand the current market direction, potential reversal points and volatility.
█ How It Works
The indicator uses a weighted moving average of historical prices to create a responsive trend line that is adjusted for volatility using standard deviation. The indicator sets upper and lower bands at intervals of two standard deviations, acting as markers for potential overbought or oversold conditions. Additionally, by comparing current and previous trend line values, the indicator identifies the trend direction, providing crucial insights for traders.
█ How to Use
Trend Identification
Use the trend line to identify the overall market direction. An upward-sloping line indicates an uptrend, while a downward-sloping line indicates a downtrend.
Volatility Assessment
Use the distance between the upper and lower bands to gauge market volatility. Wider bands indicate higher volatility, while narrower bands indicate lower volatility.
Overbought/Oversold
If the price reaches or exceeds the upper or lower bands, it may be in an overbought or oversold condition, respectively.
█ Settings
Trend Control: Adjusts the sensitivity and smoothness of the trend line. Lower values make the trend more responsive, while higher values make it smoother.
Trend Dynamic: Controls how quickly the trend adjusts to price changes. Higher values result in a slower adjustment.
Volatility: Consists of two parts - the scaling factor for volatility and the sensitivity for volatility adjustment. Adjusting these settings alters the distance between the trend lines and the price, as well as how sensitive the bands are to changes in volatility.
Squeeze Control: Influences the degree to which market squeeze is considered in the calculation, with higher values increasing sensitivity.
Enable Scalping Trend: A toggle that, when activated, makes the indicator focus on short-term trends, which is particularly useful for scalping strategies.
█ Related scripts with the same calculation philosophy
TrendCylinder
TrendSphere
Predictive Trend and Structure
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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!
Volatility Cone Forecaster Lite [PhenLabs]📊 Volatility Cone Forecaster
Version: PineScript™v6
📌Description
The Volatility Cone Forecaster (VCF) is an advanced indicator designed to provide traders with a forward-looking perspective on market volatility. Instead of merely measuring past price fluctuations, the VCF analyzes historical volatility data to project a statistical “cone” that outlines a probable range for future price movements. Its core purpose is to contextualize the current market environment, helping traders to anticipate potential shifts from low to high volatility periods (and vice versa). By identifying whether volatility is expanding or contracting relative to historical norms, it solves the critical problem of preparing for significant market moves before they happen, offering a clear statistical edge in strategy development.
This indicator moves beyond lagging measures by employing percentile analysis to rank the current volatility state. This allows traders to understand not just what volatility is, but how significant it is compared to the recent past. The VCF is built for discretionary traders, system developers, and options strategists who need a sophisticated understanding of market dynamics to manage risk and identify high-probability opportunities.
🚀Points of Innovation
Forward-Looking Volatility Projection: Unlike standard indicators that only show historical data, the VCF projects a statistical cone of future volatility.
Percentile-Based Regime Analysis: Ranks current volatility against historical data (e.g., 90th, 75th percentiles) to provide objective context.
Automated Regime Detection: Automatically identifies and labels the market as being in a ‘High’, ‘Low’, or ‘Normal’ volatility regime.
Expansion & Contraction Signals: Clearly indicates whether volatility is currently increasing or decreasing, signaling shifts in market energy.
Integrated ATR Comparison: Plots an ATR-equivalent volatility measure to offer a familiar point of reference against the statistical model.
Dynamic Visual Modeling: The cone visualization directly on the price chart provides an intuitive guide for future expected price ranges.
🔧Core Components
Realized Volatility Engine: Calculates historical volatility using log returns over multiple user-defined lookback periods (short, medium, long) for a comprehensive view.
Percentile Analysis Module: A custom function calculates the 10th, 25th, 50th, 75th, and 90th percentiles of volatility over a long-term lookback (e.g., 252 days).
Forward Projection Calculator: Uses the calculated volatility percentiles to mathematically derive and draw the upper and lower bounds of the future volatility cone.
Volatility Regime Classifier: A logic-based system that compares current volatility to the historical percentile bands to classify the market state.
🔥Key Features
Customizable Lookback Periods: Adjust short, medium, and long-term lookbacks to fine-tune the indicator’s sensitivity to different market cycles.
Configurable Forward Projection: Set the number of days for the forward cone projection to align with your specific trading horizon.
Interactive Display Options: Toggle visibility for percentile labels, ATR levels, and regime coloring to customize the chart display.
Data-Rich Information Table: A clean, on-screen table displays all key metrics, including current volatility, percentile rank, regime, and trend.
Built-in Alert Conditions: Set alerts for critical events like volatility crossing the 90th percentile, dropping below the 10th, or switching between expansion and contraction.
🎨Visualization
Volatility Cone: Shaded bands projected onto the future price axis, representing the probable price range at different statistical confidence levels (e.g., 75th-90th percentile).
Color-Coded Volatility Line: The primary volatility plot dynamically changes color (e.g., red for high, green for low) to reflect the current volatility regime, providing instant context.
Historical Percentile Bands: Horizontal lines plotted across the indicator pane mark the key percentile levels, showing how current volatility compares to the past.
On-Chart Labels: Clear labels automatically display the current volatility reading, its percentile rank, the detected regime, and trend (Expanding/Contracting).
📖Usage Guidelines
Setting Categories
Short-term Lookback: Default: 10, Range: 5-50. Controls the most sensitive volatility calculation.
Medium-term Lookback: Default: 21, Range: 10-100. The primary input for the current volatility reading.
Long-term Lookback: Default: 63, Range: 30-252. Provides a baseline for long-term market character.
Percentile Lookback Period: Default: 252, Range: 100-1000. Defines the period for historical ranking; 252 represents one trading year.
Forward Projection Days: Default: 21, Range: 5-63. Determines how many bars into the future the cone is projected.
✅Best Use Cases
Breakout Trading: Identify periods of deep consolidation when volatility falls to low percentile ranks (e.g., below 25th) and begins to expand, signaling a potential breakout.
Mean Reversion Strategies: Target trades when volatility reaches extreme high percentile ranks (e.g., above 90th), as these periods are often unsustainable and lead to contraction.
Options Strategy: Use the cone’s projected upper and lower bounds to help select strike prices for strategies like iron condors or straddles.
Risk Management: Widen stop-losses and reduce position sizes when the indicator signals a transition into a ‘High’ volatility regime.
⚠️Limitations
Probabilistic, Not Predictive: The cone represents a statistical probability, not a guarantee of future price action. Extreme, unpredictable news events can drive prices outside the cone.
Lagging by Nature: All calculations are based on historical price data, meaning the indicator will always react to, not pre-empt, market changes.
Non-Directional: The indicator forecasts the *magnitude* of future moves, not the *direction*. It should be paired with a directional analysis tool.
💡What Makes This Unique
Forward Projection: Its primary distinction is projecting a data-driven, statistical forecast of future volatility, which standard oscillators do not do.
Contextual Analysis: It doesn’t just provide a number; it tells you what that number means through percentile ranking and automated regime classification.
🔬How It Works
1. Data Calculation:
The indicator first calculates the logarithmic returns of the asset’s price. It then computes the annualized standard deviation of these returns over short, medium, and long-term lookback periods to generate realized volatility readings.
2. Percentile Ranking:
Using a 252-day lookback, it analyzes the history of the medium-term volatility and determines the values that correspond to the 10th, 25th, 50th, 75th, and 90th percentiles. This builds a statistical map of the asset’s volatility behavior.
3. Cone Projection:
Finally, it takes these historical percentile values and projects them forward in time, calculating the potential upper and lower price bounds based on what would happen if volatility were to run at those levels over the next 21 days.
💡Note:
The Volatility Cone Forecaster is most effective on daily and weekly charts where statistical volatility models are more reliable. For lower timeframes, consider shortening the lookback periods. Always use this indicator as part of a comprehensive trading plan that includes other forms of analysis.
Adaptive Volatility-Scaled Oscillator [AVSO] (Zeiierman)█ Overview
The Adaptive Volatility-Scaled Oscillator (AVSO) is a dynamic trading indicator that measures and visualizes volatility-adjusted market behavior. By scaling various metrics (such as volume, price changes, standard deviation, ATR, and Yang-Zhang volatility) and applying adaptive smoothing, AVSO helps traders identify market conditions where volatility deviates significantly from the norm.
This indicator uses standardized scaling (Z-Score logic) to highlight periods of abnormally high or low volatility relative to recent history. With gradient coloring and clear volatility zones, AVSO provides a visually intuitive way to analyze market volatility and adapt trading strategies accordingly.
█ How It Works
⚪ Scaling Metrics: The indicator scales user-selected metrics (e.g., volume, ATR, standard deviation) relative to the market and price, providing a standardized volatility measure.
⚪ Z-Score Standardization: The scaled metric is normalized using a Z-Score to measure how far current volatility deviates from its recent mean.
Positive Z-Score: Above-average volatility.
Negative Z-Score: Below-average volatility.
⚪ Adaptive Smoothing: An Adaptive EMA smooths the Z-Score, dynamically adjusting its length based on the strength of the volatility. Stronger deviations result in shorter smoothing, increasing responsiveness.
█ Unique Feature: Yang-Zhang Volatility
The Yang-Zhang volatility estimator sets this indicator apart by providing a more robust and accurate measure of volatility compared to traditional methods like ATR or standard deviation.
⚪ What Makes Yang-Zhang Volatility Unique?
Comprehensive Calculation: It combines overnight price gaps (log returns from the previous close to the current open) and intraday price movements (high, low, and close).
Accurate for Gapped Markets: Traditional volatility measures can misrepresent price movement when significant gaps occur between sessions. Yang-Zhang accounts for these gaps, making it highly reliable for assets prone to overnight price jumps, such as stocks, cryptocurrencies, and futures.
Adaptable to Real Market Conditions : By including both close-to-open returns and intraday volatility, it provides a balanced and adaptive measure that captures the full volatility picture.
⚪ Why This Matters to Traders
Better Volatility Insights: Yang-Zhang offers a clearer view of true market volatility, especially in markets with price gaps or uneven trading sessions.
Improved Trade Timing: By identifying volatility spikes and calm periods more effectively, traders can time their entries and exits with greater confidence.
█ How to Use
Identify High and Low Volatility
A high Z-Score (>2) indicates significant market volatility. This can signal momentum-driven moves, breakouts, or areas of increased risk.
A low Z-Score (<-2) suggests low volatility or a calm market environment. This often occurs before a potential breakout or reversal.
Trade Signals
High Volatility Zones (background highlight): Monitor for potential breakouts, trend continuations, or reversals.
Low Volatility Zones: Anticipate range-bound conditions or upcoming volatility spikes.
█ Settings
Source: Select the price source for scaling calculations (close, high, low, open).
Metric Measure: Choose the volatility measure:
Volume: Scales raw volume.
Close: Uses closing price changes.
Standard Deviation: Price dispersion.
ATR: Average True Range.
Yang: Yang-Zhang volatility estimate.
Bars to Analyze: Number of historical bars used to calculate the mean and standard deviation of the scaled metric.
ATR / Standard Deviation Period: Lookback period for ATR or Standard Deviation calculation.
Yang Volatility Period: Period for the Yang-Zhang volatility estimator.
Smoothing Period: Base smoothing length for the adaptive smoothing line.
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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!
Implied Volatility and Historical VolatilityThis indicator provides a visualization of two different volatility measures, aiding in understanding market perceptions and actual price movements. Remember to combine it with other technical analysis tools and risk management strategies for informed trading decisions. The two measures of volatility:
Implied Volatility: Based on the standard deviation of recent price changes, it represents the market's expectation of future volatility.
Historical Volatility: Measured by the daily high-low range as a percentage of the closing price, it reflects the actual volatility experienced recently. It is intended to be used along side the Mean and Standard Deviation Lines indicator.
Inputs:
Period (Days): Defines the number of past bars used to calculate both types of volatility.
Calculations:
Interpretation:
Comparing the lines: Divergence between the lines can indicate potential mispricing:
If the Implied Volatility is higher than the Historical Volatility, the market might be overestimating future volatility.
Conversely, if the Implied Volatility is lower, the market might be underestimating future volatility.
Monitoring trends: Track changes in both lines over time to identify potential shifts in volatility expectations or actual market behavior.
Limitations:
Assumes normality in price distribution, which may not always hold true.
Historical Volatility only reflects past behavior, not future expectations.
Consider other factors like market sentiment and news events for comprehensive volatility analysis.
Volatility Weighted Moving Average [BigBitsIO]The "Volatility Weighted Moving Average " indicator is a moving average indicator that is designed to weight certain periods of volatility more so than others, applying a greater impact on periods of high, low or average volatility. Volatility is measured throughout the volatility lookback period, and the current candle is weighted appropriately based on the indicator's weight type. Peak volatility based on the weight type is valued more to amplify the effect of the desired volatility weight.
Below are the settings used for this indicator and what they mean and do:
Moving Average Length: The lookback period for the moving average calculation.
Length To Measure Volatility: The lookback period to compare the volatility of the current candle to. Ex: This candle is high/average/low volatility compared to the candles in the last X candles
Volatility Divisions: The more volatility divisions the more precise the weighting is on candle volatility. With more volatility divisions, there are typically fewer candles that can qualify as peak volatility within the volatility weight type.
Amplify Peak Volatility In Weight Type: This is an extra weight applied to candles with peak volatility to further help weight the moving average in the direction of desired volatility.
Start Source Of Volatility: The starting point of measuring volatility. Volatility is measured as the difference in start - end source.
End Source of Volatility: The ending point of measuring volatility. Volatility is measured as the difference in start - end source.
Moving Average Source: The data source of the candle when used to calculate the moving average.
Moving Average Type: You can choose between a Volatility Weighted "Weighted Moving Average (WMA)", and a Volatility Weighted "Simple Moving Average(SMA)". The WMA and SMA respectively will somewhat resemble the actual WMA and SMA of the same moving average length, but the volatility will be weighted to shift values based on your settings.
Weight Type: The type of volatility that should be valued most. High volatility values candles with the highest volatility, average volatility values candles that are within the average range of volatility most, and the low volatility option weights candles with the least volatility the most.
Moving Average Smoothing Length (SMA): This will smooth the final line with an SMA. The weighting can produce jagged lines by itself, so it is smoothed slightly by default.
Why this indicator was made: I made this indicator because I wanted to visually interpret the effects of volatility on moving averages and if it could help identify any patterns in breakouts, trends, or consolidation periods.
The theory: Using a weight type of high volatility you might be able to identify breakouts with a sharp value incline or decline in slope. An average weight type would help identify trends as it could potentially reduce noise from very large and very small candles and focus more on the value of average candles - I believe for the theory on this one to work you would actually want to use less "Volatility Division" in order to include more average-sized candles in the peak weight. Finally, using a weight type of low volatility could help identify periods of consolidation.
Volatility Trigger IndexThe script allows to assess the volatility of an asset.
It works by calculating the rate of change and the standard deviation.
The index is useful to determine the lowest volatility periods (could be useful to look strategies) and also it determine the highest volatility periods (maybe for exits or partial closes).
It has 3 iputs:
Lenght.
Low volatility value.
High volatility value.
The low and high values are set after a visual inspection. The values changes in each time frame. Usually when the timeframe is higher the value of the index is higher as well. So the low and high levels must be changed after each time frame set.
As an idea could be used in combination with any moving average to determine the market direction and the index used as a trigger.
Volatility Cone [Loxx]When it comes to forecasting volatility, it seems that the old axiom about weather is applicable: "Everyone talks about it, but no one can do much about it!" Volatility cones are a tool that may be useful in one’s attempt to do something about predicting the future volatility of an asset.
A "volatility cone" is a plot of the range of volatilities within a fixed probability band around the true parameter, as a function of sample length. Volatility cone is a visualization tool for the display of historical volatility term structure. It was introduced by Burghardt and Lane in early 1990 and is popular in the option trading community. This is mostly a static indicator due to processor load and is restricted to the daily time frame.
Why cones?
When we enter the options arena, in an effort to "trade volatility," we want to be able to compare current levels of implied volatility with recent historical volatility in an effort to assess the relative value of the option(s) under consideration Volatility cones can be an effective tool to help us with this assessment. A volatility cone is an analytical application designed to help determine if the current levels of historical or implied volatilities for a given underlying, its options, or any of the new volatility instruments, such as VolContractTM futures, VIX futures, or VXX and VXZ ETNs, are likely to persist in the future. As such, volatility cones are intended to help the user assess the likely volatility that an underlying will go on to display over a certain period. Those who employ volatility cones as a diagnostic tool are relying upon the principle of "reversion to the mean." This means that unusually high levels of volatility are expected to drift or move lower (revert) to their average (mean) levels, while relatively low volatility readings are expected to rise, eventually, to more "normal" values.
How to use
Suppose you want to analyze an options contract expiring in 3-months and this current option has an current implied volatility 25.5%. Suppose also that realized volatility (y-axis) at the 3-month mark (90 on the x-axis) is 45%, median in 35%, the 25th percentile is 30%, and the low is 25%. Comparing this range to the implied volatility you would maybe conclude that this is a relatively "cheap" option contract. To help you visualize implied volatility on the chart given an expiration date in bars, the indicator includes the ability to enter up to three expirations in bars and each expirations current implied volatility
By ascertaining the various historical levels of volatility corresponding to a given time horizon for the options futures under consideration, we’re better prepared to judge the relative "cheapness" or "expensiveness" of the instrument.
Volatility options
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility. One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility. That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Sampling periods used
5, 10, 20, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, and 360
Historical Volatility plot
Purple outer lines: High and low volatility values corresponding to x-axis time
Blue inner lines: 25th and 75th percentiles of volatility corresponding to x-axis time
Green line: Median volatility values corresponding to x-axis time
White dashed line: Realized volatility corresponding to x-axis time
Additional things to know
Due to UI constraints on TradingView it will be easier to visualize this indicator by double-clicking the bottom pane where it appears and then expanded the y- and x-axis to view the entire chart.
You can click on each point on the graph to see what the volatility of that point is.
Option expiration dates will show up as large dots on the graph. You can input your own values in the settings.
Volatility Regime Classifier | ATRP Percentile ZonesThis indicator helps you understand the current volatility environment of any asset by comparing recent ATR-based values to its historical range.
It defines four regimes:
🔴 Low Volatility: Volatility is decreasing
🟢 Normal: Volatility is increasing but still below average
🟠 High: Volatility is elevated
🟣 Extreme: Volatility is very high compared to recent history
⚙️ How it works
We calculate the Average True Range (ATR) as a percentage of price (ATRP), then compare a short-term ATR to a longer-term one. Their difference shows whether volatility is picking up or slowing down.
To make the signal more adaptive, we look at the distribution of recent volatility over a rolling window. We compute the 50th and 70th percentiles of that history to set dynamic thresholds.
About distribution & percentiles
Volatility in financial markets doesn't follow a normal (Gaussian) distribution, it's often skewed, with sudden spikes and fat tails. That means fixed thresholds (like "ATR > 20") can be misleading or irrelevant across assets and timeframes.
Using percentiles solves this:
The 50th percentile marks the middle of the recent volatility range.
The 70th percentile captures a zone where volatility is unusually high, but not too rare, which keeps the signal usable and not overly sensitive.
These levels offer a balance:
⚖️ not too reactive, not too slow — just enough to highlight meaningful shifts.
✅ Use cases
Spot changes in market conditions
Filter or adapt strategies depending on the regime
Adjust position sizing and risk dynamically
Expected Volatility, Range, and Estimated VolatilityOverview
The Expected Volatility, Expected Range, and Estimated Volatility Indicator helps traders quantify and visualize the expected price movement of a financial instrument based on historical price changes. Unlike traditional historical volatility measures that are annualized, this indicator calculates expected volatility using a proprietary transform model directly from historical price data over a specified period. This provides an immediate, timeframe-specific estimate of expected volatility without annualization, making it more directly applicable to the current trading timeframe.
This indicator should be used with the Mean and Standard Deviation Lines to enhance analysis by combining price distribution and volatility insights.
Inputs
Volatility Period (Bars): Determines the number of bars used to calculate the expected volatility. For accurate visualization, it is recommended to set this period to be the same as the one used in the Mean and Standard Deviation Lines indicator. Adjusting this period can make the indicator more responsive to recent price changes or smooth out short-term fluctuations.
Plot Mode: Choose between "Percent" or "Base Currency" to display the indicator's outputs either as a percentage or in the asset's base currency value.
Outputs
Expected Volatility (Orange Line): Displays the expected volatility calculated using the transform model based on historical price changes over the specified period and serves as a reference for typical market movements and aiding in the identification of high-risk periods or potential breakout opportunities.
Expected Range (Red Line): Represents the expected price movement range based on the expected volatility.
Estimated Volatility (Yellow Line): Provides an alternative volatility measure based on the intraday range (high-low) relative to the previous close, offering additional insights into price fluctuations within each bar.
How to Use
Risk Management
You can use either the Expected Volatility or the Expected Range to set stop-loss and take-profit levels based on your preference. Using the Expected Volatility values will generally result in tighter stop-loss levels, potentially exiting trades earlier, while using the Expected Range may allow for more room to accommodate price fluctuations.
Historical Performance Analysis
Monitor when the Estimated Volatility (yellow line) crosses above the Expected Volatility or Expected Range lines (orange and red lines). Such crossings indicate periods where actual market volatility exceeded expected levels, providing insights into the historical effectiveness of your stop-loss or take-profit strategies.
Combined Analysis with Mean and Standard Deviation Lines
Use this indicator alongside the Mean and Standard Deviation Lines to gain a comprehensive view of both price distribution and volatility. Ensure that the Volatility Period is set to the same value in both indicators for accurate visualization and comparison. This combined approach enhances your ability to identify significant price movements and adjust your trading strategy accordingly.
Trend Analysis
Observe changes in the Expected Volatility values to identify periods of increasing or decreasing market volatility, which may signal potential trend developments or reversals.
Identifying Typical and Extreme Conditions
The Expected Volatility serves as a benchmark for typical market movements, aiding in the identification of high-risk periods or potential breakout opportunities when price action moves beyond this range.
Preference-Based Strategy
Choose between using the Expected Volatility or Expected Range based on your risk tolerance and trading strategy. The Expected Volatility provides a more conservative approach, while the Expected Range allows for greater flexibility in accommodating market fluctuations.
Additional Notes
For accurate visualization, set the Volatility Period to the same value used in the Mean and Standard Deviation Lines indicator. This alignment ensures consistency in your analysis and enhances the reliability of the insights gained from both indicators.
Be mindful that higher volatility periods can present both opportunities and increased risk; appropriate risk management practices are essential.
Important: The Expected Volatility calculated by this indicator is not annualized , unlike traditional historical volatility measures. This makes it directly applicable to the timeframe of your analysis, providing a more immediate estimate of expected price movements.
[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.
EWMA Volatility EstimatorThis script calculates EWMA Volatility (Exponentially Weighted Moving Average Volatility).
Commonly used model in financial risk management.
It estimates recent price volatility by applying more weight to the most recent returns, capturing volatility clustering while remaining responsive to fast market shifts.
The method uses a decay factor (λ) of 0.94, the standard value used in models like RiskMetrics, and converts the variance estimate into annualized volatility in percentage terms.
This is not a forecasting tool. It’s an estimator that reflects the magnitude of recent price moves in a statistically robust way.
It can be helpful for:
Understanding regime shifts in market behavior
Designing position sizing rules based on recent volatility
Filtering entries during high or low volatility phases
How It Works
Computes log returns of the closing price.
Squares the returns to get a proxy for variance.
Applies an exponential moving average to the squared returns using an equivalent EMA period based on λ = 0.94.
Converts the result to volatility by taking the square root and scaling to a percentage.
Key Characteristics
Backward-looking estimator
Reacts faster than standard rolling-window volatility
Smooths noise while still being sensitive to recent spikes
This script is educational and informational. It is not financial advice or a guarantee of performance. Always test any tool as part of a broader strategy before using it in live markets.
Volatility with Power VariationVolatility Analysis using Power Variation
The "Volatility with Power Variation" indicator is designed to measure market volatility. It focuses on providing traders with a clear understanding of how much the market is moving and how this movement changes over time.. This indicator helps in identifying potential periods of market expansion or contraction, based on volatility.
What the indicator does:
This indicator analyzes volatility which refers to the degree of variation in the returns of a financial instrument over time. It's an important measure to understand how much the price and returns of a asset fluctuates. High volatility means large price swings, meanwhile low volatility indicates smaller and consolidating movements. Realized (Historical) Volatility refers to volatility based on past price data.
Power Variation
Power Variation is an extension of the traditional methods used to calculate realized volatility. Instead of simply summing up squared returns (as done in calculating variance), Power Variation raises the magnitude of returns to a power p . This allows the indicator to capture different types of market behavior depending on the chosen value of p .
When P = 2, the Power variation behaves like a traditional variance measure. Lower values of p (e.g., p=1) make the indicator more sensitive to smaller price changes, meanwhile higher values make it more responsive to large jumps, but smaller price moves wont affect the measure that much or won't most likely.
Bipower Variation
Bipower variation is another method used to analyze the changes in price. It specifically isolates the continuous part of price movements from the jumps, which can help by understanding whether volatility is coming from regular market activity or from sharp, sudden moves.
How to Use the Indicator.
Understand Realized and Historical Volatility. Volatility after periods of low volatility you can eventually expect a expansion or an increase in volatility. Conversely, after periods of high volatility, the market often contracts and volatility decreases. If the variation plot is really low and you start seeing it increasing, shown by the standard deviation channels and moving average and you see it trending and increasing then that means you can expect for volatility to increase which means more price moves and expansions. Also if the scaling seems messed up, then use the logarithmic chart scale.
Volatility FilterThe "Volatility Filter" script is designed to measure market volatility across two different timeframes and determine whether the market is flat or trending.
It uses custom-tuned versions of four different indicators to measure volatility and distinguish between trending and ranging conditions.
The selected indicators are:
1 - Average Directional Index (ADX) Volatility
2 - Damiani Volameter
3 - Trader Pressure Index (TPI)
4 - Williams Alligator Indicator
The script calculates a filter score for both the current timeframe and a user-specified higher timeframe. It offers two types of filter scores, controlled by the 'FilterType' parameter. The filter score is then visualized on the chart as the main oscillator for the current timeframe and a filled bar for the higher timeframe.
The script utilizes a custom moving average function that provides 17 different ways to calculate a moving average, giving the user extensive flexibility in tailoring the script to their needs.
By using custom indicators and unique score calculation methods across two timeframes, this script provides a comprehensive measure of market volatility, aiding traders in identifying trending and ranging market conditions.
This script also provides two additional parameters for tuning its calculations and output, allowing to adjust the script to any trading style and the characteristics of the market being traded.
1 - Threshold: This parameter sets a threshold that the oscillator needs to surpass for the current market move to be considered as a trend. By adjusting the threshold, traders can control how much volatility is required to register a move as trending. A higher threshold will require more volatility for a trend to be recognized, meaning that the market needs to be moving more strongly for a trend to be identified.
2 - Length: This parameter is used to smooth the oscillator. It determines the number of periods used in the calculation of the moving average of the volatility filter score. A longer length will consider more data points and therefore provide a smoother line, which can be useful in accounting for the fading of trends. When trends start to lose their strength but are still present, a longer length can help in maintaining the recognition of the trend, aiding in making accurate trading decisions.
By adjusting these parameters, traders can fine-tune the script's sensitivity to market volatility and its recognition of trends, providing valuable flexibility in adapting to different market conditions and trading strategies.
VIX, ATR, and Volatility Indicatorhere what the indictor do !
The "VIX, ATR, and Volatility Indicator" combines the Volatility Index (VIX), Average True Range (ATR), and moving averages to provide insights into market volatility.
VIX (Volatility Index):
The VIX measures the expected volatility in the market over the next 30 days. A higher VIX value indicates increased market volatility, while a lower value suggests lower volatility.
ATR (Average True Range):
The ATR is a technical indicator that measures the average range between high and low prices over a specified period. It provides a sense of the market's volatility by considering price movements. Higher ATR values indicate greater volatility, while lower values indicate lower volatility.
Moving Averages:
The indicator calculates both an Exponential Moving Average (EMA) and Simple Moving Average (SMA) with a specific period (e.g., 50).
Moving averages smooth out price data to identify trends and potential areas of support or resistance.
Volatility Detection:
By comparing the current closing price to the EMA and SMA, the indicator determines if there is high volatility.
If the current closing price is higher than either the EMA or SMA, it indicates potential high volatility.
Visualization:
The VIX and ATR are typically plotted on the chart, providing a visual representation of market volatility and price range.
Additionally, markers or labels may be used to highlight periods of high volatility when the current price exceeds the moving averages.
what are the VIX and ATR
Volatility Index (VIX):
Monitor the VIX value from financial platforms or market data providers. A higher VIX value indicates increased market volatility, suggesting potential trading opportunities. Conversely, a lower VIX value indicates lower volatility, which may influence your trading strategy.
Average True Range (ATR):
Calculate the ATR manually or use charting platforms that provide ATR as an indicator.
Plot the ATR on your trading chart to visualize the range of price movements.
Determine suitable entry and exit points based on ATR values. For example, higher ATR values may indicate larger potential price swings, while lower ATR values may suggest a more stable market.
how it work
Fetching VIX Data:
The request.security function is used to fetch the daily VIX data from the "CBOE:VIX" symbol. It retrieves the closing price of the VIX for each day.
Calculating ATR:
The ta.atr function calculates the Average True Range (ATR) with a period of 14. ATR measures the average range between the high and low prices over the specified period, providing an indication of market volatility.
Calculating Moving Averages:
Two types of moving averages are calculated: Exponential Moving Average (EMA) and Simple Moving Average (SMA). Both moving averages are calculated using a period of 50, but you can adjust the period as needed.
The ta.ema function calculates the Exponential Moving Average, which places greater weight on recent prices.
The ta.sma function calculates the Simple Moving Average, which gives equal weight to all prices in the period.
Identifying High Volatility:
The indicator determines if there is high volatility by comparing the current closing price to both the EMA and SMA.
If the current closing price is higher than either the EMA or SMA, the isHighVolatility variable is set to true, indicating potential high volatility.
Plotting the Indicators:
The VIX and ATR are plotted using the plot function, assigning colors and line widths for visual differentiation.
The plotshape function is used to plot markers below the bars to indicate highly volatile periods. The isHighVolatility variable determines when the markers appear.
