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.
חפש סקריפטים עבור "Volatility"
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.
GARCH Volume Volatility [MarkitTick]Title: GARCH Volume Volatility
Description
Overview
The GARCH Volume Volatility (GV) indicator is a sophisticated quantitative tool designed to analyze the rate of change in market participation. While the vast majority of technical indicators focus on Price Volatility (how much price moves), this script focuses on Volume Volatility (how unstable the participation is).
Market volume is rarely distributed evenly; it tends to cluster. Periods of high activity are often followed by more high activity, and periods of calm tend to persist. This behavior is known as "heteroskedasticity." This script utilizes an Exponentially Weighted Moving Average (EWMA) model—a core component of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) frameworks—to model these changing variance regimes.
By isolating volume volatility from raw volume data, this tool helps traders distinguish between sustainable liquidity flows and erratic, unsustainable volume shocks that often precede market reversals or breakouts.
Methodology and Calculations
1. Logarithmic vs. Percentage Returns
The foundation of this indicator is the calculation of "Volume Returns"—the period-over-period change in volume.
- The script defaults to Logarithmic Returns. In financial statistics, log returns are preferred because they normalize data that can vary wildly in magnitude (such as cryptocurrency volume spikes), providing a more symmetric view of changes.
- Users can opt for standard percentage changes if they prefer a linear approach.
2. Variance Proxy (Squared Returns)
To measure volatility, the direction of the volume change (up or down) matters less than the magnitude. The script squares the returns to create a "Variance Proxy." This ensures that a massive drop in volume is treated with the same statistical weight as a massive spike in volume—both represent a significant change in the volatility of participation.
3. GARCH-Style Smoothing (EWMA)
Standard Moving Averages (SMA) treat all data points in the lookback period equally. However, volatility is dynamic. This script uses an EWMA model with a tunable "Lambda" (Decay Factor).
- The Recursive Formula: The current calculation relies on a weighted average of the current variance and the previous period's smoothed variance.
- Memory Effect: This allows the indicator to "remember" recent volatility shocks while gradually letting their influence fade. This mimics the GARCH process of conditional variance.
4. Dynamic Statistical Thresholds
The final output is the Volatility (square root of variance). To make this data actionable, the script calculates a dynamic upper and lower limit based on the standard deviation (Z-Score) of the volatility itself over a user-defined lookback period.
How to Use
The indicator plots a histogram that categorizes the market into four distinct volatility regimes:
1. High Volatility (Red Histogram)
Trigger: Volatility > High Band (Upper Standard Deviation).
Interpretation: This signals an extreme anomaly in volume stability. This is not just "high volume," but "erratic volume behavior." This often occurs at:
- Capitulation bottoms (panic selling).
- Euphoric tops (blow-off tops).
- Major news events or earnings releases.
2. Elevated Volatility (Maroon Histogram)
Trigger: Volatility > Mean Average.
Interpretation: The market is in an active state. Participation is changing rapidly, but within statistically normal bounds. This is common during healthy, trending moves where new participants are entering the market steadily.
3. Normal/Low Volatility (Green Histogram)
Trigger: Volatility is within the lower bands.
Interpretation: The market volume is stable. There are no sudden shocks in participation. This is typical of consolidation phases or "creeping" trends where the price drifts without significant volume conviction.
4. Extremely Low Volatility (Bright Green/Transparent)
Trigger: Volatility < Low Band.
Interpretation: The "calm before the storm." When volume volatility collapses to near-zero, it implies that the market has reached a state of equilibrium or disinterest. Historically, volatility is cyclical; periods of extreme compression often lead to violent expansion.
Settings and Configuration
Core Settings
- Use EWMA: When checked (Default), uses the recursive GARCH-style calculation. If unchecked, it reverts to a simple SMA of variance, which is less sensitive to recent shocks but more stable.
- Log Returns: Uses natural log for calculations. Highly recommended for assets with exponential growth or large volume ranges.
- Length: The baseline period for the calculation.
- Threshold Lookback: The number of bars used to calculate the Mean and Standard Deviation bands.
- EWMA Lambda: The decay factor (0.0 to 1.0). A value of 0.94 is standard for risk metrics.
-- Higher Lambda (e.g., 0.98): The indicator reacts slower and is smoother (long memory).
-- Lower Lambda (e.g., 0.80): The indicator reacts very fast to new data (short memory).
Visuals
- Show Thresholds: Toggles the visibility of the statistical bands on the chart.
- High Band (StdDev): The multiplier for the upper warning zone. Default is 1.5 deviations. Increasing this to 2.0 or 3.0 will filter for only the most extreme events.
Disclaimer This tool is for educational and technical analysis purposes only. Breakouts can fail (fake-outs), and past geometric patterns do not guarantee future price action. Always manage risk and use this tool in conjunction with other forms of analysis.
Volatility-Dynamic Risk Manager MNQ [HERMAN]Title: Volatility-Dynamic Risk Manager MNQ
Description:
The Volatility-Dynamic Risk Manager is a dedicated risk management utility designed specifically for traders of Micro Nasdaq 100 Futures (MNQ).
Many traders struggle with position sizing because they use a fixed Stop Loss size regardless of market conditions. A 10-point stop might be safe in a slow market but easily stopped out in a high-volatility environment. This indicator solves that problem by monitoring real-time volatility (using ATR) and automatically suggesting the appropriate Stop Loss size and Position Size (Contracts) to keep your dollar risk constant.
Note: This tool is hardcoded for MNQ (Micro Nasdaq) with a tick value calculation of $2 per point.
📈 How It Works
-This script operates on a logical flow that adapts to market behavior:
-Volatility Measurement: It calculates the Average True Range (ATR) over a user-defined length (Default: 14) to gauge the current "speed" of the market.
-State Detection: Based on the current ATR, the script classifies the market into one of three states:
Low Volatility: The market is chopping or moving slowly.
Normal Volatility: Standard trading conditions.
High Volatility: The market is moving aggressively.
Dynamic Stop Loss Selection: Depending on the detected state, the script selects a pre-defined Stop Loss (in points) that you have configured for that specific environment.
Position Sizing Calculation: Finally, it calculates how many MNQ contracts you can trade so that if your Stop Loss is hit, you do not lose more than your defined "Max Risk per Trade."
🧮 Methodology & Calculations
Since this script handles risk management, transparency in calculation is vital.
Here is the exact math used:
ATR Calculation: Contracts = Max Risk / Risk Per Contract
⚙️ Settings
You can fully customize the behavior of the risk manager via the settings panel:
Risk Management
-Max Risk per Trade ($): The maximum amount of USD you are willing to lose on a single trade.
Volatility Thresholds (ATR)
-ATR Length: The lookback period for volatility calculation.
-Upper Limit for LOW Volatility: If ATR is below this number, the market is "Low Volatility."
-Lower Limit for HIGH Volatility: If ATR is above this number, the market is "High Volatility." (Anything between Low and High is considered "Normal").
Stop Loss Settings (Points)
-SL for Low/Normal/High: Define how wide your stop loss should be in points for each of the three market states.
Visual Settings
-Color Theme: Switch between Light and Dark modes.
-Panel Position: Move the dashboard to any corner or center of your chart.
-Panel Size: Adjust the scale (Tiny to Large) to fit your screen resolution.
📊 Dashboard Overview
-The on-screen panel provides a quick-glance summary for live execution:
-Market State: Color-coded status (Green = Low Vol, Orange = Normal, Red = High Vol).
-Current ATR: The live volatility reading.
-Suggested SL: The Stop Loss size you should enter in your execution platform.
-CONTRACTS: The calculated position size.
-Est. Loss: The actual dollar amount you will lose if the stop is hit (usually slightly less than your Max Risk due to rounding down).
Who is this for?
-Discretionary and systematic futures traders on MNQ (/MNQ or MES also works with small adjustments)
-Anyone who wants perfect risk consistency regardless of whether the market is asleep or exploding
-Traders who hate manual position-size calculations on every trade
No repainting
Works on any timeframe
Real-time updates on every bar
Overlay indicator (no signals, pure risk-management tool)
⚠️ Disclaimer
This tool is for informational and educational purposes only. It calculates mathematical position sizes based on user inputs. It does not execute trades, nor does it guarantee profits. Past performance (volatility) is not indicative of future results. Always manually verify your order size before executing trades on your broker platform.
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
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.
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.
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.
[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.
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.
ATR Volatility AlertsOverview:
This is a dynamic alert tool based on the Average True Range (ATR), designed to help traders detect sudden price movements that exceed normal volatility levels. Whether you are trading breakouts or monitoring for abnormal spikes, this indicator visualizes these events on the chart and triggers system alerts when the price move exceeds your specified ATR multiplier.
Key Features:
Fully Customizable ATR Range:
You can adjust the ATR Length (Default: 14) and the Multiplier (Default: 1.5x).
Tip: Increase the multiplier (e.g., to 2.0 or 3.0) to catch only extreme volatility, or lower it for scalping smaller moves.
Visual Chart Signals:
Visual markers appear instantly when a bar's movement exceeds the ATR threshold.
Green Triangle: Indicates an Upward Spike.
Red Triangle: Indicates a Downward Spike.
Flexible System Alerts:
Designed to integrate seamlessly with TradingView's alert system. You can choose from three specific alert directions based on your strategy:
1.Price Spike Up: Triggers only on sharp upward moves.
2.Price Spike Down: Triggers only on sharp downward moves.
3.Bidirectional Volatility Alert: Triggers on BOTH huge pumps and dumps.
How to Set Alerts:
Click the "Create Alert" button in TradingView.
Select ATR Volatility Alerts in the "Condition" dropdown.
Choose the specific logic you need:
· Select Price Spike Up for bullish monitoring.
· Select Price Spike Down for bearish monitoring.
· Select Bidirectional Volatility Alert to watch for any volatility expansion.
Hourly Volatility Explorer📊 Hourly Volatility Explorer: Master The Market's Pulse
Unlock the hidden rhythms of price action with this sophisticated volatility analysis tool. The Hourly Volatility Explorer reveals the most potent trading hours across multiple time zones, giving you a strategic edge in timing your trades.
🌟 Key Features:
⏰ Multi-Timezone Analysis
• GMT (UTC+0)
• EST (UTC-5) - New York
• BST (UTC+1) - London
• JST (UTC+9) - Tokyo
• AEST (UTC+10) - Sydney
Perfect for tracking major market sessions and their overlaps!
📈 Dynamic Visualization
• Color-gradient hourly bars for instant pattern recognition
• Real-time volatility comparison
• Interactive data table with comprehensive statistics
• Automatic highlighting of peak volatility periods
🎯 Strategic Applications:
Day Trading:
• Identify optimal trading windows
• Avoid low-liquidity periods
• Capitalize on session overlaps
• Fine-tune entry/exit timing
Risk Management:
• Set appropriate stop losses based on hourly volatility
• Adjust position sizes for different market hours
• Optimize risk-reward ratios
• Plan around high-impact hours
Global Market Analysis:
• Track volatility across all major sessions
• Spot institutional trading patterns
• Identify quiet vs. active periods
• Monitor 24/7 market dynamics
💡 Perfect For:
• Forex traders navigating global sessions
• Crypto traders in 24/7 markets
• Day traders optimizing execution times
• Algorithmic traders fine-tuning strategies
• Risk managers calibrating exposure
📊 Advanced Features:
• Rolling 3-month analysis for reliable patterns
• Precise pip movement calculations
• Sample size tracking for statistical validity
• Real-time current hour comparison
• Color-coded visual system for instant insights
⚡ Pro Trading Tips:
• Use during major session overlaps for maximum opportunity
• Compare patterns across different instruments
• Combine with volume analysis for deeper insights
• Track seasonal variations in hourly patterns
• Build trading schedules around peak hours
🎓 Educational Value:
• Understand market microstructure
• Learn global market dynamics
• Master timezone relationships
• Develop timing intuition
🛠️ Customization:
• Adjustable lookback period
• Flexible pip multiplier
• Multiple timezone options
• Visual preference settings
Whether you're scalping the 1-minute chart or managing longer-term positions, the Hourly Volatility Explorer provides the precise timing intelligence needed for today's global markets.
Transform your trading schedule from guesswork to science. Know exactly when markets move, why they move, and how to position yourself for maximum opportunity.
#TechnicalAnalysis #Trading #Volatility #MarketTiming #DayTrading #Forex #Crypto #TradingView #PineScript #MarketAnalysis #TradingStrategy #RiskManagement #GlobalMarkets #FinancialMarkets #TradingTools #MarketStructure #PriceAction #Scalping #SwingTrading #AlgoTrading
Jurik Volatility BandsVolatility is a core concept in trading and impacts our trading strategies. Therefore, all traders should have some sort of volatility indicator displayed to gauge the current volatility and future expected moves.
Jurik Volatility Bands displays the price inside a volatility channel. In this way, we can measure the current price action in accordance with its volatility.
Usage
The indicator is mainly used for scalping and intraday trading. Whenever the price touches either the upper or lower volatility band, we can consider it a volatility move. It can lead to a pullback or breakout move. However, we know that when the volatility is high, we expect more significant price moves and should prepare ourselves for it.
Disclaimer: No financial advice, only for educational/entertainment purposes.
Cross Asset VolatilityThis script brings together a number of volatility indexes from the CBOE in one space making it easier to use rather than adding a number of different securities to one chart. One could create a template with these securities attached, but sometimes, you don't want to switch charts, for whatever reason, and adding an indicator for is quick and simple.
One note is that due some securities exhibit much larger volatility than others (i.e. oil vs bonds) and it can be difficult to see clearly those securities whose volatilities are low, and hence we have added the ability to calculate the values as a Log value to make the indicator more readable. Another way to do this is to change the Y-axis on the chart to Logarithmic while leaving the indicator at its default settings (i.e. the checkbox for using Log calculations remains unchecked).
Volatility DashboardThis indicator calculates and displays volatility metrics for a specified number of bars (rolling window) on a TradingView chart. It can be customized to display information in English or Thai and can position the dashboard at various locations on the chart.
Inputs
Language: Users can choose between English ("ENG") and Thai ("TH") for the dashboard's language.
Dashboard Position: Users can specify where the dashboard should appear on the chart. Options include various positions such as "Bottom Right", "Top Center", etc.
Calculation Method: Currently, the script supports "High-Low" for volatility calculation. This method calculates the difference between the highest and lowest prices within a specified timeframe.
Bars: Number of bars used to calculate the volatility.
Display Logic
Fills the islast_vol_points array with the calculated volatility points.
Sets the table cells with headers and corresponding values:
=> Highest Volatility: The maximum value in the islast_vol_points array
=> Mean Volatility: The average value in the islast_vol_points array,
=> Lowest Volatility: The minimum value in the islast_vol_points array, Number of Bars: The rolling window size.
[-_-] Volatility Calibrated ATRDescription:
An indicator based on ATR adjusted for volatility of the market. It uses Heikin Ashi data to find short and long opportunities and displays a dynamic stop loss level. Additionally, it has alerts for when the trend changes (which is an entry signal).
How it works:
It works by dynamically calculating the Period for ATR which depends on current volatility level that is calculated by a function that uses Standard Deviation of price. ATR is then smoothed by Weighted Moving Average and multiplied by ATR Factor, resulting in a plot that changes its colour to red when we're in a downtrend and green when in an uptrend. This plot should be used as a dynamic Stop Loss level. Trend change is determined by price crossing the dynamic Stop Loss level. The squared red and green labels appear when the trend changes, and should be used as Entry signals.
Parameters:
- Source -> data used for calculations
- ATR Factor -> higher values produce less noise and longer trends, lower values give more signals
Relative Strength Volatility Adjusted Ema [CC]The Relative Strength Volatility Adjusted Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Mar 2022) and this is his final indicator of his recent Relative Strength series. I published both of the previous indicators, Relative Strength Volume Adjusted Exponential Moving Average and Relative Strength Exponential Moving Average
This indicator is particularly unique because it uses the Volatility Index (VIX) symbol as the default to determine volatility and uses this in place of the current stock's price into a typical relative strength calculation. As you can see in the chart, it follows the price much closer than the other two indicators and so of course this means that this indicator is best for choppy markets and the other two are better for trending markets. I would of course recommend to experiment with this one and see what works best for you.
I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish!
Implied Volatility LevelsOverview:
The Implied Volatility Levels Indicator is a powerful tool designed to visualize different levels of implied volatility on your trading chart. This indicator calculates various implied volatility levels based on historical price data and plots them as dynamic dotted lines, helping traders identify significant market thresholds and potential reversal points.
Features:
Multi-Level Implied Volatility: The indicator calculates and plots multiple levels of implied volatility, including the mean and both positive and negative standard deviation multiples.
Dynamic Updates: The levels update in real-time, reflecting the latest market conditions without cluttering your chart with outdated information.
Customizable Parameters: Users can adjust the lookback period and the standard deviation multiplier to tailor the indicator to their trading strategy.
Visual Clarity: Implied volatility levels are displayed using distinct colors and dotted lines, providing clear visual cues without obstructing the view of price action.
Support for Multiple Levels: Includes additional levels (up to ±5 standard deviations) for in-depth market analysis.
How It Works:
The indicator computes the standard deviation of the closing prices over a user-defined lookback period. It then calculates various implied volatility levels by adding and subtracting multiples of this standard deviation from the mean price. These levels are plotted as dotted lines on the chart, offering traders a clear view of the current market's volatility landscape.
Usage:
Identify Key Levels: Use the plotted lines to spot potential support and resistance levels based on implied volatility.
Analyze Market Volatility: Understand how volatile the market is relative to historical data.
Plan Entry and Exit Points: Make informed trading decisions by observing where the price is in relation to the implied volatility levels.
Parameters:
Lookback Period (Days): The number of days to consider for calculating historical volatility (default is 252 days).
Standard Deviation Multiplier: A multiplier to adjust the distance of the levels from the mean (default is 1.0).
This indicator is ideal for traders looking to incorporate volatility analysis into their technical strategy, providing a robust framework for anticipating market movements and potential reversals.
[SGM GARCH Volatility]I'm excited to share with you a Pine Script™ that I developed to analyze GARCH (Generalized Autoregressive Conditional Heteroskedasticity) volatility. This script allows you to calculate and plot GARCH volatility on TradingView. Let's see together how it works!
Introduction
Volatility is a key concept in finance that measures the variation in prices of a financial asset. The GARCH model is a statistical method that predicts future volatility based on past volatilities and prediction residuals (errors).
Indicator settings
We define several parameters for our indicator:
length = input.int(20, title="Length")
p = input.int(1, title="Lag order (p)")
q = input.int(1, title="Degree of moving average (q)")
cluster_value = input(0.2,title="cluster value")
length: The period used for the calculations, default 20.
p: The order of the delay for the GARCH model.
q: The degree of the moving average for the GARCH model.
cluster_value: A threshold value used to color the graph.
Calculation of logarithmic returns
We calculate logarithmic returns to capture price changes:
logReturns = math.log(close) - math.log(close )
Initializing arrays
We initialize arrays to store residuals and volatilities:
var float residuals = array.new_float(length, 0)
var float volatilities = array.new_float(length, 0)
We add the new logarithmic returns to the tables and keep their size constant:
array.unshift(residuals, logReturns)
if (array.size(residuals) > length)
array.pop(residuals)
We then calculate the mean and variance of the residuals:
meanResidual = array.avg(residuals)
varianceResidual = array.stdev(residuals, meanResidual)
volatility = math.sqrt(varianceResidual)
We update the volatility table with the new value:
array.unshift(volatilities, volatility)
if (array.size(volatilities) > length)
array.pop(volatilities)
GARCH volatility is calculated from accumulated data:
var float garchVolatility = na
if (array.size(volatilities) >= length and array.size(residuals) >= length)
alpha = 0.1 // Alpha coefficient
beta = 0.85 // Beta coefficient
omega = 0.01 // Omega constant
sumVolatility = 0.0
for i = 0 to p-1
sumVolatility := sumVolatility + beta * math.pow(array.get(volatilities, i), 2)
sumResiduals = 0.0
for j = 0 to q-1
sumResiduals := sumResiduals + alpha * math.pow(array.get(residuals, j), 2)
garchVolatility := math.sqrt(omega + sumVolatility + sumResiduals)
Plot GARCH volatility
We finally plot the GARCH volatility on the chart and add horizontal lines for easier visual analysis:
plt = plot(garchVolatility, title="GARCH Volatility", color=color.rgb(33, 149, 243, 100))
h1 = hline(0.1)
h2 = plot(cluster_value)
h3 = hline(0.3)
colorGarch = garchVolatility > cluster_value ? color.red: color.green
fill(plt, h2, color = colorGarch)
colorGarch: Determines the fill color based on the comparison between garchVolatility and cluster_value.
Using the script in your trading
Incorporating this Pine Script™ into your trading strategy can provide you with a better understanding of market volatility and help you make more informed decisions. Here are some ways to use this script:
Identification of periods of high volatility:
When the GARCH volatility is greater than the cluster value (cluster_value), it indicates a period of high volatility. Traders can use this information to avoid taking large positions or to adjust their risk management strategies.
Anticipation of price movements:
An increase in volatility can often precede significant price movements. By monitoring GARCH volatility spikes, traders can prepare for potential market reversals or accelerations.
Optimization of entry and exit points:
By using GARCH volatility, traders can better identify favorable times to enter or exit a position. For example, entering a position when volatility begins to decrease after a peak can be an effective strategy.
Adjustment of stops and objectives:
Since volatility is an indicator of the magnitude of price fluctuations, traders can adjust their stop-loss and take-profit orders accordingly. Periods of high volatility may require wider stops to avoid being exited from a position prematurely.
That's it for the detailed explanation of this Pine Script™ script. Don’t hesitate to use it, adapt it to your needs and share your feedback! Happy analysis and trading everyone!
RECON ATR Volatility PercentageThe original Average True Range (ATR) indicator is a technical analysis indicator designed to measure volatility. The higher the ATR the higher the volatility.
The RECON ATR Volatility Percentage indicator calculates the Average True Range (ATR) as a percentage.
Suggested chart timeframes: 1h, 4h and 1D seem to produce the most useful intel but can be used on lower timeframes as well.
The Recon ATR Volatility Percentage can be utilized for identifying trading pairs with a desired amount of volatility, for example deploying a grid trading strategy on pairs that are trending up with a high amount of volatility (say over 50%) might produce desirable results.
It is important to note the ATR does not indicate price direction and can be high in both a rising or falling market.
The ATR Length, Period Look Back Length parameters as well as the color of the columns can be configured per your specifications.
