Volatility Regime OscillatorVolatility Regime Oscillator
OVERVIEW
A 0-100 oscillator confined to the one thing volatility truly does: measure and manage risk. It forecasts how large moves are likely to be, places that forecast on a self-normalising 0-100 percentile scale (calm → extreme), and reports the position-size multiplier a volatility-targeting approach would call for. You read it next to your own method and size accordingly. It places no orders, gives no entry/exit signals, and takes no view on direction. Volatility's only job here is how big, never which way — that restraint is the whole design.
WHY THESE METHODS BELONG IN ONE SCRIPT (mashup rationale)
This is not a folder of separate volatility studies — it is one pipeline, and each stage feeds the next:
Estimate. Range-based estimators (Yang-Zhang, Garman-Klass, Rogers-Satchell, Parkinson) measure realised volatility far more efficiently than close-to-close; combining them is more robust out-of-sample than trusting any single one.
Forecast. A HAR multi-horizon blend (with an optional measurement-error correction that shifts weight off the noisy fast term), EWMA, or window mean turns that estimate forward.
Character. Good/bad semivariance and vol-of-vol report whether the move is downside-driven and whether the volatility itself is stable — context a single number can't carry.
Regime. A percentile rank converts the forecast into a self-normalising 0-100 read, so the same calm/normal/high/extreme bands work on any symbol or timeframe.
Size. Volatility targeting turns the forecast into an exposure multiplier — blended toward constant exposure and gated down in extremes, the robust drawdown-reducing form.
A forecaster without a regime frame is just a number; a regime read without sizing is just a gauge; sizing without the de-risk gates is the naive targeting the research shows underwhelms. Chained, they answer one question end to end: how big is risk now, and what does that imply for size? Remove a stage and the chain breaks.
HOW IT IS DIFFERENT FROM A PLAIN VOLATILITY FORECASTER
Most volatility tools answer "what will volatility be?" This one answers "where does volatility sit, and how much should I size?" The forecast is only the input; the output is a regime percentile plus a position-sizing multiplier — vol targeting, conditional targeting, a managed/constant blend, downside scaling, and an optional stress gate — wrapped in a risk-management frame. It is a sizing and regime gauge, not a forecasting study.
HOW TO USE
Oscillator 0-100 = where current forecast vol sits in its own history (percentile). Bands: <25 calm, 25-65 normal, 65-90 high, ≥90 extreme.
Dotted marker = where your target vol sits in that distribution. Line above the marker = vol above target → size down; below = room to size up.
Suggested exposure (dashboard) = the vol-target multiplier of equity a long would use now (1.0× = 100%). Apply it to your own side.
Optional expected-move envelope on the price chart shows the size of the likely move (for stop placement) — symmetric, regime-coloured, never a direction or target.
UNIVERSAL ACROSS MARKETS
The estimators read OHLC, so it runs on any instrument and timeframe out of the box. An optional Analyse symbol input reroutes the calculation to a different instrument than the one charted. Annualisation is derived from the chart timeframe by default (so the forecast-vol % and the target marker read sensibly on any timeframe), with a manual bars-per-year override available. The India VIX stress gate is OFF by default because it reads an India-specific implied-vol symbol that would mislead on other markets — turn it on only for NIFTY / BANKNIFTY.
ACCURACY — AN HONEST NOTE
This contains the most advanced volatility methods genuinely computable from price bars. It does not and cannot contain the options-data frontier — rough-volatility calibration (rough Heston / fractional Brownian), deep-learning forecasters, or implied-vol-surface models — none of which run on chart OHLC. Even at its best, realised-vol forecasting explains only a minority of realised-vol variance. Treat this as a sharper estimate and a disciplined risk gauge, not a precise or certain forecast. The "suggested exposure" is context for your own risk process, not an instruction; the research is explicit that full vol-targeting often fails to beat constant exposure out of sample — the keepable part is tail/drawdown reduction, which is why sizing is blended toward constant exposure by default.
ORIGINALITY
The individual methods are public and credited below. The original work is the assembly: a single self-normalising oscillator that chains efficient range estimators → a measurement-error-corrected HAR forecast → a semivariance/vol-of-vol character read → a percentile regime → a blended, gated volatility-targeting exposure, with a regime-coloured expected-move envelope — a risk-and-sizing gauge rather than a forecast or a directional signal. No third-party Pine code is reused.
CONCEPT CREDIT
Volatility estimators — M. Parkinson (1980); Garman & Klass (1980); Rogers & Satchell (1991); Yang & Zhang (2000). HAR forecaster — Fulvio Corsi (2009); measurement-error (HARQ) correction — Bollerslev, Patton & Quaedvlieg (2016). EWMA / RiskMetrics — J.P. Morgan (1996). Good/bad realised semivariance — Barndorff-Nielsen, Kinnebrock & Shephard; Patton & Sheppard (2015). Path-dependent leverage effect — Julien Guyon & Jordan Lekeufack (2023). Volatility targeting / conditional targeting — Moreira & Muir (2017); Harvey et al. (2018); robustness caveats — Cederburg et al. (2020); Wang & Yan (2021). Assembly, oscillator framing, regime bands and the target marker are the author's own work. Not affiliated with, nor endorsed by, any third party.
DISCLAIMER
Educational risk-management gauge. NOT financial advice, NOT a signal, and NO guarantee of any outcome. Estimates from price can be wrong, especially through structural breaks and on low-history symbols. Trading carries risk of loss. Do your own research and manage your own risk. The author accepts no liability for any use of this script. Indicator
VAM Pro (Multi-Model) [Final]Volatility Adjusted Momentum (VAM) Pro+ is a professional quantitative tool designed to measure trend strength by normalizing momentum against market volatility. Standard momentum indicators often fail during high-volatility periods because they treat every price change the same regardless of market noise. This indicator solves that problem by scaling price changes based on their statistical significance using Z-Score logic. This Pro+ version is specifically optimized for Scalping and Intraday Trading by introducing advanced volatility estimators and mathematical horizon adjustments that superior to classic standard deviation models.
The indicator allows you to choose the most suitable volatility model for your specific asset class. The Parkinson Volatility model is highly recommended for Crypto markets because it uses the High-Low range instead of just close prices, effectively capturing intraday wicks and liquidation spikes that standard deviation often misses. For Equities and Forex, the Garman-Klass model is the most efficient choice as it utilizes the full Open-High-Low-Close data set to account for opening gaps and professional trading ranges.
The mathematical core of the script features a strict Horizon Adjustment based on the Square Root of Time rule. This aligns the one-bar volatility risk with your chosen momentum lookback period to ensure a mathematically consistent calculation. By default, the script uses Logarithmic Returns to maintain scale invariance, which is critical for assets with high percentage swings like Cryptocurrencies. To trade with VAM Pro, look for the histogram color and its relation to the Zero line. A Green histogram indicates positive volatility-adjusted momentum where bulls are dominant, while a Red histogram shows bearish dominance. Values reaching the +2.0 or -2.0 Sigma levels represent statistically extreme moves that often act as exhaustion points or precede strong mean-reversion opportunities. When the histogram crosses the yellow signal line, it provides an early warning that the current momentum is fading.
Investment involves risk. The Volatility Adjusted Momentum (VAM) Pro+ is an analytical tool and does not constitute financial advice, investment recommendations, or an offer to buy or sell any security. Past performance is not indicative of future results. Market conditions can change rapidly, and technical indicators may produce false signals. Always perform your own due diligence, use proper risk management, and consult with a certified financial advisor before making any trading decisions. The author assumes no responsibility for any financial losses incurred through the use of this script.
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[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 PulseWire 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.
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JFD-Adaptive, GKYZ-Filtered KAMA [Loxx]JFD-Adaptive, GKYZ-Filtered KAMA is a Kaufman Adaptive Moving Average with the option to make it Jurik Fractal Dimension Adaptive. This also includes a Garman-Klass-Yang-Zhang Historical Volatility Filter to reduce noise.
What is KAMA?
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average ( KAMA ) is a moving average designed to account for market noise or volatility . KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
What is Jurik Fractal Dimension?
There is a weak and a strong way to measure the random quality of a time series.
The weak way is to use the random walk index ( RWI ). You can download it from the Omega web site. It makes the assumption that the market is moving randomly with an average distance D per move and proposes an amount the market should have changed over N bars of time. If the market has traveled less, then the action is considered random, otherwise it's considered trending.
The problem with this method is that taking the average distance is valid for a Normal (Gaussian) distribution of price activity. However, price action is rarely Normal, with large price jumps occuring much more frequently than a Normal distribution would expect. Consequently, big jumps throw the RWI way off, producing invalid results.
The strong way is to not make any assumption regarding the distribution of price changes and, instead, measure the fractal dimension of the time series. Fractal Dimension requires a lot of data to be accurate. If you are trading 30 minute bars, use a multi-chart where this indicator is running on 5 minute bars and you are trading on 30 minute bars.
What is Garman-Klass-Yang-Zhang Historical Volatility?
Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-close volatility estimator for zero drift and has an efficiency of 8 times the classic close-to-close estimator. Note that when the drift is nonzero, but instead relative large to the volatility , this estimator will tend to overestimate the volatility . The Garman-Klass-Yang-Zhang Historical Volatility calculation is as follows:
GKYZHV = sqrt((Z/n) * sum((log(open(k)/close( k-1 )))^2 + (0.5*(log(high(k)/low(k)))^2) - (2*log(2) - 1)*(log(close(k)/open(2:end)))^2))
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
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Garman & Klass Estimator Historical Volatility Bands [Loxx]Garman & Klass Estimator Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Garman & Klass Estimator Historical Volatility (instead of "regular" Historical Volatility ) for bands calculation.
What is Garman & Klaus Historical Volatility?
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. The Garman and Klass estimator for estimating historical volatility assumes Brownian motion with zero drift and no opening jumps (i.e. the opening = close of the previous period). This estimator is 7.4 times more efficient than the close-to-close estimator. 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.
The Garman & Klass Estimator is as follows:
GKE = sqrt((Z/n)* sum((0.5*(log(high./low)).^2) - (2*log(2) - 1).*(log(close./open)).^2))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related indicators:
Parkinson's Historical Volatility Bands
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Yang & Zheng Extension of Garman & KlassFirst off, a huge thank you to the following people:
theheirophant: www.pulsewire.com
alexgrover: www.pulsewire.com
NGBaltic: www.pulsewire.com
This is the Yang & Zhang extension of Garman & Klass. The equation was modified to include the logarithm of the open price divided by the preceding close price. As a result, this function uses the open, high, low and close prices to estimate volatility. This modification allows the volatility estimator to account for the opening jumps, but as the original function, it assumes that the underlying follows a Brownian motion with zero drift (the historical mean return should be equal to zero). This estimator tends to overestimate the volatility when the drift is different from zero, however, for a zero drift motion, this estimator has an efficiency of eight times the classic close-to-close estimator (standard deviation).
This script allows you to transform the volatility reading. The intention of this is to be able to compare volatility across different assets and timeframes. Having a relative reading of volatility also allows you to better gauge volatility within the context of current market conditions.
For the signal lie I chose a repulsion moving average to remove choppy crossovers of the estimator and the signal. This may have been a mistake, so in the near-future I might update so that the MA can be selected. Let me know if you have any opinions either way.
References
www.rdocumentation.org
www.quantshare.com
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open. Indicator
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OHLC Volatility Estimators by @Xel_arjonaDISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is by Creative-Commons as PulseWire's regulations. Any use, copy or re-use of this code should mention it's origin as it's authorship.
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS DEBUGING CODE The models included in the function have been taken from openly sources on the web so they could have some errors as in the calculation scheme and/or in it's programatic scheme. Debugging are welcome.
WHAT'S THIS?
Here's a full collection of candle based (compressed tick) Volatility Estimators given as a function, openly available for free, it can print IMPLIED VOLATILITY by an external symbol ticker like INDEX:VIX.
Models included in the volatility calculation function:
CLOSE TO CLOSE: This is the classic estimator by rule, sometimes referred as HISTORICAL VOLATILITY and is the must common, accepted and widely used out there. Is based on traditional Standard Deviation method derived from the logarithm return of current close from yesterday's.
ELASTIC WEIGHTED MOVING AVERAGE: This estimator has been used by RiskMetriks®. It's calculation is based on an ElasticWeightedMovingAverage Standard Deviation method derived from the logarithm return of current close from yesterday's. It can be viewed or named as an EXPONENTIAL HISTORICAL VOLATILITY model.
PARKINSON'S: The Parkinson number, or High Low Range Volatility, developed by the physicist, Michael Parkinson, in 1980 aims to estimate the Volatility of returns for a random walk using the high and low in any particular period. IVolatility.com calculates daily Parkinson values. Prices are observed on a fixed time interval. n=10, 20, 30, 60, 90, 120, 150, 180 days.
ROGERS-SATCHELL: The Rogers-Satchell function is a volatility estimator that outperforms other estimators when the underlying follows a Geometric Brownian Motion (GBM) with a drift (historical data mean returns different from zero). As a result, it provides a better volatility estimation when the underlying is trending. However, this Rogers-Satchell estimator does not account for jumps in price (Gaps). It assumes no opening jump. The function uses the open, close, high, and low price series in its calculation and it has only one parameter, which is the period to use to estimate the volatility.
YANG-ZHANG: Yang and Zhang were the first to derive an historical volatility estimator that has a minimum estimation error, is independent of the drift, and independent of opening gaps. This estimator is maximally 14 times more efficient than the close-to-close estimator.
LOGARITHMIC GARMAN-KLASS: The former is a pinescript transcript of the model defined as in iVolatility . The metric used is a combination of the overnight, high/low and open/close range. Such a volatility metric is a more efficient measure of the degree of volatility during a given day. This metric is always positive.
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