️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards.
█ OVERVIEW
As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole.
█ CONCEPTS
Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations.
█ WAYS TO USE THIS INDICATOR
Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison.
When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss.
█ FEATURES
• "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations.
• "Omega Period" is the size of the sample that is used for the calculations.
• “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve.
• “Color Settings” you can set the color for each curve.
Sejarah Ketidakstabilan
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
Linear Moments█ OVERVIEW
The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values.
█ CONCEPTS
█ Four moments of a distribution
We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution.
The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution.
One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters.
█ Order Statistics
L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity.
To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required.
█ Bias and efficiency of an estimator
One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters.
Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases.
The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters.
In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively.
Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population.
█ WAYS TO USE THIS INDICATOR
The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies.
One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution.
█ FEATURES
Source Settings
Source - Select the source you wish the indicator to calculate on
Source Selection - Selec whether you wish to calculate on the source value or its log return
Moments Settings
Moments Selection - Select the L-moment you wish to be displayed
Lookback - Determine the sample size you wish the L-moments to be calculated with
Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel.
█ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values.
█ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical Volatility Rank, and Historical Volatility Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models.
Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias.
Pros:
• Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator.
Cons:
• The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.
• Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.
• Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator:
• Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.)
Garman-Klass Estimator:
• Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.
• The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility.
Rogers-Satchell Estimator:
• Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.
• The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.
• Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension:
• Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.
• The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator:
• The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.
• This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.
• 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV.
EWMA Estimator:
• EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.
• EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.
• RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.
• However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH.
Adjusted Mean Absolute Deviation Estimator:
• This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.
• It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.
• The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.
• In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility.
Adjusted Median Absolute Deviation Estimator:
• This is another estimator that does not use standard deviation to measure volatility.
• Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.
• The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
█ FEATURES
• You can select the volatility estimator models in the Volatility Model input
• Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.
• Alpha is used to adjust the Yang Zhang volatility estimator value.
• Percentile Length is used to Adjust Percentile coloring lookbacks.
• The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.
• When percentile coloring is off, it won’t show the gradient color.
• You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.
• When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
• You can choose the plot style, such as lines, columns, areas in the plotstyle input.
• When the show information panel is on, a small panel will display on the right.
• The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value.
█ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move).
█ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
Orbital Barycenter Matrix @darshaksscThe Orbital Barycenter Matrix is a visual, informational-only tool that models how price behaves around a dynamically calculated barycenter —a type of moving equilibrium derived entirely from historical price data.
Instead of focusing on signals, this indicator focuses on market structure symmetry, distance, compression, expansion, and volatility-adjusted movement.
This script does not predict future price and does not provide buy/sell signals .
All values and visuals come solely from confirmed historical data , in full compliance with TradingView policy.
📘 How the Indicator Works
1. Dynamic Barycenter (Core Mean Line)
The barycenter is calculated from a smoothed blend of historical price components.
It represents the center of mass around which price tends to oscillate.
This is not a forecast line—only a representation of historical average behavior.
2. Orbital Rings (Distance Zones)
Around the barycenter, the indicator draws several “orbital rings.”
Each ring shows a volatility-scaled distance from the barycenter using ATR-based calculations.
These rings help visualize:
How far price has drifted from its historical center
Whether price is moving in an inner, mid, or outer region
How volatility influences the spacing of the rings
Rings do not imply future targets and are informational only.
3. Orbital Extension Range
Beyond the outermost ring, a wider band (extension range) shows a high-volatility reference distance.
It represents extended displacement relative to past price behavior—not a projected target.
4. Orbit Trail (Motion Trace)
The Orbit Trail plots small circles behind price, helping visualize how price has moved through the orbital regions over time.
Colors adjust with “pressure” (distance from center), making compression and expansion easy to observe.
5. Satellite Nodes (Swing Markers)
Confirmed swing highs and lows (using fixed pivots) are marked as small dots.
Their color reflects the orbital zone they formed in, giving context to how significant or extended each pivot was.
These swing markers do not repaint because they use confirmed pivots.
6. Pressure & Distance Calculations
The indicator converts price displacement away from the barycenter into a pressure metric, scaled between 0%–100%.
Higher pressure means price is further from its historical center relative to volatility.
The dashboard displays:
Zone classification
ATR-based distance
Pressure level
A small intensity gauge
All are informational readings—no direction or forecast.
📊 Key Features
✔ Dynamic barycenter core
✔ Up to four orbital rings
✔ Informational orbital extension band
✔ Visual orbit trail showing recent movement
✔ Non-repainting satellite swing nodes
✔ Distance & pressure analytics
✔ Fully adjustable HUD
✔ Always-visible floating dashboard (screen-anchored)
✔ Zero repainting on confirmed elements
✔ 100% sourced from historical data only
✔ Policy-safe: no predictions, no signals, no targets
🎯 What to Look For
1. How close price is to the barycenter
This can reveal whether price is in:
The inner region
The mid zone
The outer region
The extended field
2. Pressure level
Shows how “stretched” price is relative to its past behavior.
3. Satellite nodes
Indicate where confirmed pivots formed and in which orbital band.
4. Ring interactions
Observe how price moves between rings—inside, outside, or oscillating around them.
5. Color changes in the orbit trail
These show changes in market compression/expansion.
🧭 How to Read the Indicator
Inner Orbit
Price close to its historical equilibrium.
Mid Orbit
Moderate displacement from typical range.
Outer Orbit
Historically extended movement.
Beyond Extension Field
Price has moved further than usual relative to historical volatility.
These are descriptive conditions only , not trade recommendations.
🛠 How to Apply It on the Chart
Use the barycenter to understand where price has historically balanced.
Observe how volatility changes the spacing between rings.
Use pressure readings to identify when price is compressed, neutral, or extended.
Use swing nodes to contextualize historical pivot formation.
Watch how price interacts with rings to better understand rhythm, velocity, and structural behavior.
This tool is meant to enhance visual understanding—not to generate trade entries or exits.
⚠️ Important Disclosure
This indicator is strictly informational.
It does not predict or project future price movement.
It does not provide buy/sell/long/short signals.
All lines, zones, and values are derived solely from past market data.
Any interpretation is at the user’s discretion.
VIX Calm vs Choppy (Bar Version, VIX High Threshold)This indicator tracks market stability by measuring how long the VIX stays below or above a chosen intraday threshold. Instead of looking at VIX closes, it uses VIX high, so even a brief intraday spike will flip the regime into “choppy.”
The tool builds a running clock of consecutive bars spent in each regime:
Calm regime: VIX high stays below the threshold
Choppy regime: VIX high hits or exceeds the threshold
Calm streaks plot as positive bars (light blue background).
Choppy streaks plot as negative bars (dark pink background).
This gives a clean picture of how long the market has been stable vs volatile — useful for trend traders, breakout traders, and anyone who watches risk-on/risk-off conditions. A table shows the current regime and streak length for quick reference.
Liquidity & Momentum Master (LMM)💎 Liquidity & Momentum Master (LMM)
A professional dual-system indicator that combines:
📦 High-Volume Support/Resistance Zones and
📊 RSI + Bollinger Band Combo Signals — to visualize both smart money footprints and momentum reversals in one clean tool.
🧱 1. High-Volume Liquidity Zones (Support/Resistance Boxes)
Conditions
Visible only on 1H and higher timeframes (1H, 4H, 1D, etc.)
Detects candles with abnormally high volume and strong ATR-based range
Separates bullish (support) and bearish (resistance) zones
Visualization
All boxes are white, with adjustable transparency (alphaW, alphaBorder)
Each box extends to the right automatically
Only the most important (Top-N) zones are kept — weaker ones are removed automatically
Interpretation
White boxes = price areas with heavy liquidity and volume concentration
Price approaching these zones often leads to bounces or rejections
Narrow spacing = consolidation, wide spacing = potential large move
💎 2. RSI Exit + BB-RSI Combo Signals
RSI Exit (Overbought/Oversold Recovery)
RSI drops from overbought (>70) → plots red “RSI” above the candle
RSI rises from oversold (<30) → plots green “RSI” below the candle
Works on 15m, 30m, 1H, 4H, 1D
→ Indicates short-term exhaustion recovery
BB-RSI Combo (Momentum Reversal Confirmation)
Active on 1H and higher only
Requires both:
✅ RSI divergence (bullish or bearish)
✅ Bollinger Band re-entry (after temporary breakout)
Combo Buy (Green Diamond)
Bullish RSI divergence
Candle closes back above lower Bollinger Band
Combo Sell (Red Diamond)
Bearish RSI divergence
Candle closes back below upper Bollinger Band
→ Confirms stronger reversal momentum compared to standard RSI signals
NY VIX Channel Trend US Futures Day Trade StrategyNY VIX Channel Trend Strategy
Summary in one paragraph
Session anchored intraday strategy for index futures such as ES and NQ on one to fifteen minute charts. It acts only after the first configurable window of New York Regular Trading Hours and uses a VIX derived daily implied move to form a realistic channel from the session open. Originality comes from using a pure implied volatility yardstick as portable support and resistance, then committing in the direction of the first window close relative to the open. Add it to a clean chart and trade the simple visuals. For conservative alerts use on bar close.
Scope and intent
• Markets. Index futures ES and NQ
• Timeframes. One to thirty minutes
• Default demo. ES1 on five minutes
• Purpose. Provide a portable intraday yardstick for entries and exits without curve fitting
• Limits. This is a strategy. Orders are simulated on standard candles
Originality and usefulness
• Unique concept. A VIX only channel anchored at 09:30 New York plus a single window trend test
• Addresses. False urgency at session open and unrealistic bands from arbitrary multipliers
• Testability. Every input is visible and the channel is plotted so users can audit behavior
• Portable yardstick. Daily implied move equals VIX percent divided by square root of two hundred fifty two
• Protected status. None. Method and use are fully disclosed
Method overview in plain language
Take the daily VIX or VIX9D value, convert it to a daily fraction by dividing by square root of two hundred fifty two, then anchor a symmetric channel at the New York session open. Observe the first N minutes. If that window closes above the open the bias is long. If it closes below the open the bias is short. One trade per session. Exits occur at the channel boundary or at a bracket based on a user selected VIX factor. Positions are closed a set number of minutes before the session ends.
Base measures
Return basis. The daily implied move unit equals VIX percent divided by square root of two hundred fifty two and serves as the distance unit for targets and stops.
Components
• VIX Channel. Top, mid, bottom lines anchored at 09:30 New York. No extra multipliers
• Window Trend. Close of the first N minutes relative to the session open sets direction
• Risk Bracket. Take profit and stop loss equal to VIX unit times user factor
• Session Window. Uses the exchange time of the chart
Fusion rule
Minimum gates count equals one. The trade only arms after the window has elapsed and a direction exists. One entry per session.
Signal rule
• Long when the window close is above the session open and the window has completed
• Short when the window close is below the session open and the window has completed
• Exit on channel touch. Long exits at the top. Short exits at the bottom
• Flat thirty minutes before the session close or at the user setting
Inputs with guidance
Setup
• Use VIX9D. Width source. Typical true for fast tone or false for baseline
• Use daily OPEN. Toggle for sensitivity to overnight changes
Logic
• Window minutes. Five to one hundred twenty. Larger values delay entries and reduce whipsaw
• VIX factor for TP. Zero point five to two. Raising it widens the profit target
• VIX factor for SL. Zero point five to two. Raising it widens the stop
• Exit minutes before close. Fifteen to ninety. Raising it exits earlier
Properties visible in this publication
• Initial capital one hundred thousand USD
• Base currency USD
• request.security uses lookahead off
• Commission cash per contract two point five $ per each contract. Slippage one tick
• Default order size method FIXED with value one contract. Pyramiding zero. Process orders on close ON. Bar magnifier OFF. Recalculate after order is filled OFF. Calc on every tick ON
Realism and responsible publication
No performance claims. Past results never guarantee future outcomes. Fills and slippage vary by venue. Shapes can move while a bar forms and settle on close. Strategy uses standard candles.
Honest limitations and failure modes
Economic releases and thin liquidity can break the channel. Very quiet regimes can reduce signal contrast. Session windows follow the exchange time of the chart. If both stop and target can be hit within one bar, assume stop first for conservative reading without bar magnifier.
Works best in liquid hours of New York RTH. Very large gaps and surprise news may exceed the implied channel. Always validate on the symbols you trade.
Entries and exits
• Entry logic. After the first window, go long if the window close is above the session open, go short if below
• Exit logic. Long exits at the channel top or at the take profit or stop. Short exits at the channel bottom or at the take profit or stop. Flat before session close by the configured minutes
• Risk model. Initial stop and target based on the VIX unit times user factors. No trail and no break even. No cooldown
• Tie handling. Treat as stop first for conservative interpretation
Position sizing
Fixed size one contract per trade. Target risk per trade should generally remain near one percent of account equity. Risk is based on the daily volatility value, the max loss from the tests for one year duration with 5min chart was 4%, while the avg loss was below <1% of the total capital.
If you have any questions please let me know. Thank you for coming by !
HV Spike Strategy (HVP + OR Breakout + Reversal + TP/SL Modes)Here is a script that I tried to make it simple, although it has several parameters, I will try to explain, here we go:
Logic: Open Range Breakout: otherwise knows as First Candle Rule, usually used for the first candle in the opening of a market session, in my strategy there is an option to use it even for Crypto that operate 24/7, how to do that? Simply by detecting Volatility from the HVP (Historical Volatility Percentile). Then the ORB logic kicks in and the first candle with high volatility gives the ranges for the trades. The proper HVP Activation Threshold has to be selected for each currency pair/index/crypto in order to have maximum profit.
Enter a trade: when the price goes 100% above/below the First Candle Rule Range. That way it is filtering fake breakouts. Also if the price reverses back into the range the strategy takes the opposite trade.
Exit a trade: SL/TP By percentage or ATR, selection in the input menu.
My intention is to avoid using lagging indicators or guessing of Price Action, purely Bull/Bear indication by the first candle.
I hope you find this helpful! Wishing all successful Trades!
Sigma Volatility BandsThis indicator models and displays bands of potential future price based on historic realized volatility.
This can be used for finding price target where there is no past price action.
The price bands are derived from Standard Deviations based on input bars back of historic volatility.
More Inputs:
Lookback = Number of bars considered
Forward Bars = Number of bars to project bands forward
There are two display modes:
Forward shifted envelopes = (see below) Draws bands of price from the Standard Deviation
Forward for Anchor Lines = Draws a wedge out number of bars forward
(Vibe coded. Message me for suggested updates and improvements)
DTR & ATR with live zonesThis indicator is designed to help traders gauge the day's volatility in real-time. It compares the current Daily True Range (DTR)—the distance between the session's high and low—to the historical Average True Range (ATR).
The main purpose is to project potential price levels where the market might reach based on its average volatility. These levels (100% ATR, 150%, 200%, etc.) can be used as price targets. For instance, if you're in a long trade, you might consider taking partial or full profits as the price approaches these upper ATR extension levels. The indicator is highly customisable, allowing you to control the appearance of the ATR lines, zones, and labels to fit your charting preferences.
Core Concepts: ATR and DTR
To use this indicator effectively, it's important to understand its two main components:
Average True Range (ATR): This is a classic technical analysis indicator that measures market volatility. It calculates the average range of price movement over a specific period (e.g., 14 days). A higher ATR means the price is, on average, moving more, while a low ATR indicates less volatility. This script uses a higher timeframe ATR (e.g., Daily) to establish a stable volatility baseline for the current trading day.
Daily True Range (DTR): This is simply the difference between the current trading session's highest high and lowest low (session high - session low). It tells you how much the price has actually moved so far today.
The indicator's logic revolves around comparing the live, unfolding DTR to the historical, baseline ATR. An on-screen table conveniently shows this comparison as a percentage, to show how volatile the day has been.
How It Works: The Dynamic & Locked Mechanism
The most clever part of this indicator is how it draws the ATR levels. It operates in two distinct phases during the trading session:
Phase 1: Dynamic Expansion (Before DTR meets ATR)
At the start of the session, the DTR is small. The indicator calculates the remaining range needed to "complete" the 100% ATR level (difference = avg_atr - dtr). It then adds this remaining amount to the session high and subtracts it from the session low. This creates a "floating" 100% ATR range that expands dynamically as the session high or low is extended.
Phase 2: The Lock-in (After DTR meets or exceeds ATR)
Once the day's range (DTR) becomes equal to or greater than the avg_atr, the day has met its "expected" volatility. At this point, the levels lock in place. The indicator intelligently determines the anchor point for the locked range.
Once this primary 100% ATR range is established (either dynamically or locked), the script projects the other levels (150%, 200%, 250%, and 300%) by adding or subtracting multiples of the avg_atr from this base.
How to Use It for Trading
The primary use of this indicator is to set logical, volatility-based price targets.
Setting Profit Targets: If you enter a long position, the upper ATR levels (100%, 150%, 200%) serve as excellent areas to consider taking profits. A move to the 200% or 250% level often signifies an overextended or "exhaustion" move, making it a high-probability exit zone. For short positions, the lower ATR levels serve the same purpose.
Assessing Intraday Momentum: The on-screen table tells you how much of the expected daily range has been used. If it's early in the session and the DTR is only at 30% of the ATR, you can anticipate more significant price movement is likely to come. Conversely, if the DTR is already at 150% of ATR, the bulk of the day's move may already be complete.
Mean Reversion Signals: If the price pushes to an extreme level (e.g., 250% ATR) and shows signs of stalling (e.g., bearish divergence on an oscillator), it could signal a potential reversal or pullback, offering an opportunity for a counter-trend trade.
Key Settings
ATR Length & Smoothing Type: These settings control how the baseline ATR is calculated. The default 14 period and RMA smoothing are standard, but you can adjust them to your preference.
Session Settings: This is crucial. You must set the Market Session and Time Zone to match the primary trading hours of the asset you are analysing (e.g., "0930-1600" for the NYSE session).
Show Lines / Show Labels / Show Zones: The script gives you full control over the visual display. You can toggle each ATR level's lines, labels, and background zones individually to avoid a cluttered chart and focus only on the levels that matter to your strategy.
HV-SMA DeltaHistorical Volatility with SMA Multiplier
Concept
This indicator acts as a "volatility explosion meter" for the market. Its core principle is to compare the current volatility with its historical average to detect moments when the market begins to "swing" with significantly more force.
The main components are as follows:
① Historical Volatility (HV) This line is an indicator of the current price volatility.
If this line moves higher, it means the price is swinging wildly (high volatility).
If this line is low, it means the price is calm or moving within a narrow range (low volatility).
② SMA x Multiplier This line functions as a "threshold" or "volatility resistance" level. It is calculated from the moving average of past volatility and then multiplied by an adjustable number (smaMultiplier) to create an upper band. In simple terms, this line tells us: "Normally, volatility should not exceed this level."
③ Difference (Histogram) This is the result of subtracting the Threshold Line (②) from the HV value (①).
Appear when the HV breaks above the threshold line. This signals that "volatility has now spiked significantly above its historical average."
Appear when the HV is still below the threshold line. This indicates that volatility remains at a normal or below-average level.
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How to Use
This indicator does not tell you the direction of the price. Instead, it indicates the "power" or "momentum" of the movement. Therefore, it should always be used in conjunction with other tools to confirm the direction.
① Look for "Volatility Breakout" signals.
② Use it to confirm the strength of a trend.
③ Use it for risk management.
You can try adjusting the smaLength and smaMultiplier values in the indicator's settings to fit the specific asset and timeframe you are trading. More volatile assets may require a higher Multiplier.
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หลักการทำงาน (Concept)
Indicator ตัวนี้เป็น "เครื่องวัดการระเบิดของความผันผวน" ในตลาด
โดยแกนหลักเป็นการเปรียบเทียบความผันผวนในปัจจุบันกับความผันผวนโดยเฉลี่ยในอดีต
เพื่อหาจังหวะที่ตลาดเริ่ม "เหวี่ยง" แรงขึ้นอย่างมีนัยสำคัญ
ส่วนประกอบหลักๆ มีดังนี้:
① Historical Volatility (HV)
เส้นนี้คือตัวชี้วัดความผันผวนของราคา ณ ปัจจุบัน
ถ้าเส้นนี้วิ่งขึ้นสูง แปลว่าราคากำลังแกว่งตัวรุนแรง (ผันผวนสูง)
ถ้าเส้นนี้อยู่ต่ำ แปลว่าราคานิ่งๆ หรือเคลื่อนไหวในกรอบแคบๆ (ผันผวนต่ำ)
② SMA x Multiplier
เส้นนี้ทำหน้าที่เป็น "เส้นเกณฑ์" หรือ "แนวต้านของความผันผวน"
ถูกคำนวณมาจากเส้นค่าเฉลี่ยของความผันผวนในอดีต
แล้วคูณด้วยตัวเลข Adjustable (sma-Multiplier) เพื่อสร้างเป็นกรอบบน
พูดง่ายๆ คือ เส้นนี้บอกเราว่า "โดยปกติแล้ว ความผันผวนไม่ควรจะเกินระดับนี้"
③ Difference (Histogram)
เป็นผลลัพธ์จากการนำค่า HV ข้อ ① มาลบกับ เส้นเกณฑ์ ข้อ ②
เกิดขึ้นเมื่อ HV ทะลุเส้นเกณฑ์ขึ้นไป
เป็นสัญญาณว่า ณ ตอนนี้ "ความผันผวนได้พุ่งสูงกว่าค่าเฉลี่ยในอดีตอย่างมีนัยสำคัญ"
เกิดขึ้นเมื่อ HV ยังอยู่ต่ำกว่าเส้นเกณฑ์
บอกว่าความผันผวนยังอยู่ในระดับปกติหรือต่ำกว่าค่าเฉลี่ย
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วิธีการนำไปใช้ (How to Use)
Indicator ตัวนี้ ไม่ได้บอกทิศทางของราคา
แต่จะบอก "พลัง" หรือ "โมเมนตัม" ของการเคลื่อนไหว
เราจึงควรใช้มันร่วมกับเครื่องมืออื่นเพื่อยืนยันทิศทางเสมอ
① มองหาสัญญาณ "การระเบิดของราคา" (Volatility Breakout)
② ใช้ยืนยันความแข็งแกร่งของเทรนด์
③ ใช้ในการบริหารความเสี่ยง
สามารถลองปรับค่า smaLength และ smaMultiplier ในการตั้งค่า Indicator
เพื่อให้เข้ากับสินทรัพย์และ Timeframe ที่เทรดได้นะ
สินทรัพย์ที่เหวี่ยงแรงๆ อาจต้องใช้ Multiplier ที่สูงขึ้น เป็นต้น
ADR - Average Daily Range [KasTrades]This is an Average Daily Range (ADR) indicator.
There are two settings for ADR:
Two Look back period ADR range (e.g. 7 and 14 days)
One Look back period ADR (e.g. 5 days only)
Two day ADR ranges are typically used in equities and index futures whereas one day ADR is typically used in forex.
The opening time by default is 17:00 New York (Eastern) time. The ranges are always calculated from the opening price of the first bar on the respected timeframe.
ADR [KasTrades]This ADR indicator has 2 options: a Range of ADR, such as 7 and 14 which is typically used for indexes, index futures and equities, or a single ADR such as a 5 day which is typically used for forex.
The session start time is 17:00 ET (NY Time) by default, this is adjustable.
You can adjust the ADR days to different ranges such as 5 and 10, or a single ADR day such as 7.
Colors of the ADR high and low are also adjustable.
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?"
Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen.
Mathematical Foundation: First Passage Time Theory
What is First Passage Time?
First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in:
Quantitative Finance: Option pricing, risk management, and algorithmic trading
Neuroscience: Modeling neural firing patterns
Biology: Population dynamics and disease spread
Engineering: Reliability analysis and failure prediction
The Mathematics Behind It
This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula:
dS = μS dt + σS dW
Where:
S = Asset price
μ = Drift (trend component)
σ = Volatility (uncertainty component)
dW = Wiener process (random walk)
Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine:
When each threshold (+X% or -X%) is likely to be hit
Which threshold is hit first (directional bias)
How often each scenario occurs (probability distribution)
🎯 How This Indicator Works
Core Algorithm Workflow:
Calculate Historical Statistics
Measures recent price volatility (standard deviation of log returns)
Calculates drift (average directional movement)
Annualizes these metrics for meaningful comparison
Run Monte Carlo Simulations
Generates 1,000+ random price paths based on historical behavior
Tracks when each path hits the upside (+X%) or downside (-X%) threshold
Records which threshold was hit first in each simulation
Aggregate Statistical Results
Calculates percentile distributions (10th, 25th, 50th, 75th, 90th)
Computes "first hit" probabilities (upside vs downside)
Determines average and median time-to-target
Visual Representation
Displays thresholds as horizontal lines
Shows gradient risk zones (purple-to-blue)
Provides comprehensive statistics table
📈 Use Cases
1. Options Trading
Selling Options: Determine if your strike price is likely to be hit before expiration
Buying Options: Estimate probability of reaching profit targets within your time window
Time Decay Management: Compare expected time-to-target vs theta decay
Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk.
2. Swing Trading
Entry Timing: Wait for higher probability setups when directional bias is strong
Target Setting: Use median time-to-target to set realistic profit expectations
Stop Loss Placement: Understand probability of hitting your stop before target
Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing.
3. Risk Management
Position Sizing: Larger positions when probability heavily favors one direction
Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty)
Hedge Timing: Know when to add protective positions based on downside probability
Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup.
4. Market Regime Detection
Trending Markets: High directional bias (70%+ one direction)
Range-bound Markets: Balanced probabilities (45-55% both directions)
Volatility Regimes: Compare actual vs theoretical minimum time
Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades.
First Hit Rate (Most Important!)
Shows which threshold is likely to be hit FIRST:
Upside %: Probability of hitting upside target before downside
Downside %: Probability of hitting downside target before upside
These always sum to 100%
⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter!
Advanced Parameters
Drift Mode
Allows you to explore different scenarios:
Historical: Uses actual recent trend (default—most realistic)
Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities)
50% Reduced: Dampens trend effect (conservative scenario)
Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets.
Distribution Type
Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users)
Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis
⚠️ Important Limitations & Best Practices
Limitations
Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM
Historical Parameters: Uses recent volatility/drift—may not predict regime shifts
No Fundamental Events: Cannot predict earnings, news, or macro shocks
Computational: Runs only on last bar—doesn't give historical signals
Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management.
Created by: Henrique Centieiro. feedback is more than welcome!
Omega ATR Indicator📖 Introduction
The Ω ATR Indicator was created to provide a more complete and professional framework for volatility analysis than the classic Average True Range (ATR).
While the traditional ATR is a useful tool, it has limitations: it delivers a simple rolling average of volatility, but it does not adapt to market regimes, it does not highlight extreme events, and it often leaves the trader with incomplete information about risk.
The Ω ATR takes the same foundation and elevates it into a multi-dimensional volatility dashboard, adding statistical layers, adaptive calculations, and clear visual references that allow traders to interpret volatility in a way that is immediately actionable.
🔎 What makes it different from a standard ATR?
This indicator introduces several features beyond the classic formula:
True Range Core – plots the raw True Range (TR) for each bar, providing a direct, bar-by-bar view of volatility impulses.
Standard & Adjusted ATR – includes both the conventional ATR (smoothed average) and an Adjusted ATR that automatically corrects for extreme conditions by incorporating percentile rescaling.
Percentile Volatility Levels – dynamically calculated extreme thresholds (99.8%, 75%, 50%, 25%), plotted as dotted levels across the chart. These act as reference lines for “normal” vs. “abnormal” volatility, useful for spotting unusual price expansions or contractions.
Linear Regression Volatility Trend – overlays a regression line of volatility, showing whether the market is moving toward expansion (rising vol), contraction (falling vol), or stability.
Monetary Value Translation – the indicator converts volatility into points, ticks, and dollar values (based on the instrument’s point value). This allows futures traders and high-value instruments users to immediately see how much volatility is “worth” in cash terms.
Interactive Table Display – a real-time statistics table is displayed directly on the chart, showing:
SMA of ATR in $ and points
Percentile-based volatility range (VAR) in $ and points
Tick equivalences, for quick position sizing
⚡ How traders can use it
The Ω ATR Indicator is designed to be versatile, fitting both discretionary traders and systematic strategy developers.
Risk Management: ATR-based stop losses and position sizing are significantly improved by using the adjusted ATR and percentile thresholds. Traders can size their positions according to volatility regimes, not just raw averages.
Breakout & Exhaustion Detection: When TR or ATR values spike above the 99.8% or 95% percentile levels, this often corresponds to breakout conditions or volatility exhaustion — useful for breakout strategies, mean-reversion setups, and volatility fades.
Market Regime Identification: The regression line helps distinguish if volatility is rising (trending environment, larger swings expected) or compressing (range-bound environment, lower risk opportunities).
Multi-Asset Flexibility: Works equally well on equities, futures, crypto, and FX. Its point/tick/dollar conversion makes it especially powerful for futures traders who need to quantify risk precisely.
Scalping to Swing Trading: On lower timeframes, it acts as a micro-volatility detector; on higher timeframes, it functions as a strategic risk gauge for position management.
⚙️ Settings and Customization
Length: The ATR lookback period (default = 34).
Shorter lengths (14–21) for intraday traders who want fast response.
Longer lengths (34–55) for swing/position traders who want smoother readings.
AVG / ADJ AVG: Toggle to display the standard ATR or the adjusted ATR.
Volatility Levels: Enable/disable up to 4 percentile-based levels (1st = 25%, 2nd = 50%, 3rd = 75%, 4th = 99.8%). Recommended: keep 3 levels active for clarity.
Color Controls: All plots and levels are fully customizable to match your chart style.
Table Display: Positioned on the chart (default: middle-right) with key values updated in real time.
🧭 Best Practices for Use
Combine with Trend Tools: Volatility readings are most powerful when combined with trend filters or volume analysis. For example, a breakout with both high volatility and trend confirmation is stronger than either alone.
ATR Stops: Use the Adjusted ATR rather than the standard one when trailing stops in highly volatile instruments like crypto or Nasdaq futures, as it adapts to outlier spikes.
Dollar Risk Translation: Use the dollar-value outputs to predefine maximum acceptable risk per trade (e.g., “I only risk $250 per position”). This bridges volatility to portfolio risk management.
Event Monitoring: Around economic events or earnings, expect volatility spikes above higher percentile levels. The indicator makes these moves instantly visible.
📌 Summary
The Ω ATR Indicator is not just “another ATR.” It is a comprehensive volatility framework that transforms volatility from a simple statistic into an actionable trading signal.
By combining:
the classic ATR,
an adjusted ATR,
percentile extremes,
regression-based volatility trends,
and real-time dollar conversions,
…this tool allows traders to precisely understand, visualize, and act on volatility in ways that a standard ATR simply cannot provide.
Whether you are scalping intraday moves, swing trading equities, or managing futures positions, the Ω ATR equips you with a professional-grade volatility dashboard that clarifies risk, highlights opportunity, and adapts across all markets and timeframes.
👉 Designed and developed by OmegaTools for traders who demand precision, clarity, and adaptability in their volatility analysis.
Realized Volatility (StdDev of Returns, %)Realized Volatility (StdDev of Returns, %)
This indicator measures realized (historical) volatility by calculating the standard deviation of log returns over a user-defined lookback period. It helps traders and analysts observe how much the price has varied in the past, expressed as a percentage.
How it works:
Computes close-to-close logarithmic returns.
Calculates the standard deviation of these returns over the selected lookback window.
Provides three volatility measures:
Daily Volatility (%): Standard deviation over the chosen period.
Annualized Volatility (%): Scaled using the square root of the number of trading days per year (default = 250).
Horizon Volatility (%): Scaled to a custom horizon (default = 5 days, useful for short-term views).
Inputs:
Lookback Period: Number of bars used for volatility calculation.
Trading Days per Year: Used for annualizing volatility.
Horizon (days): Adjusts volatility to a shorter or longer time frame.
Notes:
This is a statistical measure of past volatility, not a forecasting tool.
If you change the scale to logarithmic, the indicator readibility improves.
It should be used for analysis in combination with other tools and not as a standalone signal.
Realized Volatility (StdDev of Returns, %)📌 Realized Volatility (StdDev of Returns, %)
This indicator measures realized volatility directly from price returns, instead of the common but misleading approach of calculating standard deviation around a moving average.
🔹 How it works:
Computes close-to-close log returns (the most common way volatility is measured in finance).
Calculates the standard deviation of these returns over a chosen lookback period (default = 200 bars).
Converts results into percentages for easier interpretation.
Provides three key volatility measures:
Daily Realized Vol (%) – raw standard deviation of returns.
Annualized Vol (%) – scaled by √250 trading days (market convention).
Horizon Vol (%) – volatility over a custom horizon (default = 5 days, i.e. weekly).
🔹 Why use this indicator?
Shows true realized volatility from historical returns.
More accurate than measuring deviation around a moving average.
Useful for traders analyzing risk, position sizing, and comparing realized vs implied volatility.
⚠️ Note:
It is best used on the Daily Chart!
By default, this uses log returns (which are additive and standard in quant finance).
If you prefer, you can easily switch to simple % returns in the code.
Volatility estimates depend on your chosen lookback length and may vary across timeframes.
Monthly Expected Move (IV + Realized)What it does
Overlays 1-month expected move bands on price using both forward-looking options data and backward-looking realized movement:
IV30 band — from your pasted 30-day implied vol (%)
Straddle band — from your pasted ATM ~30-DTE call+put total
HV band — from Historical Volatility computed on-chart
ATR band — from ATR% extrapolated to ~1 trading month
Use it to quickly answer: “How much could this stock move in ~1 month?” and “Is the market now pricing more/less movement than we’ve actually been getting?”
Inputs (quick)
Implied (forward-looking)
Use IV30 (%) — paste annualized IV30 from your options platform.
Use ATM 30-DTE Straddle — paste Call+Put total (per share) at the ATM strike, ~30 DTE.
Realized (backward-looking)
HV lookback (days) — default 21 (≈1 trading month).
ATR length — default 14.
Note: TradingView can’t fetch option data automatically. Paste the IV30 % or the straddle total you read from your broker (use Mark/mid prices).
How it’s calculated
IV band (±%) = IV30 × √(21/252) (annualized → ~1-month).
Straddle band (±%) = (ATM Call + Put) / Spot to that expiry (≈30 DTE).
HV band (±%) = stdev(log returns, N) × √252 × √(21/252).
ATR band (±%) = (ATR(len)/Close) × √21.
All bands are plotted as upper/lower envelopes around price, plus an on-chart readout of each ±% for quick scanning.
How to use it (at a glance)
IV/Straddle bands wider than HV/ATR → market expects bigger movement than recent actuals (possible catalyst/expansion).
All bands narrow → likely a low-mover; look elsewhere if you want action.
HV > IV → realized swings exceed current pricing (mean-reversion or vol bleed often follows).
Pro tips
For ATM straddle: pick the expiry closest to ~30 DTE, use the ATM strike (closest to spot), and add Call Mark + Put Mark (per share). If the exact ATM strike isn’t quoted, average the two neighboring strikes.
The simple straddle/spot heuristic can read slightly below the IV-derived 1σ; that’s normal.
Keep the chart on daily timeframe—the math assumes trading-day conventions (~252/yr, ~21/mo).
ATR Dynamische Candles 1.2 (by Droes)This script visualizes ATR values as candles to the right of the last candle at today's high and today's low.
ATR Daily Viz V30.0This script visualizes ATR values as candles to the right of the last candle at today's high and low.
This is the first version.
ATR Daily Viz V30.0This script visualizes ATR values as candles to the right of the last candle at today's high and low.
This is the first version.
Synthetic VX3! & VX4! continuous /VX futuresTradingView is missing continuous 3rd and 4th month VIX (/VX) futures, so I decided to try to make a synthetic one that emulates what continuous maturity futures would look like. This is useful for backtesting/historical purposes as it enables traders to see how their further out VX contracts would've performed vs the front month contract.
The indicator pulls actual realtime data (if you subscribe to the CBOE data package) or 15 minute delayed data for the VIX spot (the actual non-tradeable VIX index), the continuous front month (VX1!), and the continuous second month (VX2!) continually rolled contracts. Then the indicator's script applies a formula to fairly closely estimate how 3rd and 4th month continuous contracts would've moved.
It uses an exponential mean‑reversion to a long‑run level formula using:
σ(T) = θ+(σ0−θ)e−kT
You can expect it to be off by ~5% or so (in times of backwardation it might be less accurate).
