MAD Adaptive Trend Score [BackQuant]MAD Adaptive Trend Score
Overview
MAD Adaptive Trend Score is a trend oscillator built from a Median Absolute Deviation-based price filter and a multi-lookback relative-position score.
The indicator first calculates a rolling median and MAD from the selected source. Price deviation from the median is then clipped to a configurable MAD envelope, producing the MAD Adaptive Filter.
The current value of that filtered series is then compared with a range of its previous values. Each comparison contributes either +1 or -1 to a Trend Score.
The result is a bounded directional score that can be used with separate bullish and bearish thresholds to create a persistent trend state.
The script includes:
Exact rolling median and MAD calculations.
MAD-based clipping of source movement.
Configurable multi-lookback Trend Score.
Separate long and short regime thresholds.
Optional filter overlay on the main chart.
Trend candle colouring and signals.
Reference levels and alerts.
MAD Adaptive Filter
The first stage calculates the rolling median of the selected Source over the MAD Length.
It then calculates Median Absolute Deviation:
MAD = Median(|X - Median(X)|)
Raw MAD is multiplied by 1.4826:
Scaled MAD = Raw MAD × 1.4826
with a minimum value based on the instrument's minimum tick.
The 1.4826 factor is commonly used to scale MAD to approximately the same scale as standard deviation when the underlying distribution is normal.
The indicator then measures:
Deviation = Source - Rolling Median
and defines the maximum permitted deviation as:
Maximum Deviation = Scaled MAD × MAD Multiplier
The source deviation is clipped to this range before being added back to the median.
Conceptually:
If Source remains inside the MAD envelope, the filter follows Source.
If Source moves above the envelope, the filter is limited to the upper MAD boundary.
If Source moves below the envelope, the filter is limited to the lower MAD boundary.
The MAD Adaptive Filter is therefore not a conventional moving average. It is a source series whose distance from its rolling median is limited by the current MAD-derived envelope.
MAD Multiplier
MAD Multiplier controls the permitted distance between the filtered value and the rolling median.
Lower values:
Create a tighter envelope.
Clip more of the source movement.
Keep the filter closer to the median.
Higher values:
Create a wider envelope.
Allow more source movement through unchanged.
Make the filter follow price more closely.
Trend Score
The second stage scores the current MAD Filter against several previous values of the same filtered series.
For every lookback between Score Lookback Start and End:
+1 if the current MAD Filter is above the historical MAD Filter.
-1 otherwise.
The final Trend Score is the sum of all comparisons.
If N historical values are being compared, the theoretical score range is:
-N to +N
For the default 1-to-45 range, 45 comparisons are made, so the score can range from -45 to +45.
What the score represents
A high positive score means the current MAD-filtered value is above most of the historical filtered values being compared.
A strongly negative score means it is above very few of them.
For example, with 45 comparisons:
A score near +45 means the current filtered value is above nearly the entire comparison range.
A score near 0 means the comparisons are more evenly divided.
A score near -45 means the current filtered value is below, or equal to, nearly all of them.
The score is therefore best understood as a relative position / trend score of the filtered series.
It is not a return forecast or probability of future direction.
Why use several lookbacks?
Comparing the current filter with only one previous value would effectively reduce the calculation to short-term slope.
Using many previous values instead measures where the current filtered level sits relative to a broader section of its history.
A steadily rising filtered series will generally move toward higher positive scores because the current value becomes greater than an increasing number of historical values.
During sustained weakness, the opposite occurs.
Score Lookback Start and End
These settings define which historical MAD Filter values participate in the score.
For example:
Start = 1
End = 45
compares the current filter against each filtered value from 1 through 45 bars ago.
A shorter range:
Responds more quickly to recent changes.
Creates a smaller score range.
A longer range:
Includes more historical comparisons.
Produces a broader measure of relative trend position.
Usually changes more gradually.
Because the score range depends on the number of comparisons, threshold settings should be chosen with the selected score range in mind.
Trend State
The script converts the Trend Score into a persistent bullish or bearish signal state.
The bullish and bearish rules are deliberately separate.
Bullish condition
The signal becomes bullish when:
Trend Score > Long Threshold
Once bullish, the state remains bullish until a valid bearish condition occurs.
Bearish condition
The signal becomes bearish when the score crosses downward through the Short Threshold:
Previous Score >= Short Threshold
Current Score < Short Threshold
The bearish condition therefore requires an actual downward threshold crossing rather than simply remaining below the level.
Why use separate thresholds?
Using different bullish and bearish levels introduces persistence into the regime.
The signal does not need to reverse whenever the score crosses zero.
For example, with:
Long Threshold = 40
Short Threshold = -6
the score must reach a strongly positive state before the model turns bullish, but the bullish state can persist through a substantial amount of score deterioration before a bearish transition occurs.
This creates a form of threshold hysteresis and reduces rapid switching around a single center level.
The thresholds are fully configurable and do not need to be symmetrical.
Initial state
The signal begins neutral.
A bullish state can be established once the Long Threshold condition is satisfied.
A bearish state requires a valid downward crossing of the Short Threshold.
Signal markers are shown only when an established bullish state changes to bearish or an established bearish state changes to bullish.
The initial transition from neutral does not produce a long/short marker.
Reference Lines
The optional dashed reference lines display the Long and Short Thresholds directly in the oscillator pane.
These levels correspond to the actual regime settings and can be useful when visually tracking how the Trend Score approaches a possible state change.
MAD Filter Overlay
The MAD Adaptive Filter can optionally be plotted directly on the main price chart.
This makes it possible to compare:
Raw price.
The rolling-median/MAD envelope response.
The active trend colour.
The overlay uses the same bullish or bearish state colour as the oscillator.
Trend Candles
Optional chart candles are coloured from the stored trend state:
Bullish state = Long Color.
Bearish state = Short Color.
The colour represents the indicator's trend regime rather than the direction of each individual candle.
Background Colour
An optional transparent background can also display the current trend regime on the main chart.
This is purely visual and does not alter the calculation.
How to interpret it
Strong positive score
The current MAD Filter is above most values in the selected historical comparison range.
This typically accompanies a relatively strong upward position in the filtered trend.
Falling score while still bullish
The filtered trend is losing relative strength, but the Short Threshold has not yet been crossed.
The persistent state therefore remains bullish.
Short Threshold crossing
The score has deteriorated far enough to cross below the selected bearish boundary, changing the stored state to bearish.
Rising score while bearish
The score can recover substantially while the trend remains bearish.
A new bullish state is not established until the score exceeds the Long Threshold.
How to use the indicator
The indicator can be used as:
A directional trend filter.
A persistent bullish/bearish regime indicator.
A way to measure the relative position of a MAD-filtered price series.
A confirmation tool alongside other price or market-structure analysis.
The score itself can also provide additional context beyond the binary trend colour.
For example, a bullish regime with a score near its maximum is different from a bullish regime whose score has already fallen substantially toward the bearish threshold.
Input Guide
MAD Length
Controls the rolling sample used to calculate the median and Median Absolute Deviation.
Shorter values adapt more quickly.
Longer values produce a broader statistical reference window.
MAD Multiplier
Controls how far the filtered source may move away from its rolling median.
Lower values produce stronger clipping.
Higher values allow the filter to follow Source more closely.
Score Lookback Start / End
Defines the historical MAD Filter values used in the Trend Score comparisons.
Long Threshold
Score level that must be exceeded to establish a bullish state.
Short Threshold
Level that must be crossed downward to establish a bearish state.
Data Window
The script exposes:
Rolling Median.
Raw MAD.
Scaled MAD.
These values can help show how the underlying MAD filter is being constructed.
Limitations
The indicator is reactive rather than predictive.
The score measures the current filtered value relative to historical filtered values; it does not estimate future returns.
Threshold selection can materially change signal frequency and persistence.
A very tight MAD Multiplier can suppress meaningful movement along with noise.
A very wide MAD Multiplier makes the filter increasingly similar to the original Source.
Long score ranges can improve persistence but also delay changes in regime.
Strong trends can keep the score near an extreme for extended periods.
Alerts
The script includes:
MAD Trend Score Long: stored signal changes from bearish to bullish.
MAD Trend Score Short: stored signal changes from bullish to bearish.
Summary
MAD Adaptive Trend Score combines two simple ideas.
First, the selected Source is constrained around a rolling median using Median Absolute Deviation. Source movement inside the MAD envelope passes through normally, while movement beyond the envelope is clipped to the current boundary.
Second, the current filtered value is compared with a configurable range of its own historical values.
Those comparisons are summed into a Trend Score, with positive values indicating that the current filtered level is above more of the historical comparison range and negative values indicating the opposite.
Separate Long and Short Thresholds then convert the score into a persistent bullish or bearish regime.
The result is a MAD-based filtered series and relative-position trend score for experimenting with trend persistence and threshold behaviour. อินดิเคเตอร์
Variance-Weighted Regression Trend [BackQuant]Variance-Weighted Regression Trend
Overview
Variance-Weighted Regression Trend is a rolling linear-regression trend indicator that adjusts the influence of observations according to the estimated variance of their regression residuals.
The script first calculates a standard ordinary least-squares regression across the selected window. It then measures the squared residuals around that fit and uses those residuals to estimate how variable the regression error has been through the sample.
Those variance estimates are converted into relative weights. Lower estimated residual variance can receive more influence, while higher estimated residual variance can receive less. A second weighted regression is then calculated using those weights.
The indicator also includes:
EMA, RMA or rolling-average residual variance.
Configurable inverse-variance weighting strength.
Weight regularization and upper/lower weight limits.
Weighted R² and slope-quality diagnostics.
Two regression-channel methods.
Optional trend-flip quality confirmation.
OLS comparison.
Linear regression projection.
Trend colouring and alerts.
Calculation
The basic process is:
Fit an ordinary least-squares regression over the Regression Length.
Calculate the squared residual of every observation around that fit.
Smooth those squared residuals to estimate local residual variance.
Add a regularization floor to reduce unstable extreme weights.
Convert variance into relative observation weights.
Clamp weights between the selected minimum and maximum.
Calculate a second weighted regression.
The weighted line is therefore influenced more by observations receiving larger relative weights and less by those receiving smaller ones.
Variance Weighting
The weighting is based on regression residual variance , not ATR, trading volume or raw price volatility.
For each point:
Residual = Source - OLS fitted value
Squared Residual = Residual²
The squared residuals are then processed using the selected Variance Model.
EMA
Uses exponential smoothing and responds more quickly to recent residual changes.
RMA
Uses a slower recursive smoothing process.
Rolling Mean
Uses a finite moving average of squared residuals.
Weight Power
Weight Power controls how strongly estimated variance affects the regression.
The raw weighting relationship is:
Weight ∝ 1 / Variance^Weight Power
0 gives equal weighting, making the final fit behave like the OLS regression.
1 applies standard inverse-variance-style weighting.
Values above 1 increase the difference between low- and high-variance observations.
Higher settings can make the regression more selective, but can also concentrate too much influence in a small part of the sample.
Variance Regularization
Very small variance estimates can otherwise create extremely large inverse weights.
The script therefore adds a fraction of the window's mean squared residual to each local variance estimate.
Higher regularization makes the weights more uniform.
Lower regularization allows stronger differences between observations.
Minimum and Maximum Relative Weight
Raw weights are normalized relative to their average before being clamped.
A relative weight above 1 means the observation has greater-than-average influence.
A value below 1 means it has less.
The Minimum Relative Weight prevents high-variance observations from effectively disappearing from the regression.
The Maximum Relative Weight prevents very low-variance observations from dominating the entire fit.
Weighted Regression
Once the final weights are calculated, the script solves a weighted linear regression:
Y = Intercept + Slope × X
The displayed line is the current endpoint of that rolling weighted regression.
Each new bar shifts the regression window and recalculates:
OLS.
Residuals.
Variance estimates.
Weights.
Weighted slope and intercept.
OLS Comparison
The optional OLS line shows the endpoint of the initial equal-weight regression.
This makes it easy to see how much the variance weighting is actually changing the result.
If Weight Power is set to 0, the weighted regression and OLS should be effectively aligned.
As the weighting becomes more aggressive, the lines may separate depending on the residual structure inside the window.
Trend State
Trend direction comes from the sign of the weighted regression slope.
Positive slope = bullish.
Negative slope = bearish.
A bullish flip occurs when the stored trend changes from bearish to bullish.
A bearish flip occurs when it changes from bullish to bearish.
Quality Confirmation
Quality Confirmation can be enabled to prevent weak slope changes from immediately flipping the trend state.
When enabled, an opposite slope must also satisfy:
Minimum Weighted R².
Minimum Slope / Standard Error.
If those conditions are not met, the existing trend state remains active even if the current slope temporarily changes sign.
Weighted R²
Weighted R² measures how well the weighted straight-line regression describes the current sample.
Higher values indicate that the weighted observations are more closely aligned with a linear fit.
Lower values indicate a less orderly linear relationship.
R² does not determine trend direction and should not be interpreted as a forecast of future performance.
Slope / Standard Error
The script calculates the absolute weighted slope relative to its estimated standard error:
|Slope| / Slope Standard Error
This is used as a practical slope-quality measure.
Higher values indicate that the fitted slope is larger relative to the estimated regression error.
It is used by the optional Quality Confirmation setting and is not presented as a formal significance test.
Regression Channels
Two channel-width methods are available.
Weighted Residual RMS
Uses the weighted root-mean-square distance of observations from the fitted regression.
This reflects the general amount of scatter around the line.
Regression Standard Error
Uses the calculated standard error of the fitted current regression value.
This normally represents a different and often narrower measure than residual RMS.
The Channel Multiplier scales whichever method is selected.
Expand During Poor Fit
When enabled, the channel becomes wider as Weighted R² decreases.
This is intended to visually reflect greater uncertainty when the current window is poorly described by a straight line.
The expansion affects only the channel width.
It does not alter the regression or trend calculation.
Projection
The Projection extends the current regression slope forward by the selected number of bars.
It is simply:
Current fitted line extended using the current slope.
It is not a separate forecasting model.
As the regression changes on new bars, the projection also changes.
Current Relative Weight
The Data Window shows the final relative weight assigned to the newest observation.
A value:
Above 1 = greater-than-average influence.
Below 1 = less-than-average influence.
This can help show how the current observation is being treated by the variance-weighting model.
Effective Sample Size
The indicator also reports:
Effective N = (Sum of Weights)² / Sum of Squared Weights
This provides a simple measure of weight concentration.
If weights are similar, Effective N remains close to the full Regression Length.
If a smaller group of observations receives most of the weight, Effective N falls.
This is useful when experimenting with aggressive Weight Power or wide weight limits.
Trend Strength
Trend Strength is used only for the regression glow.
It combines:
60% Weighted R².
40% normalized Slope / Standard Error.
It does not affect the regression or signals.
ATR(14) is used only to scale the visual width of the glow and flip bloom to the instrument.
Input Guide
Regression Length
Controls the size of the rolling regression sample.
Projection Bars
Controls how far the current fitted slope is extended visually.
Variance Length
Controls how quickly the residual-variance estimate changes.
Variance Model
Selects EMA, RMA or Rolling Mean smoothing of squared residuals.
Weight Power
Controls the strength of inverse-variance weighting.
Variance Regularization
Reduces extreme differences between weights.
Minimum / Maximum Relative Weight
Limits how little or how much influence any one observation can receive.
Channel Width
Selects Weighted Residual RMS or Regression Standard Error.
Channel Multiplier
Scales the regression channel.
Poor Fit Expansion
Optionally widens the channel as R² deteriorates.
Quality Confirmation
Requires minimum regression fit and slope quality before allowing trend flips.
How to use it
The indicator can be used as:
A regression-based trend filter.
A comparison between ordinary and variance-weighted regression.
A way to study how residual-based weighting changes a rolling trend estimate.
A trend-quality filter using R² and slope strength.
A regression channel for visualizing fit dispersion.
The OLS Comparison and Data Window values are particularly useful when testing the weighting settings, because they show whether the extra weighting is materially changing the regression or simply producing a result close to ordinary least squares.
Limitations
The variance estimates are derived from OLS residuals inside the same rolling window.
The model is a custom two-stage weighted regression rather than a full generalized least-squares procedure.
Higher Weight Power can concentrate the fit in a relatively small part of the sample.
Linear regression cannot represent every type of market structure.
High R² does not imply future trend continuation.
The forward projection is only a linear extrapolation of the current fit.
Quality Confirmation can reduce weak flips but can also delay genuine changes in direction.
Data Window
The script exposes:
Weighted Slope.
Weighted R².
Slope / Standard Error.
Weighted Residual RMS.
Regression Standard Error.
Current Relative Weight.
Effective Sample Size.
Trend Strength.
Alerts
The indicator includes:
Variance-Weighted Regression Bullish: trend changes from bearish to bullish.
Variance-Weighted Regression Bearish: trend changes from bullish to bearish.
Variance-Weighted Regression Flip: either transition occurs.
Summary
Variance-Weighted Regression Trend starts with a normal rolling OLS regression, measures the residual variance around that fit, and uses those estimates to assign relative weights to the observations in a second regression.
The weighting strength, variance smoothing, regularization and weight limits are all configurable, making it possible to move from essentially equal-weight OLS to a much more selective fit.
The final weighted slope controls the trend state, while Weighted R² and the Slope / Standard Error score can optionally be used to filter weak reversals.
Regression channels, OLS comparison, forward projection and the visual strength system provide additional context around the core weighted regression without changing the underlying trend logic.
อินดิเคเตอร์
MAD Volatility Trail [BackQuant]MAD Volatility Trail
Overview
MAD Volatility Trail is a robust trend-following overlay built from a rolling median and Median Absolute Deviation rather than a conventional moving average and standard deviation.
The indicator estimates a central price using the rolling median, measures how widely recent prices are distributed around that median using MAD, converts that dispersion into adaptive upper and lower bands, and then transforms those bands into one-sided trailing boundaries.
The result is a persistent bullish or bearish trend regime with:
A robust median-based center.
MAD-derived volatility bands.
Optional ATR minimum band width.
One-sided trailing support and resistance.
Optional median-slope confirmation.
Bullish and bearish regime flips.
Strength-reactive gradient and glow.
Post-flip bloom visualization.
Trend-coloured candles.
Signal and alert support.
The main distinction is statistical.
Most volatility trails rely on:
Means.
Standard deviation.
ATR.
MAD Volatility Trail instead uses:
Median.
Median Absolute Deviation.
Median-based statistics are substantially less sensitive to isolated extreme observations, making the framework useful when the user wants a trend structure that is less influenced by individual spikes or outliers.
Core concept
The indicator separates the problem into four stages:
Estimate a robust rolling center using the median.
Measure robust dispersion around that center using MAD.
Build upper and lower adaptive deviation bands.
Convert those raw bands into persistent trailing trend boundaries.
The resulting trail behaves conceptually like a volatility-aware regime filter, but its volatility estimate comes primarily from the empirical distribution of price around its median.
Why median instead of mean?
A conventional arithmetic mean is calculated by summing all observations and dividing by their count.
Every value directly affects the result.
This makes the mean sensitive to outliers.
Consider a simplified sample:
100
101
101
102
150
The extreme value at 150 pulls the arithmetic mean upward substantially.
The median is simply the middle observation after sorting:
Median = 101
The single extreme observation has much less influence.
This property is called robustness .
In markets, isolated large candles, gaps, liquidation events and temporary price spikes can distort mean-based statistics. Median-based calculations intentionally reduce the influence of those individual observations.
Rolling median
For each bar, the indicator collects the selected Source values across the MAD Lookback.
It then calculates the exact median of the available observations.
For an odd number of observations, the median is the middle sorted value.
For an even number, the median lies between the two central observations according to the median implementation.
The resulting value becomes the statistical center of the trail.
Unlike an EMA or RMA, the median is not recursively smoothed.
It is recomputed from the actual distribution of values inside the current rolling window.
Early-history behaviour
At the beginning of the chart, the script ignores unavailable historical values.
This means the first valid median calculations may use fewer observations than the full MAD Lookback until sufficient chart history has accumulated.
Once the complete lookback is available, the calculation uses the full selected window.
Median Absolute Deviation
After calculating the rolling median, the script measures the absolute distance of every observation from that median:
Absolute Deviation = |Value - Median|
It then takes the median of those absolute deviations:
MAD = Median(|Xi - Median(X)|)
This is the Median Absolute Deviation .
MAD measures the typical distance of observations from the median.
It serves a role similar to standard deviation, but the mathematics and statistical behaviour are different.
Why MAD is robust
Standard deviation squares deviations from the mean.
Large deviations therefore receive disproportionately large influence.
A single extreme observation can:
Move the mean.
Create a very large squared deviation.
Increase the final standard deviation substantially.
MAD does not square deviations.
It calculates absolute distance and then takes another median.
Extreme values therefore have limited ability to change the result unless enough of the underlying sample shifts.
This gives MAD a high resistance to outliers.
In practical chart terms, one unusual wick or shock candle is less likely to inflate the statistical width as dramatically as it could under a standard-deviation model.
MAD versus standard deviation
The two measures answer related but different questions.
Standard deviation
Measures squared dispersion around the arithmetic mean.
MAD
Measures median absolute dispersion around the median.
Standard deviation is highly useful when a mean-and-variance framework is desired.
MAD is useful when robustness to unusual observations is more important.
The indicator does not claim one is universally superior.
It intentionally uses MAD because the purpose is to construct a robust trend boundary.
MAD Scale
Raw MAD is not numerically identical to standard deviation.
Under a normal distribution, MAD is usually multiplied by a consistency factor of approximately 1.4826 when the goal is to make it comparable to standard deviation.
The indicator exposes this scaling directly:
Robust Deviation = Raw MAD × MAD Scale
The script default is 1.4655.
The input remains fully adjustable, so users who want the conventional normal-consistency approximation can set the factor near 1.4826.
This scale does not change the median itself.
It changes only the size of the deviation estimate used to build the bands.
Deviation Factor
After scaling MAD, the indicator applies the Deviation Factor:
MAD Width = Scaled MAD × Deviation Factor
This acts as the main sensitivity control.
Lower values:
Create narrower raw bands.
Place the trail closer to price.
Produce more frequent regime changes.
Higher values:
Create wider bands.
Require larger movement for reversals.
Produce more persistent trend states.
The MAD Scale and Deviation Factor both affect width, but they represent different concepts.
MAD Scale calibrates the statistical dispersion estimate.
Deviation Factor determines how much of that estimated dispersion is used for the trend envelope.
Raw MAD bands
The raw bands are:
Upper MAD Band = Median + Band Width
Lower MAD Band = Median - Band Width
Before trailing logic is applied, these bands can move freely upward or downward with:
The rolling median.
MAD dispersion.
Any active ATR floor.
These are statistical envelopes around the median.
They are not yet the final trend trail.
ATR Minimum Width
MAD can become extremely small when recent prices are tightly clustered.
In very low-dispersion conditions, this may place the raw bands extremely close to the median.
That can create excessive sensitivity to minor price fluctuations.
The optional ATR Minimum Width provides a secondary floor.
The script calculates:
ATR Floor = ATR(ATR Length) × ATR Floor Multiplier
When enabled:
Band Width = max(MAD Width, ATR Floor)
This means MAD remains the primary volatility model, but the bands cannot contract below the selected ATR-based threshold.
Why use an ATR floor?
MAD and ATR measure different aspects of market behaviour.
MAD measures:
Dispersion of the selected source around its rolling median.
ATR measures:
Bar-to-bar trading range.
Gaps relative to the previous close.
A market can have:
Low median dispersion.
But still produce meaningful intrabar range.
The ATR floor can prevent the trail from becoming unrealistically tight under those conditions.
ATR floor disabled
With ATR Minimum Width disabled, the entire structural width comes from:
MAD × MAD Scale × Deviation Factor
This produces the purest MAD-based version of the indicator.
ATR Length
ATR Length controls the volatility horizon used only for the optional minimum-width calculation.
It does not affect:
The rolling median.
Raw MAD.
Scaled MAD.
Note that the visual glow and bloom later in the script use a fixed ATR(14), separate from this ATR Length input.
Trailing bands
The raw MAD bands are converted into one-sided trails.
This is the stage that turns a statistical envelope into a persistent trend system.
Two independent trails are maintained:
Lower Trail.
Upper Trail.
Lower Trail
When the previous trigger remains above the previous Lower Trail, the new Lower Trail is:
max(Current Raw Lower Band, Previous Lower Trail)
This means the Lower Trail can:
Move upward.
Remain unchanged.
But cannot move downward while the condition remains active.
This creates a ratcheting support structure.
If the trigger falls below the prior Lower Trail, the trail is allowed to reset to the new raw lower band.
Upper Trail
When the previous trigger remains below the previous Upper Trail, the new Upper Trail is:
min(Current Raw Upper Band, Previous Upper Trail)
This means the Upper Trail can:
Move downward.
Remain unchanged.
But cannot move upward while the condition remains active.
This creates a ratcheting resistance structure.
If the trigger rises above the previous Upper Trail, the band can reset to the current raw upper value.
Why trailing the bands matters
A raw median-deviation envelope moves in both directions.
If those raw bands were used directly for trend changes:
The threshold itself could retreat toward price.
Small changes in median or MAD could produce unstable reversals.
The one-sided trail introduces hysteresis .
Hysteresis means that once a trend regime is established, the threshold required to reverse it remains on the opposing side.
This reduces the tendency to flip repeatedly around the rolling median.
Flip Trigger
The user can choose which series is used when evaluating trail breaks:
Close.
Source.
Close
Uses the candle close regardless of which series is used for the MAD calculation.
This is the conventional option.
Source
Uses the selected Source input.
For example, if HLC3 is selected as the Source:
The median is calculated from HLC3.
MAD is calculated from HLC3.
The trail can also be triggered by HLC3.
This keeps the center, dispersion and reversal trigger based on the same source.
Initial trend state
The trend begins in a neutral state.
Once a valid rolling median is available:
Trigger at or above Median = bullish initialization.
Trigger below Median = bearish initialization.
This initial assignment is not treated as a bullish or bearish flip.
Flip signals occur only after the indicator has already established one regime and later transitions into the opposite regime.
Bullish flip
A bullish regime change requires:
Trigger to move above the Upper Trail.
Current trend not already bullish.
Optional bullish median-slope confirmation to pass.
Once confirmed:
Trend becomes bullish.
The Lower Trail becomes the active trend boundary.
A bullish signal can be displayed.
Bearish flip
A bearish regime change requires:
Trigger to move below the Lower Trail.
Current trend not already bearish.
Optional bearish median-slope confirmation to pass.
Once confirmed:
Trend becomes bearish.
The Upper Trail becomes the active boundary.
A bearish signal can be displayed.
Active trend trail
The final displayed trend boundary depends on the regime:
Bullish = Lower Trail.
Bearish = Upper Trail.
This means the line automatically moves to the opposite side of price when a complete regime change occurs.
Median Slope Confirmation
The optional Median Slope Confirmation adds a directional requirement to trend reversals.
For a bullish flip:
Current Median > Median from Slope Lookback bars ago
For a bearish flip:
Current Median < Median from Slope Lookback bars ago
This requires the robust statistical center itself to move in the direction of the proposed new trend.
Why confirm with median slope?
Price can briefly cross a trail while the underlying median remains flat or continues moving in the opposite direction.
Slope confirmation can reject some of these events.
For example:
A bullish trail break with a still-falling median may represent:
A temporary rebound.
A liquidity sweep.
Noise inside a larger bearish structure.
Requiring the median to rise adds another layer of confirmation.
The trade-off is lag.
A genuine reversal may cross the trail before the rolling median has clearly changed direction.
Slope Lookback
Slope Lookback controls how far back the median is compared.
Lower values:
Respond more quickly.
Require only a very local median turn.
Higher values:
Require a broader directional shift.
Produce stronger confirmation.
Can delay reversals.
This same lookback is also used in the visual slope-strength calculation even when slope confirmation itself is disabled.
Break Trail On Flips
When enabled, the displayed trail is temporarily hidden on the actual regime-flip bar.
This creates a visual break between:
The previous regime’s trail.
The new regime’s trail.
Without the break, the plotting engine can draw a connecting segment from one side of the market to the other.
That connection has no analytical meaning.
Break Trail On Flips affects visualization only.
It does not affect:
Trend state.
Raw bands.
Trail calculations.
Signals.
Robust trend structure
The complete structural model can therefore be summarized as:
Rolling Median determines robust center.
MAD determines robust dispersion.
MAD Scale calibrates the dispersion.
Deviation Factor determines band distance.
Optional ATR floor prevents excessive compression.
Raw bands form the initial envelope.
Ratchet logic creates trailing support and resistance.
Opposite-trail breaks determine regime changes.
Optional median slope confirms those reversals.
This combination is what separates the indicator from simply plotting median ± MAD.
Visual strength model
The script calculates a separate Trend Strength value used only to control the presentation of the gradient and glow.
It does not alter:
Trend direction.
Trail levels.
Flip conditions.
Trend Strength combines:
Price distance from the active trail.
Absolute rolling-median slope.
Distance Strength
The script first measures:
Trail Distance = |Close - Active Trail|
This is normalized by the current band width.
The normalized distance is capped when price reaches twice the active band width away from the trail.
Conceptually:
Close to trail = low distance strength.
Far from trail = high distance strength.
This reflects how separated price is from the current structural boundary.
Slope Strength
The indicator also measures:
|Current Median - Median |
This value is normalized by the current band width and capped at one.
The purpose is to compare median movement against the current statistical width.
A steep median relative to the band width produces stronger visual slope strength.
Combined Trend Strength
The final visual strength is:
70% Distance Strength.
30% Median Slope Strength.
and is capped at one.
The distance component receives greater weight because the visual system places more emphasis on how strongly price is separated from the active trail.
Again, this number is not a probability, forecast or additional signal.
It is a visual intensity measure.
Layered gradient
The area between the active trail and current close is divided into several intermediate levels.
The script creates reference points approximately:
15% of the distance from trail to price.
35%.
60%.
82%.
Then the final segment to price.
These create five layered gradient regions.
The layers become progressively more transparent as they move away from the trail.
This gives the trail visual depth without turning the entire area between price and structure into one solid block.
Gradient direction
The geometry of the gradient is determined by whether close is above or below the active trail.
The colour itself comes from the current bullish or bearish trend regime.
The gradient therefore visualizes:
The active trend colour.
The distance between price and trail.
The relative strength of the trend visualization.
The gradient does not determine the regime.
Trend-strength gradient response
Higher Trend Strength reduces transparency in several layers.
This makes the ribbon more visible when:
Price is strongly separated from the trail.
The rolling median is moving meaningfully.
Lower strength produces a softer appearance.
This allows the visual presentation to communicate more than simple bullish or bearish state.
Flip bloom
The indicator includes a temporary post-flip bloom.
The bloom is derived from the number of bars elapsed since the most recent bullish or bearish transition.
Importantly, in the current implementation the bloom begins after the flip bar:
Flip bar: no bloom boost.
1 bar after flip: maximum bloom.
2 bars after flip: reduced bloom.
3 bars after flip: smaller residual bloom.
Afterward: bloom disappears.
The relative bloom strengths are:
1.00
0.55
0.25
This emphasizes the early bars following a newly confirmed regime change.
Why bloom after the flip?
The flip itself can optionally contain a break in the trail.
Applying the bloom to the following bars emphasizes the newly established active trail rather than drawing a large effect around a temporarily hidden flip point.
The bloom is cosmetic.
It does not modify the underlying calculations.
Trail glow
The active trail can also display a persistent glow.
Glow width is based on:
ATR(14) × a factor that increases with Trend Strength
This ATR(14) is fixed for visualization and is independent of the user-selected ATR Length used by the optional minimum-width floor.
The glow therefore becomes slightly wider as visual trend strength increases.
Two layers are used:
A tighter inner glow.
A broader outer glow.
The inner glow responds more strongly to Trend Strength and post-flip bloom.
Rolling Median display
The rolling median can be displayed independently from the trail.
This is useful for studying the difference between:
The current robust center.
The statistical raw bands.
The ratcheting trend trail.
During a bullish regime, the active Lower Trail can remain below the rolling median.
During a bearish regime, the active Upper Trail can remain above it.
The median is not itself the trend signal.
Raw MAD Bands display
The raw upper and lower MAD bands can also be shown.
These lines make it easier to see how the trailing logic differs from the unrestricted statistical envelope.
Raw bands:
Can move in either direction.
Trailing bands:
Can ratchet in only one direction while their persistence condition remains active.
The gap between raw and trailing levels illustrates the hysteresis introduced by the trend logic.
Trend candles
The script can redraw candles on the main chart using the active trend colour.
Bullish regime:
Uses the selected Bullish colour.
Bearish regime:
Uses the selected Bearish colour.
The candle colour represents the persistent trail regime, not whether each individual candle closed higher or lower.
A bearish candle can therefore remain bullish-coloured while the broader MAD Trail regime remains bullish.
Signal markers
Bullish and bearish markers appear only on complete transitions between established regimes.
A bullish marker requires:
Previous trend = bearish.
Current trend = bullish.
A bearish marker requires:
Previous trend = bullish.
Current trend = bearish.
Initial trend assignment does not generate a flip marker.
How to interpret the indicator
Bullish regime
A bullish state means price has previously broken above the opposing Upper Trail and the Lower Trail is now active.
The Lower Trail can be interpreted as:
Dynamic trend support.
A structural invalidation reference.
A trailing regime boundary.
Bearish regime
A bearish state means price has broken below the opposing Lower Trail and the Upper Trail is active.
The Upper Trail can be interpreted as:
Dynamic resistance.
A bearish invalidation reference.
A trailing regime boundary.
Price close to trail
When price approaches the active trail:
Visual distance strength decreases.
The gradient becomes softer.
The market is closer to the regime boundary.
This does not guarantee a reversal.
A healthy trend can repeatedly retest its active trail.
Price far from trail
When price moves substantially away:
Distance Strength rises.
The visual effect becomes stronger.
This indicates greater separation from the active structural boundary.
It should not automatically be interpreted as a better entry.
A market can be strongly extended and simultaneously close to exhaustion.
Median and trail rising together
During a bullish regime, a rising median combined with a rising Lower Trail indicates:
The robust center is moving upward.
The structural support boundary is also advancing.
This represents cleaner directional alignment.
Median flattening while trail remains bullish
The persistent regime can remain bullish while the median begins flattening.
This indicates:
The trend has not yet been invalidated.
But the robust center is no longer advancing as strongly.
The visual slope-strength component may weaken under this condition.
Raw band expansion
If MAD increases:
Raw bands widen.
Trail reset levels can move farther away.
This means recent source values are becoming more dispersed around the median.
Raw band contraction
If MAD falls:
The raw envelope tightens.
If the ATR floor is disabled, the structure can become substantially narrower.
If the ATR floor is enabled, contraction stops once the selected minimum width is reached.
How to use the indicator
1. Trend regime filter
Use the persistent trail state as directional context:
Bullish trail regime = prioritize long-side setups.
Bearish trail regime = prioritize short-side setups.
The trail does not define a complete trading system by itself.
2. Pullback structure
During a bullish regime, the Lower Trail can provide a dynamic reference for deeper pullbacks.
During a bearish regime, the Upper Trail can provide a reference for rallies.
The farther price moves from the trail, the greater the current structural separation.
3. Regime transitions
Bullish and bearish flips identify moments when price has crossed completely through the opposing robust-deviation trail.
These may be used as:
Trend-change alerts.
Confirmation for another entry method.
Potential exit conditions.
4. Median confirmation
Users who want more selective signals can enable Median Slope Confirmation.
This can be especially useful when:
The market is choppy.
Price frequently sweeps through statistical boundaries.
5. Pure robust-volatility mode
Disable the ATR Minimum Width to make band width depend only on:
Rolling MAD.
MAD Scale.
Deviation Factor.
This produces the purest version of the model.
6. Hybrid robust-volatility mode
Enable ATR Minimum Width when the MAD channel becomes too narrow for the instrument or timeframe.
This preserves MAD as the primary engine while adding a conventional range-based safety floor.
Input guide
Source
Series used for the rolling median and MAD calculation.
MAD Lookback
Controls the number of observations used for the rolling median and dispersion estimate.
Shorter values adapt faster.
Longer values create a broader and more stable distribution.
MAD Scale
Multiplier applied directly to raw MAD.
The commonly cited normal-distribution consistency factor is approximately 1.4826; the script default is 1.4655.
Deviation Factor
Controls the final width of the MAD envelope.
ATR Minimum Width
Prevents the active band width from falling below an ATR-derived floor.
ATR Length
Controls the ATR used by the optional floor.
ATR Floor
Controls the minimum width as a multiple of ATR.
Median Slope Confirmation
Requires the rolling median to move in the direction of a proposed trend flip.
Slope Lookback
Controls how far back the current median is compared.
It also influences the visual slope-strength calculation.
Flip Trigger
Selects Close or Source for trail-break detection.
Break Trail On Flips
Creates a visual discontinuity on transition bars.
How this differs from a standard Supertrend
A conventional Supertrend generally uses:
A price midpoint such as HL2.
ATR as the full band-width model.
MAD Volatility Trail instead uses:
Rolling median as its center.
Median Absolute Deviation as its primary width.
ATR only as an optional minimum floor.
The trail mechanics are conceptually related, but the statistical foundation is different.
How this differs from Bollinger Bands
Bollinger Bands normally use:
A moving average.
Standard deviation.
Symmetrical raw bands.
MAD Volatility Trail uses:
Rolling median.
Median Absolute Deviation.
One-sided trailing bands.
Persistent trend-state logic.
Bollinger Bands are primarily a statistical envelope.
MAD Volatility Trail converts its robust statistical envelope into a trend-regime system.
How this differs from median ± MAD alone
A simple median-MAD indicator would plot:
Median.
Median + MAD width.
Median - MAD width.
Those bands would move freely.
This indicator adds:
Ratchet logic.
Persistent bullish/bearish state.
Opposite-trail break conditions.
Optional median-slope confirmation.
Signals and alerts.
The raw statistical model is therefore only the first stage.
MAD versus ATR
ATR measures the size of trading ranges.
MAD measures dispersion of the selected source around its median.
They can behave very differently.
For example:
A volatile but mean-reverting market can have large ATR with relatively controlled median dispersion.
A persistent directional displacement can produce increasing MAD even if individual candle ranges are moderate.
The optional floor allows both concepts to coexist without replacing the MAD foundation.
Robust statistics and financial markets
Financial return and price distributions frequently contain:
Outliers.
Large jumps.
Skew.
Fat tails.
Mean-and-standard-deviation models remain extremely useful, but robust alternatives can provide different information when unusual observations are present.
Median and MAD belong to a family of robust statistical tools designed to reduce sensitivity to extreme sample values.
This does not make the resulting indicator immune to market shocks.
If enough of the rolling window moves, the median and MAD will also move.
The advantage is primarily that one isolated observation has less influence.
Strengths
Uses an exact rolling median.
Uses exact Median Absolute Deviation rather than an approximation.
More resistant to isolated outliers than mean/standard-deviation envelopes.
Provides a configurable MAD scale.
Supports a pure MAD or MAD-plus-ATR hybrid width.
Converts robust statistics into persistent trend boundaries.
Uses one-sided trail logic to reduce rapid regime switching.
Provides optional median-direction confirmation.
Separates signal logic from visual strength.
Includes dynamic gradient, glow and post-flip visualization.
Exposes raw MAD, scaled MAD, active band width and Trend Strength in the Data Window.
Limitations
The indicator is reactive rather than predictive.
Robust statistics do not eliminate whipsaws.
A very short MAD Lookback can still react sharply.
A very long lookback can delay adaptation to new regimes.
Median calculations can remain unchanged across several bars and then move discretely as the rolling sample changes.
Higher Deviation Factors reduce reversals but increase confirmation lag.
The ATR floor changes the model from pure MAD dispersion to a hybrid MAD/ATR structure.
Median Slope Confirmation can reject false breaks but also delay genuine reversals.
Extreme readings in the visual-strength system are not probabilities of continuation.
Glow and bloom are cosmetic and should not be treated as separate signals.
Computational considerations
Unlike many moving averages, the exact rolling median and MAD calculations require the script to build and process the values inside the selected window.
For each bar:
The rolling source sample is collected.
Its median is calculated.
Absolute deviations from that median are calculated.
A second median is calculated from those deviations.
Larger MAD Lookbacks therefore require more work than a simple recursive EMA or ATR calculation.
This is the cost of calculating the robust statistics directly.
Causality and live-bar behaviour
The indicator uses current and historical values without intentional future-looking references.
On completed historical bars, the model is causal.
On a live unfinished bar:
The Source can change.
The current rolling median can change.
MAD can change.
Raw bands can change.
A trail break can appear or disappear.
Users who require confirmed regime changes should evaluate signals at bar close.
Data Window
The indicator exposes four useful diagnostic values.
Raw MAD
The unscaled median absolute deviation.
Scaled MAD
Raw MAD multiplied by the selected MAD Scale.
Active Band Width
The actual band width after:
MAD scaling.
Deviation Factor.
Optional ATR minimum floor.
Trend Strength
The visual-strength score expressed from approximately 0 to 100.
This is calculated from trail distance and median movement.
It is not part of the trend-flip logic.
Alerts
The indicator includes:
MAD Trail Bullish: established bearish regime changes to bullish.
MAD Trail Bearish: established bullish regime changes to bearish.
MAD Trail Flip: either regime transition occurs.
Summary
MAD Volatility Trail builds a trend-following regime from robust statistics.
The calculation begins with an exact rolling median of the selected Source.
Rather than measuring dispersion with standard deviation, the script calculates the Median Absolute Deviation:
MAD = Median(|X - Median(X)|)
The raw MAD is scaled and multiplied by a configurable Deviation Factor to create the statistical width around the rolling median.
The resulting raw upper and lower bands are:
Median + Band Width.
Median - Band Width.
An optional ATR minimum floor prevents these bands from becoming excessively narrow during low-dispersion conditions.
The raw envelope is then transformed into one-sided trailing boundaries.
The Lower Trail can ratchet upward while price remains above it, while the Upper Trail can ratchet downward while price remains below it.
These trails create hysteresis and form the actual regime-switching structure.
A bearish regime turns bullish only when the selected trigger breaks above the opposing Upper Trail, optionally while the rolling median itself is rising.
A bullish regime turns bearish only when the trigger breaks below the Lower Trail, optionally while the median is falling.
The active Lower Trail is displayed during bullish regimes and the active Upper Trail during bearish regimes.
A separate visual-strength model measures price-to-trail distance and median slope relative to the active band width. That score controls gradient and glow intensity but does not alter signals.
The result is a robust alternative to conventional mean-, standard-deviation- and ATR-centered trend trails.
Rather than allowing individual extreme prices to dominate its statistical center and dispersion estimate, MAD Volatility Trail uses the median twice: once to define the center of the distribution and again to define the typical absolute distance from that center.
This creates a trend framework designed around robust location, robust dispersion and persistent trailing structure .
อินดิเคเตอร์
Adaptive T3 Hull [BackQuant]Adaptive T3 Hull
Overview
Adaptive T3 Hull is a configurable trend-following overlay that combines the lag-compensation structure of a Hull-style moving average with T3 smoothing and several optional mechanisms designed specifically to control overshoot, hooks and oscillating tails.
A conventional Hull construction gains responsiveness by comparing a faster and slower smoother, extrapolating their difference, and then smoothing the result again. This can produce a very responsive trend estimate, but the same lag compensation responsible for that responsiveness can also create exaggerated curvature around sharp reversals.
Adaptive T3 Hull makes that trade-off directly controllable.
The indicator replaces the traditional weighted-moving-average Hull stages with T3 smoothers and expands the basic Hull architecture with:
Adjustable fast/slow length relationships.
Adjustable Hull lag compensation.
Configurable final smoothing geometry.
Curvature-sensitive tail damping.
Optional asymmetric damping around turns.
An adaptive T3 volume factor.
An optional ATR-based velocity limiter.
Optional final lag compensation.
Trend-strength-dependent ribbon intensity.
Tail and curvature diagnostics in the Data Window.
The result is not intended to reproduce a standard HMA exactly. It is a generalized Hull-style framework in which the user can explicitly control the balance between responsiveness, smoothness and overshoot.
Core idea
Most trend smoothers face the same fundamental compromise:
More smoothing reduces noise but increases lag.
More lag compensation improves responsiveness but can create overshoot.
The Hull concept addresses lag by comparing a fast smoother with a slower smoother and projecting the difference forward.
A generalized form can be written as:
Hull Raw = Fast + Compensation × (Fast - Slow)
If Compensation is zero:
Hull Raw = Fast
No additional lag compensation is applied.
If Compensation is one:
Hull Raw = 2 × Fast - Slow
This reproduces the familiar compensation structure used in the standard Hull Moving Average.
Values between zero and one provide partial compensation.
Adaptive T3 Hull defaults to a substantially smaller compensation value. This is deliberate. It reduces the tendency for the projected line to extend beyond the fast smoother during sharp changes in direction.
The remaining responsiveness can then be controlled using the fast-length ratio, T3 characteristics and optional final generalization rather than relying entirely on aggressive Hull extrapolation.
Processing chain
The complete indicator can be understood as the following sequence:
Select the source and main Hull Length.
Derive a fast T3 length from the Fast Length Ratio.
Derive a final smoothing length from a configurable power-law relationship.
Calculate fast and slow T3 smoothers.
Measure velocity and curvature of the fast T3.
Normalize curvature using ATR.
Optionally reduce the active T3 Volume Factor during high curvature.
Recalculate the fast and slow T3 legs with the adaptive factor.
Measure the active curvature state.
Optionally reduce Hull compensation when curvature increases.
Construct the compensated fast-minus-slow T3 Hull.
Smooth that result through another T3 stage.
Optionally apply a final generalized lag-compensation stage.
Optionally limit extreme one-bar movement using ATR.
Determine trend from the final line slope.
Build a smoothed one-bar-offset ribbon around the result.
Each stage affects a different part of the lag-versus-overshoot problem.
T3 smoothing
The T3 is a multi-stage recursive smoother constructed from a sequence of exponential moving averages.
The script calculates six EMA stages:
E1 = EMA(Source)
E2 = EMA(E1)
E3 = EMA(E2)
E4 = EMA(E3)
E5 = EMA(E4)
E6 = EMA(E5)
Those stages are then combined using coefficients derived from the T3 Volume Factor.
The final T3 has the general form:
T3 = C1×E6 + C2×E5 + C3×E4 + C4×E3
where C1 through C4 change with the Volume Factor.
This construction allows T3 smoothing to maintain substantial smoothness while using coefficient-based compensation to reduce some of the lag created by repeated EMA filtering.
Important: T3 Volume Factor does not use trading volume
Despite its name, the T3 Volume Factor is not calculated from market volume.
It is a coefficient controlling the internal T3 response.
Changing it does not incorporate:
Exchange volume.
Volume profile.
OBV.
Money flow.
It changes how aggressively the internal EMA stages are combined.
Higher values generally increase compensation and responsiveness, but can also increase overshoot.
Lower values generally produce a more restrained and smoother response.
This relationship is particularly important in this indicator because Hull compensation and T3 compensation can interact.
An aggressive T3 followed by aggressive Hull extrapolation can produce substantially more tail behaviour than either technique alone.
Why combine T3 and Hull logic?
Hull-style smoothing and T3 smoothing approach lag reduction differently.
The Hull architecture uses:
A fast smoother.
A slow smoother.
The difference between them.
A final smoothing stage.
T3 uses:
Multiple recursive EMA stages.
A coefficient-controlled combination of those stages.
Adaptive T3 Hull combines both ideas.
Instead of:
Fast WMA.
Slow WMA.
Final WMA.
the indicator uses:
Fast T3.
Slow T3.
Compensated difference.
Final T3.
This produces a smoother underlying structure while retaining the ability to compensate for lag.
However, combining two lag-reduction mechanisms also makes overshoot control more important. Much of the indicator is therefore devoted to regulating that compensation dynamically.
Hull Length
Hull Length establishes the main smoothing horizon.
It is used to derive:
The slow T3 length.
The fast T3 length.
The final smoothing length.
Lower values:
React more quickly.
Track shorter trend changes.
Increase sensitivity to local curvature.
Can generate more frequent directional flips.
Higher values:
Produce broader trend estimates.
Reduce short-term variation.
Increase response delay.
Generally produce more persistent regimes.
Unlike a standard HMA, the relationship between these three smoothing stages is not fixed.
Fast Length Ratio
The fast T3 length is calculated as:
Fast Length = Hull Length × Fast Length Ratio
with the result rounded to a valid integer.
In a conventional Hull structure, the fast stage normally uses approximately half the main length.
Therefore:
Fast Length Ratio = 0.50
reproduces the familiar half-length relationship.
The default configuration uses a larger ratio, making the fast leg closer in length to the slow leg.
This matters because the difference:
Fast T3 - Slow T3
is the quantity used for lag compensation.
If the fast and slow stages are very different:
Their separation can become larger.
Hull compensation becomes stronger.
The resulting line can react faster.
Overshoot potential increases.
If their lengths are closer:
Their separation becomes smaller.
The compensation term becomes more restrained.
The final line generally becomes smoother.
Fast Length Ratio is therefore another direct control over the aggressiveness of the Hull projection.
Hull Compensation
Hull Compensation controls how much of the fast-versus-slow difference is added back to the fast T3.
The underlying formula is:
Hull Raw = Fast T3 + Effective Compensation × (Fast T3 - Slow T3)
Before adaptive damping is applied, Effective Compensation begins from the Hull Compensation input.
Compensation = 0
The raw line becomes the fast T3 itself.
No Hull-style extrapolation occurs.
Compensation = 1
The calculation becomes:
2 × Fast T3 - Slow T3
which matches the standard Hull lag-compensation form.
Compensation between 0 and 1
Only part of the fast-slow separation is extrapolated.
This creates a middle ground between:
Pure fast smoothing.
Full Hull compensation.
Compensation above 1
The difference is extrapolated even more aggressively than a conventional Hull construction.
This can create a highly responsive line, but it also increases the likelihood of:
Overshoot.
Hooks.
Large tails after sharp turns.
The default is intentionally conservative relative to a standard Hull.
What are Hull tails?
Hull-style moving averages can develop a distinctive oscillating or hooked appearance around strong reversals.
This occurs because the lag-compensation term is effectively extrapolating the difference between two smoothers.
Imagine the fast smoother accelerating upward while the slow smoother is still catching up.
The difference:
Fast - Slow
becomes positive.
Adding that difference to the fast smoother projects the result even further upward.
When price abruptly reverses, the fast smoother begins turning first while the slow smoother remains elevated.
The compensation term can then change rapidly and cause the completed Hull to:
Extend beyond the fast line.
Hook sharply.
Reverse with excessive curvature.
This is not necessarily an error in the Hull formula. It is a consequence of aggressive lag compensation.
Adaptive T3 Hull includes several independent tools for reducing this behaviour.
Final Hull smoothing
After the fast and slow T3 legs are combined, the raw Hull is smoothed again.
The final smoothing length is calculated from:
Length^Hull Smoothing Exponent × Final Smoothing Multiplier
This generalizes the standard Hull square-root stage.
A conventional HMA normally uses approximately:
sqrt(Length)
which is equivalent to:
Length^0.50
before rounding.
Hull Smoothing Exponent
The Hull Smoothing Exponent controls how strongly the final smoothing length grows as the main Hull Length increases.
Exponent = 0.50
Reproduces the square-root relationship used in the conventional Hull construction.
Exponent below 0.50
Produces a shorter final smoothing stage, particularly at larger main lengths.
This generally:
Increases responsiveness.
Allows more of the compensated movement through.
Exponent above 0.50
Creates a longer final smoothing stage.
This generally:
Reduces local variation.
Smooths more aggressively.
Adds response delay.
The script allows this relationship to be generalized instead of forcing the standard square-root rule.
Final Smoothing
Final Smoothing applies an additional multiplier to the derived root length:
Final Length = Length^Exponent × Root Multiplier
This gives a second level of control over the final stage without changing the underlying power-law relationship.
Higher values:
Increase final smoothing.
Reduce local hooks.
Slow the line.
Lower values:
Decrease final smoothing.
Increase responsiveness.
Allow more short-term curvature through.
The Smoothing Exponent controls how smoothing scales with Hull Length.
The Final Smoothing multiplier controls the overall magnitude of that final stage.
Curvature measurement
Adaptive tail damping requires a way to determine when the fast T3 is changing direction unusually quickly.
The indicator first calculates velocity:
Velocity = Fast T3 - Previous Fast T3
Previous velocity is:
Previous Velocity = Previous Fast T3 - Fast T3 two bars ago
Curvature is then approximated as the absolute change in velocity:
Curvature = |Velocity - Previous Velocity|
This is a discrete second-difference concept.
Velocity describes how quickly the smoother is moving.
Curvature describes how quickly that velocity itself is changing.
For example:
A steadily rising line can have positive velocity but low curvature.
A line suddenly flattening after a strong rise can have high curvature.
A sharp reversal can produce very high curvature.
This makes curvature particularly useful for detecting the conditions in which Hull overshoot tends to appear.
ATR normalization
Raw curvature is not directly comparable across instruments.
A $10 curvature movement is enormous for one market and negligible for another.
The script therefore normalizes curvature using ATR:
Normalized Curvature = Curvature / ATR
The result is capped at 1.
This creates an adaptive pressure measure between approximately:
0 = little curvature relative to recent range.
1 = very large curvature relative to recent range.
ATR is calculated using the Damping Normalization length.
This normalized curvature drives several optional adaptive mechanisms.
Damping Normalization
Damping Normalization controls the ATR period used when converting curvature into a relative value.
Short values:
Make the normalization respond rapidly to current volatility.
Allow damping pressure to change quickly.
Longer values:
Create a more stable volatility baseline.
Reduce rapid changes in normalized curvature.
This setting does not smooth the final T3 Hull directly.
It changes how the adaptive systems interpret curvature.
Adaptive Tail Damping
Adaptive Tail Damping dynamically reduces Hull Compensation when curvature becomes large.
The process can be summarized as:
Effective Compensation = Hull Compensation × (1 - Damping Pressure × Damping Strength)
When curvature is low:
Damping Pressure approaches zero.
Effective Compensation remains close to the selected Hull Compensation.
When curvature becomes large:
Damping Pressure increases.
Effective Compensation is reduced.
This means the indicator deliberately removes some of its lag compensation precisely when the fast T3 is bending sharply.
Why reduce compensation during curvature?
Hull compensation is most useful when the fast and slow smoothers are moving consistently in the same directional structure.
During a smooth trend:
The fast line leads the slow line.
Their separation can be used to reduce lag.
During a sharp turn:
The fast line may reverse before the slow line.
Their separation can become a poor estimate of useful forward compensation.
Extrapolating the full difference can create overshoot.
Adaptive damping therefore treats high curvature as a reason to trust the Hull extrapolation less.
Damping Strength
Damping Strength determines how much curvature can reduce Hull compensation.
At zero:
Curvature has no effect on compensation.
As the value increases:
High-curvature events remove progressively more compensation.
The line becomes more restrained around sharp turns.
At a Damping Strength of 1 and maximum normalized curvature, compensation can theoretically be reduced all the way toward zero.
This does not stop the underlying T3 from moving.
It removes the additional Hull extrapolation.
Asymmetric Turn Damping
By default, curvature damping can apply whenever the fast T3 experiences significant curvature.
Asymmetric Turn Damping makes the condition more selective.
When enabled, damping pressure is only applied when the current velocity is moving against the previous directional pace.
Conceptually:
A previously rising fast T3 is damped when its upward velocity begins weakening or reversing.
A previously falling fast T3 is damped when its downward velocity begins weakening or reversing.
This allows strong acceleration in the existing direction to retain more compensation while focusing the damping mechanism around deceleration and turning behaviour.
The purpose is to distinguish:
Curvature caused by trend acceleration.
Curvature caused by trend exhaustion or reversal.
This can preserve responsiveness during strong continuation while still suppressing tails around turns.
Adaptive T3 Volume Factor
Adaptive T3 Volume Factor provides a second curvature-sensitive damping mechanism.
Instead of changing the Hull compensation, this feature changes the internal T3 coefficient itself.
The active factor is approximately:
Active VF = Base VF × (1 - Normalized Curvature × VF Damping Strength)
subject to the configured minimum.
When curvature is low:
Active VF remains near the selected T3 Volume Factor.
When curvature rises:
Active VF is reduced.
The T3 becomes less aggressively compensated.
This attacks overshoot earlier in the processing chain.
Hull damping versus VF damping
The two mechanisms affect different stages.
Adaptive Tail Damping
changes how much:
Fast T3 - Slow T3
is extrapolated.
Adaptive T3 Volume Factor
changes how the T3 smoothers themselves are constructed.
Using both means curvature can reduce:
The aggressiveness of each T3 leg.
The aggressiveness of the Hull compensation between those legs.
This can strongly suppress tails but may also reduce responsiveness.
The controls are therefore optional and independently adjustable.
VF Damping Strength
VF Damping Strength controls how strongly curvature reduces the T3 Volume Factor.
Higher values:
Produce larger reductions during sharp curvature.
Increase smoothing around turns.
Can reduce T3 overshoot more aggressively.
Lower values:
Keep Active VF closer to the base setting.
Preserve more of the original T3 response.
Minimum VF
Minimum VF prevents the adaptive mechanism from reducing the active coefficient indefinitely.
It defines the lower bound used when Adaptive T3 Volume Factor is active.
This keeps the filter within a controlled response range during extreme curvature.
If the selected base Volume Factor is already below the requested minimum, the script does not force it upward above the base value.
Generalize Final Hull
Generalize Final Hull adds another optional lag-compensation stage after the main T3 Hull has already been completed.
A second smoothed version of the completed Hull is calculated.
The final target then becomes:
Hull Target = Hull Base + Generalization × (Hull Base - Second Hull)
This uses the same broad idea as Hull compensation:
Compare a faster estimate with a slower version.
Add part of their difference back to the faster estimate.
At zero Generalization:
The stage has no effect.
As Generalization increases:
The final result becomes more responsive.
Lag is reduced further.
Overshoot potential increases.
This option exists because the earlier tail controls allow the user to reduce aggressive compensation in the main Hull construction and, if desired, reintroduce a smaller amount of controlled responsiveness at the end.
Generalization
Generalization controls the amount of final compensation.
Lower values create subtle lag reduction.
Higher values increasingly extrapolate the difference between the first and second completed Hull smoothers.
This feature should be considered one of the more aggressive responsiveness controls in the indicator.
If the objective is maximum tail suppression, it can be left disabled.
Velocity Limiter
The Velocity Limiter addresses a different problem.
Curvature damping changes how the line is calculated.
The Velocity Limiter places a direct cap on how far the completed line is allowed to move in one bar.
The maximum permitted movement is:
Maximum Step = ATR × Max ATR / Bar
The desired change is:
Delta = Hull Target - Previous T3 Hull
That change is clamped between:
-Maximum Step
+Maximum Step
The final T3 Hull then advances by only the permitted amount.
Why use a velocity limiter?
Occasionally, a large price shock or a combination of aggressive settings can cause the completed Hull target to jump sharply.
The limiter acts as a final mechanical speed limit.
It can reduce:
Single-bar jumps.
Extreme hooks.
Shock-driven movement.
However, this comes with a clear trade-off.
If the market genuinely reprices very quickly, the limiter deliberately prevents the trend line from following the full move immediately.
It therefore introduces controlled lag.
Max ATR / Bar
This setting determines the maximum permitted single-bar movement in ATR units.
For example:
0.35 allows the completed line to move by no more than 0.35 ATR in one bar.
Lower values:
Create stronger movement suppression.
Produce smoother transitions.
Can significantly delay response to genuine breaks.
Higher values:
Interfere less often.
Allow larger legitimate moves.
The limiter is disabled by default because it is a strong constraint.
How the tail controls work together
The script provides several different ways to reduce tail behaviour because overshoot can originate at multiple stages.
Fast Length Ratio
Reduces fast-versus-slow separation.
Hull Compensation
Directly controls extrapolation of that separation.
Final Smoothing
Smooths the compensated output more heavily.
Adaptive Tail Damping
Reduces Hull compensation during curvature.
Asymmetric Turn Damping
Restricts that damping mainly to deceleration and turning behaviour.
Adaptive T3 Volume Factor
Makes the underlying T3 calculations more conservative during curvature.
Velocity Limiter
Caps the final single-bar movement.
Generalization
Moves in the opposite direction by optionally adding some final lag compensation back.
These controls are intentionally modular.
A user does not need to enable all of them.
Default design philosophy
The default settings intentionally do not reproduce a standard Hull Moving Average.
A standard Hull-like configuration would approximately use:
Fast Length Ratio near 0.50.
Hull Compensation near 1.00.
Hull Smoothing Exponent near 0.50.
Final Smoothing near 1.00.
The default Adaptive T3 Hull uses a much more restrained compensation structure.
This shifts the design away from maximum lag cancellation and toward smoother trend tracking with reduced tail behaviour.
The advanced controls then allow users to progressively move the model toward either:
More responsiveness.
More stability.
Trend determination
Trend direction is determined directly from the slope of the completed T3 Hull.
If:
Current T3 Hull > Previous T3 Hull
the direction becomes bullish.
If:
Current T3 Hull < Previous T3 Hull
the direction becomes bearish.
If the line is unchanged:
The previous state persists.
The trend does not depend on price crossing the line.
It depends on whether the adaptive T3 Hull itself is rising or falling.
Long and short signals
A long signal occurs when direction changes into the bullish state.
A short signal occurs when direction changes into the bearish state.
The markers therefore identify:
A change in slope regime.
They do not represent:
Guaranteed entries.
Price targets.
Stop levels.
Because the signal is based on local slope, more responsive configurations will naturally produce more flips during sideways conditions.
Ribbon construction
The optional band is not a conventional upper-and-lower volatility channel.
The main line is the current T3 Hull.
The secondary ribbon reference is calculated from a smoothed version of the previous-bar T3 Hull :
Ribbon Reference = WMA(T3 Hull , Band Smoothing)
The area between these two lines is filled with a gradient.
This creates visual separation between:
The current adaptive trend estimate.
A delayed and smoothed reference to its prior values.
The band therefore functions as a trend ribbon rather than a statistical volatility envelope.
Band Smoothing
Band Smoothing controls the WMA applied to the one-bar-offset Hull series.
Lower values:
Keep the ribbon reference close to the main line.
Produce a tighter band.
Respond quickly to direction changes.
Higher values:
Create a slower reference.
Widen the visual separation during sustained movement.
Create a smoother ribbon.
This input affects the visualization only.
It does not change:
The T3 Hull calculation.
Trend direction.
Signals.
Trend Strength
The indicator also calculates a normalized trend-velocity measure for visualization.
Raw strength is based on:
|Current T3 Hull - Previous T3 Hull| / ATR
and is multiplied by the Strength Sensitivity input.
The result is capped at 1 and then smoothed with an EMA.
This produces a normalized value from approximately:
0 = very little line movement relative to ATR.
1 = strong line movement relative to ATR.
This is a measure of trend-line velocity , not a statistical probability that the trend will continue.
Strength Smoothing
Strength Smoothing controls how quickly the visual strength estimate changes.
Lower values:
React quickly to acceleration and deceleration.
Create faster ribbon-intensity changes.
Higher values:
Produce steadier strength visualization.
Reduce flickering in the gradient.
It does not affect the underlying trend calculation.
Strength Sensitivity
Strength Sensitivity determines how quickly line velocity reaches the maximum normalized strength.
Higher values:
Cause smaller ATR-normalized movement to appear strong.
Increase gradient intensity more easily.
Lower values:
Require greater movement before maximum visual intensity is reached.
Strength-Weighted Gradient
When disabled, the ribbon uses a fixed gradient transparency.
When enabled, gradient intensity changes with Trend Strength.
As the T3 Hull moves more quickly relative to ATR:
The near portion of the ribbon becomes more visible.
The broader gradient becomes stronger.
When trend velocity is weak:
The ribbon becomes more subdued.
This is purely a visualization feature.
It does not alter:
Direction.
Signals.
Smoothing.
Tail damping.
Trend candles
The indicator can recolor the main chart candles according to the active T3 Hull slope state.
Bullish trend = selected Long Color.
Bearish trend = selected Short Color.
The candle colour describes the indicator regime, not the individual candle’s own open-to-close direction.
A bearish candle can therefore remain bullish-coloured while the T3 Hull is still rising.
Tail diagnostics
Several internal values are exposed in TradingView’s Data Window.
These provide insight into how the adaptive model is currently behaving.
Effective Hull Compensation
Shows the compensation actually being used after adaptive tail damping.
If adaptive damping is disabled:
It remains equal to Hull Compensation.
If damping is active:
It falls below the base value when curvature pressure increases.
This is useful for seeing when the indicator is automatically becoming more conservative.
Active T3 Volume Factor
Shows the T3 coefficient currently being used.
If Adaptive T3 Volume Factor is disabled:
It remains equal to the base Volume Factor.
When enabled:
It can decrease during high curvature.
Normalized Curvature
Shows the current curvature estimate after ATR normalization.
Values closer to 1 represent greater changes in fast-T3 velocity relative to recent range.
Trend Strength
Shows the smoothed normalized T3 Hull velocity as a percentage.
This is the same quantity used by the optional Strength-Weighted Gradient.
Tail Overshoot
The script also measures whether the final T3 Hull has extended beyond the fast T3 in the direction of the fast/slow separation.
An upper overshoot occurs when:
Fast T3 is above Slow T3.
Completed T3 Hull is above Fast T3.
A lower overshoot occurs when:
Fast T3 is below Slow T3.
Completed T3 Hull is below Fast T3.
When this happens, Tail Overshoot reports:
|T3 Hull - Fast T3| / ATR
This expresses the size of the overshoot in ATR units.
A value of zero means the completed Hull is not currently beyond the fast T3 under that definition.
This diagnostic is particularly useful when tuning:
Hull Compensation.
Damping Strength.
Fast Length Ratio.
Adaptive VF.
Final Smoothing.
Generalization.
How to interpret the indicator
Rising T3 Hull
A rising line indicates a bullish trend state.
The model’s completed combination of T3 smoothing, Hull compensation and any active damping controls is moving upward.
Falling T3 Hull
A falling line indicates a bearish trend state.
Smooth persistent slope
A stable slope with few direction changes generally indicates a cleaner trend environment for this style of filter.
Frequent colour changes
Rapid bullish/bearish transitions generally indicate:
Sideways price action.
A very responsive configuration.
Insufficient smoothing for the current market.
High normalized curvature
High curvature means the fast T3’s velocity is changing rapidly relative to ATR.
If adaptive controls are enabled, this is where:
Hull compensation may decrease.
T3 Volume Factor may decrease.
High tail overshoot
A larger Tail Overshoot value indicates the completed Hull has moved materially beyond the fast T3.
If the objective is a less tail-heavy line, possible adjustments include:
Reduce Hull Compensation.
Increase Final Smoothing.
Increase Fast Length Ratio.
Increase Damping Strength.
Enable Adaptive T3 Volume Factor.
Reduce or disable Generalization.
Enable the Velocity Limiter.
How to use the indicator
1. Trend regime filter
The most direct use is as a slope-based regime filter:
Rising T3 Hull = bullish trend state.
Falling T3 Hull = bearish trend state.
This can be combined with independent entry logic.
2. Trend transition signals
Long and short markers identify when the adaptive line changes slope direction.
These can be used as:
Regime-change alerts.
Confirmation for another setup.
Potential trailing-exit conditions.
They are not standalone guarantees of a sustained reversal.
3. Pullback reference
During a persistent trend, the T3 Hull can act as a smoothed directional reference.
Price returning toward the line while the line continues to slope in the original direction may represent a pullback within the existing regime.
4. Ribbon expansion
The distance between the current T3 Hull and its delayed WMA reference can visually highlight persistent movement.
A stronger ribbon separation can occur when the current adaptive trend estimate is moving away from its delayed historical reference.
5. Tail tuning
The Data Window diagnostics allow the indicator to be treated as a filter-design tool.
Users can observe:
When compensation is being damped.
How strongly curvature is elevated.
Whether the completed line is overshooting.
How the active T3 coefficient changes.
This can make parameter changes easier to understand than tuning solely by appearance.
Suggested tuning approaches
Smooth / reduced-tail configuration
For a calmer trend line:
Use lower Hull Compensation.
Use a larger Fast Length Ratio.
Increase Final Smoothing.
Enable Adaptive Tail Damping.
Use moderate or higher Damping Strength.
Leave Generalization disabled.
If strong shocks still create large movements:
Enable the Velocity Limiter.
Responsive configuration
For faster behaviour:
Reduce Fast Length Ratio toward the traditional half-length relationship.
Increase Hull Compensation.
Reduce Final Smoothing.
Reduce the Hull Smoothing Exponent.
Use a more aggressive T3 Volume Factor.
These changes generally increase overshoot risk.
Adaptive configuration
For responsiveness in normal conditions with additional protection near turns:
Use moderate Hull Compensation.
Enable Adaptive Tail Damping.
Enable Asymmetric Turn Damping.
Optionally enable Adaptive T3 Volume Factor.
This allows stronger compensation during smooth directional movement while automatically reducing it when the line begins to decelerate or turn.
Maximum tail-control configuration
For very aggressive tail suppression:
Low Hull Compensation.
Higher Final Smoothing.
Adaptive Tail Damping enabled.
Higher Damping Strength.
Adaptive T3 Volume Factor enabled.
Generalization disabled.
Velocity Limiter enabled.
This can create a very stable line, but the cost is additional lag.
How this differs from a standard Hull Moving Average
A conventional HMA normally uses:
WMA at half length.
WMA at full length.
2 × Fast - Slow lag compensation.
Final WMA around sqrt(Length).
Adaptive T3 Hull changes every major part of that architecture:
T3 replaces WMA.
Fast Length Ratio is configurable.
Hull Compensation is configurable.
The final smoothing exponent is configurable.
Final smoothing has an additional multiplier.
Compensation can adapt to curvature.
T3 behaviour can adapt to curvature.
Final movement can be ATR-limited.
An additional generalized compensation stage can be enabled.
It is therefore better understood as a generalized adaptive Hull framework than as a conventional HMA with a different smoothing length.
How this differs from a normal T3
A standard T3 produces one smoothed price estimate from repeated EMA stages and a fixed Volume Factor.
Adaptive T3 Hull uses multiple T3 calculations in a Hull-style structure:
Fast T3.
Slow T3.
Compensated fast-slow projection.
Final T3 smoothing.
It can also dynamically alter the T3 factor according to curvature.
The T3 is therefore a building block inside the larger trend model.
How this differs from simply smoothing an HMA
Applying an additional moving average to an HMA can reduce its tails, but it also adds lag after the overshoot has already occurred.
Adaptive T3 Hull attacks the problem at several earlier stages.
It can:
Reduce the fast-slow separation.
Reduce compensation itself.
Reduce compensation specifically around sharp turns.
Reduce the T3 factor during curvature.
Change the final Hull smoothing geometry.
Limit extreme final movement.
This provides more control than applying one additional smoothing layer to a completed HMA.
Parameter interaction
Many settings interact strongly.
Fast Ratio + Hull Compensation
A low Fast Ratio creates greater separation between fast and slow legs.
Combining that with high Hull Compensation can produce aggressive extrapolation.
Hull Compensation + Adaptive Damping
Hull Compensation defines the maximum starting compensation.
Adaptive damping determines how much of it survives during curvature.
T3 Volume Factor + Hull Compensation
Both can contribute to lag reduction.
High values in both stages may amplify overshoot.
Final Smoothing + Generalization
Final Smoothing adds lag and stability.
Generalization removes some of that lag again.
Using both allows the user to create a smooth base and then selectively reintroduce responsiveness.
Adaptive VF + Adaptive Hull Damping
Both respond to curvature but at different stages.
Enabling both can create strong protection around turns.
Velocity Limiter + all other controls
The Velocity Limiter is applied near the end of the pipeline.
It can therefore override an aggressive target generated by the preceding calculations.
Input guide
Source
Price series used by the complete indicator.
Hull Length
Primary calculation horizon.
T3 Volume Factor
Controls the internal T3 coefficient structure. It does not use trading volume.
Hull Compensation
Controls how much of the fast-minus-slow T3 separation is added to the fast T3.
Final Smoothing
Multiplies the final Hull smoothing length.
Adaptive Tail Damping
Reduces Hull Compensation during high curvature.
Damping Strength
Controls the amount of compensation reduction.
Damping Normalization
ATR horizon used to normalize curvature.
Fast Length Ratio
Controls the fast T3 length relative to the main Hull Length.
Hull Smoothing Exponent
Controls the power-law relationship used to derive the final smoothing length.
Asymmetric Turn Damping
Restricts curvature damping primarily to deceleration and turning behaviour.
Adaptive T3 Volume Factor
Reduces the T3 coefficient during high curvature.
VF Damping Strength
Controls how strongly curvature reduces the active T3 factor.
Minimum VF
Limits how far the adaptive T3 factor can be reduced.
Velocity Limiter
Caps final one-bar T3 Hull movement using ATR.
Max ATR / Bar
Defines the maximum movement allowed by the Velocity Limiter.
Generalize Final Hull
Enables an additional lag-compensation stage after the main T3 Hull.
Generalization
Controls the strength of that final compensation.
Strength-Weighted Gradient
Allows ribbon intensity to vary with normalized T3 Hull velocity.
Strength Smoothing
Smooths the visual trend-strength measure.
Sensitivity
Controls how quickly ATR-normalized movement reaches maximum visual strength.
Band Smoothing
Controls the delayed WMA reference used to build the ribbon.
Strengths
Combines T3 smoothing with a generalized Hull framework.
Directly exposes Hull lag compensation as a user control.
Provides multiple independent methods for reducing oscillating tails.
Uses ATR-normalized curvature for adaptive behaviour.
Can distinguish general curvature from decelerating/turning curvature.
Can adapt the T3 coefficient as well as Hull compensation.
Allows the standard Hull square-root smoothing relationship to be generalized.
Includes an optional ATR-based velocity limiter.
Provides optional final lag compensation for advanced tuning.
Includes real-time tail and curvature diagnostics.
Provides trend-strength-reactive visualization without altering signals.
Limitations
The indicator remains a reactive trend filter rather than a predictive model.
Increasing lag compensation generally increases overshoot risk.
Aggressive tail suppression generally increases lag.
Slope-based signals can whipsaw in ranging markets.
The large number of controls creates many interacting parameter combinations.
Over-tuning parameters to one asset or historical period can reduce robustness elsewhere.
The Velocity Limiter can delay response to genuine price shocks.
Generalization can reintroduce overshoot that earlier damping stages removed.
Trend Strength measures line velocity, not probability of continuation.
Tail Overshoot is a diagnostic relative to the fast T3, not a trading signal.
Causality and real-time behaviour
The calculations use current and historical data without intentional future references.
The indicator can therefore be evaluated causally on completed bars.
However, on a live unfinished candle:
The source can change.
The T3 stages can change.
Curvature can change.
Adaptive compensation can change.
The final slope can change.
A long or short signal can appear or disappear before bar close.
Users requiring confirmed trend transitions should evaluate signals on completed candles.
Alerts
The indicator includes three alert conditions:
T3 Hull Long: the completed T3 Hull changes into a rising trend state.
T3 Hull Short: the completed T3 Hull changes into a falling trend state.
T3 Hull Signal: either directional transition occurs.
Summary
Adaptive T3 Hull is a generalized trend smoother built around the idea that Hull-style lag compensation does not need to be fixed.
The model begins with fast and slow T3 smoothers rather than traditional WMAs. Their difference is used to compensate the fast T3 for lag, but the amount of compensation is directly configurable.
This alone allows the user to move continuously between:
A restrained fast T3.
A partially compensated Hull structure.
A conventional 2×fast-minus-slow construction.
More aggressive extrapolation.
The final smoothing stage is also generalized. Instead of forcing the conventional square-root Hull relationship, the user can control both the smoothing exponent and a separate multiplier.
The adaptive systems then focus specifically on the behaviour that often makes Hull-style smoothers difficult to tune: oscillating tails around sharp turns.
The script measures changes in fast-T3 velocity, normalizes that curvature using ATR, and can use the result to:
Reduce Hull compensation.
Reduce the T3 Volume Factor.
Apply damping only around deceleration and turns.
An optional velocity limiter provides a final ATR-based cap on extreme one-bar movement, while an optional generalized compensation stage can reintroduce controlled responsiveness after the main smoothing process.
The final line determines trend through its slope, while a delayed WMA reference forms the optional ribbon. Ribbon intensity can also respond to normalized trend velocity.
Adaptive T3 Hull is therefore designed less as one fixed moving-average formula and more as a configurable filter architecture for exploring the trade-off between lag, smoothness, responsiveness and overshoot .
Its default configuration intentionally favors a less tail-heavy response than a conventional Hull construction, while the advanced controls allow users to move the model toward either greater responsiveness or stronger damping depending on the behaviour they want from the trend filter.
อินดิเคเตอร์
Black-Litterman Allocator [BackQuant]# Black-Litterman Allocator
IMPORTANT: Concept / Educational Implementation
Black-Litterman Allocator is a research and educational concept that implements a practical version of the Black-Litterman portfolio-allocation framework inside TradingView and Pine Script.
It is intended to demonstrate how equilibrium priors, covariance estimates, subjective investor views, view confidence, mean-variance optimization, portfolio constraints, volatility targeting and portfolio backtesting can be combined into one visual allocation model.
It should not be interpreted as an institutional-grade portfolio optimizer, automated investment product, portfolio recommendation, or guarantee that the resulting allocation is optimal.
The outputs depend heavily on:
The selected asset universe.
The chart timeframe.
The covariance lookback.
The quality and synchronization of TradingView price data.
The chosen prior-weight scheme.
Risk-aversion assumptions.
The investor views entered by the user.
The confidence attached to those views.
Portfolio constraints.
Volatility-target settings.
Transaction-cost assumptions.
The optional regime filter.
The default universe and default views are examples for demonstrating the framework. They are not investment recommendations.
The script is best treated as a portfolio-allocation laboratory : a way to study how changing assumptions about equilibrium, risk, correlations and expected returns can propagate through a Black-Litterman-style allocation process.
Overview
Black-Litterman Allocator is a 15-asset cross-asset portfolio model that starts with a neutral portfolio prior, reverse-engineers the expected returns implied by that prior, optionally incorporates up to five investor views, solves for a new posterior allocation, applies portfolio constraints and volatility targeting, and then simulates the resulting portfolio through time.
The model follows a broad sequence:
Collect return history for the selected 15-asset universe.
Estimate an annualized covariance matrix.
Stabilize that matrix using diagonal covariance shrinkage.
Construct a prior portfolio.
Estimate the market risk-aversion parameter.
Reverse-optimize the prior into implied equilibrium returns.
Convert investor views into the Black-Litterman P, Q and uncertainty structure.
Blend the prior with those views to obtain posterior expected returns.
Optionally calculate posterior covariance.
Solve a mean-variance portfolio from the posterior.
Apply availability, short-selling, gross exposure and position-size constraints.
Target a desired portfolio volatility.
Apply additional leverage and gross-exposure caps.
Rebalance periodically.
Track the resulting equity curve and portfolio statistics.
The script also provides detailed visualizations showing:
Prior versus final active weights.
Equilibrium versus posterior expected returns.
The impact of individual views.
Current gross and net exposure.
Portfolio volatility and scaling.
Turnover.
Portfolio equity versus a benchmark.
Drawdown and daily returns.
A broad set of performance and risk statistics.
Why Black-Litterman exists
Traditional mean-variance optimization has an important practical weakness.
The optimizer is extremely sensitive to expected-return estimates.
Suppose several assets have similar volatility and correlation characteristics, but one asset is assigned an expected return only slightly higher than the others.
A mathematical optimizer can interpret that small difference very aggressively and allocate an unrealistic amount of capital to that asset.
Small estimation errors in expected returns can therefore produce very large changes in portfolio weights.
This is one reason unconstrained mean-variance portfolios often produce allocations that appear unstable or unintuitive.
The Black-Litterman framework was developed by Fischer Black and Robert Litterman as a way of approaching the problem from the opposite direction.
Instead of beginning with a set of independently estimated expected returns, the framework begins with an equilibrium portfolio and asks:
What expected returns would make this portfolio mathematically optimal?
Those implied returns become the prior.
Investor views are then introduced as controlled deviations from that equilibrium rather than replacing the equilibrium assumptions entirely.
This creates a useful distinction:
Prior = what the portfolio implies before the investor expresses a view.
Views = where the investor believes equilibrium is wrong.
Posterior = the combined result after balancing both sources of information.
That is the central idea behind this indicator.
Important distinction: the prior in this script
In textbook Black-Litterman, the equilibrium portfolio is often represented using market-capitalization weights.
This script is intentionally more flexible.
It provides three different prior schemes:
Equal Weight.
Inverse Volatility.
Manual Weights.
For that reason, the word equilibrium should be interpreted carefully.
If Equal Weight or Inverse Volatility is selected, the prior is a user-selected equilibrium proxy , not necessarily the true global market portfolio.
If Manual Weights is selected and the user enters representative market-cap or benchmark weights, the prior can be made closer to the traditional Black-Litterman interpretation.
This flexibility is intentional because TradingView users may want to study Black-Litterman mechanics without first sourcing a complete set of institutional market-cap weights.
Asset universe
The allocator supports fifteen simultaneously selected assets.
The default universe is designed as a broad cross-asset example containing:
Cryptocurrency.
US equities.
International equities.
Precious metals.
Energy.
The US dollar.
Long-duration Treasury exposure.
The default list includes assets such as Bitcoin, Ethereum, Solana, major equity indices, gold, silver, oil, DXY and TLT.
Every symbol can be replaced by the user.
This allows the framework to be adapted to:
Global macro portfolios.
Equity-sector portfolios.
Cryptocurrency portfolios.
ETF portfolios.
Multi-asset portfolios.
However, all assets should represent actual price series .
Market-capitalization series, synthetic quantities or unrelated non-price data should not be inserted as if they were tradable asset prices, because the resulting returns would contaminate the covariance matrix and portfolio calculations.
Data availability protection
A multi-asset allocator has a specific problem when some assets have shorter histories than others.
Suppose fourteen assets have ten years of data but the fifteenth asset was only listed six months ago.
If missing values are simply converted into zeros, the new asset may appear to have:
Almost no volatility.
Artificially stable returns.
Artificial correlations.
This is especially dangerous when using inverse-volatility weighting, because an asset with incorrectly measured near-zero volatility could receive a very large prior allocation.
The script protects against this by maintaining a separate data-availability state for every asset.
An asset is only admitted into the active universe once it has accumulated at least one complete covariance lookback of valid price history.
Until then:
Its active mask remains disabled.
It receives no prior weight.
It receives no optimized weight.
Views referencing it are ignored.
The allocation table displays it as having no usable data.
This makes the universe dynamic.
A newly listed asset can eventually become active once enough genuine history has accumulated.
Return calculations
The allocator uses two forms of return data for different purposes.
Log returns
Log returns are used for covariance estimation:
Log Return = ln(Price / Previous Price)
These are stored in a rolling history matrix.
Simple returns
Simple returns are used when compounding the simulated portfolio:
Simple Return = Price / Previous Price - 1
This distinction is deliberate.
Log returns are convenient for statistical covariance calculations, while simple returns are appropriate for directly multiplying portfolio wealth through time.
Rolling return-history matrix
The script maintains a rolling matrix containing return history for all fifteen assets.
Each row represents a historical bar and each column represents one asset.
Once the requested covariance lookback has been collected, the matrix acts as the input for the covariance engine.
Rather than recalculating years of historical data from scratch on every bar, the script operates the history as a rolling buffer.
The full Black-Litterman calculation is also performed only on rebalance events rather than continuously.
This is important because:
Covariance estimation is computationally expensive.
Matrix multiplication is expensive.
Matrix inversion is expensive.
TradingView imposes execution limits.
The indicator therefore approximates how a real asset-allocation process is normally operated: weights remain relatively stable between scheduled portfolio reviews and are recomputed at discrete intervals.
Covariance matrix
The covariance matrix is one of the central inputs to the entire model.
For N assets, covariance produces an N × N matrix.
The diagonal contains the variance of each asset.
The off-diagonal entries contain covariance between pairs of assets.
Conceptually:
Positive covariance means two assets tend to move in the same direction.
Negative covariance means they tend to move in opposing directions.
Covariance near zero suggests weaker linear co-movement.
The portfolio does not consider the risk of each asset independently.
Instead, portfolio risk depends on:
Individual asset volatility.
Portfolio weights.
The covariance relationships between every pair of assets.
This is why diversification cannot be measured simply by counting positions.
Ten highly correlated assets may behave more like one large risk exposure than ten independent exposures.
Covariance Lookback
The Covariance Lookback controls how many bars are used to estimate the covariance matrix.
Shorter windows:
Adapt more quickly.
Reflect recent correlation changes.
Contain fewer observations.
Produce noisier covariance estimates.
Longer windows:
Provide more observations.
Create more statistically stable estimates.
Adapt more slowly when correlations change.
This parameter is particularly important when the number of assets is large relative to the number of observations.
With fifteen assets, an extremely short covariance window can create a poorly conditioned or nearly singular matrix.
That can make matrix inversion unstable and produce extreme portfolio weights.
Annualization
The covariance matrix is annualized using the Trading Days per Year input.
The script supports:
252 days.
365 days.
252 is generally appropriate for traditional financial markets operating primarily on weekdays.
365 may be more appropriate for a crypto-only daily portfolio.
Mixed universes require judgement because crypto trades continuously while many traditional markets do not.
The annualization setting affects:
Covariance.
Volatility.
Return statistics.
Risk-aversion estimates.
It should therefore be selected consistently with the universe and timeframe being studied.
Covariance shrinkage
Raw sample covariance matrices can be noisy.
This is particularly problematic when:
The lookback is short.
There are many assets.
Several assets are highly correlated.
Market relationships change rapidly.
The script applies a simple fixed-coefficient shrinkage toward a diagonal covariance target.
The diagonal variances are retained.
The off-diagonal covariance terms are multiplied by:
1 - Shrinkage
Therefore:
Shrinkage = 0
leaves the sample covariance relationships largely unchanged.
Shrinkage = 1
removes the off-diagonal covariance terms and effectively treats the assets as uncorrelated for optimization purposes.
Intermediate values partially reduce estimated correlations.
This is best described as Ledoit-Wolf-style diagonal shrinkage , not as a full automatic Ledoit-Wolf estimator.
A true Ledoit-Wolf implementation estimates an optimal shrinkage intensity statistically.
Here, the user directly controls the shrinkage coefficient.
Why shrinkage can help
Portfolio optimization involves matrix inversion.
If covariance estimates are noisy, the inverse matrix can amplify those errors dramatically.
Shrinkage intentionally sacrifices some estimated correlation detail in exchange for greater numerical stability.
A moderate amount of shrinkage can therefore:
Reduce unstable allocations.
Reduce sensitivity to short-term correlation noise.
Improve matrix conditioning.
Too much shrinkage can also remove genuine diversification information.
The parameter is a bias-versus-variance trade-off.
Safe matrix inversion
Black-Litterman requires several matrix inversions.
Matrices can become singular or nearly singular when:
Assets are highly correlated.
Lookbacks are too short.
Data is incomplete.
The script checks whether the matrix is square and sufficiently non-singular before using a standard inverse.
When necessary, it falls back to a pseudo-inverse.
This does not magically make poor data reliable, but it prevents a singular matrix from immediately destroying the calculation.
A pseudo-inverse should still be interpreted cautiously because the underlying portfolio problem may be poorly conditioned.
Prior portfolio
Before Black-Litterman can estimate equilibrium returns, it requires a prior portfolio.
Three schemes are provided.
Equal Weight
Every active asset receives an equal allocation:
Weight = 1 / Number of Active Assets
This is the simplest prior.
It expresses no preference based on:
Market capitalization.
Volatility.
Expected return.
Its strength is simplicity.
Its weakness is that it assumes every asset deserves the same capital allocation regardless of risk.
Inverse Volatility
Inverse Volatility gives greater prior weight to assets with lower historical volatility.
Conceptually:
Raw Weight ∝ 1 / Volatility
The weights are then normalized.
This produces a risk-oriented prior rather than a capital-oriented prior.
Lower-volatility assets receive more weight.
Higher-volatility assets receive less.
This can be useful for diversified macro portfolios, but it has an important implication:
the quietest asset may dominate the prior.
For example, a bond or currency exposure may receive much more prior weight than cryptocurrency simply because its realized volatility is lower.
This is not a bug.
It is the direct consequence of using inverse volatility as the prior definition.
Manual Weights
Manual mode allows the user to enter fifteen raw numbers corresponding to the fifteen selected assets.
The entries are normalized automatically.
This means the values do not need to sum to 100.
The user can enter:
Percentages.
Market capitalizations.
Benchmark weights.
Relative notional values.
Only their proportions matter.
If the intention is to approximate traditional Black-Litterman market equilibrium, Manual Weights can be used to supply actual or approximate market-cap weights.
Reverse optimization
Once the prior weights are known, the model derives the returns that would make those weights consistent with mean-variance equilibrium.
The implied equilibrium excess-return vector is:
Pi = Delta × Sigma × Wprior
where:
Pi = implied equilibrium excess returns.
Delta = risk-aversion coefficient.
Sigma = covariance matrix.
Wprior = prior portfolio weights.
This is called reverse optimization .
Normal portfolio optimization asks:
Given expected returns, what weights should I own?
Reverse optimization asks:
Given the portfolio weights, what expected returns would justify owning them?
That reversal is one of the key ideas behind Black-Litterman.
Why implied returns matter
Expected returns are difficult to estimate directly.
Historical averages are noisy.
Forecast models disagree.
Small errors can create enormous portfolio changes.
Black-Litterman instead begins from a portfolio that the user considers a reasonable neutral starting point.
The model then backs out the expected returns consistent with that portfolio.
These implied returns become the equilibrium prior against which investor opinions are expressed.
Risk aversion: Delta
Delta controls the relationship between expected return and risk.
Higher Delta means:
Greater assumed aversion to risk.
A larger equilibrium return requirement for a given covariance structure and prior.
Lower Delta implies less risk aversion.
The script provides:
Auto (Implied).
Manual.
Manual Delta
Manual mode allows the user to directly select the risk-aversion coefficient.
This is useful when:
A stable assumption is preferred.
The user is reproducing an external Black-Litterman study.
The portfolio prior is known but a particular Delta is desired.
Auto Delta
Auto mode estimates Delta from the current prior portfolio.
The script estimates:
Prior portfolio variance.
An annualized return estimate over the covariance horizon.
The selected risk-free rate.
It then forms an implied risk-aversion estimate from excess return relative to variance.
The value is constrained to a practical range to prevent extreme estimates from destabilizing the optimizer.
This Auto mode is a practical implementation choice for the concept.
It should not be interpreted as a uniquely correct market risk-aversion estimate.
Tau: uncertainty in the prior
Tau is one of the most important Black-Litterman parameters.
It scales uncertainty in the equilibrium prior.
Conceptually:
Prior Uncertainty = Tau × Sigma
A smaller Tau implies stronger confidence in the equilibrium-return prior.
A larger Tau gives the model more freedom to move away from the prior when investor views are introduced.
In practical terms:
Smaller Tau
Makes the prior harder to move.
Reduces the effect of views.
Larger Tau
Increases prior uncertainty.
Allows views to exert more influence.
Tau should not be interpreted in isolation.
Its effect interacts with:
The covariance matrix.
View confidence.
View direction.
The number of views.
Investor views
The script supports up to five simultaneous investor views.
Each view contains:
A view type.
Asset A.
Optional Asset B.
Expected return Q.
Confidence.
Each view can be:
Off.
Absolute.
Relative.
The expected-return input is interpreted as an annualized expected return or annualized relative return .
Absolute views
An absolute view expresses an opinion about one asset.
For example:
“Asset A will return 10% annually.”
In matrix notation, the corresponding row of the P matrix contains:
+1 for Asset A.
0 for all other assets.
Q then contains:
0.10
for a 10% annual view.
Relative views
A relative view expresses one asset relative to another.
For example:
“Asset A will outperform Asset B by 5% annually.”
The corresponding P row contains:
+1 for Asset A.
-1 for Asset B.
0 elsewhere.
Q becomes:
0.05
This does not necessarily mean Asset A itself must return +5%.
It means:
Expected Return A - Expected Return B = 5%
Relative views are one of the most useful features of Black-Litterman because investors are often more confident about relative relationships than exact absolute returns.
It may be easier to hold the view:
“Gold will outperform equities.”
than:
“Gold will return exactly 12.4%.”
P matrix
The P matrix describes which assets each investor view references.
Each row corresponds to one active view.
Each column corresponds to one of the fifteen assets.
An absolute view creates one non-zero exposure.
A relative view creates a long-versus-short pair.
P therefore translates a verbal market opinion into portfolio mathematics.
Q vector
Q contains the expected return associated with each view.
For absolute views:
Q = expected annual asset return.
For relative views:
Q = expected annual outperformance of A relative to B.
The relationship:
P × Returns = Q
defines what the investor believes.
View confidence
Black-Litterman does not require every opinion to be treated as equally reliable.
Each view therefore receives a confidence value.
Confidence controls its uncertainty.
The basic principle is:
Low confidence = large view uncertainty.
High confidence = small view uncertainty.
The script converts intuitive percentage confidence into an Omega uncertainty term using a confidence mapping related to the Idzorek-style approach to expressing subjective confidence. User-specified confidence was developed precisely to make the otherwise difficult view-uncertainty input more interpretable.
Omega
Omega represents uncertainty in the views.
For each active view, the script first measures the variance of the corresponding view portfolio using:
P × TauSigma × P'
It then scales that variance according to confidence:
Omega = ((1 - Confidence) / Confidence) × View Variance
This has intuitive behaviour.
High confidence
If confidence approaches 100%:
(1 - c) / c approaches zero.
Omega becomes small.
The view receives substantial influence.
Low confidence
If confidence approaches zero:
(1 - c) / c becomes very large.
Omega becomes large.
The view has little effect.
The script bounds confidence away from exactly zero and one for numerical stability.
Why confidence matters
Suppose two investors both believe Bitcoin will outperform gold by 10%.
Investor A has 90% confidence.
Investor B has 20% confidence.
Their view Q is identical.
But their portfolio allocations should not necessarily be identical.
The confidence parameter allows the same directional opinion to produce very different posterior tilts.
This is one of the most useful parts of Black-Litterman.
It separates:
What you believe.
How strongly you believe it.
View disagreement: Q - PΠ
The Views table displays:
Q - PΠ
This measures how far the investor view differs from the equilibrium prior.
Suppose equilibrium already implies that Asset A will outperform Asset B by 8%.
If the user enters a relative view of 9%, the disagreement is only 1%.
The posterior may therefore change only slightly.
If the user instead enters 20%, the disagreement with equilibrium is much larger.
The same confidence level will then produce a much larger posterior adjustment.
This quantity is extremely useful because it shows that the impact of a view depends not only on the view itself, but on how different it is from what the prior already expects.
Posterior expected returns
Once P, Q and Omega have been constructed, the script calculates the Black-Litterman posterior expected-return vector.
Conceptually:
Posterior = Prior + Confidence-Weighted Adjustment
The full adjustment depends on:
Tau.
Sigma.
P.
Q.
Omega.
The disagreement Q - PΠ.
The model therefore does not simply overwrite the expected return of the named asset.
The adjustment can propagate across the entire asset universe through covariance relationships.
This is a fundamental feature of Black-Litterman.
If two assets are strongly related, a view about one may alter the posterior expectation of the other even if that second asset was not explicitly named.
Why views propagate
Suppose the user enters a strong bullish view on one equity index.
If several other equity indices are highly correlated with it, the covariance matrix tells the model that those assets are economically related.
The posterior adjustment therefore does not exist in isolation.
This means:
Views influence related assets.
Portfolio effects depend on covariance.
The same view can produce different tilts under different correlation regimes.
That behaviour is intentional.
No active views
If no usable views are active:
Posterior expected returns remain equal to the equilibrium prior returns.
The allocation is then driven by:
The prior.
Covariance.
Risk aversion.
Portfolio constraints.
Volatility targeting.
This makes the script useful even without discretionary views.
It can be used to study how the prior portfolio behaves under the optimization and risk-management layers by itself.
Posterior covariance
The script can optionally include the Black-Litterman posterior covariance adjustment.
Investor views introduce uncertainty about expected returns.
The posterior covariance calculation incorporates additional uncertainty associated with combining the prior and the views.
When enabled, the optimizer uses this adjusted covariance matrix.
When disabled, optimization uses the original covariance estimate.
The practical effect is usually more subtle than changing the expected-return vector, but it can affect:
Position sizes.
Diversification.
Volatility estimates.
View-driven tilts.
Portfolio optimization
After calculating posterior expected returns, the script solves a mean-variance allocation.
The unconstrained portfolio is conceptually:
w* = (Delta × SigmaPosterior)^-1 × PiPosterior
This converts posterior return expectations and covariance into portfolio weights.
If:
There are no views.
The prior and covariance are internally consistent.
No constraints alter the result.
the solution tends toward the prior portfolio.
Views create deviations away from that starting point.
Why unconstrained weights can be extreme
Mean-variance optimization can produce very large positive or negative positions.
This happens because matrix inversion magnifies differences between:
Expected returns.
Volatility.
Correlations.
If two assets are highly correlated but have slightly different expected returns, the optimizer may create a large long position in one and a large short position in the other.
Mathematically this can be valid.
Practically it may be unusable.
The script therefore applies several layers of portfolio constraints after the raw solution.
Data mask
Assets without sufficient price history receive zero weight regardless of what the raw optimizer produces.
This prevents incomplete covariance columns from entering the live portfolio.
Long-only mode
When Allow Short Weights is disabled:
All negative optimizer weights are clipped to zero.
The remaining positive positions are then normalized.
This converts the portfolio into a long-only allocation.
The result is no longer the exact unconstrained analytical Black-Litterman solution.
That is expected.
Real portfolios frequently require constraints that alter the theoretical optimum.
Short-enabled mode
When shorting is enabled, negative posterior weights are permitted.
This allows:
Long-short portfolios.
Relative-value expressions.
Negative allocations to assets receiving sufficiently weak posterior expectations.
Gross exposure becomes especially important in this mode because a portfolio can have low net exposure while still carrying substantial absolute risk.
For example:
+150% long.
-50% short.
= 100% net exposure.
= 200% gross exposure.
Gross Exposure
The Gross Exposure input controls the target sum of absolute portfolio weights before volatility targeting.
Gross exposure is:
Gross = Sum of |Weight|
This differs from net exposure:
Net = Sum of Weight
For long-only portfolios, gross and net are normally similar.
For long-short portfolios, they can differ significantly.
Volatility targeting
After the portfolio has been normalized, the script estimates total portfolio volatility using:
Portfolio Variance = w' × Sigma × w
Portfolio Volatility = sqrt(Portfolio Variance)
This is a full covariance-aware portfolio volatility calculation.
It does not simply average asset volatility.
The model then calculates a volatility scaling factor:
Volatility Scale = Target Volatility / Estimated Portfolio Volatility
subject to minimum and maximum limits.
If estimated portfolio volatility is below target:
Exposure can increase.
If estimated volatility is above target:
Exposure is reduced.
Why portfolio volatility matters
Suppose two assets each have 20% volatility.
A 50/50 portfolio does not necessarily have 20% volatility.
If the assets are weakly correlated, portfolio volatility may be much lower.
If they are highly correlated, it may remain close to 20%.
Using:
sqrt(w'Σw)
allows the volatility target to account for diversification.
Target Volatility
Target Volatility defines the desired annualized risk level of the portfolio before later hard caps are considered.
Examples might conceptually include:
A lower target for a defensive multi-asset portfolio.
A higher target for a crypto-focused portfolio.
The setting is not automatically appropriate simply because the portfolio reaches it.
A volatility target does not account for:
Tail risk.
Liquidity.
Gap risk.
Regime changes.
Nonlinear derivatives.
It is one risk-control dimension.
Maximum volatility-target leverage
A very low-volatility portfolio can theoretically require enormous leverage to reach a high volatility target.
The Max Vol-Target Leverage setting prevents this.
For example, if the mathematical scaling factor is 6× but the maximum leverage is 3×:
The model uses no more than 3×.
This protects against explosive leverage during unusually quiet covariance estimates.
Maximum weight per asset
After volatility targeting, every individual position is subjected to a hard position-size cap.
This ordering is important.
If the position cap were applied before leverage scaling, the volatility scaler could simply increase the capped position again.
Applying the cap afterward ensures the final position magnitude cannot exceed the selected maximum.
For example:
Max Weight = 30%
means no individual position can remain above 30% after the volatility scaling stage.
Maximum gross exposure after volatility targeting
After individual caps are applied, the portfolio is also checked against a maximum total gross exposure.
If gross exposure exceeds that maximum, every position is scaled downward proportionally.
This provides a second portfolio-level safeguard.
The result is a hierarchy:
Generate raw Black-Litterman weights.
Apply long/short rules.
Normalize initial gross exposure.
Apply volatility targeting.
Cap individual positions.
Cap final gross exposure.
Why the target may not be reached
The volatility target is not guaranteed to be achieved exactly.
Suppose the model wants to increase portfolio exposure enough to reach 15% volatility.
If doing so would violate:
Maximum leverage.
Maximum asset weight.
Maximum gross exposure.
the constraints take priority.
The resulting portfolio may therefore have volatility below the requested target.
This is intentional.
Risk limits are allowed to override the target.
Rebalancing
The complete optimizer does not run on every bar.
The user selects a Rebalance Every N Bars interval.
For a daily chart:
Approximately 21 bars corresponds roughly to one trading month.
Longer rebalance intervals:
Reduce turnover.
Reduce computation.
Allow allocations to persist longer.
Shorter intervals:
React faster to new covariance and view conditions.
Increase turnover.
Increase computational load.
The covariance matrix and Black-Litterman solve run only on rebalance events.
Forced rebalances
Two events can trigger a solve outside the normal schedule:
The regime filter changes from CASH back to ACTIVE.
The number of assets with sufficient history changes.
This prevents the portfolio from waiting many bars before responding to a material change in state.
Regime filter
The script includes an optional regime filter based on the chart symbol.
The filter compares:
A fast EMA.
A slow EMA.
When the fast EMA is above the slow EMA:
Regime = ACTIVE
When the fast EMA is not above the slow EMA:
Regime = CASH
This filter applies to the chart symbol , not individually to the fifteen assets.
That distinction is important.
If the indicator is placed on SPX, the regime filter reflects SPX.
If it is placed on Bitcoin, it reflects Bitcoin.
The regime state therefore acts as a global risk-on/risk-off switch for the entire portfolio.
CASH regime
When the regime filter turns off:
The live asset weights are flattened to zero.
The strategy stops compounding asset returns while the regime remains inactive.
When the filter turns ACTIVE again:
A new Black-Litterman solve is forced immediately.
The user should therefore choose the chart symbol intentionally if the regime filter is enabled.
Regime filter limitation
A single chart-symbol EMA regime is an intentionally simple overlay on a much more sophisticated cross-asset model.
It should not be confused with a multi-asset economic-regime model.
It answers only:
Is the fast trend of the chart symbol above its slower trend?
The regime layer can have a very large impact on historical results.
Backtests with and without it are therefore testing materially different systems.
Transaction costs
The script calculates turnover on each committed rebalance:
Turnover = Sum of |New Weight - Previous Weight|
The selected transaction-fee rate is then applied to that turnover.
This is more realistic than assuming rebalancing is free.
However, the cost model remains simplified.
It does not separately model:
Bid-ask spread.
Slippage.
Market impact.
Short borrow fees.
Financing costs.
Taxes.
Different fee schedules by asset.
The fee input should therefore be treated as an approximate portfolio-level trading-cost assumption.
Important backtest implementation note
The current implementation charges transaction fees when a new active portfolio is committed during a rebalance.
The transition that flattens the portfolio when the regime filter enters CASH is not separately charged an explicit turnover fee in the current code.
Therefore, backtests using the regime filter may slightly understate transaction costs associated with risk-off exits.
This is one reason the script should be treated as a concept rather than a production execution simulator.
No-lookahead portfolio return handling
The portfolio return for the current bar is calculated using the weights that were already active before the current rebalance solve.
Only after that return has been calculated does a new set of weights become active.
This prevents the optimizer from using newly calculated current-bar weights to capture a return that occurred before those weights could have existed.
This ordering is essential for a meaningful historical simulation.
Prior versus posterior weight chart
One of the main visual components is the paired horizontal weight chart.
Each asset receives two bars:
Prior weight.
Final active portfolio weight.
The prior represents the selected equilibrium starting allocation.
The active portfolio reflects the portfolio after:
Views.
Optimization.
Short constraints.
Gross normalization.
Volatility targeting.
Position caps.
Final gross caps.
Therefore, the visible gap between the bars represents more than the mathematical Black-Litterman posterior alone.
It represents the complete practical allocation change from prior to final active book .
If the regime filter is currently in CASH, the live active weights may be zero.
This distinction is important when interpreting the chart.
Allocation table
The Allocation Table shows each of the fifteen assets with:
Prior Weight.
Post Weight.
Delta Weight.
Equilibrium Expected Return.
Posterior Expected Return.
Prior Weight
The allocation before investor views and final portfolio construction.
Post Weight
The current active portfolio weight after the complete optimization and risk-control process.
Delta Weight
The difference between the active weight and prior weight.
Positive values indicate the asset has been increased relative to the prior.
Negative values indicate it has been reduced.
Equilibrium E
The implied return derived through reverse optimization.
Posterior E
The expected return after the active investor views have been incorporated.
Comparing equilibrium and posterior expected return is often more informative than looking only at weights.
A return expectation can change substantially while the final weight changes only modestly because:
The asset is highly volatile.
It is highly correlated with another holding.
The maximum-weight constraint binds.
Portfolio volatility limits exposure.
Views table
The Views Table shows each active view and includes:
View description.
Q.
Confidence.
Omega.
Q - PΠ.
This allows the user to inspect not only what the view says, but how strongly it conflicts with equilibrium and how uncertain it is.
Two views with identical Q values may have very different portfolio effects if:
Confidence differs.
Covariance differs.
Equilibrium expectations differ.
Current Book table
The Current Book table provides a compact summary of the active portfolio.
It includes:
ACTIVE or CASH regime.
Prior scheme.
Number of active views.
Number of rebalances.
Gross exposure.
Net exposure.
Number of live assets.
Turnover.
Risk-aversion Delta.
Tau.
Estimated portfolio volatility.
Volatility scaling factor.
This table is useful for diagnosing why the allocator currently looks the way it does.
For example:
Large view changes but small weights
may be explained by a tight volatility target or maximum-weight constraint.
Large gross but low net
may indicate significant long-short exposure.
Few live assets
means part of the universe has not yet accumulated sufficient historical data.
Equity curve
The script maintains a simulated portfolio equity curve beginning from the selected Initial Capital.
Initial Capital affects only the scale of the equity curve.
It does not affect:
Weights.
Sharpe ratio.
Volatility.
Portfolio optimization.
The equity curve compounds the historical portfolio returns generated by the active weights.
The line changes colour according to whether equity increased or decreased from the previous bar.
Benchmark Buy & Hold
A benchmark equity curve can be displayed beside the portfolio.
Both curves begin from the same nominal capital.
The benchmark is also used in:
Beta.
Alpha.
The benchmark can be changed independently from the fifteen-asset universe.
For meaningful interpretation, the benchmark should be relevant to the portfolio being studied.
A broad global macro portfolio compared only with SPX is answering a different question from an equity portfolio compared with SPX.
Daily returns
The script can optionally plot the portfolio’s per-bar percentage return.
This is useful for visually inspecting:
Return clustering.
Large gains.
Large losses.
Regime-filter cash periods.
Because it shares the pane with the equity curve, it is generally best viewed separately.
Rolling drawdown
Drawdown is measured relative to the previous portfolio-equity peak:
Drawdown = (Current Equity - Peak Equity) / Peak Equity
The result is negative while the portfolio remains below its historical high.
The visual fill becomes stronger as drawdown deepens.
The Max DD for Scaling input affects only the visual intensity scale.
It does not limit portfolio losses or modify the allocation.
Performance metrics
The metrics table includes a broad range of return and risk statistics.
Net Profit
Percentage change in portfolio equity from initial capital.
Maximum Drawdown
Largest historical peak-to-trough decline in the simulated portfolio.
Win Rate
Percentage of non-zero portfolio-return bars that were positive.
Flat CASH bars are excluded from the win/loss count.
This prevents periods where the portfolio is deliberately inactive from automatically being classified as losing periods.
Annual Mean Return
Arithmetic average per-bar portfolio return multiplied by the selected annualization factor.
This is not identical to CAGR.
Annual Standard Deviation
Per-bar return standard deviation scaled by the square root of the annualization factor.
Variance
Square of annualized standard deviation.
Sharpe Ratio
Measures annualized excess mean return relative to total return volatility using the selected risk-free rate.
Sortino Ratio
Measures return relative to downside-return variability rather than total volatility.
Omega Ratio
Compares the aggregate positive portfolio returns with the magnitude of aggregate negative portfolio returns.
Gain-to-Pain
Compares net return with the aggregate magnitude of negative returns.
CAGR
Compound annual growth rate based on beginning equity, ending equity and elapsed calendar time.
Calmar Ratio
CAGR divided by absolute maximum drawdown.
Beta
Measures covariance of portfolio returns with benchmark returns relative to benchmark variance.
Alpha
Estimates annualized portfolio return in excess of the return implied by its benchmark Beta and selected risk-free rate.
Skewness
Measures asymmetry of the historical portfolio-return distribution.
Positive skew indicates a longer or heavier positive tail.
Negative skew indicates a more pronounced negative tail.
VaR 95th Percentile
The implementation reports the fifth percentile of historical portfolio returns.
It can be interpreted as the lower-tail return threshold associated with approximately the worst 5% of observations.
It is displayed as a return value rather than converting the loss into a positive number.
Conditional VaR
Conditional VaR averages the returns in the lowest 5% tail.
This provides information about the average severity of outcomes beyond the VaR threshold.
Historical VaR and Conditional VaR rely entirely on the observed backtest sample.
They should not be interpreted as guarantees about future tail losses.
Risk-free rate
The selected Risk-Free Rate influences:
Sharpe.
Alpha.
Auto risk-aversion estimation.
Changing it therefore affects both reported performance statistics and potentially the portfolio itself when Auto Delta is enabled.
Understanding prior versus posterior
The most important conceptual visualization in the script is the difference between the prior and posterior state.
Suppose the prior allocation is:
Asset A: 20%
Asset B: 20%
Asset C: 20%
Asset D: 20%
Asset E: 20%
Now suppose the investor enters:
Asset A will outperform Asset B by 8%, with high confidence.
Black-Litterman does not simply add 8% weight to A and remove 8% from B.
Instead, the model asks:
What did equilibrium already imply about A versus B?
How uncertain is the prior?
How confident is the investor?
What is the covariance of the A-minus-B view?
How are A and B related to the rest of the portfolio?
The resulting posterior return adjustment then passes through the optimizer.
The final weights are subsequently modified by the portfolio constraints.
This explains why Black-Litterman allocations can behave very differently from manually applying arbitrary portfolio tilts.
Example: low-confidence relative view
Suppose equilibrium implies:
Expected A return = 8%
Expected B return = 7%
The equilibrium difference is 1%.
The investor believes:
A will outperform B by 5%
but assigns only 20% confidence.
The view disagrees with equilibrium, but Omega is relatively large because confidence is low.
The posterior therefore moves toward the investor view without fully accepting it.
Example: high-confidence relative view
Using the same equilibrium assumptions, suppose confidence is increased to 90%.
Omega becomes much smaller.
The investor view therefore carries much greater influence.
The posterior A-minus-B expected-return spread moves much closer toward the stated view.
The final weights may then shift significantly, subject to risk and portfolio constraints.
Example: view already priced into equilibrium
Suppose the user believes A will outperform B by 5%.
But the equilibrium prior already implies approximately 5%.
Then:
Q - PΠ ≈ 0
There is little disagreement to resolve.
Even a high-confidence view may produce only a small posterior adjustment.
This is an important property of the model.
Black-Litterman does not reward the user simply for entering a strong opinion.
The opinion must differ from equilibrium before it meaningfully changes the posterior.
Absolute versus relative confidence
Absolute views generally require greater confidence in the expected return level itself.
Relative views can be easier to interpret because the user only needs an opinion about the spread between two assets.
For example:
“Equities will return 14%.”
is a stronger forecasting statement than:
“Equities will outperform bonds by 4%.”
Neither is inherently superior.
The model supports both because portfolio managers frequently express views in both forms.
Why the model is useful conceptually
The value of Black-Litterman is not that it discovers the future.
It provides a disciplined method for converting beliefs into portfolio changes.
Without a framework, an investor may say:
“I like gold.”
“I am bearish equities.”
“Bitcoin should outperform bonds.”
but those statements do not specify:
How much the portfolio should change.
How volatility should affect the position.
How correlated assets should respond.
How conviction should change the allocation.
Black-Litterman forces those opinions into a structured portfolio context.
That is what this indicator is intended to demonstrate.
Important implementation difference from institutional Black-Litterman
The script implements the core Black-Litterman mechanics, but several choices are intentionally simplified for TradingView.
These include:
A fixed maximum universe of fifteen assets.
Up to five investor views.
User-selected fixed covariance shrinkage rather than automatically estimated shrinkage intensity.
Equal-weight and inverse-volatility priors in addition to manual market-style priors.
A simplified Auto Delta estimate.
Discrete bar-based rebalancing.
Simplified transaction costs.
A single chart-symbol regime filter.
Historical covariance from TradingView price data.
These choices make the model practical and interpretable inside Pine Script.
They also mean that results should not be compared directly with a production institutional implementation without understanding the differences.
Mixed-market data considerations
Cross-asset portfolios introduce data-alignment problems.
Cryptocurrency trades continuously.
Equities, commodities and bonds have market sessions and holidays.
Different TradingView symbols may also come from different exchanges or data providers.
The covariance matrix assumes the return observations are meaningfully aligned.
Users should therefore be careful with:
Intraday mixed-asset universes.
Assets from incompatible sessions.
Symbols with limited historical coverage.
Synthetic or non-tradable price series.
Daily or broader timeframes are generally easier to interpret for a macro allocation concept.
Backtest limitations
Historical simulation is useful for understanding behaviour, but this should not be treated as proof of future performance.
The backtest does not model every real-world implementation issue.
Examples include:
Bid-ask spreads.
Market impact.
Execution latency.
Portfolio financing.
Borrow availability.
Short borrow costs.
Taxes.
Different trading sessions.
Rebalancing at exact executable prices.
Changes in instrument availability.
Survivorship effects in a manually selected universe.
The model also uses historical covariance as an estimate of future covariance.
Correlations can change abruptly during stress periods.
The most diversified-looking portfolio based on historical data can become much more concentrated in risk when formerly independent assets begin moving together.
No automatic investment views
The script does not create investor views for the user.
Q and confidence are deliberately manual.
This is important because Black-Litterman is a framework for combining beliefs with equilibrium.
It does not tell the investor what those beliefs should be.
Views could theoretically come from:
Macro analysis.
Valuation models.
Momentum models.
Fundamental research.
Quantitative forecasts.
Discretionary judgement.
The quality of the posterior cannot exceed the quality of the assumptions provided to it.
Parameter interaction
Black-Litterman parameters should not be tuned independently.
Several important interactions exist.
Tau + Confidence
Both influence how aggressively views move the posterior.
Higher prior uncertainty combined with high view confidence can create strong posterior changes.
Covariance Lookback + Shrinkage
A short noisy covariance window may require more shrinkage for stability.
A long sample may tolerate less.
Target Volatility + Leverage Caps
A high volatility target may have little effect if maximum leverage or gross exposure is restrictive.
Views + Max Weight
A strong posterior preference for one asset may never appear fully in the active portfolio if the asset cap is binding.
Shorts + Gross Exposure
Allowing shorts can materially increase gross exposure even when net exposure looks conservative.
Rebalance Frequency + Fees
Frequent optimization allows faster adaptation but increases turnover and assumed trading cost.
Prior selection
The choice of prior is not cosmetic.
It changes the equilibrium return vector itself.
The same investor views can therefore produce different posterior portfolios depending on whether the starting prior is:
Equal Weight.
Inverse Volatility.
Market-like Manual Weights.
Users studying the framework should therefore treat prior construction as one of the primary model assumptions.
Suggested research workflow
A useful way to study the indicator is:
Begin with no investor views.
Choose a prior.
Observe the implied equilibrium returns.
Inspect the covariance-driven allocation.
Add one low-confidence relative view.
Observe Q - PΠ.
Compare equilibrium and posterior returns.
Increase confidence gradually.
Observe how the posterior and weights respond.
Add a second view.
Experiment with Tau.
Enable and disable posterior covariance.
Compare long-only and short-enabled portfolios.
Change the volatility target.
Observe when position or gross caps become binding.
This is generally more informative than immediately entering five aggressive views and trying to interpret the final result.
Example research questions
The allocator can be used to study questions such as:
How much does a 70% confidence view move the portfolio compared with 30% confidence?
How does inverse-volatility equilibrium differ from equal-weight equilibrium?
How does covariance shrinkage change portfolio concentration?
How do relative views propagate into assets not explicitly named?
How much does volatility targeting alter the raw optimizer?
How often do hard position caps bind?
How different are equilibrium expected returns from posterior expected returns?
How much turnover is generated by monthly versus weekly rebalancing?
How does a regime filter alter drawdown and opportunity cost?
These are the types of questions the concept is designed to explore.
Input guide
Initial Capital
Controls the starting dollar value of the simulated equity curve.
It does not change portfolio weights.
Trading Days/Year
Controls annualization.
Use a value consistent with the universe being studied.
Target Volatility
Sets the desired annualized portfolio-volatility target before hard leverage and weight constraints.
Transaction Fees
Approximate fee charged per unit of rebalance turnover.
Rebalance Every N Bars
Controls how frequently the full covariance and Black-Litterman solve occurs.
Allow Short Weights
Allows negative optimized weights.
Max Weight per Asset
Hard cap on individual position magnitude after volatility targeting.
Gross Exposure
Target absolute exposure before volatility scaling.
Max Gross After Vol Target
Final portfolio-level ceiling on gross exposure.
Max Vol-Target Leverage
Maximum scaling multiplier permitted by volatility targeting.
Covariance Lookback
Historical window used for covariance estimation and minimum data availability.
Covariance Shrinkage
Reduces off-diagonal covariance estimates toward zero.
Tau
Controls uncertainty in the equilibrium prior.
Use Posterior Covariance
Allows view uncertainty to modify the covariance matrix used by the optimizer.
Risk Aversion
Selects automatically estimated or manually specified Delta.
Prior Weight Scheme
Selects Equal Weight, Inverse Volatility or Manual Weights.
Investor Views
Supports up to five annualized absolute or relative return views.
Confidence
Controls the uncertainty assigned to each view.
Start Date
Defines the beginning of simulated portfolio equity.
Historical data before the date may still be used to warm up covariance estimates.
Risk-Free Rate
Used in portfolio statistics and Auto Delta estimation.
Benchmark
Used for the buy-and-hold comparison, Alpha and Beta.
Regime Filter
Optional chart-symbol fast/slow EMA filter that moves the portfolio between ACTIVE and CASH.
Prior vs Posterior visualization
Displays the difference between the selected prior allocation and current final portfolio weights.
Strengths
Implements the central Black-Litterman prior-and-views framework directly in Pine.
Supports both absolute and relative investor views.
Allows confidence to directly control view uncertainty.
Uses a complete cross-asset covariance matrix.
Includes diagonal covariance shrinkage.
Supports dynamic asset-data availability.
Provides equal-weight, inverse-volatility and manual priors.
Supports long-only and long-short allocation.
Uses covariance-aware portfolio volatility targeting.
Includes individual and portfolio-level exposure constraints.
Accounts for rebalance turnover fees.
Provides extensive allocation, view and portfolio diagnostics.
Includes a visual prior-versus-final-weight comparison.
Includes portfolio equity, benchmark and risk statistics.
Limitations
This is a concept and educational implementation, not an institutional portfolio-management system.
Historical covariance is only an estimate of future relationships.
The 15-asset universe is fixed in size.
A maximum of five views can be entered.
The prior is only a true market-equilibrium proxy if the selected weights appropriately represent one.
Equal Weight and Inverse Volatility are practical prior substitutes rather than literal global market-cap equilibrium.
The shrinkage coefficient is user-selected rather than statistically estimated.
Auto Delta is a practical approximation.
Portfolio optimization remains sensitive to inputs.
Poor views can produce poor posterior estimates.
High-confidence incorrect views can materially damage the portfolio.
Volatility targeting does not protect against all forms of risk.
Historical volatility can underestimate future crisis volatility.
Hard constraints mean the final portfolio may differ substantially from the analytical unconstrained Black-Litterman optimum.
The final volatility target may not be reached when position, leverage or gross limits bind.
The regime filter is based only on the chart symbol.
The backtest uses simplified transaction costs.
Regime-driven exits to CASH are not separately charged an explicit turnover fee in the current implementation.
Mixed-market TradingView data can contain differing sessions and histories.
Backtested performance does not establish future performance.
Historical and theoretical context
The Black-Litterman framework was developed to address practical problems encountered when applying mean-variance optimization to global portfolios.
Its central contribution is not simply another optimization equation.
It is a different way of constructing expected returns.
Instead of requiring the investor to estimate every asset’s return independently, equilibrium returns provide a coherent starting point. Investor views then alter only the parts of that equilibrium where the investor has an opinion.
This structure can be summarized as:
Start neutral.
Reverse-engineer equilibrium.
State where you disagree.
State how strongly you disagree.
Let covariance propagate those beliefs.
Re-optimize the portfolio.
The original Black-Litterman work emphasized equilibrium as a neutral starting point and allowed investor opinions about absolute or relative performance to tilt that equilibrium according to confidence.
Later work on user-specified confidence made the view-uncertainty problem easier to interpret by expressing conviction in intuitive percentage terms rather than requiring users to manually specify an abstract uncertainty covariance for every view.
This indicator takes those principles and translates them into a practical TradingView research environment.
Summary
Black-Litterman Allocator is an experimental portfolio-allocation framework designed to demonstrate how equilibrium, investor beliefs and portfolio risk can be combined inside TradingView.
The model begins with fifteen selectable assets and estimates their annualized covariance structure using historical log returns. A user-controlled shrinkage process reduces noisy cross-asset covariance estimates, while assets without sufficient historical data are excluded until a complete covariance window becomes available.
The user then selects an Equal Weight, Inverse Volatility or Manual prior portfolio.
That prior is reverse-optimized into implied equilibrium expected returns:
Pi = Delta × Sigma × Prior Weights
Up to five absolute or relative investor views can then be introduced.
Each view specifies:
What the investor expects.
Which assets the view applies to.
How confident the investor is.
Confidence is translated into view uncertainty, allowing weak opinions to create small tilts and high-confidence opinions to exert greater influence.
The Black-Litterman posterior combines those views with equilibrium while accounting for covariance relationships across the entire portfolio.
The resulting posterior expected returns are converted into an optimized allocation, after which the script applies:
Data-availability rules.
Optional long-only constraints.
Gross-exposure normalization.
Portfolio volatility targeting.
Maximum leverage.
Maximum position sizes.
Maximum gross exposure.
The portfolio is then rebalanced through time, transaction costs are approximated, an optional chart-level regime filter can move the book into CASH, and the resulting historical equity curve is compared with a selectable benchmark.
Extensive tables show:
Prior and final weights.
Equilibrium and posterior returns.
View confidence and uncertainty.
View disagreement with equilibrium.
Gross and net exposure.
Portfolio volatility.
Turnover.
Performance and risk statistics.
The purpose of the script is not to claim that Black-Litterman can identify the optimal future portfolio.
Its purpose is to make the framework tangible.
It provides a way to explore how a neutral portfolio can be translated into implied expected returns, how subjective beliefs can be incorporated without completely discarding that prior, how confidence changes the strength of those beliefs, how covariance spreads their effects across the portfolio, and how practical constraints can transform a theoretical posterior into a more realistic active allocation.
Treat the indicator as a concept, a research tool, and a visual implementation of portfolio-allocation theory rather than as an automated investment recommendation.
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Nonparametric Relative Momentum [BackQuant]Nonparametric Relative Momentum
Overview
Nonparametric Relative Momentum is a percentile-rank oscillator that measures where the current price or momentum observation sits relative to its own recent empirical history.
Unlike conventional momentum oscillators that transform price using fixed arithmetic relationships, this indicator uses rank statistics . The current observation is compared directly against the previous values in a rolling window and converted into a percentile score from 0 to 100.
The result answers a simple question:
How extreme is the current observation relative to what this market has actually done recently?
Two calculation modes are available:
Price ranks the selected price source directly.
Momentum first measures price change across a configurable horizon, then ranks that momentum against its own recent history.
The oscillator also includes:
Mid-rank handling for tied observations.
Optional output smoothing.
An EMA signal line.
Configurable overbought and oversold zones.
Stepped intensity colouring as the rank becomes more extreme.
Main-chart candle colouring from the 50 midline regime.
Alerts for midline, extreme-zone and signal-line crossings.
Why “nonparametric”?
In statistics, a parametric method generally assumes that data can be described by a particular distribution or by parameters associated with that distribution.
A nonparametric method does not require the same distributional assumption.
Percentile ranks are a classic example.
The oscillator does not need to assume that recent price changes are:
Normally distributed.
Symmetric.
Constant in volatility.
Characterised by a stable mean and standard deviation.
Instead, it works directly from the ordering of the observed data.
If the current momentum observation is greater than almost every momentum observation in the recent window, it receives a high rank.
If it is lower than almost everything observed recently, it receives a low rank.
This makes the oscillator fundamentally relative to the market’s own recent empirical distribution.
Core calculation
The calculation occurs in three stages:
Select the series to rank.
Calculate its empirical percentile rank.
Optionally smooth that rank and calculate a signal average.
The selected ranking target depends on the Rank Target input.
Price Mode
In Price mode:
Target = Selected Price Source
The current source value is compared with the previous values in the Rank Window.
This answers:
Where is current price positioned within its recent price distribution?
A value near 100 means current price is above almost every observation in the comparison window.
A value near 0 means it is below almost every observation.
A value near 50 means it sits near the middle of its recent distribution.
Because Price mode ranks the price level itself, it behaves somewhat like a stochastic or price-position oscillator, although the calculation is based on empirical ranking rather than highest-lowest range normalisation.
Momentum Mode
Momentum mode first calculates:
Momentum = Source - Source
This measures the absolute price change across the selected Momentum Length.
The resulting momentum series is then percentile-ranked over the Rank Window.
The oscillator therefore answers:
How strong is the current momentum observation compared with recent momentum observations?
This is different from asking whether price itself is historically high or low.
For example, price can be near a recent high while momentum has weakened considerably. In that situation:
Price mode may remain highly ranked.
Momentum mode may fall toward the centre or lower half of the distribution.
Conversely, price does not need to be at a long-term extreme for momentum to rank very highly if the current change is unusually strong relative to recent movements.
Why Momentum mode is different from traditional RSI
The standard Relative Strength Index developed by J. Welles Wilder compares smoothed positive and negative price changes.
Its calculation depends on the relative magnitude of average gains and average losses.
Nonparametric Relative Momentum does not use that formula.
Instead:
A momentum observation is calculated.
That observation is ranked against its own historical sample.
For this reason, Momentum mode can be thought of as a rank-based relative momentum oscillator .
Both traditional RSI and this oscillator are bounded between 0 and 100, but the meaning of those values is different.
For example:
RSI = 90
means the balance of smoothed gains versus losses has produced an RSI reading of 90.
Nonparametric Relative Momentum = 90
means the current momentum observation ranks around the upper end of its recent empirical momentum distribution.
That distinction is important.
Percentile rank calculation
For each bar, the indicator compares the current target with every observation in the preceding Rank Window.
It counts:
How many previous values are below the current value.
How many previous values are exactly equal to it.
The percentile rank is then:
Rank = 100 × (Values Below + 0.5 × Equal Values) / Window Length
This produces an oscillator between 0 and 100.
Why use rank instead of magnitude?
Consider two markets.
Market A may normally move only 0.5% over the selected momentum horizon.
Market B may routinely move 5%.
A raw momentum threshold cannot be interpreted the same way for both.
Ranking changes the question.
Instead of asking:
How many points or percent did this market move?
the oscillator asks:
How unusual is this move relative to this market’s own recent behaviour?
This allows the same 0–100 framework to adapt naturally to different price scales and volatility regimes.
Mid-rank treatment of ties
A simple percentile implementation might count only observations strictly below the current value.
That can distort the result when repeated values occur.
This indicator uses mid-rank treatment .
If historical observations equal the current value, each tie contributes one half rather than being classified entirely above or below.
For example, suppose:
40% of observations are below the current value.
20% are exactly equal.
40% are above.
The mid-rank result is:
40 + 0.5 × 20 = 50
This places the tied observation at the centre of its equal-value group.
Mid-ranks are commonly used in rank-based statistics because they provide a more balanced treatment of ties.
Rank Window
The Rank Window determines how much historical data defines the current empirical distribution.
A shorter Rank Window:
Adapts quickly.
Responds strongly to recent regime changes.
Produces more rapid movement between percentiles.
Can create noisier extreme readings.
A longer Rank Window:
Builds the ranking from a larger sample.
Produces a more stable percentile estimate.
Makes extremes harder to reach.
Responds more slowly when market behaviour changes.
The window therefore controls the memory of the oscillator.
It does not smooth the underlying target directly. It changes the reference distribution against which the target is ranked.
Momentum Length
Momentum Length is used only when Rank Target is set to Momentum.
It controls the horizon over which price change is measured:
Momentum = Current Source - Source from Momentum Length bars ago
Shorter values:
Measure faster momentum.
React to shorter impulses.
Change direction more frequently.
Longer values:
Measure broader displacement.
Focus on more persistent movement.
Ignore more short-term fluctuation.
The Momentum Length and Rank Window perform separate roles.
Momentum Length determines what movement is measured.
Rank Window determines the historical sample against which that movement is judged.
Output Smoothing
The raw percentile rank can optionally be passed through an EMA.
A value of 1 leaves the rank effectively unsmoothed.
Higher values:
Reduce rapid rank fluctuations.
Create a smoother oscillator.
Reduce short-lived extreme readings.
Introduce additional lag.
The smoothing occurs after the percentile calculation.
It does not change how observations are ranked.
The 50 midline
The oscillator is centred around 50.
A value above 50 means the current observation ranks above the midpoint of its recent distribution.
A value below 50 means it ranks below the midpoint.
The interpretation depends on the selected mode.
Price mode above 50
Current price is positioned in the upper half of its recent price distribution.
Price mode below 50
Current price is positioned in the lower half.
Momentum mode above 50
Current momentum is stronger than roughly the middle of its recent momentum observations.
Momentum mode below 50
Current momentum is weaker relative to its recent distribution.
The indicator also uses this midline to colour main-chart candles:
Above or equal to 50 = bullish colour.
Below 50 = bearish colour.
This provides a simple relative-regime view on the price chart.
Percentile extremes
Because the oscillator represents rank rather than an unbounded magnitude, readings near 0 and 100 carry a straightforward interpretation.
Near 100
The current observation is greater than almost every value in the recent comparison window.
Near 0
The current observation is lower than almost every value.
These are empirical extremes.
They do not mean price or momentum cannot become more extreme.
A value near 100 can persist while a strong trend continues because new observations may repeatedly remain near the top of the evolving distribution.
Likewise, readings near 0 can persist during sustained downside momentum.
Overbought and Oversold zones
The default static zones are:
Overbought: 90–100
Oversold: 0–10
These are configurable.
The labels “overbought” and “oversold” describe statistical location, not guaranteed reversal conditions.
An overbought reading means:
The ranked observation is near the top of its recent empirical distribution.
An oversold reading means:
It is near the bottom.
During a range, these areas may help identify local extremes.
During a persistent trend, the oscillator can remain in an extreme zone for extended periods.
The zones should therefore be interpreted together with:
Trend context.
Price structure.
Oscillator direction.
Signal-line behaviour.
Why 90/10 instead of 70/30?
Traditional RSI commonly uses 70 and 30.
That convention does not need to apply to a percentile-rank oscillator.
A rank above 90 means the current observation is in approximately the upper tail of the recent empirical sample, while a reading below 10 represents the lower tail.
Using more extreme default zones makes them intentionally selective.
Users who want broader zones can move the boundaries toward values such as 80 and 20.
Signal line
The white Moving Average line is an EMA of the final oscillator:
Signal = EMA(Percentile Rank Oscillator, Signal Length)
This provides a slower reference against which short-term rank movement can be compared.
Oscillator above signal
The percentile rank is strengthening relative to its own recent smoothed level.
Oscillator below signal
The rank is weakening.
Crossovers can be used to identify changes in short-term momentum within the broader percentile regime.
For example:
A bullish crossover below the oversold zone can indicate rank beginning to recover from an extreme.
A bearish crossover above the overbought zone can indicate deterioration from an upper-tail reading.
A crossover near 50 may represent a more neutral momentum transition.
Signal crosses should not be interpreted independently from oscillator location.
Stepped oscillator colouring
The oscillator uses stepped colour intensity based on its position relative to the 50 midline.
Above 50, colours progressively strengthen as the percentile reaches higher levels.
Below 50, bearish intensity progressively strengthens as the percentile falls.
The main regions are approximately:
50–62.5: modest positive rank.
62.5–75: strengthening positive rank.
75–90: strong positive rank.
90–99: upper-tail extreme.
99–100: exceptional upper-tail rank.
The lower half mirrors this concept:
37.5–50: modest negative rank.
25–37.5: weakening relative state.
10–25: strong negative rank.
1–10: lower-tail extreme.
0–1: exceptional lower-tail rank.
These colours do not introduce additional calculations or signals.
They visually communicate how far the oscillator has moved into its empirical distribution.
Column presentation
The percentile oscillator is plotted as columns around a histogram base of 50.
This means:
Values above 50 extend upward.
Values below 50 extend downward from the midline.
Although the numerical scale remains 0–100, this presentation visually emphasises deviation from the centre of the distribution.
The 50 level therefore functions as the oscillator’s equilibrium reference.
Price mode versus Momentum mode
The two modes answer different questions and should not be treated interchangeably.
Price Mode
Asks:
Where is price relative to its recent distribution?
This makes it useful for:
Range position.
Breakout context.
Relative price extremes.
Stochastic-like analysis.
Momentum Mode
Asks:
Where is current price change relative to the recent distribution of price changes?
This makes it useful for:
Momentum expansion.
Momentum exhaustion.
Relative impulse analysis.
Trend-strength transitions.
Momentum mode can identify weakening momentum before price itself leaves the upper part of its distribution.
Price mode can remain elevated simply because the market is still trading near recent highs.
Example: strong uptrend
Suppose price has been rising steadily.
Price Mode may remain above 90 because current price continually sits near the upper edge of its recent range.
Momentum Mode may behave differently:
It can rise toward 100 during acceleration.
Fall back toward 50 when the trend continues at a more ordinary pace.
Drop below 50 if momentum deteriorates significantly even while price remains relatively high.
This distinction can help separate price location from momentum condition .
Example: volatility regime change
Suppose a market normally changes by only small amounts, then suddenly produces a large directional move.
Raw momentum alone shows a large number.
The percentile rank provides additional context by showing whether that movement is unusual relative to the recent distribution.
If the current momentum is greater than nearly every recent observation, the oscillator moves toward 100.
If the market has already experienced many similarly large moves, the same absolute momentum may receive a much less extreme rank.
The indicator therefore adapts automatically to changing empirical behaviour without requiring fixed momentum thresholds.
Midline crossings
A crossover above 50 indicates the ranked series has moved into the upper half of its recent distribution.
A cross below 50 indicates movement into the lower half.
In Momentum mode, these crossings can be used as a simple relative momentum regime:
Above 50 = comparatively stronger momentum state.
Below 50 = comparatively weaker momentum state.
In Price mode, they indicate whether price is above or below the central portion of its recent rank distribution.
These crossings also control the optional main-chart candle colours.
Extreme-zone crossings
The indicator provides alerts when:
The oscillator crosses upward into the overbought zone.
The oscillator crosses downward into the oversold zone.
These alerts identify entry into an extreme percentile area.
They do not indicate that the extreme has ended.
For reversal-oriented analysis, a trader may instead monitor:
A subsequent exit from the zone.
A signal-line crossover.
Divergence with price.
A break in market structure.
Divergence interpretation
Because Momentum mode ranks momentum rather than price, it can also be useful for examining momentum divergence.
For example:
Price may make a higher high while the oscillator produces a lower percentile peak.
This indicates that the latest momentum observation is less exceptional relative to its recent history than it was during the previous price high.
The reverse can occur at lows.
As with conventional divergence, this is evidence of changing momentum characteristics, not confirmation that price must reverse.
How to use the indicator
1. Relative momentum regime
In Momentum mode, use the 50 midline as a simple regime reference:
Above 50 = positive relative momentum state.
Below 50 = negative relative momentum state.
2. Momentum extremes
Use the configurable zones to identify unusually high or low momentum ranks.
Rather than automatically fading these conditions, determine whether the market is:
Trending.
Exhausting.
Breaking out.
Returning toward equilibrium.
3. Signal-line transitions
Oscillator and signal-line crosses can help identify shorter-term changes in rank direction.
The location of the crossover matters.
A bullish crossover at 5 carries different context from one at 95.
4. Price-distribution analysis
Switch to Price mode when the objective is to measure where the current market sits within its recent price distribution.
This can be useful for:
Breakout analysis.
Range positioning.
Relative high/low detection.
5. Trend confirmation
Momentum remaining consistently above 50 can support an existing bullish trend.
Momentum remaining below 50 can support a bearish trend.
Repeated oscillation around 50 indicates that relative momentum is changing sides frequently.
6. Candle regime colouring
The optional overlay candles make the oscillator’s midline state visible directly on the main price chart.
This can be useful when the oscillator pane is being used primarily for extremes and signal-line analysis.
Input guide
Rank Target
Selects what is percentile-ranked.
Price ranks the source itself.
Momentum ranks its change over the selected Momentum Length.
Rank Window
Controls the empirical comparison sample.
Longer values are smoother and statistically broader. Shorter values adapt more quickly.
Momentum Length
Controls the displacement horizon in Momentum mode.
It has no effect in Price mode.
Output Smoothing
Applies optional EMA smoothing to the percentile rank.
1 produces the raw rank.
Signal Length
Controls the EMA signal line.
Shorter values follow the oscillator more closely. Longer values produce slower crossover signals.
Overbought Zone
Sets the lower boundary of the upper extreme area.
Oversold Zone
Sets the upper boundary of the lower extreme area.
How this differs from RSI
Traditional RSI:
Separates gains and losses.
Smooths their magnitude.
Calculates a relative-strength ratio.
Transforms that ratio onto a 0–100 scale.
Nonparametric Relative Momentum:
Calculates price or momentum directly.
Ranks the current observation against historical observations.
Uses no gain/loss ratio.
Uses no assumed distribution.
The identical 0–100 scale therefore represents a different statistical concept.
How this differs from Stochastic
A conventional stochastic oscillator measures where current price lies between the highest high and lowest low of a window.
Its basic concept is:
(Current - Lowest) / (Highest - Lowest)
Nonparametric Price mode instead asks how many historical observations are below the current price.
This distinction matters because the rank considers the entire empirical ordering of the sample, not only its two extreme endpoints.
Two windows can have identical highs, lows and current price but different internal distributions.
A stochastic calculation can return the same value in both cases, while percentile rank can differ because the number of observations above and below the current price is different.
How this differs from a Z-score
A Z-score measures deviation from a mean in standard-deviation units:
Z = (Current Value - Mean) / Standard Deviation
That calculation depends directly on the sample mean and dispersion.
Percentile rank depends only on ordering.
As a result, an extreme outlier can heavily alter a mean and standard deviation but has much less influence on the ordering of the remaining observations.
This is one of the reasons rank statistics can be useful when financial data contains skew, fat tails or isolated extreme moves.
Strengths
Uses a nonparametric empirical ranking process.
Requires no assumption of normality.
Produces an intuitive bounded 0–100 scale.
Adapts naturally to the recent behaviour of each market.
Supports both price-location and momentum-ranking modes.
Uses mid-ranks for tied observations.
Normalises momentum extremes without relying on fixed point or percentage thresholds.
Includes configurable smoothing and signal analysis.
Provides direct midline regime colouring on the main chart.
Limitations
A percentile rank measures relative position, not absolute magnitude.
A reading of 100 does not indicate how much larger the current observation is than the rest of the sample.
Persistent trends can remain at extreme ranks for extended periods.
Short Rank Windows can generate rapid percentile changes.
Long Rank Windows adapt more slowly to regime shifts.
Momentum mode uses absolute source change rather than percentage return, although ranking substantially reduces scale dependence within a single instrument.
Extreme readings are not automatic reversal signals.
Signal-line crosses can whipsaw in noisy conditions.
The oscillator is reactive and does not forecast future price.
Alerts
The indicator provides alerts for:
Cross Up 50: oscillator enters the upper half of its distribution.
Cross Down 50: oscillator enters the lower half.
Overbought: oscillator crosses upward through the selected upper-zone boundary.
Oversold: oscillator crosses downward through the selected lower-zone boundary.
Bull: oscillator crosses above its signal EMA.
Bear: oscillator crosses below its signal EMA.
Summary
Nonparametric Relative Momentum converts either price or momentum into an empirical percentile rank.
Instead of asking how far an observation is from a moving average, how many standard deviations it sits from a mean, or what ratio of gains to losses produced it, the indicator asks where that observation ranks relative to its own recent history.
In Price mode, it measures the relative location of price within its historical distribution.
In Momentum mode, it first calculates price displacement across a chosen horizon and then measures how exceptional that momentum is relative to recent momentum observations.
A mid-rank procedure handles tied values, optional EMA smoothing controls visual responsiveness, and a separate signal average provides crossover analysis. The 50 midline separates the upper and lower halves of the empirical distribution, while configurable overbought and oversold zones highlight the tails.
The result is a distribution-free relative momentum framework that adapts to the observed behaviour of the market rather than relying on fixed magnitude thresholds or an assumed statistical distribution.
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Tyson Uppercut Compression Spring Breakout (Viprasol)Tyson Uppercut — Compression Spring Breakout 🥊
(The name is an affectionate combat-sports homage — this is an educational pattern tool, not affiliated with or endorsed by any athlete or organization.)
CONCEPT
A spring loaded by VOLATILITY, not by swings. The tool measures the recent bar-range and requires it to be compressed — noticeably tighter than the window before it (energy coiling). Then the uppercut: one wide-range bar that bursts up out of the compression on the close = the release. Distinct from swing-decay coils; this reads raw range contraction directly from the bars.
HOW IT DETECTS
- Compression is measured on CLOSED bars only: the highest high and lowest low over a window (default 10 bars, offset by one bar).
- That compression range must be tight versus the prior, wider window (default: <= 60% of the range over twice the lookback) AND not larger than a set ATR cap.
- Release: the current bar closes above the compression high, its full bar range is at least a set ATR multiple (default 1.3x ATR), and it closes up (close > open).
- All logic runs on bar close (barstate.isconfirmed). An optional dotted live box previews an active compression before any break.
ENTRY / STOP / TARGET
- Entry: on the confirmed release close (long only).
- Stop: below the compression low, minus an ATR buffer.
- Target: entry + R multiple of risk (default 2R, adjustable).
- Drawn as a solid compression box plus an entry line and filled TP/SL zones that extend right until price touches one; the hit side thickens.
NON-REPAINTING
Compression is read from already-closed bars (a one-bar offset is used), and the release is confirmed on the bar close. A printed signal does not move or disappear afterward. The live dotted preview is informational only and is not a signal.
KEY FEATURES
- Volatility-compression detection from raw range, with an ATR height cap to avoid oversized "boxes".
- Release requires a genuinely wide breakout bar, not just a marginal close.
- Optional live compression preview; option to hide new setups while a trade is active.
- Filled, extend-until-hit TP/SL zones and an on-chart status table (open trades). Alert condition included.
INPUTS OVERVIEW
- The spring: compression window (bars), tightness fraction vs prior window, max compression height (x ATR), release bar range (x ATR), ATR length.
- The knockout: TP as R multiple, SL buffer (x ATR), minimum bars between signals, one-trade-at-a-time, hide-setup-while-in-trade.
- Visuals: compression box / entry / TP / SL colors, label offset, zone transparency, show-live toggle.
HOW TO USE
1. Add to a liquid symbol and timeframe; it is fully overlay-based.
2. Set the compression window and tightness fraction to define how coiled the range must be.
3. Raise the release ATR multiple to demand a stronger breakout bar.
4. Watch the live dotted box to anticipate setups, and study the TP/SL zones on your instrument.
5. Optionally create an alert from the built-in condition.
LIMITATIONS (read this)
- This is a pattern/education tool, not a signal service and not financial advice. It does not predict the future.
- Compression breakouts frequently fail or reverse (false breakouts are common), especially in ranging markets.
- It is long-only by design; it does not trade downside releases.
- Range readings depend on the chosen windows; different settings can materially change what counts as "compressed".
- Results depend heavily on your inputs, instrument, and timeframe. Always use your own risk management and discretion.
CREDITS
ATR uses Wilder's Average True Range. Highest/lowest range measurement uses standard public functions (ta.highest / ta.lowest). The raw-range compression-and-release detection and the trade-zone visualization are original Viprasol design.
Original Viprasol work; no third-party Pine code reused.
อินดิเคเตอร์
Innovation-Gated Hull Supertrend [BackQuant] Innovation-Gated Hull Supertrend
Overview
Innovation-Gated Hull Supertrend is an adaptive trend-following overlay that combines three distinct signal-processing components:
A Hull Moving Average projection for responsive trend estimation.
An innovation-gated recursive filter for adaptive noise reduction.
A volatility-based Supertrend applied to the filtered Hull estimate.
The indicator is designed to behave differently during quiet and active market conditions.
When the Hull estimate changes only slightly relative to recent volatility, the innovation gate restricts how much of that movement is admitted into the filtered trend estimate. The Supertrend bands can also expand during these quieter conditions, reducing sensitivity to minor fluctuations.
When a larger and statistically more meaningful change occurs, the gate opens. The recursive filter becomes more responsive, the Supertrend bands return closer to their base width, and the model is allowed to react more quickly.
The result is a trend framework that attempts to balance two competing requirements:
Remain stable when price movement is small and noisy.
Respond more quickly when new information produces a meaningful displacement.
The indicator does not predict future prices. It is a causal trend model that adapts its response according to the size of newly arriving information relative to the current volatility environment.
Core calculation chain
The complete calculation can be summarised as:
Calculate a Hull Moving Average projection from the selected price source.
Estimate current volatility using ATR, standard deviation, or a blend of both.
Compare the Hull projection with the recursive filter’s previous estimate.
Normalise that difference by volatility to calculate an innovation score.
Pass the score through a smooth logistic gate.
Use the gate to adapt the recursive filter’s measurement and process uncertainty.
Generate the innovation-filtered Hull estimate.
Optionally adapt the Supertrend band multiplier using the same gate.
Apply Supertrend logic around the filtered Hull estimate.
Generate bullish and bearish regime changes when the Supertrend changes sides.
Each stage solves a different problem.
The Hull projection provides a responsive directional input. The innovation filter decides how much of that input should be trusted. The Supertrend then converts the filtered estimate into a persistent trailing regime.
Historical background
The indicator combines ideas from several areas of technical analysis and signal processing.
Hull Moving Average
The Hull Moving Average was developed by Alan Hull as a method of reducing lag while preserving a smooth output.
Traditional moving averages face a basic trade-off:
Short averages respond quickly but contain more noise.
Long averages are smoother but react later.
The Hull Moving Average attempts to improve this balance by combining weighted moving averages of different lengths.
Its general construction is:
Fast WMA = WMA of price over approximately half the main length.
Slow WMA = WMA of price over the full length.
Raw Hull = 2 × Fast WMA - Slow WMA.
Final Hull = WMA of the Raw Hull over the square root of the main length.
The subtraction stage compensates for some of the delay introduced by the longer average. The final square-root smoothing stage reduces noise in the compensated series.
Recursive estimation and the Kalman-filter principle
The innovation filter is based on the general recursive-estimation framework associated with Kalman filtering.
The Kalman filter was developed by Rudolf E. Kálmán and became widely used in engineering, navigation, aerospace, robotics and control systems.
A recursive estimator typically follows two stages:
Predict the current state from the previous state.
Correct that prediction using the newest observation.
The correction depends on how uncertain the model is and how reliable the new observation is believed to be.
The difference between the observation and prediction is called the:
Innovation
In this indicator:
The observation is the current Hull projection.
The prediction is the previous filtered estimate.
The innovation is the difference between them.
A large innovation means the Hull projection has moved significantly away from the model’s prior estimate.
A small innovation means the new observation is close to what the model already expected.
Supertrend
Supertrend is a volatility-trailing concept built from an underlying price reference and ATR-based bands.
Its basic structure consists of:
An upper band above the reference.
A lower band below the reference.
One-sided trailing behaviour.
A regime switch when price crosses the opposing band.
In a bullish regime, the lower band acts as the active trail.
In a bearish regime, the upper band acts as the active trail.
This indicator modifies the conventional approach in two important ways:
The central reference is the innovation-filtered Hull estimate rather than a normal price midpoint.
The band multiplier can adapt according to the innovation gate.
Stage 1: Hull projection
The first stage calculates the Hull projection from the selected price source.
The script determines:
The full Hull length.
A half-length rounded to a valid integer.
A square-root length rounded to a valid integer.
It then calculates:
Fast WMA = WMA(source, half length)
Slow WMA = WMA(source, full length)
Raw Hull = 2 × Fast WMA - Slow WMA
Hull Projection = WMA(Raw Hull, square-root length)
The Hull projection is more responsive than many conventional moving averages of a similar nominal length.
However, responsiveness also means it can react to short-lived movements. For that reason, the Hull projection is not used directly as the final trend line. It becomes the observation supplied to the innovation filter.
Hull Length
The Hull Length controls the underlying trend horizon.
Lower values:
React more quickly.
Follow shorter trend legs.
Produce more local changes.
Admit more short-term noise into the next stage.
Higher values:
Produce a smoother projection.
Focus on broader trend structure.
Respond later to sudden reversals.
The Hull Length therefore controls the basic timescale of the model before any adaptive filtering or Supertrend logic is applied.
Stage 2: Volatility model
The innovation must be interpreted relative to current market conditions.
A movement of 10 points may be large in a quiet market but insignificant in a highly volatile market.
The indicator therefore normalises the innovation using a selectable volatility estimate.
Three modes are available:
ATR
Standard Deviation
Blend
ATR mode
Average True Range measures recent trading range while accounting for gaps from the previous close.
True Range is based on the greatest of:
Current high minus current low.
Absolute current high minus previous close.
Absolute current low minus previous close.
ATR then smooths True Range across the selected Volatility Length.
ATR is useful because it measures the realised movement range of the instrument.
It is sensitive to:
Wide candles.
Price gaps.
Range expansion.
Standard Deviation mode
Standard deviation measures how widely the Hull projection has varied around its recent mean.
It is a dispersion measure rather than a range measure.
Standard deviation responds to:
Variation in the selected series.
Directional displacement.
Changes in the distribution of the filtered input.
While ATR focuses on bar range, standard deviation focuses on dispersion of the Hull series itself.
Blend mode
Blend mode calculates the average of ATR and standard deviation.
Conceptually:
Blended Volatility = (ATR + Standard Deviation) / 2
This provides a combined estimate incorporating:
Observed range behaviour.
Statistical dispersion of the Hull projection.
Neither measure is universally superior. The blend attempts to reduce dependence on only one definition of volatility.
Volatility Length
The Volatility Length controls how quickly the normalisation baseline changes.
Lower values:
React faster to recent volatility changes.
Cause the innovation score to adjust more quickly.
May make the gate less stable.
Higher values:
Produce a slower volatility baseline.
Create more consistent normalisation.
May respond later when volatility changes abruptly.
The volatility estimate is prevented from falling below the instrument’s minimum tick size, avoiding unstable division during extremely quiet periods.
Stage 3: Innovation calculation
The filter begins each bar with a prediction.
In this implementation, the prediction is the previous filtered estimate.
The innovation is:
Innovation = Hull Projection - Previous Filter Estimate
The innovation may be positive or negative.
A positive value means the Hull projection is above the prior estimate.
A negative value means it is below the prior estimate.
The absolute innovation measures the size of the disagreement regardless of direction.
Innovation score
The raw innovation is normalised by current volatility:
Innovation Score = |Innovation| / Volatility
This expresses the new movement in volatility units.
For example:
A score of 0.25 means the innovation is approximately one quarter of the selected volatility measure.
A score of 1.00 means it is approximately equal to that volatility measure.
A score above 1.00 means the change is larger than the current volatility baseline.
The score is dimensionless, making it more comparable across instruments and price scales.
This is the key quantity used to determine whether the filter should remain cautious or become more responsive.
Stage 4: Logistic innovation gate
The innovation score is passed through a logistic function.
The logistic function has the form:
Gate = 1 / (1 + exp(-x))
Its output remains between zero and one.
In the indicator, the gate input depends on:
Innovation Score
Innovation Threshold
Gate Sharpness
Conceptually:
Gate Input = Sharpness × (Score - Threshold)
When the score is below the threshold:
The gate approaches zero.
The filter treats the new Hull movement cautiously.
When the score rises above the threshold:
The gate moves toward one.
The filter becomes more willing to admit the new movement.
The logistic function creates a smooth transition rather than a hard on/off switch.
This is important because a binary threshold could cause abrupt changes whenever the score moves slightly above or below one exact value.
Innovation Threshold
The Innovation Threshold determines where the gate begins moving from a quiet state toward an active state.
Higher values:
Require a larger volatility-normalised innovation.
Keep the filter conservative for longer.
Reject more moderate changes.
Lower values:
Open the gate sooner.
Increase responsiveness.
Allow smaller movements to influence the estimate.
The threshold should be interpreted in relation to the selected volatility model.
Gate Sharpness
Gate Sharpness controls how rapidly the logistic gate transitions around the threshold.
Lower sharpness:
Creates a gradual transition.
Produces a wider intermediate region.
Changes responsiveness smoothly.
Higher sharpness:
Makes the gate behave more like a hard switch.
Creates a faster transition near the threshold.
Produces stronger separation between quiet and active states.
An extremely high value can make the adaptive behaviour abrupt, while a low value may reduce the distinction between quiet and active conditions.
Admission Floor
The gate is converted into an admission value.
The Admission Floor ensures that the filter never completely ignores the Hull projection.
The admission calculation is:
Admission = Floor + (1 - Floor) × Gate
When the gate is near zero:
Admission remains near the selected floor.
When the gate is near one:
Admission approaches one.
A lower floor creates stronger filtering during quiet conditions.
A higher floor keeps the model more responsive even when innovation is small.
This setting prevents the estimator from becoming fully frozen.
Stage 5: Adaptive recursive update
The admission and gate values modify two uncertainty terms:
Measurement noise.
Process noise.
These terms control how the recursive filter balances its existing estimate against the new Hull observation.
Measurement Noise
Measurement Noise represents uncertainty in the incoming Hull projection.
Higher measurement noise tells the filter:
Trust the new observation less.
Remain closer to the previous estimate.
Produce more smoothing.
Lower measurement noise tells the filter:
Trust the Hull projection more.
Correct the estimate more aggressively.
Become more responsive.
The script adapts measurement noise using the admission value:
Adaptive Measurement Noise = Base Measurement Noise / Admission
When admission is low:
Measurement noise increases.
The new Hull movement receives less weight.
When admission is high:
Measurement noise moves closer to its base value.
The filter becomes more receptive.
Process Noise
Process Noise represents uncertainty in the filter’s current state model.
Higher process noise tells the estimator:
The underlying trend may be changing.
The previous estimate may no longer be reliable.
Allow faster adaptation.
Lower process noise tells it:
Assume the existing state remains relatively stable.
Change the estimate more cautiously.
The script increases process noise as the gate opens:
Adaptive Process Noise = Base Process Noise × (1 + Process Boost × Gate)
This creates a two-sided adaptive response.
During quiet conditions:
Measurement noise increases.
Process noise remains closer to its base level.
The filter resists small changes.
During high-innovation conditions:
Measurement noise decreases toward its normal value.
Process noise increases.
The filter becomes substantially more responsive.
Process Boost
Process Boost controls how strongly the process uncertainty expands when the gate opens.
Higher values:
Allow faster response to large innovations.
Increase the filter gain during active movement.
Can make the model more sensitive after shocks.
Lower values:
Keep behaviour closer to the base recursive filter.
Produce more controlled adaptation.
May respond more slowly to genuine regime changes.
Covariance and filter gain
The recursive filter maintains an internal covariance representing uncertainty in its estimate.
Before the new observation is processed:
Predicted Covariance = Previous Covariance + Adaptive Process Noise
The filter gain is then:
Gain = Predicted Covariance / (Predicted Covariance + Adaptive Measurement Noise)
The gain remains between zero and one.
A low gain means:
The previous estimate receives more influence.
The Hull observation receives less influence.
A high gain means:
The filter moves more strongly toward the current Hull projection.
The new estimate is:
Filtered Hull = Prediction + Gain × Innovation
The covariance is then updated for the next bar.
Why the filter is innovation-gated
A normal recursive filter may use constant process and measurement noise settings.
That means its responsiveness is broadly fixed.
This indicator changes those terms according to the size of the innovation.
The model therefore behaves differently under two broad conditions.
Quiet condition
When the Hull projection remains close to the prior estimate relative to volatility:
Innovation score is low.
Gate remains mostly closed.
Admission is limited.
Adaptive measurement noise rises.
Process noise remains lower.
Filter gain falls.
The filtered Hull changes more slowly.
Active condition
When the Hull projection moves meaningfully away from the prior estimate:
Innovation score rises.
Gate opens.
Admission approaches one.
Measurement noise decreases.
Process noise increases.
Filter gain rises.
The estimate adapts more quickly.
This allows the model to filter small movement without applying the same degree of resistance to every large move.
Stage 6: Innovation-adaptive Supertrend bands
The filtered Hull becomes the centre of the Supertrend calculation.
The initial raw bands are:
Upper Band = Filtered Hull + Factor × ATR
Lower Band = Filtered Hull - Factor × ATR
The Supertrend uses its own ATR Period, which is independent of the volatility length used by the innovation score.
This distinction is important:
Innovation volatility determines whether the filter should admit new information.
Supertrend ATR determines the distance of the trailing regime bands.
Adaptive band factor
When Adapt Bands With Innovation is enabled, the Supertrend factor changes according to the gate.
The adaptive factor is:
Adaptive Factor = Base Factor ×
When the gate is near one:
The adaptive factor approaches the base factor.
Bands become relatively tighter.
The Supertrend can respond more readily.
When the gate is near zero:
The factor expands above its base value.
Bands become wider.
Minor price fluctuations are less likely to cause a reversal.
This creates coordinated adaptation:
Quiet conditions produce stronger filtering and wider bands.
Active conditions produce faster filtering and narrower bands.
The same innovation state therefore influences both the centre estimate and the trailing threshold.
Quiet Band Expansion
Quiet Band Expansion controls how much wider the Supertrend factor becomes when the innovation gate is closed.
A value of zero disables the expansion effect even if band adaptation is enabled.
Higher values:
Create wider bands during low-innovation conditions.
Reduce quiet-market reversals.
Delay new signals until price moves further.
Lower values:
Keep the adaptive factor closer to its base setting.
Allow more responsive regime changes.
The expansion is greatest when the gate is near zero and fades as the gate opens.
Supertrend trailing logic
The raw upper and lower bands are converted into one-sided trailing bands.
The lower band is prevented from moving downward while price remains above its previous value.
The upper band is prevented from moving upward while price remains below its previous value.
This ratcheting behaviour creates:
A rising lower trail during bullish conditions.
A falling upper trail during bearish conditions.
A trend change occurs when price crosses the active opposing boundary.
In a bullish regime:
The lower band is the active Supertrend.
In a bearish regime:
The upper band is the active Supertrend.
ATR Period and Factor
ATR Period
Controls the volatility horizon used to construct the Supertrend bands.
Lower values:
React faster to current range changes.
Produce more variable band widths.
Higher values:
Produce a steadier range estimate.
Respond more slowly to sudden volatility changes.
Factor
Controls the base distance between the filtered Hull and the Supertrend bands.
Lower factors:
Create tighter bands.
Produce earlier regime changes.
Increase sensitivity to noise.
Higher factors:
Create wider bands.
Produce fewer regime changes.
Increase confirmation delay.
When adaptation is enabled, the selected factor acts as the minimum or active-condition factor. Quiet conditions may expand it further.
Trend signals
The indicator generates a long signal when the Supertrend changes into its bullish state.
It generates a short signal when the Supertrend changes into its bearish state.
The signal requires the completed calculation chain:
Hull projection.
Innovation filtering.
Adaptive band factor.
Supertrend regime change.
The plotted symbols are:
𝕃 for a bullish transition.
𝕊 for a bearish transition.
These markers identify regime changes. They are not complete trading systems and do not define stop placement, position size or profit targets.
Innovation impulse alert
The script also includes an Innovation Impulse alert.
This occurs when the innovation score crosses above the selected Innovation Threshold.
It indicates that:
The difference between the Hull projection and the recursive estimate has become large relative to volatility.
The gate is entering a more active state.
The filter is beginning to admit new information more aggressively.
An innovation impulse does not necessarily produce an immediate Supertrend reversal.
It can occur:
During acceleration within an existing trend.
At the beginning of a possible regime change.
During a temporary volatility shock.
It is therefore best interpreted as an information-arrival event rather than an automatic long or short signal.
Visual components
Hull Projection
Displays the unfiltered Hull Moving Average input.
This is useful for comparing:
The responsive raw projection.
The innovation-filtered result.
The final Supertrend.
The Hull projection will generally react first.
Filtered Hull
Displays the recursive innovation-gated estimate.
The distance between the Hull projection and filtered Hull helps illustrate the filter’s current behaviour.
During quiet conditions:
The filtered Hull may lag behind small changes.
During meaningful innovations:
It can move more rapidly toward the Hull projection.
IGH Supertrend
Displays the final volatility trail around the filtered Hull.
It is the primary regime output.
The line is coloured according to the persistent bullish or bearish trend state.
Candle colouring
Candles may be coloured according to the active Supertrend regime:
Bullish colour during the long regime.
Bearish colour during the short regime.
This provides immediate chart-wide directional context.
How to interpret the indicator
Bullish regime
A bullish regime indicates that price has crossed into the bullish side of the adaptive Supertrend structure.
The active trail is positioned below the market and can be interpreted as:
A dynamic trend boundary.
A possible pullback reference.
A regime invalidation guide.
Bearish regime
A bearish regime indicates that price has crossed into the bearish side of the adaptive structure.
The active trail is positioned above the market and may act as:
Dynamic resistance.
A rally reference.
A bearish regime invalidation guide.
Low innovation score
A low score means the current Hull movement is small relative to volatility.
The model responds by:
Filtering more strongly.
Reducing admission.
Using a lower recursive gain.
Potentially expanding the Supertrend bands.
This is intended to reduce reactions to small fluctuations.
High innovation score
A high score means the Hull projection has changed substantially relative to volatility.
The model responds by:
Opening the gate.
Increasing admission.
Increasing process uncertainty.
Raising the filter gain.
Reducing quiet-condition band expansion.
This allows a faster response when the incoming information is more significant.
Rising Hull without a trend flip
The Hull projection may turn before the filtered Hull or Supertrend.
This means:
The fast input has changed.
The adaptive filter has not yet admitted enough of that change.
The Supertrend boundary has not yet been crossed.
This is not an error. It demonstrates the staged confirmation design.
Innovation impulse without trend reversal
An innovation impulse can occur without a long or short signal.
This may indicate:
Acceleration in the existing trend.
A volatility shock.
An attempted reversal that has not crossed the Supertrend.
The Supertrend remains the final regime layer.
How to use the indicator
1. Trend regime filter
Use the active Supertrend state to filter another entry method:
Prioritise long setups during bullish regimes.
Prioritise short setups during bearish regimes.
2. Pullback framework
In a bullish regime, pullbacks toward the Supertrend may represent tests of the active trend boundary.
In a bearish regime, rallies toward the Supertrend may represent resistance tests.
A touch alone does not guarantee continuation.
3. Innovation monitoring
The innovation alert can be used to identify when the model detects a meaningful change in its input.
This may help direct attention to:
Fresh acceleration.
Breakout attempts.
Possible trend transitions.
4. Confirmation framework
The three optional lines can be read as a progression:
Hull projection changes first.
Filtered Hull adapts according to innovation.
Supertrend confirms the final regime.
This allows users to study the difference between early movement and confirmed structure.
5. Trailing risk reference
The final Supertrend may be used as a visual trailing reference.
However, it does not account for:
Account size.
Position size.
Slippage.
Liquidity.
Maximum acceptable loss.
It should not replace a complete risk-management process.
Parameter interaction
The settings should not be tuned independently without considering how they interact.
More responsive configuration
A more responsive setup may use:
Lower Hull Length.
Lower Innovation Threshold.
Higher Admission Floor.
Lower Measurement Noise.
Higher Process Noise or Process Boost.
Lower Supertrend Factor.
Lower Quiet Band Expansion.
This will generally produce earlier changes but more noise.
More conservative configuration
A more conservative setup may use:
Higher Hull Length.
Higher Innovation Threshold.
Lower Admission Floor.
Higher Measurement Noise.
Lower Process Boost.
Higher Supertrend Factor.
Higher Quiet Band Expansion.
This will generally create fewer transitions but greater delay.
Balanced interpretation
Changing several settings in the same direction can produce an extreme result.
For example:
A very low threshold, high admission floor, large process boost and tight Supertrend factor may overreact.
A very high threshold, low admission floor, high measurement noise and wide Supertrend factor may respond excessively slowly.
The appropriate balance depends on the instrument, timeframe and intended holding period.
How this differs from a standard Hull trend indicator
A standard Hull trend indicator normally uses:
Hull slope.
Price crossing the Hull.
A fast and slow Hull comparison.
This indicator instead:
Uses the Hull as an observation.
Measures its disagreement with a recursive estimate.
Normalises that disagreement by volatility.
Adapts the filter gain according to the innovation.
Applies a final Supertrend regime around the filtered result.
The Hull is therefore the beginning of the model, not the final signal.
How this differs from a fixed Kalman-style filter
A fixed recursive filter uses constant uncertainty settings.
Innovation-Gated Hull Supertrend adapts both measurement and process uncertainty according to the normalised innovation.
This means:
Small innovations are filtered more heavily.
Large innovations receive greater admission.
The response speed is therefore state dependent.
How this differs from a standard Supertrend
A standard Supertrend is commonly centred around a raw price reference such as HL2.
This indicator uses:
A responsive Hull projection.
An innovation-gated recursive estimate of that projection.
An optionally adaptive band multiplier.
The Supertrend is therefore built around a filtered trend estimate rather than raw price alone.
Strengths
Combines responsive and stable trend-processing stages.
Normalises new movement by current volatility.
Uses a smooth gate rather than a binary threshold.
Adapts measurement and process uncertainty.
Can widen trend bands during quiet conditions.
Can respond more rapidly to meaningful innovations.
Separates early movement from final regime confirmation.
Supports ATR, standard deviation and blended volatility models.
Provides trend, impulse and visual comparison outputs.
Limitations
The indicator is reactive rather than predictive.
Strong filtering can delay genuine reversals.
Responsive settings can increase whipsaws.
A large innovation may represent a temporary shock rather than a lasting trend.
Supertrend signals still depend on ATR and price crossing behaviour.
Parameter combinations can materially change the model’s behaviour.
The indicator may require different settings across assets and timeframes.
The recursive state develops from the available chart history.
Values can update while the current real-time candle is still forming.
Causality and real-time behaviour
The calculation uses current and historical observations without future-looking references.
However, like most indicators calculated on live candles, the current bar’s values can change before the candle closes.
This means:
The Hull projection may move intrabar.
The innovation score and gate may change intrabar.
A Supertrend transition may appear and disappear before confirmation.
Users requiring confirmed signals should evaluate the indicator at bar close or configure alerts accordingly.
Alerts
The indicator provides three alert conditions:
IGH ST Long: the adaptive Supertrend changes into a bullish regime.
IGH ST Short: the adaptive Supertrend changes into a bearish regime.
IGH Impulse: the normalised innovation score crosses above the selected threshold.
The impulse alert identifies increased information flow into the filter. It does not specify direction by itself because the innovation score uses the absolute size of the prediction error.
Summary
Innovation-Gated Hull Supertrend combines a responsive Hull Moving Average, a volatility-normalised innovation gate, an adaptive recursive filter and a volatility-trailing Supertrend.
The Hull projection provides an early estimate of directional movement. The recursive filter compares that projection with its prior state and measures the resulting innovation relative to ATR, standard deviation or a blend of both.
A logistic gate then determines how strongly the new movement should be admitted. During quiet conditions, the filter becomes more conservative and the Supertrend bands can expand. During meaningful displacement, the filter becomes more responsive and the bands move closer to their base width.
The final Supertrend converts the adaptive estimate into a persistent bullish or bearish regime.
The indicator is designed to make responsiveness conditional rather than fixed: small movements receive stronger filtering, while larger volatility-adjusted innovations are allowed to influence the model more quickly.
อินดิเคเตอร์
Session ATR Risk ToolSession ATR Risk Tool
## Overview
The Session ATR Risk Tool is a discretionary **risk-management and trade-planning overlay**. It sizes a stop loss from market volatility, projects fixed reward-to-risk targets (1:1, 1:2, 1:3), and draws a standard-deviation ladder so you can see your full trade geometry on the chart before you enter. It also estimates a contract count from a fixed dollar risk, and prints a context table of intraday, daily and weekly volatility.
It is built and tuned for Micro E-mini Nasdaq-100 (MNQ) intraday trading, but every parameter is exposed as an input, so it works on any symbol once you set the correct point value.
This tool does **not** generate buy/sell signals and makes no claim about win rate or profitability. It is a visualization and planning aid only.
## What makes it different
Most reward-to-risk tools place lines a fixed number of ticks or a single ATR away. This tool adds three things in one package:
1. **Two selectable stop engines.** The stop distance can be derived from either the chart-timeframe ATR (small, realistic intraday stops) or from a rolling average of completed *session* ranges (swing-sized stops). You choose which volatility regime sizes your risk.
2. **A standard-deviation ladder denominated in your own stop distance.** Instead of arbitrary fib or price-percent levels, each rung is a multiple (−0.5, 1, 2, 3, 4 by default, all editable) of the exact ATR-based stop distance, projected from entry. One "sd" on the chart always equals one unit of the risk you are actually taking.
3. **A volatility context table.** Intraday ATR, averaged session range, daily ATR(14) and weekly ATR(14) are shown side by side so the chosen stop can be judged against higher-timeframe volatility at a glance.
## How it works
- **Session range capture.** The script tracks the high and low of each completed session window (default 09:30–16:00 exchange time) and stores the high-low range. It keeps a rolling buffer of the most recent N sessions (default 10) and averages them to produce a "session ATR" in points.
- **Intraday ATR.** A standard ATR of configurable length is calculated on the chart timeframe for scalp-sized stops.
- **Stop distance.** `Stop distance = chosen basis × ATR multiplier`, where the basis is either the intraday ATR or the averaged session range. The multiplier lets you tighten or widen the stop.
- **Trade geometry.** From the entry price (live price by default, or a fixed price you type in) and the trade direction, the tool places the stop one stop-distance against you, then projects targets at 1×, 2× and 3× the stop distance for clean 1:1 / 1:2 / 1:3 reward-to-risk.
- **Standard-deviation ladder.** Each ladder rung is plotted at `entry + direction × stop distance × deviation`, giving an evenly scaled map of where price sits relative to your risk unit.
- **Position-size estimate.** Dollar risk per contract = stop distance × point value. Estimated contracts = floor(risk per trade ÷ dollar risk per contract). This is an arithmetic estimate for planning, not an order-routing instruction.
- **Higher-timeframe context.** Daily and weekly ATR(14) are pulled from confirmed higher-timeframe bars (non-repainting) for the context table.
All levels are drawn as faded horizontal rays anchored to the bar grid, so they stay locked to the candles when you pan or zoom. Drawings rebuild on the most recent bar to keep the chart clean.
## How to use it
1. Add the tool to an intraday chart of the instrument you trade.
2. Set **$ per Point** for your instrument (MNQ = 2.0, NQ = 20.0, MES = 5.0, etc.) and your **Risk per Trade ($)**.
3. Choose your **Stop Basis** — "Intraday ATR" for scalps and intraday entries, "Session Range" for wider, swing-style stops.
4. Adjust the **ATR Multiplier** to set how far the stop sits from entry. As a starting guide, roughly 1.0–2.0× with Intraday ATR on a 1–5 minute chart; if using Session Range, scale the multiplier down (around 0.10–0.20×) because the session range is much larger.
5. Set **Trade Direction** (Long or Short). Leave **Entry Price** at 0 to anchor the levels to live price, or type your actual fill price to lock the geometry in place after entry.
6. Read your plan off the chart: the Stop, the 1:1 / 1:2 / 1:3 targets, the standard-deviation ladder, and the info table showing the stop in points and dollars plus an estimated contract count.
## Inputs
- **Session Window / Sessions to Average** — defines the session and how many completed sessions feed the averaged session range.
- **Stop Basis / Intraday ATR Length / ATR Multiplier** — select and tune the volatility source for the stop.
- **Trade Direction / Entry Price** — direction toggle and optional fixed entry.
- **$ per Point / Risk per Trade ($)** — instrument tick value and account risk used for the size estimate.
- **SDev Ladder deviations** — the five editable ladder multiples.
- **Visual controls** — ray length back/forward, table toggle, and colors for up, down and entry levels.
## Notes and limitations
- The contract-count figure is an arithmetic estimate from your inputs. It is not connected to a broker and places no orders. Always confirm size and risk in your own platform.
- "Session ATR" here means the averaged high-low **range** of recent sessions, not a true-range calculation; it is intentionally a wider, regime-level measure.
- Higher-timeframe ATR values use confirmed bars to avoid repainting.
- Reward-to-risk targets are fixed geometric projections; they are not predictions of price reaching those levels.
- This script is a planning and visualization tool only. It is not financial advice and does not guarantee any outcome. อินดิเคเตอร์
อินดิเคเตอร์
Volatility Gated Supertrend [BackQuant]Volatility Gated Supertrend
Overview
Volatility Gated Supertrend is a regime-aware trend-following indicator built around a modified Supertrend engine with an integrated volatility filter . Unlike a traditional Supertrend, which flips direction whenever price crosses its trailing bands, this version introduces a gating mechanism that can block trend reversals during low-volatility conditions .
The purpose of the indicator is simple:
Keep the responsiveness and structure of a Supertrend.
Reduce false flips during sideways or compressed conditions.
Allow trend transitions primarily when volatility is expanding enough to justify participation.
The result is a smoother and more selective trend engine designed to suppress whipsaws while still reacting to meaningful directional movement.
The full source structure for the indicator can be referenced here: :contentReference {index=0}
Core idea
Traditional Supertrend indicators work well during directional markets but struggle in compressed environments:
Price repeatedly crosses the trailing bands.
Trend direction flips too frequently.
False reversals appear during chop.
This indicator attempts to solve that problem by asking:
“Is there enough volatility expansion to justify accepting a new trend?”
Instead of blindly allowing every flip, the indicator measures:
Current volatility,
Baseline volatility,
Relative expansion or compression.
Only when volatility conditions are sufficient does the trend engine allow a directional transition.
What the Supertrend is
The Supertrend is a volatility-based trailing trend indicator built from:
ATR (Average True Range)
A central price source
A directional trailing stop structure
The classic logic:
Upper band = price source + ATR × multiplier
Lower band = price source − ATR × multiplier
These bands trail price dynamically:
In bullish conditions, the lower band ratchets upward.
In bearish conditions, the upper band ratchets downward.
When price crosses one of the bands:
The trend flips direction.
This creates a clean directional regime model.
How this version differs
The major difference is the volatility gate .
A normal Supertrend asks:
“Did price cross the band?”
This indicator asks:
“Did price cross the band, and is volatility strong enough to trust the move?”
That additional filter dramatically changes behavior in sideways conditions.
ATR and volatility structure
The indicator uses two ATR measurements:
Fast ATR → current short-term volatility
Slow ATR → baseline long-term volatility
The core ratio:
Volatility Ratio = Fast ATR / Slow ATR
Interpretation:
Ratio above threshold → volatility expansion
Ratio below threshold → volatility compression
This becomes the gate logic.
Volatility Gate Logic
The gate opens only when:
Fast ATR / Slow ATR ≥ Gate Threshold
If volatility is too compressed:
The gate closes.
Trend flips are blocked.
Importantly:
The Supertrend bands still calculate normally.
Price can still cross them.
But the directional state will not update while the gate is closed.
This distinction matters because it means:
The market may technically trigger a reversal,
But the indicator intentionally ignores it if volatility conditions are weak.
Why this helps
Most trend-following systems fail in chop because:
Small meaningless moves trigger directional flips.
There is insufficient range expansion.
The market lacks trend persistence.
By requiring volatility confirmation:
Weak reversals are filtered out.
Trend state becomes more stable.
Noise is reduced.
This makes the indicator particularly useful during:
Low-volatility consolidations,
Mean-reverting conditions,
Slow drifting ranges.
Band construction
The indicator uses:
hl2 as the central source,
ATR for dynamic width,
A configurable multiplier for sensitivity.
Formulas:
Upper Band = hl2 + ATR × multiplier
Lower Band = hl2 − ATR × multiplier
The trailing logic prevents the bands from moving backward unnecessarily:
Bullish lower band only rises.
Bearish upper band only falls.
This creates the staircase-style trailing structure common in Supertrend systems.
Trend state
Trend direction is binary:
1 = bullish
-1 = bearish
A raw bullish flip occurs when:
Close > trailing upper band
A raw bearish flip occurs when:
Close < trailing lower band
However:
The trend only updates if the volatility gate is open.
This is the defining behavior of the script.
Blocked flips
One of the most important features is the visualization of blocked signals .
When:
Price crosses a band,
But volatility is insufficient,
The script:
Plots an X-cross marker,
Keeps the existing trend state,
Refuses the flip.
This gives traders visibility into:
Potential but unconfirmed reversals,
Areas of weak participation,
Fake breakouts or low-energy transitions.
Visual behavior
Trend band
The active trailing band changes color based on trend direction:
Bullish → bullish color
Bearish → bearish color
Gate closed → gated color (dimmed)
Trend fill
The script fills the space between price and the active band:
Bullish fill during bullish regimes
Bearish fill during bearish regimes
This creates a cleaner directional overlay.
Outer glow
An additional glow layer expands slightly beyond the trend band:
Adds directional emphasis,
Improves trend readability,
Visually reinforces active regime.
When the gate closes:
The band and candles dim.
This visually communicates:
“The trend engine is currently suppressing flips.”
Candle coloring
Candles can optionally inherit the trend state:
Bullish regime → bullish candles
Bearish regime → bearish candles
Gate closed → dimmed neutral appearance
This allows the indicator to function as a full-chart regime overlay.
Signal logic
Bullish signal
Occurs when:
Trend flips from bearish to bullish,
AND the gate is open.
Bearish signal
Occurs when:
Trend flips from bullish to bearish,
AND the gate is open.
Blocked signal
Occurs when:
A raw flip condition appears,
BUT volatility ratio is below threshold.
This distinction is important:
A blocked signal is not ignored information.
It is a rejected transition.
How to interpret the gate
Gate open
Volatility is active.
Market expansion is sufficient.
Trend flips are allowed.
Gate closed
Market is compressed.
Conditions are likely choppy.
Trend reversals are suppressed.
This effectively turns the indicator into a:
Trend-following system during expansion,
Trend-holding system during compression.
Why ATR ratio works well
ATR ratio is a powerful regime detector because it measures:
Current volatility relative to normal volatility.
Not just:
“Is volatility high?”
But:
“Is volatility high relative to its recent baseline?”
This adaptive behavior allows the gate to work across:
Different assets,
Different timeframes,
Different volatility environments.
Input guide
ATR Multiplier
Controls band width:
Higher = wider bands, fewer flips
Lower = tighter bands, more sensitivity
ATR Length
Controls volatility calculation for the Supertrend itself.
Fast ATR
Short-term volatility measure.
Slow ATR
Long-term baseline volatility measure.
Gate Threshold
Controls how strict the gate is:
Lower threshold = more permissive
Higher threshold = more restrictive
Example:
0.6 → allows more flips
1.0 → requires current volatility to match baseline
1.2 → requires expansion regime
Strengths
Reduces Supertrend whipsaws in chop.
Adds regime awareness.
Uses adaptive volatility filtering.
Clean trend visualization.
Blocked-signal logic provides extra context.
Limitations
Can delay reversals during early expansion.
Very high thresholds may suppress legitimate transitions.
Still fundamentally a trend-following system.
Not designed for low-volatility mean reversion trading.
Best use case
Volatility Gated Supertrend works best as:
A directional regime filter,
A swing trend overlay,
A volatility-aware trend confirmation tool,
A way to suppress noise during consolidations.
It is particularly useful for traders who:
Like Supertrend logic,
But dislike how often it flips in sideways markets.
Summary
Volatility Gated Supertrend extends the classic Supertrend framework by introducing a volatility-aware gating engine that blocks trend reversals during compressed market conditions. By comparing fast ATR against slow ATR, the script determines whether enough volatility expansion exists to justify a directional transition. The result is a cleaner, more stable trend system that retains the strengths of Supertrend logic while dramatically reducing whipsaws during low-energy market regimes. อินดิเคเตอร์
Xer0's Dual Engine Ladder AllocatorOverview
This indicator is designed for long-term investors using a "Dual Engine" portfolio strategy on M1 Finance — mixing a broad-market index fund with a leveraged counterpart in the same Pie. Instead of guessing when to buy the dip, this script provides a systematic, step-by-step roadmap for increasing your leveraged allocation as the market falls, and resetting it as the market recovers.
How It Works
The strategy is built on "Sticky All-Time High" logic. It tracks the highest close price and calculates the current drawdown from that peak, then responds with one of three scenarios:
Ladder Down (Risk On): For every defined drop step (e.g. every -5%), the indicator signals a RISK UP event — automatically calculating your new target allocation to the leveraged slice of your Pie. This forces systematic, disciplined buying at lower prices.
Recovery Reset (Risk Off): Once the market recovers by a set percentage from the bottom, the script signals a RESET — returning your allocation to the base level and locking in the gains from the dip-buying phase.
Bull Step: When the market pushes into new high territory, the script tracks each new leg up and keeps your reference point current.
Key Features
Sticky ATH Tracking: Automatically calculates true drawdown from the cycle peak
Customizable Ladder Steps: Define your own drop trigger percentage and leverage increase per step
Max Cap: Hard ceiling on leverage exposure to protect against catastrophic drawdowns
Bar Confirmation: All signals fire on daily close to avoid intraday false triggers
Visual Dashboard: Bottom-right table showing current mode, target leverage, drawdown, and recovery price target
Alert Conditions: Built-in RISK UP and RESET alerts compatible with TradingView's "Once Per Bar Close" setting
Backtested Performance (Simulated — Read Carefully)
The following results are from a Python backtest covering approximately 30 years (1996–2026), using $923/week in contributions every Friday. The strategy used two M1 Pies: Pie 1 (S&P 500 index fund / 3× S&P 500 ETF, base leverage 35%) and Pie 2 (Nasdaq-100 index fund / 3× Nasdaq-100 ETF, base leverage 25%). Tax assumptions reflect California state + federal rates for a $47K–$100K income bracket. Data prior to 2010 is synthetic, modeled from underlying index returns.
Results are hypothetical and do not represent actual trading. Past performance does not guarantee future results.
Ladder Strategy | VOO Benchmark
Total Contributed $1,395,576 | $1,395,576
Final Value (after-tax) $25,286,879 | $9,025,443
Total Return 1,711.9% | 546.7%
CAGR (on contributions) 10.1% | 6.4%
Max Drawdown -91.8% | -50.5%
Taxes Paid (CA) $5,358,907 | N/A (buy & hold)
Cash After Full Liquidation $23,500,189 | $7,171,385
The ladder strategy produced approximately 227.7% more after-tax cash than buy-and-hold VOO after full liquidation. However, the strategy experienced a maximum drawdown of -91.8% — meaning at its worst point, the portfolio lost nearly all of its value on paper. This level of volatility is not suitable for most investors and requires strong conviction and a long time horizon to hold through.
How to Use
Add this indicator to a Daily (1D) chart of your chosen index. Configure the inputs to match your risk tolerance — Base Leverage %, Drop Step %, and Max Cap %. Enter your M1 Pie name in the input field so alerts reference it by name. Set alerts using "Once Per Bar Close" and adjust your Pie allocation whenever a signal fires.
Disclaimer
This script is for informational and educational purposes only. It does not constitute financial advice. Backtested results are simulated and hypothetical — they do not account for all real-world frictions and should not be interpreted as a guarantee of future performance. Trading leveraged instruments involves significant risk, including the potential loss of your entire investment, and is not suitable for all investors. อินดิเคเตอร์
BTC Valuation Cycle [Alpha Extract]A sophisticated multi-metric Bitcoin valuation framework that synthesizes on-chain analytics including SOPR, MVRV, Price-to-Realized, and Mayer Multiple into a unified 0-100 cycle oscillator with six-tier zone classification for market cycle identification. Utilizing logistic transformation with configurable weighting and z-score normalization, this indicator delivers institutional-grade Bitcoin-specific valuation assessment with pivot-based extreme detection and comprehensive alert system. The system's weighted composite architecture combined with adaptive curve intensity enables precise calibration of cycle sensitivity while maintaining statistical validity across Bitcoin's multi-year market cycles.
🔶 Advanced Multi-Metric Synthesis Engine
Implements sophisticated composite calculation combining four distinct Bitcoin valuation metrics with configurable weighting and normalization framework. The system retrieves SOPR (Spent Output Profit Ratio), MVRV (Market Value to Realized Value), Price-to-Realized ratio, and Mayer Multiple from on-chain sources, applies z-score normalization to each metric over configurable periods, transforms via logistic function for 0-100 scaling, and generates weighted average creating unified cycle score.
// Component Score Calculation
SOPR_Centered = SOPR - 1.0
SOPR_Z = z_score(SOPR_Centered, Normalization_Length)
SOPR_Score = logistic_100(SOPR_Z, Curve_Intensity)
Price_to_Realized_Z = z_score(Price / Realized_Price, Normalization_Length)
PR_Score = logistic_100(Price_to_Realized_Z, Curve_Intensity)
MVRV_Z = z_score(Market_Cap / Realized_Cap, Normalization_Length)
MVRV_Score = logistic_100(MVRV_Z, Curve_Intensity)
Mayer_Z = z_score(Mayer_Multiple, Normalization_Length)
Mayer_Score = logistic_100(Mayer_Z, Curve_Intensity)
// Weighted Composite
Cycle = (SOPR_Score × W_SOPR + PR_Score × W_PR + MVRV_Score × W_MVRV + Mayer_Score × W_Mayer) / (W_SOPR + W_PR + W_MVRV + W_Mayer)
🔶 Understanding Bitcoin Valuation Metrics
SOPR (Spent Output Profit Ratio) measures the degree of profit for coins moved on-chain, calculated as value sold divided by value paid. Values above 1.0 indicate profitable selling (distribution), below 1.0 indicate loss-taking (capitulation). The system centers SOPR around 1.0 for normalization.
MVRV (Market Value to Realized Value) compares current market cap to realized cap (aggregate cost basis). High MVRV signals overvaluation as price exceeds average acquisition cost; low
MVRV suggests undervaluation. The system offers Ratio mode (raw MVRV), Z-Score mode (statistical deviation), or Blend mode (average of both).
Price-to-Realized Ratio directly compares current BTC price to realized price (realized cap divided by circulating supply), providing cleaner valuation signal than MVRV by removing market cap distortions.
Mayer Multiple measures price relative to 200-day moving average. Values above 2.4 historically mark tops; values near or below 1.0 mark bottoms. The system normalizes this classic technical indicator alongside on-chain metrics.
🔶 Logistic Transformation Framework
Features sophisticated logistic function application converting unbounded z-scores into bounded 0-100 range with configurable curve intensity controlling sensitivity. The system applies formula: 100 / (1 + exp(-z × k)) where z is z-score and k is curve intensity (default 0.90), creates S-curve transformation preserving relative relationships while preventing extreme outliers, and enables smooth gradient visualization across entire cycle range.
🔶 Six-Tier Cycle Zone Classification
Implements comprehensive market cycle framework dividing 0-100 range into six distinct zones with configurable thresholds representing Bitcoin's characteristic bubble and bust patterns. The system defines Bottom Extreme (default <10, accumulation zone), Cold Zone (10-25, early recovery), Lower Mid (25-40, neutral to bullish), Upper Mid (40-60, bullish), Hot Zone (60-75, late bull market), and Top Extreme (>75, euphoria/distribution) with dynamic color coding.
🔶 Pivot-Based Extreme Detection System
Provides intelligent local extreme identification using pivot high/low detection with zone threshold filtering and visual capsule markers. The system detects pivot highs above Hot Zone threshold and pivot lows below Cold Zone threshold using configurable left/right bars, creates horizontal capsule visualizations at exact extreme values with color-coded centers (red for tops, cyan for bottoms), and maintains rolling array limited to maximum capsule count for clean chart presentation.
🔶 MVRV Calculation Mode Selection
Offers three distinct MVRV calculation approaches optimizing for different market conditions and analytical preferences. Ratio mode uses raw Market Cap / Realized Cap for direct valuation comparison, Z-Score mode applies statistical normalization emphasizing deviations from historical mean, and Blend mode (default) averages both approaches balancing absolute valuation with statistical context for robust signal generation.
🔶 Configurable Metric Weighting System
Features flexible weight allocation enabling traders to emphasize preferred metrics or disable unreliable components during specific market regimes. The system accepts 0.0-N weight values for each metric (default 1.0 all equal), automatically handles missing data by excluding NA metrics from composite, recalculates weighted average dynamically, and enables custom cycle calibration based on trader's confidence in different on-chain signals.
🔶 Confirmed HTF Data Integration
Implements rigorous anti-repaint methodology using confirmed higher-timeframe values with offset preventing live bar distortion. The system retrieves all on-chain metrics from daily timeframe with 1-bar offset ensuring only completed daily candle data influences cycle score, applies identical offset to Mayer Multiple calculation, and maintains signal stability across real-time updates preventing false extreme alerts.
🔶 Comprehensive Alert Framework
Provides five distinct alert conditions covering critical cycle events and threshold breaches with descriptive messages. The system triggers Top Extreme alert on crossover above top threshold (default 90), Bottom Extreme alert on crossunder below bottom threshold (default 10), Hot Rejection alert when cycle falls from Hot Zone, Cold Reclaim alert when cycle rises from Cold Zone, and Mayer Threshold breach alert for traditional technical confirmation.
🔶 Gradient Zone Visualization Architecture
Creates intuitive color-coded area plot with six distinct color zones reflecting current cycle position through visual spectrum from cyan (extreme bottom) through purple/orange to red (extreme top). The system applies dynamic zone coloring to both area fill and cycle value display, implements configurable area transparency (default opaque), and maintains consistent color scheme across oscillator pane, table values, and capsule markers.
🔶 Real-Time Diagnostics System
Features comprehensive data availability monitoring with missing metric labels and detailed value table showing all component metrics. The system detects NA values in SOPR, Realized Price, MVRV, or Mayer Multiple, displays warning label listing unavailable metrics, and provides table overlay showing current values for Cycle score, all four components, MVRV-Z, Mayer MA, and threshold with color-coded formatting.
🔶 Performance Optimization Framework
Employs efficient calculation methods with null-safe division functions, optimized array management for capsule storage, and conditional plotting minimizing unnecessary rendering. The system includes streamlined weighted average calculation skipping NA metrics, smart capsule cleanup maintaining maximum limit through oldest-first deletion, and minimal recalculation overhead through var declarations and confirmed bar logic.
This indicator delivers sophisticated Bitcoin-specific valuation analysis through multi-metric on-chain synthesis unavailable in traditional technical indicators. By combining SOPR (profit/loss behavior), MVRV (cost basis valuation), Price-to-Realized (pure valuation), and Mayer Multiple (technical context) into unified cycle framework with statistical normalization, it provides comprehensive market cycle assessment grounded in blockchain fundamentals. The six-tier zone system maps directly to Bitcoin's characteristic 4-year halving cycles with Bottom Extreme zones historically marking generational buying opportunities and Top Extreme zones marking distribution phases. Perfect for long-term Bitcoin investors seeking data-driven cycle timing, position sizing based on valuation extremes (increase allocation in Cold/Bottom zones, reduce in Hot/Top zones), and objective framework for navigating Bitcoin's volatile multi-year cycles with alerts providing advance warning of major cycle transitions requiring portfolio reassessment. อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
Price per m2 Argentina CABA USD/m2 - SMAs (1999-2025)Overview
This indicator plots the historical USD price per square meter of apartments in CABA (Buenos Aires City), Argentina, combining annual data (1999–2011) with monthly data (2012–2025) to reconstruct a long-term residential real estate pricing series.
All values were manually digitized, cleaned, and consolidated from public reports and market datasets to create a continuous analytical framework for historical valuation analysis.
The script also includes SMA20, SMA50, and SMA100 calculated over the custom dataset to support long-term trend analysis, cycle identification, and macro structural evaluation.
Data Sources
1999–2011 (Annual): Maure Real Estate Market Reports
2012–2020 (Monthly): UCEMA Real Estate Index
2020–2025 (Monthly): RE/MAX – UCEMA Market Monitor
All datasets referenced are derived from publicly available reports and institutional publications.
How to Use This Indicator
*USE ON THE 1 MONTH TIMEFRAME*
This tool enables investors, developers, and market analysts to:
• Identify multi-year trend shifts in residential real estate valuations
• Compare pricing cycles against Argentine macroeconomic environments
• Map long-term support and resistance zones
• Detect early signs of market recovery or contraction
• Integrate real estate fundamentals with technical analysis frameworks
The moving averages help visualize structural trends that are typically less observable in traditional property datasets.
About This Work
This historical series was reconstructed and coded by Engineer Francisco Michelich through the consolidation of market research, statistical normalization, and technical analysis methodologies.
This script does not represent an official financial index and is not affiliated with the original data institutions. It is intended solely as an educational and analytical tool for visualizing long-term trends in the Buenos Aires, Argentina residential real estate market. อินดิเคเตอร์
อินดิเคเตอร์
RLPS -Simplified Long-Term Support/Resistance Levels (Shelters)// Introduction //
RLPS (Simplified Long-Term Shelters) is a streamlined indicator designed for traders who have already identified the preponderant long-term phase of their assets and want to efficiently track multiple assets using pre-calculated Fibonacci levels.
IMPORTANT: Before using this indicator, you need to have determined the date-price coordinates of the preponderant phase (i0→i1 pivots) for your asset(s). These coordinates can be obtained using our master RLP indicator (Long-Term Shelters), which automatically helps to calculates them, or through your own research and analysis.
// Theoretical Foundation //
Many traditional institutional investors use the latest higher-degree market phase that stands out from others (longest duration and greatest price change on daily timeframe) to base a Fibonacci retracement on whose levels they open long-term positions. These positions can remain open to be activated in the future even years in advance. The phase is considered valid until a new, more preponderant phase develops over time.
RLPS allows you to manually input these pre-identified phase coordinates and draw Fibonacci levels that serve as Long-Term Shelter Levels—marking future trading points (entries, exits, risk management) that remain valid for months and even years.
// Key Features //
• Supports up to 5 different assets with permanently stored phase coordinates
• Dropdown selector to quickly switch between configured assets
• No ZigZag calculation required—user provides pre-calculated coordinates
• Timeframe-agnostic: levels remain constant across all timeframes
• Works with any price source (exchange) regardless of historical data availability
• Asset Information table with visual validation (✅ Match / ❌ No Match)
• Long-Term Historical Prices (LTHP): add up to 5 psychological price levels per asset (historical highs/lows, annual opening prices, etc.)
• Customizable Fibonacci levels, colors, styles, and label formatting
• Logarithmic scale support for volatile assets like cryptocurrencies
// Quick Start Guide //
STEP 1: In TradingView, select "Bitcoin / U.S. dollar" from Bitstamp Exchange (BITSTAMP:BTCUSD).
STEP 2: Configure the chart to Daily (D) timeframe.
STEP 3: Load the RLPS indicator. Initially no drawing appears (fields are empty by default).
STEP 4: Open indicator settings and activate "Practice Asset Data Table" in the GENERAL section.
STEP 5: A table appears with sample data for 5 assets. Locate "Bitcoin on Bitstamp":
- i0 Date: 2020-03-13 18:00 | i0 Price: 3850.0
- i1 Date: 2021-11-10 18:00 | i1 Price: 69000.0
STEP 6: Copy this data to "ASSET 1 - IDENTIFICATION AND DATE-PRICE PIVOT COORDINATES".
STEP 7: Verify "Asset 1" is selected in the dropdown and close settings.
STEP 8: You should now see the yellow diagonal phase line, horizontal Fibonacci levels, and the validation table showing "✅ Match".
STEP 9: Navigate the chart to verify how Fibonacci levels align with historical support/resistance zones.
// Important Notes //
• The sample data in the Practice Table was validated in 02/2026 and serves as reference only.
• It is your responsibility to validate or update the preponderant phase of your assets over time.
• Use our master RLP indicator to automatically find and calculate preponderant phases, then transfer the coordinates here for permanent tracking.
• You can deactivate the Practice Table once you've copied the data you need.
// Shelter Indicators Ecosystem //
RLPS is part of a comprehensive ecosystem of indicators for price action analysis based on shelter levels:
RLPS (Simplified Long-Term Shelters): This indicator. Simplified version of RLP that allows manual input of previously identified preponderant phase coordinates. Ideal for permanent operations with multiple assets across different timeframes.
RLP (Long-Term Shelters): Automatically identifies the preponderant Zigzag phase that institutional investors use as a reference to project Fibonacci levels. These levels determine order placement over the following months and years.
RMP (Mid-Term Shelters): Provides the psychological shelter and resistance levels that institutional investors establish at the beginning of each year. These form the main framework that professionals use to plan entry and exit operations throughout the year.
RS (Weekly Shelters): Tactical structural analysis indicator designed to precisely track price action and manage positions during current weeks.
RID (Intra-Day Shelters): For intraday operations based on levels calculated from the daily opening price. Designed for 1H timeframes or lower, including scalping strategies.
By combining RLPS, RLP, RMP, RS, and RID, you obtain a multi-timeframe framework that provides certainty and clarity to apply strategies grounded in price action, across any time horizon: from scalping to long-term investments.
// Final Notes //
We sincerely regret to inform you that we have not included the Spanish translation previously provided in our indicators, due to our significant concern regarding the ambiguous rules on publication bans related to indicators.
Sharing motivates. Happy hunting in this great jungle!
อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
Trap Longs - Hamza NaveedTrap Longs – Hamza Naveed is an advanced Open Interest–based indicator that analyzes net longs, net shorts, delta, and ratio across multiple exchanges (Binance, BitMEX, Kraken). It visualizes institutional positioning using candles, lines, or columns, with optional VWMA/EMA smoothing, RSI strength, volume heatmaps, statistical tables, and divergence detection. Designed to identify traps, absorption, and exhaustion, this tool helps traders understand positioning shifts, liquidity behavior, and potential trend reversals beyond price action alone. อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
