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.
Indicator
Ultimate Hull SuiteGreymyst Ultimate Hull Suite
The Greymyst Ultimate Hull Suite is a premium, multi-functional trend-following indicator designed to provide traders with highly accurate, low-lag momentum signals. Built entirely from the ground up for professional trading, this suite combines advanced Moving Average variations with dynamic volatility filters and multi-timeframe analysis to offer extreme confluence in a single tool.
🌟 Core Concepts & Features
1. Advanced Hull Variations
Traditional moving averages often suffer from lag. The Hull Moving Average solves this by prioritizing recent price action. This suite allows you to toggle between three powerful variations:
HMA (Standard Hull): The classic low-lag moving average.
EHMA (Exponential Hull): Uses exponential calculations to react even faster to sudden price spikes.
THMA (Triple Hull): Offers ultra-smooth trend detection, practically eliminating market noise and false signals during choppy ranges.
2. Multi-Timeframe (MTF) Alignment
Trading against the macro trend is a common pitfall. The built-in MTF engine allows you to anchor your Hull Moving Average to a higher timeframe (e.g., viewing the 4-Hour Hull trend on a 15-minute chart). This ensures you are only taking trades that align with the dominant market direction.
3. Dynamic Volatility Bands (Hull Envelopes)
Instead of static support and resistance, this suite wraps the Hull Moving Average in ATR-based Volatility Bands.
Trend Cloud: The area between the Hull and the bands is filled with a bullish (green) or bearish (red) cloud.
Mean Reversion: When price action aggressively pierces the upper or lower bands, it signals an overextended market, warning you of potential pullbacks or mean-reversion opportunities.
4. Squeeze Momentum Confluence Filter
A trend is only as strong as the volume and volatility behind it. This indicator integrates a hidden Squeeze Volatility Engine (combining Bollinger Bands and Keltner Channels).
The Filter: Buy and Sell signals are strictly suppressed if the market is stuck in a low-volatility "squeeze" (consolidation).
Signals are only generated when the Hull changes direction AND volatility is actively expanding, keeping you out of flat, choppy markets.
5. Automated Signals & Alerts
The suite visually prints clear B (Buy) and S (Sell) markers on your chart when high-probability confluence is met (Trend Shift + Volatility Expansion).
It includes comprehensive, ready-to-use Alert Conditions so you can automate your trading via webhooks or receive notifications directly to your phone.
⚙️ How to Use It for Confluence
Trend Confirmation: Use the color of the Hull MA (Green for Up, Red for Down) as your primary directional bias.
Entry Triggers: Look for Buy/Sell markers printed by the indicator. Because of the built-in Squeeze filter, these markers represent moments where price is reversing with momentum.
Take Profit / Stop Loss: Use the outer ATR bands as dynamic profit targets or trailing stop-loss zones.
(Created by greymyst) Indicator
Volatility Hull Ribbon [BackQuant]Volatility Hull Ribbon
Overview
Volatility Hull Ribbon is a trend-following overlay built from a Hull-style moving average that replaces traditional volume weighting with volatility weighting . Instead of weighting price by traded volume, this indicator weights price by the absolute True Range of each bar, meaning bars with larger range expansion have more influence on the final trend estimate.
The goal is to create a smoother but responsive trend line that pays more attention to bars where the market actually moved with force. It then plots this volatility-weighted Hull structure as either a clean line or a ribbon-style band, with gradient fill, candle coloring, and long/short flip markers.
At a high level, the indicator does three things:
Builds a volatility-weighted moving average using True Range as the weighting source.
Applies Hull-style lag reduction to produce a faster trend-following curve.
Visualizes trend direction using slope, ribbon fill, candles, and flip signals.
Core idea
Most moving averages treat each bar equally or weight only by time. That means a quiet candle and a high-range expansion candle can have similar influence depending on the MA type.
Volatility Hull Ribbon takes a different approach:
Bars with larger True Range are treated as more important.
Bars with smaller True Range have less influence.
Recent bars are also weighted more heavily than older bars.
This creates a trend estimate that responds more strongly when the market expands, while remaining smoother during lower-energy movement.
What “volatility-weighted” means here
The custom weighting function uses:
Price source
Absolute True Range
A decreasing time weight
For each bar inside the lookback:
Weighted price contribution = source * abs(True Range ) * recency weight
Weight contribution = abs(True Range ) * recency weight
Then:
Volatility-weighted average = weighted price sum / weighted True Range sum
So price movement on wide-range bars matters more than price movement on quiet bars.
Why True Range is used
True Range captures more than just high-low movement. It accounts for gaps and previous close displacement. This makes it a broader volatility proxy than simple candle range.
Using True Range as the weight means the filter gives more importance to bars where:
Range expanded,
Price displaced aggressively,
Volatility increased,
Market participation likely intensified.
This is useful because strong trend moves often occur during volatility expansion, not during quiet drift.
Hull-style construction
The indicator then applies a Hull-style transformation to the volatility-weighted average.
The structure is:
VWHMA = VWMA_TR( 2 * VWMA_TR(src, len / 2) - VWMA_TR(src, len), sqrt(len) )
Where VWMA_TR means the custom True-Range-weighted moving average.
This follows the same logic as the classic Hull Moving Average:
Use a faster half-length average.
Use a slower full-length average.
Subtract the lagging component.
Smooth the result with sqrt(length).
The difference is that every smoothing step is volatility-weighted instead of standard weighted-average based.
Why this matters
A classic Hull Moving Average is already designed to reduce lag. This version modifies the internal weighting so the curve becomes more sensitive to volatility-backed price movement .
That means:
Large expansion bars can pull the filter faster.
Weak low-range chop has less effect.
Trend changes during strong movement can be reflected more clearly.
Trend detection
Trend direction is based on the slope of the VWHMA:
Bullish when VWHMA > VWHMA
Bearish when VWHMA < VWHMA
This is a simple but effective regime definition:
Rising volatility-weighted Hull = bullish trend pressure.
Falling volatility-weighted Hull = bearish trend pressure.
The script uses this slope state to color:
The main line,
The ribbon fill,
Optional candles,
Signal markers.
Ribbon mode
When “Plot as Band?” is enabled, the script creates a second line:
onebar_off = WMA(VWHMA , 10)
This is a delayed and smoothed version of the VWHMA. The area between the current VWHMA and this offset line becomes the ribbon.
Interpretation:
Ribbon expansion shows separation between current trend structure and its delayed reference.
Ribbon compression shows trend slowing or flattening.
A clean flip in the ribbon often coincides with trend transition.
The ribbon is not a volatility band. It is a trend displacement ribbon built from the difference between the current VWHMA and its delayed smoothed version.
Gradient fill logic
The fill is directional:
If VWHMA is above the offset line, fill intensity is stronger near the VWHMA and fades toward the offset.
If VWHMA is below the offset line, the gradient reverses.
This creates a cleaner visual than a flat fill because it emphasizes the active side of the ribbon.
In practice:
Strong bright ribbon = trend line leading the delayed reference.
Faded/narrow ribbon = weaker separation.
Ribbon reversal = trend pressure has shifted.
Signal logic
Signals are generated when the VWHMA slope changes direction:
Long signal: crossover(VWHMA, VWHMA )
Short signal: crossunder(VWHMA, VWHMA )
This means:
A long signal prints when the current VWHMA turns upward relative to the previous value.
A short signal prints when the current VWHMA turns downward.
These are slope-flip signals, not price crossover signals.
Important interpretation
A signal does not mean “buy blindly” or “sell blindly.” It means the volatility-weighted trend estimate has changed direction. The quality of the signal depends on:
Market structure,
Higher timeframe trend,
Volatility conditions,
Whether the ribbon is expanding or compressing.
Candle coloring
When enabled, candles are painted according to the VWHMA slope:
Bullish slope = long color.
Bearish slope = short color.
This makes the indicator easier to read as a regime overlay. You can quickly see when the market is consistently aligned with the volatility-weighted trend.
How to use it
1) Trend filter
Use the VWHMA color as a bias filter:
Only favor longs when the VWHMA is rising.
Only favor shorts when the VWHMA is falling.
2) Trend transition tool
Slope flips can identify early trend shifts:
Long marker = VWHMA has turned upward.
Short marker = VWHMA has turned downward.
Because the filter is Hull-style and volatility-weighted, it can react faster than slower trend filters while still suppressing some low-range noise.
3) Ribbon strength reading
The ribbon gives additional context:
Expanding ribbon = stronger separation and cleaner trend pressure.
Contracting ribbon = momentum weakening.
Ribbon flattening = chop or transition risk.
4) Pullback structure
In strong trends, price often respects the VWHMA or ribbon area:
Bull regime: pullbacks into the ribbon can act as support.
Bear regime: rallies into the ribbon can act as resistance.
5) Volatility-backed trend confirmation
Because large True Range bars influence the calculation more, this tool is useful for identifying whether trend changes are being supported by actual range expansion.
If price moves but the VWHMA does not respond strongly, the move may lack volatility-backed confirmation.
Input guide
Price Source
Defines the input series used for the calculation. Close is standard, but hl2, hlc3, or ohlc4 can be used for smoother structural behavior.
Lookback Period
Controls the smoothing length:
Lower values = faster response, more signals, more noise.
Higher values = smoother trend, fewer flips, more lag.
Plot as Band
Enables the ribbon view using the delayed smoothed VWHMA reference.
Line Width
Controls the main line thickness when not relying heavily on band mode.
Show Trend Candles
Paints candles by current trend state.
Show Signals
Toggles the long/short slope-flip markers.
Strengths
Uses volatility-weighted smoothing instead of equal weighting.
Combines volatility sensitivity with Hull-style lag reduction.
Clean ribbon visualization for trend displacement.
Simple slope-based regime interpretation.
Works well as a trend overlay or bias filter.
Limitations
Slope flips can still whipsaw in sideways markets.
Large wick bars can influence the filter strongly because True Range is used as weight.
It does not measure volume, despite using a VWMA-style internal function.
It is a trend tool, not a complete trading system.
Best use case
Volatility Hull Ribbon works best when used as a visual trend structure layer:
Use color for bias.
Use ribbon expansion/compression for strength.
Use slope flips for regime transitions.
Use price interaction with the ribbon for pullback context.
Summary
Volatility Hull Ribbon is a Hull-style trend overlay that replaces traditional weighting with True Range weighting, making the moving average more responsive to volatility-backed price movement. It builds a low-lag volatility-weighted Hull curve, compares it to a delayed smoothed reference to form a ribbon, and uses slope changes to define trend direction and signals. The result is a clean, responsive trend ribbon that highlights when volatility-backed trend pressure is rising, fading, or reversing. Indicator
Hull MA Trend Zones [AGPro Series]Hull MA Trend Zones
📌 Overview
Hull MA Trend Zones is a premium HMA trend overlay built for traders who want a cleaner way to read Hull Moving Average direction, slope quality, and pullback behavior.
The script is centered on one clear idea: a strong HMA trend should not only move above or below a moving average; it should show measurable slope, orderly ribbon structure, and controlled pullback behavior around the active HMA path.
Instead of presenting a crowded moving average wall, the script uses a focused three-line HMA structure, a subtle pullback band, and concept-native trend zones that are tied directly to the active Hull MA state.
⚙️ How It Works
The engine calculates a fast HMA, an anchor HMA, and a slow HMA.
The anchor HMA is the main decision line. Its slope is normalized with ATR so the script can judge whether the current Hull MA movement is weak, transitional, or directional.
The ribbon structure then checks whether the fast, anchor, and slow HMA lines are aligned. This separates clean trend movement from mixed or unstable movement.
Finally, the pullback layer evaluates whether price is extending away from the HMA, testing the HMA zone, holding the HMA zone, rejecting from the HMA zone, or failing the active trend path.
🧭 What The Script Shows
- HMA ribbon for clean trend direction.
- ATR-based HMA pullback band around the anchor HMA.
- Rectangular HMA slope zones created from active directional states.
- Confirmed, quality-gated Bull Turn and Bear Turn labels.
- Optional Hold and Reject labels for pullback events.
- A compact AGPro panel with HMA State, Slope Strength, Pullback Status, and Quality Score.
📊 AGPro Panel
The panel is designed for fast scanning without taking over the chart.
HMA State shows whether the active read is bullish, bearish, transitional, or neutral.
Slope Strength converts the anchor HMA slope into a clear percentage-style reading.
Pullback Status explains whether price is extending, testing, holding, rejecting, or failing the HMA trend zone.
Quality Score combines slope strength, ribbon alignment, and pullback behavior into a single 0-100 reading.
🎯 What Makes It Different
Hull MA Trend Zones is not a generic moving average ribbon, not a ribbon compression map, and not a broad support/resistance tool.
Its focus is narrower and more practical: HMA slope, HMA trend-zone behavior, and pullback-to-HMA quality.
The rectangular zones are not drawn as generic support or resistance. They are HMA slope zones created from the active trend state and the ATR-sized HMA pullback area. This keeps the script visually useful while avoiding overlap with broader zone, corridor, or compression-style indicators.
The default visual design is intentionally restrained. Pullback labels are optional, turn labels require confirmation and a minimum quality score, and old zones are capped so the chart keeps a cleaner premium look on both intraday and higher-timeframe charts.
🔧 Key Settings
Fast HMA Length controls the responsive side of the ribbon.
Anchor HMA Length controls the main trend path, slope state, pullback band, and panel logic.
Slow HMA Length helps identify whether the HMA ribbon is aligned or still transitional.
Slope Lookback and Trend Slope Threshold control how selective the HMA state engine is.
Zone Width ATR controls the height of the HMA pullback band and slope-zone area.
Zone Forward Bars controls how far the active slope zone projects while the same trend state remains valid.
Turn Confirmation and Minimum Turn Label Score control how selective the default turn labels are.
Label Cooldown Bars, Max Visible Labels, and Label Offset ATR keep chart density suitable for publication-quality screenshots.
Panel Location, Panel Theme, Label Font Size, and Panel Font Size are adjustable.
✅ Suggested Use
Use Hull MA Trend Zones to study trend continuation, Hull MA pullback quality, HMA slope transitions, and cleaner moving-average trend behavior.
It is especially useful when you want an HMA-focused overlay that remains readable on active charts and avoids the clutter of large multi-average systems.
The script is designed as a public-free AGPro Series tool with a clean visual identity, a focused HMA concept, and a PulseWire-safe publication structure. Indicator
MULTIVITAMIN1. What is MULTIVITAMIN?
MULTIVITAMIN is a custom-built, open-source algorithmic scalping indicator designed for PulseWire. Its primary goal is to capture high-probability momentum trades by combining pure Price Action (PA) mechanics with advanced trend and volatility filters, specifically engineered to avoid "chop" (ranging/flat markets) and deliver healthy, actionable signals.
2. What does it cover? (Core Modules)
Price Action Engine: The core of the system. It detects real-time structural market shifts by identifying Engulfing patterns, Order Blocks (OB) with volume confirmation, and Fair Value Gaps (FVG).
Trend & Momentum Filters: Utilizes a multi-timeframe Hull Moving Average (HMA) for signal smoothing, a 50-period EMA for macro trend direction, and an ADX filter to ensure the market has sufficient momentum before entering a trade.
Pro Walls (Anti-Chop System): Features advanced, optional defensive mechanisms: an ATR-based volatility filter (ensuring candle size is larger than average market noise) and an EMA slope filter (ensuring the trend line is actually pointing up or down, not moving sideways).
Volume Supported Breakout (BKR): An optional hybrid feature that looks for extreme volume spikes (compared to a 20-period SMA) combined with local high/low breakouts.
Lifecycle Management: A built-in state machine (Idle, Long, Short) that tracks the trade's status, preventing overlapping entries and managing exits based on user-defined criteria.
3. What is its operational logic?
Step 1: Scanning & Setup: The script continuously monitors price action for base entry conditions.
Step 2: The Gauntlet (Filtering): A setup must pass through all active user filters (EMA, ADX, Volatility, etc.) before being validated.
Step 3: State Execution: If all conditions align, the internal bot state shifts from 0 (Idle) to 1 or -1, plotting an ENTER shape on the chart and triggering an external webhook alert.
Step 4: The Exit: The bot remains in this state until a valid exit condition (like a reverse PA signal) is met.
4. ⚠️ CRITICAL: How to Set Up Alerts (The Anti-Repainting Rule) ⚠️
If you are using the "Exit on Reverse Signal" (Ters Sinyalde Çık) feature in the settings, you MUST configure all your PulseWire alerts (both ENTER and EXIT alerts) to trigger "Once Per Bar Close".
Why? If you set them to "Once Per Bar" (anlık tetikleme), intra-bar price fluctuations (wicks) will trigger premature exit/entry orders to your exchange before the candle officially closes, leading to repainting on the chart and ghost orders in your account. Always wait for the candle to close to confirm the structural shift! Indicator
Multi-Timeframe EMA SMA HMA LR Proximity & Alerts [HYPR-run]DESCRIPTION:
Nine moving averages from Weekly down to chart timeframe on one chart.
Weekly 10 SMA, Daily 50/100/200 EMA/SMA, 4hr 200 SMA, plus chart-TF
10 EMA, 200 SMA, Hull MA, and Linear Regression. See where price sits
relative to every meaningful institutional level without switching
timeframes.
The proximity filter is the key feature. Enable all nine MAs, set a
threshold, and only lines near current price appear. The Daily 200 SMA
at 20% away? Hidden. When price drops toward it, the line shows up
automatically. Your chart stays clean and the levels that matter are
always visible.
DISCOVERING EDGE
We have found that managing risk in mature assets with the 50d, 100d,
200d, and 10w is highly effective, simple and a methodology shared
amongst experienced investors and traders. This indicator interprets
that positioning across 16 configurations with a 7-tier color gradient,
so you see structural health at a glance. "Oh, it's bouncing on the
50dma right now, there may be a set-up in play..."
POSITIONAL CONTEXT vs STATIC MA OVERLAY
Static overlays show every MA with no interpretation of what the
positioning means. This indicator color-codes 16 above/below
configurations weighted by MA significance (200d and 10w are
heavyweights), surfaces bounce/reject events ranked by importance,
and shows % distance to each curve so you know exactly how much of a
move is needed for price to converge.
- Events fire independently of display toggles; a hidden 200d SMA
that price just bounced off still shows "Bouncing 200d" in the
dashboard.
- 7-tier positioning gradient weighted by MA significance (200d and
10w are heavyweights) shows structural health in one glance.
- Webhook alerts on configurable MA cross (9 options from 10w to
linear regression) with full bar filter.
FEATURES
- 9 moving averages from Weekly down to chart timeframe
- Proximity filter: hides irrelevant MAs far from price
- Bounce/reject detection at each MA level
- Two alert systems: XO/XU cross + bounce/reject on selected MA
- Bounce/reject alerts fire when price wicks into selected MA (support/resistance hold)
- Dashboard: row 1 positioning context (above/below each MA), row 2 live events (bouncing, rejecting, XO, XU)
- Dashboard dark/light theme toggle for any chart background
- Polyline rendering (smooth lines, no staircase artifacts)
- End-of-line labels with % distance from price
- Toggle each MA independently
HOW IT WORKS
Higher timeframe MAs are pulled via request.security and rendered as
polylines for smooth display on any chart timeframe. The proximity check
runs on every bar: if the distance between price and a given MA exceeds
the threshold %, the polyline is not drawn. When price approaches, the
line appears. Alerts fire independently of display toggles.
DASHBOARD
Two-row dynamic dashboard that updates every bar.
- Row 1 (positioning): which MAs price is above or below, grouped with
"&" separators. The Weekly 10 SMA is separated as the anchor by a
pipe. 7-tier color gradient based on how many of the four key MAs
(50d, 100d, 200d, 10w) price is above, with heavyweight distinction
(200d and 10w carry more weight than 50d/100d): bright green (all
four), green (3/4 with both heavyweights), dark green (3/4 missing a
heavyweight), yellow (2/4), dark red (1/4 with a heavyweight), red
(1/4 only lightweight), bright red (none)
- Row 2 (events): up to 3 simultaneous events, most significant MA first
(w10 → d200 → d100 → d50 → 4h200). Bouncing (support holding),
rejecting (resistance holding), XO (crossover), XU (crossunder). Color
intensity uses a 2D significance matrix: MA weight x event type.
Brightgreen for a w10 bounce; yellow for a d200 cross; darkgreen for
idle above d50. Dark gray when idle
- Runs independently of display toggles; events fire for all MAs even if
the line is hidden by the proximity filter
DEFAULT CONFIGURATION
Weekly 10 SMA (white), Daily 50 EMA (yellow), and Daily 200 SMA (purple)
are on by default. Proximity filter on at 5%. These three levels are the
most commonly watched institutional reference points.
POSITIONING TABLE (row 1, all 16 configurations)
BADGE COLOR (header, positioning x event combination)
ALERTS
Two alert systems. XO/XU fires when price crosses the selected MA with a
full bar filter (body >= 66.6% of range, rejects doji/wick-heavy bars).
Bounce/Reject fires when price wicks into the selected MA from the correct
side and closes confirming support (bounce) or resistance (reject). Both
fire JSON payloads; works with any webhook receiver.
CREDITS
No external libraries or third-party code used. Indicator
Exponential Hull Momentum [BackQuant]Exponential Hull Momentum
Overview
Exponential Hull Momentum is a normalized momentum oscillator built from an Exponential Hull Moving Average -style transformation. Its purpose is to measure whether smoothed directional pressure is pushing toward the upper or lower end of its own recent range, while keeping the response faster and cleaner than a plain moving-average oscillator.
At a high level, the script does three things:
Builds a fast, low-lag smoothed series using an Exponential Hull-style calculation.
Normalizes that series against its own rolling high-low range so the output fits into a bounded oscillator-style scale centered around zero.
Optionally smooths the oscillator with a selectable moving average so you can use a secondary signal line or regime filter.
The final result is an oscillator that tries to answer:
Is momentum pushing toward the strong positive end of its recent range?
Is momentum collapsing toward the negative end?
Is the current move still expanding, or is it rolling over relative to its own smoothed state?
What this indicator is actually measuring
This indicator is not measuring raw returns, not measuring RSI-style up/down closes, and not measuring volatility. It is measuring the position of a low-lag smoothed price transform within its own recent rolling range .
That distinction matters.
It means:
Positive values indicate the Exponential Hull series is in the upper half of its recent normalized range.
Negative values indicate it is in the lower half of its recent normalized range.
Extreme positive values suggest strong upward momentum persistence.
Extreme negative values suggest strong downward momentum persistence.
Because it is normalized, the oscillator is less about absolute price level and more about relative momentum state .
Where the “Hull” idea comes from
The Hull Moving Average family exists to solve a classic moving-average problem:
If you smooth more, you reduce noise but increase lag.
If you smooth less, you reduce lag but increase noise.
Alan Hull’s core idea was to combine moving averages in a way that compensates for lag before applying a final smoothing stage. The classic HMA uses weighted moving averages. This script uses the same structural idea, but with EMAs instead , producing an Exponential Hull-style moving average .
So instead of a classic HMA, the script constructs:
A fast EMA on half-length input.
A slower EMA on full-length input.
A lag-compensated intermediate value using 2 * fast - slow.
A final EMA smoothing pass using sqrt(length).
This is why it is called Exponential Hull Momentum . The “Hull” part refers to the lag-reduction structure, the “Exponential” part comes from using EMA instead of WMA.
The EHMA calculation step by step
The core function is:
EHMA(_src, _length) =
EMA( 2 * EMA(_src, _length / 2) - EMA(_src, _length), round(sqrt(_length)) )
Let’s break that down.
1) Fast EMA on half length
EMA(_src, _length / 2)
This reacts quickly to recent price changes.
2) Slow EMA on full length
EMA(_src, _length)
This is smoother and more delayed.
3) Lag compensation
2 * fastEMA - slowEMA
This is the critical step. It pushes the result toward the faster average while subtracting part of the slower lagging component. Conceptually, it behaves like a “de-lagged” smoother. It is related in spirit to reduced-lag constructions like DEMA and TEMA, though implemented in a Hull-style framework.
4) Final smoothing
EMA(lag_compensated_series, sqrt(length))
This final pass cleans up the compensated series so it remains usable as a smooth momentum engine rather than a noisy de-lagged line.
So the oscillator’s underlying subject is not raw price, but this EHMA subject series .
Why use EHMA instead of a plain EMA or raw price
A raw price oscillator is often too noisy. A plain EMA oscillator is smoother, but can still lag too much. EHMA tries to balance:
Faster reaction than a standard EMA.
Cleaner shape than a raw de-lagged transform.
More sensitivity to directional bursts.
That makes it useful for momentum work, especially when you want:
Earlier momentum regime shifts.
Cleaner trend-state transitions.
A bounded oscillator rather than an overlay line.
Normalization: turning the EHMA into an oscillator
After computing the EHMA subject, the script normalizes it using its own rolling lowest and highest values over a user-defined normalization period:
lowest = lowest(subject, norm_period)
highest = highest(subject, norm_period)
plotosc = (subject - lowest) / (highest - lowest) - 0.50
This transforms the EHMA series into a bounded range centered around zero.
Interpretation:
If subject is near the rolling highest, plotosc approaches +0.5.
If subject is near the rolling lowest, plotosc approaches -0.5.
If subject is near the middle of the rolling range, plotosc is near 0.
So the oscillator is essentially:
Where is the current EHMA value sitting within its recent high-low envelope?
Why normalization matters
Without normalization, the EHMA value itself would still be in price units, which makes comparison harder across:
Different assets,
Different timeframes,
Different price regimes.
Normalization gives you a common scale:
-0.5 to +0.5, centered at 0
That makes the output much easier to use as a momentum state tool.
What the oscillator values mean
Near +0.5
The EHMA subject is pressing against the upper end of its rolling range. This usually means:
Strong bullish momentum,
Persistent upward movement in the smoothed series,
A possible “stretched” positive momentum condition.
Near -0.5
The EHMA subject is pressing against the lower end of its rolling range. This usually means:
Strong bearish momentum,
Persistent downward movement,
A possible stretched downside state.
Near 0
The EHMA subject is near the midpoint of its recent range. This can mean:
Momentum is neutral,
Momentum is transitioning,
The market is compressing or chopping relative to recent structure.
Important nuance about the oscillator scale
This is not a z-score . It is not measuring “standard deviations from mean.” It is a min-max style range normalization . That means:
The output depends on the recent highest and lowest subject values.
If the rolling range changes sharply, oscillator sensitivity can change too.
The same oscillator value does not imply the same statistical rarity across all contexts.
It is best read as a relative range-position momentum oscillator , not as a probabilistic metric.
Signal line / moving average layer
The script optionally applies a second smoothing layer directly to the oscillator:
sig_ma = MA(plotosc, malen, matype)
You can choose from many MA types:
SMA
EMA
DEMA
TEMA
RMA
WMA
HMA
T3
ALMA
LINREG
VWMA
This signal line is not required for the core oscillator to work. It is a secondary interpretation layer that can be used for:
Momentum confirmation,
Cross-based entry logic,
Smoothing out the oscillator for regime filtering,
Visual comparison between raw momentum and smoothed momentum.
The script note suggests that if you want to use the MA more like a signal histogram, you can change its style to columns in the style menu.
Why a selectable MA matters
Different traders want different signal characteristics:
SMA/EMA for classic smoothing,
DEMA/TEMA for lower lag,
HMA/T3/ALMA for smoother trend-state filtering,
LINREG for slope-sensitive behavior,
VWMA if you want volume-weighted smoothing.
This makes the indicator more flexible without changing the core EHMA oscillator.
Color gradient logic
The oscillator columns are colored using thresholded intensity zones rather than a continuous gradient function. The color changes as the oscillator moves further away from zero.
For positive values:
Weak positive: lighter cyan/green tones.
Moderate positive: stronger green.
Strong positive: bright green.
Extreme positive near +0.5: intense bright green.
For negative values:
Weak negative: orange/red tint.
Moderate negative: deeper red.
Strong negative: bright red.
Extreme negative near -0.5: intense red.
This means the plot does two jobs at once:
Direction from sign,
Relative momentum intensity from color saturation.
So even without reading the value numerically, you can see whether momentum is:
Barely positive,
Strongly positive,
Barely negative,
Or deeply negative.
Static levels and what they mean
The script draws fixed zones:
+0.5 and +0.4
-0.4 and -0.5
0 midline
These create:
An upper “overbought / strong positive momentum” zone from 0.4 to 0.5
A lower “oversold / strong negative momentum” zone from -0.4 to -0.5
A midline at 0 separating positive from negative momentum territory
Important:
These are momentum extreme zones , not traditional RSI overbought/oversold zones.
Strong trends can stay pinned near +0.5 or -0.5 for long periods.
Extreme readings do not automatically mean reversal.
The fill between the upper and lower static boundaries just makes those zones easier to identify visually.
Midline logic
The zero line is the most important structural level in the oscillator:
Above 0 = EHMA is in the upper half of its recent range, positive momentum regime.
Below 0 = EHMA is in the lower half of its recent range, negative momentum regime.
The alert conditions are built on this exact logic:
Long alert on crossover above 0
Short alert on crossunder below 0
So the core directional interpretation is midline-based.
How to interpret the indicator in practice
1) Momentum regime
The cleanest use is as a regime filter:
Above 0: positive momentum bias.
Below 0: negative momentum bias.
This alone can already be useful for:
Filtering entries,
Avoiding countertrend setups,
Aligning with the dominant smoothed momentum state.
2) Momentum intensity
The closer the oscillator moves toward +0.5 or -0.5, the stronger the recent momentum relative to its own normalized range.
This can help distinguish:
Weak trend drift,
Healthy trend continuation,
Momentum surge / expansion,
Potential exhaustion zones.
3) Transition behavior
Watch how the oscillator behaves around 0:
Fast thrust through 0 often signals a fresh momentum shift.
Repeated chop around 0 often signals indecision or sideways conditions.
A flattening oscillator after an extreme reading often shows momentum deterioration before price fully turns.
4) Using the moving average signal
If enabled, the MA of the oscillator can help identify:
When raw momentum is accelerating away from smoothed momentum,
When momentum is rolling over,
Whether the oscillator move is broad and sustained or only a short burst.
A common interpretation:
Oscillator above signal MA and above zero = strong bullish momentum structure.
Oscillator below signal MA and below zero = strong bearish momentum structure.
Divergence between oscillator and signal MA = momentum fading or transitioning.
What makes this different from RSI or stochastic-style oscillators
This script is structurally different from standard oscillators.
Compared to RSI
RSI is based on the ratio of average up closes to down closes. It measures directional internal strength of return behavior.
EHMA Momentum instead:
Starts from a low-lag smoothed price transform,
Then asks where that transform sits in its recent range.
So it is more “structure-relative momentum” than “up/down return balance.”
Compared to Stochastic
Stochastic asks where price closes relative to recent high-low range.
EHMA Momentum asks where the EHMA-smoothed subject sits relative to its own recent subject range.
That means:
It is less raw than stochastic,
More smoothed,
Potentially less noisy,
And more focused on directional structure than candle location.
Parameter behavior
Exponential Hull Calculation Period (len)
Controls how the EHMA subject is built.
Very low values make the subject extremely reactive.
Higher values smooth the subject more and reduce sensitivity.
Since the default is very small, this script is designed to be sharp and responsive by nature.
Normalization Period (norm_period)
Controls the rolling high-low range used to normalize the subject.
Higher values create a broader historical range and smoother normalization.
Lower values make the oscillator adapt faster, but it can become more jumpy and “range-reset” more often.
Signal MA Period and Type
Controls how smooth the optional secondary line is.
Shorter MA = faster cross behavior.
Longer MA = slower, steadier confirmation.
Strengths of this approach
Fast response because of the Exponential Hull construction.
Easy interpretation because of bounded normalized output.
Works well as a regime filter via the zero line.
Intensity is visually clear from both height and color.
Flexible because of optional multi-type signal smoothing.
Limitations and what to watch for
Because the oscillator is min-max normalized, extreme values can persist in strong trends.
A rolling highest/lowest normalization can make the oscillator “reset” as old extremes leave the window.
On very low lengths, the EHMA can become highly reactive and potentially noisy.
Zero-line crosses can whipsaw in sideways markets, especially if normalization is too short.
So this tool is best used with context:
Trend structure,
Market regime,
Higher timeframe bias,
Or combined with the signal MA and price action.
Summary
Exponential Hull Momentum is a normalized momentum oscillator built from an EMA-based Hull-style smoothing engine. It first creates a low-lag Exponential Hull series, then normalizes that series within its own rolling high-low range so the output oscillates around zero between roughly -0.5 and +0.5. Positive values indicate the EHMA subject is pressing into the upper half of its recent range, negative values indicate the lower half, and the distance from zero reflects relative momentum strength. Static zones highlight extreme positive and negative momentum states, while an optional multi-type moving average can be used as a secondary signal or smoothing layer. Indicator
Kalman Hull Trend Score [BackQuant]Kalman Hull Trend Score
Overview
Kalman Hull Trend Score is a trend-strength and regime-evaluation indicator that combines two ideas, Kalman filtering and Hull-style smoothing, then measures persistence of that filtered trend using a rolling score. The goal is to produce a cleaner, more stable trend read than typical moving average tools, while still reacting fast enough to be practical in live markets.
Instead of treating a moving average as a simple line you cross, this indicator turns the filtered trend into an oscillator-like score that answers: “Is the smoothed trend consistently progressing, or is it stalling and degrading?”
Core idea
The indicator is built from two components:
A Kalman-based smoothing engine that estimates price state and reduces noise adaptively.
A Hull-style construction that uses multiple Kalman passes to create a responsive, low-lag trend filter.
Once the Kalman Hull filter is built, a persistence score is calculated by comparing the current Kalman Hull value to many past values. The result is a trend score that rises in sustained trends and compresses or flips during deterioration.
Why Kalman instead of standard smoothing
Traditional moving averages apply fixed smoothing rules regardless of market conditions. A Kalman filter behaves differently, it is designed to estimate an underlying state in noisy data, adjusting how much it “trusts” new price information versus prior estimates.
This script exposes that behavior through two key controls:
Measurement Noise: how noisy the observed price is assumed to be.
Process Noise: how much the underlying state is allowed to evolve from bar to bar.
Together, these settings let you tune the balance between smoothness and responsiveness without relying on blunt averaging alone.
Kalman filter mechanics (conceptual)
Each update cycle follows the classic structure:
Prediction: assume the state continues, and expand uncertainty by process noise.
Update: compute Kalman Gain, then blend the new price observation into the estimate.
Correction: reduce uncertainty based on how much the filter accepted the new information.
When measurement noise is higher, the filter becomes more conservative, smoothing harder. When process noise is higher, the filter adapts faster to regime changes, but can become more reactive.
Check out the original script:
Kalman Hull construction
The “Hull” component is not a standard HMA built from WMAs. Instead, it recreates the Hull idea using Kalman filtering as the smoothing primitive. The structure follows the same intent as HMA, reduce lag while keeping the line smooth, but does it with Kalman passes:
Apply Kalman smoothing over multiple effective lengths.
Combine them using the Hull-style weighting logic.
Run the combined output through another Kalman pass to finalize smoothing.
The result is a Kalman Hull filter that aims to track trend with less jitter than raw price, and less lag than slow averages.
Another Kalman Hull with Supertrend
Trend scoring logic
The trend score is computed by comparing the current Kalman Hull value to past Kalman Hull values over a fixed lookback range (1 to 45 bars in this script):
If current kalmanHMA > kalmanHMA , add +1
If current kalmanHMA < kalmanHMA , add -1
This produces a persistence score rather than a simple direction signal. Strong trends where the filter keeps advancing will accumulate positive comparisons. Weak trends, chop, or reversals will cause the score to flatten, decay, or flip negative.
Interpreting the score
Read the score as trend conviction and persistence:
High positive values: bullish persistence, the filtered trend is progressing consistently.
Low positive values: trend exists but is fragile, progress is slowing.
Near zero: indecision, range behavior, frequent challenges to structure.
Negative values: bearish persistence or sustained deterioration in the filtered trend.
The rate of change matters:
Score expansion suggests trend is gaining traction.
Score compression often signals consolidation or exhaustion.
Fast flips usually accompany regime transitions.
Signal thresholds and regime transitions
User-defined thresholds convert the score into regimes:
Long threshold: score must exceed this level to confirm bullish persistence.
Short threshold: a crossunder of the score triggers bearish regime transition.
This is intentionally conservative. Long bias is maintained while the score holds above the long threshold. Short transitions are event-triggered on breakdown via crossunder, helping avoid constant flipping during minor noise.
Signals are only plotted on regime changes (first bar of the flip), keeping them clean for alerts and backtests.
Visual presentation
The indicator provides multiple layers depending on how you want to use it:
Kalman Hull Trend Score oscillator, color-coded by active regime.
Optional Kalman Hull filter plotted on the price chart for structure context.
Optional threshold reference lines for quick regime mapping.
Optional candle coloring and background shading for instant readability.
You can run it as a pure score panel or as a combined panel + on-chart trend overlay.
How to use in practice
Trend filtering
Favor long setups when the score remains above the long threshold.
Reduce directional aggression when score compresses toward zero.
Treat a short-threshold breakdown as a regime risk event, not just a signal.
Trend quality assessment
Rising score supports continuation trades and adds confidence to breakouts.
Flat or falling score warns that trend persistence is fading.
If price trends but score fails to expand, trend may be weak or liquidity-driven.
Trade management
Use the Kalman Hull line as dynamic structure reference on chart.
Use score deterioration to scale out before a full regime flip.
Use regime flips as confirmation for bias shifts rather than prediction.
Tuning guidelines
Measurement Noise
Higher: smoother filter, fewer false shifts, slower to adapt.
Lower: more responsive, more sensitive to microstructure noise.
Process Noise
Higher: adapts quicker to sudden changes, but can become twitchy.
Lower: steadier state estimate, but slower during sharp regime transitions.
A practical approach is to first tune measurement noise until the Kalman Hull line matches the “clean trend structure” you want, then adjust process noise to control how quickly it reacts when the regime genuinely changes.
Summary
Kalman Hull Trend Score transforms a Kalman-based Hull-style trend filter into a quantified persistence oscillator. By combining adaptive Kalman smoothing with low-lag Hull logic and a rolling comparison score, it provides a cleaner read on trend quality than basic moving averages or single-condition trend tools. It is best used as a regime filter, trend strength gauge, and structure-aware trade management layer.
Indicator
Kalman Hull Kijun [BackQuant]Kalman Hull Kijun
A trend baseline that merges three ideas into one clean overlay, Kalman filtering for noise control, Hull-style responsiveness, and a Kijun-like Donchian midline for structure and bias.
Context and lineage
This indicator sits in the same family as two related scripts:
Kalman Price Filter
This is the foundational building block. It introduces the Kalman filter concept, a state-estimation algorithm designed to infer an underlying “true” signal from noisy measurements, originally used in aerospace guidance and later adopted across robotics, economics, and markets.
Kalman Hull Supertrend
This is the original script made, which people loved. So it inspired me to create this one.
Kalman Hull Kijun uses the same core philosophy as the Supertrend variant, but instead of building a Supertrend band system, it produces a single structural baseline that behaves like a Kijun-style reference line.
What this indicator is trying to solve
Most trend baselines sit on a bad trade-off curve:
If you smooth hard, the line reacts late and misses turns.
If you react fast, the line whipsaws and tracks noise.
Kalman Hull Kijun is designed to land closer to the middle:
Cleaner than typical fast moving averages in chop.
More responsive than slow averages in directional phases.
More “structure aware” than pure averages because the baseline is range-derived (Kijun-like) after filtering.
Core idea in plain language
The plotted line is a Kijun-like baseline, but it is not built from raw candles directly.
High level flow:
Start with a chosen price stream (source input).
Reduce measurement noise using Kalman-style state estimation.
Add Hull-style responsiveness so the filtered stream stays usable for trend work.
Build a Kijun-like baseline by taking a Donchian midpoint of that filtered stream over the base period.
So the output is a single baseline that is intended to be:
Less jittery than a simple fast MA.
Less laggy than a slow MA.
More “range anchored” than standard smoothing lines.
How to read it
1) Trend and bias (the primary use)
Price above the baseline, bullish bias.
Price below the baseline, bearish bias.
Clean flips across the baseline are regime changes, especially when followed by a hold or retest.
2) Retests and dynamic structure
Treat the baseline like dynamic S/R rather than a signal generator:
In uptrends, pullbacks that respect the baseline can act as continuation context.
In downtrends, reclaim failures around the baseline can act as continuation context.
Repeated back-and-forth around the line usually means compression or chop, not clean trend.
3) Extension vs compression (using the fill)
The fill is meant to communicate “distance” and “pressure” visually:
Large separation between price and baseline suggests expansion.
Price compressing into the baseline suggests rebalancing and decision points.
Inputs and what they change
Kijun Base Period
Controls the structural memory of the baseline.
Higher values track broader swings and reduce flips.
Lower values track tighter swings and react faster.
Kalman Price Source
Defines what data the filter is estimating.
Close is usually the cleanest default.
HL2 often “feels” smoother as an average price.
High/Low sources can become more reactive and less stable depending on the market.
Measurement Noise
Think of this as the main smoothness knob:
Higher values generally produce a calmer filtered stream.
Lower values generally produce a faster, more reactive stream.
Process Noise
Think of this as adaptability:
Higher values adapt faster to changing conditions but can get twitchy.
Lower values adapt slower but stay stable.
Plotting and UI (what you see on chart)
1) Adaptive line coloring
Baseline turns bullish color when price is above it.
Baseline turns bearish color when price is below it.
This makes the state readable without extra panels.
2) Gradient “energy” fill
Bull fill appears between price and baseline when above.
Bear fill appears between price and baseline when below.
The goal is clarity on separation and control, not decoration.
3) Rim effect
A subtle band around price that only appears on the active side.
Helps highlight directional control without hiding candles.
4) Candle painting (optional)
Candles can be colored to match the current bias.
Useful for scanning many charts quickly.
Disable if you prefer raw candles.
Alerts
Long state alert when price is above the baseline.
Short state alert when price is below the baseline.
Best used as a bias or regime notification, not a standalone entry trigger.
Where it fits in a workflow
This is a context layer, it pairs well with:
Market structure tools, BOS/MSB, OBs, FVGs.
Momentum triggers that need a regime filter.
Mean reversion tools that need “do not fade trends” context.
Limitations
No baseline eliminates chop whipsaws, tuning only manages the trade-off.
Settings should not be copy pasted across assets without checking behavior.
This does not forecast, it estimates and smooths state, then expresses it as a structural baseline.
Disclaimer
Educational and informational only, not financial advice.
Not a complete trading system.
If you use it in any trading workflow, do proper backtesting, forward testing, and risk management before any live execution.
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Keltner Hull Suite [QuantAlgo]🟢 Overview
The Keltner Hull Suite combines Hull Moving Average positioning with double-smoothed True Range banding to identify trend regimes and filter market noise. The indicator establishes upper and lower volatility bounds around the Hull MA, with the trend line conditionally updating only when price violates these boundaries. This mechanism distinguishes between genuine directional shifts and temporary price fluctuations, providing traders and investors with a systematic framework for trend identification that adapts to changing volatility conditions across multiple timeframes and asset classes.
🟢 How It Works
The calculation foundation begins with the Hull Moving Average, a weighted moving average designed to minimize lag while maintaining smoothness:
hullMA = ta.hma(priceSource, hullPeriod)
The indicator then calculates true range and applies dual exponential smoothing to create a volatility measure that responds more quickly to volatility changes than traditional ATR implementations while maintaining stability through the double-smoothing process:
tr = ta.tr(true)
smoothTR = ta.ema(tr, keltnerPeriod)
doubleSmooth = ta.ema(smoothTR, keltnerPeriod)
deviation = doubleSmooth * keltnerMultiplier
Dynamic support and resistance boundaries are constructed by applying the multiplier-scaled volatility deviation to the Hull MA, creating upper and lower bounds that expand during volatile periods and contract during consolidation:
upperBound = hullMA + deviation
lowerBound = hullMA - deviation
The trend line employs a conditional update mechanism that prevents premature trend reversals. The system maintains the current trend line until price action violates the respective boundary, at which point the trend line snaps to the violated bound:
if upperBound < trendLine
trendLine := upperBound
if lowerBound > trendLine
trendLine := lowerBound
Directional bias determination compares the current trend line value against its previous value, establishing bullish conditions when rising and bearish conditions when falling. Signal generation occurs on state transitions, triggering alerts when the trend state shifts from neutral or opposite direction:
trendUp = trendLine > trendLine
trendDown = trendLine < trendLine
longSignal = trendState == 1 and trendState != 1
shortSignal = trendState == -1 and trendState != -1
The visualization layer creates a trend band by plotting both the current trend line and a two-bar shifted version, with the area between them filled to create a visual channel that reinforces directional conviction.
🟢 How to Use This Indicator
▶ Long and Short Signals: The indicator generates long/buy signals when the trend state transitions to bullish (trend line begins rising) and short/sell signals when transitioning to bearish (trend line begins falling). These state changes represent structural shifts in momentum where price has broken through the adaptive volatility bands, confirming directional commitment.
▶ Trend Band Dynamics: The spacing between the main trend line and its shifted counterpart creates a visual band whose width reflects trend strength and momentum consistency. Expanding bands indicate accelerating directional movement and strong trend persistence, while contracting or flattening bands suggest decelerating momentum, potential trend exhaustion, or impending consolidation. Monitoring band width provides early warning of regime transitions from trending to range-bound conditions.
▶ Preconfigured Presets: Three optimized parameter sets accommodate different trading styles and timeframes. Default (14, 20, 2.0) provides balanced trend identification suitable for daily charts and swing trading, Fast Response (10, 14, 1.5) delivers aggressive signal generation optimized for intraday scalping and momentum trading on 1-15 minute timeframes, while Smooth Trend (18, 30, 2.5) offers conservative trend confirmation ideal for position trading on 4-hour to daily charts with enhanced noise filtration.
▶ Built-in Alerts: Three alert conditions enable automated monitoring - Bullish Trend Signal triggers on long setup confirmation, Bearish Trend Signal activates on short setup confirmation, and Trend Change alerts on any directional transition. These notifications allow you to respond to regime shifts without continuous chart monitoring.
▶ Color Customization: Five visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and display preferences, ensuring optimal contrast and visual clarity across trading environments.
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Triple HMA Colored [Chichomax]Triple HMA Colored Indicator Description
The Triple HMA Colored indicator is a sophisticated technical analysis tool designed to enhance trend identification by displaying three Hull Moving Averages (HMAs) on your chart, each with fully customizable periods and dynamic color settings. This indicator is built on the refined HMA calculation method, which leverages weighted moving averages (WMAs) to generate smooth and responsive trend lines with minimal lag.
Key Features:
- Triple HMA Setup:
Displays three HMAs, each computed with different, user-configurable periods, enabling multi-timeframe analysis in a single indicator.
- Dynamic Color Coding:
Each HMA line is color-coded based on its directional movement. When the current HMA value exceeds the previous value, the line is drawn in the designated "up" color, and when it falls below, it switches to the "down" color. This provides immediate visual cues for trend shifts.
- Customizable Inputs:
Users can adjust the period lengths for each of the three HMAs and select from six different color options (two for each HMA) directly from the indicator’s settings panel, ensuring that the tool can be tailored to match various trading strategies and visual preferences.
- Efficient Trend Detection:
By combining the speed of WMAs with the smoothness of the Hull Moving Average, this indicator offers a reliable method to detect market momentum changes, making it a valuable asset for both trend-following and counter-trend strategies.
Ideal for traders who demand flexibility and clarity in their chart analysis, the Triple HMA Colored indicator simplifies the process of tracking market trends across multiple timeframes while providing clear, visual signals for potential entry and exit points. Indicator
MTF EHMA & HMA Insights [FibonacciFlux]MTF EHMA & HMA Insights
Overview
The Multi-Timeframe EHMA, HMA, and Midline with Fill script is a powerful technical analysis tool designed for traders seeking to enhance their market insights and decision-making processes. By integrating two advanced moving averages—Exponential Hull Moving Average (EHMA) and Hull Moving Average (HMA)—along with a dynamic midline, this indicator provides a comprehensive view of market trends across multiple timeframes.
Key Features
1. Dual Moving Averages
- Exponential Hull Moving Average (EHMA) :
- Offers a rapid response to price changes, making it particularly useful for identifying short-term trends.
- Utilizes a unique calculation method that reduces lag, allowing traders to react quickly to market movements.
- Hull Moving Average (HMA) :
- Known for its smoothness and ability to filter out noise, the HMA presents a clear picture of the underlying trend.
- The HMA is specifically designed to achieve a balance between responsiveness and smoothness, enabling traders to make informed decisions.
2. Midline Calculation
- Dynamic Midline (m) :
- The midline is calculated as the average of EHMA and HMA, providing a neutral reference point for evaluating price movements.
- It visually represents market sentiment; a rising midline suggests bullish conditions, while a declining midline indicates bearish trends.
3. Visual Components
- Fill Areas :
- Color-coded fills between the EHMA and HMA enhance visual clarity by indicating the relative position of these moving averages.
- The fill color dynamically changes based on the relationship between the two averages (green for EHMA below HMA and red for EHMA above HMA), allowing traders to quickly assess market conditions.
4. Signal Generation and Alerts
- Buy/Sell Signals :
- The indicator generates buy signals when the midline crosses above its previous value, indicating a potential upward trend.
- Conversely, sell signals are triggered when the midline crosses below its previous value, suggesting a possible downward movement.
- Alert Conditions :
- Built-in alerts notify traders in real-time when significant changes occur, allowing them to act swiftly on potential trading opportunities.
- Customizable alert messages ensure traders receive relevant information tailored to their strategies.
Technical Details
Input Parameters
- Timeframe Settings :
- Traders can customize the timeframes for both EHMA and HMA, enabling them to adapt the indicator to different trading styles and market conditions.
- Length Settings :
- Adjustable lengths for both moving averages impact their sensitivity, allowing traders to optimize their performance based on volatility and market dynamics.
Plotting and Visualization
- Plotting :
- The script plots the EHMA, HMA, and midline directly on the chart for easy visualization.
- Signal labels (BUY and SELL) are displayed prominently, helping traders to identify potential entry and exit points without ambiguity.
Benefits
1. Clarity and Insight
- The combination of EHMA, HMA, and midline provides a clear and concise visual representation of market trends, aiding traders in making informed decisions.
2. Flexibility
- Customizable parameters allow traders to tailor the indicator to their specific needs, making it suitable for various market conditions and trading styles.
3. Efficiency
- Real-time alerts and visual signals minimize response times, enabling traders to capitalize on opportunities as they arise.
4. Enhanced Trading Conditions
- When utilizing the Fibonacci number 144 on a daily chart, the indicator facilitates optimal trading conditions:
- "The entry was made before the bubble began, using 144 as the Fibonacci variable."
- "The exit occurred right before the bubble burst, or alternatively, a short position was initiated."
- "When the next bubble started, a long entry was made again."
- "Despite some lag, the position was exited and a long entry was made."
- "The exit or short entry took place at the second double top peak."
- "A short position was already established before the double top formation occurred."
- On a 4-hour chart, traders can effectively set stop losses at HMA levels, achieving a risk-reward ratio between 4 and 8.
- Additionally, analyzing the 15-minute chart with a multi-timeframe approach allows for more precise entry points.
Conclusion
The Multi-Timeframe EHMA, HMA, and Midline with Fill script is a robust tool for traders looking to enhance their technical analysis capabilities. By combining multiple moving averages with a dynamic midline and alert system, this indicator offers a comprehensive approach to understanding market trends. Its flexibility, clarity, and efficiency make it an invaluable asset for both novice and experienced traders alike.
Important Note
As with any trading tool, it is crucial to conduct thorough analysis and risk management when using this indicator. Past performance does not guarantee future results, and traders should always be prepared for potential market fluctuations. Indicator
Versatile Moving Average StrategyVersatile Moving Average Strategy (VMAS)
Overview:
The Versatile Moving Average Strategy (VMAS) is designed to provide traders with a flexible approach to trend-following, utilizing multiple types of moving averages. This strategy allows for customization in choosing the moving average type and length, catering to various market conditions and trading styles.
Key Features:
- Multiple Moving Average Types: Choose from SMA, EMA, SMMA (RMA), WMA, VWMA, HULL, LSMA, and ALMA to best suit your trading needs.
- Customizable Inputs: Adjust the moving average length, source of price data, and stop-loss source to fine-tune the strategy.
- Target Percent: Set the percentage difference between successive profit targets to manage your risk and rewards effectively.
- Position Management: Enable or disable long and short positions, allowing for versatility in different market conditions.
- Commission and Slippage: The strategy includes realistic commission settings to ensure accurate backtesting results.
Strategy Logic:
1. Moving Average Calculation: The selected moving average is calculated based on user-defined parameters.
2. Entry Conditions:
- A long position is entered when the entry source crosses over the moving average, if long positions are enabled.
- A short position is entered when the entry source crosses under the moving average, if short positions are enabled.
3. Stop-Loss: Positions are closed if the stop-loss source crosses the moving average in the opposite direction.
4. Profit Targets: Multiple profit targets are defined, with each target set at an incremental percentage above (for long positions) or below (for short positions) the entry price.
Default Properties:
- Account Size: $10000
- Commission: 0.01% per trade
- Risk Management: Positions are sized to risk 80% of the equity per trade, because we get very tight stoploss when position is open.
- Sample Size: Backtesting has been conducted to ensure a sufficient sample size of trades, ideally more than 100 trades.
How to Use:
1. Configure Inputs: Set your preferred moving average type, length, and other input parameters.
2. Enable Positions: Choose whether to enable long, short, or both types of positions.
3. Backtest and Analyze: Run backtests with realistic settings and analyze the results to ensure the strategy aligns with your trading goals.
4. Deploy and Monitor: Once satisfied with the backtesting results, deploy the strategy in a live environment and monitor its performance.
This strategy is suitable for traders looking to leverage moving averages in a versatile and customizable manner. Adjust the parameters to match your trading style and market conditions for optimal results.
Note: Ensure the strategy settings used for publication are the same as those described here. Always conduct thorough backtesting before deploying any strategy in a live trading environment. Strategy
SuperTrend Fisher [AlgoAlpha]🚀🌟 Introducing the "Super Fisher" by AlgoAlpha, a sophisticated and versatile tool crafted for the discerning trader. This innovative indicator merges the precision of the Fisher Transform with the adaptability of the SuperTrend methodology, offering a fresh perspective on market analysis. 📈🔍
Key Features:
🔶 Customizable Settings: Tailor the indicator to your trading style with adjustable inputs like "Fair-value Period" and "EMA Length". Choose your preferred "Up Color" and "Down Color" for a personalized visual experience.
🔶 Advanced Fisher Transform: At the heart of this tool is the Fisher Transform, an algorithm renowned for pinpointing potential price reversals by normalizing asset prices.
🔶 Integrated SuperTrend Functionality: This feature adds a layer of trend analysis, using the refined Fisher Transform values to generate dynamic, trend-following signals.
🔶 Enhanced Visualization: Clearly distinguishable bullish and bearish market phases, thanks to the color-coded plots of Fisher Transform and SuperTrend values.
🔶 Overbought/Oversold Levels: Visual plots and fills for these levels provide additional insights into market extremities.
🔶 Configurable Alerts: Stay informed with alerts for critical market movements like crossing the zero line or the SuperTrend.
Logic:
The "Super Fisher" operates on a sophisticated algorithm:
1. Fisher Transform Calculation: It starts by calculating the Detrended Price Oscillator (DPO) and its standard deviation. These values are then transformed using the Fisher Transform formula, which is subsequently smoothed with a Hull Moving Average.
2. SuperTrend Integration: The SuperTrend function employs the Fisher Transform values to create a dynamic trend-following tool. It calculates upper and lower bands and determines which one to use for market direction based on whether the fisher is above or below the bands, offering an insightful view of the price trend.
3. Overbought/Oversold Identification: The tool plots specific levels to indicate overbought and oversold conditions, aiding in the identification of potential reversal points.
Here's a closer look at the core calculations:
Calculates the Fisher Transform:
value = 0.0
value := round_(.66 * ((src - low_) / (high_ - low_) - .5) + .67 * nz(value ))
fish1 = 0.0
fish1 := .5 * math.log((1 + value) / (1 - value)) + .5 * nz(fish1 )
fish1 := ta.hma(fish1, l)
Calculates the SuperTrend:
supertrend(factor, atrPeriod, srcc) =>
src = srcc
atr = atrr(srcc, atrPeriod)
upperBand = src + factor * atr
lowerBand = src - factor * atr
prevLowerBand = nz(lowerBand )
prevUpperBand = nz(upperBand )
lowerBand := lowerBand > prevLowerBand or srcc < prevLowerBand ? lowerBand : prevLowerBand
upperBand := upperBand < prevUpperBand or srcc > prevUpperBand ? upperBand : prevUpperBand
int direction = na
float superTrend = na
prevSuperTrend = superTrend
if na(atr )
direction := 1
else if prevSuperTrend == prevUpperBand
direction := srcc > upperBand ? -1 : 1
else
direction := srcc < lowerBand ? 1 : -1
superTrend := direction == -1 ? lowerBand : upperBand
How to Use:
📊 To maximize the potential of the "Super Fisher", follow these steps:
1. Customize Settings: Adjust the inputs to match your trading preferences. This includes setting the periods for the Fisher Transform and SuperTrend, as well as choosing colors for better visualization.
2. Analyze the Market: Observe the Fisher Transform and SuperTrend plots to gauge market direction. Pay special attention to color changes, as they indicate shifts in market sentiment.
3. Identify Extremes: Use the overbought and oversold plots to understand potential reversal points.
4. Set Alerts: Utilize the alert functionality to stay informed about significant market movements, ensuring you never miss an opportunity.
🔥 In summary the "Super Fisher" is a comprehensive market analysis tool designed to enhance your trading insights and decision-making process. 📉🌟🚨 Indicator
Range Filter x Hull SuiteRange Filter x Hull Suite
This indicator is a hybrid of two popular indicators, with a twist; namely the Range Filter (Guikroth version) and the Hull Suite (by Insilico) .
Originally developed as a 1 minute trend following strategy and traded during the New York Session for it's typically high volume / likely trending nature, it provides entry signals based on the following logic:
For bullish entry signals:
The first bullish* candle (*defined by the Range Filter bar color logic, blue by default - which is not necessarily technically a bullish candle as defined by the OHLC values) which appears after the consolidation candles (also defined by the Range Filter bar color logic, orange by default), and where the Hull Suite moving average is also bullish.
For bearish entry signals:
The first bearish* candle (*defined by the Range Filter bar color logic, red by default - which is not necessarily technically a bearish candle as defined by the OHLC values) which appears after the consolidation candles (also defined by the Range Filter bar color logic, orange by default), and where the Hull Suite moving average is also bearish.
The indicator aims to filter out signals where possible consolidation is occurring and comes with styling options and alternative filter options such as a triple moving average trend detection method. Signals can also be filtered by a specific trading session. Standard options for the Range Filter and Hull Suite settings are also able to be customised within the settings menu.
Alerts
Various alerts are built-in, including the custom entry signals unique to this strategy.
Note : The above features listed above are accurate at the time of publishing, but may be altered in future.
Many thanks to guikroth & Insilico for sharing their open source indicators, and also to the original developer of the strategy itself for sharing it. Indicator
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