Nonparametric Relative Momentum [BackQuant]Nonparametric Relative Momentum
Overview
Nonparametric Relative Momentum is a percentile-rank oscillator that measures where the current price or momentum observation sits relative to its own recent empirical history.
Unlike conventional momentum oscillators that transform price using fixed arithmetic relationships, this indicator uses rank statistics . The current observation is compared directly against the previous values in a rolling window and converted into a percentile score from 0 to 100.
The result answers a simple question:
How extreme is the current observation relative to what this market has actually done recently?
Two calculation modes are available:
Price ranks the selected price source directly.
Momentum first measures price change across a configurable horizon, then ranks that momentum against its own recent history.
The oscillator also includes:
Mid-rank handling for tied observations.
Optional output smoothing.
An EMA signal line.
Configurable overbought and oversold zones.
Stepped intensity colouring as the rank becomes more extreme.
Main-chart candle colouring from the 50 midline regime.
Alerts for midline, extreme-zone and signal-line crossings.
Why “nonparametric”?
In statistics, a parametric method generally assumes that data can be described by a particular distribution or by parameters associated with that distribution.
A nonparametric method does not require the same distributional assumption.
Percentile ranks are a classic example.
The oscillator does not need to assume that recent price changes are:
Normally distributed.
Symmetric.
Constant in volatility.
Characterised by a stable mean and standard deviation.
Instead, it works directly from the ordering of the observed data.
If the current momentum observation is greater than almost every momentum observation in the recent window, it receives a high rank.
If it is lower than almost everything observed recently, it receives a low rank.
This makes the oscillator fundamentally relative to the market’s own recent empirical distribution.
Core calculation
The calculation occurs in three stages:
Select the series to rank.
Calculate its empirical percentile rank.
Optionally smooth that rank and calculate a signal average.
The selected ranking target depends on the Rank Target input.
Price Mode
In Price mode:
Target = Selected Price Source
The current source value is compared with the previous values in the Rank Window.
This answers:
Where is current price positioned within its recent price distribution?
A value near 100 means current price is above almost every observation in the comparison window.
A value near 0 means it is below almost every observation.
A value near 50 means it sits near the middle of its recent distribution.
Because Price mode ranks the price level itself, it behaves somewhat like a stochastic or price-position oscillator, although the calculation is based on empirical ranking rather than highest-lowest range normalisation.
Momentum Mode
Momentum mode first calculates:
Momentum = Source - Source
This measures the absolute price change across the selected Momentum Length.
The resulting momentum series is then percentile-ranked over the Rank Window.
The oscillator therefore answers:
How strong is the current momentum observation compared with recent momentum observations?
This is different from asking whether price itself is historically high or low.
For example, price can be near a recent high while momentum has weakened considerably. In that situation:
Price mode may remain highly ranked.
Momentum mode may fall toward the centre or lower half of the distribution.
Conversely, price does not need to be at a long-term extreme for momentum to rank very highly if the current change is unusually strong relative to recent movements.
Why Momentum mode is different from traditional RSI
The standard Relative Strength Index developed by J. Welles Wilder compares smoothed positive and negative price changes.
Its calculation depends on the relative magnitude of average gains and average losses.
Nonparametric Relative Momentum does not use that formula.
Instead:
A momentum observation is calculated.
That observation is ranked against its own historical sample.
For this reason, Momentum mode can be thought of as a rank-based relative momentum oscillator .
Both traditional RSI and this oscillator are bounded between 0 and 100, but the meaning of those values is different.
For example:
RSI = 90
means the balance of smoothed gains versus losses has produced an RSI reading of 90.
Nonparametric Relative Momentum = 90
means the current momentum observation ranks around the upper end of its recent empirical momentum distribution.
That distinction is important.
Percentile rank calculation
For each bar, the indicator compares the current target with every observation in the preceding Rank Window.
It counts:
How many previous values are below the current value.
How many previous values are exactly equal to it.
The percentile rank is then:
Rank = 100 × (Values Below + 0.5 × Equal Values) / Window Length
This produces an oscillator between 0 and 100.
Why use rank instead of magnitude?
Consider two markets.
Market A may normally move only 0.5% over the selected momentum horizon.
Market B may routinely move 5%.
A raw momentum threshold cannot be interpreted the same way for both.
Ranking changes the question.
Instead of asking:
How many points or percent did this market move?
the oscillator asks:
How unusual is this move relative to this market’s own recent behaviour?
This allows the same 0–100 framework to adapt naturally to different price scales and volatility regimes.
Mid-rank treatment of ties
A simple percentile implementation might count only observations strictly below the current value.
That can distort the result when repeated values occur.
This indicator uses mid-rank treatment .
If historical observations equal the current value, each tie contributes one half rather than being classified entirely above or below.
For example, suppose:
40% of observations are below the current value.
20% are exactly equal.
40% are above.
The mid-rank result is:
40 + 0.5 × 20 = 50
This places the tied observation at the centre of its equal-value group.
Mid-ranks are commonly used in rank-based statistics because they provide a more balanced treatment of ties.
Rank Window
The Rank Window determines how much historical data defines the current empirical distribution.
A shorter Rank Window:
Adapts quickly.
Responds strongly to recent regime changes.
Produces more rapid movement between percentiles.
Can create noisier extreme readings.
A longer Rank Window:
Builds the ranking from a larger sample.
Produces a more stable percentile estimate.
Makes extremes harder to reach.
Responds more slowly when market behaviour changes.
The window therefore controls the memory of the oscillator.
It does not smooth the underlying target directly. It changes the reference distribution against which the target is ranked.
Momentum Length
Momentum Length is used only when Rank Target is set to Momentum.
It controls the horizon over which price change is measured:
Momentum = Current Source - Source from Momentum Length bars ago
Shorter values:
Measure faster momentum.
React to shorter impulses.
Change direction more frequently.
Longer values:
Measure broader displacement.
Focus on more persistent movement.
Ignore more short-term fluctuation.
The Momentum Length and Rank Window perform separate roles.
Momentum Length determines what movement is measured.
Rank Window determines the historical sample against which that movement is judged.
Output Smoothing
The raw percentile rank can optionally be passed through an EMA.
A value of 1 leaves the rank effectively unsmoothed.
Higher values:
Reduce rapid rank fluctuations.
Create a smoother oscillator.
Reduce short-lived extreme readings.
Introduce additional lag.
The smoothing occurs after the percentile calculation.
It does not change how observations are ranked.
The 50 midline
The oscillator is centred around 50.
A value above 50 means the current observation ranks above the midpoint of its recent distribution.
A value below 50 means it ranks below the midpoint.
The interpretation depends on the selected mode.
Price mode above 50
Current price is positioned in the upper half of its recent price distribution.
Price mode below 50
Current price is positioned in the lower half.
Momentum mode above 50
Current momentum is stronger than roughly the middle of its recent momentum observations.
Momentum mode below 50
Current momentum is weaker relative to its recent distribution.
The indicator also uses this midline to colour main-chart candles:
Above or equal to 50 = bullish colour.
Below 50 = bearish colour.
This provides a simple relative-regime view on the price chart.
Percentile extremes
Because the oscillator represents rank rather than an unbounded magnitude, readings near 0 and 100 carry a straightforward interpretation.
Near 100
The current observation is greater than almost every value in the recent comparison window.
Near 0
The current observation is lower than almost every value.
These are empirical extremes.
They do not mean price or momentum cannot become more extreme.
A value near 100 can persist while a strong trend continues because new observations may repeatedly remain near the top of the evolving distribution.
Likewise, readings near 0 can persist during sustained downside momentum.
Overbought and Oversold zones
The default static zones are:
Overbought: 90–100
Oversold: 0–10
These are configurable.
The labels “overbought” and “oversold” describe statistical location, not guaranteed reversal conditions.
An overbought reading means:
The ranked observation is near the top of its recent empirical distribution.
An oversold reading means:
It is near the bottom.
During a range, these areas may help identify local extremes.
During a persistent trend, the oscillator can remain in an extreme zone for extended periods.
The zones should therefore be interpreted together with:
Trend context.
Price structure.
Oscillator direction.
Signal-line behaviour.
Why 90/10 instead of 70/30?
Traditional RSI commonly uses 70 and 30.
That convention does not need to apply to a percentile-rank oscillator.
A rank above 90 means the current observation is in approximately the upper tail of the recent empirical sample, while a reading below 10 represents the lower tail.
Using more extreme default zones makes them intentionally selective.
Users who want broader zones can move the boundaries toward values such as 80 and 20.
Signal line
The white Moving Average line is an EMA of the final oscillator:
Signal = EMA(Percentile Rank Oscillator, Signal Length)
This provides a slower reference against which short-term rank movement can be compared.
Oscillator above signal
The percentile rank is strengthening relative to its own recent smoothed level.
Oscillator below signal
The rank is weakening.
Crossovers can be used to identify changes in short-term momentum within the broader percentile regime.
For example:
A bullish crossover below the oversold zone can indicate rank beginning to recover from an extreme.
A bearish crossover above the overbought zone can indicate deterioration from an upper-tail reading.
A crossover near 50 may represent a more neutral momentum transition.
Signal crosses should not be interpreted independently from oscillator location.
Stepped oscillator colouring
The oscillator uses stepped colour intensity based on its position relative to the 50 midline.
Above 50, colours progressively strengthen as the percentile reaches higher levels.
Below 50, bearish intensity progressively strengthens as the percentile falls.
The main regions are approximately:
50–62.5: modest positive rank.
62.5–75: strengthening positive rank.
75–90: strong positive rank.
90–99: upper-tail extreme.
99–100: exceptional upper-tail rank.
The lower half mirrors this concept:
37.5–50: modest negative rank.
25–37.5: weakening relative state.
10–25: strong negative rank.
1–10: lower-tail extreme.
0–1: exceptional lower-tail rank.
These colours do not introduce additional calculations or signals.
They visually communicate how far the oscillator has moved into its empirical distribution.
Column presentation
The percentile oscillator is plotted as columns around a histogram base of 50.
This means:
Values above 50 extend upward.
Values below 50 extend downward from the midline.
Although the numerical scale remains 0–100, this presentation visually emphasises deviation from the centre of the distribution.
The 50 level therefore functions as the oscillator’s equilibrium reference.
Price mode versus Momentum mode
The two modes answer different questions and should not be treated interchangeably.
Price Mode
Asks:
Where is price relative to its recent distribution?
This makes it useful for:
Range position.
Breakout context.
Relative price extremes.
Stochastic-like analysis.
Momentum Mode
Asks:
Where is current price change relative to the recent distribution of price changes?
This makes it useful for:
Momentum expansion.
Momentum exhaustion.
Relative impulse analysis.
Trend-strength transitions.
Momentum mode can identify weakening momentum before price itself leaves the upper part of its distribution.
Price mode can remain elevated simply because the market is still trading near recent highs.
Example: strong uptrend
Suppose price has been rising steadily.
Price Mode may remain above 90 because current price continually sits near the upper edge of its recent range.
Momentum Mode may behave differently:
It can rise toward 100 during acceleration.
Fall back toward 50 when the trend continues at a more ordinary pace.
Drop below 50 if momentum deteriorates significantly even while price remains relatively high.
This distinction can help separate price location from momentum condition .
Example: volatility regime change
Suppose a market normally changes by only small amounts, then suddenly produces a large directional move.
Raw momentum alone shows a large number.
The percentile rank provides additional context by showing whether that movement is unusual relative to the recent distribution.
If the current momentum is greater than nearly every recent observation, the oscillator moves toward 100.
If the market has already experienced many similarly large moves, the same absolute momentum may receive a much less extreme rank.
The indicator therefore adapts automatically to changing empirical behaviour without requiring fixed momentum thresholds.
Midline crossings
A crossover above 50 indicates the ranked series has moved into the upper half of its recent distribution.
A cross below 50 indicates movement into the lower half.
In Momentum mode, these crossings can be used as a simple relative momentum regime:
Above 50 = comparatively stronger momentum state.
Below 50 = comparatively weaker momentum state.
In Price mode, they indicate whether price is above or below the central portion of its recent rank distribution.
These crossings also control the optional main-chart candle colours.
Extreme-zone crossings
The indicator provides alerts when:
The oscillator crosses upward into the overbought zone.
The oscillator crosses downward into the oversold zone.
These alerts identify entry into an extreme percentile area.
They do not indicate that the extreme has ended.
For reversal-oriented analysis, a trader may instead monitor:
A subsequent exit from the zone.
A signal-line crossover.
Divergence with price.
A break in market structure.
Divergence interpretation
Because Momentum mode ranks momentum rather than price, it can also be useful for examining momentum divergence.
For example:
Price may make a higher high while the oscillator produces a lower percentile peak.
This indicates that the latest momentum observation is less exceptional relative to its recent history than it was during the previous price high.
The reverse can occur at lows.
As with conventional divergence, this is evidence of changing momentum characteristics, not confirmation that price must reverse.
How to use the indicator
1. Relative momentum regime
In Momentum mode, use the 50 midline as a simple regime reference:
Above 50 = positive relative momentum state.
Below 50 = negative relative momentum state.
2. Momentum extremes
Use the configurable zones to identify unusually high or low momentum ranks.
Rather than automatically fading these conditions, determine whether the market is:
Trending.
Exhausting.
Breaking out.
Returning toward equilibrium.
3. Signal-line transitions
Oscillator and signal-line crosses can help identify shorter-term changes in rank direction.
The location of the crossover matters.
A bullish crossover at 5 carries different context from one at 95.
4. Price-distribution analysis
Switch to Price mode when the objective is to measure where the current market sits within its recent price distribution.
This can be useful for:
Breakout analysis.
Range positioning.
Relative high/low detection.
5. Trend confirmation
Momentum remaining consistently above 50 can support an existing bullish trend.
Momentum remaining below 50 can support a bearish trend.
Repeated oscillation around 50 indicates that relative momentum is changing sides frequently.
6. Candle regime colouring
The optional overlay candles make the oscillator’s midline state visible directly on the main price chart.
This can be useful when the oscillator pane is being used primarily for extremes and signal-line analysis.
Input guide
Rank Target
Selects what is percentile-ranked.
Price ranks the source itself.
Momentum ranks its change over the selected Momentum Length.
Rank Window
Controls the empirical comparison sample.
Longer values are smoother and statistically broader. Shorter values adapt more quickly.
Momentum Length
Controls the displacement horizon in Momentum mode.
It has no effect in Price mode.
Output Smoothing
Applies optional EMA smoothing to the percentile rank.
1 produces the raw rank.
Signal Length
Controls the EMA signal line.
Shorter values follow the oscillator more closely. Longer values produce slower crossover signals.
Overbought Zone
Sets the lower boundary of the upper extreme area.
Oversold Zone
Sets the upper boundary of the lower extreme area.
How this differs from RSI
Traditional RSI:
Separates gains and losses.
Smooths their magnitude.
Calculates a relative-strength ratio.
Transforms that ratio onto a 0–100 scale.
Nonparametric Relative Momentum:
Calculates price or momentum directly.
Ranks the current observation against historical observations.
Uses no gain/loss ratio.
Uses no assumed distribution.
The identical 0–100 scale therefore represents a different statistical concept.
How this differs from Stochastic
A conventional stochastic oscillator measures where current price lies between the highest high and lowest low of a window.
Its basic concept is:
(Current - Lowest) / (Highest - Lowest)
Nonparametric Price mode instead asks how many historical observations are below the current price.
This distinction matters because the rank considers the entire empirical ordering of the sample, not only its two extreme endpoints.
Two windows can have identical highs, lows and current price but different internal distributions.
A stochastic calculation can return the same value in both cases, while percentile rank can differ because the number of observations above and below the current price is different.
How this differs from a Z-score
A Z-score measures deviation from a mean in standard-deviation units:
Z = (Current Value - Mean) / Standard Deviation
That calculation depends directly on the sample mean and dispersion.
Percentile rank depends only on ordering.
As a result, an extreme outlier can heavily alter a mean and standard deviation but has much less influence on the ordering of the remaining observations.
This is one of the reasons rank statistics can be useful when financial data contains skew, fat tails or isolated extreme moves.
Strengths
Uses a nonparametric empirical ranking process.
Requires no assumption of normality.
Produces an intuitive bounded 0–100 scale.
Adapts naturally to the recent behaviour of each market.
Supports both price-location and momentum-ranking modes.
Uses mid-ranks for tied observations.
Normalises momentum extremes without relying on fixed point or percentage thresholds.
Includes configurable smoothing and signal analysis.
Provides direct midline regime colouring on the main chart.
Limitations
A percentile rank measures relative position, not absolute magnitude.
A reading of 100 does not indicate how much larger the current observation is than the rest of the sample.
Persistent trends can remain at extreme ranks for extended periods.
Short Rank Windows can generate rapid percentile changes.
Long Rank Windows adapt more slowly to regime shifts.
Momentum mode uses absolute source change rather than percentage return, although ranking substantially reduces scale dependence within a single instrument.
Extreme readings are not automatic reversal signals.
Signal-line crosses can whipsaw in noisy conditions.
The oscillator is reactive and does not forecast future price.
Alerts
The indicator provides alerts for:
Cross Up 50: oscillator enters the upper half of its distribution.
Cross Down 50: oscillator enters the lower half.
Overbought: oscillator crosses upward through the selected upper-zone boundary.
Oversold: oscillator crosses downward through the selected lower-zone boundary.
Bull: oscillator crosses above its signal EMA.
Bear: oscillator crosses below its signal EMA.
Summary
Nonparametric Relative Momentum converts either price or momentum into an empirical percentile rank.
Instead of asking how far an observation is from a moving average, how many standard deviations it sits from a mean, or what ratio of gains to losses produced it, the indicator asks where that observation ranks relative to its own recent history.
In Price mode, it measures the relative location of price within its historical distribution.
In Momentum mode, it first calculates price displacement across a chosen horizon and then measures how exceptional that momentum is relative to recent momentum observations.
A mid-rank procedure handles tied values, optional EMA smoothing controls visual responsiveness, and a separate signal average provides crossover analysis. The 50 midline separates the upper and lower halves of the empirical distribution, while configurable overbought and oversold zones highlight the tails.
The result is a distribution-free relative momentum framework that adapts to the observed behaviour of the market rather than relying on fixed magnitude thresholds or an assumed statistical distribution.
Indicator
Tyson Uppercut Compression Spring Breakout (Viprasol)Tyson Uppercut — Compression Spring Breakout 🥊
(The name is an affectionate combat-sports homage — this is an educational pattern tool, not affiliated with or endorsed by any athlete or organization.)
CONCEPT
A spring loaded by VOLATILITY, not by swings. The tool measures the recent bar-range and requires it to be compressed — noticeably tighter than the window before it (energy coiling). Then the uppercut: one wide-range bar that bursts up out of the compression on the close = the release. Distinct from swing-decay coils; this reads raw range contraction directly from the bars.
HOW IT DETECTS
- Compression is measured on CLOSED bars only: the highest high and lowest low over a window (default 10 bars, offset by one bar).
- That compression range must be tight versus the prior, wider window (default: <= 60% of the range over twice the lookback) AND not larger than a set ATR cap.
- Release: the current bar closes above the compression high, its full bar range is at least a set ATR multiple (default 1.3x ATR), and it closes up (close > open).
- All logic runs on bar close (barstate.isconfirmed). An optional dotted live box previews an active compression before any break.
ENTRY / STOP / TARGET
- Entry: on the confirmed release close (long only).
- Stop: below the compression low, minus an ATR buffer.
- Target: entry + R multiple of risk (default 2R, adjustable).
- Drawn as a solid compression box plus an entry line and filled TP/SL zones that extend right until price touches one; the hit side thickens.
NON-REPAINTING
Compression is read from already-closed bars (a one-bar offset is used), and the release is confirmed on the bar close. A printed signal does not move or disappear afterward. The live dotted preview is informational only and is not a signal.
KEY FEATURES
- Volatility-compression detection from raw range, with an ATR height cap to avoid oversized "boxes".
- Release requires a genuinely wide breakout bar, not just a marginal close.
- Optional live compression preview; option to hide new setups while a trade is active.
- Filled, extend-until-hit TP/SL zones and an on-chart status table (open trades). Alert condition included.
INPUTS OVERVIEW
- The spring: compression window (bars), tightness fraction vs prior window, max compression height (x ATR), release bar range (x ATR), ATR length.
- The knockout: TP as R multiple, SL buffer (x ATR), minimum bars between signals, one-trade-at-a-time, hide-setup-while-in-trade.
- Visuals: compression box / entry / TP / SL colors, label offset, zone transparency, show-live toggle.
HOW TO USE
1. Add to a liquid symbol and timeframe; it is fully overlay-based.
2. Set the compression window and tightness fraction to define how coiled the range must be.
3. Raise the release ATR multiple to demand a stronger breakout bar.
4. Watch the live dotted box to anticipate setups, and study the TP/SL zones on your instrument.
5. Optionally create an alert from the built-in condition.
LIMITATIONS (read this)
- This is a pattern/education tool, not a signal service and not financial advice. It does not predict the future.
- Compression breakouts frequently fail or reverse (false breakouts are common), especially in ranging markets.
- It is long-only by design; it does not trade downside releases.
- Range readings depend on the chosen windows; different settings can materially change what counts as "compressed".
- Results depend heavily on your inputs, instrument, and timeframe. Always use your own risk management and discretion.
CREDITS
ATR uses Wilder's Average True Range. Highest/lowest range measurement uses standard public functions (ta.highest / ta.lowest). The raw-range compression-and-release detection and the trade-zone visualization are original Viprasol design.
Original Viprasol work; no third-party Pine code reused.
Indicator
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
Session ATR Risk ToolSession ATR Risk Tool
## Overview
The Session ATR Risk Tool is a discretionary **risk-management and trade-planning overlay**. It sizes a stop loss from market volatility, projects fixed reward-to-risk targets (1:1, 1:2, 1:3), and draws a standard-deviation ladder so you can see your full trade geometry on the chart before you enter. It also estimates a contract count from a fixed dollar risk, and prints a context table of intraday, daily and weekly volatility.
It is built and tuned for Micro E-mini Nasdaq-100 (MNQ) intraday trading, but every parameter is exposed as an input, so it works on any symbol once you set the correct point value.
This tool does **not** generate buy/sell signals and makes no claim about win rate or profitability. It is a visualization and planning aid only.
## What makes it different
Most reward-to-risk tools place lines a fixed number of ticks or a single ATR away. This tool adds three things in one package:
1. **Two selectable stop engines.** The stop distance can be derived from either the chart-timeframe ATR (small, realistic intraday stops) or from a rolling average of completed *session* ranges (swing-sized stops). You choose which volatility regime sizes your risk.
2. **A standard-deviation ladder denominated in your own stop distance.** Instead of arbitrary fib or price-percent levels, each rung is a multiple (−0.5, 1, 2, 3, 4 by default, all editable) of the exact ATR-based stop distance, projected from entry. One "sd" on the chart always equals one unit of the risk you are actually taking.
3. **A volatility context table.** Intraday ATR, averaged session range, daily ATR(14) and weekly ATR(14) are shown side by side so the chosen stop can be judged against higher-timeframe volatility at a glance.
## How it works
- **Session range capture.** The script tracks the high and low of each completed session window (default 09:30–16:00 exchange time) and stores the high-low range. It keeps a rolling buffer of the most recent N sessions (default 10) and averages them to produce a "session ATR" in points.
- **Intraday ATR.** A standard ATR of configurable length is calculated on the chart timeframe for scalp-sized stops.
- **Stop distance.** `Stop distance = chosen basis × ATR multiplier`, where the basis is either the intraday ATR or the averaged session range. The multiplier lets you tighten or widen the stop.
- **Trade geometry.** From the entry price (live price by default, or a fixed price you type in) and the trade direction, the tool places the stop one stop-distance against you, then projects targets at 1×, 2× and 3× the stop distance for clean 1:1 / 1:2 / 1:3 reward-to-risk.
- **Standard-deviation ladder.** Each ladder rung is plotted at `entry + direction × stop distance × deviation`, giving an evenly scaled map of where price sits relative to your risk unit.
- **Position-size estimate.** Dollar risk per contract = stop distance × point value. Estimated contracts = floor(risk per trade ÷ dollar risk per contract). This is an arithmetic estimate for planning, not an order-routing instruction.
- **Higher-timeframe context.** Daily and weekly ATR(14) are pulled from confirmed higher-timeframe bars (non-repainting) for the context table.
All levels are drawn as faded horizontal rays anchored to the bar grid, so they stay locked to the candles when you pan or zoom. Drawings rebuild on the most recent bar to keep the chart clean.
## How to use it
1. Add the tool to an intraday chart of the instrument you trade.
2. Set **$ per Point** for your instrument (MNQ = 2.0, NQ = 20.0, MES = 5.0, etc.) and your **Risk per Trade ($)**.
3. Choose your **Stop Basis** — "Intraday ATR" for scalps and intraday entries, "Session Range" for wider, swing-style stops.
4. Adjust the **ATR Multiplier** to set how far the stop sits from entry. As a starting guide, roughly 1.0–2.0× with Intraday ATR on a 1–5 minute chart; if using Session Range, scale the multiplier down (around 0.10–0.20×) because the session range is much larger.
5. Set **Trade Direction** (Long or Short). Leave **Entry Price** at 0 to anchor the levels to live price, or type your actual fill price to lock the geometry in place after entry.
6. Read your plan off the chart: the Stop, the 1:1 / 1:2 / 1:3 targets, the standard-deviation ladder, and the info table showing the stop in points and dollars plus an estimated contract count.
## Inputs
- **Session Window / Sessions to Average** — defines the session and how many completed sessions feed the averaged session range.
- **Stop Basis / Intraday ATR Length / ATR Multiplier** — select and tune the volatility source for the stop.
- **Trade Direction / Entry Price** — direction toggle and optional fixed entry.
- **$ per Point / Risk per Trade ($)** — instrument tick value and account risk used for the size estimate.
- **SDev Ladder deviations** — the five editable ladder multiples.
- **Visual controls** — ray length back/forward, table toggle, and colors for up, down and entry levels.
## Notes and limitations
- The contract-count figure is an arithmetic estimate from your inputs. It is not connected to a broker and places no orders. Always confirm size and risk in your own platform.
- "Session ATR" here means the averaged high-low **range** of recent sessions, not a true-range calculation; it is intentionally a wider, regime-level measure.
- Higher-timeframe ATR values use confirmed bars to avoid repainting.
- Reward-to-risk targets are fixed geometric projections; they are not predictions of price reaching those levels.
- This script is a planning and visualization tool only. It is not financial advice and does not guarantee any outcome. Indicator
Indicator
Volatility Gated Supertrend [BackQuant]Volatility Gated Supertrend
Overview
Volatility Gated Supertrend is a regime-aware trend-following indicator built around a modified Supertrend engine with an integrated volatility filter . Unlike a traditional Supertrend, which flips direction whenever price crosses its trailing bands, this version introduces a gating mechanism that can block trend reversals during low-volatility conditions .
The purpose of the indicator is simple:
Keep the responsiveness and structure of a Supertrend.
Reduce false flips during sideways or compressed conditions.
Allow trend transitions primarily when volatility is expanding enough to justify participation.
The result is a smoother and more selective trend engine designed to suppress whipsaws while still reacting to meaningful directional movement.
The full source structure for the indicator can be referenced here: :contentReference {index=0}
Core idea
Traditional Supertrend indicators work well during directional markets but struggle in compressed environments:
Price repeatedly crosses the trailing bands.
Trend direction flips too frequently.
False reversals appear during chop.
This indicator attempts to solve that problem by asking:
“Is there enough volatility expansion to justify accepting a new trend?”
Instead of blindly allowing every flip, the indicator measures:
Current volatility,
Baseline volatility,
Relative expansion or compression.
Only when volatility conditions are sufficient does the trend engine allow a directional transition.
What the Supertrend is
The Supertrend is a volatility-based trailing trend indicator built from:
ATR (Average True Range)
A central price source
A directional trailing stop structure
The classic logic:
Upper band = price source + ATR × multiplier
Lower band = price source − ATR × multiplier
These bands trail price dynamically:
In bullish conditions, the lower band ratchets upward.
In bearish conditions, the upper band ratchets downward.
When price crosses one of the bands:
The trend flips direction.
This creates a clean directional regime model.
How this version differs
The major difference is the volatility gate .
A normal Supertrend asks:
“Did price cross the band?”
This indicator asks:
“Did price cross the band, and is volatility strong enough to trust the move?”
That additional filter dramatically changes behavior in sideways conditions.
ATR and volatility structure
The indicator uses two ATR measurements:
Fast ATR → current short-term volatility
Slow ATR → baseline long-term volatility
The core ratio:
Volatility Ratio = Fast ATR / Slow ATR
Interpretation:
Ratio above threshold → volatility expansion
Ratio below threshold → volatility compression
This becomes the gate logic.
Volatility Gate Logic
The gate opens only when:
Fast ATR / Slow ATR ≥ Gate Threshold
If volatility is too compressed:
The gate closes.
Trend flips are blocked.
Importantly:
The Supertrend bands still calculate normally.
Price can still cross them.
But the directional state will not update while the gate is closed.
This distinction matters because it means:
The market may technically trigger a reversal,
But the indicator intentionally ignores it if volatility conditions are weak.
Why this helps
Most trend-following systems fail in chop because:
Small meaningless moves trigger directional flips.
There is insufficient range expansion.
The market lacks trend persistence.
By requiring volatility confirmation:
Weak reversals are filtered out.
Trend state becomes more stable.
Noise is reduced.
This makes the indicator particularly useful during:
Low-volatility consolidations,
Mean-reverting conditions,
Slow drifting ranges.
Band construction
The indicator uses:
hl2 as the central source,
ATR for dynamic width,
A configurable multiplier for sensitivity.
Formulas:
Upper Band = hl2 + ATR × multiplier
Lower Band = hl2 − ATR × multiplier
The trailing logic prevents the bands from moving backward unnecessarily:
Bullish lower band only rises.
Bearish upper band only falls.
This creates the staircase-style trailing structure common in Supertrend systems.
Trend state
Trend direction is binary:
1 = bullish
-1 = bearish
A raw bullish flip occurs when:
Close > trailing upper band
A raw bearish flip occurs when:
Close < trailing lower band
However:
The trend only updates if the volatility gate is open.
This is the defining behavior of the script.
Blocked flips
One of the most important features is the visualization of blocked signals .
When:
Price crosses a band,
But volatility is insufficient,
The script:
Plots an X-cross marker,
Keeps the existing trend state,
Refuses the flip.
This gives traders visibility into:
Potential but unconfirmed reversals,
Areas of weak participation,
Fake breakouts or low-energy transitions.
Visual behavior
Trend band
The active trailing band changes color based on trend direction:
Bullish → bullish color
Bearish → bearish color
Gate closed → gated color (dimmed)
Trend fill
The script fills the space between price and the active band:
Bullish fill during bullish regimes
Bearish fill during bearish regimes
This creates a cleaner directional overlay.
Outer glow
An additional glow layer expands slightly beyond the trend band:
Adds directional emphasis,
Improves trend readability,
Visually reinforces active regime.
When the gate closes:
The band and candles dim.
This visually communicates:
“The trend engine is currently suppressing flips.”
Candle coloring
Candles can optionally inherit the trend state:
Bullish regime → bullish candles
Bearish regime → bearish candles
Gate closed → dimmed neutral appearance
This allows the indicator to function as a full-chart regime overlay.
Signal logic
Bullish signal
Occurs when:
Trend flips from bearish to bullish,
AND the gate is open.
Bearish signal
Occurs when:
Trend flips from bullish to bearish,
AND the gate is open.
Blocked signal
Occurs when:
A raw flip condition appears,
BUT volatility ratio is below threshold.
This distinction is important:
A blocked signal is not ignored information.
It is a rejected transition.
How to interpret the gate
Gate open
Volatility is active.
Market expansion is sufficient.
Trend flips are allowed.
Gate closed
Market is compressed.
Conditions are likely choppy.
Trend reversals are suppressed.
This effectively turns the indicator into a:
Trend-following system during expansion,
Trend-holding system during compression.
Why ATR ratio works well
ATR ratio is a powerful regime detector because it measures:
Current volatility relative to normal volatility.
Not just:
“Is volatility high?”
But:
“Is volatility high relative to its recent baseline?”
This adaptive behavior allows the gate to work across:
Different assets,
Different timeframes,
Different volatility environments.
Input guide
ATR Multiplier
Controls band width:
Higher = wider bands, fewer flips
Lower = tighter bands, more sensitivity
ATR Length
Controls volatility calculation for the Supertrend itself.
Fast ATR
Short-term volatility measure.
Slow ATR
Long-term baseline volatility measure.
Gate Threshold
Controls how strict the gate is:
Lower threshold = more permissive
Higher threshold = more restrictive
Example:
0.6 → allows more flips
1.0 → requires current volatility to match baseline
1.2 → requires expansion regime
Strengths
Reduces Supertrend whipsaws in chop.
Adds regime awareness.
Uses adaptive volatility filtering.
Clean trend visualization.
Blocked-signal logic provides extra context.
Limitations
Can delay reversals during early expansion.
Very high thresholds may suppress legitimate transitions.
Still fundamentally a trend-following system.
Not designed for low-volatility mean reversion trading.
Best use case
Volatility Gated Supertrend works best as:
A directional regime filter,
A swing trend overlay,
A volatility-aware trend confirmation tool,
A way to suppress noise during consolidations.
It is particularly useful for traders who:
Like Supertrend logic,
But dislike how often it flips in sideways markets.
Summary
Volatility Gated Supertrend extends the classic Supertrend framework by introducing a volatility-aware gating engine that blocks trend reversals during compressed market conditions. By comparing fast ATR against slow ATR, the script determines whether enough volatility expansion exists to justify a directional transition. The result is a cleaner, more stable trend system that retains the strengths of Supertrend logic while dramatically reducing whipsaws during low-energy market regimes. Indicator
Xer0's Dual Engine Ladder AllocatorOverview
This indicator is designed for long-term investors using a "Dual Engine" portfolio strategy on M1 Finance — mixing a broad-market index fund with a leveraged counterpart in the same Pie. Instead of guessing when to buy the dip, this script provides a systematic, step-by-step roadmap for increasing your leveraged allocation as the market falls, and resetting it as the market recovers.
How It Works
The strategy is built on "Sticky All-Time High" logic. It tracks the highest close price and calculates the current drawdown from that peak, then responds with one of three scenarios:
Ladder Down (Risk On): For every defined drop step (e.g. every -5%), the indicator signals a RISK UP event — automatically calculating your new target allocation to the leveraged slice of your Pie. This forces systematic, disciplined buying at lower prices.
Recovery Reset (Risk Off): Once the market recovers by a set percentage from the bottom, the script signals a RESET — returning your allocation to the base level and locking in the gains from the dip-buying phase.
Bull Step: When the market pushes into new high territory, the script tracks each new leg up and keeps your reference point current.
Key Features
Sticky ATH Tracking: Automatically calculates true drawdown from the cycle peak
Customizable Ladder Steps: Define your own drop trigger percentage and leverage increase per step
Max Cap: Hard ceiling on leverage exposure to protect against catastrophic drawdowns
Bar Confirmation: All signals fire on daily close to avoid intraday false triggers
Visual Dashboard: Bottom-right table showing current mode, target leverage, drawdown, and recovery price target
Alert Conditions: Built-in RISK UP and RESET alerts compatible with PulseWire's "Once Per Bar Close" setting
Backtested Performance (Simulated — Read Carefully)
The following results are from a Python backtest covering approximately 30 years (1996–2026), using $923/week in contributions every Friday. The strategy used two M1 Pies: Pie 1 (S&P 500 index fund / 3× S&P 500 ETF, base leverage 35%) and Pie 2 (Nasdaq-100 index fund / 3× Nasdaq-100 ETF, base leverage 25%). Tax assumptions reflect California state + federal rates for a $47K–$100K income bracket. Data prior to 2010 is synthetic, modeled from underlying index returns.
Results are hypothetical and do not represent actual trading. Past performance does not guarantee future results.
Ladder Strategy | VOO Benchmark
Total Contributed $1,395,576 | $1,395,576
Final Value (after-tax) $25,286,879 | $9,025,443
Total Return 1,711.9% | 546.7%
CAGR (on contributions) 10.1% | 6.4%
Max Drawdown -91.8% | -50.5%
Taxes Paid (CA) $5,358,907 | N/A (buy & hold)
Cash After Full Liquidation $23,500,189 | $7,171,385
The ladder strategy produced approximately 227.7% more after-tax cash than buy-and-hold VOO after full liquidation. However, the strategy experienced a maximum drawdown of -91.8% — meaning at its worst point, the portfolio lost nearly all of its value on paper. This level of volatility is not suitable for most investors and requires strong conviction and a long time horizon to hold through.
How to Use
Add this indicator to a Daily (1D) chart of your chosen index. Configure the inputs to match your risk tolerance — Base Leverage %, Drop Step %, and Max Cap %. Enter your M1 Pie name in the input field so alerts reference it by name. Set alerts using "Once Per Bar Close" and adjust your Pie allocation whenever a signal fires.
Disclaimer
This script is for informational and educational purposes only. It does not constitute financial advice. Backtested results are simulated and hypothetical — they do not account for all real-world frictions and should not be interpreted as a guarantee of future performance. Trading leveraged instruments involves significant risk, including the potential loss of your entire investment, and is not suitable for all investors. Indicator
BTC Valuation Cycle [Alpha Extract]A sophisticated multi-metric Bitcoin valuation framework that synthesizes on-chain analytics including SOPR, MVRV, Price-to-Realized, and Mayer Multiple into a unified 0-100 cycle oscillator with six-tier zone classification for market cycle identification. Utilizing logistic transformation with configurable weighting and z-score normalization, this indicator delivers institutional-grade Bitcoin-specific valuation assessment with pivot-based extreme detection and comprehensive alert system. The system's weighted composite architecture combined with adaptive curve intensity enables precise calibration of cycle sensitivity while maintaining statistical validity across Bitcoin's multi-year market cycles.
🔶 Advanced Multi-Metric Synthesis Engine
Implements sophisticated composite calculation combining four distinct Bitcoin valuation metrics with configurable weighting and normalization framework. The system retrieves SOPR (Spent Output Profit Ratio), MVRV (Market Value to Realized Value), Price-to-Realized ratio, and Mayer Multiple from on-chain sources, applies z-score normalization to each metric over configurable periods, transforms via logistic function for 0-100 scaling, and generates weighted average creating unified cycle score.
// Component Score Calculation
SOPR_Centered = SOPR - 1.0
SOPR_Z = z_score(SOPR_Centered, Normalization_Length)
SOPR_Score = logistic_100(SOPR_Z, Curve_Intensity)
Price_to_Realized_Z = z_score(Price / Realized_Price, Normalization_Length)
PR_Score = logistic_100(Price_to_Realized_Z, Curve_Intensity)
MVRV_Z = z_score(Market_Cap / Realized_Cap, Normalization_Length)
MVRV_Score = logistic_100(MVRV_Z, Curve_Intensity)
Mayer_Z = z_score(Mayer_Multiple, Normalization_Length)
Mayer_Score = logistic_100(Mayer_Z, Curve_Intensity)
// Weighted Composite
Cycle = (SOPR_Score × W_SOPR + PR_Score × W_PR + MVRV_Score × W_MVRV + Mayer_Score × W_Mayer) / (W_SOPR + W_PR + W_MVRV + W_Mayer)
🔶 Understanding Bitcoin Valuation Metrics
SOPR (Spent Output Profit Ratio) measures the degree of profit for coins moved on-chain, calculated as value sold divided by value paid. Values above 1.0 indicate profitable selling (distribution), below 1.0 indicate loss-taking (capitulation). The system centers SOPR around 1.0 for normalization.
MVRV (Market Value to Realized Value) compares current market cap to realized cap (aggregate cost basis). High MVRV signals overvaluation as price exceeds average acquisition cost; low
MVRV suggests undervaluation. The system offers Ratio mode (raw MVRV), Z-Score mode (statistical deviation), or Blend mode (average of both).
Price-to-Realized Ratio directly compares current BTC price to realized price (realized cap divided by circulating supply), providing cleaner valuation signal than MVRV by removing market cap distortions.
Mayer Multiple measures price relative to 200-day moving average. Values above 2.4 historically mark tops; values near or below 1.0 mark bottoms. The system normalizes this classic technical indicator alongside on-chain metrics.
🔶 Logistic Transformation Framework
Features sophisticated logistic function application converting unbounded z-scores into bounded 0-100 range with configurable curve intensity controlling sensitivity. The system applies formula: 100 / (1 + exp(-z × k)) where z is z-score and k is curve intensity (default 0.90), creates S-curve transformation preserving relative relationships while preventing extreme outliers, and enables smooth gradient visualization across entire cycle range.
🔶 Six-Tier Cycle Zone Classification
Implements comprehensive market cycle framework dividing 0-100 range into six distinct zones with configurable thresholds representing Bitcoin's characteristic bubble and bust patterns. The system defines Bottom Extreme (default <10, accumulation zone), Cold Zone (10-25, early recovery), Lower Mid (25-40, neutral to bullish), Upper Mid (40-60, bullish), Hot Zone (60-75, late bull market), and Top Extreme (>75, euphoria/distribution) with dynamic color coding.
🔶 Pivot-Based Extreme Detection System
Provides intelligent local extreme identification using pivot high/low detection with zone threshold filtering and visual capsule markers. The system detects pivot highs above Hot Zone threshold and pivot lows below Cold Zone threshold using configurable left/right bars, creates horizontal capsule visualizations at exact extreme values with color-coded centers (red for tops, cyan for bottoms), and maintains rolling array limited to maximum capsule count for clean chart presentation.
🔶 MVRV Calculation Mode Selection
Offers three distinct MVRV calculation approaches optimizing for different market conditions and analytical preferences. Ratio mode uses raw Market Cap / Realized Cap for direct valuation comparison, Z-Score mode applies statistical normalization emphasizing deviations from historical mean, and Blend mode (default) averages both approaches balancing absolute valuation with statistical context for robust signal generation.
🔶 Configurable Metric Weighting System
Features flexible weight allocation enabling traders to emphasize preferred metrics or disable unreliable components during specific market regimes. The system accepts 0.0-N weight values for each metric (default 1.0 all equal), automatically handles missing data by excluding NA metrics from composite, recalculates weighted average dynamically, and enables custom cycle calibration based on trader's confidence in different on-chain signals.
🔶 Confirmed HTF Data Integration
Implements rigorous anti-repaint methodology using confirmed higher-timeframe values with offset preventing live bar distortion. The system retrieves all on-chain metrics from daily timeframe with 1-bar offset ensuring only completed daily candle data influences cycle score, applies identical offset to Mayer Multiple calculation, and maintains signal stability across real-time updates preventing false extreme alerts.
🔶 Comprehensive Alert Framework
Provides five distinct alert conditions covering critical cycle events and threshold breaches with descriptive messages. The system triggers Top Extreme alert on crossover above top threshold (default 90), Bottom Extreme alert on crossunder below bottom threshold (default 10), Hot Rejection alert when cycle falls from Hot Zone, Cold Reclaim alert when cycle rises from Cold Zone, and Mayer Threshold breach alert for traditional technical confirmation.
🔶 Gradient Zone Visualization Architecture
Creates intuitive color-coded area plot with six distinct color zones reflecting current cycle position through visual spectrum from cyan (extreme bottom) through purple/orange to red (extreme top). The system applies dynamic zone coloring to both area fill and cycle value display, implements configurable area transparency (default opaque), and maintains consistent color scheme across oscillator pane, table values, and capsule markers.
🔶 Real-Time Diagnostics System
Features comprehensive data availability monitoring with missing metric labels and detailed value table showing all component metrics. The system detects NA values in SOPR, Realized Price, MVRV, or Mayer Multiple, displays warning label listing unavailable metrics, and provides table overlay showing current values for Cycle score, all four components, MVRV-Z, Mayer MA, and threshold with color-coded formatting.
🔶 Performance Optimization Framework
Employs efficient calculation methods with null-safe division functions, optimized array management for capsule storage, and conditional plotting minimizing unnecessary rendering. The system includes streamlined weighted average calculation skipping NA metrics, smart capsule cleanup maintaining maximum limit through oldest-first deletion, and minimal recalculation overhead through var declarations and confirmed bar logic.
This indicator delivers sophisticated Bitcoin-specific valuation analysis through multi-metric on-chain synthesis unavailable in traditional technical indicators. By combining SOPR (profit/loss behavior), MVRV (cost basis valuation), Price-to-Realized (pure valuation), and Mayer Multiple (technical context) into unified cycle framework with statistical normalization, it provides comprehensive market cycle assessment grounded in blockchain fundamentals. The six-tier zone system maps directly to Bitcoin's characteristic 4-year halving cycles with Bottom Extreme zones historically marking generational buying opportunities and Top Extreme zones marking distribution phases. Perfect for long-term Bitcoin investors seeking data-driven cycle timing, position sizing based on valuation extremes (increase allocation in Cold/Bottom zones, reduce in Hot/Top zones), and objective framework for navigating Bitcoin's volatile multi-year cycles with alerts providing advance warning of major cycle transitions requiring portfolio reassessment. Indicator
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Indicator
Price per m2 Argentina CABA USD/m2 - SMAs (1999-2025)Overview
This indicator plots the historical USD price per square meter of apartments in CABA (Buenos Aires City), Argentina, combining annual data (1999–2011) with monthly data (2012–2025) to reconstruct a long-term residential real estate pricing series.
All values were manually digitized, cleaned, and consolidated from public reports and market datasets to create a continuous analytical framework for historical valuation analysis.
The script also includes SMA20, SMA50, and SMA100 calculated over the custom dataset to support long-term trend analysis, cycle identification, and macro structural evaluation.
Data Sources
1999–2011 (Annual): Maure Real Estate Market Reports
2012–2020 (Monthly): UCEMA Real Estate Index
2020–2025 (Monthly): RE/MAX – UCEMA Market Monitor
All datasets referenced are derived from publicly available reports and institutional publications.
How to Use This Indicator
*USE ON THE 1 MONTH TIMEFRAME*
This tool enables investors, developers, and market analysts to:
• Identify multi-year trend shifts in residential real estate valuations
• Compare pricing cycles against Argentine macroeconomic environments
• Map long-term support and resistance zones
• Detect early signs of market recovery or contraction
• Integrate real estate fundamentals with technical analysis frameworks
The moving averages help visualize structural trends that are typically less observable in traditional property datasets.
About This Work
This historical series was reconstructed and coded by Engineer Francisco Michelich through the consolidation of market research, statistical normalization, and technical analysis methodologies.
This script does not represent an official financial index and is not affiliated with the original data institutions. It is intended solely as an educational and analytical tool for visualizing long-term trends in the Buenos Aires, Argentina residential real estate market. Indicator
Indicator
RLPS -Simplified Long-Term Support/Resistance Levels (Shelters)// Introduction //
RLPS (Simplified Long-Term Shelters) is a streamlined indicator designed for traders who have already identified the preponderant long-term phase of their assets and want to efficiently track multiple assets using pre-calculated Fibonacci levels.
IMPORTANT: Before using this indicator, you need to have determined the date-price coordinates of the preponderant phase (i0→i1 pivots) for your asset(s). These coordinates can be obtained using our master RLP indicator (Long-Term Shelters), which automatically helps to calculates them, or through your own research and analysis.
// Theoretical Foundation //
Many traditional institutional investors use the latest higher-degree market phase that stands out from others (longest duration and greatest price change on daily timeframe) to base a Fibonacci retracement on whose levels they open long-term positions. These positions can remain open to be activated in the future even years in advance. The phase is considered valid until a new, more preponderant phase develops over time.
RLPS allows you to manually input these pre-identified phase coordinates and draw Fibonacci levels that serve as Long-Term Shelter Levels—marking future trading points (entries, exits, risk management) that remain valid for months and even years.
// Key Features //
• Supports up to 5 different assets with permanently stored phase coordinates
• Dropdown selector to quickly switch between configured assets
• No ZigZag calculation required—user provides pre-calculated coordinates
• Timeframe-agnostic: levels remain constant across all timeframes
• Works with any price source (exchange) regardless of historical data availability
• Asset Information table with visual validation (✅ Match / ❌ No Match)
• Long-Term Historical Prices (LTHP): add up to 5 psychological price levels per asset (historical highs/lows, annual opening prices, etc.)
• Customizable Fibonacci levels, colors, styles, and label formatting
• Logarithmic scale support for volatile assets like cryptocurrencies
// Quick Start Guide //
STEP 1: In PulseWire, select "Bitcoin / U.S. dollar" from Bitstamp Exchange (BITSTAMP:BTCUSD).
STEP 2: Configure the chart to Daily (D) timeframe.
STEP 3: Load the RLPS indicator. Initially no drawing appears (fields are empty by default).
STEP 4: Open indicator settings and activate "Practice Asset Data Table" in the GENERAL section.
STEP 5: A table appears with sample data for 5 assets. Locate "Bitcoin on Bitstamp":
- i0 Date: 2020-03-13 18:00 | i0 Price: 3850.0
- i1 Date: 2021-11-10 18:00 | i1 Price: 69000.0
STEP 6: Copy this data to "ASSET 1 - IDENTIFICATION AND DATE-PRICE PIVOT COORDINATES".
STEP 7: Verify "Asset 1" is selected in the dropdown and close settings.
STEP 8: You should now see the yellow diagonal phase line, horizontal Fibonacci levels, and the validation table showing "✅ Match".
STEP 9: Navigate the chart to verify how Fibonacci levels align with historical support/resistance zones.
// Important Notes //
• The sample data in the Practice Table was validated in 02/2026 and serves as reference only.
• It is your responsibility to validate or update the preponderant phase of your assets over time.
• Use our master RLP indicator to automatically find and calculate preponderant phases, then transfer the coordinates here for permanent tracking.
• You can deactivate the Practice Table once you've copied the data you need.
// Shelter Indicators Ecosystem //
RLPS is part of a comprehensive ecosystem of indicators for price action analysis based on shelter levels:
RLPS (Simplified Long-Term Shelters): This indicator. Simplified version of RLP that allows manual input of previously identified preponderant phase coordinates. Ideal for permanent operations with multiple assets across different timeframes.
RLP (Long-Term Shelters): Automatically identifies the preponderant Zigzag phase that institutional investors use as a reference to project Fibonacci levels. These levels determine order placement over the following months and years.
RMP (Mid-Term Shelters): Provides the psychological shelter and resistance levels that institutional investors establish at the beginning of each year. These form the main framework that professionals use to plan entry and exit operations throughout the year.
RS (Weekly Shelters): Tactical structural analysis indicator designed to precisely track price action and manage positions during current weeks.
RID (Intra-Day Shelters): For intraday operations based on levels calculated from the daily opening price. Designed for 1H timeframes or lower, including scalping strategies.
By combining RLPS, RLP, RMP, RS, and RID, you obtain a multi-timeframe framework that provides certainty and clarity to apply strategies grounded in price action, across any time horizon: from scalping to long-term investments.
// Final Notes //
We sincerely regret to inform you that we have not included the Spanish translation previously provided in our indicators, due to our significant concern regarding the ambiguous rules on publication bans related to indicators.
Sharing motivates. Happy hunting in this great jungle!
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Trap Longs - Hamza NaveedTrap Longs – Hamza Naveed is an advanced Open Interest–based indicator that analyzes net longs, net shorts, delta, and ratio across multiple exchanges (Binance, BitMEX, Kraken). It visualizes institutional positioning using candles, lines, or columns, with optional VWMA/EMA smoothing, RSI strength, volume heatmaps, statistical tables, and divergence detection. Designed to identify traps, absorption, and exhaustion, this tool helps traders understand positioning shifts, liquidity behavior, and potential trend reversals beyond price action alone. Indicator
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Scalp Boost LONG✦ Overview
Scalp Boost LONG is a visual tool designed to highlight potential short-term upward impulses.
A signal is generated only when multiple market conditions align at the candle close, combining momentum dynamics, local probability shifts, and abnormal volume behavior.
The indicator does not repaint.
✦ Concept
The tool focuses on selective situations where the market shows signs of micro-breakout potential.
If all internal conditions are confirmed — a LONG event is displayed.
If not — the chart remains clean.
This builds a low-noise signal model, prioritizing quality over frequency.
✦ Signal Logic
The LONG signal requires confirmation of all core conditions:
• Local impulse dynamics
Identifies short-term acceleration suggesting a breakout from a compressed price structure.
• Probability beyond a statistical zone
Uses relative breakout probability instead of fixed levels, checking whether price exceeds expected local ranges.
• Abnormal volume activity
Highlights candles with monetary flow above a custom threshold, signaling increased market interest.
• Anti-overheat filter
Conditions avoiding exhausted or low-momentum phases where continuation is less likely.
Only when all filters are aligned a LONG marker appears.
✦ Visual Structure
The chart display is intentionally minimal:
• ROC Curve
Subdued line, showing short-term momentum without distraction.
• LONG Marker
Green triangle below the candle on confirmed events.
• Candle Highlight
Soft background highlight on the signal bar.
• Volume Marker
Small red dot at the bottom of candles with abnormal monetary flow.
All visual elements appear only on candle close.
✦ Alerts
A clean event structure is available for notifications:
LONG Signal
This allows receiving alerts during chart analysis or in automated workflows while keeping full control over decision-making.
✦ Notes & Guidelines
This tool:
is not a trading system,
does not provide targets or stops,
may trigger against the dominant trend,
should be combined with the user’s own methodology.
Signals are rare by design.
Do not interpret each event as a trend continuation — it highlights conditions, not outcomes.
✦ Suggested Use
-(Non-mandatory ideas for advanced users)
-identifying potential micro-breakouts,
-timing entries around volume spikes,
-adding context to scalping models,
-filtering impulsive moves from noise.
-suitable for a 5-minute timeframe
The indicator can be helpful as a confirmation layer, not a standalone decision tool.
Indicator
Session Opening Range Breakout (ORBO)This strategy automates a classic Opening Range Breakout (ORBO) approach: it builds a price range for the first minutes after the market opens, then looks for strong breakouts above or below that range to catch early directional moves.
Concept
The idea behind ORBO is simple:
The first minutes after the session open are often highly informative.
Price forms an “opening range” that acts as a mini support/resistance zone.
A clean breakout beyond this zone can lead to high-momentum moves.
This script turns that logic into a fully backtestable strategy in PulseWire.
How the strategy works
Opening Range Session
Default session: 09:30–09:50 (exchange time)
During this window, the script tracks:
orHigh → highest high within the session
orLow → lowest low within the session
This forms your Opening Range for the day.
Breakout Logic (after the window ends)
Once the defined session ends:
Long Entry:
If the close crosses above the Opening Range High (orHigh),
→ strategy.entry("OR Long", strategy.long) is triggered.
Short Entry:
If the close crosses below the Opening Range Low (orLow),
→ strategy.entry("OR Short", strategy.short) is triggered.
Only one opening range per day is considered, which keeps the logic clean and easy to interpret.
Daily Reset
At the start of a new trading day, the script resets:
orHigh := na
orLow := na
A fresh Opening Range is then built using the next session’s 09:30–09:50 candles.
This ensures entries are always based on today’s structure, not yesterday’s.
Visuals & Inputs
Inputs:
Opening range session → default: "0930-0950"
Show OR levels → toggle visibility of OR High / Low lines
Fill range body → optional shaded zone between OR High and OR Low
Chart visuals:
A green line marks the Opening Range High.
A red line marks the Opening Range Low.
Optional yellow fill highlights the entire OR zone.
Background shading during the session shows when the range is currently being built.
These visuals make it easy to see:
Where the OR sits relative to current price
How clean / noisy the breakout was
How often price respects or rejects the opening zone
Backtesting & Optimization
Because this is written as a strategy():
You can use PulseWire’s Strategy Tester to view:
Win rate
Net profit
Drawdown
Profit factor
Equity curve
Ideas to experiment with:
Change the session window (e.g., 09:15–09:45, 10:00–10:30)
Apply to different:
Markets: indices, FX, crypto, stocks
Timeframes: 1m / 5m / 15m
Add your own:
Stop Loss & Take Profit levels
Time filters (only trade certain days / times)
Volatility filters (e.g., ATR, range size thresholds)
Higher-timeframe trend filter (e.g., only take longs above 200 EMA)
Strategy
Strategy
Indicator
TitanGrid L/S SuperEngineTitanGrid L/S SuperEngine
Experimental Trend-Aligned Grid Signal Engine for Long & Short Execution
🔹 Overview
TitanGrid is an advanced, real-time signal engine built around a tactical grid structure.
It manages Long and Short trades using trend-aligned entries, layered scaling, and partial exits.
Unlike traditional strategy() -based scripts, TitanGrid runs as an indicator() , but includes its own full internal simulation engine.
This allows it to track capital, equity, PnL, risk exposure, and trade performance bar-by-bar — effectively simulating a custom backtest, while remaining compatible with real-time alert-based execution systems.
The concept was born from the fusion of two prior systems:
Assassin’s Grid (grid-based execution and structure) + Super 8 (trend-filtering, smart capital logic), both developed under the AssassinsGrid framework.
🔹 Disclaimer
This is an experimental tool intended for research, testing, and educational use.
It does not provide guaranteed outcomes and should not be interpreted as financial advice.
Use with demo or simulated accounts before considering live deployment.
🔹 Execution Logic
Trend direction is filtered through a custom SuperTrend engine. Once confirmed:
• Long entries trigger on pullbacks, exiting progressively as price moves up
• Short entries trigger on rallies, exiting as price declines
Grid levels are spaced by configurable percentage width, and entries scale dynamically.
🔹 Stop Loss Mechanism
TitanGrid uses a dual-layer stop system:
• A static stop per entry, placed at a fixed percentage distance matching the grid width
• A trend reversal exit that closes the entire position if price crosses the SuperTrend in the opposite direction
Stops are triggered once per cycle, ensuring predictable and capital-aware behavior.
🔹 Key Features
• Dual-side grid logic (Long-only, Short-only, or Both)
• SuperTrend filtering to enforce directional bias
• Adjustable grid spacing, scaling, and sizing
• Static and dynamic stop-loss logic
• Partial exits and reset conditions
• Webhook-ready alerts (browser-based automation compatible)
• Internal simulation of equity, PnL, fees, and liquidation levels
• Real-time dashboard for full transparency
🔹 Best Use Cases
TitanGrid performs best in structured or mean-reverting environments.
It is especially well-suited to assets with the behavioral profile of ETH — reactive, trend-intraday, and prone to clean pullback formations.
While adaptable to multiple timeframes, it shows strongest performance on the 15-minute chart , offering a balance of signal frequency and directional clarity.
🔹 License
Published under the Mozilla Public License 2.0 .
You are free to study, adapt, and extend this script.
🔹 Panel Reference
The real-time dashboard displays performance metrics, capital state, and position behavior:
• Asset Type – Automatically detects the instrument class (e.g., Crypto, Stock, Forex) from symbol metadata
• Equity – Total simulated capital: realized PnL + floating PnL + remaining cash
• Available Cash – Capital not currently allocated to any position
• Used Margin – Capital locked in open trades, based on position size and leverage
• Net Profit – Realized gain/loss after commissions and fees
• Raw Net Profit – Gross result before trading costs
• Floating PnL – Unrealized profit or loss from active positions
• ROI – Return on initial capital, including realized and floating PnL. Leverage directly impacts this metric, amplifying both gains and losses relative to account size.
• Long/Short Size & Avg Price – Open position sizes and volume-weighted average entry prices
• Leverage & Liquidation – Simulated effective leverage and projected liquidation level
• Hold – Best-performing hold side (Long or Short) over the session
• Hold Efficiency – Performance efficiency during holding phases, relative to capital used
• Profit Factor – Ratio of gross profits to gross losses (realized)
• Payoff Ratio – Average profit per win / average loss per loss
• Win Rate – Percent of profitable closes (including partial exits)
• Expectancy – Net average result per closed trade
• Max Drawdown – Largest recorded drop in equity during the session
• Commission Paid – Simulated trading costs: maker, taker, funding
• Long / Short Trades – Count of entry signals per side
• Time Trading – Number of bars spent in active positions
• Volume / Month – Extrapolated 30-day trading volume estimate
• Min Capital – Lowest equity level recorded during the session
🔹 Reference Ranges by Strategy Type
Use the following metrics as reference depending on the trading style:
Grid / Mean Reversion
• Profit Factor: 1.2 – 2.0
• Payoff Ratio: 0.5 – 1.2
• Win Rate: 50% – 70% (based on partial exits)
• Expectancy: 0.05% – 0.25%
• Drawdown: Moderate to high
• Commission Impact: High
Trend-Following
• Profit Factor: 1.5 – 3.0
• Payoff Ratio: 1.5 – 3.5
• Win Rate: 30% – 50%
• Expectancy: 0.3% – 1.0%
• Drawdown: Low to moderate
Scalping / High-Frequency
• Profit Factor: 1.1 – 1.6
• Payoff Ratio: 0.3 – 0.8
• Win Rate: 80% – 95%
• Expectancy: 0.01% – 0.05%
• Volume / Month: Very high
Breakout Strategies
• Profit Factor: 1.4 – 2.2
• Payoff Ratio: 1.2 – 2.0
• Win Rate: 35% – 60%
• Expectancy: 0.2% – 0.6%
• Drawdown: Can be sharp after failed breakouts
🔹 Note on Performance Simulation
TitanGrid includes internal accounting of fees, slippage, and funding costs.
While its logic is designed for precision and capital efficiency, performance is naturally affected by exchange commissions.
In frictionless environments (e.g., zero-fee simulation), its high-frequency logic could — in theory — extract substantial micro-edges from the market.
However, real-world conditions introduce limits, and all results should be interpreted accordingly. Indicator
