Adaptive Hurst Cycle Engine [SpokoStocks]Adaptive Hurst Cycle Engine measures the cycles that are actually present in price, reads where the market sits inside each of them right now, projects the combined cycle forward, and scores its own cycle lows against real price lows so you can see on the chart how well it is doing. Nothing is drawn with hindsight and nothing moves once it is on the chart.
WHAT YOU SEE ON THE PRICE CHART
• A cycle-model line: price with the noise removed and the measured cycles kept. Where it hugs price, the cycles explain the movement; where it drifts, they do not.
• A dashed projected path continuing that model forward from the last bar, built from the current amplitude and phase of each cycle.
• Bars coloured by cycle position, from teal at a trough through violet to crimson at a peak.
• Composite lows scored after the fact: a teal triangle where the composite low landed on a real price low, a small grey triangle where it missed. The dashboard reports the hit rate on the chart you are looking at.
WHAT YOU SEE IN THE PANE
• The composite cycle, the sum of the measured components, with a two-tone fill.
• A diamond at each composite low, placed one bar after the turn and never moved.
• A dashed projection of the composite with the next projected low labelled.
• A background shade when two or more cycles are near a trough at the same time.
• The individual components can be switched on behind a toggle.
DASHBOARD
For each cycle: measured period, spectral power, whether it is rising or falling, and the bar count and date of its next low. Below that: the dominant period, how many cycles are synchronised near a trough, the projected composite low, and the low-accuracy score.
HOW IT WORKS
1. A roofing filter, a high-pass at the longest period and a super-smoother at the shortest, isolates the band in which cycles can exist. Both filters are from John Ehlers' published work.
2. An autocorrelation periodogram, also after Ehlers, measures spectral power for every period in the range on every bar and smooths it over time, so the spectrum reflects persistent cycles rather than single swings. Power is normalised so 100% is the strongest period seen recently.
3. The cycle set is either the strongest spectral peaks above your minimum power (Measured peaks) or Hurst's nominal model built from the dominant period in 2:1 ratios (Harmonic set). Periods are smoothed to prevent jitter. If no peak clears the threshold, the dominant period is used so the chart is never empty.
4. Each cycle is extracted with a band-pass filter tuned to its period. The output and its quadrature give the phase, which tells the script how many bars remain to the next low and peak, and the amplitude.
5. The composite is the sum of the components, Hurst's principle of summation. The projection extends each component as a cosine from its current phase and amplitude and adds them; on the price chart the same projection is placed on top of the trend with its recent slope.
6. Accuracy scoring: when the composite turns up, the previous bar is a composite low. Once the bars on both sides are known, that bar's low is compared with the lowest low of the surrounding window. A match within a quarter of an ATR is a hit. Hits and total are accumulated and shown as a percentage.
HOW TO USE IT
• Check the hit rate first. It tells you, on this symbol and timeframe, how often the composite lows have coincided with price lows.
• Read spectral power per cycle. Below about 50% a cycle is weak on this chart and its dates deserve less weight.
• The next-low column is the working forecast for each cycle; the composite low is where they combine, and the confluence shade marks the windows Hurst's model treats as the most important lows.
• Use the projected path as an expectation, and the bar colours to see at a glance which side of the cycle the market is on.
NON-REPAINTING AND LAG
All filters are recursive and use only closed bars. Composite-low diamonds are confirmed one bar after the turn and fixed. The projection is a forecast redrawn only forward of the current bar. Every causal cycle filter carries a delay of one to two bars; the phase readout and the projection exist to give notice of a low before it happens rather than to remove that delay. The hit and miss triangles are the one deliberately delayed element: they are a scorecard drawn once the surrounding window has closed, not a live signal.
LIMITATIONS
• The spectrum needs roughly twice the longest period of history to settle; the first bars on a chart are unreliable.
• Dates in the dashboard count chart bars and ignore weekends and closed sessions.
• Cycles change; a projection is only as good as the persistence of the cycles behind it.
• The hit rate is descriptive, not a guarantee, and depends on the accuracy window you set.
The roofing filter and autocorrelation periodogram follow John Ehlers, Cycle Analytics for Traders (2013). The cycle-set selection, phase-based low forecasts, composite projection on price, phase colouring and on-chart accuracy scoring are original to this script. インジケーター
Market Path Forecast [BOSWaves]Market Path Forecast - Swing-Calibrated Directional Forecast with Confidence Cone, Structure-Snapped Levels, and Adaptive Horizon
Overview
Market Path Forecast is a swing-calibrated probabilistic directional forecast system that derives its target price, forecast duration, and cone width entirely from the statistical properties of the instrument's own historical swing behavior, where the path cone, level placement, and forecast horizon all adapt continuously to the accumulated record of completed swings rather than applying fixed ATR multiples or arbitrary projection distances.
Instead of projecting fixed percentage moves or static ATR extensions, the system accumulates the percentage size and bar duration of each completed directional swing into weighted sample arrays, computes the weighted average and standard deviation of those samples, and uses these statistics to estimate where the current swing is likely to travel and how long it is likely to take. The resulting forecast is not a generic technical projection but a statistically calibrated estimate derived from the instrument's actual measured movement history.
This creates a forecast framework that is self-calibrating to each instrument and timeframe. Instruments with large consistent swings produce wide confident cones pointing to distant targets. Instruments with small erratic swings produce narrower cones with closer targets. The confidence interval setting scales the cone width relative to the measured historical variance, allowing the trader to choose whether to view the tight central tendency or the broader probable range. Structure snap alignment pulls forecast levels toward nearby historical pivot prices, anchoring statistically derived targets to structurally significant levels. And the adaptive horizon dynamically adjusts the projection duration as the current swing develops, so the cone length reflects how much time is estimated to remain rather than a fixed number of bars.
Price is therefore evaluated against a forecast that reflects the instrument's own statistical swing personality rather than a generic overlay applied identically regardless of how the instrument actually moves.
Conceptual Framework
Market Path Forecast is founded on the principle that the most reliable basis for a directional price forecast is the statistical distribution of the instrument's own completed swing history, and that both the target level and the confidence around that target should derive from measured historical variance rather than from fixed indicator parameters.
Traditional forecast tools apply static extensions, fixed ATR projections, or Fibonacci ratios that carry no relationship to how the specific instrument actually moves. This framework replaces static projection with statistical estimation, accumulating a rolling weighted sample of historical swing sizes and durations and deriving forecast parameters from that sample on every bar. Recent swings receive greater weight than older ones, ensuring the forecast adapts dynamically to evolving market behavior while maintaining the stability that comes from a sufficient sample of historical evidence.
Three core principles guide the design:
Forecast targets, durations, and cone widths should derive from the statistical properties of the instrument's own swing history rather than from fixed parameters, ensuring every element of the projection reflects actual measured behavior rather than generic assumptions.
The confidence cone should scale with historical swing variance through a statistically meaningful confidence interval parameter, so traders understand they are viewing a fraction of the measured probability distribution rather than an arbitrary visual band.
Forecast levels should be snapped toward nearby historical structure prices where they exist within the configurable snap range, anchoring statistically derived targets to the structural price levels that may have influenced prior swing reversals.
This shifts directional forecasting from fixed-parameter projection into instrument-specific statistical estimation where all visual elements adapt to the instrument's own historical behavior.
Theoretical Foundation
The indicator combines swing detection through highest and lowest lookback comparison, recent-weighted average and standard deviation calculation across historical swing percentage moves and bar durations, directional forecast derivation from the appropriate bull or bear sample arrays, momentum-adjusted path curvature using EMA difference normalization, structure-snap level alignment using nearest historical pivot within the configurable ATR search radius, and historical support and resistance zone construction from separate pivot detection with age-based expiry and break detection.
Swing direction is tracked by monitoring whether the current highest or lowest lookback value is being set by the current high or low, with confirmed swing points registered when price rotates away from a prior extreme. Each completed directional leg contributes its percentage move and bar duration to separate bull and bear sample arrays using a weighted push that replaces oldest samples beyond the configured maximum. The weighted average applies linearly increasing weights from oldest to most recent, giving recent swings proportionally greater influence. Standard deviation is computed from the same weighted scheme, producing a variance measure that reflects recent behavior more than distant history. The forecast target is calculated as a percentage move from the swing origin, with the extension factor derived from the deviation ratio to scale the extension level beyond the primary target.
Four internal systems operate in tandem:
Swing History Engine : Detects confirmed swing direction changes, measures the percentage move and bar duration of each completed leg, and accumulates these into directional and combined weighted sample arrays that feed all downstream forecast calculations.
Statistical Forecast Engine : Derives weighted average target percentage and duration from the directional sample arrays, falls back to combined samples when directional sample count is insufficient, calculates the standard deviation for cone width scaling, and applies minimum spacing enforcement to prevent levels from overlapping.
Path and Level Rendering System : Constructs the three-layer confidence cone using eased smooth interpolation with momentum-derived curvature, and renders up to six forecast levels as three-layer box zones with structure-snapped prices, directional coloring, and configurable label display.
Historical Structure System : Independently detects pivot highs and lows at the configured structure pivot length, maintains active zone boxes with age-based fading and break detection, stores pivot prices in a rolling array that feeds the structure snap function for all forecast levels, and enforces maximum zone count and age limits.
This design ensures the forecast derives entirely from measured historical behavior while the structure snap layer connects statistically derived levels to structurally significant prices where they exist in proximity.
How It Works
Market Path Forecast evaluates price through a sequence of swing-calibrated and statistically derived processes:
Swing Direction Tracking : On each bar, the highest high and lowest low over the configured swing length are compared to the current bar. When the current high sets the lookback high, direction tracks bullish. When the current low sets the lookback low, direction tracks bearish. Confirmed swing points are registered when price rotates away from the prior extreme.
Swing Sample Accumulation : On each confirmed swing direction change, the completed leg's percentage move and bar duration are calculated and pushed into the appropriate directional and combined sample arrays with size capping at the configured maximum. Bull legs accumulate into the bull arrays and bear legs into the bear arrays.
Weighted Forecast Derivation : The weighted average of the directional sample array provides the forecast percentage move. The weighted average of the duration array provides the forecast bar count. The weighted standard deviation of the directional array provides the variance measure for cone scaling. When fewer than three directional samples exist, the combined arrays are used as fallback.
Adaptive Horizon Calculation : The estimated remaining bars for the current swing are calculated by subtracting elapsed bars from the estimated total duration and clamping to the configured minimum and maximum. When adaptive horizon is disabled, the fixed bar count is used instead.
Target Calculation : The primary target is derived from the swing origin price adjusted by the forecast percentage in the forecast direction, with a minimum distance floor enforced as an ATR multiple to prevent targets from forming too close to current price.
Level Derivation : Target 1, 2, and 3 are placed at 40, 70, and 100 percent of the base distance. The extension level is placed beyond Target 3 using a factor derived from the deviation-to-mean ratio. The opposite structure reference and invalidation level are placed on the opposing side of price.
Structure Snap Application : Each raw level price is tested against the rolling historical structure price array. If a matching structural high or low exists within the ATR snap range on the correct side of price, the level is blended toward that structural price by the configured snap strength.
Minimum Spacing Enforcement : After snapping, all levels are adjusted to maintain a minimum separation equal to twice the zone ATR width, preventing levels from overlapping regardless of snap results.
Cone Construction : The base band half-width is derived from the greater of the ATR floor and the price-converted standard deviation, clamped to a maximum fraction of the distance to Target 3, then multiplied by the confidence interval setting. Smooth eased interpolation builds the outer, inner, and center polyline paths between current price and the Target 3 level with momentum-derived curvature applied.
Historical Structure Zone Management : Pivot highs and lows detected at the structure pivot length receive dual-layer zone boxes that extend rightward each bar, fade with cubic age scaling, convert to dotted broken style when price closes through them, and expire after the configured maximum age or break age.
Together, these elements form a continuously updating forecast system where every visual element adapts to the instrument's measured swing history and structural price environment.
Interpretation
Market Path Forecast should be interpreted as a statistically calibrated swing forecast with a probabilistic confidence cone and structure-aligned target levels:
Forecast Path Cone : The three-layer cone extending from current price represents the probable range of price paths based on historical swing behavior. The outer layer covers the full confidence interval width. The inner layer covers approximately 55 percent of the cone width. The center line represents the weighted average expected path.
Cone Width : A wide cone indicates high historical swing variance where completed swings varied significantly in size. A narrow cone indicates consistent swing behavior with low variance. The confidence interval setting controls how many standard deviations of historical variance the cone spans.
Cone Curvature : The cone bends in the direction of current EMA momentum, reflecting whether the trend currently has upside or downside momentum bias that may influence the directional path of the developing swing.
Target 1, 2, 3 Levels : Three-layer zone boxes at progressively greater distances represent the expected first, intermediate, and primary swing completion levels derived from the weighted average of historical swings at 40, 70, and 100 percent of the base distance.
Extension Level : Beyond Target 3, the extension level marks where larger-than-average swings have historically reached, scaled by the ratio of standard deviation to mean swing size. A larger extension factor indicates that historical swings have been more variable and have occasionally traveled significantly beyond average.
Support / Resistance Level : The opposing-direction level on the near side of price identifies the closest structural reference in the opposing direction, representing the level where a counter-swing could develop before the forecast target is reached.
Invalidation Level : The furthest opposing level marks the price beyond which the current swing forecast would be statistically invalidated, representing the distance at which counter-directional movement exceeds what is consistent with the current swing remaining intact.
Historical Structure Zones : Green support zones and red resistance zones from historical pivot detection provide the structural price environment that both informs the forecast level snap function and serves as ongoing structural reference for price interaction monitoring.
Broken Structure Zones : Zones that have been closed through convert to dotted style with faded coloring, indicating the former level has been breached and may now function in the opposing structural role.
Cone width, target level placement, snap alignment to structure, and invalidation level distance collectively provide more forecast context than any element in isolation.
Signal Logic & Visual Cues
Market Path Forecast generates two directional signals tied to swing direction changes:
Bullish Forecast : Triggered when swing direction flips from bearish to bullish, resetting the forecast origin to the confirmed swing low and projecting the cone and levels upward toward the statistically estimated bull swing targets.
Bearish Forecast : Triggered when swing direction flips from bullish to bearish, resetting the forecast origin to the confirmed swing high and projecting the cone and levels downward toward the statistically estimated bear swing targets.
Each forecast reset incorporates the newly completed swing into the weighted sample arrays before generating the next projection, ensuring every forecast benefits from the most recent available behavioral evidence.
Alert generation covers bullish and bearish forecast direction changes for systematic swing-based monitoring workflows.
Strategy Integration
Market Path Forecast fits within swing-calibrated directional and statistical target-based trading approaches:
Target-Based Exit Planning : Use the three forecast target levels as a staged exit framework, planning partial position reductions at T1, T2, and T3 rather than targeting a single fixed level, allowing structured progression through the statistically estimated swing completion zone.
Cone Containment Monitoring : Monitor whether price is staying within the inner confidence cone or pressing against the outer boundaries as a real-time swing health indicator. Price persistently hugging the outer cone boundary in the forecast direction suggests above-average momentum. Price compressing toward the center early in the forecast suggests weakening follow-through.
Extension Level Context : Use the extension level as a target for high-momentum setups where the deviation-to-mean ratio is elevated, indicating that historical swings have occasionally extended significantly beyond the average. A larger gap between T3 and the extension level reflects greater historical variability.
Invalidation Level Risk Management : Use the invalidation level as the maximum tolerable counter-directional excursion, beyond which the current swing forecast is no longer statistically consistent with historical behavior and the position rationale is undermined.
Structure Snap Confluence : Prioritize levels that have been snapped to nearby structural pivot prices over purely statistically derived levels, as these represent locations where both the measured swing expectation and historical price structure align simultaneously.
Confidence Interval Calibration : Use a lower confidence interval such as 0.5 for tight conviction analysis where you want to see only the central tendency of the forecast. Use 1.5 or 2.0 to visualize the broader probability range that captures less typical swing outcomes.
Technical Implementation Details
Swing Detection : Highest and lowest lookback comparison with direction tracking and confirmed point registration on price rotation
Sample Arrays : Weighted push accumulation for bull, bear, and combined percentage and duration arrays with configurable maximum size
Forecast Statistics : Linearly increasing weight scheme for weighted average and standard deviation with directional to combined fallback below minimum sample threshold
Cone Construction : Eased smooth interpolation with momentum-normalized EMA curvature across configurable step count for outer, inner, and center polyline paths
Level System : Six forecast levels with percentage-of-base-distance placement, deviation-ratio extension scaling, structure snap blending, minimum spacing enforcement, and three-layer zone box rendering
Structure System : Pivot-based zone detection with dual-layer boxes, rolling structure price array for snap function, cubic age fading, break detection with style conversion, and configurable zone count and age limits
Performance Profile : Last-bar rendering with full polyline and object rebuild each update, configurable level count for object management
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Intraday swing forecasting with shorter swing length and fewer historical swings for fast adaptation to intraday directional changes
15 - 60 min : Session-level swing projection with balanced swing length and moderate sample count for meaningful statistical accumulation across typical session swings
4H - Daily : Swing-level directional forecasting with longer swing detection and larger sample count for statistically robust estimates derived from significant structural moves
Suggested Baseline Configuration:
Swing Length : 16
Historical Swings : 20
Volatility Length : 200
Adaptive Forecast Horizon : Enabled
Confidence Interval (SD) : 1.0
Path Curvature : 0.45
Number of Levels : 6
Structure Snap Strength : 0.65
Show Historical Structure : Enabled
Show Forecast Path : Enabled
Show Forecast Levels : Enabled
These suggested parameters should be used as a baseline; their effectiveness depends on the instrument's swing frequency, historical swing consistency, and preferred forecast horizon, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Forecast targets too close to price : Decrease Minimum Target Distance toward 1.0 to allow targets to form closer to price, or increase Historical Swings to accumulate more samples that may include larger average moves.
Forecast targets too far from price : Increase Minimum Target Distance to enforce greater separation, or decrease Historical Swings to weight more recent and potentially smaller swing samples more heavily.
Cone too wide or too narrow : Adjust Confidence Interval to expand or contract the cone relative to the measured standard deviation of historical swings, using 0.5 for a tight central tendency view or 2.0 for a broad probability range.
Forecast flipping too frequently : Increase Swing Length to require more bars on each side of a confirmed swing extreme, filtering shorter-term oscillations from the swing detection.
Forecast too slow to update : Decrease Swing Length toward 6 for faster swing confirmation, or decrease Historical Swings to allow the weighted average to adapt more quickly to recent behavior changes.
Levels not snapping to structure : Increase Structure Snap Range to widen the ATR distance within which structural pivot prices attract forecast levels, or increase Structure Snap Strength toward 1.0 for stronger magnetic pull toward nearby structure.
Too many historical structure zones : Reduce Maximum Zones to limit visible structural zones, or decrease Maximum Zone Age to expire older zones sooner and keep the chart focused on more recent structural history.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Instruments with consistent swing behavior where historical percentage moves and durations cluster tightly, producing low variance forecasts with narrow confident cones that accurately reflect the instrument's typical directional tendency
Trending markets where completed swings accumulate rapidly and the weighted sample arrays update frequently, keeping the forecast calibrated to current momentum characteristics
Swing-based trading approaches where statistically derived target levels replace arbitrary Fibonacci or ATR projections with instrument-specific measurements of where swings have historically terminated
Structure-rich instruments where the snap function can align statistically derived levels with meaningful historical pivot prices, creating confluence between statistical expectation and structural significance
Reduced Effectiveness:
Instruments with highly erratic swing behavior where percentage moves vary widely between legs, producing large standard deviations and wide uncertain cones that reduce the specificity of target level placement
Range-bound or choppy markets where swing detection fires frequently on minor oscillations, populating the sample arrays with small inconsistent measurements that undermine forecast reliability
Instruments with insufficient completed swings within the sample window where the fallback to combined arrays may produce forecasts that blend bull and bear statistical properties inappropriately
Very short timeframes where completed swings are so numerous and small that the weighted average converges on noise-level movements without statistical significance
Markets undergoing structural regime changes where historical swing statistics are no longer representative of current behavior, making the weighted average a poor estimate of future swing potential until sufficient new samples accumulate
Integration Guidelines
Confluence : Combine with BOSWaves structural tools, volume analysis, or momentum indicators to validate forecast direction and target level interactions with broader analytical context before committing to swing-based trade plans
Sample Count Awareness : Monitor whether the forecast is drawing on directional or combined samples by assessing how many completed swings in the current direction exist within the historical window. Fewer than three directional samples means the forecast is using combined statistics that blend both directions.
Cone Evolution Monitoring : Track cone width changes across successive forecast resets as a volatility regime indicator. Progressively widening cones across multiple swings suggest increasing swing size variability. Narrowing cones suggest the instrument is entering a more consistent swing rhythm.
Structure Snap Validation : When a level snaps significantly from its raw statistical position to a nearby structural pivot, treat the snapped level with elevated confidence as it represents simultaneous statistical expectation and structural significance.
Invalidation Discipline : Respect the invalidation level as a hard position management boundary. A close beyond the invalidation level indicates counter-directional movement that exceeds the statistical parameters of the current forecast, warranting position reassessment regardless of other analytical factors.
Disclaimer
Market Path Forecast is a professional-grade swing-calibrated statistical forecast and structural analysis tool. It uses weighted historical swing statistics with confidence interval scaling and structure snap alignment but does not predict future price movements with certainty. All forecasts represent statistical estimates based on historical behavior and carry inherent uncertainty that increases with forecast horizon. Results depend on market conditions, instrument swing consistency, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates order flow context, structural analysis, and comprehensive risk management. インジケーター
Fibonacci Path Profile [MantisAlgo]Fibonacci Path Profile is a historical swing-path analysis tool that compares the active market structure with similar Fibonacci swing patterns from the past and visualizes the historical distribution of the next D and E swing points.
The indicator combines the current A→B impulse, B→C retracement or extension, and live C→Now progress to continuously refine the historical sample.
🟢 ABC STRUCTURE
The indicator automatically detects alternating swing highs and swing lows and organizes them into an A-B-C structure.
Bullish ABC
A→B = upward impulse
B→C = downward retracement
The next D swing develops upward from C
Bearish ABC
A→B = downward impulse
B→C = upward retracement
The next D swing develops downward from C
A→B is used as the base swing for all subsequent Fibonacci measurements.
🟢 FIBONACCI MATCHING
The B→C movement is measured relative to the A→B impulse:
B→C Ratio = |B − C| / |A − B|
Historical cases are matched using:
Bullish or Bearish ABC direction
B→C Fibonacci range
Live C→Now progress
The B→C ranges are:
0–23.6%
23.6–38.2%
38.2–50%
50–61.8%
61.8–78.6%
78.6–100%
100–127.2%
127.2–161.8%
161.8%+
Up to 100% is classified as a Retrace, while values above 100% are classified as an Extension.
* To preserve a usable historical sample, all B→C values above 161.8% are grouped into a single 161.8%+ matching range rather than being divided into additional extension classes.
🟢 LIVE C→NOW MATCHING
The indicator also measures how far the active move has progressed from C relative to the B→C range.
C→Now = current progress from C relative to |B − C|
As price develops, this progress is used to filter historical cases that remained valid through a similar stage of the move.
🟢 D & E PATH
The profiles show the historical locations of the next two swing points:
D PathDistribution of the next swing point after C.
E PathDistribution of the following swing point after D.
Both D and E locations are normalized relative to the B→C range, allowing historical structures of different absolute sizes to be compared on the same basis.
The E Path is also separated into three structural outcomes:
🟥 B Break — E moves beyond the B level
🟨 No Break — E remains between B and C
🟦 C Break — E moves beyond the C level
The displayed percentages represent the weighted share of each outcome among the currently matched historical cases.
* For the D and E profiles, values beyond the displayed ±161.8% range are grouped into the outermost top or bottom bin rather than split into additional bins.
🟢 REAL-TIME PROFILE
The active profile is continuously recalculated as price develops.
This can update:
C→Now progress
matched historical cases
D Path distribution
E Path distribution
B Break / No Break / C Break probabilities
Because D and E represent the active forward path, both profile boxes are always displayed to the right of the current candle.
🟢 DYNAMIC C
Before a new D swing is confirmed, the B→C retracement may continue to a new extreme.
In that case, the active C point is updated:
Bullish ABC → C moves to a new lower low
Bearish ABC → C moves to a new higher high
This prevents an unfinished B→C leg from being treated as a completed swing.
🟢 WEIGHT
Two weighting methods are available:
Recent
More recent historical cases receive greater weight.
Weight = 1 / (1 + Age / 1500)
where Age is measured in bars.
Equal
Every matched historical case receives the same weight.
Use Recent to emphasize newer market behavior or Equal to view the unweighted historical distribution.
🟢 HOW TO USE
Use the indicator to evaluate how similar historical structures developed from the current setup.
D Path highlights where the next swing historically tended to form.
E Path shows how price developed after that D swing.
B Break / No Break / C Break summarizes the historical structural outcome.
C→Now continuously refines the sample as the active move progresses.
The profiles represent historical swing distributions, not traded volume.
They are designed to provide a probabilistic view of the active structure rather than a fixed Fibonacci price target.
🟢 DISCLAIMER
This indicator is provided for informational and educational purposes only and does not constitute financial or investment advice.
Historical patterns and probabilities do not guarantee future results. All trading and investment decisions remain the sole responsibility of the user. インジケーター
Precedent [ThrowMaster]===============================================================
WHAT IT IS
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Precedent does not predict. It measures.
Every time a defined market event confirms on your chart, Precedent
records what price actually did over the following N bars. Once enough
comparable records have accumulated, it displays the empirical
distribution of those recorded outcomes: how far price travelled, how
often it reached a given distance, and how many bars that took.
The question it answers is narrow and deliberately so:
"On this symbol, on this timeframe, when this kind of event happened
at this kind of price level in this kind of market condition, what
followed afterwards, and across how many cases?"
Every number shown is measured from the visible history of the chart you
are looking at. Nothing is imported from another market, no outcome
percentages are hard-coded, and no distribution shape is assumed. If the
chart has not yet produced enough comparable cases, the indicator stays
silent and tells you how many it has.
This is a context tool. It produces no buy or sell signals, no entry
prices, and no stop levels, and it is not designed to be used as one.
Please read the section titled THE MOST IMPORTANT WARNING before using
it on a live chart.
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HOW IT WORKS
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1. LEVEL MAP
A running inventory of prices that carry structural meaning is
maintained bar by bar:
- Swing pivots confirmed with a symmetrical left/right lookback
(default 21 bars each side for external structure, 5 for internal).
- Equal highs and equal lows: when a new pivot lands within the merge
tolerance of an existing level, that level's touch count increases
rather than a second level being created.
- Unfilled fair value gaps: a three-bar imbalance where the current
bar's low is above the high from two bars ago (or the mirror for
the bearish case). Each additional gap overlapping the same price
adds to that level's weight, so three gaps stacked at one price
are recorded as one level carrying three factors.
- Order blocks: the extreme of the last opposite-coloured candle
immediately before a displacement bar, where displacement means a
body in the top 15 percent of the last 100 bodies AND the move
takes out the most recent internal pivot. Displacement alone is not
enough; it must be tied to a structural break.
- Previous day and previous week high and low, requested with a
one-bar offset so no unclosed higher-timeframe data is used.
Each level accumulates a WEIGHT equal to the number of independent
factors coinciding there, plus a bonus for repeated touches and for age
beyond 200 bars. Two factors closer together than the merge tolerance
(default 0.25 x ATR) are treated as one level with two factors, never as
two levels. This prevents an order block that naturally sits inside a
fair value gap from being counted twice.
A level whose weight reaches the MAJOR threshold (default 4) is
classified MAJOR; weight 1 to 3 is MINOR; anything else is NONE.
Note on interpretation: a heavily touched level is treated as more
SIGNIFICANT, not as stronger. Repeatedly tested highs and lows are
exactly the prices that attract sweeps. The indicator does not assume
which way that resolves; it measures what actually followed.
2. EVENT CLASSES
Six event types are detected. Every one of them locks its state at bar
close.
SWP Sweep Price trades beyond a mapped level and closes back
inside it, with a wick in the top quartile of the
last 100 wicks on that side.
SHF Shift A close beyond the most recent confirmed external
swing, in either direction (break of structure or
change of character).
SQZ Squeeze Bollinger Bands (20, 2.0) contract entirely inside
Keltner Channels (20, 1.5 x ATR) for at least five
consecutive bars, then expand back out.
CLX Climax Volume in the top 5 percent of the last 200 bars
combined with a bar range in the top 10 percent.
Where volume is unavailable, range plus body size is
used instead and the dashboard states which.
REJ Reject A bullish or bearish engulfing bar, or a pin bar with
a wick in the top quartile of the last 100, but only
when it occurs at a mapped level. A rejection candle
floating in empty space is not recorded at all.
DIV Divergence Price makes a lower low while cumulative flow makes a
higher low, or the mirror case, measured at confirmed
pivots. Flow is signed by body position within the
bar range and scaled by volume where volume exists.
Divergence is measured against volume-weighted flow, not against an
oscillator. An oscillator derived from price and then compared back to
price adds no independent information; volume is a separate data source.
Two events of the same class are never recorded closer together than the
full horizon. This is a deliberate statistical constraint: it costs
sample size, and it buys the guarantee that no two stored outcomes share
an overlapping future.
3. CONTEXT SCORE
Three voices contribute to an additive score from 0 to 100. Nothing
gates. No voice can block a signal; each only adds weight.
STRUCTURE 35 Whether the recent sequence of confirmed swing highs
and lows agrees with the event's direction.
FLOW 30 The percentile rank of the bar's signed flow over the
last 200 bars, cut to one fifth when its sign
disagrees with the event direction.
HTF 35 Whether the higher timeframe close sits above or below
its own 50-period EMA, in agreement with the event.
The score is then discounted by regime and renormalised back to a 0-100
scale, so scores remain comparable across regimes:
RANGE structure x 0.70 (structure breaks constantly and means
little inside a range)
TRANSITION higher tf x 0.80 (higher timeframe bias is least
reliable while it is turning)
TREND flow x 0.85 (large volume is ordinary in a trend
and therefore less informative)
One correction is applied automatically: Climax and Divergence are
themselves defined from flow, so for those two classes the flow weight
is halved and the freed weight is split evenly between structure and
higher timeframe. Without this, the flow voice would confirm an event
that flow itself created.
The score is converted into a two-level tier by comparing it to the 60th
percentile of past scores for the same event class on this chart. There
is no fixed cut-off number.
4. SIGNATURE AND BACKOFF
Each recorded event is filed under a discrete key:
event class x location class x regime x direction x score tier
Direction is never merged at any level, because upward and downward
outcomes are not symmetrical.
When a new event confirms, the engine looks for stored outcomes sharing
that key. If fewer than the minimum sample (default 20) exist, it drops
the finest component and looks again:
L3 event + location + regime + direction + tier
L2 event + location + regime + direction
L1 event + regime + direction
L0 event + direction
The first level with a sufficient sample is used, and the dashboard
always states which level was used and how many records it contained.
If even L0 is short, nothing is drawn and the dashboard shows
CALIBRATING with the current count.
Seeing L1 or L0 rather than L3 is normal, not a fault. Non-overlapping
sampling produces a limited number of independent cases per chart, and
the backoff exists precisely to handle that honestly rather than
displaying a percentage built on four observations.
5. OUTCOME STORE
For each recorded event the engine tracks, for the following H bars
(default 24):
- excursion at H/4, H/2, 3H/4 and H, expressed in R where R is the
ATR(14) value at the event bar
- maximum favourable and maximum adverse excursion
- the bar number at which the move first reached +1R, +2R and +3R,
or zero if it never did
The record is written to the store only after H bars have fully elapsed.
A projection displayed today is therefore built exclusively from events
that had already finished before it was issued. This is a structural
property of the design, not a discipline that has to be maintained.
6. WHAT IS DRAWN
- An empirical quantile fan. The outer envelope traces the 5th and
95th percentiles of the matched outcomes at each of the four
checkpoints; the inner envelope traces the 25th and 75th; the
dashed centre line traces the 50th. The shape is asymmetric and
heavy-tailed whenever the underlying data is, because the values
are measured percentiles rather than a fitted curve.
- A target line. The median maximum favourable excursion of the
matched set is converted to a price, then snapped to a mapped
structural level if one lies within half an ATR. Statistics choose
the zone; structure chooses the exact price. The label states
"level" when a snap occurred and "stat" when it did not.
- Hit rate and expected bars. Both are read at the nearest whole R
ring (+1R, +2R or +3R) to the target distance, and the ring is
named on the label. Hit rate is the share of matched records that
reached that ring within H bars. The bar count is the median
first-passage time among those records that reached it.
7. RUN TRACKER
A run begins at a confirmed structure shift and ends at the next
confirmed shift in the opposite direction. Within a run, occurrences of
each event class and direction are counted. When the run closes, one
record per class is stored: how many had occurred before the reversal.
The observation unit is therefore the run, not the event. This matters:
counting events directly would produce heavily overlapping samples,
since several events inside one run share the same future. Counting runs
does not.
The panel answers a question most tools ignore entirely: given that a
third bearish divergence has now printed in this uptrend, in what
fraction of past runs on this chart did the reversal arrive by the
third, and in what fraction did the run extend to a fourth or beyond.
8. SELF-AUDIT
Every displayed projection resolves into exactly one of four states, and
these are never merged:
HIT the target was reached first
ADV the -1R reference was reached first
AMB both were touched inside the same bar, so the order is
unknowable from bar data and the case is discarded rather
than claimed
EXP H bars elapsed with neither touched
The dashboard reports the running counts, and separately compares the
average hit rate the tool projected against the hit rate it actually
realised. If those two numbers diverge, the tool is telling you its own
estimates are miscalibrated on this chart.
Two further panels report whether the classification axes carry any
information at all: median outcome for tier A versus tier B, and median
outcome for MAJOR versus MINOR versus NONE locations. If a pair does not
separate, that axis is not contributing, and you are meant to see that.
===============================================================
WHAT MAKES IT ORIGINAL
===============================================================
- Outcome statistics are conditioned on a discrete event signature
and computed from the chart's own history, rather than assumed from
a parametric distribution or imported as fixed percentages.
- Projection targets are snapped onto mapped structural levels, so
the displayed price is a real level rather than a quantile value
floating in empty space.
- The hierarchical backoff makes sparse conditioning explicit: the
display always names the level of specificity that was achievable
and the sample size behind it.
- Sequence statistics use the completed run as the observation unit,
which removes the sample overlap that direct event counting creates.
- The indicator scores its own past projections against outcomes and
displays projected versus realised hit rate on the chart.
===============================================================
HOW TO READ THE CHART
===============================================================
HORIZONTAL LINES
The image below shows the level map alone, with the projection layer
switched off, so the two grades of level can be compared directly: gold
solid lines mark MAJOR levels, thin dotted lines mark MINOR ones.
Two families of horizontal line exist, and they are drawn so that they
can never be confused with each other.
THE LEVEL MAP owns thin dotted lines and gold:
Gold, solid, width 2 A MAJOR level: four or more independent
structural factors coincide at this price.
Washed white, DOTTED, A MINOR level: one to three factors.
width 1
THE TARGET FAMILY owns solid width-2 lines in mint, coral and slate.
No target line is ever drawn dotted or at width 1.
Mint, solid, width 2, The target of the live projection.
full opacity Extends to the right. Only one is live at
a time.
Coral, dotted, width 1 The -1R adverse reference of the live
projection. Removed the moment the
projection resolves. This is a measurement
boundary used to classify the outcome. It
is NOT a stop loss and must not be used as
one.
Once a projection resolves, its target line keeps its full width and
stays solid. Only its colour and opacity change:
Mint, faded HIT: price reached it within the horizon.
Coral, faded ADV: price reached the -1R reference
first.
Slate blue-grey, faded EXP or AMB: the horizon elapsed with
neither touched, or both were touched in
the same bar and the case was discarded.
Resolved lines are retained deliberately. A price that the statistics
selected, and that was then snapped onto a structural level, often
remains structurally relevant afterwards, and it is useful to see where
those prices were. The number retained is configurable and defaults to
six.
Read the fade as expiry of a claim, not as expiry of the price. The
faded line is a record that this price was once selected as a target and
of what happened next. It is not a live target and carries no ongoing
claim about the future.
The image below shows several resolved projections on one chart, so the
three outcome colours can be compared side by side: a faded mint line
where price reached the target, a faded coral line where it reached the
adverse reference first, and a faded slate line where the horizon
elapsed without either being touched. The markers carry the matching
three-letter outcome. Projections that did not work out are shown here
deliberately; the indicator records its own failures and so should its
description.
Level lines are redrawn on each new bar and only levels within six ATR
of current price are displayed, up to fourteen at a time.
THE FAN
Outer shaded band 5th to 95th percentile of matched
historical outcomes.
Inner shaded band 25th to 75th percentile.
Dashed centre line 50th percentile, the median path.
Teal the event pointed upward.
Coral the event pointed downward.
The fan is frozen at the moment the projection is issued and is never
recalculated. It reaches forward exactly H bars.
The next image shows a single live projection close up: the two shaded
bands, the dashed median path, the mint target line, and the label
carrying hit rate, sample size and remaining bars. Note that the bands
are not symmetrical around the median, because they are measured
percentiles rather than a fitted curve.
EVENT MARKERS
A small label prints at each recorded event, below the bar for upward
events and above for downward ones. It contains a three-letter code and
a number:
SWP sweep SHF shift SQZ squeeze
CLX climax REJ rejection DIV divergence
The number is the count of that event class and direction so far inside
the current run. "DIV 3" means this is the third divergence of that
direction since the last structure shift.
When a projection resolves, its marker gains a suffix and changes
colour:
HIT green target reached first
ADV red the -1R reference reached first
AMB amber both touched in the same bar; discarded
EXP amber the horizon elapsed with neither touched
THE TARGET LABEL
Three lines at the right end of the projection:
line 1 the target price, followed by "level" if it was snapped onto
a mapped structural level or "stat" if no level was near
line 2 hit rate and sample size, for example: hit 61% n=38
line 3 while the projection is live: bars remaining and the R ring
the hit rate refers to. Once it resolves, this line is
replaced by "RESOLVED" followed by HIT, ADV, AMB or EXP, and
the whole label takes the matching colour.
The bar count was frozen when the projection was issued and only counts
down. Nothing behind it is recalculated.
THE DASHBOARD
EVENT class, direction, and sequence number in this run
LOCATION MAJOR / MINOR / NONE and the level weight
REGIME RANGE / TRANS / TREND, the context score, tier
SIGNATURE backoff level used and sample size
WITH q50 q75 q95 terminal excursion quantiles measured ALONG the
event's own direction, in R
AGAINST q25 q05 the same distribution's tail measured AGAINST the
event's direction, in R
TARGET price and hit rate
ETA bars remaining and the R ring, or "no open
projection"
RUN EXT how far the current run has extended, in R
FLOW SOURCE "volume" or "proxy"
SEQ 1 / 2 / 3 / 4+ share of past completed runs that reversed after
that many events of this class
RUNS median median count before reversal, and number of runs
TIER A / B median outcome in R for each tier, with counts
LOC MAJ/MIN/NONE median outcome in R for each location class
LEDGER running totals of hit, adv, amb and exp
CALIBRATION average projected hit rate against realised
The dashboard is reproduced below at readable size, since every claim
made in the HOW IT WORKS section is meant to be verifiable there: the
backoff level actually used, the sample size behind it, the quantiles,
the sequence distribution, the two axis-health rows, and the running
comparison of projected against realised hit rate.
WITH and AGAINST are measured relative to the event's own direction, not
relative to the chart. For a downward event, a WITH value of +1.4R means
price fell by 1.4 ATR, and an AGAINST value of -1.8R means price rose by
1.8 ATR before the horizon closed. AGAINST is the row that tells you how
violent the route can be, and it is the row most worth reading before
deciding on any position size.
===============================================================
THE MOST IMPORTANT WARNING
===============================================================
DO NOT TREAT THE GREEN LINE AS A TARGET TO TRADE TOWARD, AND DO NOT
TREAT ANY MARKER AS AN ENTRY SIGNAL.
You will observe the following, and it is the single most dangerous
thing about this indicator:
The projection expires. The bars run out. The label reads "closed". And
then, twenty or fifty bars later, price finally reaches the green line.
It is tempting to read that as the tool having been right after all. It
was not, and here is why that reading destroys accounts:
1. The green line is snapped to a structural level. Structural levels
get revisited eventually, because that is what they are. Price
arriving there after the horizon has expired is not evidence of
anything. It is what levels do.
2. The expected bar count is a median of the cases that reached the
ring. By definition, roughly half of the successful cases took
longer than that. "The estimate elapsed and it is not there yet"
is an ordinary outcome, not a malfunction.
3. Route is not measured. The indicator records where price ended up
and how far it travelled. It does not promise that the path there
was survivable. Price can travel far against you first, and still
arrive. The AGAINST row is the only place the route appears at all,
and even there it is a summary, not a guarantee.
A faded line is a closed case. It has no ETA, no live claim and no
implication that price is still heading there. If price later reaches a
faded line, that is not a delayed hit. It is a structural level being
revisited, which is what structural levels do.
That third point is what actually costs money. A leveraged position
opened on the strength of a hit rate can be liquidated by an adverse
excursion long before the target is reached. The account is closed; the
target being reached afterwards is irrelevant to it. This is not a
remote scenario. It is the ordinary case whenever leverage is applied to
a statistic that describes destinations rather than routes.
The -1R red line does not protect you either. It is a measurement
boundary chosen so outcomes can be classified consistently. It is not a
risk parameter and was never sized to be one.
Precedent is a context tool. It tells you what has typically followed
this kind of moment on this chart, with the sample size attached. Entry
timing, position size, stop placement, leverage and the decision to
trade at all remain entirely yours and must come from a method this
indicator does not contain and does not attempt to contain.
If the only thing you take from a projection is "hit 61 percent, so buy"
you have misread it. The intended reading is closer to: "in 38
comparable cases on this chart, price reached this level within 24 bars
in 61 percent of them, taking a median of 9 bars, and the adverse tail
of that distribution ran to -1.8R."
===============================================================
LIMITATIONS AND REPAINT POLICY
===============================================================
REPAINT BEHAVIOUR, ELEMENT BY ELEMENT
- Event detection, tier assignment and score sampling are locked at
bar close. They do not change afterwards.
- Swing pivots confirm with a delay equal to the pivot length in bars
(default 21). A level cannot appear before its pivot is confirmed.
That delay is the cost of not looking ahead, and it is not avoided
anywhere in this script.
- Higher timeframe values are requested with a one-bar offset, so
only completed higher timeframe bars are used. On the chart
timeframe this means the higher timeframe bias lags by one higher
timeframe bar.
- The fan, the target line and the adverse line are computed once, at
the confirmed bar that issued them, and are never recalculated.
- The dashboard and the countdown update live within the forming bar
by design. The countdown only subtracts from a number that was
frozen at issue; no statistic behind it is recomputed.
KNOWN LIMITATIONS
- The statistics are descriptive, not predictive. They summarise what
has already happened on the chart in front of you. They are not a
forecast and are not out-of-sample.
- Sample sizes are small by construction. Requiring non-overlapping
outcomes limits the number of independent cases available, which is
why the backoff and the visible sample counts exist.
- Until enough completed outcomes exist, nothing is drawn at all. The
image below shows that state: the dashboard reports CALIBRATING and
the current count against the required minimum, and no fan, target
or hit rate appears anywhere on the chart.
- Chart history is finite. On low timeframes the available bars may
cover only a matter of weeks, and possibly only one market regime.
Treat a large sample drawn from a single regime with caution.
- Outcomes are measured at bar resolution. When a bar touches both
the target and the adverse reference, the order is unknowable and
the case is marked ambiguous and discarded rather than assumed.
- Statistics apply only to events this indicator itself defined. If
you identify a setup it did not mark, no displayed percentage
describes it. Borrowing a number from a different occasion is a
misuse.
- Where a symbol provides no usable volume, flow falls back to a
body-position proxy on the same percentile scale. The dashboard
states which is in use. Mixing the two would corrupt the store, so
the fallback applies to the whole session or not at all.
- Changing the higher timeframe from Auto to Manual changes what the
signature key means. The stored outcomes are therefore cleared and
rebuilt from zero when you do it. This is intentional.
- Nothing here is tuned. There is no optimiser and no fitted weight.
Thresholds are percentiles over disclosed rolling windows. If a
parameter is changed, the level map and the statistics change with
it, and the sample must accumulate again.
===============================================================
DISCLAIMER
===============================================================
This script is published for educational and analytical purposes. It is
not financial advice, not a recommendation to buy or sell any
instrument, and not a trading system. It produces no entry signals, no
stop levels and no position sizing.
Historical measurement does not indicate future results. Markets change
regime, and a distribution measured on past bars may not describe the
next ones. Trading carries risk of loss, and leveraged trading carries
risk of total loss. Any decision taken while this indicator is on the
chart remains entirely the responsibility of the person taking it.
インジケーター
Volatility Cone & Analog Path ProjectionVolatility Cone & Analog Path Projection — Forward Price Envelope with Fractal Replay and Terminal Probability Distribution
Overview
Nearly every overlay on TradingView describes the past: where price has been, where volume traded, where structure broke. This tool points in the other direction. It builds a forward projection zone from the current bar using three independent layers — a realized-volatility cone, a replay of the historically most similar price fractals, and a terminal probability profile that combines both into a distribution of possible outcomes at the projection horizon.
The result is not a forecast. It is a bounded expectation: a visual answer to "given how this instrument has actually been moving, what range is normal over the next N bars, and where has price historically ended up after conditions that looked like this?"
Conceptual Framework
Price uncertainty grows with the square root of time, not linearly. A 24-bar projection is not 24 times as wide as a 1-bar projection — it is roughly 4.9 times as wide. Traders who size targets and stops on a straight-line mental model consistently misjudge what is achievable in a given number of bars.
The cone makes that curvature visible. Its width at each future bar is sigma * sqrt(t), where sigma is the standard deviation of log returns over the volatility window. Three nested bands are drawn, so you can immediately see which targets sit inside the ordinary range, which sit at the statistical edge, and which would require an exceptional move.
The Gaussian model alone, however, is a poor description of real markets: returns have fat tails, and volatility clusters. The analog layer addresses this by ignoring models entirely and asking an empirical question instead — what actually happened, historically, after the market printed this exact shape?
How It Works
Volatility estimation. Log returns are computed bar to bar. Their standard deviation over the volatility window gives the per-bar sigma; their mean gives the drift. Drift can be included or excluded from the cone's centerline.
Cone construction. For each future bar t from 1 to the horizon, the upper and lower bounds are close * exp(drift*t ± k*sigma*sqrt(t)) for each of the three band multipliers. Each band is rendered as a closed polygon with layered transparency, producing depth from the centerline outward.
Fingerprint extraction. The most recent N bars of log returns are z-scored — mean removed, divided by their own standard deviation. This makes the pattern scale-invariant: the same shape is recognised whether it happened during a quiet range or a volatile expansion, and at any price level.
Historical scan. Every candidate window inside the scan depth is z-scored the same way and compared to the current fingerprint by summed squared difference. Lower distance means a closer shape match. Candidates that overlap an already-selected match without improving on it are rejected, so the top results are not five copies of the same event shifted by one bar.
Forward replay. For each of the top matches, the bars that followed it are converted into a relative path and re-anchored to the current close. The path each analog is drawing forward is exactly the move that occurred after that historical fingerprint — nothing is fitted or optimised. Paths ending above the current price are drawn bullish, below bearish, and a thick median line traces the bar-by-bar median across all analogs.
Terminal probability profile. At the projection horizon a horizontal distribution is built across the cone's full range. Each row's density blends the Gaussian probability implied by the volatility model with an empirical kernel centred on each analog's endpoint. The Model Weight input controls that mix: 1.0 is purely theoretical, 0.0 is purely historical, and the default sits between them. The widest row — the mode of the blended distribution — is marked as the most probable zone.
Interpretation
Cone bands define what is statistically ordinary. A target beyond the outer band within the horizon is not impossible, it is simply rare — treat it accordingly when planning holding time.
Cone width itself is information. A narrow cone means compressed volatility, which historically resolves into expansion. A wide cone means the market is already moving; chasing inside it carries a worse risk profile.
Analog dispersion matters more than analog direction. Five paths that fan out in all directions means the current shape carried no historical edge. Five paths clustering in one direction is the meaningful configuration.
Best Match Quality in the panel scores how closely the nearest historical fingerprint resembles the present one. Below roughly 60%, treat the analog layer as noise and rely on the cone alone.
The most probable zone is where the blended distribution peaks. It is a magnet-style reference, not a target — the distribution is wide by construction.
Volatility Regime compares short-window volatility to the full window. Expanding means the cone is likely to understate near-term movement; contracting means the opposite.
Settings
Setting Effect
Projection Horizon Bars projected forward. Also the endpoint of the profile
Volatility Window Sample size for sigma and drift. Longer = smoother, slower to adapt
Include Drift Tilts the cone with the window's mean return
Inner / Mid / Outer Band Sigma multipliers for the three layers
Fingerprint Length Bars compared for similarity. Shorter = more matches, less specific
Scan Depth How far back to search for analogs
Number of Analogs How many historical paths to replay
Profile Rows / Width Resolution and horizontal size of the terminal distribution
Model Weight Gaussian versus empirical blend in the distribution
Redraw on Bar Close Only Recommended on. The scan is heavy; this runs it once per bar
Limitations — read this
This is not a prediction and must not be traded as one. The cone describes a statistical range under an assumption of stable volatility. Real volatility is not stable, and returns have fatter tails than the Gaussian model implies, so moves outside the outer band occur more often than the model suggests.
Analog matching is weak evidence. A few dozen bars of shape similarity is a small sample; markets are non-stationary and a pattern that resolved one way in the past carries no obligation to repeat. The paths are historical context, not a probability statement about the future.
Nothing repaints, but the whole projection is recomputed each bar. Yesterday's cone is not preserved — the drawing always reflects current data only. It is anchored to the last bar by design.
On low-volume, illiquid, or heavily gapped instruments the return distribution is distorted and both layers degrade.
No entries, no stops, no targets, no signals. This is a context tool for sizing expectations and holding time. インジケーター
Reversal Trap Probability Bands [BigBeluga]🔵 OVERVIEW
The Reversal Trap Probability Bands is an advanced technical indicator created by BigBeluga to identify and trade fakeout traps around market extremes. Traditional envelope or band indicators often fail because traders blindly enter breakouts that quickly reverse into whipsaw losses. In order to provide a solution to this problem, this indicator combines volatility-based envelope channels with a dynamic probability tracking engine, measuring historical RSI buckets to calculate real-time win probabilities for reversal traps.
The indicator aims to visualize institutional exhaustion and subsequent mean-reversion expansions. The core element of its calculation involves tracking baseline moving averages alongside outer volatility bounds defined as:
upper_band = basis + (multiplier * vola)
lower_band = basis - (multiplier * vola)
where basis is an exponential moving average of length envelope_len , and vola is the ATR volatility measure scaled by multiplier . Higher values of envelope_len and multiplier allow the indicator to filter out routine market noise and isolate major structural exhaustion points.
🔵 FEATURES
The system utilizes a multi-layered matrix structure to provide actionable market intelligence:
1 — Volatility Envelope & Basis Engine
envelope_len = input.int(55, "Envelope Smoothness") : Controls the responsiveness and smoothness of the central baseline.
upper_band & lower_band : Dynamic outer boundaries that shade gradient fills to visualize upper and lower market extremes.
2 — Reversal Trap Detection & RSI Probability Tracking
trap_window = input.int(10, "Trap Window (Candles)") : Defines the maximum candle count allowed outside the bands before invalidating a fakeout setup.
rsi_bucket = math.max(0, math.min(10, math.round(rsi / 10))) : Automatically categorizes momentum into distinct RSI tiers to calculate real-time win probability rates.
3 — Dynamic Target, Stop, & Signal Management
Bull_Stop = ta.lowest(low, 2) - atr & Bear_Stop = ta.highest(high, 2) + atr : Calculates volatility-adjusted safety padding for active trade management.
Signal Labels & Targets: Plots clear entry notifications displaying win probability percentages, along with dashed target and stop lines.
🔵 HOW TO USE
Apart from the basic visualization of volatility extremes, this tool can also act in alternative ways to support decision-making:
Identify Reversal Traps: Wait for price to break outside the upper or lower envelope boundaries and subsequently close back inside within the defined trap_window .
Evaluate Win Probability: Check the probability percentage displayed on the trap signal label (backed by historical RSI bucket tracking) before entering a trade.
Manage Risk with Stops and Targets: Use the projected dashed target lines (anchored to the basis line) and ATR-padded stop lines to execute and protect positions.
🔵 NOTES
Why this implementation is unique:
It moves beyond static band indicators by integrating a self-learning historical database that calculates live win probabilities based on momentum buckets.
The automated target and stop-loss line projection engine provides clear visual roadmaps for every triggered setup.
The script is fully optimized for Pine Script version 6, utilizing high-performance array tracking (`var int bull_total = array.new_int(11, 0)`) for smooth execution.
Note: Because the win probability engine evaluates historical trade performance dynamically in real time, initial signals on a freshly loaded chart may display "Tracking..." until sufficient sample data is recorded.
インジケーター
FractalMemoryLib [Jayadev Rana]FractalMemoryLib packages the pattern-memory engine used by the Fractal Memory Projection indicator and the Fractal Memory Strategy so any script can import it.
WHAT IT DOES
The library finds the historical window whose movement shape most resembles the most recent bars (mean squared distance between stdev-normalized log returns), replays what followed that window as a projected close path, and sizes stops and targets adaptively by volatility regime.
EXPORTED FUNCTIONS
logRet(src) - one-bar log return of a series.
bestMatch(src, winLen, scanDepth, gapAhead) - scans up to scanDepth bars back and returns the offset of the most similar window plus a 0-100 similarity score. gapAhead reserves bars after the match for a projection.
analogPath(src, offset, fcLen, scaleF) - array of fcLen projected closes built by replaying the returns that followed the match, rescaled by scaleF (for example current ATR over ATR at the match).
adaptiveR(atrLen, rankLen, base) - volatility-adaptive unit risk: ATR times (base plus its 0-1 percentile rank), plus the rank itself. Call on every bar.
volRegime(volRank) - "Low", "Normal" or "High" label from the rank.
targets(entry, dirSign, unitR, slMult) - stop loss and TP1/TP2/TP3 at 1R, 2R and 3R.
USAGE NOTES
Call adaptiveR on every bar for ta consistency. bestMatch and analogPath are loop-heavy; for display purposes call them on the last bar only, and make sure the chart has at least scanDepth plus gapAhead bars of history. When the library itself is added to a chart it draws a small demo projection line from the best analog.
The analog projection is a statistical reference to a similar past episode, not a prediction, and not financial advice. ライブラリ
Fractal Memory Strategy [Jayadev Rana]Fractal Memory Strategy trades the same engine as the Fractal Memory Projection indicator: it looks for the historical episode most similar to current price action, and only takes trend flips that agree with how that episode played out. Exits scale out at three volatility-adaptive targets.
HOW IT DECIDES
An ATR trailing stop tracks the trend. When it flips, the last 30 closes are converted to normalized log returns and compared against past windows by mean squared distance. The bars that followed the best analog give a net direction; the flip is only traded when the analog direction agrees (the filter can be disabled). Orders are processed on bar close, so no lookahead is involved. For visual context the strategy also draws the 50-candle ghost projection beyond the last bar - it is display-only and never affects order logic.
ENTRIES AND EXITS
On a confirmed bullish flip with agreement the strategy closes any short and enters long; the mirror applies to shorts. One unit of risk R equals ATR times (1.2 plus the ATR percentile rank over 200 bars), so targets and stops widen in volatile regimes and tighten in quiet ones. Position exits: one third at 1R, one third at 2R, the remainder at 3R, with a stop at 1.5R (all adjustable). Direction can be restricted to long-only or short-only.
PROPERTIES USED IN THE PUBLISHED BACKTEST
10,000 initial capital, 10 percent of equity per trade, 0.01 percent commission per order, 2 ticks slippage, no pyramiding, orders on close. These are deliberately conservative; adjust them to match your own broker before drawing any conclusion.
PANEL
Match similarity, volatility regime, forecast direction, closed trade count and win rate.
NOTES
The analog projection is a statistical reference, not a prediction, and past behaviour does not guarantee anything about the future. Results vary by symbol and timeframe; test on your own market with realistic costs before considering any live use. This is an educational tool, not financial advice. ストラテジー
Structural Language ModelOverview
Structural Language Model treats price action as a language. Each bar is tokenised into one of five structural symbols, and a low-order Markov model learns the grammar — the probability of what comes next given the recent context. Instead of "match the nearest historical shape" (fragile, overfit-prone k-NN), it estimates P(next token | last k tokens): a nonparametric conditional-move model that proves or disproves itself, live, on your symbol. It is a research/forecast read, not a signal service.
The five-symbol grammar
Every bar becomes one token, built from robust intrabar primitives (gap-immune, no fragile sweep/FVG detection), with adaptive thresholds so the alphabet stays balanced across symbols and timeframes:
X− down impulse · d ordinary down · c compression / indecision · u ordinary up · X+ up impulse
The model then learns grammar like c → X+ (breakout), X+ → X− (reversal), runs of u/X+ (trend), X+ → c (exhaustion), using order-1 or order-2 transition counts with Laplace smoothing, updated online.
Why these parts are one tool
The tokeniser turns raw OHLC into a balanced, information-rich alphabet — without it the Markov counts are dominated by whatever token is most common.
The Markov model reads out, each bar, a directional bias (P up-ish − P down-ish), a predictability score (how peaked the next-token distribution is, via normalized entropy), a structural-surprise spike (−log P of the token that just printed — a grammar break), and the full next-bar probability ladder.
The harness is the part that makes it honest. It's prequential (predict-then-update: each transition is scored from counts that exclude its own outcome, so every score is out-of-sample), it runs a walk-forward in-sample vs out-of-sample split with Wilson 95% intervals, and it draws a reliability curve — binning OOS predictions by predicted P(up) and showing the realized up-rate per bin. A rising, significant curve = real calibrated information; a flat one = none. Remove any part and you can no longer answer "is this model actually calibrated on this market?"
How to use it
Read the directional bias line against its conviction bands as context, not a trigger, and check predictability for how peaked the forecast is. Then read the harness — the model is only worth trusting where the out-of-sample up-lean lift is above 1 and/or the reliability spread is positive and significant (✓sig). A flat or insignificant curve means there's no calibrated edge here; treat it as descriptive only, or try another symbol/timeframe. The dashboard has a Compact layout (default: forecast + the one calibration line that matters) and a Pro layout (the full ladder, in/out-of-sample lift, and the three-bin reliability curve). Bias-turn crosses are optionally mirrored on the price chart. It is never a standalone signal.
Non-repainting
Tokens and counts update only on confirmed bars, and the score for bar t uses counts as they stood before bar t's transition was added — nothing reads its own future. The live next-bar forecast naturally refines as the current bar forms (it's a forecast, not a settled statistic). All harness figures are out-of-sample by construction.
Honest limits
OHLCV only. A per-bar tokeniser maximises samples but is coarser than a swing/event grammar (a documented future extension). Any edge is typically modest and market/timeframe-dependent — directional forecasting on noisy price is hard, and no indicator has an inherent edge. That's exactly why the harness is built in: validate it before trusting it.
Outputs for other scripts
Generic EXP_* plots — bias, predictability, structural surprise, live P(next up-ish), and the OOS lift — are published to the Data Window for use from other scripts via input.source().
Concept credits
Markov chains / n-gram language models — A. Markov (1913); C. Shannon (1948)
Prequential (predict-then-update) evaluation — A. P. Dawid (1984)
Additive (Laplace) smoothing — P.-S. Laplace
Entropy — C. Shannon (1948)
Wilson score interval — E. B. Wilson (1927)
Synthesis and Pine implementation are the author's own; no third-party Pine code reused.
Disclaimer
Research and education only. Not financial advice, not a signal service, not a guarantee of future results. Validate with your own testing, apply realistic costs, and manage risk. インジケーター
インジケーター
BEDROCK Gated Macro Spot Cycle ModelBEDROCK condenses several independent long-term Bitcoin valuation models into a single transparent 0–100 score, then maps that score onto a seven-tier ladder running from deep value to cycle-top risk — with capitulation and euphoria gates that hold back the two most common false signals at each extreme. It is built for spot investors making multi-month and multi-year allocation decisions, not for short-term trading.
The reference card below shows how to read the model and how each tier behaves:
█ WHAT IT DOES
BEDROCK answers one question: where does price sit inside its macro cycle right now? Rather than a single oscillator, it scores a basket of slow-moving valuation measures, normalizes each to a common 0–100 "cheap → expensive" scale, blends them into a weighted composite, and classifies the result into an actionable tier with a suggested accumulation or distribution size. A higher composite means greater long-term value and lower risk; a lower composite means the market is stretched and risk is rising.
█ HOW IT WORKS
The composite is built from four independent blocs, any of which can be reweighted or disabled:
Trailing cost-basis bloc — price relative to the 200-week SMA, the 2-year SMA, and the 200-day SMA (Mayer Multiple). These three are deliberately collapsed into a single averaged, bounded factor so the moving-average family is represented once and cannot dominate the score through collinearity.
Drawdown from all-time high — how far price has fallen from its peak. The heaviest-weighted leg by default.
Weekly RSI — long-term momentum, as secondary confirmation.
MVRV Z-Score (optional) — an on-chain valuation leg you can enable and feed from an external source.
Each metric is mapped to 0–100 through its own linear calibration between a deep-value anchor and an expensive anchor, so every leg speaks the same language before being combined. Weights are auto-renormalized over whatever blocs are actually available — so the model stays coherent on early history where the 200-week isn't yet populated, or when MVRV is turned off. It simply reweights the parts it has.
█ THE TWO GATES — THE CORE IDEA
A raw valuation score has two classic failure modes: it screams "generational buy" on the first leg down of a bear market, and it screams "top" every time price gets mildly extended. BEDROCK addresses both with directional gates that only ever cap the tier toward the middle — they never fabricate a signal, and they never block the core accumulate or trim reads.
Capitulation gate (bottom) — the two deepest tiers stay locked until the market shows genuine capitulation. Generational requires a large drawdown from the all-time high (or a deeply negative MVRV-Z); Deep Accumulation requires price at or below its 200-week basis. Until then the score is capped at Accumulation, so you keep buying value without prematurely committing everything.
Euphoria gate (top) — the two riskiest tiers stay locked until multiple independent overheating signs agree across the 200-week multiple, weekly RSI, the Mayer Multiple, and MVRV-Z. Euphoria requires at least one confirmation; Cycle Top requires at least two. This is what stops the model from calling a top on every rally.
Because both gates only cap toward neutral, accumulation signals are never suppressed and trim signals are never suppressed. The gates restrain only the extreme calls, and only until the evidence is actually there.
█ READING THE INDICATOR
Composite line — the 0–100 score, colored by its current tier.
Background — shaded by tier for at-a-glance cycle context.
Threshold lines — the tier boundaries.
Markers — gated triangles mark transitions into accumulation tiers (up) and distribution tiers (down).
Data table — live composite, current tier, suggested action, both gate states, and every underlying metric (200W and 2Y multiples, Mayer, drawdown, weekly RSI, MVRV).
█ THE SEVEN TIERS
Generational Value — extremely rare deep value; aggressive accumulation.
Deep Accumulation — excellent value; size up.
Accumulation — good value; keep building.
Neutral / Hold — fairly valued; hold.
Expensive / Trim — above fair value; begin scaling out.
Euphoria / Distribute — high risk; distribute and protect profit.
Cycle Top / Exit — extreme; high-probability macro top.
Each tier also outputs a suggested DCA-in or trim-out multiplier, so the signal is sized rather than binary.
█ HOW TO USE IT
Use it on Bitcoin spot or index charts such as BITSTAMP:BTCUSD or $BINANCE:BTCUSDT.
Weekly is the primary timeframe; daily works as a secondary view.
Accumulate through tiers 1–3, hold in tier 4, scale down in tiers 5–6, and treat tier 7 as exit territory.
Built-in alerts fire on entry into each accumulation and distribution tier (gated).
█ WHAT MAKES IT ORIGINAL
BEDROCK is not a single valuation ratio dressed up as an oscillator. The combination is the point: a transparent additive composite over independent metrics, a deliberate collinearity fix that collapses the moving-average family into one bounded bloc, a dual directional-gate system that suppresses the two most common false signals at both extremes without ever blocking the core reads, and sized accumulate/trim output instead of a bare number. Every metric, weight, calibration anchor, and gate threshold is exposed as an input, so the entire model is auditable and tunable — nothing is hidden.
█ NOTES & LIMITATIONS
BEDROCK is a long-horizon valuation tool, not a precise top/bottom timer and not a short-term trading system. It is designed to keep you positioned in the statistically favorable portion of the cycle, not to nail exact turns. Several display themes are included. This script is for educational purposes only and is not financial advice — size your own risk and do your own research. インジケーター
Daily Volume ForecastDaily Volume Forecast
What this indicator does
Daily Volume Forecast projects the full-day trading volume of the current, still-running day — its expected value at the closing bell. It is designed for the Daily chart , where the live bar already carries the volume accumulated so far; the indicator scales that partial volume up to an estimate for the whole session.
The goal is to answer a simple question intraday: is today on track for above- or below-average volume? — before the day is actually over.
How it works
Partial volume so far: On the Daily chart the current bar's volume is the cumulative volume traded since the session open. This is the basis that gets extrapolated.
Elapsed session time: On a daily bar the bar's own hour/minute is the open time, not the current time. The indicator therefore derives the elapsed time from the real wall-clock (timenow) in the instrument's exchange timezone, relative to the configured session, so it knows how far the day has progressed.
Two forecast methods are available:
Intraday Profile (recommended): Intraday volume (e.g. 5-minute) is pulled per day via request.security_lower_tf and averaged into a typical volume-by-time-of-day curve . Because real volume is U-shaped (heavy at the open and close, light at midday), this curve captures how much of a day's volume is normally done by the current time. The forecast is current_volume / expected_fraction_done_by_now, which adapts automatically to the instrument's own shape. Until enough intraday history exists, it falls back to the linear method.
Linear: Assumes volume is spread evenly across the session: forecast = current_volume × (session_length / elapsed_minutes). Simple, requires no extra data, but overestimates the remainder in the morning because it ignores the U-shape.
Live update: The estimate is computed on the current (last) bar and updates as the day develops. When the market is closed, elapsed time clamps to the full session, so the forecast converges to the actual day's volume.
Display
Volume columns of the actual daily volume (historical and current), coloured by up/down day — green when close ≥ open, red otherwise.
Forecast line for the projected full-day volume, extended to the right edge (trackprice) on the current bar.
Label on the last bar showing the estimate (e.g. "Est. 3.2M").
Info table (optional, bottom-right) with the selected method, the forecast, percent of the session elapsed, the current volume, and the number of days used to build the profile.
Settings
Forecast Method: Intraday Profile or Linear.
Trading Session: Session string (default 0930-1600 for US RTH); adjust to the instrument (e.g. 0900-1730 EU stocks, 0000-2400 for 24h / crypto / FX).
Profile Resolution (min): Intraday resolution used to build the volume curve (Intraday Profile only).
Show Historical Daily Volumes: Toggle the volume columns.
Show Info Table: Toggle the table.
Colours for up and down volume.
How to use it
Apply it to a Daily chart (a warning label appears on other timeframes). Use it to gauge participation in real time — to confirm breakouts on rising projected volume, to flag unusually quiet days, or as a context filter alongside a price strategy. The Intraday Profile method is recommended whenever the instrument has a pronounced intraday volume shape; Linear is a lightweight fallback that needs no intraday history.
Notes
request.security_lower_tf provides only a limited amount of recent intraday history (a TradingView/plan limit), so the profile is built from the most recent available days and rolls forward naturally.
The profile method needs a few completed days to warm up; until then it uses the linear fallback.
The session string must match the instrument, otherwise the elapsed-time scaling — and therefore the forecast — is distorted. Extended-hours volume is not considered.
This script is an analysis tool and does not constitute financial advice. A volume forecast is an extrapolation, not a guarantee of the day's outcome.
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Daily Volume Forecast — Deutsch
Was macht dieser Indikator?
Daily Volume Forecast prognostiziert das gesamte Tagesvolumen des aktuellen, noch laufenden Tages — den zu erwartenden Wert zum Handelsschluss. Er ist für den Daily-Chart ausgelegt, wo die laufende Kerze bereits das bisher kumulierte Volumen trägt; der Indikator skaliert dieses Teilvolumen auf eine Schätzung für die ganze Session hoch.
Ziel ist es, schon während des Tages eine einfache Frage zu beantworten: Läuft der heutige Tag auf über- oder unterdurchschnittliches Volumen hinaus? — bevor der Tag tatsächlich vorbei ist.
Wie es funktioniert
Bisheriges Teilvolumen: Auf dem Daily-Chart ist das Volumen der aktuellen Kerze das seit Session-Eröffnung kumulierte Volumen. Das ist die Basis, die hochgerechnet wird.
Verstrichene Session-Zeit: Auf einer Tageskerze ist deren Stunde/Minute die Eröffnungszeit, nicht die aktuelle Uhrzeit. Der Indikator leitet die verstrichene Zeit deshalb aus der realen Uhrzeit (timenow) in der Börsen-Zeitzone des Instruments ab, relativ zur eingestellten Session — so weiss er, wie weit der Tag fortgeschritten ist.
Zwei Prognosemethoden stehen zur Wahl:
Intraday Profile (empfohlen): Über request.security_lower_tf wird das Intraday-Volumen (z. B. 5-Minuten) je Tag erhoben und zu einer typischen Volumenkurve nach Tageszeit gemittelt. Da reales Volumen U-förmig ist (viel bei Eröffnung und Schluss, wenig am Mittag), erfasst diese Kurve, welcher Anteil des Tagesvolumens zur aktuellen Uhrzeit normalerweise schon gehandelt ist. Die Prognose ist aktuelles_Volumen / erwarteter_Anteil_bis_jetzt und passt sich automatisch an die Form des Instruments an. Bis genug Intraday-Historie vorliegt, greift der lineare Fallback.
Linear: Nimmt an, dass das Volumen gleichmässig über die Session verteilt ist: Prognose = aktuelles_Volumen × (Session-Länge / verstrichene_Minuten). Einfach, ohne Zusatzdaten, überschätzt aber am Vormittag den Rest, weil die U-Form ignoriert wird.
Live-Update: Die Schätzung wird auf der aktuellen (letzten) Kerze berechnet und aktualisiert sich im Tagesverlauf. Bei geschlossenem Markt wird die verstrichene Zeit auf die volle Session begrenzt, sodass die Prognose gegen das tatsächliche Tagesvolumen konvergiert.
Anzeige
Volumen-Säulen des tatsächlichen Tagesvolumens (historisch und aktuell), eingefärbt nach Up/Down-Tag — grün wenn close ≥ open, sonst rot.
Prognose-Linie für das hochgerechnete Tagesvolumen, auf der aktuellen Kerze bis zum rechten Rand verlängert (trackprice).
Label am letzten Balken mit der Schätzung (z. B. "Est. 3.2M").
Info-Tabelle (optional, unten rechts) mit gewählter Methode, Prognose, Anteil der bereits verstrichenen Session, aktuellem Volumen und Anzahl der für das Profil genutzten Tage.
Einstellungen
Forecast Method: Intraday Profile oder Linear.
Trading Session: Session-String (Standard 0930-1600 für US-RTH); an das Instrument anpassen (z. B. 0900-1730 EU-Aktien, 0000-2400 für 24h / Krypto / FX).
Profile Resolution (min): Intraday-Auflösung zum Aufbau der Volumenkurve (nur Intraday Profile).
Show Historical Daily Volumes: Volumen-Säulen ein-/ausblenden.
Show Info Table: Tabelle ein-/ausblenden.
Farben für Up- und Down-Volumen.
Verwendung
Auf einen Daily-Chart anwenden (auf anderen Timeframes erscheint ein Hinweis-Label). Geeignet, um die Marktbeteiligung in Echtzeit einzuschätzen — etwa zur Bestätigung von Ausbrüchen bei steigender prognostizierter Beteiligung, zum Markieren ungewöhnlich ruhiger Tage oder als Kontext-Filter neben einer Kursstrategie. Die Methode Intraday Profile wird empfohlen, wenn das Instrument eine ausgeprägte Intraday-Volumenform hat; Linear ist ein leichtgewichtiger Fallback ohne Intraday-Historie.
Hinweise
request.security_lower_tf liefert nur eine begrenzte Menge jüngster Intraday-Historie (TradingView-/Abo-Limit), daher basiert das Profil auf den jüngsten verfügbaren Tagen und rollt natürlich mit.
Die Profil-Methode braucht einige abgeschlossene Tage zum "Aufwärmen"; bis dahin greift der lineare Fallback.
Der Session-String muss zum Instrument passen, sonst werden die Zeit-Skalierung — und damit die Prognose — verzerrt. Vor-/Nachbörsliches Volumen wird nicht berücksichtigt.
Dieses Skript ist ein Analyse-Werkzeug und stellt keine Anlageberatung dar. Eine Volumenprognose ist eine Hochrechnung, keine Garantie für den Tagesausgang.
インジケーター
BreakoutBreakout is an overlay indicator for identifying pre-breakout trade context using supply/demand zones, higher-timeframe alignment, moving-average structure, and confluence scoring.
This script is for analysis and planning only. It does not place orders or guarantee results.
What it does
Breakout helps visualize potential long and short setups before price breaks out of a key zone. It combines:
- Key magnet supply/demand zones (strongest reaction levels only)
- HTF trade zones auto-anchored by chart timeframe
- Pre-trade setup boxes with entry, stop, and target
- Diamond markers for high-confluence turning points
- Liquidity levels for potential sweep/SFP context
- Optional info panel with bias, levels, and active setup details
How it works
Magnet zones: pivot highs/lows cluster into levels. Levels price revisits gain strength. Only the strongest are drawn.
HTF trade regime: each chart timeframe uses two higher anchor timeframes. Long ideas can be filtered to HTF demand zones; shorts to HTF supply zones. Zones use confirmed HTF swing highs/lows with ATR-based width and reach.
Setup engine: setups form when zone context, trend/MA structure, confidence score, and cooldown align. Stops use risk percent, MA support/resistance, swing levels, and a max distance cap. Targets use a configurable minimum risk/reward.
Diamond signals: require structure (SFP, reversal, or MA setup), zone context, and supporting momentum/trend. Optional volume spike and RSI divergence add confluence. Not every pivot prints a diamond.
Volume and divergence: abnormal volume spikes (relative volume and z-score) can combine with RSI divergence in diamonds and setup scoring.
MTF data uses request.security() with barmerge.lookahead_off to avoid lookahead on higher-timeframe context.
How to use
1. Add Breakout to your chart.
2. Choose your execution timeframe.
3. Keep HTF zone filter on for higher-timeframe alignment.
4. Wait for price at HTF zone plus key magnet zone, then diamond or setup box with confidence at or above Min Confidence.
5. Use entry, stop, and target from the active setup box for planning.
6. Manage risk with your own rules.
Main settings
- Risk: risk per trade, min R/R, max hold bars
- Filters: min confidence, approach distance, cooldown, volume, vol spike and divergence
- HTF zones: trade inside HTF S/D only, auto anchors, zone width and reach
- Display: zones, setups, liquidity, labels, info panel, EMAs, diamond spacing
Alerts
- Breakout Long Setup
- Breakout Short Setup
- Breakout Approaching Demand
- Breakout Approaching Supply
Alerts reflect script conditions at alert time. They are not trade recommendations.
Disclaimer
Educational and informational use only. Not financial advice. Trading involves risk, including loss of capital. Past results do not guarantee future performance. Use at your own discretion. インジケーター
Strategy Forecast EngineThe Strategy Forecast Engine is a regime-based Monte Carlo forecasting tool that estimates the future return distribution of trend-following strategies across different market environments. The model identifies the current market regime, conditions forecasts on historical returns observed during comparable regimes, and generates thousands of potential future price paths using Monte Carlo simulation. The resulting return distribution is presented through percentile projections and a structured, color-coded table that provides a comprehensive assessment of the forecast.
First, the model identifies the current market regime using the selected trend-following strategy. Users can choose between a moving-average crossover strategy, a volatility-based trailing stop strategy, or a combined strategy that incorporates both approaches. Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). By default, the model applies an asymmetric design in which conflicting signals default to bullish unless neutral regimes are enabled in the menu. Market regimes are determined as follows:
Bullish Trend Regime = (Fast MA – Slow MA) > (ATR × Trend Margin)
Bearish Trend Regime = (Fast MA – Slow MA) < –(ATR × Trend Margin)
Bullish Volatility Regime = Price > (Highest Price – (Volatility × Stop Factor))
Bearish Volatility Regime = Price < (Lowest Price + (Volatility × Stop Factor))
Bullish Combined Regime = Bullish Trend Regime and Bullish Volatility Regime
Bearish Combined Regime = Bearish Trend Regime and Bearish Volatility Regime
Once the current regime has been identified, the model collects all historical logarithmic returns that occurred during the same regime beginning from the selected start date. Only returns from the matching regime are used to generate the forecast, allowing projections to be conditioned on historically comparable market environments rather than treating all historical observations as equally relevant. If duration-adjusted forecast is enabled in the menu, the model further restricts the sample pool to returns from regimes that were at least as mature as the current regime.
The Monte Carlo simulation engine then generates thousands of possible future price paths over the selected forecast horizon. Each simulation randomly samples historical returns from the sample pool associated with the current regime and compounds them forward to generate a potential future price path. This process is repeated for the specified number of simulations to produce a broad range of possible future outcomes. The random seed controls reproducibility, ensuring that identical settings produce identical forecasts. Once all individual simulations have been completed, the resulting return distribution is summarized using percentile projections:
95% = 5% of simulations ended above this level and 95% ended below it.
75% = 25% of simulations ended above this level and 75% ended below it.
Median = 50% of simulations ended above this level and 50% ended below it.
25% = 25% of simulations ended below this level and 75% ended above it.
5% = 5% of simulations ended below this level and 95% ended above it.
The upper quartile (75%) and lower quartile (25%) define the Interquartile Range (IQR), which contains the middle 50% of all simulated outcomes and represents the central range of the projected outcome distribution. The upper and lower tail percentiles can be set to 10% (90% / 10%), 5% (95% / 5%), or 1% (99% / 1%). The default setting is 5%, which captures the middle 90% of simulated outcomes. At 10%, the range captures 80% of simulated outcomes, while at 1%, the range captures 98% of simulated outcomes. To further evaluate the risk/reward characteristics of the forecast, the model includes a built-in table with the following metrics:
Regime = Current market regime based on the selected strategy configuration.
Duration = Percentile rank of current regime duration relative to past regimes.
Forecast = Percentile rank of current duration including the forecast horizon.
Win Rate = Percentage of profitable simulations relative to total simulations.
Profit Factor = Ratio of total simulated profits to total simulated losses.
Expectancy = Average expected percentage return across all simulations.
Reward/Risk = Ratio of upper quartile return to lower quartile return.
Asymmetry = Ratio of selected upper tail return to selected lower tail return.
Skewness = Ratio of upside potential to downside risk relative to the median.
Sample Size = Number of historical returns available for the current regime.
Frequency = Percentage of historical returns belonging to the current regime.
In summary, the Strategy Forecast Engine is a comprehensive forecasting tool designed to help investors evaluate the return distribution of trend-following strategies based on the current market regime. By combining regime detection with Monte Carlo simulation, the model conditions forecasts on historical returns observed during comparable market regimes to estimate the distribution of potential outcomes and their associated risk/reward characteristics. While the model provides valuable insight into historical return patterns, investors should remain mindful that historical market behavior may not necessarily persist under future market conditions. インジケーター
Nasan Stretch - Velocity Quadrant Plot# Stretch/Velocity Quadrant Plot
The **Stretch/Velocity Quadrant Plot** is a multi-asset market regime and momentum tool designed to track up to **11 symbols simultaneously**.
By plotting **Velocity** against **Stretch**, the indicator shows where each asset sits within the lifecycle of a trend and how it is moving between expansion, exhaustion, reversal, and recovery phases.
It is especially useful for:
* comparing several stocks on one chart,
* identifying leadership and rotation,
* spotting early momentum changes,
* distinguishing strong trends from overextended moves,
* and evaluating how likely a stock is to remain in or leave its current market phase.
## Core Concept
The model combines short-, medium-, and long-term behavior using:
* 13-period EMA
* 21-period EMA
* 34-period EMA
The three horizons are blended using customizable weights.
All values are normalized by ATR, allowing stocks with very different prices and volatility levels to be compared on the same scale.
## Velocity — X-Axis
Velocity measures the rate of change of the three EMAs.
Each EMA uses a separate lookback:
* EMA 13 velocity over 8 bars
* EMA 21 velocity over 13 bars
* EMA 34 velocity over 21 bars
The result represents the speed and direction of the underlying trend.
* Positive Velocity: trend momentum is improving
* Negative Velocity: trend momentum is weakening
## Stretch — Y-Axis
Stretch measures how far price has moved away from its EMA structure.
It is calculated as the ATR-normalized distance between price and the 13-, 21-, and 34-period EMAs.
* Positive Stretch: price is trading above its trend structure
* Negative Stretch: price is trading below its trend structure
* Larger absolute values: price is farther from equilibrium
## The Four Market Quadrants
### Q1 — Bullish Expansion
**Positive Velocity / Positive Stretch**
The asset is above its trend structure and momentum is accelerating.
This is typically associated with:
* strong uptrends,
* momentum continuation,
* leadership,
* and expanding price strength.
A stock moving from Q4 into Q1 may represent a developing recovery turning into a confirmed expansion phase.
### Q2 — Bearish Reversion
**Negative Velocity / Positive Stretch**
Price remains above its trend structure, but momentum is weakening.
This can indicate:
* an aging uptrend,
* loss of momentum,
* consolidation,
* profit-taking,
* or the beginning of a pullback.
Stocks that are highly stretched in Q1 and begin rotating into Q2 may be entering an exhaustion phase.
### Q3 — Bearish Expansion
**Negative Velocity / Negative Stretch**
Price is below its trend structure and downside momentum is increasing.
This is typically associated with:
* established downtrends,
* accelerating weakness,
* distribution,
* and bearish continuation.
### Q4 — Bullish Reversion
**Positive Velocity / Negative Stretch**
Price remains below its trend structure, but momentum has turned positive.
This can signal:
* improving conditions,
* mean reversion,
* early recovery,
* or a potential transition back into bullish expansion.
A rotation from Q4 into Q1 is one of the most important bullish transitions on the chart.
## Multi-Asset Tracking
The indicator supports up to **11 editable symbols**.
Each asset is displayed with:
* its own color,
* a historical trail,
* a current-position marker,
* and an automatically generated ticker label.
This makes it easy to compare:
* relative trend strength,
* market rotation,
* momentum leadership,
* and differences in trend maturity.
## Trailing Paths
Each ticker includes a configurable historical trail.
The trail shows how the asset arrived at its current position and whether it is:
* accelerating,
* slowing,
* rotating,
* reversing,
* or moving sideways near a quadrant boundary.
The direction of the trail is often as important as the current quadrant.
## True Range Variability Clouds
Optional ribbons are drawn around each asset’s trail using:
The calculation is blended over the same 8-, 13-, and 21-bar horizons used by the Velocity model.
Cloud thickness changes at every point along the trail:
* Narrow cloud: volatility is stable and movement is more orderly
* Wide cloud: volatility is inconsistent, noisy, or unstable
This provides an additional layer of information beyond direction and momentum.
The center trail shows where the asset is moving.
The surrounding cloud shows how reliable or disorderly that movement has been.
## Position-Sensitive Quadrant Statistics
A dedicated statistics table can be assigned to one requested ticker.
The table displays:
* current quadrant,
* current uninterrupted stay,
* average historical duration in each quadrant,
* number of completed quadrant runs,
* current Velocity and Stretch values,
* and transition probabilities over several forward horizons.
The transition model does not rely only on the quadrant name.
Each quadrant is divided into a 3 × 3 internal grid based on:
* Velocity magnitude: Low, Medium, or High
* Stretch magnitude: Low, Medium, or High
This creates 36 possible origin states:
The model therefore estimates probabilities such as:
> From Q1 with High Velocity and Medium Stretch, what is the probability of being in Q1, Q2, Q3, or Q4 after 5, 10, 15, or 21 bars?
This provides a more detailed transition model than treating every point inside the same quadrant as identical.
## Customizable Smoothing
The Velocity, Stretch, and cloud calculations can be smoothed using:
* EMA
* RMA
* SMA
* WMA
* or no smoothing
An optional second smoothing pass is also available for users who prefer slower, cleaner trails.
## How to Use the Indicator
The indicator is best used to evaluate trend rotation rather than as a standalone buy or sell signal.
Examples:
* Multiple stocks rotating from Q4 into Q1 may indicate broad market recovery.
* A leader remaining in Q1 with a narrow cloud may indicate persistent, orderly momentum.
* A highly stretched stock in Q1 rotating toward Q2 may be losing momentum.
* A stock in Q4 with rising Velocity may be developing an early recovery setup.
* A widening cloud may warn that the apparent move is becoming less stable.
* A stock near a quadrant boundary may be more likely to switch states than one deep inside a quadrant.
The strongest interpretation comes from combining:
* current quadrant,
* trail direction,
* cloud width,
* distance from the quadrant boundaries,
* and the transition-probability table.
## Important Note
This indicator is intended as a visual and statistical market-analysis tool.
It does not predict future prices with certainty and should be used together with:
* trend confirmation,
* volume,
* support and resistance,
* fundamentals,
* earnings risk,
* and position-sizing rules.
インジケーター
Self Calibrating Probability ChannelSELF-CALIBRATING PROBABILITY CHANNEL
A forecast channel whose width is set by conformal prediction, tuned by a parameter-free online calibrator, and proven on your own chart. You pick a coverage level - say 90% - and the indicator shows you, live, the percentage it has actually achieved over recent bars, on every timeframe. Most bands assert a width; this one measures whether the width was right and corrects itself until it is, with nothing to tune.
WHAT IT IS
Bollinger Bands, Keltner Channels, Donchian Channels and standard-deviation regression channels all draw a width from a formula and ask you to trust it. None of them tell you what fraction of price actually landed inside. A "2 standard deviation" band is only a true 95% band if returns are normally distributed and stationary - which markets are not - so the real hit-rate drifts, usually without the user ever knowing.
This indicator inverts that. It forecasts where price should be next bar, measures how wrong that forecast has actually been, and builds the band directly from the empirical distribution of those errors. Then it watches its own hit-rate bar by bar and self-corrects. The result is a channel that earns its stated confidence level instead of assuming it - and reports, honestly, where it is and isn't holding.
THE METHOD (plain language)
1. Forecast path. Each bar, a one-step-ahead forecast of price is formed. You can pick a Kalman level-and-velocity tracker, a linear-regression slope, an EMA projection, or an anchored VWAP - or leave it on Auto, which runs all of them and blends them online by recent accuracy, so the centre line self-calibrates too. The forecast for the current bar uses only prior bars, so it is genuinely out-of-sample.
2. Error window. The gap between forecast and outcome is the forecast error. A rolling window of recent errors is kept, stored in volatility (ATR) units so the band breathes with the market. Each error is recorded only after its band has already been scored, so the band never includes the bar it is being tested on.
3. Conformal bands. For a chosen confidence level, the band edges sit at the matching quantiles of the recent error distribution (split-conformal prediction). Because it uses the actual error quantiles - including their skew - the bands are asymmetric when the errors are, rather than forcing a symmetric width. Four levels are drawn at once (50 / 70 / 90 / 95%) as nested zones, so the channel doubles as a probability heatmap: the dark core is where price spends most of its time, the faint outer edge marks rare excursions.
4. Parameter-free self-calibration (DtACI). After each bar the indicator checks whether price fell inside each level and nudges the width to hold the target. Rather than asking you to pick a calibration speed, it runs several speeds as competing "experts" and continuously blends them by how well each has tracked coverage recently (Dynamically-tuned Adaptive Conformal Inference). There is no rate to tune - the calibration tunes itself.
5. Live coverage proof, including by regime. The dashboard shows, for every level, the target versus the actually-achieved coverage over a rolling window, each tagged calibrated / under / over. It also reports the realised 90% coverage broken down by market regime - so you can see, for instance, that the band holds 92% in a quiet range but 87% in a volatile breakout. You are not asked to trust the band; you are shown its track record on the symbol, timeframe and regime in front of you.
6. Forward cone. A widening cone projects the likely range several bars ahead. Its width is built from actual multi-step forecast errors (not a square-root-of-time assumption), and its centre curves as projected momentum decays rather than extrapolating in a straight line. An optional bootstrap cloud resamples the real errors into sample forward paths - a direct picture of the distribution the bands come from.
7. Context and early warning. A two-axis regime read (trend strength x volatility) labels conditions; a turbulence detector watches for clustering of outer-band breaches and flags, in advance, when coverage is likely to degrade; a coiled-spring marker notes when a compressed range begins to expand; and an optional higher-timeframe row shows whether the larger trend agrees.
WHY THESE PARTS BELONG TOGETHER (one engine, not a bundle)
This is a single forecasting loop, not a collection of separate indicators sharing a chart. Each part is a required step, and removing any one breaks the whole:
- The forecast path produces an expected price and a drift. Without it there is no quantity whose error can be measured.
- The conformal band converts that path's own recent errors into prediction intervals. Without the forecast there is no error to bound; without the band the forecast is an unqualified guess.
- The online self-calibration adjusts the band to hold the target hit-rate as conditions change. Without it the intervals slowly drift out of calibration and the stated confidence becomes false.
- The live coverage readout verifies the loop is actually working, overall and per regime. It is the proof step a formula-based band cannot offer.
- The context layers (regime, turbulence early-warning, graded breaches, compression-release, higher-timeframe agreement) all read the same forecast errors and exist only to tell you WHEN the interval is most trustworthy and when it is about to fail.
So the components are not combined for convenience; they form a closed measure-and-correct cycle - forecast, bound the error, recalibrate, verify - which is precisely why they are published as one script rather than several overlays.
WHAT MAKES IT DIFFERENT
Conformal prediction is a distribution-free framework - its coverage guarantee holds for any underlying distribution given exchangeable errors, with no assumption that returns are Gaussian. It is standard in machine-learning uncertainty quantification but essentially absent from charting tools, which lean almost entirely on standard-deviation or ATR multiples. Pairing it with a parameter-free online recalibrator, a self-weighting forecast centre, and an on-chart coverage readout - including a per-regime breakdown - is the original contribution here. No moving-average envelope, regression channel or volatility band can state "I targeted 90% and have actually delivered 90% over the last 250 bars, and here is exactly where I don't" - this one can, and shows it.
WHAT YOU SEE ON THE CHART
- A multi-zone channel around a forecast centre line, shaded from the high-probability core out to the rare-excursion edge, coloured by forecast direction, and adaptive to dark or light chart backgrounds.
- A widening forward cone, optionally filled with a faint cloud of resampled paths.
- Right-side labels marking the forecast and the 90 / 95% edges as price levels.
- Small triangles when price breaks beyond the outer band; a ring when that breach is also high-quality (graded on displacement, close position, volume, range expansion and structure); an amber diamond when a quiet range starts to wake up.
- A dashboard with the live forecast, the 90% band range and where price sits within it, the full calibration table, the per-regime coverage, a reliability score, the forecast bias, the sample count, the calibration mode, and an optional higher-timeframe row.
- A plain-language "how to read" key, so the chart is approachable without any statistics background.
HOW TO READ AND USE IT
Mean reversion: when price reaches the outer (90 / 95%) zone in a ranging regime, it is statistically stretched and tends to revert toward the centre line. The "band position" readout and the calibration table tell you how stretched, and how trustworthy that edge currently is.
Trend continuation: a sustained walk along one side of the channel, especially with the cone tilted that way and the higher-timeframe row aligned, indicates a directional regime rather than noise.
Anomaly / breakout: a plain triangle is a volatility event; a ringed one is the same event confirmed as high-quality. A turbulence flag warns that the bands may be about to lose calibration.
Reliability and regime: treat the bands as most actionable when reliability is high, the calibration rows read "calibrated", and turbulence is quiet. The per-regime coverage tells you which conditions the channel is currently most trustworthy in.
SETTINGS OVERVIEW
- Forecast path (Auto / Kalman / Linear Regression / EMA / Anchored VWAP) and smoothing lengths.
- Calibration: residual window, recency window, volatility normalisation, parameter-free DtACI on/off (with a manual ACI rate as fallback), coverage-evaluation window.
- Forward projection length, cone momentum decay, optional bootstrap cloud.
- Anomaly sensitivity, swing pivot length, coiled-spring thresholds, turbulence sensitivity.
- Higher-timeframe context, price source, and full theme controls.
The price source is selectable and volume is borrowed where a symbol reports none, so it works across futures, equities, forex and crypto on any timeframe. Defaults read well intraday; longer windows suit higher timeframes.
HONESTY AND LIMITATIONS
- Non-repainting: each bar's forecast uses only prior bars, each error is recorded only after its band is scored, anomalies confirm on bar close, and the higher-timeframe row uses the last confirmed higher-timeframe value. Historical bands do not change after the fact.
- Conformal coverage is a statistical expectation over a window, not a per-bar guarantee. In a sharp regime break the realised hit-rate will dip until the window and calibrator re-adapt - and the dashboard, including its per-regime breakdown, shows that dip honestly rather than hiding it.
- The bands describe the distribution of short-horizon forecast error. They are a probabilistic context for price, not a prediction of direction and not a trading system.
- Calibration needs enough samples; on a fresh chart the channel needs its warm-up window before the figures are meaningful, and the cone needs a few extra bars beyond that.
This script is for research and education. It is not financial advice and not a solicitation to trade. Markets carry risk; test any tool on your own data and timeframe, and make your own decisions.
インジケーター
インジケーター
Volatility Forecast [EXCAVO]Forward Projection of the Bollinger Envelope with Adaptive Horizon and Slope Clamp
The Volatility Forecast takes the classical Bollinger Bands and
projects the basis and the bands forward by a configurable number of bars.
Slopes of the basis, standard deviation and ATR are estimated from linear
regression over a lookback window, then extrapolated through a smooth
curve into the right side of the chart. Small orange dots mark band
reclaim events on confirmed closed bars.
The forecast horizon adapts to the chart timeframe so the projection
stays meaningful at every TF, and a slope clamp prevents the bands from
ballooning into unrealistic territory after sharp regime shifts.
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▸ HOW TO USE
Step 1 → Add the indicator. The current Bollinger Bands are
plotted on the chart and a dashed envelope extends to the
right with the projected basis and bands.
Step 2 → Read the projection. The projected upper and lower
bands show the most likely volatility envelope over the
next bars under the current trend and volatility regime.
Wider end = expansion expected; narrower end = compression.
Step 3 → Use the reclaim dots. A small orange dot below a bar
marks a confirmed bull band reclaim (price tagged the lower
band and pulled back inside). A dot above a bar marks a
bear reclaim. These are context, not entries.
Step 4 → Check the dashboard. The top right panel reads the
projection mode, current width vs its rolling average,
band state, and the last reclaim.
Step 5 → Combine with structure. The envelope pairs well with
trend and structure tools. A breakout that aligns with an
expanding projected envelope tends to continue; one against
a contracting envelope tends to fade.
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▸ HOW IT CALCULATES
◆ Bollinger Bands
Basis is a simple moving average of the source over Length (default 20).
Standard deviation is computed over the same window. Bands are basis plus
or minus Multiplier times standard deviation (default 2.0). These are the
solid plotted lines on the chart.
◆ Linear Regression Slopes
For the projection, the algorithm estimates per-bar slopes from a linear
regression of the basis, the standard deviation, and the ATR over the
Slope Lookback window (default 40). Slope is taken as the difference
between the linreg value at offset 0 and offset 1 - the per-bar drift
the regression expects to continue.
◆ Slope Clamp
Each slope is then clamped to a safety bound so that the cumulative
projected displacement stays sensible. End to end, the projected basis
cannot drift more than two current band-widths, and the projected
standard deviation or ATR cannot grow more than 50% of its current value.
Sign of the slope is preserved so trend direction is intact, only the
magnitude is bounded. This keeps the projection meaningful after sharp
regime shifts.
◆ Forward Projection
Three modes turn slopes into a projected envelope across the forecast
horizon:
Linear extends basis and width on a straight line using the
current slope at every step.
Smooth Curve (default) eases from the current value toward a
dynamic endpoint via a smoothstep curve so the projection has a
natural arc instead of a hard linear extrapolation.
Adaptive Volatility drives the projected width with ATR slope
instead of standard-deviation slope. Useful when volatility is
regime-dependent and the ATR captures it better than stdev.
A projection floor at 50% ensures the envelope never collapses to a
single point on declining-volatility regimes.
◆ Auto Timeframe Forecast
Forecast Bars defaults to Auto, which picks the horizon from the chart
timeframe: 40 bars on 4h and below, 20 on Daily, 10 on Weekly, 6 on
Monthly+. Manual override is available for operators who want a fixed
bar count regardless of timeframe.
◆ Band Reclaim Markers
A bull reclaim fires when the prior bar's low touched the lower band,
the current bar's low has pulled back above the lower band, and the
close sits below the basis. A bear reclaim is symmetric on the upper
band. Cooldown of Length bars prevents same-direction stacking. The
optional Trend MA filter keeps bull marks only above the MA and bear
marks only below. By default markers fire only on confirmed closed bars
(no repaint); Real-time Markers can be enabled if intra-bar feedback is
preferred.
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▸ WHAT MAKES IT DIFFERENT
◆ Adaptive Horizon
The forecast horizon scales with the chart timeframe. The projection
shows a comparable arc on intraday, daily, weekly and monthly without
manual tuning per chart.
◆ Slope Clamp Safety Net
Linear-regression slopes can overshoot after sharp moves or on long
horizons. The clamp caps the cumulative displacement so the projection
cannot grow into unrealistic ranges, regardless of the underlying slope.
◆ Three Projection Modes
Linear, Smooth Curve and Adaptive Volatility cover the common shapes a
volatility envelope can take. Smooth Curve uses smoothstep easing for
a natural arc; Adaptive Volatility ignores stdev drift and tracks ATR
instead.
◆ Confirmed Reclaim Markers
Small orange dots above or below the bar mark band reclaim events. They
fire on confirmed closed bars by default (no repaint), with an optional
real-time mode for operators who prefer intra-bar feedback. A trend-MA
filter keeps the bias clean.
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▸ DASHBOARD
Real-time panel with the current state read:
Mode - Linear / Smooth Curve / Adaptive Volatility
Width vs Avg - current band width relative to its rolling average
Band State - where price sits in the bands (Above Upper / Below Lower / Upper Half / Lower Half)
Last Marker - direction and bars since the last band reclaim
Legend table explains every on-chart element. Both panels toggle in the
Dashboard settings.
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▸ SETTINGS
Bollinger Bands
Source - close (price series used for basis and stdev)
Length - 20 (lookback for basis and stdev)
Multiplier - 2.0 (band width in stdev units)
Basis / Band / Fill Colors - default palette
Forecast Envelope
Forecast Bars Mode - Auto (adapts to chart TF) or Manual
Forecast Bars (Manual) - 40 (used when Mode = Manual)
Slope Lookback - 40 (linreg window for slope estimation)
Mode - Smooth Curve (Linear / Smooth Curve / Adaptive Volatility)
Projection Style / Width / Colors - dashed, default palette
Fill Projection - ON
Reclaim Markers
Show Markers - ON
Real-time Markers - OFF (no repaint by default; closed bars only)
Filter by Trend MA - ON
Trend MA Type - SMA (SMA / EMA / WMA / HMA)
Trend MA Length - 100
Marker Color - orange (#FF8C00)
Show Trend MA - OFF
Dashboard
Show Dashboard - ON
Dashboard Position - Top Right
Show Legend - ON
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▸ ALERTS
Two directional alertconditions are exposed:
Bull Band Reclaim - fires on a confirmed bull reclaim event
Bear Band Reclaim - fires on a confirmed bear reclaim event
Set the alert condition to "Once Per Bar Close" for clean, non-repainting
delivery. Trend-MA filter and cooldown apply to alerts the same way they
apply to the on-chart markers.
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Best regards,
EXCAVO
Disclaimer
Trading involves significant risk. This indicator is a technical analysis
tool and does not constitute financial advice, investment recommendations,
or a guarantee of future results. Past indicator behavior does not
guarantee future performance. Always use proper risk management and your
own judgment.
インジケーター
AI Trend Detector | Adaptive Signals [NeuraLib Machine Learning]🔷 AI Trend Detector | Adaptive Signals
AI Trend Detector is a NeuraLib-powered Machine Learning indicator. It trains a compact supervised neural model on confirmed historical movement, then uses the current market state to estimate Bear , Neutral , and Bull pressure.
The model output is converted into a clean visual system:
Trend Oscillator : A 0-100 pressure gauge. Lower values suggest bullish pressure or oversold conditions. Higher values suggest bearish pressure or overbought conditions.
Adaptive MA Cloud : A main-chart adaptive moving average with an AI-biased cloud that expands as model pressure moves away from neutral.
Confirmed Triangles : Optional chart markers for overbought and oversold interactions, with modes for zone entry, zone exit, or confirmed rotation inside a zone.
Dashboard : A compact readout showing the current state, signal value and confidence.
Triangle Alerts : Alert conditions tied to the same confirmed marker logic shown on the chart.
Directional Confidence : An optional 0-100 line showing the stronger directional model probability, calculated from the larger of Bull or Bear pressure. It does not include Neutral probability, so it reflects directional conviction rather than overall model certainty.
This is not a fixed crossover system. The signals are the visual layer of a model-driven trend pressure engine.
---
🔷 How The Model Learns
Each bar contributes a compact feature row based on price movement, adaptive MA context, and distance from the adaptive baseline. NeuraLib stores these rows in a rolling dataset, normalizes the inputs, and trains the model on recent time-series windows.
The model is trained as a 3-class classifier:
Bear
Neutral
Bull
Historical training examples use future-resolved movement to create their target class, but only after that movement has already occurred. This is the supervised learning setup: the model learns from completed historical outcomes, then applies its learned weights to the current live feature window.
The exposed settings allow users to experiment with model size, learning rate, training frequency, smoothing, trend horizon, and signal behavior.
---
🔷 Model Architecture
The model uses a compact temporal classification architecture:
Flattened state window : Recent feature rows are combined into one temporal input.
Temporal convolution stack : Conv1D-style layers extract short-term structure from the recent market sequence.
Global average pooling : The temporal output is compressed into a compact state representation.
Dense classifier head : One or two dense layers process the pooled state.
Three output logits : The model produces Bear, Neutral, and Bull logits, which are converted into display probabilities.
This keeps the model small enough for Pine Script while still giving it a true sequence-learning structure rather than a simple crossover or rule-based signal engine.
---
🔷 Reading The Signals
The oscillator is intentionally inverted for intuitive market reading:
Low values : Oversold or bullish pressure.
Mid values : Balanced or neutral pressure.
High values : Overbought or bearish pressure.
Triangles can be configured through the Triangle trigger setting:
Crossing into : Prints when the oscillator crosses into an overbought or oversold zone.
Going out of : Prints when the oscillator exits an overbought or oversold zone.
Rotation inside zone : Prints when the signal forms a confirmed turn while still inside the zone.
In rotation mode, Rotation confirmation controls how many bars must pass without breaking the candidate peak or trough before the marker is accepted. Rotation triangles print on the confirmation bar, not on the older pivot bar.
The adaptive MA cloud is visual only. The model is not trained on the shifted cloud edge. The cloud simply applies model pressure around the adaptive MA baseline.
---
⚠️ Repainting And Signal Timing
The training and signal system is designed around confirmed bars:
Training rows are pushed on confirmed bars.
Triangle signals are gated with barstate.isconfirmed .
Rotation markers print on the confirmation bar.
No negative plot offsets are used to move markers into the past.
The smoothing path uses current and past values only.
Because this model does not train on the full price history, but instead learns from the most recent N bars, repainting may occur when the script is reloaded at a later date. This happens because the model may begin training from a different market environment.
To help preserve the original model state, adjust the Historical Train Window setting to account for any new bars that have been added since the original run.
---
⚠️ Limitations
Machine Learning inside Pine Script is powerful, but it is still bounded by TradingView's execution model.
The model is compact by design.
Training history is bounded for performance.
Changing hyperparameters rebuilds the model.
Signals depend on the chosen horizon, threshold, smoothing, and triangle mode.
The model estimates directional pressure. It does not know your entries, exits, risk, fees, or position sizing.
This indicator is best treated as a model-based market pressure tool, not as a complete trading system by itself.
This indicator is powered by the NeuraLib Deep Learning Runtime
Disclaimer: This indicator is an analytical and educational tool. It does not guarantee future results, signal accuracy, or financial gain. Past behavior does not ensure future behavior. Use it as one component in a broader trading process, under your own responsibility. Conceptual architecture and quantitative development by Alien_Algorithms.
インジケーター
Market State Forecast Projection EngineThis indicator is a **forecast projection tool**. It looks at the current market environment, searches history for the most similar environments, then plots what usually happened afterward. It is not trying to predict the future with certainty. It is saying: “When the market looked like this before, what tended to happen next?”
The engine defines the current market environment using three things:
* **Trend**, based on moving averages.
* **Momentum**, based on RSI.
* **Volatility**, based on ATR.
Then it finds the closest historical matches, studies their future paths, and draws a forecast line with optional upper and lower bands.
---
## What You See on the Chart
### Forecast Midline
The main forecast line shows the **average path** of the selected historical matches.
In simple terms:
* If similar past situations usually moved higher, the line slopes up.
* If similar past situations usually moved lower, the line slopes down.
* If similar past situations were mixed, the line may be flat or choppy.
### Upper Band
The upper band shows the stronger side of historical outcomes.
It means:
* Some similar historical setups moved better than the average.
* The upper band gives you a visual idea of the upside range from those past examples.
* It is not a guaranteed target.
### Lower Band
The lower band shows the weaker side of historical outcomes.
It means:
* Some similar historical setups moved worse than the average.
* The lower band gives you a visual idea of downside risk from those past examples.
* It is not a guaranteed support level.
### Band Width
The space between the bands matters.
* Tight bands mean historical outcomes were more consistent.
* Wide bands mean historical outcomes were scattered and less reliable.
* A forecast with wide bands should be treated with more caution.
---
## Main Inputs
### Non-Repaint Mode
**Default: On**
This controls whether the forecast uses the live candle or the last completed candle.
Use **Non-Repaint Mode On** when:
* You want more stable signals.
* You want the forecast to update only after the candle closes.
* You care about cleaner historical testing.
Use **Non-Repaint Mode Off** when:
* You want the forecast to react during the current live candle.
* You accept that the forecast may change before the candle closes.
For most use cases, leave this **On**.
---
## Model Group
### Forecast Horizon
This controls how far into the future the indicator projects.
Example:
* On a daily chart, `20` means 20 trading days.
* On a 1-hour chart, `20` means 20 hours.
* On a 5-minute chart, `20` means 20 five-minute candles.
Use a lower value when:
* You are trading short-term moves.
* You want a tighter forecast window.
* You do not want the projection stretched too far.
Use a higher value when:
* You are looking for swing-trade context.
* You want to see the broader projected path.
* You are using higher timeframes.
A practical range is usually:
* `10–20` for shorter-term analysis.
* `20–50` for swing-style analysis.
---
### Search Depth
This controls how much history the engine searches.
Example:
* `1000` means the engine searches roughly 1,000 prior bars.
* `2000` means it searches more history.
* `500` means it searches less history.
Use a higher Search Depth when:
* You want a larger historical sample.
* You are on a short timeframe with lots of bars.
* You want more possible market-state comparisons.
Use a lower Search Depth when:
* You want the model to focus on more recent market behavior.
* You are on a slower chart like daily or weekly.
* You want less influence from older market regimes.
The tradeoff is simple:
* More history gives more examples.
* Less history may be more relevant to the current market regime.
---
### Pattern Matches
This controls how many of the closest historical matches are used.
This is one of the most important inputs.
If set to `30`, the engine finds the **30 closest historical market states** and builds the forecast from those.
Use fewer matches when:
* You want stricter, more specific comparisons.
* You want only the closest historical examples.
* You are okay with a forecast that may be more reactive.
Use more matches when:
* You want a smoother forecast.
* You want less noise from individual examples.
* You want a broader historical sample.
General interpretation:
* `10–20` = stricter, more selective.
* `25–40` = balanced.
* `50+` = broader, smoother, but less specific.
---
### Weight Closer Matches
This controls whether the best matches receive more influence.
When turned **On**:
* The closest historical matches matter more.
* Weaker matches still count, but less heavily.
* The forecast becomes more focused on the most similar examples.
When turned **Off**:
* Every selected match is treated equally.
* The forecast becomes more democratic.
* A very close match and a weaker match have the same influence.
For most users, leave this **On**.
---
## Advanced Model Inputs
### Forecast Model
This chooses how the engine defines the market environment.
All models use:
* EMA trend.
* RSI momentum.
* ATR volatility.
The difference is how each model emphasizes those ingredients.
---
### Conservative
Use **Conservative** when you want a slower, smoother model.
It is designed to:
* React less aggressively.
* Favor more stable market environments.
* Put more importance on trend and volatility.
* Reduce noisy forecast changes.
Best for:
* Daily charts.
* Swing trading.
* Slower-moving stocks or ETFs.
* Users who want fewer false shifts.
---
### Balanced
Use **Balanced** as the general-purpose default.
It is designed to:
* Give trend, momentum, and volatility a normal balance.
* Work across many markets.
* Avoid being too slow or too fast.
Best for:
* Most users.
* Most chart timeframes.
* General market forecasting.
* Starting point before testing other models.
---
### Aggressive
Use **Aggressive** when you want a faster model.
It is designed to:
* React more quickly to changing momentum.
* Give more influence to short-term market shifts.
* Be more sensitive to fresh moves.
Best for:
* Intraday trading.
* Fast-moving markets.
* Crypto.
* Momentum names.
* Traders who want earlier, more responsive shifts.
The downside is that it may be noisier.
---
### Trend Following
Use **Trend Following** when you want the model to emphasize persistent directional moves.
It is designed to:
* Care more about trend structure.
* Care less about short-term momentum noise.
* Favor markets that continue moving in the same direction.
Best for:
* Strong trending stocks.
* Indexes.
* Breakout environments.
* Higher-timeframe directional trading.
This model is less ideal in sideways or choppy markets.
---
### Mean Reversion
Use **Mean Reversion** when you want the model to focus on stretched conditions.
It is designed to:
* Emphasize momentum extremes.
* Look for environments where price may snap back or reverse.
* Care less about long-term trend persistence.
Best for:
* Range-bound markets.
* Overbought/oversold setups.
* Countertrend analysis.
* Shorter-term reversal ideas.
This model may fight strong trends, so use it carefully in momentum-heavy markets.
---
## Historical Lookback Inputs
### Lookback Bars
This lets you move the forecast backward in time.
Example:
* `0` means current forecast.
* `50` means show what the forecast would have looked like 50 bars ago.
* `250` means show what the forecast would have looked like 250 bars ago.
Use this for:
* Visual backtesting.
* Studying old setups.
* Checking whether the forecast was useful historically.
* Comparing forecast paths against what actually happened.
This is one of the most valuable testing features.
---
### Lock to Candle
This lets you anchor the forecast to a specific candle time instead of a simple bar offset.
Use it when:
* You want to test a specific time of day.
* You trade a regular session open.
* You want repeatable historical anchors.
Example:
* You can lock to the 13:30 UTC candle, which often corresponds to the U.S. stock market open during daylight saving time.
When this is off, the indicator uses **Lookback Bars** instead.
---
### Days Back
This works with **Lock to Candle**.
It tells the indicator how many matching anchor candles to go back.
Example:
* `0` = most recent matching candle.
* `1` = one matching session back.
* `2` = two matching sessions back.
Use this when:
* You want to test the most recent open.
* You want to test yesterday’s open.
* You want to step through past sessions one by one.
---
### Hour UTC
This is the UTC hour used for candle locking.
Use it with **Minute UTC** to identify the exact candle you want.
Example:
* `13` means 13:00 UTC.
* Combined with `30`, it means 13:30 UTC.
This is useful because TradingView symbols and sessions can vary, but UTC gives a consistent anchor.
---
### Minute UTC
This is the UTC minute used for candle locking.
Example:
* Hour UTC = `13`
* Minute UTC = `30`
Together, that means:
* Lock to the 13:30 UTC candle.
Use this for precise historical testing.
---
### Auto Previous Session
This controls what happens if today’s target candle has not printed yet.
When turned **On**:
* The indicator automatically uses the most recent previous matching candle.
* This keeps the forecast visible even before today’s target time exists.
When turned **Off**:
* If today’s target candle has not printed, the lock may show no match and fall back.
For most users, leave this **On**.
---
## Bias Logic Inputs
### Bias Threshold %
This controls how strong the bull or bear probability must be before the indicator labels the forecast bullish or bearish.
Example:
* If Bias Threshold is `60`, Bull Probability must be at least 60% before a bullish label can appear.
* If Bear Probability is at least 60%, a bearish label can appear.
Use a lower threshold when:
* You want more frequent bias labels.
* You are okay with weaker directional evidence.
Use a higher threshold when:
* You want stricter signals.
* You only want stronger historical agreement.
Practical range:
* `60%` = balanced.
* `70%+` = more conservative.
* `50–55%` = loose and more signal-heavy.
---
### Minimum Bull/Bear Edge %
This controls how large the gap must be between Bull Probability and Bear Probability.
Example:
* Bull Probability = 65%
* Bear Probability = 35%
* Edge = 30 percentage points
If the minimum edge is `15`, this would qualify.
But:
* Bull Probability = 58%
* Bear Probability = 42%
* Edge = 16 percentage points
This may still fail if Bull Probability is below the Bias Threshold.
This input prevents weak differences from being labeled as strong directional bias.
Use a higher edge when:
* You want cleaner bias labels.
* You want the model to avoid borderline calls.
Use a lower edge when:
* You want more frequent directional bias.
* You accept more uncertainty.
---
## Display Inputs
### Show Forecast Midline
This turns the main forecast line on or off.
Turn it **On** when:
* You want to see the projected average path.
Turn it **Off** when:
* You only want the info box probabilities.
* You want a cleaner chart.
---
### Show Confidence Bands
This turns the upper and lower forecast bands on or off.
Turn it **On** when:
* You want to see the historical range of outcomes.
* You care about uncertainty.
* You want to know whether the forecast is tight or messy.
Turn it **Off** when:
* You only want the central forecast.
* The chart feels too cluttered.
---
### Band Width Multiplier
This controls how wide the bands are.
Higher values make the bands wider.
Lower values make the bands tighter.
Use lower values when:
* You want a cleaner, tighter visual range.
* You want bands closer to the average forecast.
Use higher values when:
* You want to see a broader range of historical outcomes.
* You want a more conservative uncertainty envelope.
Default `1.0` is a good starting point.
---
## Forecast Midline Style Inputs
### Forecast Midline Color
Controls the color of the main projection line.
The default aqua color makes it visually distinct from price candles.
### Forecast Midline Width
Controls how thick the midline is.
Use a thicker line when:
* You want the forecast to stand out.
* You are using a busy chart.
Use a thinner line when:
* You want a cleaner chart.
* You use many overlays.
### Forecast Midline Type
Controls whether the line is:
* Solid.
* Dashed.
* Dotted.
Solid is usually best for the main forecast line.
---
## Upper Band Style Inputs
### Upper Band Color
Controls the color of the upper forecast band.
The default green tone suggests upside range.
### Upper Band Width
Controls how thick the upper band is.
A thin dashed line usually works best because it should be secondary to the midline.
### Upper Band Type
Controls whether the upper band is solid, dashed, or dotted.
Dashed is usually best because it visually communicates “range” rather than “target.”
---
## Lower Band Style Inputs
### Lower Band Color
Controls the color of the lower forecast band.
The default red tone suggests downside range.
### Lower Band Width
Controls how thick the lower band is.
A thin line keeps it useful without dominating the chart.
### Lower Band Type
Controls whether the lower band is solid, dashed, or dotted.
Dashed is usually best for the same reason as the upper band.
---
## Info Box Inputs
### Show Info Box
This turns the dashboard on or off.
Turn it **On** when:
* You want the probabilities and diagnostics visible.
* You are actively evaluating the forecast.
Turn it **Off** when:
* You only want the chart projection.
* You want a cleaner visual layout.
---
### Info Box Position
Controls where the dashboard appears.
Options:
* Top Left.
* Top Right.
* Bottom Left.
* Bottom Right.
Use the position that interferes least with price action on your chart.
---
### Text Size
Controls the dashboard text size.
Use:
* **Tiny** for compact charts.
* **Small** for normal use.
* **Normal** if you want easier reading.
* **Large** for presentations or large monitors.
---
### Background
Controls the info box background color.
A darker background usually works best on most TradingView chart themes.
### Border
Controls the info box border color.
This helps separate the dashboard from the chart.
### Header Text
Controls the title/header text color.
### Header Background
Controls the top header row background.
This gives the dashboard its polished look.
---
## Info Box Metrics
### Bull Prob %
This shows the weighted percentage of selected historical matches that ended bullish.
Simple meaning:
> Of the similar historical market states, how many tended to move up?
A high number means bullish outcomes dominated the selected historical matches.
---
### Bear Prob %
This shows the weighted percentage of selected historical matches that ended bearish.
Simple meaning:
> Of the similar historical market states, how many tended to move down?
A high number means bearish outcomes dominated the selected historical matches.
---
### Direction Bias
This shows the final label after applying the bias rules.
Possible outputs:
* Bullish.
* Bearish.
* None.
* Weak Data.
* No Matches.
A bullish or bearish label only appears when the probability and edge requirements are met.
---
### Bull/Bear/Flat
This shows how many selected matches ended:
* Bullish.
* Bearish.
* Flat.
Example:
* `18 / 9 / 3`
This means:
* 18 bullish historical outcomes.
* 9 bearish historical outcomes.
* 3 flat historical outcomes.
This gives you a quick look at the underlying distribution.
---
### Match Count
This shows how many historical matches were actually used.
If Pattern Matches is set to `30`, Match Count should usually show `30`.
If it shows less, there may not have been enough valid historical data.
---
### Fit Quality
This tells you how closely the selected historical matches resemble the current market state.
High Fit Quality means:
* The current market environment closely resembles the selected historical examples.
Low Fit Quality means:
* The engine found matches, but they were not very close.
Important:
* Fit Quality is not win rate.
* Fit Quality is not probability.
* Fit Quality is not accuracy.
* It only measures how good the historical comparisons are.
Best interpretation:
* High Fit Quality + strong Bull/Bear Probability = more compelling.
* High Fit Quality + split probabilities = similar markets existed, but outcomes were mixed.
* Low Fit Quality = be cautious.
---
### Model
This shows which Forecast Model is active.
Examples:
* Balanced.
* Conservative.
* Aggressive.
* Trend Following.
* Mean Reversion.
This is useful for screenshots and reviewing past setups.
---
### Anchor
This tells you where the forecast is anchored.
Examples:
* `0 bars · NR` means current forecast using Non-Repaint Mode.
* `50 bars · NR` means historical forecast from 50 bars ago.
* `Locked` means it is anchored to a specific UTC candle.
This helps you know whether you are looking at a current forecast or a historical replay.
---
### Search Depth
This shows the actual number of bars being searched.
It may be lower than your input if the chart does not have enough loaded history.
---
## Best Practical Way to Use It
A clean workflow would be:
* Start with **Balanced** model.
* Keep **Non-Repaint Mode On**.
* Use **Pattern Matches around 30**.
* Use **Search Depth around 1000**.
* Watch **Fit Quality**.
* Watch **Bull/Bear Probability**.
* Treat the forecast line as a scenario path, not a guaranteed prediction.
* Use **Lookback Bars** to test whether the forecast was historically useful.
* Avoid trusting any forecast where the bands are very wide and probabilities are split.
The strongest setup is usually when:
* Fit Quality is high.
* Bull or Bear Probability is clearly dominant.
* The forecast bands are not extremely wide.
* The projection agrees with price structure.
インジケーター
Helios Volatility Forecast [JOAT]Helios Volatility Forecast
Helios Volatility Forecast is a Yang-Zhang volatility estimator with regime classification, a volatility cone (historical percentile bands), an HMA-smoothed forecast line, and a position-size suggestion. Volatility is classified into four regimes (LOW / NORMAL / ELEVATED / EXTREME) by percentile rank against its own history. Cross-pane elements paint a soft regime tint and a position-multiplier suggestion onto the price chart.
What makes it different
Most volatility indicators use a simple close-to-close standard deviation, which discards intraday range information and ignores overnight gaps. The Yang-Zhang estimator combines four components — overnight close-to-open variance, intraday open-to-close variance, and a Rogers-Satchell range term — into a single estimator that is more accurate than close-to-close for instruments that gap.
A 4-band volatility cone (5th, 25th, 50th, 75th, 95th percentile of the past 100 bars) is plotted around the current volatility, with gradient fills bracketing tails and the interquartile range.
A 4-regime classifier (LOW / NORMAL / ELEVATED / EXTREME by percentile thresholds at 25, 65, 90) drives a cross-pane tint on the price chart and a numeric position-size multiplier suggestion. The suggestion scales inversely with realized vol — wider sizes in low-vol regimes, halved sizes in extreme-vol regimes.
An HMA forecast line projects the smoothed vol trajectory ahead. Forecast-crossing-realized alerts fire when expansion or contraction is imminent.
How it works
Yang-Zhang formula combines overnight return, intraday return, and Rogers-Satchell range term, weighted by k = 0.34 / (1.34 + (len + 1) / (len - 1)).
Percentile rank of sigma_yz over a 100-bar history equals vol_pct.
Regime classification: LOW below 25, NORMAL 25 to 65, ELEVATED 65 to 90, EXTREME above 90.
HMA of sigma_yz equals the forecast. Forecast direction equals the sign of (forecast minus current).
Position-size multiplier equals clamp(1.5 minus vol_pct / 100, 0.3, 1.5).
Vol-of-vol (stdev of recent realized vol) feeds a regime stickiness indicator.
Reading the chart
In-pane : regime-tinted volatility line (vivid mint for LOW, neutral white for NORMAL, amber for ELEVATED, vivid red for EXTREME), HMA forecast line with direction-color flow, five vol-cone percentile lines.
Cross-pane : soft regime tint background on the price chart, plus a Size x0.50 EXTREME vol label updating each bar.
A vol-of-vol panel as a sub-strip at the top of the pane.
Five right-edge cone percentile labels (p5 / p25 / p50 / p75 / p95).
A current-vol percentile rank label.
Regime change timeline labels on the price chart at each regime transition.
Cross-pane vol-cone touch markers when vol crosses p95 (breakout) or p5 (contraction).
A regime stickiness indicator (how long the regime has been in its current state).
Forward expected-range lines on the price chart (close plus or minus forecast times ATR scalar).
Signals
Regime up / down (any percentile-bucket transition)
Extreme vol entry
Low vol entry
Vol breakout (sigma crosses above p95 of its own history)
Vol contract (sigma crosses below p5)
Vol Z-shock up / down (when vol z-score exceeds plus or minus 2)
Forecast cross up / down (forecast vs realized)
All gated on barstate.isconfirmed or barstate.ishistory. No future references. No lookahead_on.
Inputs
Volatility : Yang-Zhang window, regime percentile lookback, forecast HMA length.
Visual : bullish (low vol) color, bearish (extreme vol) color, elevated (amber) color, cone toggle, forecast toggle, cross-pane candles toggle, regime pulse toggle.
Dashboard : position, size.
How traders use this
Position sizing : scale entries inversely with the regime. Full size in LOW, default in NORMAL, half in ELEVATED, third in EXTREME. The multiplier label provides the suggested factor.
Volatility breakouts : vol crossing above p95 historically precedes large directional moves. Tighten trailing stops or reduce holding time.
Volatility contraction : vol crossing below p5 historically precedes range / chop. Reduce directional bias. Consider mean-reversion strategies.
Regime-aware stops : in ELEVATED or EXTREME regimes, ATR-based stops should be wider. In LOW regimes, tighter. The pos-mult label codifies this implicitly.
Limitations
Yang-Zhang assumes log-normal returns and lognormality breaks down during fat-tail events (it under-estimates vol in true crash regimes).
Percentile classification needs sufficient history. The default 100-bar lookback can be lengthened for stable instruments.
The position-size multiplier is a heuristic, not a portfolio-management recommendation. Combine with your own risk-management framework.
The HMA forecast lags slightly behind real-time changes. Treat as smoothed trend, not pinpoint prediction.
Compatibility
Pine Script v6 open-source indicator (pane plus cross-pane). Any symbol, any timeframe. Cross-pane elements use force_overlay=true. No request.security calls.
Defaults
20-bar Yang-Zhang window, 100-bar regime lookback, 5-bar HMA forecast, mint / red / amber palette, top-right medium dashboard.
Credits
Yang-Zhang estimator from D. Yang and Q. Zhang, Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices , Journal of Business (2000).
インジケーター
Probability Horizon - Bayesian SVJD Model# Probability Horizon — Bayesian SVJD Model
## What it does
Probability Horizon is a forward-looking probability projection tool. It does **not** generate buy or sell signals, does **not** act as a strategy, and does **not** place orders. Its single purpose is to display, at each bar, where price **may** be over a user-set horizon, drawn as a forward cone with three percentile lines (25%, 50%, 75%) plus an 8-row diagnostic dashboard.
The cone is the visual answer to two questions the indicator computes every bar:
1. **How wide should the distribution of forward outcomes be?** — set by expected total variance over the horizon.
2. **Should the distribution tilt up or down?** — set by a Bayesian-averaged probability of an up-move.
The wider the cone, the more uncertain the model is. The more the cone tilts, the more directionally confident the model is. A flat, narrow cone means "I expect range-bound, low-vol conditions." A wide, steeply tilted cone means "I expect a directional move under elevated variance."
## Why these components are combined (justification for the mashup)
This script combines several quantitative methods — KAMA, z-score, Haar wavelet, Kalman filter, Hamilton regime-switching, Hawkes process, Heston stochastic volatility, Merton jump-diffusion, Bayesian model averaging, and a calibration tracker. To a reviewer this can look like a collection of indicators bolted together, but it is not. It is **one** statistical model — a Stochastic Volatility Jump-Diffusion (SVJD) framework — whose pieces are mathematically required to produce a forward probability distribution.
Each component has a specific structural role:
- **Forward variance estimation.** A probability cone needs a forward-variance number. The naive choice is realized volatility × √horizon (pure Brownian motion), but this ignores two well-documented facts about financial returns: variance is mean-reverting (Heston, 1993) and returns have fat tails from discrete jumps (Merton, 1976). The Heston + jump-diffusion combination addresses both. The script uses the Heston integrated-variance closed form for the mean-reverting diffusion component, adds a Merton jump-variance contribution, and optionally adds a vol-of-vol uncertainty term. The result is a horizon-dependent variance estimate that dynamically narrows when volatility is elevated (expected to decay back to mean) and widens when volatility is depressed (expected to rise).
- **Directional probability.** To tilt the cone, a probability of direction is required. A single signal is unreliable, so five orthogonal sub-models each output their own P(up):
- A short-term KAMA-trend model
- A z-score mean-reversion model
- A Haar wavelet decomposition model (price denoised into trend + cycle + noise; signal fires only on the trend band)
- A Kalman-adaptive smoothing model (smoothing factor adapts to noise level in real time)
- A Hamilton 3-state regime model with Gaussian observation likelihoods (Bull / Bear / Range)
- **Sub-model combination.** The five P(up) values are combined via Bayesian model averaging with online weight updates. Each bar, after a fixed evaluation horizon, every sub-model is scored by log-loss against the actual outcome. Weights update via exponential decay: a model that predicted correctly gains weight; a model that failed loses weight. A minimum weight floor prevents any model from being silenced completely, so it can recover if its regime returns. Weights are renormalised to sum to 1.
- **Crisis dampening.** A Hawkes self-exciting point process monitors volatility clustering. Each large absolute return is treated as an event that boosts the process's intensity by α and decays exponentially at rate β. When intensity rises above a threshold multiple of its baseline, the final Bayesian probability is shrunk toward 0.5 — the higher the intensity, the stronger the shrinkage. This is how the model says "I have no idea — treat this as a coin flip" during regime breaks.
- **Self-correcting calibration.** A calibration tracker logs every prediction and checks the realized outcome after a fixed horizon. Predictions are binned by predicted probability (50–60%, 60–70%, etc.). If a bin's actual historical hit rate is below its predicted midpoint, future predictions in that bin are shrunk further toward 0.5. This is the model's honesty mechanism: it learns from its own miscalibration and tones itself down where it has been overconfident.
Each component answers a specific structural question. Remove any one and a specific capability disappears: no Heston → cone width does not adapt to vol regime; no calibration → no self-correction; no Hawkes → no crisis dampening; no Bayesian averaging → one model dominates and the system becomes brittle.
## How the components interact (data flow)
Every confirmed bar, the script executes the following pipeline:
1. **Measure** five raw market dimensions: KAMA slope, z-score vs trend, realized-vol percentile, OBV/price divergence, higher-TF trend.
2. **Each sub-model** maps its directional bias and strength to a probability in . The maximum single-model probability is capped at 0.60 because empirical calibration on multiple markets showed that anything higher is overconfident.
3. **Bayesian model averaging** combines the five sub-model probabilities into a single raw P(up), weighted by each sub-model's recent log-loss accuracy.
4. **Hawkes modifier** is applied: if intensity is above its warning threshold, the raw P(up) is pulled toward 0.5 in proportion to how far above threshold the intensity is.
5. **Calibration shrinkage** is applied: the post-Hawkes probability is checked against its calibration bin's historical hit rate, and shrunk further toward 0.5 if that bin has been overconfident.
6. **Forward variance** is computed separately: Heston integrated variance + Merton jump variance + optional vol-of-vol uncertainty, all over the projection horizon.
7. **The cone is drawn** with width set by the square root of forward variance and tilt set by the final shrunk probability. Three percentile lines (P25, P50, P75) are plotted from the current bar to the horizon endpoint.
Direction (sub-model probabilities → Bayesian average → Hawkes modifier → calibration shrinkage) is one half of the pipeline. Variance (Heston + jumps + vol-of-vol) is the other half. They meet at the cone, where one determines tilt and the other determines width.
## How to use it
**On the chart.** The cone shows the model's current view of the forward distribution. The P50 line is the median expected level given the implied drift. The P25 and P75 lines bracket the interquartile range. If P25 and P75 are roughly equidistant from current price, the model has no strong directional view; if the cone tilts noticeably up or down, the Bayesian probability favours that direction. A wider cone means more uncertainty; a narrower cone means tighter forward variance.
**The optional Monte Carlo cloud** (off by default) overlays bootstrap-resampled forward paths drawn from the asset's actual recent returns. Unlike the cone, it makes no Gaussian assumption — it shows the empirical distribution of forward outcomes given the asset's own recent return history.
**The 8-row dashboard** (top-right) is where the model exposes its full state:
- **Row 1 — Verdict.** Current direction (Bull / Bear / Neutral) and final P(up) percentage.
- **Row 2 — Direction.** A 10-character probability bar plus the size of the calibration shrinkage applied in percentage points.
- **Row 3 — 5 Models.** Up/down icons for each sub-model and the agreement count (e.g., "4/5 agree"). Trust the verdict more when 4 or 5 of 5 agree; trust it less when only 3 of 5 agree.
- **Row 4 — Regime.** Combined market regime (Quiet Bull / Quiet Bear / Volatile-Range / Crisis) plus the Hamilton dominant state.
- **Row 5 — Vol.** Volatility state vs long-run mean: HIGH (above mean, cone narrowing as vol decays), LOW (below mean, cone widening as vol rises), or AT MEAN. Includes the current sigma.
- **Row 6 — Risk.** Hawkes status: ✓ calm or ⚠ CRISIS. When CRISIS fires, the probability has been shrunk toward 0.5.
- **Row 7 — Honesty.** This is the most important diagnostic. It shows the actual historical hit rate for predictions in the 50–60% probability bin, with a Wilson confidence interval. If the system has predicted 55% many times and the actual rate is 53–58%, it is well-calibrated. If actual is below 50%, the system is currently overconfident and shrinkage is active.
- **Row 8 — Samples.** Total confirmed predictions and a trust level (low / warm-up / OK). Trust the cone less when total samples are below 50; trust it most when samples exceed 200.
## Suitable timeframes
The script auto-scales internal lookbacks to the chart timeframe relative to a 15-minute reference, so the same defaults work across 15-minute, 1-hour, 4-hour, and daily charts without manual tuning. Below 15 minutes the Hamilton 3-state model is automatically disabled because sample sizes become too small for reliable likelihood estimation; the other four sub-models continue to operate.
## What is original
Combining KAMA, wavelets, Kalman, Hamilton, Heston, Merton jumps, and a Hawkes process is not by itself new — these are all published methods. The original aspects of this script are:
- The **specific combination**: a five-model Bayesian ensemble for direction, with Hawkes-process crisis dampening and a self-correcting calibration tracker, all wrapped around a Heston + jump-diffusion variance estimate. I am not aware of a public Pine Script that combines all of these into a single probability cone with this data flow.
- The **calibration shrinkage mechanism**: the model logs every prediction, scores it after a fixed horizon, and applies bin-specific shrinkage to future predictions in bins where it has been overconfident. This is a self-correcting honesty layer that runs entirely on-chart, with Wilson confidence intervals and optional regime-specific calibration tables (Bull / Bear / Range).
- The **transparent diagnostic dashboard**: rather than hiding the model behind a single line, the dashboard exposes the verdict, model agreement, regime, vol state, crisis indicator, calibration quality, and sample size in eight rows. Users can see at a glance not only what the model thinks, but how much to trust it.
## Limitations (please read)
- **Pine Script cannot perform true maximum-likelihood estimation.** Heston and jump-diffusion parameters are estimated using approximation methods (AR(1) regression on log-variance, exponential moving averages for jump moments, rolling averages for long-term variance). The directional behaviour is correct — when vol is high, the cone narrows; when jumps are frequent, the cone widens — but exact parameter values are not equivalent to those a quantitative research desk would produce with MLE on tick data.
- **OHLCV data only.** No order-book data, no alternative-data feed, no options input, no fundamental input.
- **Calibration needs sample accumulation.** The Honesty row shows "warming up" until at least ~30 confirmed predictions have matured. Pine has no cross-session persistence, so calibration is rebuilt from chart history each time the indicator loads on a new chart.
- **Monte Carlo is deterministic.** Paths use a Linear Congruential Generator seeded by bar_index for reproducibility within a session. The same chart at the same moment produces the same cloud. This is intentional.
- **This is a probability indicator, not a strategy.** There are no backtest results, no equity curve, no position management. The script cannot tell you what to do; it only tells you what its model currently thinks the distribution of forward outcomes is.
- **Past calibration does not guarantee future calibration.** Market regimes change, parameters drift, and the cone should be treated as a visualisation aid rather than a prediction.
## Inputs of note
The defaults work without modification on most liquid instruments at most timeframes. Inputs worth knowing:
- **Auto-scale lookbacks** (on by default) — keeps the same defaults usable across timeframes.
- **Bayesian learning rate η** (default 0.15) — how fast sub-model weights adapt. Higher = faster but noisier.
- **Min Bayesian Probability** (default 0.62) — the threshold the dashboard uses to call a bar "Bull" or "Bear" rather than "Neutral".
- **Heston κ floor / ceiling** — bounds the mean-reversion-speed estimate. Defaults (0.02 to 0.30) handle most markets.
- **Hawkes warning threshold** (default 2.0× baseline) — when crisis dampening kicks in.
- **Monte Carlo Cloud** (off by default) — overlays bootstrap paths. Turn on if you want an empirical (non-Gaussian) view of forward outcomes alongside the cone.
## Disclaimer
This script is published for educational and analytical purposes only. It does not constitute financial advice, investment advice, a recommendation to buy or sell any financial instrument, or a solicitation of any transaction. The author is not a registered investment adviser and nothing in this script should be construed as personalised investment guidance.
Past performance does not guarantee future results. The probability projections shown by this indicator are model outputs, not forecasts of what will actually happen. Trading and investing involve substantial risk of loss and are not suitable for every investor. Users are solely responsible for their own trading decisions and for verifying that any approach is appropriate for their personal financial situation, risk tolerance, and applicable regulations.
The author and Market_Logic_India accept no liability for any losses, damages, or trading outcomes resulting from the use, misuse, or interpretation of this script. Use at your own risk.
インジケーター
Markov Forecaster PRO🟦 Markov Forecaster PRO is a regime-classification and probability-forecasting engine built on a discrete-time Markov chain over three states — Bull, Bear, Sideways. Every bar is labelled from its rolling N-bar log return; the labels feed a 3×3 transition matrix that is power-iterated for the stationary distribution and exponentiated for forward-probability cones (P¹, P³, P⁵, P^horizon). Unlike the dozens of textbook Markov indicators on TradingView, this one layers four original refinements on top of the standard chain construction — each addressing a well-known weakness of the memoryless Markov assumption.
The indicator integrates seven analytical layers — adaptive regime classification, semi-Markov duration tracking, sample-size disclosure, pending-regime early warning, forward-probability forecasting, look-ahead-free backtesting with fees and slippage, and multi-timeframe confluence — each rendered on a single overlay chart through a regime ribbon, three-layer neon glow signals, and four theme-aware dashboard panels.
Built with statistical honesty in mind. The backtest charges configurable commission and slippage on every entry and exit, the transition matrix flags rows with insufficient data, the duration-conditional probabilities are shown alongside the unconditional ones, and the documentation is explicit about what the model can and cannot predict.
🟦 HOW THE CORE ENGINE WORKS
**Regime Classification**
Each bar, the engine measures the rolling N-bar log return:
logRet = log(close / close )
The bar is labelled by comparing this return against the configured boundary:
- `logRet > +threshold` → BULL
- `logRet < −threshold` → BEAR
- otherwise → SIDEWAYS
The classification runs every bar with no look-ahead. The choice of threshold determines how reactive the regime label is, and this is where the first refinement enters.
**Adaptive Threshold (k · σ · √N)**
Traditional Markov regime indicators use a fixed percentage cut — e.g. "±5 % over 20 bars". This collapses on real markets: the same 5 % is trivial in a 2017 mania and never reached in 2023 chop. The fix is to scale the boundary with realised volatility:
threshold_adaptive = k × σ × √N
where σ is the per-bar log-return standard deviation over a configurable window (default 100 bars). Under a random walk, k = 1.0 cuts at the 16th / 84th percentiles; k = 2.0 at the 2.5th / 97.5th percentiles. The default k = 1.5 reproduces classic ±1.5-sigma thresholds.
Fixed-percentage mode is still available for users who want to lock the threshold deliberately.
**Regime Confidence**
Once classified, the move's strength is normalised relative to the active boundary:
confidence = |logRet| / threshold
| Confidence | Tier | Visual |
|---|---|---|
| < 1.0× | weak | ▱▱▱ |
| 1.0× – 2.0× | moderate | ▰▱▱ |
| 2.0× – 3.0× | strong | ▰▰▱ |
| ≥ 3.0× | stretched | ▰▰▰ |
The confidence value drives the ribbon transparency (in Adaptive Intensity mode), feeds the High Confidence alert (≥ 2.5× trigger), and is reported in the Status dashboard.
🟦 SEMI-MARKOV DURATION BUCKETS
**The Memoryless Problem**
A standard Markov chain says: "Given I'm in Bull, the probability of staying Bull tomorrow is X — regardless of whether Bull started yesterday or 200 bars ago." This is the memoryless property, and on real markets it's wrong. A 200-day-old Bull regime carries different mean-reversion risk than a 5-day-old one.
**The Refinement**
Markov Forecaster PRO additionally builds two CONDITIONAL transition matrices:
- `P_young` — transitions counted when the source regime's age was below its empirical average duration
- `P_mature` — transitions counted when the source regime's age was at or above the average
Both matrices are constructed in parallel with the main P, using the same per-bar bucketing logic and updated continuously. The self-transition probabilities for the current regime are then surfaced in the Status dashboard:
P young / mature 91% / 64%
The user reads this as: "When this regime was young (under its avg duration), it continued 91 % of the time. When mature, only 64 %." On a long-running regime this is the canonical signal that mean-reversion risk is rising — without the rest of the chain math being polluted.
A minimum of 10 samples per bucket is required before a value is shown; below that the cell reports "—" rather than display an unreliable probability.
🟦 FORWARD PROBABILITY CONE
**Matrix Exponentiation**
The 3×3 transition matrix P encodes one-bar-ahead probabilities. To project further out, the matrix is multiplied by itself:
P¹ = P — next bar
P³ = P × P × P — 3 bars out
P⁵ = P × P × P × P × P — 5 bars out
P^h = repeated h times — user-configured horizon
The Forecast Cone panel renders all four horizons for each of the three destination regimes, conditioned on the current regime. A trader reading the row "BULL" sees the probability the market will be in Bull at each horizon, given the current regime.
**Stationary Distribution**
Power-iterating the matrix to convergence yields the stationary distribution — the long-run probability of being in each regime, independent of starting state. With 50 iterations (default), any well-behaved 3×3 stochastic matrix is essentially converged.
stat + stat + stat = 1.0
This is rendered as the "long-run" row in the Forecast panel and the "Long-run share" cell in the Status panel.
**Honest Limitation**
The cone uses the UNCONDITIONAL matrix (averaged over all regime ages). For duration-conditional probabilities, the Status panel's P cell is the relevant readout. This split is explicit in both the cone footer label and the Forecast input tooltip.
🟦 SAMPLE-SIZE DISCLOSURE
A probability is only as reliable as the data behind it. Markov Forecaster PRO surfaces sample size in three places:
**Per-row sample count in the Transition Matrix**
A fifth column "n" in the matrix panel reports the number of transitions from each source regime. The cell is colored by reliability tier:
| Sample N | Tier | Color |
|---|---|---|
| ≥ 100 | high | foreground |
| 30 – 99 | moderate | dim |
| < 30 | low | divergent (warning) |
A row with fewer than 30 transitions is flagged because three-decimal probabilities derived from sparse data are noise, not signal.
**Total Sample N in the Status panel**
The Status dashboard's "Sample N" cell sums all transition counts and reports a global reliability tier:
| Total N | Tier |
|---|---|
| ≥ 200 | high (full color) |
| 50 – 199 | moderate (foreground) |
| < 50 | low (divergent warning) |
**Matrix footer**
The matrix panel's footer also shows the total N in compact notation (e.g. "N = 1.8k") for at-a-glance check.
The goal of this layer is honesty: a freshly-loaded chart with 30 bars of history should NOT display the same matrix as a 10-year chart, and the reliability tier makes the difference obvious without the user having to inspect counts manually.
🟦 PENDING-REGIME EARLY WARNING
**The Lookback Lag**
Because the regime is classified from log(close / close ), the official regime label inherently lags — by the time the threshold is crossed, the move is already N bars old. This is a structural feature of the model, not a bug, but it can be partially mitigated.
**Pending Logic**
Inside Sideways, when the log return reaches 70 % of either boundary, the dashboard fires an early-warning cue:
distance_fraction = max(|logRet| / threshold, ...)
isPending = (regime == SIDE) AND (distance_fraction ≥ 0.70)
The Status panel's "Pending" cell displays the direction the return is leaning toward and the current fraction:
⚠ ▲ BULL 87%
Color matches the leaning regime. The Pending Regime alert (default OFF, opt-in) fires on the first bar a pending state is entered.
This is not a regime change signal — it's a "watch this" cue, triggered roughly 30 % before the official threshold is crossed. Used alongside the official regime change, it gives the user advance notice without compromising the threshold's strictness.
🟦 LOOK-AHEAD-FREE BACKTEST
**The Look-Ahead Trap**
`regime` is derived from `log(close / close )`, which contains today's close. Allocating today's return to today's regime is look-ahead bias — the strategy would "know" today's regime before today's close, which is impossible in real-time trading. Most published Markov backtests have this bug.
**The Fix**
Markov Forecaster PRO allocates positions on the PRIOR bar's confirmed regime:
regForAlloc = regime // yesterday's confirmed regime
If yesterday's regime was Bull, we are long today. The strategy is realisable in real time because the previous bar's regime is known when the current bar opens.
This means the strategy is delayed by one bar relative to the regime label — and that's the correct, honest treatment. If a Bull→Bear flip happens on bar t, the strategy takes bar t's loss (still long from regime =Bull) and exits at bar t+1.
**Fees and Slippage**
Every Bull entry and exit pays the configured per-fill cost:
costFrac = feesPct/100 + slippageBps/10000
costPerFill = log(1 − costFrac) // negative log-space cost
The cumulative cost is debited from the Bull log-return total:
Bull gross = exp(bullLogR) − 1
Bull net = exp(bullLogR + bullCostLogR) − 1
A round-trip pays the fee + slippage twice. With defaults (0.10 % fee, 5 bps slippage), each round-trip costs roughly 0.30 % of equity in log space.
**Display**
The Backtest panel renders:
| Field | Value |
|---|---|
| Per-regime rows | GROSS cumulative log return (no fees) |
| Strategy row | NET cumulative (fees applied) vs Buy-and-Hold |
| Methodology footer | trade count · fee % · slippage bps |
The headline strategy result is the NET number — the realistic outcome a trader would have experienced. The gross numbers are kept for diagnostic comparison.
**What This Is Not**
This is a diagnostic backtest, not a tradable strategy. There is no position sizing, no risk management, no overnight financing, no shorting. It tells you whether "long when prior bar was Bull, flat otherwise" would have beaten buy-and-hold after fees — nothing more.
🟦 MULTI-TIMEFRAME CONFLUENCE
The same regime logic runs on a user-configured higher timeframe via `request.security` with `lookahead = barmerge.lookahead_off` and `gaps = barmerge.gaps_off` (anti-repaint mandatory). The result is reported in the Status dashboard's HTF block:
| State | Display | Color |
|---|---|---|
| HTF regime matches LTF regime | ✓ ALIGNED | bull |
| HTF regime differs from LTF | ⚠ DIVERGENT | bear |
| Insufficient HTF data | — | foreground |
Divergent regimes are common at trend turns — the LTF flips before the HTF catches up. Aligned regimes carry higher conviction. A separate alert ("MTF Confluence") fires on regime entries only when the HTF agrees.
Recommended pairings:
| Chart | HTF |
|---|---|
| 1H | D |
| 4H | W |
| D | W |
Use at least 3× your chart timeframe — anything closer and the two regimes track each other with no information gain.
🟦 VISUAL LAYER
**Regime Ribbon**
The chart background is tinted to the current regime color with three style options:
| Style | Behaviour |
|---|---|
| Subtle | Fixed 92 % transparency (price stays hero) |
| Bold | Fixed 75 % transparency (easy to scan from far) |
| Adaptive Intensity | Transparency scales with confidence (60 % – 95 %) |
In Adaptive Intensity mode, a strong directional move (confidence ≥ 3×) renders the ribbon at full intensity; a weak move stays faint. The ribbon doubles as a visual confidence meter.
**Three-Layer Neon Glow Signals**
On every confirmed regime change (after the Min Hold filter), the indicator drops a three-layer halo on the chart:
| Layer | Size | Transparency | Purpose |
|---|---|---|---|
| Outer | size.large | 80 % | Soft halo |
| Middle | size.normal | 50 % | Mid-glow |
| Core | size.small | 0 % | Bright center |
Bull markers (▲) render below the bar; Bear (▼) and Sideways (◆) render above. The Min Hold input (default 4 bars) requires a new regime to persist before its flip is drawn — kills label spam in choppy zones without affecting the underlying transition counts.
**Confidence Tags (optional)**
An off-by-default toggle adds the confidence multiplier to each signal arrow ("BULL 2.3×"), useful for screen captures and analysis.
🟦 DASHBOARDS
Four theme-aware panels, each independently togglable and positionable:
**Status Panel** (default: Bottom Left)
Compact live readout — current regime, age, confidence, pending direction, average duration, young/mature bucket, P young vs mature, expected remaining bars, long-run share, sample size, and HTF alignment. 16 rows base, 19 with HTF block enabled.
**Transition Matrix Panel** (default: Top Right)
3×3 next-bar P matrix with diagonal-highlighted self-transition cells. The fifth column reports per-row sample size with reliability tier coloring. Matrix footer shows total N.
**Forecast Cone Panel** (default: Middle Right)
Forward probability for each destination regime at horizons +1, +3, +5, and +configured. Steady-state row shows the long-run distribution. Current regime is reported at the bottom for context.
**Backtest Panel** (default: Bottom Right)
Per-regime gross cumulative return, average per-bar, and the bar count. Strategy row shows NET return vs buy-and-hold. Methodology footer lists trade count, fee, and slippage.
All four panels share the same theme palette and adapt to Dark / Light display mode. Text size is independently configurable (Tiny / Small / Normal / Large).
🟦 COLOR THEMES
Ten cohesive palettes tuned to the Apex design system, each defining three regime axes (Bull, Bear, Sideways):
| Theme | Character | Bull | Bear | Sideways |
|---|---|---|---|---|
| Prism | Classic | Forest green | Crimson | Slate grey |
| Focus | Default | Cyan steel | Deep orange | Cool blue-grey |
| Solar | Warm | Amber | Indigo red | Lavender grey |
| Frost | Cool | Sky blue | Soft lavender | Pale steel |
| Laser | Neon | Lime green | Hot crimson | Charcoal grey |
| Aurora | Bright | Gold | Scarlet | Warm beige |
| Plasma | Electric | Aqua | Magenta | Slate teal |
| Bloom | Soft | Mint | Hot pink | Blue-grey |
| Eclipse | Deep | Navy | Dark crimson | Steel grey |
| Carbon | Minimal | Near-white | Mid-grey | Dark grey |
One theme selection drives every visual component: ribbon, glow signals, all four dashboard headers, regime-colored cells, diagonal matrix highlights, and HTF alignment color.
**Dark / Light Display Mode**
Dashboard chrome (background, foreground, borders, section dividers) flips between dark-on-bright and bright-on-dark. The regime axis colors remain consistent across modes — only the panel chrome changes.
🟦 ALERT SYSTEM
Six alert conditions, each independently togglable:
| Alert | Condition |
|---|---|
| Bull Regime Entry | Regime flipped to BULL (after Min Hold confirmation) |
| Bear Regime Entry | Regime flipped to BEAR |
| Sideways Regime Entry | Regime flipped to SIDEWAYS (default OFF) |
| High Confidence | confidence ≥ 2.5× threshold, first bar of crossing |
| MTF Confluence | Regime change + HTF agrees |
| Pending Regime | Inside Sideways, log return ≥ 70 % of either boundary (default OFF) |
All alerts fire on confirmed bar close and use the standard `alertcondition` mechanism. The Min Hold filter applies to entry alerts — a new regime must persist Min Hold bars before its entry alert fires, matching the on-chart glow markers.
The Sideways and Pending alerts are default-off because they can fire more frequently than the other types — opt-in by design.
🟦 SETTINGS REFERENCE
**Theme**
- Theme — One of 10 Apex palettes. Default: Focus
- Display Mode — Dark / Light. Default: Dark
**Regime Logic**
- Threshold Mode — Adaptive (k·σ·√N) / Fixed (%). Default: Adaptive
- Lookback Window — Bars for the rolling log return. Default: 20
- Adaptive k — Sigma multiplier. Default: 1.5
- Fixed Bull Threshold — Used only in Fixed mode. Default: 5.0 %
- Fixed Bear Threshold — Used only in Fixed mode. Default: 5.0 %
- Volatility Window — Bars for the per-bar stdev. Default: 100
- Min Hold — Bars a new regime must persist for label drawing. Default: 4
**Forecast**
- Forecast Horizon — Bars projected by the right-most cone column. Default: 10
- Stationary Power — Power-iteration count. Default: 50
**Regime Ribbon**
- Show Regime Ribbon — Toggle. Default: ON
- Ribbon Style — Subtle / Bold / Adaptive Intensity. Default: Adaptive Intensity
**Signal Labels**
- Show Regime Change Signals — Toggle. Default: ON
- Glow Effect — Three-layer halo toggle. Default: ON
- Show Confidence on Signal — Adds multiplier tag (e.g. "BULL 2.3×"). Default: OFF
**Multi-Timeframe**
- Enable HTF Confluence — Toggle. Default: ON
- HTF Resolution — Higher timeframe. Default: D
**Backtest**
- Trading Fee (% per fill) — Per-side commission. Default: 0.10 %
- Slippage (bps per fill) — Per-side slippage in basis points. Default: 5
**Dashboards**
- Show Status / Matrix / Forecast / Backtest — Independent toggles. Default: all ON
- Dashboard Size — Tiny / Small / Normal / Large. Default: Small
**Panel Positions**
- Status Panel — 9-position grid. Default: Bottom Left
- Matrix Panel — Default: Top Right
- Forecast Panel — Default: Middle Right
- Backtest Panel — Default: Bottom Right
**Alerts**
- Bull / Bear / Sideways Regime Entry — Independent toggles
- High Confidence — Default: ON
- MTF Confluence — Default: ON
- Pending Regime — Default: OFF
🟦 COMPATIBILITY
Works on all asset classes and all timeframes in TradingView Pine Script v6.
- Crypto: Spot, futures, perpetual contracts
- Forex: All pairs
- Equities: Stocks, ETFs, indices
- Commodities: Metals, energy, agriculture
- Timeframes: 1m through Monthly
The adaptive threshold normalises by per-bar realised volatility, making the regime classification volatility-agnostic across assets without manual recalibration. The same default settings work on BTCUSDT daily, SPY weekly, and EURUSD 4H — only the HTF resolution input should be adjusted to match the chart timeframe.
🟦 TECHNICAL NOTES
- Pine Script v6
- `max_labels_count = 500`, `max_lines_count = 100`, `max_bars_back = 5000`
- No repainting — all regime classifications are computed on confirmed bar close. The HTF request uses `lookahead = barmerge.lookahead_off` and `gaps = barmerge.gaps_off`
- Regime change debouncing uses `ta.barssince` to avoid runtime-indexed history reads (which can trip "cannot determine max_bars_back" in Pine v6)
- Heavy computation (matrix exponentiation, stationary distribution, dashboard rendering) is gated on `barstate.islast` to run once per chart render
- Transition counting uses `barstate.isconfirmed` to avoid double-counting the live bar
- Backtest accumulators charge fees at trade boundaries — entries and exits detected by `regForAlloc != regForAlloc `
- Duration buckets use the SOURCE regime's age at the time of transition for classification; the threshold is the empirical average duration of that regime, computed continuously
- Matrix multiplication is implemented as an unrolled 3×3 flat-array routine for portability and speed
- Empty-row fallback to uniform 1/3 in the transition matrix prevents NaN propagation when a regime has not appeared in visible history
🟦 LIMITATIONS — READ THIS
This indicator is statistically honest about what it can and cannot do. Three known limitations:
1. **The Markov assumption is partially violated.** Markets are not memoryless. The duration buckets (Section: Semi-Markov Duration Buckets) mitigate this but do not eliminate it.
2. **Forward probabilities are not predictions.** They are conditional probabilities under the chain assumption. A "Bull 58 % at +10 bars" reading does not mean "58 % chance the next 10 bars are bullish" — it means "given a long-run sample of similar starting states, 58 % were in Bull at +10 bars". Use the cone as ONE input alongside other analysis.
3. **The regime label lags by N bars.** This is structural — the rolling log return necessarily looks back. The Pending early warning partially mitigates this but cannot eliminate the lag. Treat the official regime change as a confirmation, not a leading signal.
🟦 DISCLAIMER
This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. The hypothetical backtest is a diagnostic tool — there is no position sizing, no risk management, and no consideration of overnight financing, dividends, or other real-world frictions beyond the configured fee and slippage. Always conduct your own analysis and apply proper risk management. インジケーター
