Produk
Komunitas
Pasar
Broker
Lebih lanjut

Indikator dan strategi
Forecast

Forecast

Upcoming Market Sessions Upcoming Market Sessions This lightweight script projects the exact starting times of the major global trading sessions directly into the future blank space of your chart. It features a customizable timezone selection and individual toggle switches for each market session to keep your workspace clean. Why this script was created: This indicator is built to make forecasting significantly easier by clearly highlighting upcoming session starts for the next 24 hours. When drawing out future price action, it is easy to find yourself constantly checking the bottom time axis to see which session your setup will land in. This script gives a direct visual indication of exactly when you need to be back at your charts, and instantly shows if your forecasted move is landing inside a high-volume session for your traded instrument. Features: Future Projections: Draws clean, vertical session lines into the future empty space where candles haven't formed yet. Flexible Session Toggles: Turn individual session lines (Sydney, Tokyo, London, New York) on or off depending on your asset. Customizable Inputs: Easily adjust the exact start hours (HHMM) and matching display colors directly in the indicator settings.

Indikator

Diupdate

BEDROCK Gated Macro Spot Cycle Model BEDROCK 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.

Indikator

oleh AlphaFibStein

Daily Volume Forecast Daily 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. ═══════════════════════════════════════ 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.

Indikator

oleh Tools-for-Trader

Breakout Breakout 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.

Indikator

oleh RickyTSpanish

Diupdate

Adaptive Swing Timing Engine [AlgoPoint]Adaptive Swing Timing Engine Most swing indicators explain where a market reversal occurred after it has already happened. Adaptive Swing Timing Engine focuses on a different dimension: time. The indicator studies the number of bars between confirmed swing highs and swing lows, learns how bullish and bearish price legs have behaved historically, and projects statistical timing windows in which the next meaningful turn may become more likely. Instead of presenting a single deterministic reversal date, the engine displays a probable timing range, a central estimate, a confidence score, and a near-term reversal probability. HOW IT WORKS 1. VOLATILITY-ADAPTIVE SWING DETECTION The engine identifies meaningful swing highs and lows using a hybrid reversal threshold based on: • Average True Range • Minimum percentage movement • Minimum swing duration • Confirmed candle closes This allows the detector to adapt automatically as market volatility expands or contracts. Three ready-to-use sensitivity profiles are included: • Responsive — Detects smaller and more frequent market turns • Balanced — Designed for general swing analysis • Major Moves — Focuses on larger structural rotations • Custom — Gives full control over the detection threshold 2. DIRECTIONAL CYCLE MEMORY Bullish and bearish price legs are analyzed separately. The model maintains independent historical distributions for: • Swing Low → Swing High durations • Swing High → Swing Low durations • Swing High → Swing High full cycles • Swing Low → Swing Low full cycles This matters because bullish and bearish legs do not always develop at the same speed. A market may rise gradually but decline rapidly, or behave in the opposite way. The engine preserves this timing asymmetry instead of forcing every movement into a single average. 3. ROBUST STATISTICAL MODEL Simple averages can be distorted by unusually long trends or abnormal market events. To reduce this problem, the engine uses: • Median-based cycle estimation • Median Absolute Deviation outlier control • Recency-weighted observations • Direction-specific cycle history • Full-cycle agreement analysis Recent market behavior receives greater influence, while extreme historical durations are prevented from dominating the forecast. 4. AGE-MATCHED CYCLE ANALYSIS A confirmed swing may already be several bars old by the time the reversal threshold is completed. The engine accounts for this confirmation delay. When enough history is available, it only compares the current movement with previous cycles that survived beyond the current cycle age. This prevents the model from projecting a reversal point that has already passed before the swing was confirmed. 5. PROBABILISTIC TIMING PROJECTION The indicator can project up to three possible future swing turns. Each projection contains: • Main Timing Window — The central historical reversal range • Wider Uncertainty Zone — An optional broader probability range • Dashed Center Line — The model’s central timing estimate • Expected Turn Type — Possible Swing High or Possible Swing Low • Confidence Score — A 0–100 assessment of model quality Later projections are displayed with reduced confidence because uncertainty naturally increases as the forecast horizon expands. READING THE CHART H / L MARKERS H identifies a confirmed Swing High. L identifies a confirmed Swing Low. The marker is placed on the actual historical extreme, but only after the swing has been confirmed. CONFIRMATION DOTS Optional confirmation dots show the exact candle on which the indicator had enough information to confirm the swing. This makes the difference between the historical extreme and the real-time confirmation moment fully transparent. SWING PATH The swing path connects confirmed market turning points and provides a clean visual representation of the active cycle structure. COLORED TIMING WINDOW The projected colored area represents the main period in which the next swing turn may become statistically more likely. It should be treated as an attention zone, not as an automatic entry signal. DASHED CENTER LINE The dashed vertical line represents the center of the projected timing distribution. It is not a guaranteed reversal bar. SUMMARY PANEL The dashboard provides a simplified real-time overview of: • Expected next swing type • Estimated timing • Main reversal window • Confidence level • Current cycle age • Near-term reversal chance • Number of learned cycles • Historical model error and coverage NEAR-TERM REVERSAL CHANCE The near-term percentage is calculated from comparable historical cycles. For example, if the current cycle has lasted 20 bars, the model checks previous cycles that also lasted beyond 20 bars and measures how many ended within the selected future horizon. This is an empirical historical probability, not a guaranteed forecast. CONFIDENCE SCORE The confidence score evaluates the quality of the timing model using several factors: • Number of available cycle samples • Stability of historical cycle durations • Direction-specific data availability • Agreement between half-cycle and full-cycle models • Historical forecast calibration • Availability of age-matched observations Confidence measures model consistency. It does not represent the probability that the market must reverse. HOW TO USE 1. Start with the Balanced sensitivity profile. 2. Allow the indicator to collect the required number of confirmed cycles. 3. Monitor the projected timing window as the current cycle matures. 4. Treat an active timing window as a period of increased attention. 5. Look for independent price confirmation such as: • Market structure change • Support or resistance reaction • Liquidity sweep • Momentum exhaustion • Volume expansion • Rejection candle • Higher-timeframe confluence 6. Use Responsive mode for shorter market rotations and Major Moves mode for broader structural swings. The timing model is designed as an analytical context tool. It should not be used as a standalone buy or sell system. NON-REPAINTING AND CONFIRMATION The swing engine processes confirmed candle closes. Confirmed swing events are not continuously moved or recalculated after completion. Historical H and L labels are positioned on the actual swing extremes only after sufficient reversal movement has confirmed them. Optional confirmation dots reveal when those swing points became known to the model. The script does not use future price data or lookahead calculations. Because swing confirmation requires price to move away from an extreme, every confirmed swing system naturally contains some detection delay. The indicator displays this process transparently rather than hiding it. ALERTS The script includes alert conditions for: • Main Reversal Window Started • Projection Center Reached • Projection Became Overdue • Swing High Confirmed • Swing Low Confirmed LIMITATIONS Market cycles are not fixed. A sudden volatility expansion, news event, structural breakout, or persistent directional trend can cause the current movement to last significantly longer or shorter than its historical average. The model also requires a minimum number of completed cycles before producing a meaningful projection. On newly listed assets, limited chart histories, or very high swing thresholds, the learning phase may take longer. The indicator forecasts timing conditions only. It does not provide a price target, guarantee a reversal, or predict the direction and magnitude of the next move. Adaptive Swing Timing Engine is a probabilistic analysis framework — not a promise of future market behavior. Elevate Your Trading ▶ qalphatrade.com Trade with mathematical certainty, not emotional guesswork.

Indikator

Strategy Forecast Engine The 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.

Indikator

oleh QuantitativeAlpha

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.

Indikator

oleh federalTacos5392b

Diupdate

Self Calibrating Probability Channel SELF-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.

Indikator

oleh Market_Logic_India

TripleQMatrix: HTF Projection Nico The Higher Timeframe (HTF) Projection is a professional trading tool designed to solve the "tunnel vision" that often occurs when scalping on lower timeframes like the 30-second or 15-minute charts. What is it? It takes a higher timeframe candle (like a 15-minute bar) and projects its data—Open, High, Low, and Close—directly onto your lower-timeframe chart. It essentially gives you a "ghost" of the larger market context while you are focused on micro-scalping. How to use it effectively Using this tool correctly is all about **Contextual Alignment**: 1. Regime Filtering: Use the projected candle's color (or the "Chop/Runner" status) to determine your market bias. If the HTF box is Gray (Chop): Avoid directional scalping. The market is in equilibrium, and institutional participation is too low for a sustained run. If the HTF box is Green/Red (Runner): This confirms the current 15-minute trend. Look to only take scalp trades that align with this direction (e.g., if the box is Red/Bearish, prioritize short-selling opportunities). 2. Projection as a "Magnet": Large institutions often trade based on the levels of these HTF candles. Use the projected **High** and **Low** lines as potential "magnets." If price is trending toward the HTF High, anticipate institutional selling (rejection) at that level. 3. The "Close" Countdown: The timer label tells you exactly when the 15-minute candle will finalize. Scalpers often experience increased volatility in the final 60 seconds of a 15-minute bar as institutions "mark the close." Use this countdown to protect your open positions. The Institutional Workflow Entry: Wait for a short-term reversal signal (like your **Institutional Rejection** indicator) that happens within the range of the projected HTF candle. Exit: Set your profit targets based on the HTF High/Low levels, as these are the zones where the "Big Money" is likely to take profits or re-enter. By keeping the HTF projection on your chart, you are no longer just scalping "price action"; you are scalping in harmony with the timeframe that institutional algorithms use to structure their daily entries.

Indikator

oleh TripleQMatrix

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. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ 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. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ 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. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ 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. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ 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. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ 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 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ 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. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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.

Indikator

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.

Indikator

oleh Alien_Algorithms

Market State Forecast Projection Engine This 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.

Indikator

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).

Indikator

oleh officialjackofalltrades

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.

Indikator

oleh Market_Logic_India

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.

Indikator

oleh a_jabbaroff

Signal Projection Explorer Many traders focus on building full strategies right away — combining entries, exits, stop-losses, take-profits, filters, and position sizing. But there is a problem with that approach: 👉 It often hides the true quality of the underlying signal. When multiple layers are added on top (risk management, opposite signals, overlays), it becomes very difficult to answer a simple but critical question: “Is this signal actually good on its own?” 🎯 What this indicator does This tool is designed to analyze raw signals in isolation. Instead of jumping straight into a full strategy, it lets you explore: 👉 What tends to happen to the price after a signal occurs? 🔍 How it works The script detects signals in historical data (Golder Cross in this script). It collects all occurrences of those signals. For each signal, it tracks price performance over the next X bars. It then builds a distribution of outcomes and projects it forward from the current price. 📈 What you see on the chart Instead of a single prediction, you get a range of historical outcomes: 🔴 Worst P&L → maximum adverse move after the signal 🟢 Best P&L → best-case outcome 🔵 25th percentile → lower bound of typical outcomes 🟠 75th percentile → upper bound of typical outcomes ⚪ Mean → average path 🟣 Median → typical (robust) path All of these are projected forward from the current price, giving you an intuitive view of possible scenarios. 📋 Stats Table The table summarizes key metrics at the selected projection horizon: Number of signals used Final P&L for each line (Worst / Best / Percentiles / Mean / Median) Distribution metrics like Spread and IQR This gives you a quick read on: Expected return Risk range Outcome dispersion 🧠 Why this matters This tool helps you: Separate signal quality from strategy complexity Understand risk vs reward before adding filters Avoid overfitting strategies on weak signals Build better systems from strong foundations ⚠️ Important note This is not a prediction tool. It shows historical tendencies based on past signals — not guaranteed future outcomes. Always use it as: a research tool a context layer not a standalone trading system 🚀 Final thought Before optimizing entries, exits, and risk… 👉 Make sure your signal itself has an edge. This indicator helps you see that clearly.

Indikator

oleh QuantNomad

Diupdate

Pattern Match Price Projection Pattern Match Price Projection is a price forecasting tool built on historical analog analysis. It scans past market data to find segments of price action that closely resemble the most recent pattern, then uses what followed those similar setups to construct a forward projection. Instead of relying on a single historical match, the model aggregates multiple high-quality analogs and produces an averaged path, along with adaptive upper and lower bands that reflect the dispersion of those outcomes. The projection is derived from return-based pattern matching, with optional normalization to focus on structural similarity rather than magnitude. This allows the model to identify recurring behaviors across different volatility regimes and price levels. The result is a forward estimate that reflects how the market has historically behaved after similar conditions, rather than a purely reactive or lagging indicator. A visual overlay of the current pattern is included to provide context for what the model is evaluating in real time, making it easier to interpret why a projection is being generated. The forecast itself can be anchored either to the most recent closing price or to a smoothed price reference, depending on preference. Non-repaint mode ensures the projection remains stable during an active candle, while live mode allows it to update dynamically as price evolves. This tool is best used as a contextual framework for anticipating potential price paths based on historical precedent. It does not attempt to predict exact outcomes, but instead highlights probabilistic structure derived from prior market behavior.

Indikator

Diupdate

Aegis Quantum Oracle [wjdtks255]Aegis Quantum Oracle: Energy Velocity Forecast ■ OVERVIEW The Aegis Quantum Oracle is a next-generation momentum forecasting framework designed to identify structural market shifts through Energy Velocity Analysis. By calculating the acceleration and deceleration of capital flow, this system predicts potential reversals before they manifest in price action. ■ TECHNICAL ARCHITECTURE Luminous Smoothing Engine: Utilizes an EMA-based pre-filter to eliminate market noise, ensuring that the energy calculation is based on high-conviction price data. Quantum Velocity Vector: The core algorithm calculates histVelocity—the second derivative of momentum—to track the rate of change in market energy. Oracle Thresholding: Signals are normalized against a 50-period historical average (avgEnergy) to maintain statistical significance across different volatility regimes. ■ PREDICTIVE FORECASTING (ORACLE DOTS) Energy Ignition (Green Dots): A leading indicator for bullish reversals, appearing when bearish momentum bottoms out and buy-side velocity begins to accelerate. Energy Freezing (Red Dots): A leading indicator for bearish reversals, appearing when bullish momentum reaches an exhaustion peak and sell-side velocity takes over.

Indikator

Diupdate

Forward-Projecting Opportunity Cone [ChartPrime]🔶 OVERVIEW In a market defined by uncertainty, the Forward-Projecting Opportunity Cone provides a mathematically grounded roadmap for price action. Instead of predicting a single direction, this tool uses historical volatility and standard deviation to project a "cone" of high-probability price boundaries into the future. By calculating the Expected Move based on annualized volatility, the indicator visualizes where price is statistically likely to remain over a specified window, helping traders identify trend exhaustion, mean reversion opportunities, and low-probability "tail events." 🔶 THE MATHEMATICS OF PROBABILITY The indicator operates on the principle of Standard Deviation ( σ (sigma)) applied to logarithmic returns. It calculates the annualized volatility of the asset and projects it forward using the "Square Root of Time" rule: 1σ Zone (68.2% Probability): The "Normal" range. Most price action occurs here. 2σ Zone (95.4% Probability): The "Exhaustion" range. Reaching these levels often suggests a significant move is underway or nearing a limit. 3σ Zone (99.7% Probability): The "Extreme" range. Price hitting the 3rd sigma is a rare event often followed by sharp mean reversion or "Black Swan" momentum. 🔶 THE COMMAND CENTER: DYNAMIC DASHBOARD The integrated dashboard is designed to act as a real-time risk-assessment engine, providing deep insights into the asset's current "volatility health" and the structural parameters of the projection. Annualized Volatility: Displays the raw percentage of the asset's movement potential over a year based on the current timeframe. Volatility Percentile: Contextualizes current volatility by ranking it against the last 252 bars. It tells you if the market is currently "quiet" or "explosive" compared to its own history. Volatility Regime: Automatically classifies the market into LOW (Teal), NORMAL (Violet), or HIGH (Amber) regimes, allowing you to adjust your strategy based on the current environment. Sigma Distance & Direction: Tracks exactly how many standard deviations price has moved from the anchor point and whether it is trending above or below the mean. Expected % Moves: Provides precise mathematical targets for 1 σ (sigma), 2 σ (sigma), and 3 σ (sigma) moves in percentage terms for your specific projection window. Anchor Price: Displays the exact price level where the statistical model begins. This allows for pinpoint accuracy when measuring moves from a specific Pivot High or Low. Projection Window: Shows the duration (in bars) that the current cone extends into the future, defining the specific time horizon for the probability model. 🔶 CORE VISUAL INDICATIONS The Pivot-Anchor "Cone Lock": Unlike standard moving bands, this can anchor to a fixed structural point (Pivot High/Low). This allows you to measure the probability of a specific trend's lifecycle from its inception. Real-Time Zone Tracking: The indicator provides active labels like "Inside 1 σ (sigma)" for stable trends or "Beyond 3 σ (sigma) ✦" to warn of extreme volatility blowouts. Gradient Probability Mapping: The cone utilizes a fading visual engine. As the projection moves further into the future, the colors soften, representing the natural increase in market "entropy" and uncertainty over time. 🔶 TRADING APPLICATIONS Mean Reversion: When price pierces the 3 σ (sigma) (Amber) zone , it is in a territory that occurs less than 1% of the time. This is a high-conviction signal to look for reversal setups. Trend Stability: A healthy, institutional-led trend typically grinds along the 1 σ (sigma) (Teal) boundary . If price remains here, the trend is sustainable. Mathematical Take-Profits: Use the 1 σ (sigma) and 2 σ (sigma) levels at the tip of the cone to set take-profit targets that are statistically realistic for the current market state. 🔶 CONCLUSION The Forward-Projecting Opportunity Cone is more than a volatility band; it is a structural probability engine. By anchoring math to price action and providing a comprehensive dashboard of risk metrics, it helps traders remain objective, avoid chasing extremes, and understand exactly where "normal" ends and "opportunity" begins.

Indikator

oleh ChartPrime

Transform Swing Forecast Signal Name: Transform Candle Swing Reversal Explorer Searchable Name: Transform Swing Forecast Signal Short title: TFX Forecast Signal Summary Transform Candle Swing Reversal Explorer is a simplified exploratory script designed to visualize transform-style directional movement, swing/reversal framing, and hypothetical forecast candles on TradingView charts. It is meant to help users inspect chart structure, directional shifts, and possible path-expansion behavior in a lightweight format. It is also meant for users who want to better understand transform candles and transform-style movement in a simpler script, and for users who are looking to develop, create, test, or modify their own engine/script from an open source starting point for personal use, instead of needing the full operational engine source to do that. Users who want a pre-built ready-to-test/use operational engine should search for Transform Entry Exit Reversal. How it works The script builds a simple causal transform path from raw price and displays it as wickless transform candles plus a transform path line. It uses a lightweight alternating turning-point method to mark possible swing/reversal areas and a simplified forecast model to project hypothetical future candles and targets. This explorer version is focused on visualization and exploratory chart reading. It does not include the best-fit transform engine, advanced pivot-timing/replay structure, or the full execution/trade-management architecture used in Transform Entry Exit Reversal. The goal is to demonstrate the category and some of its possibilities in a simpler script format. It is also intended to serve as a lighter open source starting point for users who want to better understand transform candles and transform-style movement, and develop/create, test, or modify their own personal script/engine from that foundation. Forecast model note Forecast candles in this script are hypothetical path projections, not literal future predictions. They are intended to show a possible future movement/path-expansion sketch, not an exact real-world directional or target-hit probability. Features Wickless transform candle display Transform path line Alternating swing/reversal markers Hypothetical forecast candles Forecast target lines and labels Small status table Simple trend-flip alert Who it’s for This script is best suited for traders and researchers who want a lightweight exploratory tool for studying transform-style chart structure, swing/reversal framing, and hypothetical future path behavior. It is especially suited for users who want to better understand transform candles and transform-style movement in a simpler script, and for users who want to develop, create, test, or modify their own personal script/engine from a lighter open source starting point. Who it’s not for This script is not best suited for users looking for advanced replay diagnostics, execution-ready trade management, or a complete transform/pivot engine workflow. It is also not best suited for users mainly looking for a pre-built ready-to-test/use operational engine, since that is the role of Transform Entry Exit Reversal. Final note This is an exploratory swing/reversal visualization script. Its purpose is to demonstrate a simplified transform-candle, swing/reversal, and forecast-candle concept that can help users understand the broader category. It is also intentionally suited to users who want to better understand transform candles and transform-style movement in a simpler open source script, and create, test, or modify their own personal script/engine, rather than use Transform Entry Exit Reversal directly.

Indikator

oleh AceInfinity

Diupdate

Transform Candle Swing Reversal Explorer Name: Transform Candle Swing Reversal Explorer Short title: TFC Swing Reversal Explorer Summary Transform Candle Swing Reversal Explorer is a simplified exploratory script designed to visualize transform-style directional movement, swing/reversal framing, and hypothetical forecast candles on TradingView charts. It is meant to help users inspect chart structure, directional shifts, and possible path-expansion behavior in a lightweight format. It is also meant for users who want to better understand transform candles and transform-style movement in a simpler script, and for users who are looking to develop, create, test, or modify their own engine/script from an open source starting point for personal use, instead of needing the full operational engine source to do that. Users who want a pre-built ready-to-test/use operational engine should search for Transform Candle Pivot Swing Reversal Engine. How it works The script builds a simple causal transform path from raw price and displays it as wickless transform candles plus a transform path line. It uses a lightweight alternating turning-point method to mark possible swing/reversal areas and a simplified forecast model to project hypothetical future candles and targets. This explorer version is focused on visualization and exploratory chart reading. It does not include the best-fit transform engine, advanced pivot-timing/replay structure, or the full execution/trade-management architecture used in Transform Candle Pivot Swing Reversal Engine. The goal is to demonstrate the category and some of its possibilities in a simpler script format. It is also intended to serve as a lighter open source starting point for users who want to better understand transform candles and transform-style movement, and develop/create, test, or modify their own personal script/engine from that foundation. Forecast model note Forecast candles in this script are hypothetical path projections, not literal future predictions. They are intended to show a possible future movement/path-expansion sketch, not an exact real-world directional or target-hit probability. Features Wickless transform candle display Transform path line Alternating swing/reversal markers Hypothetical forecast candles Forecast target lines and labels Small status table Simple trend-flip alert Who it’s for This script is best suited for traders and researchers who want a lightweight exploratory tool for studying transform-style chart structure, swing/reversal framing, and hypothetical future path behavior. It is especially suited for users who want to better understand transform candles and transform-style movement in a simpler script, and for users who want to develop, create, test, or modify their own personal script/engine from a lighter open source starting point. Who it’s not for This script is not best suited for users looking for advanced replay diagnostics, execution-ready trade management, or a complete transform/pivot engine workflow. It is also not best suited for users mainly looking for a pre-built ready-to-test/use operational engine, since that is the role of Transform Candle Pivot Swing Reversal Engine. Final note This is an exploratory swing/reversal visualization script. Its purpose is to demonstrate a simplified transform-candle, swing/reversal, and forecast-candle concept that can help users understand the broader category. It is also intentionally suited to users who want to better understand transform candles and transform-style movement in a simpler open source script, and create, test, or modify their own personal script/engine, rather than use Transform Candle Pivot Swing Reversal Engine directly.

Indikator

oleh AceInfinity

Diupdate

Swing Structure Forecast [BOSWaves]Swing Structure Forecast - Statistical Swing Projection System with Volatility-Adaptive Support and Resistance Detection Overview Swing Structure Forecast is a statistically-driven swing analysis system that maps directional price structure through confirmed pivot identification, where support and resistance zones construct automatically at each swing extreme and a probabilistic forecast beam projects the next swing leg using aggregated historical swing measurements. Rather than applying fixed price targets, universal extension ratios, or lagging directional filters, zone boundaries, forecast direction, and projection magnitude are governed by structural pivot confirmation, ATR-proportioned zone sizing, and rolling statistical measurement of completed swing history across a configurable sample window. This produces a continuously refreshed structural map alongside a data-grounded forward projection. Zones breathe with volatility cycles and forecasts are calibrated to the instrument's own measured behaviour rather than theoretical constants or fixed multiples. Price is therefore assessed against structurally-anchored zones derived from confirmed swing pivots, with directional expectations built from the statistical record of prior completed legs rather than external reference points. Conceptual Framework Swing Structure Forecast is built on the premise that genuine support and resistance originate at confirmed swing extremes, and that the statistical character of completed swing legs contains meaningful information about the magnitude and duration of the move that will follow. Standard projection methodologies apply predetermined ratios that treat every instrument and market condition as interchangeable. This framework instead extracts magnitude expectations from the instrument's own swing record, building an evidence base from recent completed legs and distilling it into a statistically-grounded projection originating at the current confirmed pivot. Three core principles shape the design: Support and resistance zones should originate at structurally confirmed swing highs and lows, not at indicator crossovers, arbitrary distances, or price patterns lacking pivot confirmation. Zone width must respond to prevailing volatility, expanding proportionally when ATR is elevated and compressing when market conditions quieten. Forecast targets and projection uncertainty should be derived from the distribution of the instrument's own recent swing history, with variability expressed visually rather than hidden behind a single projected level. This repositions price structure work from passive historical reference into an active, instrument-specific projection framework that updates with each new confirmed swing. Theoretical Foundation The indicator unifies structural pivot detection, ATR-responsive zone construction, rolling statistical aggregation, and Fibonacci extension mapping. Swing highs and lows are established through a rolling highest/lowest comparison across a configurable lookback window, accepting only pivots surrounded by sufficient structural confirmation on both sides. A 200-period ATR provides a slow-moving, stable volatility reference that scales zone thickness and beam width proportionately across varying instruments and timeframes. Completed swing percentages and durations populate a rolling sample array, with three aggregation modes — weighted, average, and median — giving users direct control over how heavily recent legs are weighted against older history. Standard deviation across this sample governs beam width, producing narrow projections when swing history is consistent and widening the beam when prior legs have varied significantly in magnitude. Four internal systems work in coordination: Pivot Detection Engine : Confirms swing highs and lows through multi-bar structural comparison, withholding confirmation until price movement validates the extreme and eliminating repainting. Zone Construction System : Builds dual-layer ATR-proportioned boxes at each confirmed pivot, applying progressive opacity reduction with age and monitoring for structural breach events. Forecast Engine : Processes the rolling swing sample through the selected statistical method and casts the next projected leg as a smoothed cone beam originating at the current pivot, scaled by historical variance. Fibonacci Extension System : Deploys individually toggleable extension levels beyond the primary forecast target, each with a fully configurable ratio for defining continuation objectives. This structure keeps the structural map and forward projection permanently coupled, refreshing in unison whenever a new swing confirms. How It Works Swing Structure Forecast processes price through a structured sequence of pivot-aware operations: Pivot Confirmation : Bar highs and lows are continuously compared against a rolling window of configurable length. A swing high locks in once price retreats sufficiently from the peak; a swing low locks in once price advances sufficiently from the trough, ensuring no repainting occurs. Zone Placement : A dual-layer box anchors at each confirmed pivot. An outer boundary encloses the broader reaction area and an inner zone concentrates the higher-probability interaction region. Age-Based Fading : Zone opacity diminishes progressively as elapsed bars accumulate since formation, weighting recent structural levels visually above older historical context. Breach Detection : A close beyond a zone's anchor level triggers conversion to a dotted outline and initiates an automatic removal sequence, purging invalidated structure from the chart. Swing Recording : Each completed leg is logged as a percentage magnitude and a bar duration into the rolling sample array, capped at the user-defined sample count with oldest entries discarded first. Statistical Aggregation : The selected method, weighted, average, or median, resolves the sample into an expected magnitude and duration for the forthcoming swing leg. Beam Construction : A three-layer cone extends forward from the current pivot anchor using smoothstep-eased interpolation, with width proportional to sample standard deviation and opacity grading across nested layers. Target Zone : A bounding box placed at the beam terminus presents the projected price level and expected percentage move, with box height communicating the degree of forecast uncertainty. Fibonacci Extensions : Configurable ratio levels project beyond the primary target, establishing pre-mapped objectives for continuation moves that exceed the base projection. These processes collectively sustain a live structural framework and a statistically-grounded projection that regenerates with every newly confirmed swing pivot. Interpretation Swing Structure Forecast should be read as a structural boundary map combined with a probabilistic directional projection: Support Zones (Green) : Constructed at confirmed swing lows, marking price regions where prior downside pressure exhausted and upward reversals originated. Resistance Zones (Red) : Established at confirmed swing highs, identifying areas where prior upside pressure stalled and downward reversals began. Zone Opacity : Communicates structural age. Vivid zones reflect recent pivot formation; subdued zones represent older levels retained for broader historical context. Broken Zones : Transition to faint dotted outlines on breach, preserved as reference markers without visually competing with structurally intact levels. Forecast Beam : Extends forward from the most recently confirmed pivot, projecting the statistically expected next leg. Cone width encodes uncertainty drawn from sample variance. Narrow Beam : Prior swing history shows consistent magnitude, indicating relatively high projection confidence. Wide Beam : Prior swing history shows significant variability, indicating greater uncertainty and warranting additional confirmation before acting. Target Zone and Label : Mark the statistically derived price destination alongside expected percentage move and absolute price level. Fibonacci Extensions : Pre-mapped levels beyond the primary target defining structured continuation objectives for extended directional moves. Path Markers : Dot markers positioned along the beam centerline with opacity fading toward the target, conveying projected trajectory and directional progression. Structural context, beam width, and sample consistency are more significant than any individual projected value in isolation. Signal Logic and Visual Cues Swing Structure Forecast operates through two principal visual frameworks: Structural Zones : Continuously maintained support and resistance boxes anchored at confirmed pivots. Intact zones carry unbroken structural relevance; broken zones document levels that price has already closed through and structurally dismissed. Forecast Beam : Repositions automatically on every new swing confirmation, simultaneously refreshing the beam geometry, target zone, path markers, and Fibonacci extensions to reflect the updated pivot origin and current statistical aggregation. Alert conditions trigger on confirmed swing high and swing low events, supporting systematic structural monitoring without requiring active chart observation. Strategy Integration Swing Structure Forecast applies across structure-based, mean-reversion, and trend-continuation trading methodologies: Structure-Referenced Entries : Treat intact zones as interaction boundaries for entry decisions, assigning greater weight to recently formed levels over aged, heavily faded structure. Instrument-Calibrated Targets : Use the statistical projection as a primary take-profit reference built from the instrument's own measured swing history rather than applied universal ratios. Beam Width Conviction Scaling : Adjust confirmation requirements relative to current beam width. Wide beams call for additional validation before committing; narrow beams reflect historically stable swing magnitude. Fibonacci Continuation Planning : Reference extension levels beyond the primary target when trending conditions suggest the initial projection may be exceeded. Broken Zone Flip Monitoring : Track recently breached zones as candidate reversal levels where former support may transition to resistance and vice versa following structural invalidation. Multi-Timeframe Structural Context : Reference higher-timeframe zones as macro boundaries while applying lower-timeframe forecast projections for entry precision and target identification. Sample Population Patience : Defer high-conviction treatment of projection outputs until the sample window has accumulated sufficient completed swings, particularly on instruments or timeframes with limited history. Technical Implementation Details Core Engine : Rolling highest/lowest pivot detection with configurable lookback and no-repaint confirmation logic Zone Construction : Dual-layer ATR-proportioned boxes with progressive opacity fading, breach detection, and automatic invalidation removal Statistical Model : Weighted, average, or median aggregation across configurable rolling sample with standard deviation uncertainty scaling Forecast Geometry : Smoothstep-eased three-layer polyline beam with standard deviation width scaling and graduated opacity Target Visualisation : Projection label with percentage move and price level enclosed by uncertainty-proportioned target box Fibonacci System : Five independently toggleable extension levels with fully configurable ratios Alert Coverage : Swing high confirmation and swing low confirmation events Performance Profile : Optimised for real-time execution across all timeframes with configurable zone capacity and sample limits Optimal Application Parameters Timeframe Guidance: 1 - 15 min : Near-term swing structure with short-horizon projection for intraday approaches 1H - 4H : Intraday to multi-session structural mapping with intermediate forecast range Daily - Weekly : Macro swing structure identification with extended projection targets Suggested Baseline Configuration: Swing Length : 16 Zone Width (ATR) : 0.3 Max Level Age : 300 bars Samples : 20 Method : Weighted Forecast Bars : 5 Fib Extensions : 1.0, 1.272, 1.618 active These suggested parameters serve as a starting baseline; their effectiveness varies with the instrument's volatility profile, characteristic swing cadence, and preferred zone density, so incremental adjustment across multiple session types is recommended before drawing performance conclusions. Parameter Calibration Notes Apply the following refinements to adjust behaviour without modifying core logic: Zones too wide : Lower Zone Width (ATR) to narrow zone boundaries, particularly on lower timeframes where ATR values produce oversized zones relative to typical price movement. Too many zones forming : Raise Swing Length to impose stricter structural requirements before a pivot qualifies for zone creation. Beam excessively wide : Sample history contains high variance. Raise Samples to dilute outlier legs or switch to Median to limit their influence on the projected magnitude. Projection slow to reflect recent behaviour : Lower Samples or switch to Weighted method to concentrate projection weight on the most recently completed swing legs. Significant pivots going undetected : Lower Swing Length to increase sensitivity and qualify shorter structural moves as confirmed pivots. Forecast visual range misaligned with chart : Modify Forecast Bars to adjust how far projection visuals extend rightward without altering the underlying price target calculation. Stale levels persisting on chart : Reduce Max Level Age to accelerate removal of older unbroken zones, keeping structural reference anchored to recent pivot history. Adjustments should be applied incrementally and assessed across varied session conditions rather than calibrated against a single market period. Performance Characteristics High Effectiveness: Markets exhibiting rhythmic swing sequences with clearly defined structural turning points Instruments where volatility follows identifiable expansion and contraction patterns that ATR captures proportionately Trend-continuation approaches targeting measured extensions derived from the instrument's own swing record Mean-reversion strategies using confirmed structural zones as primary entry and exit reference boundaries Reduced Effectiveness: Directionless, low-conviction conditions generating frequent shallow pivots that populate the sample with structurally insignificant measurements Event-driven or gap-heavy sessions producing swing magnitudes that are unrepresentative of normal instrument behaviour Instruments with erratic or non-stationary volatility profiles where ATR-based proportioning loses consistency Early sessions on a given timeframe before sufficient completed swings have accumulated to produce statistically reliable projections Integration Guidelines Confluence : Pair with BOSWaves volume tools, order flow indicators, or broader market structure analysis to reinforce zone and forecast interpretation Sample Discipline : Reserve high-conviction treatment for projections generated once the sample window is fully populated with completed swings Breach Acceptance : Treat breached zones as structurally void and resist anchoring expectations to levels price has already invalidated with a closing breach Beam Width Respect : Read a wide beam as a requirement for additional confirmation before acting, not permission to disregard the projection entirely Directional Consistency : Sustain bias aligned with the current forecast direction until a newly confirmed swing pivot shifts the projection origin Timeframe Confluence : Highest-quality structural setups emerge when active zones and forecast direction correspond across multiple timeframes simultaneously Disclaimer Swing Structure Forecast is a professional-grade swing structure and statistical forecasting tool. All projections are derived from historical swing behaviour and represent probabilistic expectations rather than assured outcomes. Performance depends on the consistency of prior swing history, prevailing market conditions, parameter selection, and disciplined application. BOSWaves recommends deploying this indicator as one component within a comprehensive analytical framework incorporating trend context, volume analysis, and rigorous risk management practices.

Indikator

Spline Quantile Regression Channel [LuxAlgo]The Spline Quantile Regression Channel indicator implements an advanced non-linear regression model to fit a flexible, multi-level channel over recent price action. Unlike standard linear regression which identifies the mean trend, this tool fits specific price percentiles (quantiles) using cubic splines, providing robust support and resistance zones that adapt to market volatility and non-linear structures. 🔶 USAGE The indicator is designed to provide a sophisticated view of the current trend and its extremes. By fitting cubic splines to specific quantiles, the script offers a "bendable" channel that can follow complex price movements more accurately than traditional straight-line regressions. 🔹 Trend Identification The median line (default 0.5 quantile) represents the central tendency of the price action. When the spline is sloping upward, it indicates a non-linear bullish regime; a downward slope indicates a bearish regime. 🔹 Support and Resistance The upper and lower bands represent the specified extremes (e.g., the 90th and 10th percentiles). These act as dynamic boundaries: Prices reaching the upper band often indicate overextended conditions within the current lookback period. Prices reaching the lower band suggest the asset is trading at the lower end of its recent distribution. 🔹 Forecasting The indicator projects the calculated spline into the future using a dashed line. This forecast is a mathematical extrapolation of the current non-linear trend, helping traders visualize where the price distribution is headed if the current momentum and curvature persist. 🔶 DETAILS The script employs several advanced mathematical concepts to ensure accuracy and stability: Cubic Spline Basis: The model uses a piecewise polynomial basis ($1, x, x^2, x^3$) combined with truncated power functions at "knots." This allows the curve to change its curvature locally, adapting to swings that a simple polynomial cannot capture. Quantile Optimization: Instead of minimizing squared errors (OLS), the script uses an Iteratively Reweighted Least Squares (IRLS) solver to minimize the "check function." This allows the script to target specific percentiles of the price data. Numerical Stability: To prevent matrix overflows common in high-degree polynomial calculations, the script standardizes price data (Z-score) and scales time coordinates between 0 and 1 before performing matrix inversion. 🔶 SETTINGS 🔹 Spline Configuration Lookback Period: The number of historical bars used to fit the spline regression. Larger windows result in a more "macro" trend, while smaller windows react quickly to recent changes. Internal Knots: Determines the "flexibility" of the spline. More knots allow the curve to follow price swings more tightly, while fewer knots yield a smoother, more rigid curve. 🔹 Optimization IRLS Iterations: The number of optimization passes for the solver. Higher values improve the accuracy of the quantile fit, especially in volatile markets. Forecast Length: The number of bars to project the calculated spline into the future. 🔹 Quantile Levels Upper Quantile: The specific percentile for the upper band (e.g., 0.95 for the top 5%). Median Quantile: The central percentile (typically 0.5 for the median). Lower Quantile: The specific percentile for the lower band (e.g., 0.05 for the bottom 5%). 🔹 Visuals Colors: Individual color settings for the upper, median, and lower bands. Line Width: Controls the thickness of the polylines rendered on the chart.

Indikator

11 22 33 44 55 66 77 88

Dibuat oleh manusia

Data pasar tertentu disediakan oleh ICE Data Services. Data referensi tertentu disediakan oleh FactSet. Hak Cipta © 2026 FactSet Research Systems Inc.Hak Cipta © 2026, American Bankers Association. Basis data CUSIP disediakan oleh FactSet Research Systems Inc. Seluruh hak dilindungi. Dokumen SEC dan dokumen lainnya disediakan oleh Quartr.© 2026 TradingView, Inc.

Lebih dari sekadar produk

Superchart

Penyaring

Saham
ETF
Obligasi
Koin crypto
Pasangan CEX
Pasangan DEX
Pine

Heatmap

Saham
ETF
Koin crypto

Kalender

Ekonomi
Perolehan
Dividen
IPO

Produk lainnya

Aliran berita
Portofolio
Grafik Fundamental
Kurva Imbal Hasil
Options
Peta Makro
Skrip Pine®

Aplikasi

Mobile
Desktop

Komunitas

Jejaring sosial
Tembok Cinta
Refer teman
Tata Tertib
Moderator

Ide-Ide

Trading
Edukasi
Pilihan editor

Skrip Pine

Indikator & strategi
Wizard
Freelancer
Paid Space

Peralatan & langganan

Fitur-fitur
Harga Akun
Data pasar
Paket hadiah

Trading

Ikhtisar
Broker
Perbandingan broker
The Leap

Penawaran spesial

Kontrak berjangka CME Group
Kontrak berjangka Eurex
Bundel saham AS

Tentang perusahaan

Tentang kami
Misi luar angksa
Blog
Pusat Bantuan
Karir
Media kit

Merchandise

Toko TradingView
Kartu tarot untuk trader
C63 TradeTime

Kebijakan & keamanan

Syarat penggunaan
Pernyataan Penyangkalan
Kebijakan Privasi
Kebijakan Cookie
Pernyataan Aksesibilitas
Tips keamanan
Program Pencari Bug
Halaman status

Solusi bisnis

Widget
Perpustakaan charting
Lightweight Charts™
Advanced Chart
Platform Trading

Peluang pertumbuhan

Periklanan
Integrasi broker
Program partner
Program Edukasi

Komunitas

Jejaring sosial
Tembok Cinta
Refer teman
Tata Tertib
Moderator

Ide-Ide

Trading
Edukasi
Pilihan editor

Skrip Pine

Indikator & strategi
Wizard
Freelancer
Paid Space

Solusi bisnis

Widget
Perpustakaan charting
Lightweight Charts™
Advanced Chart
Platform Trading

Peluang pertumbuhan

Periklanan
Integrasi broker
Program partner
Program Edukasi