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Regime-filter

AI SuperTrend [PickMyTrade]THE PROBLEM WITH A FIXED MULTIPLIER Every standard SuperTrend applies the same ATR multiplier across all market conditions — the same constant during a strong trending breakout, a narrow choppy range, and a volatility spike. A value calibrated for one regime is miscalibrated for the others. Most traders compensate by manually switching timeframes or parameters. This script automates that decision. The question it asks: what if the ATR multiplier were selected from historical bars that most resembled the current market regime — matched by Hurst state and volatility rank — rather than set by the user as a fixed constant? ──────────────────────────────────────── THREE AI LAYERS Layer 1 — Hurst Exponent (Regime Gate) The Hurst Exponent is computed via Rescaled Range (R/S) Analysis. It measures the degree of long-range dependence (memory) in the price series: H ≥ 0.55 → Persistent (trending) — SuperTrend active, signals enabled H ≈ 0.50 → Random walk — candles turn gray, no signal generated H ≤ 0.45 → Anti-persistent (mean-reverting) — signals suppressed The Hurst gate is the first filter. Signals only fire when market structure is historically associated with persistence — not randomness or mean-reversion. Layer 2 — Garman-Klass Volatility Rank Garman-Klass (1980) estimates realized volatility from OHLC prices rather than close-to-close returns, capturing intrabar price range and making it more sensitive to volatility changes. The current reading is percentile-ranked against recent history (0–100%) and used as the second feature dimension for the KNN search. Layer 3 — KNN Multiplier Optimizer K-Nearest Neighbors searches a rolling memory bank of feature pairs from previous bars. For each current bar it finds the K most similar historical bars by Euclidean distance in that 2D feature space. From those neighbors it retrieves the ATR multipliers that were in effect — weighted by the profitability of the bar that followed. The result is the AI Multiplier: a context-aware value drawn from the most similar past conditions, not a fixed constant. ──────────────────────────────────────── WHAT YOU SEE ON THE CHART Candle color — the defining visual. Orange = bull trend regime, blue = bear trend regime, gray = random walk or mean-reverting. The regime state is readable on every bar without checking the table. Signals — ● (circle) marks trend flips with Hurst ≥ 0.65, the high-conviction threshold. ▲▼ (triangle) marks standard threshold crossings. No signal fires in gray (random or mean-reverting) regimes. SL / TP lines — dashed lines drawn automatically at each signal bar, sized from current ATR × the AI Multiplier active at that bar. Info table (top right) — live display of Hurst value, Regime label, Direction, AI Multiplier, Vol Rank, and KNN memory bar count. Shows WARMUP until KNN has stored enough bars to begin optimizing. ──────────────────────────────────────── HOW TO USE A signal requires all three conditions to be true simultaneously: KNN is warmed up (table shows ● LIVE) Hurst confirms a trending regime (H ≥ Trend Threshold input) Price is on the correct side of the EMA filter Circle signals (●) indicate Hurst has exceeded 0.65 — stronger persistence than the standard threshold. Triangle signals (▲▼) are at the user-defined threshold. Gray candles indicate the market is not in a trending regime; reducing exposure or standing aside is appropriate during those periods. The regime background shading (faint orange or blue fill) shows when the SuperTrend is in an active directional state. ──────────────────────────────────────── INPUTS AI Engine — Hurst Lookback, KNN Neighbors (K), KNN Memory (bars), Trend Threshold, Mean-Revert Threshold SuperTrend — ATR Length, Base Multiplier, Volatility Window, EMA Period Visual — Bull/Bear colors, SL/TP lines toggle, SL ATR Multiplier, Risk:Reward ratio, Regime Background Display — Zen Mode (hides labels and table), Show Info Table ALERTS Three alert conditions: Long Signal, Short Signal, Any Signal. ──────────────────────────────────────── NOTES KNN requires warmup equal to the KNN Memory setting before the AI Multiplier activates. During warmup the base multiplier scaled by Hurst and volatility rank is used instead. No repainting. All signals are confirmed on bar close. KNN stores lagged values only. Garman-Klass citation: Garman, M. & Klass, M. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business , 53(1), 67–78.
Indicator
by PickMyTrade_Official
2
Anchored VWAP Reversion ChannelAnchored VWAP Reversion Channel — Regime-Gated Fade Framework ## What this script does This is an **analytical study** that frames mean-reversion ("fade") setups around an **anchored, volume-weighted regression channel**, then **gates** those setups by a statistical market-state test and **scores** them against their own forward outcomes. It does not place orders and it is not a signal service — its purpose is to let you see, on your own instrument and timeframe, whether fading a stretched move actually has an edge, instead of assuming it does. It plots one channel (a centre line plus inner/outer residual-σ bands), marks fade setups at the outer band, draws supporting context (volume-profile POC / value area, untested prior-session POCs, momentum divergences, liquidity sweeps, and multi-timeframe trend lines), and reports a compact validation panel. ## Why these components are combined (mashup rationale) Fading an extreme is really three separate questions, and no single classic indicator answers all three. Stacking look-alike indicators just echoes one input, so this tool deliberately combines **three non-redundant lenses and makes them check each other**: 1. **WHERE is price stretched?** — A **volume-weighted polynomial regression** anchored at the most recent swing pivot, with **residual-σ bands**. Because the curve tilts with the active leg, an outer-band tag stays meaningful even inside a trend, where a flat cumulative VWAP would not. A **volume profile** anchored to the *same* window supplies POC and value area, and prior-session POCs that have never since been traded through become **reversion targets**. 2. **Is a reversion actually firing here?** — Three orthogonal **tells** evaluated only at the band: a **close-back rejection**, a **band-confluent momentum divergence**, and an **equal-high/low liquidity sweep** (stop-run). Crucially, all three are derived from the same stretch, so their agreement is shrunk by a **design-effect correction** (effective-sample-size): three correlated echoes are not allowed to masquerade as three independent confirmations. 3. **Is the market in a reverting state at all?** — A **regime gate** combining a **variance-ratio test** and a **reversion-trust correlation** only lets a fade through when recent increments are offsetting (mean-reverting) rather than compounding (trending). The pieces are not bolted together side by side: they share **one geometry** (the anchored channel) and **one volatility unit** (residual σ / ATR), and each can veto the others. A band tag with no tell does nothing; a tell with no reverting regime does nothing. The design goal is to **suppress** low-quality fades — into a trend, mid-range, or backed by a single echoed tell — more than to generate them. ## The honesty layer (what makes this more than a drawing) Every fade that fires is logged and, a fixed horizon later, **resolved**: its forward return is measured in ATR units and tabulated **with the regime gate ON versus OFF**, reporting follow-through %, whipsaw %, a Wilson 95% confidence interval, and the **mean return per fade**. A per-fade series also exports to the Data Window so you can study the full return distribution offline. The gate has to **beat its own ungated baseline** to justify itself — the framework is built to be tested, not trusted blindly. ## How to use it 1. Set the **Price source** (group 01). It works on any symbol and any market; volume-based parts need a real volume feed. 2. A fade **arms** when price tags the outer band **and** at least one tell prints, then **passes** only if the regime gate reports a reverting state. Solid triangles are gated fades; the target is the centre line or the nearest untested POC. 3. Read the panel top-down: does **Gate ON** beat **Gate OFF** on both follow-through and mean R, with non-overlapping intervals and a reasonable sample size? If not, the edge is not present on this symbol/timeframe — change them rather than forcing the trade. 4. The signal lives on **higher intraday timeframes**; one-minute data is mostly noise. ## Defaults Shipped tuned for **NSE:NIFTY** index futures on intraday timeframes (sources, pivot lengths, value-area %, and the Tuesday-style weekly session context reflect that instrument). Every value is exposed as an input — change the **Price source** and the relevant lengths to run the framework on any other instrument or market. ## What is original The original work is the **coordination**, not any single formula: an anchored polynomial-regression channel used as a reversion frame, three decorrelated band tells fused by a design-effect shrink, a statistical regime gate, and a built-in A/B + forward-return validation harness — combined so each lens can veto the others and the whole thing reports its own hit rate. It is not a re-skin of one indicator. ## Concept credits (techniques are standard; this implementation is original) Anchored VWAP (standard); volume-weighted least-squares / polynomial regression (standard); residual-σ channel (standard); Volume Profile, Value Area and POC — Market Profile, Steidlmayer / CBOT; Variance-Ratio test — Lo & MacKinlay (1988); design effect / effective sample size — Kish (1965); proportion confidence interval — Wilson (1927); ATR trailing stop / Supertrend (classic, used for the multi-timeframe context lines); RSI — Wilder; Stochastic — Lane. ## Disclaimer For research and education only. This is an analytical study, **not** financial advice, **not** a recommendation, and **not** a guarantee of future results. All statistics shown are **in-sample** on loaded history, close-to-close, without costs or slippage — a study aid, not a backtest. Mean reversion fails in trends and through regime breaks. Do your own research and manage your own risk.
Indicator
by Market_Logic_India
1
Covenant Regime Atlas [JOAT]Covenant Regime Atlas Introduction Covenant Regime Atlas is an open-source Pine Script v6 market-regime indicator built to classify directional state through trend, expansion, persistence, and retest quality. Its purpose is not to predict the next trade by itself, but to create a durable bias layer that tells the trader whether the market is developing a bullish regime, a bearish regime, or a maturing directional environment worth respecting. The problem this script solves is context instability. Many traders can spot a moving-average crossover or a burst in ATR, but that alone does not answer whether the regime is actually mature, whether momentum has real separation, or whether recent retests are behaving consistently with the dominant trend. Covenant Regime Atlas addresses this by blending multiple regime components into one overlay and dashboard. The script uses a dual-mid framework derived from EMA and HMA references, ATR-scaled cloud and envelope bands, persistence measurement, heat normalization, slope impulse, and retest memory. This lets it move beyond a simple bullish-versus-bearish cross and instead describe whether the regime is developing, mature, expanding efficiently, or internally cooling. The result is an indicator for traders who want a cleaner read of bias before interpreting any trigger tool. It is especially useful as a regime filter for execution indicators and strategies that should behave differently in mature directional flow versus unstable transition periods. Core Concepts 1. Directional Mid Versus Structural Mid The script creates a fast directional midpoint and a slower structural midpoint using blended EMA and HMA references. The spread between those two curves forms the backbone of regime direction. float directionalMid = math.avg(emaFast, hmaFast) float structuralMid = math.avg(emaSlow, hmaSlow) bool trendBull = directionalMid > structuralMid This gives the regime engine more shape than a single moving average crossover. The directional mid measures active flow. The structural mid measures slower context. 2. Regime Strength Through Separation And Heat Regime strength is calculated from ATR-normalized spread plus the distance of normalized heat from its midpoint. In other words, the regime is strongest when the fast and slow structures are well separated and price is also positioned decisively within its recent range. This helps avoid overvaluing tiny directional crosses that occur with little actual separation or energy. 3. Persistence And Maturity Every regime needs time to prove itself. The script counts how long the current directional condition has been intact and compares that against a user-defined persistence floor. Once the threshold is met, the regime is treated as mature rather than merely developing. This matters because a fresh directional flip is different from a directional condition that has held for many bars and survived multiple retest opportunities. 4. Retest Memory After a mature regime forms, the indicator watches for controlled retests of the directional midpoint. Bull retests occur when price revisits the midline from above and closes back above it. Bear retests use the opposite condition. The last retest is stored as a dotted line and extended forward until it becomes irrelevant. This gives the trader a simple memory of where the market most recently confirmed trend participation. 5. Pulse, Expansion, And Efficiency The script also measures volatility expansion, slope impulse, heat drift, trend separation percentage, and directional travel efficiency. These metrics allow the dashboard to distinguish between a mature regime that is expanding forcefully and one that is mature but internally cooling or grinding. Features Bull and bear regime classification: Uses fast-versus-slow blended midpoints to define directional control Maturity logic: Distinguishes developing regimes from mature ones using persistence counting ATR-scaled cloud and envelope: Frames the current directional corridor directly on the chart Retest memory engine: Stores the latest mature-regime retest level for forward reference Initiation band: Preserves the regime start envelope so traders can judge distance from the original launch zone Pulse ribbon: Adds a compact visual band around price to reflect internal heat conditions Regime backdrop shading: Tints the chart according to the active directional state Detailed dashboard: Displays strength, heat, persistence, expansion, slope pulse, retest distance, maturity, efficiency, and more Confirmed-bar alerts: Includes mature bias, retest, expansion, continuation, efficient trend, and heat-reset conditions Data-window outputs: Exposes regime internals for systematic reading or comparison Visual Elements Directional cloud: The gap between the fast and slow regime mids shows whether the market is operating with clean separation Envelope bands: ATR-based boundaries help frame the active directional corridor around price Initiation band: The regime launch area stays visible so users can measure how far the trend has traveled from origin Retest line memory: The latest confirmed retest is preserved as a direct chart reference Backdrop and pulse ribbon: Context shading and the pulse band make regime character readable without overloading the chart Best Practices Treat mature regimes differently from developing ones because the same trigger can behave very differently in each state Watch heat drift when a regime remains mature but starts losing internal energy Use retest memory to frame participation zones rather than chasing every extension away from the midline Give more weight to regimes that show both persistence and expansion instead of one without the other Use the atlas as a context engine first and an alert source second Input Parameters Trend Engine: Fast Length: Sets the faster directional reference Slow Length: Sets the slower structural reference Heat Window: Defines the range-normalization window for heat calculations ATR Length: Controls volatility normalization Cloud Width Factor: Sets the width of the directional cloud and envelope Retest Engine: Show Retest Memory: Toggles retest storage and line rendering Retest Cooldown Bars: Prevents retests from firing too frequently Persistence Floor: Sets how many bars are required before a regime is considered mature Show Initiation Band: Displays the preserved start range of the current regime Maturity Window: Controls maturity scaling and travel-efficiency measurements Display: Show Dashboard toggle Show Regime Backdrop toggle Show Pulse Ribbon toggle Independent bull, bear, neutral, and panel colors How to Use This Indicator Step 1: Read Regime Tag And Strength Begin with the dashboard’s regime tag. It tells you whether the market is bullish or bearish and whether that state is still developing or already mature. Pair that with the strength reading to avoid confusing a weak directional bias with a strong one. Step 2: Check Persistence And Expansion Persistence tells you how long the regime has survived. Expansion tells you whether volatility is supporting the move. A mature regime with positive expansion usually deserves more respect than a new regime with weak expansion. Step 3: Use Retest Memory As A Structural Anchor When the retest line is present, it marks the last meaningful participation check inside the trend. That line can help frame whether the current move is still building from a healthy base or drifting too far away from supportive structure. Step 4: Watch Heat Drift And Efficiency Heat drift helps show whether the regime is internally warming or cooling. Efficiency tells you whether directional travel has been orderly. These readings are helpful when deciding whether the trend still looks clean or is becoming unstable. Step 5: Use It As The Bias Layer For Other Tools Covenant Regime Atlas is best used as a bias filter. It helps define whether you should be thinking continuation, pullback participation, or caution. Pair it with your own trigger logic rather than using the regime alone as a full trading plan. Indicator Limitations A developing regime can fail before reaching maturity, especially in choppy markets Retest memory is useful for context, but the stored retest level is not guaranteed to hold on future tests Efficiency and heat drift are descriptive metrics, not predictive guarantees of continuation The indicator can still classify a directional state during periods where execution conditions are poor for actual trading Originality Statement Covenant Regime Atlas is original in the way it blends trend separation, maturity, retest memory, expansion, and efficiency into a unified regime overlay. It is not just a moving-average cloud with added cosmetics: It separates directional identity from maturity, allowing the user to distinguish developing and established regimes It stores retest memory as a living structural feature instead of relying only on static crossover logic It combines heat, slope, expansion, and efficiency into one dashboard so regime quality can be judged from multiple dimensions It preserves the initiation band of the current regime, which gives context that typical trend overlays do not maintain Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Regime readings describe current market structure and internal state based on historical prices. They do not guarantee future movement or profitable trading decisions. Always use independent judgment and proper risk management. -Made with passion by jackofalltrades
Indicator
by officialjackofalltrades
Delta Pressure Ledger [JOAT]Delta Pressure Ledger Introduction Delta Pressure Ledger is an open-source lower-pane pressure model built entirely from chart-derived proxies. It combines anchored VWAP context, candle pressure, volume impulse, crowding stretch, volatility pressure, and settlement skew into a normalized composite ledger that classifies whether pressure is balanced, directional, crowded, or stressed. The problem this script solves is hidden market pressure. Many traders rely on unavailable data feeds or vendor-only metrics to estimate crowding or liquidation risk. Delta Pressure Ledger uses only chart-accessible inputs and standardizes them through z-score normalization so pressure states can still be read in a consistent way across instruments. Core Concepts 1. Chart-Derived Pressure Proxy The script estimates directional pressure from candle settlement, intrabar range occupation, and volume impulse rather than external order flow feeds. 2. Anchored VWAP Context Pressure is interpreted relative to anchored value, allowing the user to distinguish directional expansion from overstretched crowding. 3. Z-Score Normalization All sub-engines are normalized over a configurable lookback, which makes the composite reading more portable across symbols and timeframes. 4. Crowding and Stress Logic The script tracks when price and derived sentiment become stretched enough to imply elevated liquidation or unwind risk. 5. Composite Verdict Pressure, crowding, volatility, and skew are merged into one verdict state so the user can quickly determine whether the market is orderly, imbalanced, or stressed. Features Anchored VWAP context: Session, weekly, or monthly value anchor Pressure engine: Candle and volume-derived directional pressure model Crowding engine: Stretch and behavioral excess detection Volatility and skew layers: Pressure quality and instability are separated from raw direction Normalized composite score: All sub-engines standardized into one comparable ledger Risk meter: Liquidation-style stress estimate derived from crowding and instability Confirmed-bar transitions: State changes and alerts are held to confirmed bars Top-right dashboard: Regime, pressure, crowding, volatility, risk, composite score, and last confirmed flip How to Use This Indicator Step 1: Read the composite verdict The verdict gives the fastest summary of whether the market is balanced, directionally pressured, or entering a crowded stress state. Step 2: Separate pressure from crowding A bullish pressure reading with low crowding is different from a bullish pressure reading with extreme crowding and high risk. Step 3: Respect risk transitions When the risk meter moves into elevated territory, directional continuation setups deserve more caution. Indicator Limitations This script uses chart-derived proxies rather than exchange-level liquidation or true open-interest feeds Normalized readings can still behave differently across asset classes with unusual volume structure Stress conditions can remain elevated for extended periods during strong trends The script classifies pressure and risk context; it does not execute trades by itself Originality Statement Delta Pressure Ledger is original in the way it builds a portable, chart-derived pressure and crowding framework without depending on unavailable external feeds, while still organizing the result into a normalized composite and risk ledger. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Derived pressure and crowding models can be wrong, especially during atypical market events. Use proper risk management and independent judgment.
Indicator
by officialjackofalltrades
ROC Regime Filter [HYPR-run]DESCRIPTION: A reliable universal regime filter across all assets, all timeframes. Rate of change filter that classifies price action into regime states. A suite of smoothed EMAs feeds a layered ROC engine that detects when fast momentum aligns with, or diverges from, slow structure. The filter measures; it doesn't predict. When all ROC layers stack in the same direction (parallel alignment), the trend is confirmed by arithmetic. When fast ROC diverges from slow, the regime shifts. The lag is the cost of certainty. Sweet spot is 1hr to 1D; lower timeframes get noisy. DISCOVERING EDGE In order to gain a persistent, mechanical edge in which trades are permitted and which are filtered out, we explored a more meaningful expression of regime classification using layered multiple ROC periods to detect when fast momentum aligns with or diverges from slow structure. This resilient regime filter has been the backbone for our automated strategies since 2021. LAYERED ROC vs SINGLE-INDICATOR REGIME A single RSI or ADX reading flattens the market into binary (trending/not trending). Layered ROC alignment separates six distinct states, each with different permissible trade types, so the filter matches the complexity of what the market is actually doing. Six regime states gate every decision; the combination of regime color + ROC slope is the trade filter, not either one alone. Phase transitions (green to yellow, orange to green) are the actionable signals; static states just confirm what's already happening. Webhook alerts fire on macro pivots (accumulation/distribution inflections) at the regime transition, not after the move has run. FEATURES - Six regime states from layered ROC alignment (see color legend below) - Early trend detection when all layers accelerate in parallel - ROC 200 line with regime-colored gradient fill - Macro pivot detection: strong trend exhausting into sideways, scored by where ROC 200 sits relative to its all-time range - Accumulation/distribution context in dashboard - ROC 200 pivot high/low divergence markers on main chart - Consolidation markers with conviction scoring (normal vs extreme) - Gradient candle overlay (ROC Sticks; toggle on/off) - Two-row dashboard: row 1 = macro context (accumulation/distribution), row 2 = current regime state with directional qualifier and slope - Dashboard dark/light theme toggle for any chart background - Full ROC stack in data window for manual analysis - Webhook alerts on macro pivots (accumulation/distribution) HOW IT WORKS ROC alignment is the core signal. When all layers stack in the same direction, that's strong trend territory (green). When fast ROC diverges from the slower layers while slow structure still holds, the engine reclassifies from strong trend to sideways (yellow), flagging a pullback rather than trend failure. Deeper corrections where intermediate layers fall below the structural anchor fire orange, indicating a correction within the primary trend. Macro pivots fire at the inflection: strong trend exhausting into sideways for the first time. The consolidation score layers this with where ROC 200 sits in its all-time range. Consolidation at extreme ROC readings (bright green/red dots) is the highest-conviction signal for reversal. HOW TO USE Read the regime color, not the price. Green = strong trend long, red = strong trend short, orange = deeper correction, yellow = short pullback, white = directionless. Use regimes as a directional gate: longs during green, shorts during red. Yellow flags a pullback within trend; wait for resolution back to green/red before re-entering. Orange is a deeper correction; patience or fade with confirmation from other tools. The highest-edge signals come from regime transitions, not static states. Watch for: green breaking into yellow (macro pivot, potential reversal), extended yellow resolving back to green (continuation re-entry), and the ROC slope within a regime (slope rising in orange = trend about to resume). The data window shows the full ROC stack across all layers. When fast ROC diverges from slow, that signals continuation or reversion. MACRO CONTEXT (Dashboard Row 1) REGIME COLOR LEGEND (Dashboard Row 2) ALERTS Macro pivot long fires when accumulation is detected (bull inflection). Macro pivot short fires when distribution is detected (bear inflection). Create alert: condition = this indicator, "Any alert() function call". Paste your webhook URL, set Open-ended, create. Alert payload is built into the script; works with any webhook receiver. CREDITS Advance/Decline gradient function: LucF
Indicator
by HYPR-run
Kalman Regime [Jamallo](2025) Intro Kalman Regime filters price through two stages — a Gaussian kernel weighted average followed by an adaptive Kalman filter — to produce a smooth, noise-resistant baseline. ATR is used throughout to make the system volatility-aware. Breakdown The Gaussian kernel pre-smooths price using a bell-shaped weight profile that blends recency bias (recent bars matter more) with center localization (edge bars contribute less). This pre-smoothed value is then passed into an adaptive Kalman filter, which recursively estimates the "true" price state by balancing how much to trust the new measurement versus the prior state — with that balance dynamically scaled by current ATR. The resulting baseline drives regime detection through a two-tier state machine. A strong signal fires when price breaks the ATR envelope and the baseline slope confirms direction. A weaker signal fires when price crosses the baseline and slope exceeds an ATR-scaled threshold. This dual-gate structure reduces whipsaws without adding lag. END A two-stage price filter combining a Gaussian kernel pre-smoother with an adaptive Kalman filter, using ATR envelopes and slope confirmation for regime detection.
Indicator
by Jamallo22
Synthetic IV Rank [UAlgo]Synthetic IV Rank is a volatility analysis indicator that creates a practical proxy for IV Rank when direct options implied volatility data is not available. Instead of reading an options chain, the script estimates a synthetic volatility series from price action using a Yang Zhang style historical volatility model, then normalizes that value into a 0 to 100 rank across a user defined lookback window. This makes the tool especially useful for traders who want an IV Rank style workflow on instruments or markets where true implied volatility is unavailable, limited, or inconsistent. The indicator can be used on stocks, indices, forex, commodities, and crypto, with a dedicated Crypto Mode for annualization based on 365 days. The script is built as a separate pane indicator and focuses on clear regime awareness. It plots a smooth IV Rank line, adds visual threshold references for high and low volatility zones, and shows a live status label on the most recent bar with both the current rank and the raw synthetic volatility value. The overall design makes it suitable for fast regime checks, mean reversion context, premium selling style filters, and volatility expansion monitoring. Important note: This is a synthetic IV Rank approximation based on historical price volatility. It is not a substitute for true options implied volatility from an options chain. 🔹 Features 🔸 1) Synthetic IV Rank Using a Yang Zhang Style Volatility Model The indicator estimates volatility from price data using a Yang Zhang style framework, then converts that estimate into an IV Rank style percentile scale. This gives users an IV Rank like signal even when broker feeds do not provide options implied volatility. 🔸 2) Engine Based Design with Structured Inputs The script uses a custom VolatilityEngine type to organize the core calculation parameters: hv_length for volatility estimation length lookback_length for IV Rank normalization window annual_factor for market specific annualization This structure makes the logic easier to maintain and extend. 🔸 3) Stock and Crypto Annualization Modes The engine supports two annualization conventions through a simple input toggle: 252 day convention for traditional markets 365 day convention for crypto markets This is a practical detail because the same raw volatility process can produce different annualized values depending on the asset class. 🔸 4) Robust IV Rank Normalization with Safe Fallback The script computes IV Rank by comparing the current synthetic volatility value against the lowest and highest values over the selected lookback period. If the range collapses to zero, the script safely assigns a neutral rank of 50 instead of causing a division issue. This improves reliability in flat or low variance periods. 🔸 5) Clear Regime Visualization The indicator includes a strong visual layout designed for quick interpretation: A main IV Rank line A mid reference line at 50 High and low threshold markers at 80 and 20 Gradient fills that visually emphasize extreme zones This helps users recognize volatility regime shifts at a glance. 🔸 6) Dynamic Last Bar Status Labels On the latest bar, the script prints a compact status label that shows: Current IV Rank value Regime label such as EXTREME FEAR, COMPLACENCY, or NEUTRAL It also adds a secondary information label showing the raw Yang Zhang synthetic volatility percentage. This gives both normalized context and raw measurement at the same time. 🔸 7) Practical Regime Classification The script uses simple but effective thresholds for regime tagging: Above 80 signals elevated volatility conditions Below 20 signals compressed volatility conditions Between 20 and 80 is treated as neutral These thresholds align with common IV Rank style interpretation used in discretionary and systematic workflows. 🔹 Calculations 1) Engine Initialization On the first bar, the script initializes the custom volatility engine and stores: The Yang Zhang calculation length The IV Rank lookback window The annualization factor based on asset class mode If Crypto Mode is enabled, the annual factor uses the square root of 365. Otherwise it uses the square root of 252. 2) Synthetic Volatility Source Series The indicator computes a Yang Zhang style historical volatility estimate from OHLC data. The model combines multiple variance components so it can capture more market behavior than a simple close to close volatility series. The script calculates: A first variance component labeled as overnight in the comments An open to close variance component A Rogers Satchell style intraday variance component from high, low, open, and close relationships 3) First Variance Component (Commented as Overnight) In the current implementation, the first component is built from the logarithmic return of close / close , and its rolling variance is measured over the user defined length. This is important to note because the code comments describe an overnight style component, while the actual formula uses consecutive closes in the current version. 4) Open to Close Variance Component The script computes the logarithmic open to close return log(close / open) and then applies a rolling variance over the selected length. This measures intrabar movement relative to the bar open. 5) Rogers Satchell Intraday Variance Component To improve intraday variance estimation, the script calculates a Rogers Satchell style daily variance term using four logarithmic relationships derived from high, low, open, and close. It then smooths this with a rolling simple average across the same volatility length. This component is useful because it incorporates more intrabar range information than a simple close based measure. 6) Yang Zhang Weighting Factor The script computes a weighting coefficient k that depends on the volatility length. This weight balances the contribution of the open to close variance and the Rogers Satchell component in the final combined variance. The implementation follows the standard Yang Zhang style weighting formula shown in the script comments. 7) Combined Synthetic Volatility and Annualization After computing the variance components, the script combines them into a single Yang Zhang style variance estimate, takes the square root to obtain volatility, and annualizes the result with the engine annual factor. The final synthetic volatility value is expressed as a percentage. In practical terms, this output is the raw volatility series that the script later converts into Synthetic IV Rank. 8) IV Rank Calculation The IV Rank logic measures where the current synthetic volatility sits relative to its historical range over the selected lookback: It finds the lowest synthetic volatility in the lookback window It finds the highest synthetic volatility in the lookback window It scales the current value to a 0 to 100 rank If the lookback range is flat, the script assigns 50.0 as a neutral fallback. This normalization step is what makes the output behave like an IV Rank style regime indicator rather than a raw volatility plot. 9) Regime State Classification On the latest bar, the script assigns a text state from the current IV Rank: EXTREME FEAR when rank is above 80 COMPLACENCY when rank is below 20 NEUTRAL otherwise The label color also changes with the state, which improves visual scanning. 10) Visual Layer Logic The visualization includes: A plotted IV Rank line Hidden boundary plots for fill anchors Visible threshold markers at 80 and 20 A midpoint reference line at 50 Gradient fills for high volatility and low volatility zones The fill logic visually intensifies as the IV Rank moves deeper into an extreme zone, helping users identify volatility compression and expansion phases quickly. 11) What This Indicator Represents in Practice This script is best understood as a normalized historical volatility regime tool designed to mimic the workflow of IV Rank when direct implied volatility data is not available. It is excellent for context filtering and regime awareness, but it should not be interpreted as a true options market implied volatility feed.
Indicator
by UAlgo