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Volatilityindicator

Adaptive Flow | AnonycryptousAdaptive Flow | Anonycryptous Description & user manual Why this indicator is different Most trend-following indicators use a fixed moving average. A 50-period EMA moves at the same speed whether the market is trending hard or grinding sideways. It cannot distinguish between a clean directional move and noise. It gives the same weight to a volatile consolidation as it does to a strong impulse. The result is signals that arrive late in trends and fire repeatedly during chop. Adaptive Flow works differently. At its core is a Kaufman Adaptive Moving Average — a moving average that measures the efficiency of price movement on every single bar and adjusts its own speed accordingly. When price is moving cleanly in one direction with low noise, KAMA accelerates toward price. When price is churning sideways with high volatility and no direction, KAMA nearly stops moving. It adapts. Automatically. Without requiring any manual intervention. This adaptive behavior feeds into an ATR-based trailing stop that uses a ratchet mechanism — it only moves in the direction of the trade and locks in the trend floor or ceiling. When KAMA crosses the trailing stop, the trend flips. A signal fires. But a signal alone is not enough context. Adaptive Flow adds a macro trend filter via the EMA 200, a session-anchored VWAP as a value reference, and an optional noise gate that prevents KAMA from updating on insignificant price moves. Every component has a purpose. Together they form a complete adaptive trend system that tells you not just what the trend is, but whether the conditions are right to act on it. Important notice Adaptive Flow generates signals based on technical indicator alignment. These signals are not financial advice. They do not predict the future. They do not guarantee profitability. All trading decisions are made entirely by the user. Always manage your own risk. Always apply your own judgment. 1. Overview Adaptive Flow is an adaptive trend-following indicator built around a Kaufman Adaptive Moving Average with an ATR-based trailing stop. It includes a macro trend filter, a session-anchored VWAP, a noise gate, three presets, and a live dashboard. What it includes: - Kaufman Adaptive Moving Average with configurable efficiency ratio period and fast/slow smoothing constants - ATR trailing stop with ratchet mechanism and cloud fill between KAMA and the stop line - Noise filter gate that prevents KAMA from updating on insignificant price moves - EMA 200 macro trend filter with higher timeframe input support - Session-anchored VWAP with daily and custom session modes and timezone selection - Dual-layer triangle signals at trend reversals, filtered by the EMA 200 - Bar coloring reflecting the current trend state - Three presets: default for swing, fast for scalping, smooth for position trading - Live dashboard showing trend direction, KAMA position, EMA 200 status, VWAP position, noise filter state, and last signal 2. KAMA — Kaufman Adaptive Moving Average KAMA is the engine of Adaptive Flow. On every bar it calculates an Efficiency Ratio — a measure of how directionally efficient recent price movement is relative to total volatility over the lookback period. When the Efficiency Ratio is high — price is moving strongly in one direction with low noise — KAMA accelerates toward price using the fast smoothing constant. When the Efficiency Ratio is low — price is churning with high volatility and no clear direction — KAMA slows to a near-flat line using the slow smoothing constant. The result is a moving average that naturally tightens during trends, tracking price closely, and dampens during consolidation, filtering noise before it reaches the signal layer. Key inputs: KAMA length — the lookback window for the Efficiency Ratio calculation. Shorter periods (5–10) increase sensitivity to recent price swings. Longer periods (20–30) require more sustained directional movement before KAMA accelerates. Fast EMA length — the smoothing speed during trending conditions. Lower values make KAMA respond aggressively during strong trends. Default of 2 is the standard fast constant. Slow EMA length — the smoothing speed during ranging conditions. Higher values anchor KAMA firmly during choppy conditions. Default of 30 provides strong noise rejection. 3. ATR trailing stop The ATR trailing stop is the primary signal line plotted on the chart. It trails KAMA at a distance of ATR multiplied by the configured multiplier and uses a ratchet mechanism. In an uptrend the stop only moves upward, locking in the trend floor. In a downtrend it only moves downward, locking in the ceiling. The stop never moves against the direction of the trade. When KAMA crosses the trailing stop, the trend flips and the stop resets on the opposite side. A reversal signal fires at that bar. The fill zone between KAMA and the trailing stop line is rendered as a color cloud that reflects the current trend direction. Cloud transparency is configurable. Key inputs: ATR length — the volatility measurement period. Shorter periods respond quickly to volatility changes. Longer periods produce a more stable band. ATR multiplier — the distance of the trailing stop from KAMA. Lower values produce tighter stops with more frequent signals. Higher values allow trends more room and reduce whipsaws. 4. Noise filter The noise filter is an optional gate that prevents KAMA from updating on insignificant price moves. When enabled, KAMA only advances when the price displacement exceeds ATR multiplied by the noise threshold. Moves smaller than that threshold are ignored, keeping the trend line stable during low-momentum conditions. When disabled, KAMA updates every bar at full adaptive speed. This is optimal for strong trending environments where maximum responsiveness is required. On lower timeframes keep the threshold low (0.1–0.2) to avoid over-filtering fast price moves. 5. EMA 200 — macro trend filter The EMA 200 acts as a regime filter. Bullish reversal signals only fire when price is above the EMA 200, confirming a macro uptrend. Bearish reversal signals only fire when price is below the EMA 200, confirming a macro downtrend. This eliminates counter-trend entries and aligns every signal with the dominant market structure. The EMA 200 supports a higher timeframe input. On lower timeframes (1–15 minutes) it is strongly recommended to set this to 1H or 4H. The native EMA 200 on a 1 minute chart only covers a few hours of data and provides little macro context. A label showing the current EMA 200 value appears at the right edge of the chart for quick reference. The line can be hidden while keeping the EMA 200 active as a hidden signal filter. 6. VWAP — session-anchored volume weighted average price VWAP provides a volume-weighted price reference that resets at the start of each session. It represents the average price paid weighted by volume — a key reference for intraday value and institutional order flow. Two session modes are available: Daily — VWAP resets each calendar day. Standard reference for intraday traders. Custom — VWAP is anchored to a specific session defined by start and end times. Use this to isolate the Asia, London, or New York session, or any custom window. A timezone selector ensures correct boundary alignment regardless of exchange. A label showing the current VWAP value appears at the right edge of the chart. The line can be hidden while VWAP continues to calculate internally. On higher timeframes VWAP resets less frequently and its intraday reference value diminishes. Consider using it as a broader session value area reference in those cases. 7. Signals Trend reversal signals fire when KAMA crosses the ATR trailing stop. They are rendered as dual-layer triangle markers directly on the chart at the trailing stop line. A solid inner triangle marks the precise reversal point. An outer semi-transparent triangle creates a glow effect for visibility on busy charts. Up triangles indicate bullish reversals. Down triangles indicate bearish reversals. All signals are filtered by the EMA 200. Bullish signals only fire above the EMA 200. Bearish signals only fire below it. Bar coloring reflects the current trend state continuously between signals. 8. Presets Three preset configurations are available. Selecting a preset overrides the core calculation parameters. Visual settings and VWAP configuration remain under manual control. -Default — swing trading on 4H and daily charts KAMA 14 | Fast 2 | Slow 30 | ATR 14 | Multiplier 3.5 | Noise 0.3 Balanced efficiency ratio period with standard fast/slow constants. Adaptive behavior across most market conditions with moderate noise filtering. -Fast — scalping on 1 minute to 15 minute charts KAMA 6 | Fast 2 | Slow 15 | ATR 7 | Multiplier 1.8 | Noise 0.15 Short efficiency ratio period reacts immediately to momentum shifts. Tight ATR band and low multiplier keep stops close. Minimal noise filtering preserves full adaptive speed. -Smooth — position trading on daily and weekly charts KAMA 21 | Fast 3 | Slow 40 | ATR 21 | Multiplier 5.0 | Noise 0.5 Extended efficiency ratio period demands sustained momentum before accelerating. Wide slow constant and aggressive noise filtering ensure only high-conviction moves generate signals. 9. Dashboard The dashboard displays the live state of all indicator components and updates on every bar close. Rows displayed: - Trend — current state: bullish or bearish - KAMA — whether KAMA is above or below the ATR trailing stop - EMA 200 — whether price is above or below the macro filter - VWAP — whether price is above or below session value - Noise filter — whether the noise gate is active or disabled - Signal — last signal fired: buy, sell, or none The dashboard is available in three sizes (tiny, small, normal) and four positions (top left, top right, bottom left, bottom right). 10. Settings reference Calculation parameters - Price source: close, high, low, HL2, or open - KAMA length: efficiency ratio lookback period - Fast EMA length: smoothing speed during trending conditions - Slow EMA length: smoothing speed during ranging conditions - ATR length: volatility measurement period - ATR multiplier: trailing stop distance from KAMA - Enable noise filter: toggle the price displacement gate - Noise filter threshold: minimum ATR multiple required to advance KAMA - Preset configuration: default, fast, or smooth Visualization settings - Show EMA 200 and its value label - EMA 200 timeframe: leave empty for chart timeframe or set to higher timeframe - EMA 200 color - Color preset: classic (green/red) or custom - Bullish and bearish trend colors - Cloud transparency - Bar color transparency - Show trend reversal signals - Enable bar coloring VWAP settings - VWAP session: daily or custom - Timezone: UTC, exchange, or major financial center timezone - Session window: start and end time in HH:MM format (custom mode) - Show VWAP line and value label - VWAP color Dashboard settings - Show dashboard - Dashboard size: tiny, small, or normal - Dashboard position: top left, top right, bottom left, or bottom right 11. How to use 11.1 Lower timeframes (1 minute to 15 minutes) Use the fast preset as a starting point. Set the EMA 200 timeframe to 60 (1H) or 240 (4H) — never leave it on the chart timeframe at these levels, as the native EMA 200 only covers a few hours of data and provides no macro context. Set the VWAP session to daily or to the active session with the correct timezone. Keep the noise filter enabled at 0.15. For 1 minute and 3 minute scalping, consider reducing the ATR multiplier to 1.5 if stops feel too wide. Only take bullish signals when price is above the HTF EMA 200 and above VWAP. Only take bearish signals when price is below both. Avoid signals that fire within the first few bars of a new VWAP session before it is statistically meaningful. 11.2 Higher timeframes (1H, 4H, daily) Use the default preset for 1H and 4H. Use the smooth preset for daily and weekly charts. The EMA 200 can remain on the chart timeframe at these levels. Watch for KAMA cloud expansion — the cloud widening indicates momentum building and a healthy trend. Cloud narrowing indicates KAMA is slowing, signaling possible consolidation ahead. The dashboard EMA 200 and VWAP rows both aligned with the signal direction produce the highest conviction setups. 11.3 What to watch KAMA cloud widening — momentum building, trend is healthy. KAMA cloud narrowing — KAMA slowing, possible consolidation ahead. ATR trailing stop flip — confirmed trend change, signal fires here. EMA 200 and VWAP aligned with signal direction — highest conviction setup. Dashboard signal row — use as final confirmation before acting. 12. Disclaimer This indicator is provided for educational and informational purposes only. All outputs are based on historical price action calculations and do not guarantee future results. Trading financial instruments involves significant risk of loss. Past performance does not indicate future results. Use at your own discretion.
Pine Script® 인디케이터
Anonycryptous의
ATC Bollinger Band Percentile v1.1What It Is The ATC Bollinger Band Percentile (ATC BBP) is a dual-layer oscillator that tells you two things simultaneously: where price sits inside its Bollinger envelope right now, and whether the current volatility environment is compressing, neutral, or expanding — measured against real historical data, not a hardcoded threshold. Most Bollinger Band tools give you the bands. This one gives you the context behind the bands. ________________________________________ Who It's Built For ATC BBP is designed for retail traders who already use Bollinger Bands or have tried them but found the raw %B reading too noisy or too vague to act on. If you've ever looked at a squeeze setup and wondered whether the bands were actually tight or just tighter than yesterday, this indicator was built to answer that question directly. It works best for traders who use volatility as a filter before entering trend or breakout trades, want a cleaner and less reactive version of %B, or are building toward understanding normalized, statistically-grounded indicators. ________________________________________ Core Concept Bollinger Bands place price in a dynamic envelope built from a moving average and standard deviation. The %B reading converts that envelope into a 0–100 scale: 100 means price is sitting on the upper band, 0 means price is on the lower band, and 50 means price is at the midpoint. That's useful, but the raw reading is noisy and the bands themselves don't tell you whether they're wide or narrow relative to history. A band can look visually compressed on your chart and still be wider than it's been 75% of the time — or vice versa. ATC BBP solves both problems. It smooths %B with a Hull Moving Average to reduce reactive noise, and it scores the current bandwidth as a percentile against a rolling window of its own history — so you always know objectively whether compression is real. ________________________________________ ATC Upgrades Over Standard %B HMA Smoothing on %B The raw %B line reacts sharply to every candle. ATC BBP applies a Hull Moving Average to %B before it's plotted, cutting noise while preserving responsiveness. The raw %B is still available in the data window for comparison, but the smoothed version drives everything you see. You can adjust the smoothing length or disable it entirely. Bandwidth Percentile Scoring This is the core ATC enhancement. Instead of asking "are the bands narrow?", ATC BBP asks "are the bands narrow relative to the last 125 bars of bandwidth history?" The bandwidth percentile is computed by ranking the current bandwidth against every value in the lookback window. A reading of 8% means the bands are tighter right now than they've been on 92% of recent bars. That's a real squeeze signal — not an eyeball call. Empirical Zone Thresholds with Hysteresis The %B zone boundaries are not hardcoded round numbers. The defaults are set at empirically sensible levels and are fully adjustable. More importantly, every state transition — both the squeeze state and the %B zone — uses a configurable hysteresis band so the indicator doesn't flicker at the edges. Once a state is entered, it takes a meaningful move to exit it. ________________________________________ What's on the Chart ATC BBP plots in a separate pane below your price chart. The %B Line The smoothed %B oscillator on a 0–100 scale. The line changes color dynamically to reflect the current zone: green shades when price is in the lower portion of the bands, red shades in the upper portion, neutral grey for mid-range. When price tags or exceeds either band, the color deepens to full intensity. A fill between the %B line and the 50-level midline gives an immediate read on whether price is in the upper or lower half of the range. Horizontal Reference Lines Five levels mark the key zones: lower extreme (0), lower quartile (20), midline (50), upper quartile (80), and upper extreme (100). Low-opacity colored background shading tints each zone — red above the upper quartile, green below the lower quartile, neutral in the middle. Squeeze Pressure Bar Along the bottom of the pane, a colored bar marks the current squeeze state. Amber indicates a tight squeeze — bandwidth in the lowest percentile tier. Light yellow indicates a developing or loose squeeze. Blue indicates active volatility expansion. When no state is active, the bar disappears — the absence of color is meaningful. Diamond markers appear at the bar when a squeeze begins and again when expansion starts, so state transitions are never missed on a busy chart. ________________________________________ HUD Breakdown The corner HUD (top right by default) gives you a live read of both indicator layers without having to inspect chart values: Volatility — current squeeze state label: Tight Squeeze, Loose Squeeze, Expansion, or Neutral, color-coded to match the pressure bar BW %-ile — the bandwidth percentile as a number, followed by a 10-block progress bar showing where current bandwidth sits on a visual scale from fully compressed to fully expanded %B Zone — a text label for where price is in the envelope: Below Lower Band, Lower Quartile, Mid Range, Upper Quartile, or Above Upper Band %B Reading — the smoothed %B value as a number The HUD supports dark and light themes and can be repositioned to any corner of the pane. ________________________________________ Logic Layers The indicator runs two independent state machines, each with its own hysteresis logic. Squeeze State Machine Four states: Tight Squeeze (bandwidth percentile below the tight threshold), Loose Squeeze (between tight and loose thresholds), Neutral (mid-range bandwidth), and Expansion (above the expansion threshold). State transitions require the bandwidth percentile to move beyond the threshold by the hysteresis amount before the state flips. This prevents toggling at the boundary on marginal readings. %B Zone State Machine Five zones tracking price location within the envelope: Below Lower Band, Lower Quartile, Mid Range, Upper Quartile, and Above Upper Band. The same hysteresis logic applies — once price enters a zone, it stays classified there until it moves decisively into the next zone. The two machines run independently. You can be in a tight squeeze while price is in the upper quartile — which is a very different setup than a tight squeeze with price at the midline. The HUD shows both readings simultaneously so you always have the full picture. ________________________________________ Alerts Seven alert conditions are built in. BBP: Tight Squeeze Started — fires when the squeeze state first enters the tight tier. Use this to monitor compression setups across instruments before they break. BBP: Tight Squeeze Released — fires when the tight squeeze breaks. This is the exit from compression, which may precede expansion or resolve back to neutral — both are meaningful. BBP: Expansion Started — fires when bandwidth percentile crosses above the expansion threshold, confirming that volatility is breaking out of compression. BBP: Price Above Upper Band — fires when %B reaches or exceeds 100, meaning price has tagged or broken through the upper band. BBP: Price Below Lower Band — fires when %B reaches or falls below 0, meaning price has tagged or broken through the lower band. BBP: %B Cross Above 50 — fires when smoothed %B crosses above the midline. Price location bias has shifted to the upper half of the envelope. BBP: %B Cross Below 50 — fires when smoothed %B crosses below the midline. Price location bias has shifted to the lower half. ________________________________________ How to Trade With It ATC BBP is a context indicator, not a signal generator. It tells you the volatility environment and price location so you can filter and frame your setups — it does not issue buy or sell signals on its own. Step 1 — Check the Squeeze State First Before anything else, look at the HUD Volatility row and the pressure bar. Tight Squeeze means the market is coiling. Expansion means it's already moving. Neutral means neither is happening. This single read tells you what kind of market you're in before you look at anything else. Step 2 — Use Squeeze Context to Filter Breakout Setups A tight squeeze is the setup condition for a potential expansion — it does not tell you which direction. When bandwidth is in the lowest 8–10 percentile of its history, start watching price action for the break, but wait for directional confirmation from your primary setup criteria before trading it. The squeeze tells you energy is building. Your edge tells you which way it breaks. Step 3 — Use %B to Read Location Within the Setup Once you have a directional bias, %B tells you where price currently sits in the envelope. If you're looking for a long entry and %B is already above 80, price is extended toward the top of the range — it may be better to wait for a pullback toward the 50 midline. If %B is mid-range or lower quartile heading into a long setup, there's more room to run before hitting band resistance. Step 4 — Look for Squeeze-Plus-Zone Confluence The highest-value reads come when both layers line up. A tight squeeze with %B at mid-range or lower quartile means compression is present and price has room to move higher if the break is bullish — watch for expansion to confirm with %B rising through 50. Expansion with %B crossing above 50 means volatility is moving and location bias is shifting bullish simultaneously — often the clearest confirmation that a breakout is real. Expansion with %B above 100 means price is already through the upper band in an expanding environment — valid in strong trends, a caution flag in range conditions. Step 5 — Use Alerts for Multi-Instrument Monitoring If you're running ATC BBP across multiple instruments or timeframes, set the Tight Squeeze Started and Expansion Started alerts. These fire the moment a state changes so you're never watching the wrong chart while a setup develops elsewhere. ________________________________________ Settings Reference Bollinger Bands BB Length (default 30) — period for the moving average and standard deviation calculation. Optimized default for QQQ. Increase for slower, more structural readings; decrease for more reactive readings on faster instruments. BB StdDev Multiplier (default 1.6) — number of standard deviations for the band width. Optimized default for QQQ. Lower values tighten the bands and will increase the frequency of upper/lower extreme readings. BB Source (default Close) — price source for the band calculation. Smoothing %B HMA Smoothing (default 8) — Hull Moving Average length applied to %B. Set to 1 to disable smoothing and plot the raw %B line. Squeeze Quality Bandwidth Percentile Window (default 125) — rolling lookback used to rank the current bandwidth. Larger windows produce more stable percentile readings against longer historical context. Tight Squeeze Threshold (default 8) — bandwidth percentile below this level is classified as a tight squeeze. Loose Squeeze Threshold (default 25) — bandwidth percentile between the tight threshold and this level is classified as a developing squeeze. Expansion Threshold (default 75) — bandwidth percentile above this level is classified as active expansion. State Hysteresis (default 3.0) — neutral band around each threshold. A state must be exceeded by this amount before the classification changes, preventing flicker on marginal readings. %B Zones Upper Quartile (default 80) — %B above this is classified as Upper Quartile zone. Lower Quartile (default 20) — %B below this is classified as Lower Quartile zone. Upper Extreme (default 100) — %B at or above this is classified as Above Upper Band. Lower Extreme (default 0) — %B at or below this is classified as Below Lower Band. Visuals Shade %B Zones — toggles the background zone tinting on the oscillator pane. Show Squeeze Pressure Bar — toggles the colored state bar and diamond markers at the bottom of the pane. Color inputs for all states are fully adjustable if you prefer a different palette. ________________________________________ Recommended Instruments and Timeframes ATC BBP is tested and validated on ES, NQ, CL, GC, SPY, QQQ, major equities, and major FX pairs. Recommended timeframes are 5m, 15m, 1h, 4h, and 1D. Default settings are optimized for QQQ. When applying to other instruments, the BB Length, StdDev Multiplier, and Bandwidth Percentile Window are the primary settings to adjust for the instrument's typical volatility profile. ________________________________________
Pine Script® 인디케이터
ATConcepts의
Bollinger Walk Quality [AGPro Series]📌 Bollinger Walk Quality Bollinger Walk Quality is built for one specific question: when price starts walking the outer Bollinger Band, is that walk strong enough to support continuation, or is the structure beginning to fail? Many Bollinger Band tools focus on band touches, volatility contraction, width percentiles, or squeeze-style expansion setups. This script takes a different approach. It studies the behavior that comes after expansion: persistent upper-band or lower-band walking, directional pressure around the outer lane, pullback quality inside the band structure, and the point where the walk begins to lose control. The goal is to make Bollinger Band continuation easier to read directly on the chart. Instead of treating every band touch as meaningful, the script scores whether price is staying in the correct outer lane, whether the Bollinger basis supports the active direction, whether pressure is persisting across multiple bars, and whether a pullback is holding in a healthy continuation pocket. 🧭 What The Script Measures 1. Walk Side The panel identifies whether the active environment is an Upper Walk, Lower Walk, Cooling Walk, or Neutral state. Upper Walk means price is persistently operating near the upper Bollinger lane while the basis supports upward continuation pressure. Lower Walk means price is persistently operating near the lower Bollinger lane while the basis supports downward continuation pressure. Cooling states appear when a recent walk is no longer fully active, but the script is still monitoring pullback quality and failure risk for a limited memory window. 2. Band Pressure Band Pressure is a quality score for the active or developing walk. It combines outer-lane position, persistence, band contact behavior, basis slope support, and candle direction. A higher score means the walk has stronger structural pressure. A lower score means price is no longer showing durable outer-band control. This keeps the script focused on continuation quality instead of simple band interaction. The chart can show a band touch, but the panel helps separate a weak touch from a real band-walk environment. 3. Pullback Quality Healthy band walks often reset toward the middle of the Bollinger structure before continuing. Bollinger Walk Quality tracks that reset through a concept-native pullback pocket. The pullback pocket is not a generic support or resistance rectangle. It is projected from the current Bollinger structure and represents the area where a band walk can cool off while still keeping its continuation profile intact. The panel can show states such as Awaiting Pullback, Testing Pocket, Clean Hold, or Weak Reset. This helps distinguish a controlled continuation reset from a walk that is losing quality. 4. Failure Risk Failure Risk is designed to catch the moment when an active or recently active band walk starts to break down. It rises when price loses the outer lane, pressure falls, the basis slope weakens, or price begins to threaten the Bollinger basis. The failure label is intentionally restrained. It appears only on fresh failure events, so the chart remains clean and readable instead of filling with repeated warnings. 🎯 Core Features - Outer-band walk detection for upper and lower Bollinger continuation phases - Band pressure scoring based on lane position, persistence, slope support, and band contact behavior - Pullback-to-band quality state for continuation resets - Projected pullback pockets that show where a healthy walk can reset without losing structure - Failure warning labels when the active walk loses pressure or threatens the basis - Clean AG Pro panel with walk side, band pressure, pullback quality, and failure risk - Adjustable panel location, theme, font sizes, label spacing, label clearance, zone projection, and visual density 📊 Visual Design The default view is designed for a clean public TradingView chart. The script displays Bollinger Bands, highlights the active walk track, projects a limited number of pullback zones, and uses event labels with spacing controls. Labels are anchored outside the local candle envelope so they do not disappear inside candles or cover important price action. The chart remains active enough to be visually informative, but the object count and label density are controlled by default. This keeps the indicator suitable for repeated use across different symbols and timeframes. 🧩 Panel The AG Pro panel summarizes the current state in four compact rows: - Walk Side - Band Pressure - Pullback Quality - Failure Risk Panel location, panel theme, panel font size, label font size, label spacing, label clearance, zone projection, zone opacity, and object limits are all adjustable from the settings menu. 🔎 How This Is Different Bollinger Walk Quality is not a squeeze map and does not rely on compression logic. It does not use Keltner confirmation, volatility percentile ranking, or width contraction states. Its lane is continuation after expansion: - Is price walking the band? - Which side controls the walk? - Is the walk supported by pressure and basis slope? - Is the pullback still healthy? - Is the walk beginning to fail? That makes it a separate Bollinger Band workflow from squeeze-focused tools. It is designed for traders who want to evaluate trend continuation rhythm, pullback behavior, and outer-band pressure in a more structured way. ✅ Best Use Cases - Studying Bollinger Band walk continuation - Reviewing trend pressure after expansion - Identifying controlled pullbacks inside active band-walk environments - Monitoring when a walk is cooling or losing quality - Comparing upper-band and lower-band pressure across market phases - Keeping Bollinger Band continuation analysis clean and visual ⚙️ Settings Overview Engine settings control the Bollinger length, multiplier, source, basis slope lookback, and ATR normalization. Band Walk Logic settings control the outer-lane depth, minimum walk persistence, minimum walk score, pullback memory, and failure warning threshold. Visual settings control Bollinger Bands, walk highlight, pullback zones, event labels, label spacing, label count, label offset, label clearance, and label font size. Pullback Zone settings control zone projection length, maximum visible zones, and opacity. Panel settings control the information panel, location, theme, and font size. 🏁 Design Intent Bollinger Walk Quality is designed to make Bollinger Band continuation more readable without turning the chart into a crowded signal dashboard. The script focuses on structure, persistence, pullback quality, and failure behavior. It gives the chart a clear visual rhythm while keeping the panel concise and the labels controlled. This makes the script useful as a premium public Bollinger continuation tool and keeps it clearly differentiated from Bollinger squeeze, compression, and volatility expansion maps.
Pine Script® 인디케이터
AGProLabs의
Volatility Managed Kelly LeverageThe Volatility Managed Kelly Leverage (VMKL) indicator is a tool that dynamically adjusts position sizing based on forecasted market volatility. It helps you to optimize leverage exposure by systematically reducing risk during high volatility periods and increasing exposure when markets are calm. VMKL adapts in real-time to changing market conditions, potentially generating alpha while smoothing volatility and reducing maximum drawdown. This indicator implements the Optimal Volatility Plus Mean Strategy (OVPMS) from one of my favorite leverage papers: " Alpha Generation and Risk Smoothing using Managed Volatility " by Tony Cooper (2010) These are the key findings from the paper, which this indicator translates to real life: Volatility is predictable while returns are not Dynamic leverage based on volatility forecasts can generate significant excess returns The strategy reduces volatility of volatility (vovo), kurtosis, and maximum drawdown Tested on 125+ years of market data across multiple global indices The OVPMS strategy (translated into this indicator) returned 12.6% annual return vs 7.0% for buy-and-hold, with the same volatility as the underlying index. Outstanding. The indicator calculates optimal leverage using a three-step process 1. Volatility Forecasting Uses Exponential Weighted Moving Average (EWMA): σ²(t) = λ·σ²(t-1) + (1-λ)·r²(t-1) This predicts next-day volatility from recent price movements 2. Return Prediction Expected Return = a × σ^(b+1) Where: a = Power coefficient (baseline return, default: 0.10) b = Power exponent (return-volatility relationship, default: -1.76 for SPY) σ = Forecasted volatility The negative exponent means returns decrease as volatility increases - a well-documented market behaviour. 3. Optimal Leverage Calculation Full Kelly Leverage = μ / σ² Actual Leverage = Full Kelly × Kelly Fraction × Caps × Smoothing The Kelly Criterion provides the theoretically optimal leverage, which is then reduced via: Kelly Fraction: Safety margin (default 75% = three-quarter Kelly) Leverage Caps: Hard maximum and minimum limits Smoothing: SMA to reduce rebalancing frequency The Core Insight: Volatility varies over time (volatility of volatility), and this variation is costly. By targeting consistent volatility through dynamic leverage: Reduces volatility drag - Compounding works better with stable volatility Reduces drawdowns - Automatically deleverages before crashes Reduces kurtosis - Fewer extreme return events Generates alpha - Exploits the return-volatility relationship The indicator calculates optimal leverage in real-time using EWMA volatility forecasting and Kelly Criterion mathematics, automatically detecting market regimes from CASH to VERY AGGRESSIVE and respective leverages. The statistics table shows Full Kelly leverage, Kelly Fraction leverage, forecasted volatility, predicted returns, and current regime. Settings Guide Please check the informational "i" in setting to get a lot more info. You can also use preset configurations: Conservative (Safe) Kelly Fraction: 0.50 Max Leverage: 2.0x Lambda: 0.97 Sensitivity: Enhanced Moderate (Balanced) ⭐ DEFAULT Kelly Fraction: 0.75 Max Leverage: 3.0x Lambda: 0.94 Sensitivity: Enhanced Aggressive (Maximum) Kelly Fraction: 1.0 Max Leverage: 5.0x Lambda: 0.90 Sensitivity: Standard Paper Replication (Academic) Kelly Fraction: 1.0 Max Leverage: 3.0x Lambda: 0.94 Sensitivity: Standard Adaptive: ON Smoothing: 1 Remember: LEVERAGE MAGNIFIES BOTH GAINS AND LOSSES Let me know if you have questions! By Henrique Centieiro
Pine Script® 인디케이터
HenriqueCentieiro의
2
Trend Quality Band [EXCAVO]Volatility-Ranked SuperTrend That Tightens in Calm Markets and Widens in Volatile Ones The Trend Quality Band is an adaptive trend-following indicator built on a SuperTrend core whose band width is controlled entirely by ATR percentile rank. Instead of a fixed multiplier, the band compresses when current volatility is historically low and expands when it is historically high - giving the trend line room when it needs it and keeping it tight when conditions allow. This is not a standard SuperTrend with a different parameter. The driving mechanism is a statistical rank: each bar, the current ATR is compared against its full lookback history to produce a 0-100% rank, which then scales the band multiplier non-linearly between configurable compress and expand factors. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ HOW TO USE Step 1 → Add the indicator. The adaptive trend band appears immediately. Blue band below price = bullish trend. Red band above price = bearish. Step 2 → Watch for flip signals. Triangles mark the bar where the trend changed direction. Bullish flip = triangle below bar. Bearish = above. Step 3 → Read the ATR Rank in the dashboard. Below 35% = low volatility (compressed band, trend holds tight). Above 65% = high volatility (expanded band, wider room before a flip fires). Step 4 → Use the Regime label. "Low Vol" means the band is near its narrowest - trends confirmed here are high quality. "High Vol" means the band is expanded to filter noise in choppy conditions. Step 5 → Set alerts for Bullish or Bearish Trend Flip to be notified on confirmed direction changes. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ HOW IT CALCULATES ◆ ATR Percentile Rank On every bar, the indicator computes the ATR using the configured length, then ranks it against its own history over the Rank Lookback period: atr_rank = percentrank(ATR, lookback) / 100 This produces a value from 0 to 1. A rank of 0.1 means current ATR is lower than 90% of recent history (unusually quiet). A rank of 0.9 means current ATR exceeds 90% of recent history (unusually volatile). ◆ Non-Linear Multiplier Scaling The rank is mapped to a scaling factor using three zones defined by the Compress Below and Expand Above thresholds (default 0.35 / 0.65): t = clamp((atr_rank - compress_thresh) / (expand_thresh - compress_thresh), 0, 1) rank_scale = compress_factor + t x (expand_factor - compress_factor) eff_mult = base_multiplier x rank_scale When rank is below the compress threshold, rank_scale approaches the Compress Factor (default 0.65). When rank exceeds the expand threshold, rank_scale approaches the Expand Factor (default 1.55). Between the thresholds, the scale interpolates linearly. The result: a multiplier that ranges from approximately 1.6x (base 2.5 x 0.65) to 3.9x (base 2.5 x 1.55) depending on regime. ◆ SuperTrend Ratchet Logic The adaptive multiplier feeds a standard SuperTrend ratchet: upper_raw = src + eff_mult x ATR lower_raw = src - eff_mult x ATR lower_band = close > lower_band ? max(lower_raw, lower_band ) : lower_raw upper_band = close < upper_band ? min(upper_raw, upper_band ) : upper_raw The ratchet means the active band can only move in the direction of the trend - it never widens to catch a missed flip. A flip fires when price closes beyond the active band. The direction variable tracks the current trend state. ◆ Regime Classification The rank is compared against the two thresholds to produce a named regime: Low Vol (rank < 0.35), Normal (0.35-0.65), or High Vol (rank > 0.65). This regime label and the live multiplier value are shown in the dashboard so the trader can see exactly why the band is positioned where it is. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ WHAT MAKES IT DIFFERENT ◆ ATR Percentile Rank as the Sole Driver Most adaptive SuperTrend variants modulate bands using the Efficiency Ratio (a directional momentum measure) or volatility ratios against a moving average. This indicator uses a statistical rank: the current ATR is compared against its entire recent history rather than a smoothed baseline. A rank of 80% means the current bar is more volatile than 80% of recent bars - a precise, context-aware signal that a fixed multiplier or a ratio cannot capture. ◆ Compress-and-Expand Band Behavior The two-threshold system creates three distinct regimes. In the Low Vol regime the band compresses toward the Compress Factor - trend flips require less movement, keeping the indicator responsive during quiet trends. In the High Vol regime the band expands toward the Expand Factor - preventing false flips during breakouts and spike-heavy conditions. The Normal regime interpolates between the two, creating a smooth transition rather than a step function. ◆ Live Multiplier Transparency The dashboard shows the actual effective multiplier value on every bar. This tells the trader exactly how wide the band currently is and why. When the multiplier reads 1.6x the band is near its tightest. When it reads 3.8x the band is near its widest. No guesswork about how the indicator is behaving. ◆ Non-Repainting by Default Flip signals are gated by barstate.isconfirmed - they only fire on the closed bar. The Allow Repainting input is available for users who prefer real-time updates, but the default ensures historical flips on the chart are accurate. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ DASHBOARD Real-time panel (top right by default) shows the current state: Trend - BULLISH or BEARISH, colored by direction ATR Rank - current ATR percentile rank as a percentage (0-100%) Regime - Low Vol / Normal / High Vol based on rank thresholds Multiplier - effective band multiplier currently in use Band - current price level of the active trend band Legend table (bottom left) identifies all visual elements on the chart: ━ (blue) - Bullish trend band ━ (red) - Bearish trend band ┅ (orange) - Midline EMA, color reflects volatility regime ▲ (blue) - Bullish trend flip ▼ (red) - Bearish trend flip ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ SETTINGS Core Settings ATR Length - 14 (lookback for ATR calculation) Base Multiplier - 2.5 (center-point multiplier at rank = 0.5) Source - 0 = HL2, 1 = Close, 2 = HLC3 ATR Rank Settings Rank Lookback - 200 bars (history used for percentile rank) Compress Below - 0.35 (rank threshold for low volatility regime) Expand Above - 0.65 (rank threshold for high volatility regime) Compress Factor - 0.65 (multiplier scale at minimum rank) Expand Factor - 1.55 (multiplier scale at maximum rank) Visualization Bullish Color - blue (customizable) Bearish Color - red (customizable) Signal Color - orange (customizable) Show Band Fill - ON (semi-transparent fill between band and price) Show Flip Signals - ON (arrow shapes at trend flip bars) Show Background - OFF (chart background colored by trend) Show Smooth Band - ON (EMA-smoothed band line, removes staircase appearance) Smooth Length - 5 (EMA period for band smoothing) Show Midline - ON (EMA of price colored by volatility regime) Midline Length - 20 (EMA period for the midline) Dashboard Show Dashboard - ON Show Legend - ON Dashboard Position - Top Right Alert Settings Allow Repainting - OFF JSON Alerts - OFF ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ ALERTS Bullish Trend Flip - trend changed from bearish to bullish on bar close Bearish Trend Flip - trend changed from bullish to bearish on bar close Trend Flip - any direction change JSON payloads include action, direction, ticker, price, timeframe, and indicator. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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.
Pine Script® 인디케이터
EXCAVO의
GARCH Volumetric Cloud [MarkitTick]💡 The GARCH Volumetric Cloud is a highly advanced, institutional-grade trend-following and volatility-tracking indicator designed to filter market noise and pinpoint high-probability trend reversals. By synergizing a dynamic volatility engine inspired by conditional heteroskedasticity models with the smoothing properties of synthetic Heikin-Ashi price action, this tool offers traders a multi-dimensional perspective on market dynamics. It goes beyond simple price crossovers by mathematically confirming that a structural shift in trend is supported by an underlying surge in market volatility. This dual-verification approach significantly reduces the likelihood of entering false breakouts or getting trapped in ranging, low-momentum environments. ✨ Originality and Utility Standard technical indicators often rely on a single dimension of market data, such as moving averages for trend or the Average True Range for volatility. This script breaks the mold by calculating a real-time variance proxy based on squared logarithmic returns, effectively bridging the gap between academic quantitative finance and retail charting. The originality lies in its "Volatility Gatekeeper" mechanism. The system only validates trend signals when the market is experiencing a mathematically significant expansion in variance, preventing the underlying Heikin-Ashi cloud from signaling entries during dormant or strictly mean-reverting phases. Furthermore, the script calculates synthetic Heikin-Ashi values internally without relying on secondary chart inputs or delayed security calls. This ensures seamless integration, zero lookahead bias, and absolute synchronization with the current timeframe. It combines this with a fully integrated JSON webhook alert system, making it an all-in-one solution for both manual discretionary traders and automated systematic execution. 🔬 Methodology and Concepts The core methodology is divided into two distinct processing engines that operate in parallel and converge to generate actionable signals. ● Volatility Engine The system first determines the period-over-period return, giving the user the option to utilize logarithmic returns for superior statistical normalization. These returns are squared to calculate raw variance. To model volatility clustering (the tendency for volatile periods to cluster together), the script applies an Exponentially Weighted Moving Average (EWMA) to the squared returns. This EWMA acts as a dynamic variance proxy, prioritizing recent market shocks while retaining a memory of historical data, governed by the Lambda decay factor. The square root of this variance proxy is taken to return the value to a standard volatility scale. A localized volatility threshold is established by calculating a Simple Moving Average and Standard Deviation of this volatility proxy. A "High Volatility" state is triggered when the current volatility exceeds the moving average plus a user-defined multiple of the standard deviation. ● Trend Cloud Engine The script derives mathematical Heikin-Ashi price points (Open, High, Low, Close) independently of the user's primary chart type. Four distinct Exponential Moving Averages (EMAs) are applied sequentially to the synthetic Heikin-Ashi Close price. The structural trend state is determined by the relationship between the Fast EMA and the Slow EMA. When the Fast EMA crosses above the Slow EMA, the internal state shifts to Bullish. When it crosses below, the state shifts to Bearish. 🎨 Visual Guide The visual interface of the indicator is designed to provide immediate situational awareness through color-coded elements and structural bands. ● Synthetic Heikin-Ashi Candles The indicator plots custom candles directly on the chart, overriding the standard visual noise. Bullish Theme: Colored in vivid Cyan (#00E5FF) when the underlying cloud structure is in an upward trend. Bearish Theme: Colored in distinct Pink/Red (#FF3D71) when the underlying cloud structure shifts downward. The bodies, borders, and wicks are synchronized to these specific themes to maintain a clean visual hierarchy. ● The Moving Average Cloud Cloud L1 (Fast): Plotted as a solid line with 40 percent opacity. Cloud L4 (Slow): Plotted as the foundational boundary line, also at 40 percent opacity. Cloud Spine: A thicker, central moving average derived from the midpoint of the inner EMAs, drawn at 20 percent opacity to serve as a micro-support/resistance level within the broader cloud structure. Gradient Fills: The space between the four EMA lines is filled with cascading color opacities (50 percent, 65 percent, and 78 percent), creating a three-dimensional visual depth that expands during strong trends and pinches during consolidation. 📖 How to Use Applying this indicator requires an understanding of its dual-verification logic. It is not designed to trade every crossover, but rather to isolate structural shifts. ● Identifying Opportunities A valid Long signal occurs when the Fast EMA crosses above the Slow EMA, but only if the previous candle was mathematically classified as being in a "High Volatility" state by the GARCH engine. A valid Short signal occurs when the Fast EMA crosses below the Slow EMA under the exact same high-volatility prerequisite. Visually, traders should look for a color shift in the cloud and candles, accompanied by a sharp widening of the cloud structure. ● Automation and Execution The script calculates a dynamic Stop Loss based on the lowest low of the last 5 bars for long positions, and the highest high of the last 5 bars for short positions. The Take Profit is mechanically projected using a strict 1:1.5 risk-to-reward ratio based on the calculated Stop Loss distance. These parameters are packaged into a JSON payload and fired via webhook at the exact moment the signal bar closes and confirms, ensuring zero repainting and immediate execution for connected bots. ⚙️ Inputs and Settings The indicator provides deep customization options, allowing traders to tune the engines to specific assets and timeframes. ● GARCH Volume Engine Use EWMA: Toggles between the exponentially weighted variance model and a simple moving average of variance. Log Returns: Enables logarithmic return calculations for more accurate financial time-series modeling. Variance Length: Defines the lookback period for the initial variance baseline. Threshold Lookback: Sets the window for the standard deviation bands applied to the final volatility proxy. EWMA Lambda: The decay factor for the weighted average. A standard setting of 0.94 mirrors classic RiskMetrics methodology. High Volatility Band: The standard deviation multiplier required to trigger a "High Volatility" validation state. ● Cloud & Trend Engine Cloud Fast Length: The lookback for the primary reactive EMA. Cloud Mid-Fast Length: The first internal structural EMA. Cloud Mid-Slow Length: The second internal structural EMA. Cloud Slow Length: The foundational EMA that determines the overall baseline trend. ● Webhook Actions (Automation) Action Long: The string identifier sent in the JSON payload when a bullish setup is confirmed. Action Short: The string identifier sent in the JSON payload when a bearish setup is confirmed. 🔍 Deconstruction of the Underlying Scientific and Academic Framework The architectural foundation of this script is deeply rooted in quantitative financial theory, specifically drawing from time-series econometrics and signal processing. The volatility engine is a deterministic approximation of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. In standard financial mathematics, asset returns do not exhibit constant variance; instead, they experience periods of clustered turbulence and clustered calm. By calculating the squared log returns, the script isolates the magnitude of price movement independently of directional drift. The application of an Exponentially Weighted Moving Average (EWMA) to these squared returns serves as the conditional variance estimator. The Lambda parameter acts as the memory decay coefficient. By setting this coefficient high (e.g., 0.94), the model ensures that the volatility proxy reacts aggressively to sudden market shocks (such as a macroeconomic data release or institutional block order) while slowly decaying back to the mean, perfectly mirroring the theoretical decay of implied volatility in options pricing. Parallel to the econometric variance modeling, the script employs a cascaded digital filter design via the Heikin-Ashi EMA cloud. The Heikin-Ashi transformation modifies the standard Open-High-Low-Close data points to incorporate previous period averages, inherently introducing an autoregressive smoothing effect that diminishes high-frequency market noise. By passing this pre-smoothed data through a series of four Exponential Moving Averages, the system applies a multi-pole low-pass filter. The dispersion between the Fast EMA and Slow EMA represents the momentum vector of the trend. The final gating logic, which demands that a structural moving average crossover must be contemporaneous with a statistically significant deviation in the EWMA variance proxy, is a sophisticated method of reducing Type I errors (false positives) in algorithmic trend-following systems. ⚠️ Disclaimer All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
Pine Script® 인디케이터
MarkitTick의
Adaptive Spectral Bands [JOAT]Adaptive Spectral Bands Introduction The Adaptive Spectral Bands indicator is a six-layer Hann Window FIR filter ribbon combined with volatility-adaptive ATR envelopes, a three-state regime classifier, and automated support/resistance zone discovery. The Hann window is a well-known digital signal processing technique that applies a raised cosine weighting function to price data, producing a filter with near-zero overshoot and a steep frequency rolloff. The result is a smoothed trend line that turns earlier at genuine inflection points without the lag spikes characteristic of exponential moving averages. The core problem this solves: standard moving average ribbons use EMAs or SMAs which introduce phase lag proportional to their length, creating late entries. The Hann FIR ribbon resolves at the mathematically optimal balance between lag reduction and frequency separation — no other common moving average achieves this simultaneously. Core Concepts 1. Hann Window FIR Filter The filter applies raised cosine weights across a lookback window. Each weight is computed as: hannFilt(src, length) => float filt = 0.0 float coef = 0.0 for i = 1 to length float w = 1.0 - math.cos(2 * math.pi * i / (length + 1)) filt += src * w coef += w filt / coef This produces a symmetric bell-shaped kernel. The six ribbon layers use progressively wider copies of this filter (base length, base + spacing, base + 2×spacing, etc.), creating a ribbon that visually encodes trend momentum — wide separations signal strong trends, compression signals consolidation. 2. Volatility-Adaptive ATR Bands The outer bands expand and contract based on the current volatility percentile rank relative to a lookback period. This is not a fixed-multiplier Bollinger Band — the multiplier itself adapts: float vol_rank = ta.percentrank(atr_14, i_adapt_len) / 100.0 float dyn_mult = i_base_mult * (1.0 + i_adapt_str * (vol_rank - 0.5) * 2.0) float upper_band = h0 + dyn_mult * atr_14 float lower_band = h0 - dyn_mult * atr_14 During compression (low ADX, low ATR rank), bands tighten around the central Hann line. During expansion, bands widen, automatically containing breakout candles within the volatility envelope. This eliminates the problem of static bands that produce false breakouts in trending markets. 3. Three-State Volatility Regime Classifier ADX is used as the regime signal, with two configurable thresholds: Low Volatility / Compression: ADX below lower threshold. Ribbon layers are tightly stacked. Market is in accumulation or range contraction. Fade-the-band strategies may apply. Transitional: ADX between thresholds. Directional conviction is building. Ribbon is beginning to separate. Expansion / Trending: ADX above upper threshold. Ribbon layers are fully separated. Breakout confirmation. Momentum strategies applicable. The background and candle colors change with regime, providing at-a-glance context without requiring a separate ADX panel. 4. Automated S/R Zone Discovery When the leading Hann layer crosses the second layer, the local price extreme at that bar is recorded as a support or resistance level. These crossovers mark inflection points where trend direction is shifting — the price level at that bar frequently becomes a structural reference in subsequent sessions: bool cross_up = ta.crossover(h0, h1) bool cross_down = ta.crossunder(h0, h1) if cross_up and bar_index - last_sr_bar >= i_sr_gap sr_price := low // create support zone box Zones are spaced by a minimum bar count to avoid clustering. Old zones are managed by a shift-and-delete array pattern so the chart stays clean. Features Six-Layer Hann Ribbon: Progressively wider FIR filters creating a gradient ribbon from fast to slow Adaptive ATR Bands: Volatility-rank-adjusted envelopes that breathe with market conditions Three-State Regime Classifier: Low / Transitional / Expansion states with color coding Auto S/R Zones: Ribbon crossover points recorded as support/resistance boxes with configurable spacing Candle Coloring: Optional bar tinting by volatility regime (compression blue / expansion amber) 10-Row Dashboard: Displays regime label, ADX value, volatility rank, ribbon direction, band width, active S/R zone count, and more Alerts: Ribbon crossover bullish, ribbon crossover bearish, regime change to expansion, regime change to compression Input Parameters Hann Filter: Source: Price input for the filter (default: close) Base Length: Core period of the Hann FIR filter (default: 20, range: 4–500). The six ribbon layers are derived from this. Ribbon Spacing: Gap between each successive ribbon layer (default: 3). Larger values create a wider, more visible ribbon. Show Ribbon: Toggle ribbon visibility (default: on) Adaptive Bands: Enable Adaptive Bands: Toggle ATR envelope rendering (default: on) Base Multiplier: Core ATR distance for the bands (default: 2.0) Volatility Lookback: Period for ATR percentile rank calculation (default: 50) Adaptation Strength: 0 = fixed multiplier, 1 = maximum volatility adaptation (default: 0.4) Volatility Regime: ADX Length: Period for ADX computation (default: 14) Low Threshold: ADX below this = Compression regime (default: 20) High Threshold: ADX above this = Expansion regime (default: 35) S/R Zones: Auto S/R Zones: Enable ribbon-crossover-based zone discovery (default: on) Min Zone Spacing: Minimum bar distance between consecutive S/R zones (default: 30) Visualization: Bullish / Bearish / Compression / Expansion colors: Fully customizable Color Candles by Regime: Optional regime-based candle tinting (default: off) Dashboard: Position: Top Right, Top Left, Bottom Right, Bottom Left (default: Top Right) How to Use This Indicator Step 1: Identify the Regime The dashboard shows the current regime label (Compression / Transitional / Expansion) and the ADX value. In compression, wait. In expansion, trade. The volatility rank shows where current ATR sits in its historical distribution — above 70th percentile is high volatility. Step 2: Read the Ribbon Direction When h0 (fastest layer) is above h1 and h1 above h2, the ribbon is bullish and fully aligned. A crossover of h0 over h1 is the initial signal; a full stack alignment is the confirmation. Step 3: Respect the Adaptive Bands Price touching the upper band in expansion often marks a continuation point — the band is expanding to contain the trend. The same touch during compression is a fade signal. The regime state determines which interpretation applies. Step 4: Trade S/R Zone Retests When price retraces to a recently discovered S/R zone, look for ribbon alignment in the same direction as the original break. The zone marks where the Hann crossover occurred, which is the most statistically significant structural inflection point available from the ribbon. Originality Statement This indicator is original in its combination of Hann Window FIR filter ribbons with adaptive ATR bands and regime-conditioned S/R zone discovery. Its use on TradingView is justified because: The Hann FIR filter produces strictly lower phase lag at equivalent frequency cutoff than EMA or DEMA — a mathematically demonstrable property that common Pine implementations do not exploit Volatility percentile rank as the band multiplier modulator creates self-regulating envelopes that require no manual retuning between high and low volatility periods Combining ADX regime state with ribbon structure separates directional signals from noise — the same crossover signal carries different weight in compression versus expansion Auto S/R zone discovery from FIR crossovers creates an objective level-finding method anchored to frequency-domain turning points, not arbitrary pivot lookbacks Limitations The Hann FIR filter is a causal, finite impulse response filter — it responds to all price history within its window equally weighted by the cosine kernel. It cannot predict future turning points; it identifies them as they occur. ADX is a lagging indicator. Regime classification based on ADX will sometimes enter expansion state after the move has partially occurred. Auto S/R zones are derived from ribbon crossovers, which means they lag the actual price turn by the filter's inherent smoothing delay. Zones mark inflection areas, not exact price pivots. On very short timeframes (sub-5m), Hann filter smoothing may be excessive relative to the noise level, making regime signals less reliable. Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any instrument. All trading involves risk of loss. Past performance of structural patterns does not guarantee future results. Always use proper risk management. -Made with passion by jackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Volatility Terrain Engine [JOAT] Volatility Terrain Engine Introduction Volatility Terrain Engine is a pane-based oscillator that measures the current volatility regime using the ratio between a fast ATR and a slow ATR, combined with a percentile rank of current volatility within a historical window. The indicator classifies every bar into one of three states — Expansion, Compression, or Transition — and identifies squeeze conditions (volatility compressing well below its average) and expansion bursts (volatility accelerating rapidly). The oscillator, centered at zero, makes it immediately clear whether volatility is expanding or contracting relative to its baseline. Volatility regime is one of the most underappreciated dimensions of market analysis. A trend-following strategy applied during volatility compression produces poor results because the market is not moving directionally with sufficient energy. A mean-reversion strategy applied during volatility expansion gets stopped out repeatedly because the market is generating outsized moves. Identifying the current volatility terrain before applying any strategy is a prerequisite for selecting the appropriate approach. Core Concepts 1. Fast/Slow ATR Ratio The primary oscillator compares a short-period ATR (default 14) against a long-period ATR (default 100). Their ratio, centered at 1.0, is shifted to center at 0.0 by subtracting 1. Values above 0 mean recent volatility is expanding relative to the longer-term baseline; values below 0 mean it is contracting. This ratio is more informative than ATR alone because it provides context — the same ATR value means different things in a historically volatile versus historically calm market. 2. Percentile Rank The ATR percentile rank answers: where does today's volatility sit within its historical distribution? A 90th percentile reading means volatility is higher than 90% of observations in the lookback period. This is used to classify whether the current environment is historically extreme or within normal parameters. 3. Squeeze and Expansion Burst Detection A squeeze is defined as fast ATR falling below 82% of slow ATR and also below its own 20-bar average. This double condition filters single-bar dips. A squeeze represents stored energy — the market is coiling. An expansion burst is defined as the ATR ratio exceeding 1.25 with the fast ATR making successive higher values. This marks the initial stages of a volatility explosion. 4. Signal Line and Histogram The oscillator is triple-processed: EMA of ratio → SMA signal → histogram. The histogram shows the divergence between the oscillator and its signal, providing a leading read on whether volatility momentum is building or fading. Features Volatility Regime Oscillator: Gradient-colored histogram bars centered at zero Squeeze Detection: Dashboard alert and dot marker when squeeze conditions are active Expansion Burst Markers: Dot markers when volatility breaks out from compression ATR Percentile Band: Normalized ATR rank plotted as a secondary line Zone Fills: Expansion and compression zones filled with transparent color 8-Row Dashboard: Regime, ATR values, ratio, percentile rank, squeeze status, oscillator values Input Parameters Fast ATR Period: Short-term volatility measurement (default: 14) Slow ATR Period: Long-term volatility baseline (default: 100) Percentile Lookback: Historical window for rank calculation (default: 252) Signal Smoothing: Signal line period (default: 9) Expansion, Compression, and Transition color inputs How to Use This Indicator Compression → Expansion Transition The most significant signal is when a squeeze resolves into an expansion burst. This represents a volatility state change — the market has been coiling and is now releasing energy. The direction of that release is not predicted by this indicator; it must be determined using price structure and other context. Oscillator Zero Cross The oscillator crossing from negative to positive territory indicates that short-term volatility has exceeded the long-term baseline. This is not a trade signal — it is a condition indicator confirming that the market is entering a higher-energy phase. High Percentile + Expansion When the oscillator is in expansion territory and the ATR percentile rank is above 80, the market is experiencing historically significant volatility. Stops must be sized accordingly. Limitations ATR is backward-looking. Sudden volatility spikes from news events will appear in the oscillator only after those bars close The squeeze condition uses fixed multipliers (0.82 for the ATR ratio threshold). Markets with different typical volatility profiles may require adjustments to these thresholds The percentile lookback of 252 bars requires approximately one year of daily data or equivalent for the rank to be historically meaningful. On shorter data sets the rank will be computed on whatever bars are available but will be less statistically robust This indicator classifies current conditions only. It does not predict when a squeeze will resolve or in which direction Originality Statement The dual-ATR ratio approach combined with percentile ranking provides more contextual information than either measure alone. The squeeze detection using a double condition (ratio below threshold and below its own moving average) produces more reliable squeeze identification than a single-condition approach. The four-state histogram coloring (expanding positive, fading positive, expanding negative, fading negative) provides more nuanced momentum information than standard positive/negative coloring. Disclaimer This indicator is for educational and informational purposes only. Volatility regime classification does not predict price direction. A squeeze does not guarantee a subsequent expansion, and the direction of any expansion is unknowable from volatility data alone. Always use appropriate risk management. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Apex Volatility Squeeze & Breakout [Pineify]Apex Volatility Squeeze & Breakout The Apex Volatility Squeeze & Breakout indicator is a dynamic volatility analysis tool that identifies market compression (squeeze) phases and highlights potential breakout opportunities in real time. Built on the well-established principles of Bollinger Bands and Keltner Channels, this indicator synthesizes both into a single, unified volatility channel with a clear three-state trend system — giving traders an intuitive and actionable view of market volatility conditions on any symbol and timeframe. Key Features Unified volatility channel combining Bollinger Band width and Keltner Channel (ATR) threshold Three-state trend detection: Bullish, Bearish, and Squeeze (consolidation) Smoothed bands using EMA to reduce noise and false signals Color-coded volatility cloud with dynamic candle coloring Built-in Buy and Sell breakout signal labels Alert conditions for automated trading notifications Fully customizable parameters for lookback period, multipliers, smoothing, and colors How It Works This indicator operates on a core concept: when volatility contracts, a breakout is imminent . Here is how the calculation pipeline works: A Simple Moving Average (SMA) of the close price is calculated over the user-defined lookback period to establish the basis line. Standard deviation of price is computed and scaled by a Band Multiplier to determine the upper and lower Bollinger-style volatility bands. The Average True Range (ATR) is computed over the same period and scaled by a Squeeze Multiplier to establish the Keltner Channel threshold. A squeeze state is detected when the Bollinger Band width (scaled standard deviation) is less than the Keltner Channel width (scaled ATR) — this means volatility has compressed below normal levels. Both the upper band, lower band, and basis are smoothed using an Exponential Moving Average (EMA) to produce a clean, noiseless visual channel. A trend state machine determines the current market phase: Bullish (+1) when price trades above the upper band outside a squeeze, Bearish (-1) when price trades below the lower band outside a squeeze, and Squeeze (0) when volatility is compressed. Trading Ideas and Insights The squeeze state is the most critical signal this indicator provides. When the bands contract and the channel turns orange (default squeeze color), the market is consolidating and building energy. Traders should watch closely for the following scenarios: A bullish breakout occurs when price crosses above the upper smoothed band after a squeeze period, indicating upward momentum. A bearish breakout occurs when price crosses below the lower smoothed band after a squeeze period, indicating downward momentum. During the squeeze phase, traders may choose to reduce position sizes and wait for directional confirmation. The color transition of the volatility cloud — from orange (squeeze) to green (bullish) or red (bearish) — provides a clear visual cue for trend changes. How Multiple Indicators Work Together This script merges two complementary volatility methodologies into a single coherent system: Bollinger Bands (Standard Deviation) measure statistical volatility — how far price deviates from the mean. They expand during volatile markets and contract during quiet markets. Keltner Channels (ATR) measure range-based volatility — the average true range of price movement. They provide a more stable, less reactive volatility baseline. By comparing these two measures, the indicator identifies squeeze conditions: when the faster-reacting Bollinger Band width falls below the slower ATR threshold, it signals abnormal compression. This is the classic "squeeze" concept pioneered by John Carter's TTM Squeeze, adapted here with EMA smoothing for cleaner signals. The EMA smoothing layer is applied on top of the raw bands to eliminate whipsaw noise. This creates a smoother channel that is easier to read and trade, especially on lower timeframes. The trend state machine ties everything together by classifying the market into three clear phases, which then drive the dynamic coloring of the bands, cloud fill, and candle colors — creating a unified visual experience. Unique Aspects Unlike standard squeeze indicators that only display a binary squeeze/no-squeeze state, this indicator provides a full directional trend classification with three states, color-coded across all visual elements. The EMA-smoothed volatility channel is visually cleaner than traditional Bollinger Bands or Keltner Channels, making it easier to identify trend direction at a glance. The volatility cloud fill adapts its color in real time based on the current trend state, providing immediate visual feedback on whether the market is trending bullish, bearish, or consolidating. Candle coloring is also driven by the volatility state, allowing traders to spot trend alignment without needing to inspect the bands directly. How to Use Add the indicator to your chart. It overlays directly on the price chart. Watch for the orange squeeze zone — this indicates low volatility and a potential breakout ahead. When the channel transitions from orange to green, the market is breaking out bullish. When it turns red, the breakout is bearish. Use the BUY and SELL labels as entry signals when price escapes the squeeze zone and crosses the smoothed bands. Set up alerts using the built-in alert conditions ("Bullish Volatility Breakout" and "Bearish Volatility Breakout") to receive notifications without watching the chart. Combine with volume analysis or momentum oscillators for additional confirmation of breakout strength. Customization Lookback Length (default: 20) — The period for calculating the SMA, Standard Deviation, and ATR. Shorter periods make the indicator more reactive; longer periods produce smoother, more reliable signals. Band Multiplier (StDev) (default: 2.0) — Controls the width of the volatility bands. Higher values create wider bands and fewer breakout signals. Squeeze Multiplier (ATR) (default: 1.5) — Sets the threshold for squeeze detection. Lower values detect squeezes more aggressively; higher values require stronger compression. Band Smoothing (default: 5) — The EMA smoothing period applied to the bands. Higher values produce smoother bands with more lag. Color Settings — Fully customizable colors for bullish, bearish, and squeeze states. Candle Coloring — Toggle on/off to color candles based on the current volatility trend state. Conclusion The Apex Volatility Squeeze & Breakout indicator offers traders a clean, intuitive way to identify low-volatility squeeze conditions and potential breakout points. By fusing Bollinger Band width analysis with Keltner Channel ATR thresholds and applying EMA smoothing, it delivers a unified volatility channel that is both visually elegant and analytically powerful. Whether you trade stocks, forex, crypto, or futures, this tool helps you spot the moments when the market is coiling for its next big move — and positions you to act on it with confidence.
Pine Script® 인디케이터
Pineify의
1
Possible Reversal Zone DetectorThis indicator is a comprehensive tool designed to identify potential market reversal zones and mean-reversion opportunities. It utilizes a dynamic volatility channel to detect when the price becomes overextended and is likely to reverse its short-term direction. To eliminate false signals during strong trends, it comes packed with multiple highly customizable technical filters, including RSI, Volume Spikes, and Price Action patterns. Furthermore, it visually assists traders by drawing automated Risk/Reward boxes directly on the chart upon signal generation. Key Features & Mechanics: Dynamic Volatility Channel: The core of the indicator relies on an EMA baseline and a Standard Deviation multiplier. It creates an envelope around the price action. Signals are generated when the price pierces these outer boundaries and shows rejection. Volume Spike Filter (Default: ON): A reversal is much stronger when backed by heavy volume. This filter ensures that a signal is only valid if the current volume exceeds the moving average of the volume by a specified multiplier. Automated Risk/Reward Boxes (Default: ON): Once a valid reversal signal is confirmed, the indicator instantly plots a customizable Risk/Reward box (Default 1:2 RR, 1% Stop Loss) on your chart. This allows you to visually plan your trade, target, and invalidation level effortlessly. RSI Filter (Default: OFF): When enabled, it checks if the asset is mathematically overbought or oversold before confirming a reversal from the channel boundaries. Price Action Filters (Default: OFF): For the ultimate "sniper" entry, you can require the indicator to look for specific candlestick patterns—such as a Pinbar (Wick Rejection) or an Engulfing pattern—at the exact moment the price tests the channel boundary. How to Use: Wait for the visual arrows (Yellow for Bullish, Red for Bearish) to appear. These indicate that the price has breached the volatility band and satisfied your selected filters (like volume spikes). Evaluate the automatically drawn Risk/Reward box to see if the setup matches your risk management strategy. Customization: Every aspect of this indicator is adjustable. You can tweak the channel sensitivity, volume requirements, RSI levels, and the exact dimensions of the Risk/Reward boxes to fit any timeframe or asset class. Disclaimer: This script is for educational purposes only and does not constitute financial advice. Always use proper risk management.
Pine Script® 인디케이터
sohaibnori의
2
Echelon Regime Gauge [JOAT]Echelon Regime Gauge Introduction The Echelon Regime Gauge is an open-source market regime detection and session awareness indicator built in Pine Script v6. It classifies the current market state into one of six regimes — Trend Up, Trend Down, Range, Volatile, Squeeze, or Mixed — using a combination of SMA alignment, VWAP slope analysis, and Bollinger Band volatility metrics. On top of regime detection, the indicator provides session identification (Asian, London, New York, Kill Zones, Power Hour), volatility state tracking (Expansion, Contraction, Squeeze), R-squared trend quality measurement, historical volatility percentile, Wyckoff effort/result analysis, and an institutional activity score that colors candles by multi-factor heatmap logic. The core question this indicator answers is: "What kind of market am I in right now?" Knowing whether the market is trending, ranging, squeezing, or volatile changes everything about how you should trade — from entry type to stop placement to position sizing. This indicator provides that context in real-time with a confidence percentage and a comprehensive HUD dashboard. Why This Indicator Exists Most traders apply the same strategy regardless of market conditions. A breakout strategy in a ranging market produces whipsaws. A mean-reversion strategy in a trending market produces losses. This indicator solves the context problem by providing a clear, quantified regime classification: Regime Detection: Six distinct market states, each requiring different trading approaches. The classification uses three independent inputs — SMA alignment, VWAP slope direction, and Bollinger Band percentile — to produce a robust, multi-factor regime reading. Regime Confidence: A 0-100 score indicating how clearly the market fits the detected regime. High confidence means the classification is strong and reliable. Low confidence suggests transitional or ambiguous conditions. Session Awareness: Identifies the current trading session (Asian, London, New York) and highlights Kill Zones (London 2-5am, NY 7-10am) and Power Hour (3-4pm) — the periods when institutional activity is highest and moves are most significant. Volatility State: Tracks whether volatility is in Expansion, Contraction, Squeeze, or Normal state. Squeeze conditions (Bollinger width in the bottom 10th percentile) often precede explosive moves. Trend Quality (R-Squared): Measures how linear and clean the current trend is on a 0-1 scale. R-squared above 0.6 indicates a clean, tradeable trend. Below 0.4 indicates choppy, random price action. Institutional Activity Score: A multi-factor score (0-100) computed from body ratio, volume ratio, R-squared, Bollinger Band position, and VWAP distance. This score drives the candle heatmap coloring. How Regime Detection Works The regime engine combines three independent analytical dimensions: Dimension 1 — SMA Alignment: The indicator calculates three Simple Moving Averages (default 20, 50, 200). When all three are aligned in order (20 > 50 > 200), the market is in bull alignment. When reversed (20 < 50 < 200), bear alignment. Any other configuration is diverged/mixed. Dimension 2 — VWAP Slope: The VWAP (Volume Weighted Average Price) slope is calculated over a configurable lookback and normalized by ATR to make it comparable across instruments. A normalized slope above the threshold indicates upward momentum. Below the negative threshold indicates downward momentum. Within the threshold band indicates flat/ranging conditions. Dimension 3 — Volatility Percentile: Bollinger Band width percentile rank over 120 bars determines volatility state. Below the 10th percentile is a squeeze. Above the configurable expansion percentile (default 75th) is expansion. ATR percentile rank provides a secondary volatility measure. These three dimensions combine into the regime classification: Squeeze: BB width in bottom 10th percentile — volatility compression, potential breakout imminent Trend Up: Bull SMA alignment AND positive VWAP slope — clear directional momentum upward Trend Down: Bear SMA alignment AND negative VWAP slope — clear directional momentum downward Volatile: ATR in expansion percentile WITHOUT SMA alignment — high volatility but no clear trend direction Range: Flat VWAP slope WITHOUT SMA alignment — sideways, mean-reverting conditions Mixed: Conditions do not clearly fit any category — transitional state Regime Confidence Calculation Each regime has its own confidence formula based on how strongly the inputs support the classification: Trend Up/Down: SMA alignment (40pts) + slope direction (30pts) + slope magnitude (up to 30pts) Range: Flat slope (40pts) + no alignment (30pts) + low ATR percentile (up to 30pts) Squeeze: Low BB percentile (70%) + low ATR percentile (30%) Volatile: High ATR percentile (70%) + high BB percentile (30%) Mixed: Fixed at 25 — low confidence by definition The confidence is displayed as both a number and a visual bar (||||......) in the HUD, making it easy to assess at a glance. Session and Time-of-Day Analysis The indicator identifies seven session states with configurable timezone (default America/New_York): Asia (7pm-3am): Low volatility, range-building session. Quality: Low. London (3am-9:30am): Increasing volatility, often sets the day's direction. Quality: Medium. London Kill Zone (2-5am): Peak London institutional activity. Quality: High. New York (9:30am-4pm): Highest volume session for US instruments. Quality: Medium. NY Kill Zone (7-10am): Peak NY institutional activity, overlap with London. Quality: High. Lunch (10am-12pm): Low conviction, choppy price action. Quality: Low. Power Hour (3-4pm): End-of-day positioning, often produces strong moves. Quality: High. Kill Zones are highlighted with a subtle gold background tint. Lunch hours receive a dark tint as a visual warning of low-quality conditions. Institutional Signal System The indicator detects and labels six types of institutional events using a priority-based system with cooldowns to prevent label stacking: P1 — BULL/BEAR CONFLUENCE (highest priority): Full alignment of SMA, VWAP slope, R-squared trend quality, regime confidence > 70, and price vs VWAP. This is the strongest possible directional signal — all factors agree. P2 — MAJOR GOLDEN/DEATH CROSS: Mid SMA (50) crosses the Slow SMA (200). These are rare, high-impact structural events that signal major trend shifts. P2b — GOLDEN X / DEATH X: Fast SMA (20) crosses Mid SMA (50). More frequent than major crosses but still significant structural events. P3 — REGIME CHANGE: The regime classification changes from one state to another. Labels show the new regime name. P4 — SQZ BREAK: Squeeze releases into expansion with price above (bull) or below (bear) the fast SMA. These are high-energy breakout events. P5 — VWAP RECLAIM/REJECT: Price crosses above VWAP with positive slope (reclaim) or below with negative slope (rejection). VWAP is the institutional benchmark — reclaiming or losing it is significant. P6 — DISP (Displacement, lowest priority): Large-body candles (body > 70% of range, body > 2x average) indicating aggressive institutional order flow. Each signal checks a cooldown counter before firing. Higher-priority signals suppress lower-priority ones within the cooldown window, ensuring the chart shows only the most important signal at any given time. Candle Heatmap Coloring When enabled, candles are colored based on the current regime and trend quality rather than simple bull/bear direction: Bull Aligned + Clean Trend: Bright aurora lime (bullish candles) / aurora green (bearish candles) Bull Aligned: Aurora green / aurora teal Bear Aligned + Clean Trend: Aurora pink / aurora purple Bear Aligned: Aurora purple / aurora pink Squeeze: Aurora gold / warm orange Neutral: Ice blue / arctic blue This coloring scheme makes it immediately obvious what regime the market is in without looking at the HUD — the entire chart changes character with the regime. Advanced Metrics R-Squared Trend Quality: Calculated as the square of the correlation between close price and bar_index over a configurable period. Values above 0.7 indicate a clean, linear trend. Values below 0.4 indicate choppy, non-directional price action. This metric helps distinguish between trending markets that are tradeable and trending markets that are too choppy to trade reliably. Historical Volatility: Annualized standard deviation of log returns, displayed as a percentage with percentile ranking over 252 bars. This provides a longer-term volatility context beyond the Bollinger-based squeeze detection. Wyckoff Effort/Result: Volume divided by range — when this ratio is high (high volume, small range), institutional absorption is occurring. The indicator detects these events and displays them in the HUD. Regime Duration: Counts how many bars the current regime has persisted. Long-duration regimes are more established. Short-duration regimes may be transitional. Institutional Score: A 0-100 composite from body ratio, volume ratio, R-squared, BB position extremes, and VWAP distance. Higher scores indicate more institutional-quality price action. HUD Dashboard The HUD displays 12 metrics with color-coded values: Regime State with regime-specific color Confidence score with visual bar (||||......) VWAP Slope direction (Rising/Falling/Flat) Volatility state with squeeze duration counter Current Session name Session Quality rating (High/Medium/Low) SMA Alignment (Bull Aligned/Bear Aligned/Diverged) R-Squared trend quality (Clean/Moderate/Choppy) Historical Volatility with percentile classification Regime Duration in bars Institutional Activity Score Input Parameters Regime Detection: Fast/Mid/Slow SMA: Moving average periods (default: 20/50/200) VWAP Slope Lookback: Period for slope calculation (default: 20) Slope Threshold: Normalized slope threshold for trend detection (default: 0.12) Volatility: ATR Length: Period for ATR calculation (default: 14) Bollinger Length/Multiplier: BB parameters (default: 20/2.0) Squeeze Lookback: Percentile rank period (default: 120) Expansion Percentile: ATR percentile threshold for expansion (default: 75) Sessions: Show Session Zones: Toggle session background highlighting Timezone: Configurable timezone (default: America/New_York) Highlight Kill Zones: Toggle kill zone emphasis Visual: Regime Background: Toggle regime-adaptive background tinting SMA Trend Ribbon: Toggle ribbon fill between fast and mid SMA Candle Heatmap Coloring: Toggle institutional activity-based candle colors Show Prior Day H/L: Toggle PDH/PDL reference lines HUD Panel: Toggle with configurable position How to Use This Indicator Step 1: Check the Regime Before entering any trade, check what regime the market is in. Trend Up/Down = directional strategies. Range = mean-reversion. Squeeze = wait for breakout. Volatile = reduce size or stand aside. Step 2: Verify Confidence A regime reading with 80+ confidence is reliable. Below 50 suggests the market is in transition — be cautious. Step 3: Check the Session Kill Zones and Power Hour produce the most reliable moves. Lunch hour and Asian session moves are less reliable for most instruments. Step 4: Watch for Signals BULL/BEAR CONFLUENCE is the highest-conviction signal — all factors agree. SQZ BREAK signals high-energy breakouts. VWAP RECLAIM/REJECT provides key level context. Step 5: Use Trend Quality for Filtering R-squared above 0.6 means the trend is clean and tradeable. Below 0.4 means the trend is choppy — consider waiting for cleaner conditions. Best Practices The regime classification is most reliable on timeframes of 5 minutes and above Session features are most relevant for instruments with clear session structures (equities, futures, major forex) Squeeze conditions can persist for extended periods. Do not assume a squeeze will break immediately. Regime transitions (Mixed state) are the most dangerous periods. Consider reducing exposure during transitions. The institutional score is a heuristic — use it as one input among many, not as a standalone signal SMA crosses are lagging by nature. They confirm trend changes rather than predict them. Combine this indicator with structure or momentum tools for entry timing — this indicator provides context, not entries. Limitations Regime detection uses lagging indicators (SMAs, BB percentile). Regime changes are confirmed after they occur, not predicted in advance. The six-regime classification is a simplification. Real markets exist on a continuum, not in discrete states. Session times are hardcoded for EST timezone sessions. Instruments traded primarily in other timezones may need different session definitions. R-squared measures linearity, not direction. A perfectly linear downtrend has the same R-squared as a perfectly linear uptrend. The institutional activity score is estimated from available data (body ratio, volume, BB position). True institutional activity detection requires order flow data not available in Pine Script. VWAP resets daily on most instruments. Intraday VWAP slope is most meaningful for day trading timeframes. Kill Zone highlighting assumes EST-based session times. Adjust the timezone input for your local market. Technical Implementation Built with Pine Script v6 using: Three-dimensional regime classification (SMA alignment + VWAP slope + BB volatility) Regime confidence scoring with per-regime formulas Session detection using time() with configurable timezone R-squared trend quality via ta.correlation() Annualized historical volatility with percentile ranking Wyckoff effort/result absorption detection Multi-factor institutional activity scoring Priority-based signal system with 6 tiers and cooldown anti-overlap Volatility-adaptive gradient background coloring Aurora-themed SMA ribbon with alignment-responsive colors 10 alert conditions covering regime changes, SMA crosses, absorption, and clean trend detection Originality Statement This indicator is original in its comprehensive regime detection and session awareness integration. While SMA alignment and Bollinger squeeze are established concepts, this indicator is justified because: The three-dimensional regime classification (SMA + VWAP slope + BB volatility) produces a more robust state detection than any single method Regime confidence scoring quantifies how clearly the market fits the detected state, providing actionable uncertainty information Session awareness with quality ratings integrates time-of-day context directly into the regime framework R-squared trend quality measurement distinguishes between clean tradeable trends and choppy non-directional trends The priority-based signal system with 6 tiers ensures only the most important events are displayed Candle heatmap coloring driven by regime and trend quality provides immediate visual context The Aurora Borealis theme with volatility-adaptive background creates a dynamic visual environment that changes character with market conditions Squeeze duration tracking and regime duration counting provide temporal context for current conditions 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 detection classifies current market conditions based on historical data — it does not predict future regime changes. Markets can transition between regimes without warning. Squeeze conditions do not guarantee breakouts. Session quality ratings are generalizations that may not apply to all instruments or market conditions. Past regime patterns do not guarantee future behavior. Always use proper risk management and never risk more than you can afford to lose. The author is not responsible for any losses incurred from using this indicator. -Made by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
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.
Pine Script® 인디케이터
Jamallo22의
Institutional Decision Engine [JOAT]Institutional Decision Engine Introduction The Institutional Decision Engine is a comprehensive, unified trading system that integrates six distinct analytical engines into a cohesive decision-making framework. This is not just another indicator - it's a complete trading intelligence system designed to replicate the analytical approach of institutional trading desks. By combining market regime classification, structural analysis, momentum pressure, volatility intelligence, directional bias, and signal qualification into one unified system, this engine provides the holistic market analysis that professional traders rely on for consistent success. This tool is built for serious traders who understand that successful trading requires multiple layers of analysis and confirmation. Whether you're a systematic trader needing a complete decision framework, a discretionary trader seeking comprehensive market intelligence, or an algorithm developer requiring robust signal generation, this engine provides the institutional-grade analysis needed to trade with the confidence and precision of professional market participants. Why This Engine Exists Most traders use fragmented indicators that provide conflicting signals, leading to confusion and poor decisions. This engine solves that fundamental problem by: Unified Framework: Six engines working together as one cohesive system Regime-Adaptive Logic: Automatically adjusts analysis based on market conditions Multi-Layer Confirmation: Requires confluence across multiple analytical dimensions Signal Qualification: Objectively scores and grades every potential signal Risk Intelligence: Dynamic risk management based on market volatility and structure Visual Clarity: Comprehensive visualization of all analytical components The engine transforms the chaotic world of multiple indicators into a single, unified source of market truth that provides clear, actionable trading intelligence. Core Components Explained Engine 1: Market Regime Classification The first engine identifies the current market environment: // Regime Classification: 0=Neutral, 1=Trending, 2=Ranging, 3=Volatile int market_regime = 0 if volatility_state == 1 and adx_value < i_trend_threshold market_regime := 3 // Volatile Expansion else if adx_value >= i_trend_threshold market_regime := 1 // Trending else if volatility_state == -1 market_regime := 2 // Ranging/Consolidation // Regime Strength (0-100) float regime_strength = 0.0 if market_regime == 1 regime_strength := math.min(adx_value / 50.0 * 100, 100) else if market_regime == 2 regime_strength := math.min((1 - volatility_ratio) / (1 - i_contraction_mult) * 100, 100) Regime types: Trending: Strong directional markets with ADX > 25 Ranging: Low volatility consolidation phases Volatile: High volatility, chaotic conditions Neutral: Transition periods between defined states Regime Strength: How strongly the market exhibits regime characteristics Regime classification determines which strategies are appropriate and how risk should be managed. Engine 2: Structural Behavior Analysis The second engine maps market structure and key levels: // Structure Analysis bool higher_high = not na(last_swing_high) and not na(prev_swing_high) and last_swing_high > prev_swing_high bool lower_low = not na(last_swing_low) and not na(prev_swing_low) and last_swing_low < prev_swing_low bool higher_low = not na(last_swing_low) and not na(prev_swing_low) and last_swing_low > prev_swing_low bool lower_high = not na(last_swing_high) and not na(prev_swing_high) and last_swing_high < prev_swing_high // Structure Score (0-100) float structure_score = 0.0 structure_score += structure_bias == 1 ? 30 : structure_bias == -1 ? 0 : 15 structure_score += higher_high ? 20 : lower_low ? 0 : 10 structure_score += bos_bullish ? 30 : bos_bearish ? 0 : 15 Structure components: Swing Points: Key highs and lows defining market structure Market Structure: Higher highs/higher lows (bullish) or lower highs/lower lows (bearish) Break of Structure: Confirmation of trend changes Liquidity Zones: Equal highs/lows where orders cluster Structure Score: Quantifies structural quality (0-100) Structural analysis identifies the levels where professional traders place orders. Engine 3: Momentum Pressure Analysis The third engine measures buying and selling pressure: // Composite Momentum Score float momentum_bull_score = 0.0 momentum_bull_score += wt_bullish ? 25 : 0 momentum_bull_score += rsi_bullish ? 25 : 0 momentum_bull_score += weighted_pressure > 0.1 ? 25 : weighted_pressure > 0 ? 12.5 : 0 momentum_bull_score += macd_bullish ? 25 : 0 // Net Momentum State float net_momentum = momentum_bull_score - momentum_bear_score int momentum_state = net_momentum > 25 ? 1 : net_momentum < -25 ? -1 : 0 Momentum components: WaveTrend: Trend-following momentum oscillator RSI: Relative strength with momentum filter Pressure Analysis: Volume-weighted buying/selling pressure MACD: Trend acceleration and deceleration Momentum State: Bullish, bearish, or neutral momentum Momentum analysis confirms the strength and timing of potential moves. Engine 4: Volatility Intelligence Layer The fourth engine analyzes volatility cycles and squeezes: // Squeeze Detection bool squeeze_on = bb_lower > kc_lower and bb_upper < kc_upper bool squeeze_off = bb_lower < kc_lower or bb_upper > kc_upper // Volatility Cycle Phase int vol_cycle_phase = 0 if squeeze_on and squeeze_duration > 5 vol_cycle_phase := 1 // Compression else if squeeze_off and squeeze_duration > 0 vol_cycle_phase := 2 // Expansion Trigger else if volatility_ratio > 1.2 vol_cycle_phase := 3 // Active Expansion // Adaptive Multipliers float stop_multiplier = vol_cycle_phase == 3 ? 1.5 : vol_cycle_phase == 1 ? 0.8 : 1.0 float target_multiplier = vol_cycle_phase == 3 ? 1.3 : vol_cycle_phase == 1 ? 1.5 : 1.0 Volatility components: Bollinger Bands: Standard deviation-based volatility Keltner Channels: ATR-based volatility Squeeze Detection: Volatility compression patterns Cycle Phases: Compression, trigger, expansion, normal Adaptive Multipliers: Dynamic risk adjustments Volatility intelligence ensures risk management adapts to market conditions. Engine 5: Directional Bias Model The fifth engine establishes directional conviction: // Bias Computation float bullish_bias = 0.0 bullish_bias += ma_bullish_stack ? 30 : 0 bullish_bias += price_above_structure ? 20 : 0 bullish_bias += close > ma_anchor ? 15 : 0 bullish_bias += pos_di > neg_di ? 20 : 0 bullish_bias += slopes_aligned_bull ? 15 : 0 // Net Bias float net_bias = bullish_bias - bearish_bias int bias_direction = net_bias > i_bias_threshold / 2 ? 1 : net_bias < -i_bias_threshold / 2 ? -1 : 0 Bias components: MA Stack: Fast/slow/anchor moving average relationships Price Position: Where price sits relative to MAs ADX Direction: +DI vs -DI for trend confirmation MA Slopes: Directional momentum of moving averages Bias Strength: 0-100 indicating directional conviction Directional bias provides the primary directional framework for trading decisions. Engine 6: Signal Qualification System The sixth engine evaluates and qualifies all signals: // Confluence Scoring int bull_confluence = 0 bull_confluence += market_regime == 1 and trend_direction == 1 ? 2 : 0 bull_confluence += structure_bias == 1 ? 1 : 0 bull_confluence += bos_bullish ? 1 : 0 bull_confluence += momentum_state == 1 ? 2 : 0 bull_confluence += bias_direction == 1 ? 2 : 0 bull_confluence += squeeze_off and net_momentum > 0 ? 1 : 0 // Qualification Check bool bull_qualified = bull_confluence >= i_min_confluence bool bear_qualified = bear_confluence >= i_min_confluence // Final Signal Generation bool long_signal = bull_qualified and bull_trigger and bars_since_bull > i_signal_cooldown and bar_confirmed and market_regime != 3 Qualification components: Confluence Score: Points from each engine (max 10) Minimum Threshold: Required confluence for signals (default: 5) Signal Triggers: Entry conditions (crossovers, breakouts, etc.) Cooldown Management: Prevents overtrading Quality Grades: A-D grades based on confluence score Signal qualification ensures only high-probability setups are traded. Visual Elements Directional Cloud: Dynamic cloud showing trend and conviction Signal Markers: Clear entry signals with quality grades Risk Levels: Visual stop loss and target levels Structure Points: Marked swing highs and lows Squeeze Background: Volatility compression indication Signal Background: Signal strength background shading Moving Averages: Color-coded MA system Dashboard: Comprehensive intelligence panel The dashboard displays: 1. Current market regime and strength 2. Trend direction and bias scores 3. Momentum state and pressure readings 4. Volatility cycle and squeeze status 5. Structure analysis and bias 6. Signal qualification and grade 7. Risk metrics and multipliers 8. Active position information Input Parameters Regime Engine: ADX Period: Trend strength calculation (default: 14) Trend Threshold: Minimum ADX for trend (default: 25) Volatility Multipliers: Expansion/contraction thresholds Structure Engine: Swing Sensitivity: Pivot detection sensitivity (default: 10) Structure Confirmation: Bars for confirmation (default: 3) Show Liquidity: Display liquidity zones Momentum Engine: Pressure Period: Pressure calculation (default: 14) WaveTrend Settings: Channel and average periods RSI Period: Momentum oscillator (default: 14) Volatility Layer: Bollinger Settings: Period and deviation Keltner Settings: Period and multiplier Adaptive Stops: Enable dynamic stops Signal Qualification: Minimum Confluence: Required score (default: 5) Signal Cooldown: Bars between signals (default: 5) Minimum R:R: Risk/reward requirement (default: 1.5) How to Use This Engine Step 1: Understand Market Regime Check the dashboard for current regime. Avoid trading in volatile regimes (red), focus on trending regimes (green), and adapt strategy for ranging regimes (purple). Step 2: Assess Directional Bias Look for strong bias scores (>60) with MA stack confirmation. The bias should be clear across multiple components before considering entries. Step 3: Confirm Momentum Ensure momentum supports the directional bias. Look for pressure in the direction of trade and momentum acceleration. Step 4: Verify Structure Entries near structural levels have higher probability. Look for BOS confirmation and avoid trading against established structure. Step 5: Check Volatility Be aware of volatility cycles. Squeeze releases offer high-probability breakout opportunities. Adjust stops based on volatility multipliers. Step 6: Qualify Signals Only take signals with 5+ confluence points. A-grade signals (8+ points) offer the highest probability and deserve larger position sizing. Best Practices Always trade in the direction of the dominant bias Higher confluence scores mean higher probability setups Respect regime changes - they signal strategy adjustments Use the directional cloud as primary trend guidance Place stops using the volatility-adjusted levels Scale out at multiple targets as provided Avoid trading during volatile regimes unless experienced Wait for A-grade setups rather than forcing mediocre trades Keep a trade journal tracking regime/bias combinations Never override the system's risk management without strong reason Strategy Integration This engine is a complete trading system: Use signal qualification as primary entry filter Apply regime-based position sizing Import bias scores for trend confirmation Use structure levels for stop placement Integrate volatility multipliers for risk management Export all engine outputs for custom strategies Technical Implementation Built with Pine Script v6 featuring: Six-engine architecture with unified signal processing Advanced regime detection with ADX/ATR analysis Comprehensive structure analysis with swing detection Multi-factor momentum scoring system Volatility cycle analysis with squeeze detection Directional bias calculation with multiple confirmations Signal qualification with confluence scoring Dynamic risk management with adaptive multipliers Comprehensive visualization with directional cloud Real-time dashboard with 12 key metrics Export functions for complete system integration The code uses confirmed bars throughout to prevent repainting and ensure reliable signals. Originality Statement This engine is original in its comprehensive integration of six distinct analytical systems into a unified decision framework. While individual components (ADX, moving averages, RSI, etc.) are established tools, this engine is justified because: It synthesizes six independent analytical engines into one cohesive system The regime-adaptive logic automatically adjusts behavior based on market conditions Signal qualification provides objective, numerical evaluation of trade quality The directional cloud visualization offers intuitive trend analysis Dynamic risk management adapts to volatility and structure Comprehensive dashboard presents all critical metrics in one view Each engine contributes unique insights: regime shows when to trade, structure shows where, momentum shows timing, volatility shows how much, bias shows direction, and qualification shows quality The engine solves the real problem of indicator overload and conflicting signals Export functions enable complete system integration and customization This is institutional-grade analysis typically available only to professional traders The engine's value lies in providing a complete, unified trading intelligence system that eliminates analysis paralysis and provides clear, actionable signals based on comprehensive market analysis. Disclaimer This engine is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. This is a comprehensive analysis tool, not a guaranteed profit system. Even with comprehensive analysis, markets can behave unpredictably due to news events, economic data, or changes in market structure. Past performance of the system does not guarantee future results. The engine's signals are mathematical calculations based on historical patterns and should be used with proper risk management. Always use stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose on any single trade, regardless of signal quality or confluence score. The author is not responsible for any losses incurred from using this engine. Users assume full responsibility for all trading decisions made using this system. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Volatility Regime Engine [JOAT]Volatility Regime Engine Introduction The Volatility Regime Engine is a sophisticated volatility analysis tool designed to identify market cycles through expansion and contraction patterns. This indicator goes beyond simple volatility measurement by classifying volatility into distinct regimes, detecting squeeze patterns, and forecasting potential volatility shifts. It's built for traders who understand that volatility is not just noise but a predictable cycle that creates trading opportunities when properly understood. Volatility is the lifeblood of markets - it creates opportunities, determines risk, and influences strategy selection. This engine provides institutional-grade volatility analysis that helps traders adapt their approach to current market conditions. Whether you're a day trader adjusting stop distances, a swing trader timing entries after volatility contractions, or a position trader sizing positions based on volatility forecasts, this tool provides the critical volatility intelligence needed for superior decision-making. Why This Indicator Exists Most traders treat volatility as a single number (like ATR) without understanding its cyclical nature and predictive properties. This indicator addresses that limitation by: Regime Classification: Identifies whether volatility is expanding, contracting, or normal, allowing strategy adaptation Squeeze Detection: Pinpoints volatility compression patterns that often precede significant price moves Cycle Analysis: Tracks volatility cycles to identify optimal entry and exit timing Forecasting Capability: Uses mean reversion principles to predict likely volatility shifts Adaptive Multipliers: Provides dynamic stop loss and target multipliers based on current volatility Historical Context: Places current volatility in percentile context for better decision making The engine solves the critical problem of using static risk management in dynamic volatility environments. By understanding where you are in the volatility cycle, you can anticipate market behavior and position yourself accordingly. Core Components Explained 1. Multi-Layer ATR Analysis The indicator uses three ATR timeframes to capture volatility across different horizons: // Multiple ATR timeframes float atr_fast = ta.atr(i_atr_fast) float atr_slow = ta.atr(i_atr_slow) float atr_baseline = ta.sma(ta.atr(i_atr_slow), i_atr_baseline) // ATR ratios float atr_ratio = atr_baseline > 0 ? atr_slow / atr_baseline : 1.0 float atr_momentum = atr_fast / atr_slow ATR layers: Fast ATR (7 periods): Captures immediate volatility changes Slow ATR (21 periods): Medium-term volatility trend Baseline ATR (50 periods smoothed): Long-term volatility average ATR Ratio: Current volatility relative to baseline (key for regime detection) ATR Momentum: Short-term volatility acceleration/deceleration The ATR ratio is the primary driver of regime classification - values above 1.4 indicate expansion, below 0.6 indicate contraction. 2. Squeeze Detection System The indicator uses the classic TTM Squeeze concept with enhanced features: // Squeeze state bool squeeze_on = bb_lower > kc_lower and bb_upper < kc_upper bool squeeze_off = bb_lower < kc_lower and bb_upper > kc_upper // Squeeze duration tracking var int squeeze_duration = 0 if squeeze_on squeeze_duration := squeeze_duration + 1 else squeeze_duration := 0 // Squeeze intensity (longer squeeze = more explosive release) float squeeze_intensity = math.min(float(squeeze_duration) / 20.0 * 100, 100) Squeeze components: Bollinger Bands: Measure volatility through standard deviation Keltner Channels: Measure volatility through ATR Squeeze On: BB inside KC indicates volatility compression Squeeze Duration: Time in compression - longer durations build more energy Squeeze Intensity: Percentage score of compression buildup Squeeze Release: Transition from compression to expansion Squeeze releases are among the most reliable volatility signals - they often precede significant price moves. 3. Historical Volatility Analysis For additional confirmation, the indicator calculates statistical volatility: f_historical_vol(int period, int annual_days) => float log_return = math.log(close / close ) float hv = ta.stdev(log_return, period) * math.sqrt(annual_days) * 100 hv float hv_current = i_use_hv ? f_historical_vol(i_hv_len, i_hv_annual) : 0 float hv_avg = i_use_hv ? ta.sma(hv_current, i_hv_len * 2) : 0 float hv_ratio = hv_avg > 0 ? hv_current / hv_avg : 1.0 HV features: Log Returns Calculation: Statistically sound volatility measurement Annualization: Converts to annualized volatility percentage HV Ratio: Current volatility relative to historical average HV Regime: High/low volatility classification Confirmation Layer: Validates ATR-based regime detection Historical volatility adds a statistical layer that confirms what the ATR analysis is showing. 4. Volatility Regime Classification The indicator classifies volatility into four distinct states: // Raw regime based on ATR ratio int raw_vol_regime = 0 if atr_ratio >= i_exp_thresh raw_vol_regime := 1 // Expansion else if atr_ratio <= i_con_thresh raw_vol_regime := -1 // Contraction // Confirmed regime with bar count filter var int regime_counter = 0 var int confirmed_vol_regime = 0 if raw_vol_regime == raw_vol_regime and raw_vol_regime != 0 regime_counter := math.min(regime_counter + 1, i_regime_confirm + 1) else if raw_vol_regime != raw_vol_regime regime_counter := 1 if regime_counter >= i_regime_confirm confirmed_vol_regime := raw_vol_regime Regime types: Expansion (ATR ratio > 1.4): High volatility, wide ranges, increased risk Contraction (ATR ratio < 0.6): Low volatility, narrow ranges, preparing for breakouts Normal (0.6 < ATR ratio < 1.4): Balanced volatility, normal market conditions Transitioning: Regime changes requiring confirmation before acting Regime confirmation prevents whipsaws by requiring multiple bars in the same regime before classification. 5. Volatility Cycle Phases Beyond simple regimes, the indicator identifies where you are in the volatility cycle: // Cycle phases: 0=neutral, 1=building, 2=peak, 3=declining, 4=trough var int vol_cycle_phase = 0 float atr_slope = atr_slow - atr_slow float atr_accel = atr_slope - nz(atr_slope ) if confirmed_vol_regime == 1 if atr_accel > 0 vol_cycle_phase := 1 // Building expansion else vol_cycle_phase := 2 // Peak expansion else if confirmed_vol_regime == -1 if atr_accel < 0 vol_cycle_phase := 3 // Declining to contraction else vol_cycle_phase := 4 // Trough contraction Cycle phases: Building Expansion: Volatility increasing, acceleration positive Peak Expansion: High volatility but decelerating Declining to Contraction: Volatility decreasing rapidly Trough Contraction: Low volatility stabilizing Neutral: Transition periods between phases Cycle analysis helps anticipate the next phase and prepare strategy adjustments. 6. Adaptive Multipliers The indicator provides dynamic multipliers for risk management: // Dynamic stop multiplier based on regime float adaptive_stop_mult = switch confirmed_vol_regime 1 => 1.5 // Wider stops in expansion -1 => 0.8 // Tighter stops in contraction => 1.0 // Normal // Dynamic target multiplier float adaptive_target_mult = switch confirmed_vol_regime 1 => 2.0 // Larger targets in expansion -1 => 1.2 // Smaller targets in contraction => 1.5 // Normal Adaptive features: Stop Multiplier: Adjusts stop distance based on volatility regime Target Multiplier: Scales profit targets to volatility conditions Risk Adjustment: Helps maintain consistent risk across volatility regimes Export Functions: Available for integration with trading systems These multipliers help maintain consistent risk-to-reward ratios across different volatility environments. Visual Elements Multi-Layer Histogram: Core volatility ratio with gradient coloring Glow Effects: Intensity-based glow around extreme volatility Squeeze Momentum: Separate plot showing squeeze building/release Cycle Momentum: Volatility cycle acceleration/deceleration Background Shading: Regime-based background colors Signal Markers: Premium volatility signals with labels Dashboard: Real-time volatility metrics and forecasts The dashboard displays: 1. Current volatility regime and strength 2. Cycle phase and momentum 3. ATR ratio and percentage 4. Percentile ranking of current volatility 5. Squeeze status and duration 6. Quality score of current setup 7. Volatility forecast (expansion/contraction) 8. Adaptive multipliers for risk management Input Parameters ATR Settings: Fast ATR Period: Short-term volatility (default: 7) Slow ATR Period: Medium-term volatility (default: 21) Baseline Period: Long-term volatility average (default: 50) Regime Thresholds: Expansion Threshold: ATR ratio for expansion regime (default: 1.4) Contraction Threshold: ATR ratio for contraction regime (default: 0.6) Regime Confirmation: Bars for regime confirmation (default: 3) Squeeze Detection: Bollinger Period: BB calculation period (default: 20) Bollinger Multiplier: BB standard deviation (default: 2.0) Keltner Period: KC calculation period (default: 20) Keltner Multiplier: KC ATR multiplier (default: 1.5) Visual Settings: Color Scheme: Customizable colors for each regime Glow Effects: Enable/disable visual enhancements Dashboard Display: Show/hide metrics panel Signal Labels: Control signal label frequency How to Use This Indicator Step 1: Identify Current Regime Check the dashboard for the current volatility regime. In expansion (red), expect larger ranges and adjust stops wider. In contraction (blue), prepare for potential breakouts. Normal conditions (purple) allow standard trading approaches. Step 2: Monitor Squeeze Patterns Watch for squeeze onset (compression) and duration. Longer squeezes (high intensity) often lead to more explosive releases. The squeeze release signal is one of the most reliable volatility breakout patterns. Step 3: Analyze Cycle Phase Understanding the cycle phase helps anticipate the next move. Building expansion suggests continued volatility, while peak expansion warns of potential contraction ahead. Step 4: Use Percentile Context The ATR percentile shows how current volatility compares to historical levels. Extremely high percentiles (>90) suggest mean reversion to lower volatility, while low percentiles (<10) suggest expansion is likely. Step 5: Apply Adaptive Multipliers Use the provided stop and target multipliers to adjust your risk management to current conditions. This maintains consistent risk across different volatility environments. Step 6: Watch for Premium Signals Premium expansion signals (squeeze release + high volatility + HV confirmation) offer high-probability breakout opportunities. Premium contraction signals (early contraction + low HV) suggest optimal entry points before breakouts. Best Practices Use the indicator to adapt your strategy to volatility conditions rather than fighting them Squeeze releases are most reliable when they occur after long compression periods (>10 bars) Volatility expansion often follows news events - be aware of economic calendars In low volatility environments, reduce position size but increase stop distance proportionally High volatility periods offer larger profit potential but require wider stops and smaller position sizes The volatility forecast is mean-reversion based - extreme volatility tends to revert to normal Combine with trend analysis for best results - volatility expansion in the direction of trend is powerful Use the adaptive multipliers in your automated strategies for dynamic risk management Monitor the cycle phase to anticipate regime changes before they occur Keep a volatility journal to track how different instruments behave in various regimes Strategy Integration This indicator is designed to integrate seamlessly with other trading systems: Export plots provide volatility data for strategy consumption Adaptive multipliers can be imported for dynamic risk management Regime classification can filter trades based on volatility conditions Squeeze signals can trigger breakout strategies Cycle analysis can optimize entry/exit timing Quality scores can weight signal strength in composite systems The indicator includes 12 export functions for integration: ATR Ratio Export: Normalized volatility level Vol Regime Export: Current regime classification (-1, 0, 1) ATR Percentile Export: Historical volatility context Adaptive ATR Export: Volatility-adjusted ATR value Stop Multiplier Export: Dynamic stop adjustment factor Target Multiplier Export: Dynamic target adjustment factor Squeeze State Export: Binary squeeze on/off signal Squeeze Momentum Export: Squeeze building/release momentum Vol Score Export: Normalized volatility score (-100 to +100) Technical Implementation Built with Pine Script v6 featuring: Multi-timeframe volatility analysis across three ATR periods Statistical historical volatility calculation with log returns Advanced squeeze detection with duration and intensity tracking Regime classification with confirmation logic to prevent whipsaws Cycle phase analysis using slope and acceleration Adaptive multiplier system for dynamic risk management Comprehensive visualization with multi-layer glow effects Real-time dashboard with 11 key volatility metrics Alert conditions for all major volatility events Export functions for strategy integration The code uses confirmed bars for all calculations to prevent repainting and ensure reliable signals. Originality Statement This indicator is original in its comprehensive approach to volatility analysis and regime classification. While individual components (ATR, Bollinger Bands, Keltner Channels) are established tools, this indicator is justified because: It synthesizes multiple volatility measurement approaches into a unified framework The regime classification system provides actionable market state information Cycle phase analysis adds predictive capability beyond simple volatility measurement The squeeze detection system includes duration and intensity scoring for signal quality Adaptive multipliers provide practical risk management adjustments based on volatility Historical volatility adds statistical confirmation to price-based volatility measures The forecasting system uses mean reversion principles for volatility prediction Comprehensive visualization makes complex volatility concepts accessible and actionable Export functions enable integration with other trading systems Each component contributes unique insights: ATR shows current volatility, squeeze shows compression, HV shows statistical volatility, cycles show direction, and multipliers provide practical application The indicator's value lies in transforming volatility from a single number into a rich, multi-dimensional analysis that helps traders understand not just how volatile the market is, but where it is in the volatility cycle and what that means for trading opportunities. 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. Volatility analysis is a tool for understanding market conditions, not a prediction system. Volatility patterns can change suddenly due to market events, news, or changes in market structure. Past volatility patterns do not guarantee future behavior. The indicator's signals are mathematical calculations based on historical patterns and should be used in conjunction with other forms of analysis. Always use proper risk management, including stop losses and position sizing appropriate for current volatility conditions. High volatility periods require smaller position sizes due to increased risk, while low volatility periods may require wider stops to avoid premature exits. The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this system. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
QTP - Breakout ForecasterQTP - Breakout Forecaster 🔬 Overview The QTP Breakout Forecaster uses a statistical probability engine to forecast the likelihood of price breaking above or below its current consolidation range. Instead of reacting to breakouts after they happen, this indicator tells you the probability of a breakout occurring within a configurable number of future bars — giving you time to prepare entries before the move begins. Built on a random walk model with Normal CDF approximation, it translates raw volatility and price position into clean, actionable probability readings. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ How It Works The indicator defines a consolidation range using the highest high and lowest low over a configurable lookback period. It then calculates the statistical probability that price will exceed either boundary within the forecast horizon, using the standard deviation of log returns as the volatility input. The model assumes a geometric random walk — the same framework used in options pricing. The Normal CDF (Abramowitz & Stegun approximation) converts the distance-to-boundary into a percentage probability. When price sits near the upper boundary with low volatility, bullish breakout probability rises. When volatility compresses (squeeze), both probabilities increase as the range tightens relative to expected movement. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 Key Features ◆ Statistical Probability Engine — Uses Normal CDF approximation on log-return volatility to calculate the mathematical probability of price exceeding the consolidation range. Not a pattern — a calculation. ◆ Volatility Squeeze Detection — Compares current ATR to its historical average. When the squeeze reading exceeds 50%, volatility is compressed and a breakout becomes statistically more likely. Above 70% triggers an alert condition. ◆ Bias Indicator — Automatically classifies the current state as BULLISH, BEARISH, or NEUTRAL based on the differential between the two breakout probabilities. Displayed in the dashboard with color coding. ◆ Neon Glow Effect — A wider, semi-transparent line renders behind each probability line, creating a high-visibility neon effect in electric blue (bullish) and hot pink (bearish). Toggleable on/off. ◆ Donchian Channel Overlay — The consolidation range is plotted directly on the main price chart with a subtle fill, showing exactly where the upper and lower boundaries sit. Toggleable on/off. ◆ Breakout Arrows on Main Chart — When either probability crosses above 50%, a triangle arrow appears on the price chart — blue up-arrow for bullish, pink down-arrow for bearish. These fire at the moment of statistical significance, not after the breakout. ◆ Gradient Probability Fill — The area between each probability line and the zero line fills with a gradient that intensifies as probability rises, giving instant visual feedback on breakout strength. ◆ Professional Dashboard — Floating table displays bullish probability, bearish probability, bias direction, squeeze intensity, and forecast horizon in a compact, dark-themed panel. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📐 How To Use Add the indicator to your chart — the probability oscillator appears in a lower panel with a black background, and the consolidation range appears on the main chart. Watch the bullish probability line (blue) — when it rises above 50%, the model calculates that price is more likely than not to break above the range within the forecast horizon. Watch the bearish probability line (pink) — when it rises above 50%, a downside breakout is statistically favored. The squeeze reading in the dashboard tells you how compressed volatility is. High squeeze (above 50%) means the range is tight relative to historical movement — breakouts from these conditions tend to be explosive. Use the bias indicator to quickly assess directional lean. BULLISH means the upside probability significantly exceeds downside. BEARISH is the opposite. NEUTRAL means both sides are roughly equal — watch for the first one to cross 50%. Adjust the Forecast Horizon to match your trading timeframe. Shorter horizons (5-10 bars) give more immediate signals. Longer horizons (20-50 bars) capture bigger consolidation breakouts. The Range Length controls how far back the consolidation boundaries are calculated. Shorter lengths (10-15) react to recent structure. Longer lengths (30-50) define major support and resistance zones. Combine with a trend indicator — when the breakout forecaster shows high bullish probability AND price is above a key moving average, conviction for a long entry is highest. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Settings Overview ◆ Range Length — Lookback period for consolidation high/low (default: 20) ◆ Forecast Horizon — Number of future bars for probability calculation (default: 10) ◆ Volatility Lookback — Period for log-return standard deviation (default: 50) ◆ Bullish / Bearish Colors — Customizable color scheme ◆ Fill Transparency — Controls the gradient fill intensity (default: 70) ◆ Neon Glow Effect — Toggle the glow behind probability lines ◆ Show Range on Chart — Toggle the Donchian channel overlay on the main chart ◆ Dashboard — Enable/disable, position, and size controls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔔 Alerts 3 built-in alert conditions ready for automation: • Bullish Breakout Forecast — bullish probability crossed above 50% • Bearish Breakout Forecast — bearish probability crossed above 50% • High Squeeze Detected — volatility squeeze intensity above 70%, breakout imminent ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ Disclaimer This indicator is a technical analysis tool designed to assist with market analysis. It does not constitute financial advice. Past performance is not indicative of future results. Always use proper risk management and perform your own due diligence before making trading decisions.
Pine Script® 인디케이터
QTP-CB의
Adaptive Volatility Matrix [JOAT]Adaptive Volatility Matrix Introduction The Adaptive Volatility Matrix (AVM) is an advanced open-source volatility regime classification indicator that combines Bollinger Band Width Percentile (BBWP), ATR percentile analysis, regime transition prediction, volatility clustering detection, and historical regime statistics to classify market conditions into distinct volatility regimes. This indicator helps traders adapt their strategies to current market conditions by systematically identifying when volatility is expanding, contracting, or transitioning between regimes. Unlike basic volatility indicators that simply plot ATR or Bollinger Bands, AVM employs a sophisticated dual-metric system that combines BBWP (measuring price range compression/expansion) with ATR percentile (measuring absolute volatility) to create a combined volatility score (0-100%). The indicator then classifies this score into five distinct regimes and predicts regime transitions through momentum analysis. Why This Indicator Exists This indicator addresses the challenge of adapting trading strategies to volatility conditions. Different market regimes require different approaches - mean reversion works in low volatility, breakout strategies work in expansion, and risk management becomes critical in extreme volatility. AVM systematically reveals: BBWP Analysis: Measures Bollinger Band width percentile to identify compression/expansion cycles ATR Percentile: Tracks normalized ATR percentile to measure absolute volatility levels Combined Volatility Score: Weighted average (60% BBWP, 40% ATR) for robust regime classification Regime Classification: Five distinct regimes (Extreme Expansion, Expansion, Normal, Contraction, Extreme Contraction) Transition Prediction: Momentum-based forecasting of next regime with probability Volatility Clustering: Detects sustained high/low volatility periods Historical Statistics: Tracks regime duration and frequency for context Each component provides unique intelligence. BBWP shows compression cycles, ATR shows absolute volatility, combined score provides robust classification, regime system categorizes conditions, transition prediction anticipates changes, clustering detects persistence, and statistics provide historical context. Core Components Explained 1. BBWP (Bollinger Band Width Percentile) Calculation BBWP measures where current Bollinger Band width ranks relative to historical width: f_calculate_bbwp(int length, int lookback) => float basis = ta.sma(close, length) float dev = ta.stdev(close, length) float bb_width = (dev * 2) / basis * 100 // Calculate percentile rank int count = 0 for i = 1 to lookback if bb_width > nz(bb_width ) count += 1 float bbwp = (count / lookback) * 100 BBWP ranges from 0-100%: - 0-20%: Extreme compression (volatility squeeze) - 20-40%: Contraction (below average volatility) - 40-60%: Normal (average volatility) - 60-80%: Expansion (above average volatility) - 80-100%: Extreme expansion (volatility breakout) 2. ATR Percentile Analysis ATR percentile measures where current normalized ATR ranks historically: f_atr_percentile(int period, int lookback) => float atr_val = ta.atr(period) float natr = close > 0 ? (atr_val / close) * 100 : 0.0 float percentile = ta.percentrank(natr, lookback) Normalized ATR (NATR) accounts for price level differences, making volatility comparable across different price ranges. Percentile ranking shows where current volatility sits in historical distribution. 3. Combined Volatility Score & Regime Classification The combined score weights BBWP more heavily than ATR percentile: float combined_score = (bbwp_value * 0.6) + (atr_percentile * 0.4) f_classify_regime(float bbwp_val, float atr_perc, float exp_th, float con_th, float ext_th) => string regime = "Normal" int regime_code = 0 if bbwp_val >= ext_th or atr_perc >= ext_th regime := "Extreme Expansion" regime_code := 4 else if bbwp_val >= exp_th or atr_perc >= exp_th regime := "Expansion" regime_code := 3 // Additional classifications... Five regime classifications: 1. Extreme Contraction (code 1): Both metrics <30%, volatility squeeze 2. Contraction (code 2): One metric <40%, below average volatility 3. Normal (code 0): Both metrics 40-60%, average conditions 4. Expansion (code 3): One metric >70%, above average volatility 5. Extreme Expansion (code 4): Both metrics >85%, volatility breakout 4. Regime Transition Prediction AVM predicts next regime through momentum analysis: float regime_momentum = combined_score - combined_score string momentum_direction = regime_momentum > 2 ? "Accelerating" : regime_momentum < -2 ? "Decelerating" : "Stable" string predicted_regime = regime_code == 4 and regime_momentum < -5 ? "→ Expansion" : regime_code == 3 and regime_momentum < -3 ? "→ Normal" : // Additional predictions... "Stable" float transition_prob = math.min(math.abs(regime_momentum) * 10, 100) Transition probability (0-100%) based on momentum magnitude. >50% probability triggers warning. 5. Volatility Clustering Detection Clustering identifies sustained high/low volatility periods: int cluster_lookback = 20 float cluster_threshold = 70.0 int high_vol_count = 0 for i = 0 to cluster_lookback - 1 if combined_score >= cluster_threshold high_vol_count += 1 float cluster_ratio = high_vol_count / cluster_lookback * 100 bool in_vol_cluster = cluster_ratio >= 60 // 60% of bars are high vol string cluster_strength = cluster_ratio >= 80 ? "Strong" : cluster_ratio >= 60 ? "Moderate" : cluster_ratio >= 40 ? "Weak" : "None" Clusters indicate persistent volatility conditions that tend to continue. 6. Historical Regime Statistics AVM tracks regime history for context: var array regime_history = array.new_int(0) var array regime_durations = array.new_int(0) if regime_changed array.push(regime_history, regime_code) array.push(regime_durations, bars_in_regime) // Calculate statistics float avg_expansion_duration = exp_sum / exp_cnt float avg_contraction_duration = con_sum / con_cnt float duration_ratio = bars_in_regime / avg_expansion_duration bool regime_extended = duration_ratio > 1.5 Statistics show if current regime is extended (>1.5x average duration), suggesting potential transition. Visual Elements Combined Score Line: Main plot (0-100%) with regime-based coloring ATR Percentile Overlay: Circles showing ATR percentile for comparison Histogram: Gradient-colored bars showing volatility score with regime colors Reference Lines: 70% (expansion), 50% (neutral), 30% (contraction), 85% (extreme) Background Zones: Regime-colored backgrounds (purple for expansion, yellow for contraction) Transition Warnings: ⚠ symbols when transition probability >50% BBWP Percentile Bands: 20th, 50th, 80th percentile circles for context Dashboard: Real-time metrics including regime, score, BBWP, ATR%, trend, duration, momentum, transition prediction, cluster status, duration ratio, historical stats Input Parameters BBWP Parameters: BBWP Length: Bollinger Band period (default: 13) BBWP Lookback: Historical comparison period (default: 252) ATR Analysis: ATR Period: ATR calculation period (default: 14) ATR Percentile Lookback: Historical ranking period (default: 100) Regime Classification: Expansion Threshold: Score for expansion regime (default: 70%) Contraction Threshold: Score for contraction regime (default: 30%) Extreme Threshold: Score for extreme regimes (default: 85%) Visualization: Show Regime Zones: Toggle background coloring Show Histogram: Toggle volatility histogram Show ATR Overlay: Toggle ATR percentile circles How to Use This Indicator Step 1: Identify Current Regime Check dashboard "Regime" row. Adjust strategy based on classification. Step 2: Monitor Combined Score Score >70% = expansion (use breakout strategies) Score <30% = contraction (use mean reversion) Score 40-60% = normal (use balanced approach) Step 3: Check Momentum Direction "Accelerating" = volatility increasing "Decelerating" = volatility decreasing "Stable" = no significant change Step 4: Watch for Transition Warnings ⚠ symbols indicate >50% probability of regime change. Prepare to adjust strategy. Step 5: Assess Cluster Status "Strong" or "Moderate" cluster = persistent conditions likely to continue Step 6: Consider Duration Ratio Ratio >1.5x = extended regime, higher probability of mean reversion Best Practices Use regime classification to select appropriate trading strategies Extreme contraction often precedes volatility breakouts - prepare for expansion Extreme expansion often mean-reverts - reduce position sizes Transition warnings provide early signal to adjust risk management Volatility clusters suggest persistence - don't fight the regime Extended regimes (>1.5x average) have higher reversal probability BBWP and ATR percentile divergence suggests regime uncertainty Historical statistics provide context for current regime duration Combine with directional indicators - AVM shows conditions, not direction Indicator Limitations Regime classification is backward-looking - transitions lag actual changes BBWP calculation is computationally intensive on large lookback periods Transition predictions are probabilistic, not deterministic Extreme regimes can persist longer than expected during major events Historical statistics require sufficient data (50+ regime changes) Clustering detection has fixed lookback - may miss longer-term patterns Combined score weighting (60/40) may not be optimal for all instruments Regime thresholds may need adjustment for different markets Technical Implementation Built with Pine Script v6 using: Custom BBWP calculation with percentile ranking ATR percentile analysis with normalized ATR Weighted combined score (60% BBWP, 40% ATR) Five-tier regime classification system Momentum-based transition prediction with probability Volatility clustering detection (20-bar lookback) Historical regime tracking with arrays (last 50 regimes) Duration ratio calculation vs historical averages BBWP percentile bands (20th, 50th, 80th) Adaptive background coloring based on regime and duration Comprehensive dashboard with 12 metrics The code is fully open-source and can be modified to suit individual trading styles. Originality Statement This indicator is original in its comprehensive volatility regime classification approach. While BBWP and ATR are established concepts, this indicator is justified because: It combines BBWP and ATR percentile into weighted combined score for robust classification The five-tier regime system provides granular volatility categorization Momentum-based transition prediction with probability quantification is unique Volatility clustering detection identifies persistent regime conditions Historical regime statistics provide context for current regime duration Duration ratio calculation identifies extended regimes with mean reversion potential BBWP percentile bands add additional context layers Adaptive background intensity based on regime stability Each component contributes unique information: BBWP shows compression cycles, ATR shows absolute volatility, combined score provides robust classification, regime system categorizes conditions, transition prediction anticipates changes, clustering detects persistence, statistics provide context, and duration ratio identifies extremes. The indicator's value lies in presenting these complementary perspectives simultaneously with unified regime framework. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Regime classifications do not guarantee future volatility behavior. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. -Made with passion by officialjackofalltrades
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officialjackofalltrades의
Volatility Regime Switch [Metrify]VRS is a regime classifier that tries to separate two things most indicators mix together: direction and tradability. It doesn’t just ask "is price above/below a line?". it estimates whether the market is currently behaving more like a trend regime or a noise/chop regime, then adapts its switching logic and trailing structure accordingly. The output is a state machine (bull/bear) with a volatility-normalized corridor, plus explicit markers for switch accepted vs switch rejected. Core idea: switching should depend on regime Most trend flip tools fail in choppy markets because they apply the same confirmation rules everywhere. VRS tries to avoid that by measuring a continuous regime score: trreg ≈ how “trend-like” conditions are nsreg ≈ how “noise-like” conditions are That regime estimate is then used to: shape the trailing band distance (wider in chop, tighter in trend), change the required confirmation for a switch (more strict in noise), and demand follow-through after a candidate switch (acceptance check). Regime estimation: how it decides “trend-like” vs “noise-like” The regime score is built from three normalized features, then blended using inverse-variance weighting again: Efficiency ratio (ER): Measures directional efficiency: net displacement over a horizon vs total movement. Trends have higher efficiency; chop has lower. ADX-like trend strength: A custom ADX calculation is normalized (adxn), giving a bounded “trend strength” component. Volatility ratio (fast/slow): Compares fast ATR to slow ATR and normalizes it. This helps distinguish active expansion vs quieter conditions. These three components are combined into trreg (0..1). Noise regime is nsreg = 1 - trreg. The important part is it can behave differently when the market is structurally trending versus when it is structurally noisy. The anchor + adaptive bands: how the corridor is built VRS uses two EMAs: a fast EMA (emaf) a slower EMA (emas) It then creates an anchor that interpolates between them based on regime: when trend regime is strong (trreg high), the anchor leans toward the fast EMA (more responsive) when noise regime is strong (nsreg high), it leans toward the slow EMA (more stable) Band distance is bdist = volc * bmult, and bmult is also regime-dependent: in noise, bmult becomes larger → bands widen → fewer false flips in trend, bmult tightens → better trailing sensitivity Finally, the trailing bands (fup, flo) use a classic "non-decreasing band" logic similar to trailing-stop structures: the band only moves in the favorable direction unless price invalidates it, preventing constant band oscillation. Bias and conviction layer A switch is not triggered merely by close above/below a band. VRS computes conviction, which mixes: Intra-bar price action bias Two normalized elements are used: CLV (close location value) inside the candle range and body direction/strength relative to candle range. Both are Z-scored and squashed (atan-based) to avoid extreme outliers dominating. Trend bias Difference between fast and slow EMA, normalized by volatility, then Z-scored and squashed. Displacement breakout quality If price breaks above fup or below flo, it computes a breakout 'distance' normalized by volatility, then converts it into a Z-score relative to recent breakout behavior (dbullz, dbearz) These get blended into a conviction signal that is smoothed, and then compared against a dynamic trigger threshold built from the average + stdev of conviction magnitude. A flip should happen when price action + trend bias + breakout quality jointly exceed what is normal for this market recently. Practical reading notes VRS generally behaves best when read as "current regime context + boundary + switch events" rather than as a constant entry/exit engine. In trending conditions, the trail will tend to hug price more tightly and switches will be less frequent. In noisy conditions, the corridor widens and the script becomes more conservative, often producing rejected switch attempts rather than rapid flips. The rejected-switch markers (yellow X) are explicit evidence that the script detected an attempted regime change but did not see enough acceptance. For discretionary use, those rejection points can be useful as information about failed break attempts or lack of follow-through. This script is designed to be adaptive, but it still has structural constraints. It uses volatility normalization and regime weighting to reduce parameter brittleness, yet extreme regime changes (sudden volatility spikes, news-driven moves, illiquid gaps) can still cause behavior that looks late or 'overly strict', because acceptance and confirmation are intentionally conservative in high-noise conditions. Conversely, on very smooth trend legs, the trail can appear tight and switches may look clean, but that depends on how the chosen lengths match the instrument’s tempo. Also, because this is a state machine with acceptance logic, you should expect situations where price briefly breaks a boundary and then returns—those are exactly the environments that produce rejected switches. The indicator surfaces that behavior explicitly instead of hiding it.
Pine Script® 인디케이터
Metrify의
Jensen's Convexity ScoreThe Jensen Convexity Score is a quantitative indicator that measures asymmetric volatility patterns in asset returns, identifying securities with positive convexity (higher upside volatility than downside volatility). It's based on Jensen's inequality from probability theory and it helps investors to identify assets with favorable risk-return asymmetries. I was inspired to write this indicator while reading the book "The Statistical Consequences of Fat Tails by Nassim Nicholas Taleb. "The payoff of a human venture is, in general, inversely proportional to what it is expected to be... Convexity is one way to remedy the problem of prediction." Nassim Nicholas Taleb Jensen's Inequality Jensen's inequality states that for a convex function f and random variable X: E ≥ f(E ) For concave functions, the inequality reverses. This fundamental principle reveals that: Convex payoffs benefit from volatility (options, venture capital, growth stocks) Concave payoffs suffer from volatility (short options, leveraged positions, meme stocks) The Convexity Score Formula The indicator calculates a normalized convexity score by comparing upside and downside volatility. It's a simple but beautiful expression: Convexity Score = / (σ_total)² × 100 Where: - σ_up = Standard deviation of positive log returns - σ_down = Standard deviation of negative log returns - σ_total = Standard deviation of all log returns Interpretation: Positive scores (+10 to +50+): Upside volatility exceeds downside volatility → Convex opportunity Negative scores (-10 to -50+): Downside volatility exceeds upside volatility → Concave risk Near zero (-10 to +10): Symmetric volatility → Gaussian-like behavior How The Indicator Works - Core Calculation Step 1: Return Separation - Computes log returns over a lookback period (default: 50 and 120 bars) - Separates returns into upside moves (positive) and downside moves (negative) Step 2: Volatility Measurement - Calculates standard deviation for upside returns (σ_up) - Calculates standard deviation for downside returns (σ_down) - Calculates total volatility (σ_total) Step 3: Score Normalization - Computes convexity score as variance ratio - Multiplies by 100 for percentage display - Calculates historical percentile ranking (252-bar lookback) Step 4: Momentum Analysis - Linear regression slope on 20-bar score history - Identifies improving vs deteriorating convexity patterns Use Cases Asset Screening & Selection Scan multiple stocks to identify: - High positive convexity (score > +25): Assets with asymmetric upside potential - High negative convexity (score < -25): Assets to avoid or hedge Portfolio Construction - Overweight assets with persistent positive convexity - Underweight or hedge assets with persistent negative convexity - Build "barbell" portfolios: safe assets + high convexity opportunities Risk Management - Deteriorating convexity (score declining, negative momentum): Early warning signal - Extreme negative readings (< -40): Potential tail risk, reduce exposure - Improving convexity (score rising, positive momentum): Building positive asymmetry Market Regime Analysis - High average convexity: Risk-on environment, growth favored - Low/negative convexity: Risk-off environment, defensive positioning Interpretation Guide Strong Buy Signals (Score > +25)" Upside volatility significantly exceeds downside Buy Signals (Score +10 to +25): Moderate positive asymmetry Neutral (Score -10 to +10): Symmetric volatility profile Caution Signals (Score -25 to -10): Downside volatility exceeds upside Avoid/Hedge Signals (Score < -25): Strong negative convexity Why Convexity Matters Nassim Taleb emphasizes that in an uncertain world, convexity is more valuable than prediction: "If you have favorable asymmetries, or positive convexity, options being a special case, then in the long run you will do reasonably well, outperforming the average in the presence of uncertainty." Traditional risk metrics (Sharpe ratio, volatility) assume symmetric distributions. The Jensen Convexity Score reveals the hidden structure: - Positive convexity: Benefits from volatility (antifragile) - Negative convexity: Harmed by volatility (fragile) Let me know if you have any suggestions :) - Henrique Centieiro
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HenriqueCentieiro의
4
Vortex Volatility Engine [Pineify]Vortex Volatility Engine: Advanced Directional Volatility Momentum Indicator with Squeeze Detection The Vortex Volatility Engine is a sophisticated momentum oscillator that combines directional volatility analysis with squeeze detection to identify high-probability trading opportunities. Unlike traditional volatility indicators that simply measure price dispersion, this indicator decomposes volatility into bullish and bearish components, revealing the underlying directional strength of market movements. By integrating True Range normalization, directional momentum filtering, and dynamic volatility bands, it provides traders with a comprehensive framework for detecting volatility compression periods (squeezes) and subsequent explosive breakout signals. Key Features Directional Volatility Decomposition: Separates upward and downward price movements into distinct volatility components, revealing which side of the market controls momentum. Squeeze Detection System: Identifies low-volatility consolidation periods by comparing short-term True Range against long-term baselines, signaling potential setup phases for major moves. Dynamic Volatility Bands: Adaptive upper and lower bands calculated using standard deviation, creating context-aware thresholds that adjust to changing market conditions. Breakout Signal Generation: Automated detection of volatility expansion events when the delta crosses outside dynamic bands during non-squeeze periods. Four-Color Momentum Gradient: Visual differentiation between strong/weak bullish and bearish momentum using bright and dim color coding. Comprehensive Alert System: Real-time notifications for bullish breakouts, bearish breakouts, squeeze initiation, and squeeze release events. How It Works The Vortex Volatility Engine employs a multi-stage calculation methodology that transforms raw price data into actionable volatility intelligence: Stage 1: True Range Baseline Calculation The indicator begins by calculating a smoothed True Range using the Running Moving Average (RMA) method over the user-defined lookback period (default 14 bars). True Range captures the maximum of three values: current high-low range, absolute value of high minus previous close, and absolute value of low minus previous close. This provides a comprehensive measure of actual market volatility that accounts for gaps and limit moves. Stage 2: Directional Volatility Separation Price changes are decomposed into two distinct components: Upward Volatility (up_vol): Captures only positive price changes using math.max(ta.change(close), 0), then smooths with RMA to create an average upward momentum measure. Downward Volatility (dn_vol): Captures only negative price changes (converted to positive values) using math.max(-ta.change(close), 0), then smooths with RMA to create an average downward momentum measure. Stage 3: Power Normalization Both directional volatility components are normalized against the True Range baseline and converted to a 0-100 scale: Bull Power = (up_vol / tr_smooth) × 100 Bear Power = (dn_vol / tr_smooth) × 100 This normalization ensures that the indicator adapts to different volatility regimes and instruments. Values above 50 indicate bullish dominance, while values below 50 indicate bearish dominance. Stage 4: Vortex Delta Calculation The core signal is derived by subtracting Bear Power from Bull Power, creating the Vortex Delta. This delta is then smoothed using a Simple Moving Average (default 5 periods) to reduce noise and prevent false signals. Positive delta values indicate bullish volatility dominance, while negative values indicate bearish dominance. Stage 5: Squeeze Detection Logic The indicator compares short-term True Range (using the primary length parameter) against a long-term True Range baseline (3× the primary length). When short-term volatility falls below a user-defined threshold (default 0.8× the long-term average), a squeeze condition is triggered. This identifies consolidation periods where the market is "coiling" before a potential explosive move. Stage 6: Dynamic Band Construction Upper and lower volatility bands are constructed using: Basis Line: SMA of the Vortex Delta over 2× the primary length Standard Deviation: Calculated over the same period Upper Band: Basis + (StdDev × Band Multiplier) Lower Band: Basis - (StdDev × Band Multiplier) These bands create adaptive thresholds that expand during high-volatility periods and contract during low-volatility periods, providing context-aware breakout detection. Trading Ideas and Insights Breakout Trading Strategy The primary trading methodology involves waiting for squeeze periods followed by volatility expansion breakouts: Identify Squeeze: Watch for gray dots appearing on the zero line, indicating volatility compression. Wait for Release: Monitor for the squeeze to fire (gray dots disappear), signaling the end of consolidation. Confirm Direction: Enter trades when the Vortex Delta crosses above the upper band (bullish breakout) or below the lower band (bearish breakout) during non-squeeze periods. Momentum Confirmation: Prioritize trades where the histogram shows bright colors (strong momentum) rather than dim colors (weak momentum). Trend Continuation Signals Use the histogram color gradient to identify trend strength: Bright green bars (delta > 0 and increasing) suggest strong bullish momentum continuation Dark green bars (delta > 0 but decreasing) suggest weakening bullish momentum, potential reversal warning Bright red bars (delta < 0 and decreasing) suggest strong bearish momentum continuation Dark red bars (delta < 0 but increasing) suggest weakening bearish momentum, potential reversal warning Mean Reversion Opportunities When the Vortex Delta reaches extreme levels outside the dynamic bands but fails to sustain momentum (color shifts from bright to dim), this can signal exhaustion and potential mean reversion back toward the zero line or basis. How Multiple Indicators Work Together The Vortex Volatility Engine integrates four distinct analytical components into a unified framework: 1. True Range Foundation Provides the volatility baseline that normalizes all other calculations, ensuring the indicator adapts to different market conditions and instruments without requiring recalibration. 2. Directional Momentum Decomposition By separating upward and downward price movements, the indicator reveals which side controls the market. This is superior to simple momentum oscillators that only show net movement, as it exposes the underlying battle between bulls and bears. 3. Squeeze Detection System The squeeze component acts as a filter and setup identifier. By comparing short-term versus long-term volatility, it identifies periods of compression that historically precede significant directional moves. This prevents traders from taking breakout signals during normal volatility fluctuations. 4. Dynamic Volatility Bands The standard deviation-based bands provide context-aware thresholds that adapt to changing market regimes. During high-volatility periods, the bands widen to prevent false signals. During low-volatility periods, they contract to capture smaller but significant moves. Synergistic Integration: These components work together in a logical sequence: True Range establishes the baseline → Directional decomposition reveals momentum → Squeeze detection identifies setup periods → Dynamic bands define breakout thresholds. Each component enhances the others, creating a robust system that filters noise while capturing genuine volatility expansion events. Unique Aspects Volatility Normalization Method: Unlike standard vortex indicators that use absolute price differences, this engine normalizes directional movements against True Range, creating a percentage-based power metric that works across all timeframes and instruments. Four-Tier Color Coding: The histogram uses a sophisticated color gradient that distinguishes not just direction (bullish/bearish) but also momentum strength (accelerating/decelerating), providing instant visual feedback on trend quality. Squeeze Filtering Logic: Breakout signals are only generated during non-squeeze periods, preventing false signals during consolidation and ensuring traders only act on genuine volatility expansion events. Dual Smoothing Approach: Applies RMA smoothing to directional components and SMA smoothing to the final delta, balancing responsiveness with noise reduction. Multi-Timeframe Volatility Analysis: Uses different lookback periods for squeeze detection (3× primary length) versus band calculation (2× primary length), creating a hierarchical volatility framework. How to Use Adding to Your Chart Add the Vortex Volatility Engine to your chart - it displays in a separate pane below price. The histogram shows the Vortex Delta (bullish/bearish volatility balance). Gray circular dots on the upper and lower edges represent dynamic volatility bands. Gray dots on the zero line indicate squeeze periods (low volatility compression). Small colored circles on the histogram mark breakout signals. Interpreting Signals Histogram Above Zero: Bullish volatility dominance - upward price movements are stronger than downward movements. Histogram Below Zero: Bearish volatility dominance - downward price movements are stronger than upward movements. Bright Green Bars: Strong bullish momentum (delta positive and increasing) - trend continuation likely. Dark Green Bars: Weak bullish momentum (delta positive but decreasing) - potential reversal warning. Bright Red Bars: Strong bearish momentum (delta negative and decreasing) - downtrend continuation likely. Dark Red Bars: Weak bearish momentum (delta negative but increasing) - potential reversal warning. Squeeze Dots Present: Market in consolidation, volatility compressed - prepare for potential breakout. Bullish Breakout Signal: Delta crosses above upper band during non-squeeze - enter long positions. Bearish Breakout Signal: Delta crosses below lower band during non-squeeze - enter short positions. Setting Up Alerts Right-click on the indicator and select "Add Alert" Choose from four alert conditions: Bullish Vortex Breakout, Bearish Vortex Breakout, Squeeze Started, Squeeze Fired Configure notification preferences (popup, email, webhook, etc.) Alerts will trigger automatically when conditions are met, allowing hands-free monitoring Customization Volatility Length (Default: 14) Controls the lookback period for True Range and directional momentum calculations. Lower values (8-10) create a more responsive indicator suitable for intraday trading and volatile markets. Higher values (20-30) produce smoother signals better suited for swing trading and trending markets. Adjust based on your trading timeframe and market conditions. Signal Smoothing (Default: 5) Applies final smoothing to the Vortex Delta histogram to reduce noise and prevent whipsaws. Lower values (2-3) maintain responsiveness but may generate more false signals. Higher values (7-10) create cleaner signals but with increased lag. Use lower smoothing for scalping, higher smoothing for position trading. Squeeze Threshold (Default: 0.8) Determines how strict the squeeze detection is. Lower values (0.6-0.7) require more significant volatility compression, resulting in fewer but higher-quality squeeze signals. Higher values (0.9-1.0) detect squeezes more frequently but may include less significant consolidations. Adjust based on your preferred trade frequency. Volatility Band Multiplier (Default: 2.0) Controls the width of the dynamic volatility bands. Lower values (1.5-1.8) create tighter bands that generate more frequent breakout signals but with higher false positive rates. Higher values (2.5-3.0) create wider bands that filter out noise but may miss smaller breakout opportunities. Optimize based on backtesting results for your specific instrument and timeframe. Recommended Settings by Trading Style Scalping (1-5 min charts): Length: 8, Smoothing: 3, Squeeze: 0.7, Bands: 1.8 Day Trading (15-60 min charts): Length: 14, Smoothing: 5, Squeeze: 0.8, Bands: 2.0 Swing Trading (4H-Daily charts): Length: 21, Smoothing: 7, Squeeze: 0.85, Bands: 2.5 Conclusion The Vortex Volatility Engine represents a comprehensive approach to volatility-based momentum trading by combining directional decomposition, squeeze detection, and adaptive band analysis into a single, cohesive framework. By normalizing volatility components against True Range and implementing multi-stage filtering logic, it provides traders with high-probability setup identification and breakout confirmation signals. Whether you're trading breakouts from consolidation, following momentum trends, or identifying exhaustion reversals, this indicator delivers the volatility intelligence needed to make informed trading decisions across all timeframes and market conditions.
Pine Script® 인디케이터
Pineify의
Nova Statistical Filtering Oscillator [Pineify]Nova Statistical Filtering Oscillator - Advanced Mean Reversion Trading Tool A sophisticated statistical oscillator that identifies overbought and oversold market conditions using Z-Score normalization and dynamic volatility bands The Nova Statistical Filtering Oscillator (NSFO) is an advanced technical analysis tool designed to help traders identify potential mean reversion opportunities in financial markets. By combining statistical price normalization with adaptive smoothing techniques, this indicator provides a unique approach to detecting extreme price conditions and anticipating momentum reversals. Z-Score based statistical analysis Triple Exponential Moving Average (TEMA) smoothing Dynamic volatility bands Automatic mean reversion signal generation Gradient color visualization Customizable parameters Statistical overbought/oversold detection Noise reduction through TEMA smoothing Adaptive bands that adjust to market volatility Clear buy and sell signal indicators Visual gradient coloring for quick analysis Configurable alert conditions The NSFO operates through a sophisticated three-step statistical process that transforms raw price data into actionable trading signals. Step 1: Z-Score Calculation The indicator first calculates the Z-Score, which measures how many standard deviations the current price is from its historical mean. This statistical normalization allows traders to objectively identify when price has moved to statistically extreme levels. The formula uses a configurable lookback period (default 20 bars) to compute both the simple moving average (mean) and standard deviation. A Z-Score greater than +2 or less than -2 indicates statistically rare extreme conditions, occurring approximately only 5% of the time. Step 2: TEMA Smoothing Raw Z-Score values can be volatile and produce choppy signals. The NSFO applies Triple Exponential Moving Average (TEMA) smoothing to reduce market noise while maintaining responsiveness. TEMA combines three successive EMAs using the formula: 3×EMA1 - 3×EMA2 + EMA3, achieving smoother results with less lag compared to traditional moving averages. Step 3: Dynamic Volatility Bands Unlike static overbought/oversold levels, the NSFO creates adaptive bands based on the volatility of the smoothed oscillator itself. This self-referential approach automatically adjusts to changing market conditions - becoming wider during volatile markets and narrower during calm periods. The bands are calculated using the standard deviation of the smoothed oscillator multiplied by a configurable factor (default 2.0). The NSFO is built on the fundamental principle of mean reversion - the statistical observation that prices tend to return to their average over time. This concept has been validated through decades of market research and forms the basis of many successful trading strategies. Statistical Extremes as Trade Setups : When the oscillator reaches the upper or lower bands, it indicates the price has moved to a statistically extreme level. These conditions often precede reversals as the market corrects itself back toward the mean. Momentum Confirmation : The gradient coloring system provides instant visual feedback on current momentum. When the oscillator transitions from extreme levels toward the center, it confirms that momentum is shifting and the reversal may be gaining strength. Time Frame Flexibility : The NSFO performs well across various time frames, from intraday charts to weekly frames. Shorter time frames generate more signals but require tighter risk management, while longer time frames produce fewer but potentially more reliable signals. The NSFO uniquely combines three distinct technical concepts into a cohesive system: Statistical Analysis (Z-Score) : Provides the mathematical foundation for identifying extreme price conditions using probability theory. This transforms subjective "overbought" or "oversold" labels into quantifiable statistical measurements. Moving Average Smoothing (TEMA) : Reduces the inherent noise in raw statistical values while maintaining sensitivity to genuine price movements. The triple-layer EMA approach minimizes lag that typically affects simple moving averages. Volatility-Based Bands : Creates adaptive thresholds that automatically adjust to current market conditions. This dynamic approach outperforms fixed-level indicators by accounting for the reality that market volatility changes over time. The synergy between these three components creates a robust system where each element addresses a weakness in the others. The Z-Score provides statistical rigor, TEMA adds smoothness for actionable signals, and dynamic bands ensure the indicator remains effective across varying market conditions. The Nova Statistical Filtering Oscillator offers several distinctive features that set it apart from traditional technical indicators: Self-Referential Adaptation : The volatility bands are calculated from the oscillator's own standard deviation, creating a truly adaptive system that automatically adjusts to current market conditions without manual parameter changes. Gradient Visualization : The color gradient provides at-a-glance understanding of current market position relative to statistical extremes, making it easy to identify both extreme conditions and transitions. Zero Baseline Clarity : The central baseline at zero represents the statistical mean, providing an immediate visual reference point for understanding whether price is above or below its historical average. Clean Signal Generation : Buy and sell signals are generated only when the oscillator crosses back from extreme levels, filtering out premature entries and focusing on confirmed reversals. Using the NSFO effectively requires understanding both its signals and its limitations. Here's how to integrate it into your trading strategy: Identifying Overbought Conditions : When the oscillator crosses below the upper band (red line), it indicates the price has extended statistically above its average. This suggests a potential selling opportunity as the market may revert toward the mean. Identifying Oversold Conditions : When the oscillator crosses above the lower band (green line), it indicates the price has dropped statistically below its average. This suggests a potential buying opportunity as the price may bounce back toward the mean. Signal Confirmation : Wait for the oscillator to cross back toward the center (zero line) before entering a trade. This confirms the reversal is underway rather than just touching the extreme level. Trend Context : Always consider the broader trend direction. In strong trending markets, the oscillator may remain at extreme levels for extended periods. Use additional trend indicators for confirmation. Important Note: No indicator guarantees profitable trades. Always use proper risk management and consider combining NSFO with other technical or fundamental analysis tools for better decision-making. The NSFO provides several customizable parameters to suit different trading styles and market conditions: Statistical Window (default: 20) : Controls the lookback period for calculating mean and standard deviation. Higher values produce more stable readings but respond slower to price changes. Lower values are more responsive but may generate more false signals. Smoothing Length (default: 10) : TEMA period for reducing noise in the Z-Score. Higher values create smoother lines with more lag, while lower values are more responsive but potentially noisier. Bands Multiplier (default: 2.0) : Controls the width of dynamic bands in standard deviations. Higher values create wider bands, reducing false signals but potentially missing some opportunities. Lower values generate more signals but with increased noise. Color Customization : Traders can customize bullish (green) and bearish (red) colors to match their visual preferences or other indicators in their chart setup. The Nova Statistical Filtering Oscillator represents a sophisticated approach to identifying statistical extremes in financial markets. By combining Z-Score normalization with TEMA smoothing and dynamic volatility bands, it provides traders with a powerful tool for detecting potential mean reversion opportunities. Its adaptive nature ensures effectiveness across various market conditions, while the clear visual signals make it accessible to traders of all experience levels. Whether used as a primary trading system or as a confirmation tool alongside other indicators, the NSFO offers a unique perspective on market dynamics that can enhance your trading decisions. Remember to always test any new indicator thoroughly on historical data before using it with real capital, and maintain disciplined risk management practices regardless of the signals generated. This indicator is provided for educational and informational purposes only. Past performance does not guarantee future results. Trading financial markets involves substantial risk, and you should only trade with capital you can afford to lose. Always conduct your own research and consider your financial situation before making any trading decisions.
Pine Script® 인디케이터
Pineify의
Momentum Decay Field [BullByte]Momentum Decay Field : Physics-Based Momentum Energy Visualization WHAT IS THE MOMENTUM DECAY FIELD? The Momentum Decay Field (MDF) models price momentum as a decaying physical energy field using an exponential half-life model. MDF visualizes impulse energy on the price chart, detects exhaustion and energy divergence, and stores past exhaustion zones as reference levels. This is an informational visualization tool - not a buy/sell signal generator. In nuclear science, every radioactive element emits energy that decays exponentially over time following a precise mathematical law. MDF applies this exact principle to price momentum. Every strong price impulse carries kinetic energy. That energy does not last forever. It fades, weakens, and eventually depletes - just like a radioactive isotope losing its charge. MDF measures this process in real time and wraps price in a living energy field that expands, contracts, changes color, and ultimately signals exhaustion when the energy runs out. This is not a moving average. Not an oscillator. Not a mashup of existing tools . It is a standalone physics engine that models momentum as a decaying energy system directly on the price chart. THE CORE PHYSICS The engine is built on the exponential decay formula from nuclear physics: E(t) = E0 x exp(-lambda x t) Where: E(t) is the remaining energy at time t (measured in bars after the impulse) E0 is the initial energy assigned to the impulse at the moment it occurs lambda is the decay constant, derived from the half-life: lambda = ln(2) / Half-Life t is the number of bars elapsed since the impulse The half-life is the number of bars it takes for energy to decay to exactly 50% of its initial value. After two half-lives, 25% remains. After three, 12.5%. This creates the smooth, predictable decay curve visible in the dashboard sparkline. When the adaptive half-life option is enabled, stronger impulses automatically receive a longer half-life. This means powerful moves with high volume and velocity sustain their energy field longer before exhausting, while weak impulses fade quickly. This mirrors real market behavior where institutional-grade moves carry momentum further than retail noise. HOW IMPULSES ARE DETECTED Not every candle qualifies as an impulse. The detection engine requires three conditions to be met simultaneously: First , the candle body size (distance between open and close) must exceed a user-defined multiple of the Average True Range. This ensures only candles with genuine directional magnitude qualify. Second , the total candle range (high to low) must also exceed an ATR-based threshold. This confirms the move had real price displacement, not just a wide body on a small-range bar. Third , the body-to-range ratio must exceed 0.45. This filters out doji candles, pin bars, and high-wick candles that suggest indecision rather than conviction. Only candles where the body represents a significant portion of the total range pass through. When an impulse qualifies, its initial energy (E0) is calculated by combining three factors: the normalized body magnitude (body size divided by ATR), a velocity boost (how fast price moved over the velocity window compared to average), and an optional volume boost (current bar volume relative to the rolling volume average). The result is capped at 5.0 to prevent extreme outliers from dominating the field. THE ENERGY FIELD : WHAT YOU SEE ON THE CHART The energy field is the colored aura that appears around price. It communicates the current momentum state through three visual properties: Width: The field expands when energy is high and contracts as energy decays. At full energy, the field reaches its maximum width (controlled by the Max Field Width setting). As energy approaches zero, the field collapses to nothing. The width is always proportional to ATR so it adapts automatically to any instrument and timeframe. Color: The field color morphs through three stages as energy decays. At high energy levels (above 55%), the field displays the bright directional color - the bullish theme color for upward impulses, the bearish theme color for downward impulses. As energy drops into the mid range (20-55%), the color transitions through warm amber, a universal warning tone indicating momentum is fading. Below 20%, the field shifts to cool gray, signaling that momentum is nearly spent. This color morphing happens smoothly and continuously on every bar. Layering: The field is rendered in up to three concentric tiers - inner, mid, and outer - each progressively more transparent. The inner layer sits closest to price and is most opaque. The mid layer extends further with reduced opacity. The outer layer provides the widest aura at the lowest visibility. This creates a natural gradient that makes the field feel organic rather than blocky. THREE VISUAL MODES Clean mode renders no energy field at all. Only the signal markers (diamond, EXH, WEAK) and the dashboard appear on the chart. This mode is designed for traders who want a completely uncluttered chart and only care about the event markers and data panel. The momentum engine still runs in the background, powering the dashboard metrics and signals. Standard mode activates the inner and mid field layers along with the energy spine - a central reference line running through the price that pulses with momentum. This is the default mode and provides a balanced view of the energy state without overwhelming the chart. Full mode enables all three field tiers and adds candle tinting, where the candle bodies themselves are colored according to the current energy state and direction. High-energy bars glow with the directional color while depleted bars take on neutral tones. This mode provides maximum visual information for traders who want to see the full energy picture at a glance. SIGNAL MARKERS : WHAT EACH ONE MEANS Diamond (below bar for bullish, above bar for bearish): This marks the bar where a qualified impulse was detected and energy was injected into the system. It tells you that the decay physics engine has been activated or refreshed with new energy. Not every strong candle produces a diamond - the cooldown system prevents clustering by enforcing a minimum number of bars between consecutive impulse markers. EXH (cross shape, amber color): This marks the bar where the energy ratio dropped below the exhaustion threshold. It means the momentum that was driving price has fully depleted according to the decay model. These exhaustion points often coincide with areas where price pauses, reverses, or enters consolidation. The anti-repeat filter ensures that the same directional exhaustion does not fire consecutively without a change in context - either a new direction must establish or sufficient time must pass. WEAK (triangle shape, orange color): This marks an energy divergence. It appears when price makes a new high (for bullish) or new low (for bearish) but the impulse energy behind that move is at least 25% weaker than the previous impulse in the same direction. The price is extending but the fuel behind it is diminishing. This is a momentum divergence detected purely through the energy physics model, not through any traditional oscillator comparison. When WEAK fires on the same bar as an impulse, the diamond marker is automatically suppressed because the WEAK label already communicates that an impulse occurred - it simply carried less energy than its predecessor. EXHAUSTION MEMORY ZONES When an exhaustion event occurs, the indicator stores the price range of that bar as a memory zone. These zones are rendered as semi-transparent colored rectangles that extend from the exhaustion bar to the right edge of the chart. Bullish exhaustion zones use the bullish theme color and bearish exhaustion zones use the bearish theme color. These zones represent price levels where momentum previously failed to sustain. They often act as areas of interest for future price action because they mark locations where aggressive directional energy was fully consumed. Fresh zones appear more opaque and gradually fade as they age, eventually becoming fully transparent after the user-configured fade period. The Max Zones Displayed setting controls how many zones appear simultaneously. Only the most recent zones are shown. The Zone Fade Period controls how many bars a zone remains visible before disappearing. A three-stage memory management system runs continuously to prevent the zone array from growing unbounded on long-history charts: old zones beyond twice the fade period are pruned, the array is capped at a safe maximum, and the rendering loop enforces a drawing budget to stay within platform limits. THE DASHBOARD : EVERY ELEMENT EXPLAINED The dashboard uses a fully opaque dark background that renders identically on both light and dark chart themes. Every cell has its own explicit background color so nothing is inherited from the chart environment. ENERGY: A 16-character progress bar showing remaining energy as a visual fill gauge, followed by the numerical percentage. The bar transitions from bright green (charged) through cyan (active) to amber (decaying) to orange (fading) to red (depleted). PHASE: Displays the current energy state classification. CHARGED means energy is above 70% - momentum is strong and active. ACTIVE means 45-70% - momentum is present and sustaining. DECAYING means 20-45% - momentum is fading but not yet spent. FADING means energy is above the exhaustion threshold but below 20% - momentum is weak. DEPLETED means energy has fallen below the exhaustion threshold - the current cycle has exhausted. The directional bias (BULL, BEAR, or FLAT) appears alongside the phase to show which direction the current energy cycle belongs to. E0 INITIAL: Shows the initial energy magnitude that was assigned to the current impulse cycle when it began. The classification label (Extreme, Strong, Moderate, Light) provides quick context about how powerful the originating impulse was. HALF-LIFE: Displays the current half-life in bars - how long it takes for energy to decay to 50%. When adaptive half-life is enabled, this value changes with each impulse based on its strength. The ELP (elapsed) counter shows how many bars have passed since the last impulse event. ETA TO EXH: The predicted number of bars remaining until energy reaches the exhaustion threshold. This is calculated by solving the decay equation for time. The visual countdown bar (filled and empty blocks) provides a quick gauge of how close exhaustion is. When the current cycle has already exhausted, this displays EXHAUSTED in red. DECAY CURVE: A 20-character sparkline that projects how energy will diminish over approximately three half-lives into the future. Each character represents a future time step. The curve reads from left (current moment, marked by NOW) toward the right (future). Tall blocks indicate high energy, dots indicate near-zero energy. This gives traders a visual preview of the entire remaining decay trajectory at a glance. IMPULSES / EXHAUSTIONS / DIVERGENCES: Lifetime counters showing how many of each event type have occurred since the indicator was loaded on the chart. These provide context about the current session's activity level. CHART EXAMPLE Chart 1: ENERGY FIELD DYNAMICS & IMPULSE DETECTION What This Chart Represents This chart demonstrates the Momentum Decay Field's core visual engine showing how the energy field expands, contracts, and morphs in real-time as momentum cycles through impulse, sustain, and decay phases. Key Visual Elements Highlighted: 1. ENERGY FIELD COLOR MORPHING The aura transitions through three distinct color phases as energy decays: - Bright directional color (green for bullish, magenta for bearish) = High energy (>55% remaining) - Warm amber/orange = Mid-energy decay phase (20-55% remaining) - Cool gray = Low energy, momentum nearly spent (<20% remaining) 2. FIELD WIDTH DYNAMICS The energy band's width is directly proportional to remaining energy: Wide, expansive field = Strong active momentum with high kinetic energy Contracting field = Energy decaying, momentum losing strength Thin/collapsed field = Minimal momentum, approaching exhaustion Width scales with ATR to adapt across all instruments and timeframes 3. IMPULSE SIGNAL MARKERS (Diamonds) Pink/Green diamond markers identify the exact bars where qualified momentum impulses occurred: Below price = Bullish impulse (upward energy injection) Above price = Bearish impulse (downward energy injection) Each diamond represents a new E0 (initial energy) assignment Cooldown system prevents marker clustering during volatile periods 4. MULTI-CYCLE MOMENTUM VISUALIZATION The chart captures several complete momentum lifecycles: Fresh impulses creating wide, bright energy fields Progressive decay causing field contraction and color shift Direction changes showing energy field polarity reversals Energy stacking when same-direction impulses occur during active cycles 5.ENERGY DIVERGENCE SIGNAL (WEAK Marker) Orange triangle with "WEAK" label Triangle Up Position (Below Price): Signal Type: Bearish energy divergence Location: Bottom of the move after the bullish run What It Detected: Price made a new low BUT the impulse energy behind that low was 25%+ weaker than the previous bearish impulse CHART 2: EXHAUSTION SIGNALS, MEMORY ZONES & DASHBOARD DEEP DIVE What This Chart Represents This chart demonstrates the complete signal ecosystem of the Momentum Decay Field indicator, showcasing exhaustion detection, energy divergence, memory zones, and a full dashboard metrics breakdown. The chart captures a Solana (SOL/USDT) 15-minute timeframe showing the transition from strong bearish momentum through exhaustion to reversal with weakening energy. 1. ENERGY FIELD EVOLUTION LEFT SIDE (Early Period - 1:00 to 10:00): Bright magenta field = Strong bearish energy active Wide field width = High momentum magnitude Color morphing visible = Transition from bright magenta → brown/amber as energy decays Field shows downtrend = Bearish impulse energy driving price lower 2. EXHAUSTION SIGNAL (EXH Marker) Meaning: Momentum energy fully depleted - energy ratio dropped below 8% threshold Location: Above the price bars (bearish exhaustion that preceded the bullish reversal) Trigger Conditions Met: Energy ratio < 0.08 (exhaustion threshold) Minimum E0 > 0.5 (filters noise) Minimum 3 bars elapsed since impulse Cooldown period satisfied (8 bars between signals) Anti-repeat filter passed (different direction or sufficient time gap) What Happened Here: The strong bearish momentum (magenta field on left) progressively decayed until energy was fully consumed. The EXH marker fired at the exact bar where the decay equation calculated remaining energy had fallen below the 8% threshold. 3. EXHAUSTION MEMORY ZONE (Green Rectangle) Large semi-transparent green box Zone Type: Bullish exhaustion memory zone Price Range: Captures the high-low range of the bar where bullish momentum exhausted Color: Green with ~92% opacity at creation, fading over time Border: Green border at ~78% opacity Extension: Extends from exhaustion bar to right edge of chart (+10 bars beyond current) Why It Matters: Price levels where momentum exhausted represent areas where directional energy was fully consumed. Market often "remembers" these levels as they frequently become magnets for future price action, areas of consolidation, or reversal zones. 4.ENERGY SPINE (Coloured Line Through Price Center) Visible as dark line running through candle midpoints: Technical Details: Plots at hlc3 (average of high, low, close) Color matches field color, opacity varies with energy level Only visible when energy > 12% Provides visual anchor showing momentum center of gravity Style: Line breaks (plot.style_linebr) to avoid connecting across gaps Purpose: The spine acts as a dynamic momentum equilibrium line. When field is wide, spine shows the center of energy distribution. As field contracts, spine becomes the last visible element before total energy depletion. Helps traders identify the core momentum flow without relying solely on field boundaries. RECOMMENDED SETTINGS AND TIMEFRAMES The default settings are calibrated for intraday trading on timeframes between 1 minute and 15 minutes. The indicator works on all timeframes and all instruments but the default parameters are optimized for the pace of day trading where impulse-to-exhaustion cycles complete within a visible chart window. For scalping on 1-minute charts, consider reducing the Base Half-Life to 5-6 bars and the Impulse Lookback to 8-10. This makes the system more responsive to the rapid impulse cycles on lower timeframes.- For swing trading on 1-hour or 4-hour charts, consider increasing the Base Half-Life to 12-16 bars and the Impulse Threshold to 2.0-2.5. This filters out intrabar noise and focuses on only the most significant impulses that drive multi-hour moves. For daily charts, increase the Half-Life to 15-20 bars and enable Adaptive Half-Life to let strong institutional moves sustain their field across multiple sessions. The Volume Weighting option is recommended to be kept enabled on instruments with reliable volume data (stocks, futures, major crypto pairs). For forex pairs where volume represents tick count rather than actual traded volume, consider disabling it or treating it as a secondary factor. THOUGHT BEHIND THE INDICATOR The concept originated from a simple observation: every momentum move in the market follows a lifecycle that mirrors energy decay in physics. A strong impulse starts with maximum energy, sustains for a period, then gradually loses force until it is fully spent. Traditional momentum indicators attempt to measure this through oscillators that lag behind price. MDF takes a fundamentally different approach by modeling the decay process itself using established physics rather than measuring its symptoms through lagging mathematical transformations. The exponential decay model was chosen specifically because it matches the empirical behavior of momentum in financial markets. Momentum does not decay linearly - it holds relatively steady in the early phase after an impulse, then accelerates its decline as it approaches exhaustion. This is precisely the shape of an exponential decay curve and is why the half-life model produces a more accurate representation than a simple moving average or percentage-based countdown. The energy divergence detection was added after observing that many trend reversals are preceded by a sequence of price extremes with diminishing impulse energy. The price chart shows strength (higher highs or lower lows) while the underlying energy tells a different story (each push requires less fuel). This hidden weakness is invisible on a standard chart but becomes immediately apparent when measured through the energy model. WHAT THIS INDICATOR DOES NOT DO This indicator does not generate buy or sell signals. It does not tell you when to enter or exit a trade. It does not predict future price direction. It measures and visualizes the current state of momentum energy to help you make more informed decisions within your own trading framework. The exhaustion markers identify zones where momentum energy has depleted according to the physics model. Price may reverse at these zones, consolidate, or continue in the same direction after a new impulse injects fresh energy. The markers are informational reference points, not trading signals. The energy divergence detection (WEAK markers) highlights instances where price extremes are driven by weakening energy. This is an observation about momentum quality, not a prediction about price direction. Divergences can persist through multiple cycles before price responds. NON REPAINT BEHAVIOUR Every calculation in this indicator uses only confirmed bar data (open, high, low, close, volume) from completed bars. No future data is referenced. No calculations change retroactively after a bar closes. The impulse detection, energy assignment, decay computation, exhaustion detection, and divergence detection all operate exclusively on confirmed historical data. What you see on a closed bar will never change. DISCLAIMER Trading financial instruments involves substantial risk of loss. Past energy states and exhaustion patterns do not guarantee future price behavior. This tool visualizes momentum dynamics to support analysis - it does not provide financial advice. The author assumes no responsibility for trading decisions or losses. Always employ proper risk management and never risk capital you cannot afford to lose. - BullByte
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Average CandleAverage Candle is a simple volatility display indicator. It calculates the average candle range on a chosen higher timeframe (most commonly Daily) over a selected number of candles, converts that average range into pips, and shows the result as a small label on the chart. Traders can use it to quickly see “typical” movement for the day (or any timeframe) and adjust expectations for entries, stop loss distance, take profit targets, and position sizing.
Pine Script® 인디케이터
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