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Cup & Handle (Zeiierman)█ Overview Cup & Handle (Zeiierman) is a classic continuation-pattern scanner that detects both bullish Cup+Handle and bearish Inverted Cup+Handle structures using a compact pivot stream. It’s designed to highlight rounded reversals back to a “rim” level, followed by a smaller pullback (“handle”) before a potential continuation move. ⚪ What It Detects A Cup & Handle (Bull) forms when price makes a rounded decline from a left rim, bottoms, then climbs back to a similar right rim. After returning to the rim, price forms a handle (a smaller pullback) that stays within an allowed retracement range. This pattern often precedes a bullish continuation attempt. An Inverted Cup & Handle (Bear) is the mirrored version. Price makes a rounded rise to a left rim, tops, then declines back to a similar right rim. After returning to that rim, price forms a handle (a smaller bounce) that stays within the allowed retracement range. This pattern often precedes a bearish continuation attempt. █ How It Works ⚪ 1) Pivot Extraction (Swing Compression) The script first converts raw candles into a small set of meaningful swing pivots using ta.pivothigh() and ta.pivotlow() with Pivot span. A pivot is accepted only after it is confirmed by the lookback window, which helps reduce noise. Key effect: Higher Pivot span = fewer, stronger pivots (cleaner patterns) Lower Pivot span = more pivots (more patterns, more noise) ⚪ 2) Pattern Framing (4-Point Structure) When at least four pivots exist, the script maps them into a fixed sequence: For a bull Cup+Handle sequence: High → Low → High → Low These are treated as: L = left rim pivot B = cup bottom pivot R = right rim pivot H = handle pivot For a bear inverted Cup+Handle sequence: Low → High → Low → High Mapped similarly, but inverted. This “4-pivot” structure is the minimum shape needed to define a cup and a handle without overfitting. ⚪ 3) Rim Similarity Filter (Cup Quality Control) The script checks if the left rim and right rim are close enough to be considered a proper cup rim: Rim similarity tolerance (%) controls this. Lower tolerance = only very clean symmetric rims Higher tolerance = allows uneven rims (more detections) ⚪ 4) Handle Depth Filter (Reject Weak or Messy Handles) The handle is validated by measuring how deep it retraces relative to the cup depth: Handle Retraction = |rim − handle| / |rim − bottom| The handle must fall between: Handle retrace min Handle retrace max This prevents: tiny “non-handle” wiggles (too shallow) deep pullbacks that break the structure (too deep) █ How to Use ⚪ Interpreting a Bull Cup & Handle Treat it like a continuation setup built around a key breakout level: Cup forms Handle forms Breakout happens above this level Once price returns to this breakout zone and the handle stays controlled, the structure may attempt to continue upward. Common behaviors after a clean signal: Push above the breakout level Brief retest/acceptance near the breakout zone Continuation toward the projected target if momentum holds ⚪ Interpreting a Bear Inverted Cup & Handle Treat it like a bearish continuation/rollover setup built around the same breakout concept: Cup forms (inverted) Handle forms Breakout happens below this level Once price returns to this breakout zone and the handle stays controlled, the structure may attempt to continue downward. Common behaviors after a clean signal: Drop below the breakout level Retest from underneath Continuation toward the projected target if selling pressure persists █ Settings Pivot span – pivot sensitivity. Higher = smoother pivots, fewer signals. Lower = more pivots, more signals/noise. Rim similarity tolerance (%) – rim quality filter. Lower = stricter symmetry, higher = more permissive detection. Handle retrace min – minimum handle depth (filters weak handles). Handle retrace max – maximum handle depth (filters messy/deep handles). Invalidation (handle max retrace %) – “maximum tolerated damage” for handle move before the structure is considered broken. Require breakout confirmation – only trigger when price closes beyond the rim in the expected direction. Target multiplier (× cup depth) – scales how far the projection target is. Lower = closer targets; 1.0 = classic depth target. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Chỉ báo Pine Script®
của Zeiierman
7
Smart Pivot Trend█ OVERVIEW Smart Pivot Trend is a market structure–based trend indicator that combines swing pivots, volatility adaptation (ATR), and dynamic range levels to determine which side of the market is in control — buyers or sellers. Instead of moving averages, trend direction is defined through structural breaks inside pivot ranges. The indicator visualizes the active trend, evolving market structure, and historical support/resistance levels created at moments of control shifts. It helps identify trend transitions, structure breaks, and areas where price has an increased probability of reaction. █ CONCEPT Built around adaptive swing structure. The core idea is that trend emerges from market structure, not from price relative to an average. - Swing highs and swing lows form the current structural range. - Two internal percentage-based levels inside this range act as decision zones. - Break above the upper level → bullish control. - Break below the lower level → bearish control. To prevent structure from becoming outdated during strong moves, pivots are dynamically adjusted when price deviates beyond ATR × multiplier. This mechanism makes the structure volatility-aware rather than static. As a result, the indicator combines: - a dynamic, living market structure (active pivot trend) - static “market memory” levels marking previous control shifts █ FEATURES Calculations - Swing pivots as the foundation of market structure - Internal range levels as structural decision zones - ATR-based adaptive pivot correction (volatility-aware structure) - Smooth Factor — controls the degree of structural correction relative to price; defines how fast pivots adapt during strong moves - Trend change detection through structural range breaks Visualization - Active trend line based on current structure - Historical support/resistance levels plotted at trend flips - Triangles marking breaks of those levels - Gradient fill between price and the active trend line - Trend-based coloring (green = bullish, red = bearish) - Optional candle coloring based on current structural trend (bullish / bearish control) Signals - BUY / SELL — on structural trend changes - Bullish Break / Bearish Break — when historical levels are broken - Impulse breaks (when candles break levels with strong momentum) Alerts - Trend change to bullish - Trend change to bearish - Resistance break - Support break █ HOW TO USE Main settings: - Swing Length — sensitivity of swing detection - Lower / Upper Level — internal structural decision levels - ATR Length / Multiplier — influence of volatility on pivot adaptation - Smooth Factor — speed of structural adjustment to price - Visual options — colors, hiding lines, deleting broken levels, color candles by trend Trend logic: - Price above active pivot low → bullish structure - Price below active pivot high → bearish structure █ APPLICATION Trend-following - The indicator can act as a directional filter for signals from other tools. - Entries are taken only when signals from external indicators (e.g., RSI, MACD, momentum tools, price action setups, breakout systems) align with the current Smart Pivot Trend direction. - Highest probability occurs when entries happen during pullbacks to the active trend line in the direction of the prevailing structure. Market structure shifts - A trend flip represents a transfer of control between buyers and sellers. - These moments often precede larger moves because the swing structure changes. Breakout trading - Historical levels mark areas where control previously changed. - Their break often leads to volatility expansion and impulsive movement. Pullback trading - The active trend line acts as dynamic support/resistance. - Pullbacks to this line in strong trends often provide favorable risk-to-reward setups. █ ADAPTATION TO TRADING STYLE The Swing Length and Smooth Factor parameters allow the indicator to be tailored to different trading styles: Shorter Swing Length + higher Smooth Factor - structure reacts faster - more frequent trend shifts - suitable for scalping and intraday trading Longer Swing Length + lower Smooth Factor - slower structural changes - filters minor fluctuations - better suited for swing trading and longer-term positions This allows the indicator to function both as a fast micro-structure engine and as a stable higher-level trend filter. █ NOTES - This is a structural analysis tool, not a standalone trading system - Best results come when combined with key S/R levels, higher timeframe context, and price action - In ranging markets, trend flips may occur more frequently — a natural behavior of structure-based systems
Chỉ báo Pine Script®
của Uncle_the_shooter
Smart Money Concepts 2026🔘 The Smart Money Concepts (SMC) 2026 indicator is an institutional-grade trading tool built to give traders a measurable edge by automating key SMC price-action events and highlighting high-probability areas of interest. Alerts using TradingView built-in alerts system. Strength ranking to highlight stronger zones. Market structure mark-up. OB/FVG/BB detection. NRP algo, all zones do not repaint. 🩶 Smart Money Concepts (SMC) 2026 🗂️ User Guide & Trading Protocol 1.0 🧾 Executive Overview ◼️ This protocol explains the indicator’s features, how to read its data, and how to apply it inside a structured, confluence-based trading plan. ▫️ The system is engineered to de-clutter charts, focus attention on high-conviction zones, and support disciplined execution. ________________________________________ 2.0 ⚙️ Core Feature Compendium 🩶 The indicator integrates advanced components for a complete SMC market view. ⚙️ Feature 📌 Description Market Structure ||| Plots BOS + CHOCH to define trend and potential reversals. ||| ✅ ON Order Blocks (OB) ||| Detects bullish/bearish OBs showing institutional supply/demand zones. ||| ✅ ON Fair Value Gaps (FVG) ||| Flags imbalances price often revisits to rebalance (key entry areas). ||| ✅ ON Breaker Blocks (BB) ||| Finds failed/mitigated OBs that break and become strong reversal zones. ||| ⛔ OFF Premium & Discount ||| Draws Premium (sell) / Discount (buy) from latest major swing range. ||| ✅ ON Liquidity Zones ||| Marks EQH/EQL where stop liquidity is likely to rest. ||| ✅ ON Strength Rating (0–10) ||| Scores each OB/FVG by momentum, size, and session context for quality filtering. ||| ✅ ON Integrated Alerts ||| Native alerts when new OB/FVG forms so you don’t miss setups. ||| ✅ ON BTCUSD with Smart Money Concepts 2026 ________________________________________ 3.0 🧭 Zone Information Panel 🔍 Every OB and FVG zone includes an info panel for fast decision-making. 🧩 Data Point ||| 📌 Meaning / How to Use It Type ||| Identifies zone type (e.g., Bullish OB, Bearish FVG). Strength ||| Proprietary 0–10 score. ◾ Primary quality filter: > 6.5 preferred. Session ||| Session where the zone formed: Asian / London / New York (London/NY often stronger). Age ||| Bars since creation. Older unmitigated zones can still react strongly. Distance ||| Current price distance from zone midpoint in pips/points (proximity context). Pips/Points ||| Total height of the zone. Tighter zones can improve R:R efficiency. ________________________________________ 4.0 🛡️ Trading Methodology & Protocol 🩶 The edge is not trading every zone. The edge is: ◻️ Select high-strength zones → treat as AOIs → demand confirmation → execute with discipline. ________________________________________ 4.1 🎯 High-Probability Reversal Strategy 1) 🧩 Identify the Area of Interest (AOI) 🔘 Scan for Order Blocks / Fair Value Gaps with: ◼️ Strength ≥ 6.5 (quality threshold) ▫️ Add conviction by location: • Short bias: AOI in Premium • Long bias: AOI in Discount TSLA with Smart Money Concepts 2026 ________________________________________ 2) ⏱️ Wait for Price to Test the Zone 🔘 Let price trade into the high-strength OB/FVG. ◻️ Do not front-run entries. 🧷 Alerts ||| Set an alert for price entering the zone so you’re ready for execution. ________________________________________ 3) 🧠 Seek Confirmation for Entry Most Critical Step 🔍 Confirmation reduces failure risk. On a lower timeframe (e.g., zone on 1H → confirm on 5m/15m), look for one or more: 📍 Confirmation Type ||| What You Want To See Market Structure Shift ||| LTF CHOCH against the move into the zone. Momentum Divergence ||| RSI/MACD divergence (LL in price + HL in oscillator for longs; inverse for shorts). Engulfing Candle ||| Strong bullish/bearish engulfing showing decisive rejection. ________________________________________ 4) 📐 Trade Execution Rules 🔘 Execute only after confirmation prints. 🧾 Rule ||| Execution Standard Entry ||| After a clear confirmation signal closes. Stop Loss ||| Just beyond the distal end of the zone. Bearish OB/FVG SL ||| Place SL above the zone high. Bullish OB/FVG SL ||| Place SL below the zone low. Take Profit ||| Target logical liquidity: opposing high/low, opposing OB/FVG, nearby EQH/EQL. ________________________________________ Brent oil with Smart Money Concepts 2026 ⬛🛠️ Key Features Overview ⚙️ Feature 📌 Description Zone Strength Ranking ||| Each zone is dynamically scored from 1–10 based on its age and number of retests. Fresher, less-tested zones are stronger, helping prioritize high-impact levels. Real-Time Distance ||| Each active zone’s info label shows the exact distance in pips from current price to the zone edge for quick risk/opportunity assessment. Trading Session Tracking ||| Zones are tagged by formation session (Asian / London / New York) for added context—high-volume session zones often matter more. Advanced ATR Filtering ||| Volatility-based filters control zone quality: set min/max zone height and optionally enforce a consistent zone height using ATR. Minimum Zone Distance ||| Reduces clutter by requiring a minimum number of bars between new zones, ensuring zones are distinct and well-separated. Built on Pine Script v6 ||| Uses the newest Pine Script version for better efficiency, reliability, and smoother handling of complex logic/drawings. ________________________________________ 5.0 ✅ Conclusion 🩶 The SMC 2026 indicator is most powerful when used as a structured decision framework, not a blind signal generator. 🔘 Its core value is systematically identifying + scoring high-probability institutional zones. ◼️ By following this protocol—prioritize Strength ≥ 6.5, align with Premium/Discount, and require confirmation—you elevate consistency, clarity, and execution discipline.
Chỉ báo Pine Script®
của ProjectSyndicate
2
ICT Rejection Block [KTY]ICT Rejection Block Indicator This indicator automatically detects and displays Rejection Blocks based on ICT (Inner Circle Trader) methodology. Rejection Blocks are price zones formed by candles with long wicks, indicating strong buying or selling rejection at that level. Automatic Detection - Identifies candles with significant wick-to-body ratio - Rejection High (Red): Long upper wick showing buying pressure rejected - Rejection Low (Green): Long lower wick showing selling pressure rejected Multi-Timeframe Support - Display rejection blocks from two different timeframes simultaneously (LTF & HTF) - HTF rejection blocks carry more significance 1. Identify rejection blocks on your chart 2. Watch for price reaction when re-entering the rejection zone 3. Combine with Order Block, FVG, or Market Structure for confluence 4. Use rejection block levels as reference for stop-loss placement Pro Tips: - HTF rejection blocks (1H+) are more reliable - Rejection block aligned with OB or FVG increases significance - Multiple rejection blocks at similar levels indicate strong S/R zone LTF: Enable and select lower timeframe HTF: Enable and select higher timeframe Rejection Block Count: Number of rejection blocks to display per type Colors: Customize colors for rejection high and low Show Mitigated Rejection Blocks: Display broken zones in gray Rejection High Detected Rejection Low Detected Rejection High Mitigated Rejection Low Mitigated This indicator is designed for educational purposes. Rejection blocks do not guarantee price reversal. Always combine with proper risk management. If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
Chỉ báo Pine Script®
của Coin-TeacherKim
2
Best Buying & Selling Flip Zone @MaxMaserati 3.0Best Buying & Selling Flip Zone 3.0 🐂🐻 Best Buying & Selling Flip Zone 3.0 is an advanced, multi-timeframe Price Action tool designed to identify high-probability institutional supply and demand zones. By analyzing candle range and body size (Expander vs. Normal candles), this indicator categorizes market structure shifts into three distinct tiers of strength (A+++, A++, A+). It includes a built-in Trade Manager, Volume Tracking, and a unique "Defender/Attacker" Multi-Timeframe (MTF) entry confirmation system. 🚀 Key Features Multi-Timeframe Analysis: Monitor Higher Timeframe (HTF) zones while trading on a Lower Timeframe (LTF). Tiered Setup Grading: Automatically classifies zones based on the strength of the candle engulfing action (King Slayer, Crusher, Drift). Smart Entry Confirmation: The script can wait for price to tap an HTF zone and then automatically search for a confirmation pattern on the current timeframe before signaling a trade. Built-in Trade Management: Visualizes Entry, Stop Loss (SL), and Take Profit (TP) levels with customizable Risk:Reward ratios. Volume Tracking: Monitors the volume utilized to create a zone and tracks "remaining" volume as price tests the zone. Zone Deletion Logic: Automatically removes zones that have been invalidated by either a wick or a candle close. 🧠 How It Works: The "A-Grade" Logic The indicator analyzes candles based on their body-to-range ratio to define "Expander" (Explosive move) vs. "Normal" candles. It then looks for engulfing behaviors to create zones: A+++ (King Slayer): Logic: A Bullish Expander engulfs a Bearish Expander (or vice versa). Significance: This is the strongest signal, indicating a massive shift in momentum where aggressive buyers completely overwhelmed aggressive sellers. A++ (Crusher): Logic: A Bullish Expander engulfs a Bearish Normal candle. Significance: Strong momentum overcoming standard price action. High probability. A+ (Drift): Logic: A Bullish Normal candle engulfs a Bearish Normal candle. Significance: A standard flip zone. Good for continuation plays but less aggressive than KS or CR setups. 🛠️ Functionality Guide 1. General Filters & Timeframes Higher Timeframe: Select a timeframe higher than your chart (e.g., Select 4H while trading on 15m). The indicator will draw the major zones from the 4H. Deletion Logic: Wick (Hard): Zone is removed immediately if price touches the invalidation level. Close (Soft): Zone is removed only if a candle closes past the invalidation level. 2. LTF Entry Confirmation (The "Master" Switch) When Show LTF Entry Logic is enabled, the indicator does not signal immediately upon an HTF zone creation. Instead: It waits for the price to retraced and touch the HTF zone. Once touched, it scans the current timeframe for a valid flip setup (KS, CR, or DR). It creates a tighter entry box and draws trade lines only when this confirmation occurs. 3. Trade Management Risk:Reward: Set your desired RR (e.g., 2.0). SL Padding: Add breathing room (ticks) to your Stop Loss. SL Source: Choose between a safer Stop Loss (based on the HTF zone) or a tighter Stop Loss (based on the LTF confirmation candle). 4. Volume Stats Labels display the volume involved in the zone's creation. As price taps the zone, the volume is "depleted" from the label, giving you insight into the remaining order flow absorption. 🎨 Visual Customization Colors: Fully customizable colors for Buyers (Green) and Sellers (Red) zones across all three strength tiers. Labels: Toggle technical names, touch counts, and timeframe labels. Lines: Option to show "Aggressive Open Lines" to mark the exact opening price of the flip zone extended forward. ⚠️ Disclaimer This tool is for educational purposes and chart analysis assistance only. Past performance of a setup (A+++/King Slayer) does not guarantee future results. Always manage risk and use this in conjunction with your own trading strategy.
Chỉ báo Pine Script®
của MaxMaserati
DEMA Volatility SuperTrend | RakoQuantDEMA Volatility SuperTrend is a clean trend-regime indicator built for volatile markets such as crypto. It combines a Double Exponential Moving Average (DEMA) baseline with a standard deviation volatility envelope, then applies classic SuperTrend trailing logic to produce persistent bullish and bearish regimes. This tool is designed for traders who want a smooth but responsive trend structure without relying on ATR alone. Core Concept This indicator answers one simple question: Are we currently in a bullish trend regime or a bearish trend regime? It does this by building a dynamic volatility corridor around a DEMA baseline and flipping only when price breaks beyond the active band. How It Works 1. DEMA Baseline (fast + low lag) A DEMA is used instead of a normal EMA to reduce lag while maintaining smooth trend behavior. 2. Volatility Engine (Standard Deviation) Volatility bands are created using: Raw Source Volatility Classic standard deviation behavior Residual vs Baseline Volatility Measures deviations from the DEMA baseline for cleaner regime detection Band formula: Upper Band = baseline + multiplier × stdev Lower Band = baseline − multiplier × stdev 3. SuperTrend Trailing Regime Logic Instead of flipping every touch, the bands trail using SuperTrend persistence rules: Bull regime → active lower band acts as support Bear regime → active upper band acts as resistance Flips occur only when price breaks beyond the trailing band. Visual System Bull regime: Ice-Blue active band Bear regime: Violet active band Optional faint inactive bands provide structure Optional fill highlights the active regime corridor Optional candle painting matches the regime state instantly Alerts Included Bull Flip Alert → regime turns bullish Bear Flip Alert → regime turns bearish Perfect for automation or regime-based filtering. How to Use ✅ Trend filter for swing trading ✅ Regime confirmation layer for systems ✅ Works best on higher timeframes (4H / 1D) ✅ Combine with momentum or breakout triggers for entries Inputs Summary DEMA Length → baseline responsiveness Volatility Length + Multiplier → band width + sensitivity Volatility Mode → raw vs residual volatility Flip Source → Close or HL2 for regime switching Visual toggles → fill, candles, inactive rails Screenshot Placement 📸 Example chart / screenshot: Tip: show one bullish flip + one bearish flip with candle painting enabled.
Chỉ báo Pine Script®
của RakoQuant
1
AMT Orderflow Profile + Imbalance Highlight + DashboardAMT Orderflow Profile + Imbalance Highlight + Dashboard This indicator is a price-bin-based orderflow profile designed to expose where aggressive participation is concentrated and sustained, not just where volume traded. Unlike traditional volume profiles that show where activity occurred, this script focuses on how volume behaved inside price, separating buying and selling pressure and highlighting only statistically dominant imbalance. 🔹 Why This Script Is Original Most volume profiles and orderflow tools suffer from one or more of the following: Single-bin imbalance noise Repeating alerts from already-accepted imbalance Visual imbalance that does not align with alerts No distinction between fresh initiative vs historical volume This script solves those issues by combining price-bin profiling, directional volume classification, and strict imbalance persistence rules into one unified model. The result is a contextual orderflow tool, not a signal spammer. 🔹 How It Works (Concepts) Price-Based Binning The script divides the price range of the lookback window into fixed bins. Directional Volume Separation Buy volume: candles closing above open Sell volume: candles closing below open Bin-Level Imbalance Calculation A bin is imbalanced only when one side controls a configurable percentage of total volume: Side Volume ÷ (Buy + Sell Volume) ≥ Threshold Persistence Requirement (Noise Filter) Imbalance is only considered valid when it appears across 3 or more consecutive bins, filtering out isolated prints. Fresh Print Enforcement Alerts trigger only when imbalance first appears, never while it persists or after it has already been accepted by price. 🔹 Visual Output Each bin is drawn as a horizontal box Imbalanced bins display: Bold borders Highlighted background Text label: BUY IMB or SELL IMB Box width represents relative volume intensity Alerts are mathematically locked to these visual labels, ensuring perfect alignment between what you see and what you’re alerted on. 🔹 How Traders Use It This tool is best used for: Identifying initiative buying or selling Spotting absorption vs acceptance Confirming auction direction within a larger framework Providing orderflow context alongside VWAP, IB, CVD, or market structure It is not intended as a standalone entry signal, but as a confirmation and context engine. 🔹 Alerts (Non-Repainting) BUY alert → fresh 3+ bin buy-side imbalance SELL alert → fresh 3+ bin sell-side imbalance Alerts do not repeat unless imbalance fully disappears and reappears ⚠️ Notes Candle-based volume (not tick footprint) Non-repainting Designed for futures and liquid markets Best used with clean charts for clarity
Chỉ báo Pine Script®
của samb817
ICT Market Structure [KTY]ICT Market Structure Indicator Overview This indicator automatically detects and displays Market Structure based on ICT (Inner Circle Trader) methodology. Market structure analysis identifies trend direction and potential reversal points by tracking swing highs and lows. Understanding structure is fundamental to ICT trading concepts. Key Features Internal & External Structure Internal Structure: Short-term swings for quick trend detection (displayed with dashed lines) External Structure: Long-term swings for major trend identification (displayed with solid lines) Choose to display Internal, External, Both, or None CHoCH & BOS Detection CHoCH (Change of Character): First sign of potential trend reversal BOS (Break of Structure): Confirmation of trend continuation Internal labels: lowercase (choch/bos) External labels: uppercase (CHOCH/BOS) Equal Highs & Equal Lows EQH: Multiple highs at similar price levels — liquidity pool above EQL: Multiple lows at similar price levels — liquidity pool below Smart money often sweeps these levels before reversing Swing Point Labels HH (Higher High): Uptrend continuation HL (Higher Low): Uptrend confirmation LH (Lower High): Downtrend continuation LL (Lower Low): Downtrend confirmation How to Use Identify the trend using HH/HL (bullish) or LH/LL (bearish) patterns Wait for CHoCH as the first signal of potential reversal Confirm with BOS in the new direction Watch EQH/EQL levels for potential liquidity sweeps Combine with OB, FVG, Liquidity zones for higher probability setups Pro Tips: External structure is more reliable than internal structure CHoCH after liquidity sweep = high probability reversal Multiple timeframe analysis increases accuracy Internal CHoCH can provide early entries, but with higher risk Settings SettingDescriptionStructure TypeSelect INTERNAL, EXTERNAL, ALL, or NONEInternal Structure ColorsCustomize bullish/bearish colors for internal structureExternal Structure ColorsCustomize bullish/bearish colors for external structureEQL & EQHToggle equal highs/lows display with custom colorsSwing PointsToggle HH/HL/LH/LL labels with custom color Alerts Structure Alerts: 🟢 Bullish CHoCH (Internal) 🔴 Bearish CHoCH (Internal) 🟢 Bullish CHOCH (External) 🔴 Bearish CHOCH (External) 🟢 Bullish BOS (Internal) 🔴 Bearish BOS (Internal) 🟢 Bullish BOS (External) 🔴 Bearish BOS (External) Equal Levels Alerts: 🔴 Equal Highs (EQH) 🟢 Equal Lows (EQL) Swing Point Alerts: 📈 Higher High (HH) 📈 Higher Low (HL) 📉 Lower High (LH) 📉 Lower Low (LL) Notes This indicator is designed for educational purposes Internal structure provides faster signals but more noise External structure is slower but more reliable Always combine with proper risk management If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
Chỉ báo Pine Script®
của Coin-TeacherKim
Momentum Adaptive EMA | RakoQuantMomentum Adaptive EMA is a trend-following moving average system designed to dynamically adjust its responsiveness based on market momentum. Instead of using a fixed smoothing speed like a normal EMA, this indicator becomes fast in strong moves and slow in choppy conditions, producing a cleaner adaptive trend structure. This version also introduces a secondary POT Moving Average for smooth regime confirmation. Core Idea This indicator answers one key question: Is momentum accelerating enough to justify a faster trend response? By adapting the EMA’s smoothing factor in real time, the indicator avoids the two classic problems of moving averages: Lag in strong trends Whipsaws in sideways markets How It Works 1. Momentum-Based Adaptivity Engine The indicator measures momentum using a Rate-of-Change style move: ROC = current price − price N bars ago That momentum is normalized by volatility: Momentum Strength = |ROC| ÷ stdev(ROC) This produces a clean, scale-independent momentum score. 2. Adaptive EMA (Dynamic Alpha) Instead of a constant EMA alpha, smoothing is adjusted between: Alpha Min → slow mode (stable markets) Alpha Max → fast mode (strong trend markets) Adaptivity is controlled by: k (Strength Parameter) High momentum → EMA reacts faster Low momentum → EMA smooths more 3. POT Moving Average (Weighted Trend Anchor) A second moving average is calculated using a Power-Weighted POT MA, where the most recent values receive heavier weight: Stronger emphasis on recent trend shifts Smooth confirmation without volatility bands This creates a clean dual-average regime filter: Adaptive EMA = fast regime line POT MA = slower structure anchor Regime Signals Trend regime is defined by crossovers: Bullish regime: Adaptive EMA crosses above POT MA Bearish regime: Adaptive EMA crosses below POT MA Optional persistence keeps regimes stable instead of flipping constantly. Visual System Bull regime → Ice Blue trend state Bear regime → Navy trend state Candle painting optionally matches the active regime The result is a clean institutional trend overlay with adaptive behavior. Alerts Included Bull Break Alert → Adaptive EMA crosses ABOVE POT MA Bear Break Alert → Adaptive EMA crosses BELOW POT MA Useful for automation or confirmation systems. How to Use ✅ Trend filter for directional trading ✅ Adaptive MA replacement for classic EMA systems ✅ Works well on higher timeframes (4H / 1D) ✅ Combine with breakouts, momentum triggers, or volume tools for entries Inputs Summary Momentum Length → speed of momentum detection Normalization Length → volatility scaling window Alpha Min / Alpha Max → slow vs fast response bounds Adaptivity Strength (k) → aggressiveness of adaptation POT Length + Power → smoothing of the confirmation MA Persistent Regime Toggle → stability vs live switching Candle Paint Toggle → visual regime clarity Screenshot Placement 📸 Example chart / screenshot: (insert image here) Tip: show a strong bull trend + one bearish flip so users understand the adaptive behavior.
Chỉ báo Pine Script®
của RakoQuant
1
RLP V4.3 -Long Term Support/Resistance Levels (Refuges-Shelters)// Introduction // We have utilized the Zigzag library technology from ©Trendoscope Pty Ltd for Zigzag generation, allowing users the freedom to choose which of the different Zigzags calculated by Trendoscope as "Levels and Sub-Levels" is most suitable for generating ideal phases for evaluation and selection as "most preponderant phases" over long-term periods of any asset, according to its particular behavior based on its age, volatility, and price trend. // Theoretical Foundation of the Indicator // Many traditional institutional investors use the latest higher-degree market phase that stands out from others (longest duration and greatest price change on daily timeframe) to base a Fibonacci retracement on whose levels they open long-term positions. These positions can remain open to be activated in the future even years in advance. The phase is considered valid until a new, more preponderant phase develops over time, at which point the same strategy is repeated. // Indicator Objectives // 1) Automatically find the latest most preponderant long-term phase of an asset, analyzing it on daily timeframe while considering whether the long-term market trend is bullish or bearish. 2) Draw a Fibonacci Retracement over the preponderant phase (reversed if the phase is bullish). 3) The indicator automatically numbers and locates the 3 most preponderant phases, selecting Top-1 for initial Fibo drawing. 4) If the user disagrees with the indicator's automatic selection, they have the freedom to choose any of the other 2 Top phases for the Fibo drawing and its levels. 5) If the user disagrees with the amplitude or frequency of the initially drawn Zigzag phases, they can modify the Zigzag calculation algorithm parameters until one of the Top-3 matches the phase they had in mind. 6) As an experimental bonus, the indicator runs a popularity contest (CP) of "bullseye" daily price (OHLC) matches, subject to user-defined tolerance ranges, against all Fibo levels of the Top 3 selected phases, to verify which phase the market prices are validating as the most popular for placing trades. Contest results are displayed in the POP. CONTEST column of the Top-3 phases table. If the contest detects a change in the winning phase, a switch can be enabled to activate an alert that the user can utilize with TradingView's alert creator to display an alarm, send an email, etc. 7) This indicator was designed for users to find the preponderant long-term phase of their assets and manually record the date-price coordinates of the i0-i1 anchors of the preponderant phase. The Top-1 phase coordinates are shown in the Top-3 phases table where they can be captured. The date-price coordinates of all HH and LL pivots, from all Zigzag phases, can be displayed via a switch. With the pivots, the user can select a different phase than those automatically found by the indicator, according to the conclusions of their own research. Subsequently, the user can forget about this RLP indicator for a while and move on to apply in their normal trading our RLPS indicator (Simplified Long-Term Shelters), in which they can draw and simultaneously track the long-term shelters of up to 5 different assets, simply by entering their corresponding date-price coordinates, previously located with this RLP indicator or through their own observation. // Additional Notes // 1) As of the this V4.3 publication date (01/2026), the Zigzag generation parameters were adjusted by default to find the long-term preponderant phases for the following assets: Bitcoin, Ethereum, Bitcoin futures BTC1! (all generated due to the 2020-2021 pandemic). It also provides by default the confirmed preponderant phases for the following assets: Apple, Google, Amazon, Microsoft, PayPal, NQ1!, ES1! and SP500 Cash. 2) Prices, phases, and levels shown on the graphic chart correspond to results obtained using daily Bitcoin data from the Bitstamp exchange, BTCUSD:BITSTAMP (popular here in Europe). 3) Any error corrections or improvements that can be made to the phase selection algorithms or the CP phase popularity contest algorithm will be highly appreciated (statistics and mathematics, among many other sciences, are not particularly our strong suit). 4) We sincerely regret to inform you that we have not included the Spanish translation previously provided, due to our significant concern regarding the ambiguous rules on publication bans related to indicators. 4) Sharing motivates. Happy hunting in this great jungle!
Chỉ báo Pine Script®
của Masewal_aj_p_oolom
Batoot Algo PureBatoot Algo (Pure Analysis Mode) Indicator Overview Batoot Algo is an advanced technical analysis indicator based on: Price Action and geometric chart patterns Higher Timeframe (HTF) trend filtering Volume confirmation Breakout & Retest logic Head & Shoulders pattern detection Analysis-only indicator. No Buy/Sell labels on the chart. Alerts and Dashboard only. The goal is clean charts and smarter trading decisions. --- Entry Modes Aggressive (Breakout) Immediate entry on breakout Requires: Confirmed breakout High volume Optional trend alignment Conservative (Retest) Breakout → Wait for retest → Confirmation candle Reduces false signals Suitable for patient trading --- HTF Trend Filter Uses EMA crossover on higher timeframe: EMA 50 EMA 200 EMA50 > EMA200 → Bullish EMA50 < EMA200 → Bearish Filter can be enabled or disabled in settings. --- Price Patterns Detected Automatically detects and draws: Bullish / Bearish Flags Channels Triangles / Pennants Rising Wedge (Bearish) Falling Wedge (Bullish) The area between support and resistance lines is dynamically filled based on the pattern. --- Yellow Candle (High Volume) Yellow candles indicate High Volume. Triggered when: Current candle volume >= Average volume of last 20 candles × volume multiplier Default multiplier: 1.5 Confirms strong breakouts. Not a standalone entry signal. --- Head & Shoulders Detection Supports: Head & Shoulders (Bearish) Inverse Head & Shoulders (Bullish) Neckline drawn automatically. Breakout validated with volume. Pattern status shown in Dashboard. --- Dashboard Displays: Entry Mode (Aggressive / Conservative) HTF Trend Current Pattern Head & Shoulders Status Market Status: ENTRY BUY, ENTRY SELL, WAIT RETEST, SCANNING --- Alerts Alerts trigger only when: Pattern confirmed Breakout / Retest logic satisfied High volume confirmed Trend filter (if enabled) passes No trade labels plotted on chart. --- License & Attribution Licensed under Creative Commons Attribution 4.0 (CC BY 4.0) Free to use and modify. Attribution required. Removing or changing the author name is not allowed. --- This indicator is for technical analysis purposes only and is not financial advice. Always use proper risk management. --- Clean chart, smart analysis, better trading decisions.
Chỉ báo Pine Script®
của nazaralgeze
ED by bigmmED by bigmm identifies significant price divergences from the 200-period Exponential Moving Average (EMA) by analyzing closing and opening price extremes. This tool marks the three most recent candles with the largest percentage deviations. Key Features EMA200 Analysis: Uses the 200-period Exponential Moving Average as the primary reference level for measuring price deviations Deviation Calculation: Computes percentage-based deviations for both closing (below EMA) and opening (above EMA) prices Top 3 Extremes: Identifies and marks only the three most recent maximum deviations for each direction Visual Simplicity: Uses minimalistic green and red dots for clear visual identification without chart clutter Historical Analysis: Evaluates the last 1440 bars (approximately 3 years on daily timeframe) to find significant deviation patterns Recommended Usage Best used on higher timeframes (H4, D1, W1) for the following reasons: Reduced Noise: Higher timeframes filter out market noise and provide cleaner deviation signals Trend Context: EMA200 carries more significance on daily and weekly charts as a major trend indicator Strategic Signals: Extreme deviations on higher timeframes often correspond to important support/resistance levels and potential reversal zones Reduced False Signals: Longer timeframes minimize whipsaws and provide more reliable extreme readings Position Trading: Ideal for swing traders and position traders who base decisions on daily or weekly price action
Chỉ báo Pine Script®
của BIGMarketMaker
2
Buyers & sellers Candle Control Dominance Zone @MaxMaserati 3.0Description The Buyers & Sellers Candle Control Dominance Zone is a surgical price-action tool designed to identify and project key supply and demand zones derived from candle anatomy across multiple timeframes. By splitting candles into "Sellers Control" (upper wick/shadow) and "Buyers Control" (lower wick/shadow) regions, this script visualizes exactly where price rejection and absorption are occurring. With the new HTF Engine, you can now view these institutional rejection zones from a Higher Timeframe (e.g., 4H) while trading on a Lower Timeframe (e.g., 15m). How it Works The indicator identifies specific "Control Zones" based on the battle between buyers and sellers: Live Control (Current & HTF): Real-time monitoring of the developing candle. See a 4H wick forming live while watching the 1m chart. Last Closed Control (Current & HTF): Projects the zones from the most recently completed candle. Dominance Zones (BuBC & BeBC): BuBC (Bullish Body Close): A "Dominance Zone" triggered when a candle closes above the previous candle's high. Signifies strong bullish momentum. BeBC (Bearish Body Close): A "Dominance Zone" triggered when a candle closes below the previous candle's low. Signifies aggressive selling pressure. Key Features Multi-Timeframe (MTF) Overlay: Plot 4H, Daily, or Weekly control zones directly on your lower timeframe scalping charts. Smart Labeling: HTF labels automatically update to show the zone type (e.g., "Sellers Control (Live) ") and whether the last candle was a Dominance candle (BuBC/BeBC). Dynamic Extension: Zones are projected forward to help you catch retests of rejection levels. Alerts Included: Built-in alerts trigger when price crosses into a Dominance Zone (BuBC/BeBC), allowing you to set it and forget it. Can be use as: Support & Resistance: Use Buyers Control zones (lower wicks) as demand zones for longs and Sellers Control zones (upper wicks) as supply zones for shorts. Trend Confirmation: A BuBC zone often acts as a launchpad for continued upside. If price falls back into a BuBC zone and rejects, it is a high-probability continuation signal. Fractal Entry: Use the HTF zones to find the "Big Picture" levels, then use the Current TF zones to refine your entry with precision. Settings Display Filter: Toggle Current TF zones (Live, Closed, BuBC, BeBC) independently. Higher Timeframe Settings: Enable/Disable HTF overlay and select your preferred timeframe (e.g., 240 for 4H). Visuals: Fully adjustable transparency, colors, and extension lengths to keep your chart clean.
Chỉ báo Pine Script®
của MaxMaserati
ICT Supply & Demand [KTY]ICT Supply & Demand Indicator This indicator automatically detects and displays Supply and Demand zones based on swing highs and lows. Supply and Demand zones are horizontal support/resistance areas where price previously showed strong buying or selling pressure. Automatic Detection - Supply Zone (Red): Formed at swing highs where selling pressure was strong - Demand Zone (Green): Formed at swing lows where buying pressure was strong - Zones are automatically removed when price breaks through Dynamic Extension - Zones extend automatically as new bars form - Clear visual labels showing SUPPLY and DEMAND 1. Identify Supply and Demand zones on your chart 2. Watch for price reaction when re-entering the zone 3. Combine with Order Block, FVG, or Market Structure for confluence 4. Use zones as reference for take-profit or stop-loss targets Pro Tips: - Zones that align with OB or FVG have higher significance - Multiple touches on a zone increase chance of breakout - Fresh (untested) zones tend to have stronger reactions Show Supply & Demand Zones: Toggle zone display on/off Supply Zone Color: Customize supply zone color Demand Zone Color: Customize demand zone color Label Color: Customize text color Supply Zone Detected Demand Zone Detected Supply Zone Broken Demand Zone Broken This indicator is designed for educational purposes. Supply and Demand zones do not guarantee price reversal. Always combine with proper risk management. If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
Chỉ báo Pine Script®
của Coin-TeacherKim
Funnelzon Graded Buy and Sell Signals (LITE) MFI MTFFunnelzon Buy and Sell Signals (EMA Zones) – LITE is a lightweight overlay indicator built for scalping and short-term trading. It generates BUY/SELL signals, grades each signal (A+ to F), and provides a clean Confirmation Box that summarizes multi-timeframe context so you can make faster, more structured decisions. How it works Signal Engine (LTF) Signals are triggered using an ATR-based “scalp helper” logic with adjustable sensitivity. A stop-state system helps reduce repeated or noisy entries. Signal Scoring & Grades (A+ → F) When a signal appears, it is evaluated by a context pipeline that considers: Adaptive momentum/flow (AMF) ALMA trend alignment Support/Resistance proximity Swing structure behavior Market regime / trend strength (ADX-based) The result is a score mapped to a grade: A+ / A = strongest signals B / C = mixed conditions D / F = low-quality conditions Optional Filters MFI Filter: Helps avoid signals that do not meet Money Flow conditions. HTF Confirmation (MTF): Uses HTF1 and HTF2 bias. Choose strict filtering or soft alignment. Confirmation Box (Dashboard) The box displays: HTF State: Trend Long / Trend Short / HTF Conflict / Neutral Market Mode: Trend / Pullback / Conflict Trade Bias: Long-only / Short-only / Wait ENTRY NOW? = “YES” when HTF bias and LTF signal align MFI status + HTF1/HTF2 direction Optional Structure Tools EMA overlays: 9 / 12 / 20 / 50 / 100 / 200 Auto Supply/Demand zones (pivot-based, ATR thickness, configurable extension and limits) Best practices (recommended workflow) Prefer trading A+ / A signals only. Trade in the direction of HTF State when possible. If Market Mode shows PULLBACK or CONFLICT, reduce risk or wait for better alignment. Use Supply/Demand zones and EMAs for structure (targets, invalidation, and bias). Important: Confirmation with Stochastic + MACD This script is a signal + context tool, not a guarantee. To validate signal confirmation, it is strongly recommended to use: Stochastic Oscillator (momentum/exhaustion confirmation) MACD (trend momentum and direction confirmation) Only take trades when the script signal and your confirmation indicators agree. Alerts Includes alert conditions for: Buy Signal Sell Signal Any Signal ENTRY NOW (HTF + LTF aligned) ENTRY NOW Long / ENTRY NOW Short Disclaimer This indicator is for educational purposes and does not constitute financial advice. Always backtest, manage risk, and confirm signals with your own rules.
Chỉ báo Pine Script®
của funnelzon
1
ICT Premium & Discount [KTY]ICT Premium & Discount Indicator This indicator automatically displays Premium and Discount Zones based on ICT (Inner Circle Trader) methodology. Premium & Discount zones divide the current price range into upper and lower areas based on swing highs and lows. This helps traders understand where price sits within the broader range. Three-Zone Structure - Premium Zone (Red): Upper portion of the range - Equilibrium (Gray Dashed): Middle 50% line, fair value reference point - Discount Zone (Green): Lower portion of the range Multi-Timeframe Support - Display zones from two different timeframes simultaneously (LTF & HTF) - HTF zones carry more significance than LTF zones Dynamic Range Calculation - Automatically identifies swing high and low for the selected timeframe - Zones update as new highs/lows form 1. Identify the current zone - Is price in Premium, Equilibrium, or Discount? 2. Combine with Market Structure - Use CHoCH/BOS to confirm directional bias 3. Look for confluence - OB, FVG, or Liquidity zones within Premium/Discount add significance 4. Use Equilibrium as reference - Price often reacts around the 50% level Pro Tips: - HTF zones (4H, 1D) are more significant than LTF zones - Most effective when combined with other ICT concepts - Ranging markets may see price oscillate between zones without clear direction Show Premium & Discount Zones: Toggle zone display on/off LTF: Enable and select lower timeframe for zone calculation HTF: Enable and select higher timeframe for zone calculation Price Entered Premium Zone Price Entered Discount Zone This indicator is designed for educational purposes. Always combine with proper risk management. If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
Chỉ báo Pine Script®
của Coin-TeacherKim
EMA Based TMA Bands [NeuraAlgo]EMA Based TMA Bands Overview EMA Based TMA Bands is a volatility-adaptive trend and reversal indicator that combines a Triangular Moving Average (TMA) with EMA-weighted smoothing and dynamic deviation bands. It is designed to identify trend direction, overextended price conditions, and potential reversal points with high visual clarity. The indicator plots a central TMA line along with three upper and three lower volatility bands, automatically adapting to market conditions. Core Concepts 1. Triangular Moving Average (TMA) The TMA is calculated using triangular weighting, giving more importance to central bars. This creates a smoother and more stable average compared to SMA or EMA. The TMA acts as the main equilibrium price level. 2. EMA-Weighted Enhancement An additional EMA-style weighting is applied using a custom coefficient. This allows fine-tuning between smoothness and responsiveness. Lower coefficient = smoother behavior Higher coefficient = faster reaction to price changes Volatility Bands The bands are calculated using a weighted variance model: Positive and negative deviations are tracked separately. This allows asymmetric volatility response in bullish and bearish conditions. Band Structure Inner Band – Primary deviation Middle Band – 1.15× deviation ● Outer Band – 1.30× deviation These bands help identify: ● Overbought and oversold zones ● Volatility expansion and contraction ● Mean reversion opportunities Trend Detection Trend direction is determined by the slope of the TMA, normalized by ATR. ● Bullish Trend: TMA slope rising beyond threshold ● Bearish Trend: TMA slope falling beyond threshold ● Flat Market: No significant slope The TMA line automatically changes color based on trend state. Trading Signals Buy Signal A buy signal is triggered when: ● Price previously closes below the lower band ● A bullish candle forms on the current bar ● Suggests rejection of lower volatility zone Sell Signal A sell signal is triggered when: ● Price previously closes above the upper band ● A bearish candle forms on the current bar ● Suggests rejection of upper volatility zone Signals are displayed as small triangle markers on the chart. Inputs Main Settings ● TMA Period: Length of the triangular moving average ● EMA Period: Length of EMA-weighted smoothing ● EMA Coefficient: Controls EMA influence ● Band Deviation: Controls band width ● Price Source: Input price (default: HLC3) ● Trend Threshold: Sensitivity of trend detection Art Settings ● Bullish Color: Color used for bullish bands and signals ● Bearish Color: Color used for bearish bands and signals Best Use Cases ● Trend continuation trading ● Mean reversion strategies ● Volatility expansion setups ● Support and resistance visualization Notes ● Best used on intraday to swing timeframes ● Works well with price action confirmation ● Not a repainting indicator, but smoothing introduces natural lag Developed by NeuraAlgo
Chỉ báo Pine Script®
của NeuraAlgo
Bubble Risk ModelThe question of whether markets can be objectively assessed for overextension has occupied financial researchers for decades. Charles Kindleberger, in his seminal work "Manias, Panics, and Crashes" (1978), documented that speculative bubbles follow remarkably consistent patterns across centuries and asset classes. Yet identifying these patterns in real time remains notoriously difficult. The Bubble Risk Model attempts to address this challenge not by predicting crashes, but by systematically measuring the statistical characteristics that historically precede fragile market conditions. The theoretical foundation draws from two distinct research traditions. The first is the work on regime-switching models pioneered by James Hamilton (1989), who demonstrated that economic time series often exhibit discrete shifts between different behavioral states. The second is the literature on tail risk and market fragility, most notably articulated by Nassim Taleb in "The Black Swan" (2007), which emphasizes that extreme events carry disproportionate importance and that traditional risk measures systematically underestimate their probability. Rather than attempting to build a probabilistic model requiring assumptions about underlying distributions, the Bubble Risk Model operates as a deterministic state-inference system. This distinction matters. Lawrence Rabiner's foundational tutorial on Hidden Markov Models (1989) established the mathematical framework for inferring hidden states from observable data through Bayesian updating. The present model borrows the conceptual architecture of states and transitions but replaces probabilistic inference with rule-based logic. States are not computed through forward-backward algorithms but inferred through deterministic thresholds. This trade-off sacrifices theoretical elegance for practical robustness and interpretability. The measurement framework rests on four empirically grounded components. The first captures trailing twelve-month returns, reflecting the well-documented momentum effect identified by Jegadeesh and Titman (1993), who found that securities with strong past performance tend to continue outperforming over intermediate horizons. The second component measures trend persistence as the proportion of positive daily returns over a quarterly window, drawing on the research by Campbell and Shiller (1988) showing that price trends exhibit serial correlation that deviates from random walk assumptions. The third normalizes the distance between current prices and their long-term moving average by volatility, addressing the cross-sectional comparability problem noted by Fama and French (1992) when analyzing assets with different variance characteristics. The fourth component calculates return efficiency as the ratio of returns to realized volatility, a concept related to the Sharpe ratio but stripped of distributional assumptions that often fail in practice. The aggregation methodology deliberately prioritizes worst-case scenarios. Rather than averaging component scores, the model uses quantile-based aggregation with an explicit tail penalty. This design choice reflects the asymmetric error costs in bubble detection: failing to identify fragility carries greater consequences than occasional false positives. The approach aligns with the precautionary principle advocated by Taleb and colleagues in their work on fragility and antifragility (2012), which argues that systems exposed to tail risks require conservative assessment frameworks. Normalization presents a particular challenge. Raw metrics like year-over-year returns are not directly comparable across asset classes with different volatility profiles. The model addresses this through percentile ranking over multiple historical windows, typically two and five years. This dual-window approach provides regime stability, preventing the normalization from adapting too quickly during extended bull markets where elevated readings become statistically normal. The methodology draws on the concept of lookback bias documented by Lo and MacKinlay (1990), who demonstrated that single-window statistical measures can produce misleading results when market regimes shift. The state machine introduces controlled inertia into the system. Once the model enters a particular state, transitions become progressively more difficult as the state matures. This transition resistance mechanism prevents rapid oscillation near threshold boundaries, a problem that plagues many indicator-based systems. The concept parallels the hysteresis effects described in economic literature by Dixit (1989), where systems exhibit path dependence and resist returning to previous states even when underlying conditions change. Volatility regime detection adds contextual interpretation. Research by Engle (1982) on autoregressive conditional heteroskedasticity established that volatility clusters, with periods of high volatility tending to follow other high-volatility periods. The model scales its maturity thresholds inversely with volatility: in calm markets, states mature slowly and persist longer; in turbulent markets, information decays faster and states become more transient. This adaptive behavior reflects the empirical observation that low-volatility environments often precede significant market dislocations, as documented by Brunnermeier and Pedersen (2009) in their work on liquidity spirals. The confidence metric addresses internal model consistency. When individual components diverge substantially, the overall score becomes less reliable regardless of its absolute level. This approach draws on ensemble methods in machine learning, where disagreement among predictors signals increased uncertainty. Dietterich (2000) provides theoretical justification for this principle, demonstrating that ensemble disagreement correlates with prediction error. Distribution drift detection monitors whether the model's calibration remains valid. By comparing recent score distributions to longer historical baselines, the model can identify when market structure has shifted sufficiently to potentially invalidate its historical percentile rankings. This self-diagnostic capability reflects the concern raised by Andrews (1993) about parameter instability in time series models, where structural breaks can render previously estimated relationships unreliable. The cross-asset analysis extends the framework beyond individual securities. By calculating scores for multiple asset classes simultaneously and measuring their correlation, the model distinguishes between idiosyncratic overextension affecting a single asset and systemic conditions affecting markets broadly. This differentiation matters for portfolio construction, as documented by Longin and Solnik (2001), who found that correlations between international equity markets increase significantly during periods of market stress. Several limitations deserve explicit acknowledgment. The model cannot identify timing. Overextended conditions can persist far longer than rational analysis might suggest, a phenomenon documented by Shiller (2000) in his analysis of speculative episodes. The model provides no mechanism for determining when fragile conditions will resolve. Additionally, the cross-asset analysis lacks lead-lag detection, meaning it cannot distinguish whether assets became overextended simultaneously or sequentially. Finally, the rule-based nature of state inference means the model cannot express graduated probability assessments; states are discrete rather than continuous. The philosophical stance underlying the model is one of epistemic humility. It does not claim to identify bubbles definitively or predict their collapse. Instead, it provides a systematic framework for measuring characteristics that have historically been associated with fragile market conditions. The distinction between information and action remains the user's responsibility. States describe current conditions; how to respond to those conditions requires judgment that no quantitative model can provide. Practical guide for traders This section translates the model's outputs into actionable intelligence for both retail traders managing personal portfolios and professional traders operating within institutional frameworks. The interpretation differs not in kind but in scale and consequence. Understanding the score The primary output is a continuous score ranging from zero to one. Lower scores indicate elevated bubble risk; higher scores suggest more sustainable market conditions. This inverse relationship may seem counterintuitive but reflects the model's construction: it measures how extreme current conditions are relative to historical norms, with extremity mapping to fragility. A score above 0.50 generally indicates normal market conditions where standard investment approaches remain appropriate. Scores between 0.30 and 0.50 represent an elevated zone where caution is warranted but not alarm. Scores below 0.30 enter the extreme territory where historical precedent suggests increased fragility. These thresholds are not magical boundaries but represent statistical rarity: a score below 0.30 indicates conditions that occur in roughly the bottom quintile of historical observations. For retail traders, a score in the normal range means continuing with established strategies without modification. In the elevated range, this might mean pausing new position additions while maintaining existing holdings. In the extreme range, retail traders should consider whether their portfolio could withstand a significant drawdown and whether their time horizon permits waiting for recovery. For professional traders, the score integrates into broader risk frameworks: normal conditions permit full risk budgets, elevated conditions might trigger reduced position sizing or tighter stop losses, and extreme conditions could warrant defensive positioning or increased hedging activity. Reading the states The model classifies conditions into three discrete states: Normal, Elevated, and Extreme. These states differ from the continuous score by incorporating persistence and transition resistance. A market can have a score temporarily dipping below 0.30 without triggering an Extreme state if the condition proves transient. The Normal state indicates business as usual. Market conditions fall within historical norms across all measured dimensions. For retail traders, this means standard portfolio management applies. For professional traders, full strategy deployment remains appropriate with normal risk parameters. The Elevated state signals heightened attention. At least one dimension of market behavior has moved outside normal ranges, though not to extreme levels. Retail traders should review portfolio concentration and ensure diversification remains intact. Professional traders might reduce leverage slightly, tighten risk limits, or increase monitoring frequency. The Extreme state represents statistically rare conditions. Multiple dimensions show readings that historically occur infrequently. Retail traders should seriously evaluate whether they can tolerate potential drawdowns and consider reducing exposure to volatile assets. Professional traders should implement defensive protocols, potentially reducing gross exposure, increasing cash allocations, or adding protective positions. Interpreting transitions State transitions carry more information than states themselves. The model tracks whether conditions are entering, persisting in, or exiting particular states. An Entry into Extreme represents the most important signal. It indicates a regime shift from normal or elevated conditions into territory associated with historical fragility. For retail traders, this warrants immediate portfolio review. For professional traders, this typically triggers predefined defensive protocols. Persistence in a state indicates stability. Whether Normal or Extreme, persistence suggests the current regime has become established. For retail traders, persistence in Extreme over extended periods actually reduces immediate concern; the dangerous moment was the entry, not the continuation. For professional traders, persistent Extreme states require maintained vigilance but do not necessarily demand additional action beyond what the initial entry triggered. An Exit from Extreme suggests improving conditions. For retail traders, this might warrant cautious return to normal positioning over time. For professional traders, exits permit gradual normalization of risk budgets, though institutional memory typically counsels slower reentry than the mathematical signal might suggest. Duration and its meaning The model distinguishes between Tactical, Accelerating, and Structural durations in critical zones. Tactical duration (10-39 bars in critical territory) represents short-term overextension. Many Tactical episodes resolve without significant market disruption. Retail traders should note the condition but need not take dramatic action. Professional traders might implement modest hedges or reduce marginal positions. Accelerating indicates Tactical duration combined with actively deteriorating scores. This combination historically precedes more significant corrections. Retail traders should consider lightening positions in their most volatile holdings. Professional traders typically implement more substantial hedges. Structural duration (40+ bars in critical territory) indicates persistent overextension that has become a market feature rather than a temporary condition. Paradoxically, Structural conditions are both more concerning and less immediately actionable than Accelerating conditions. The market has demonstrated ability to sustain extreme readings. Retail traders should maintain heightened awareness but recognize that timing remains impossible. Professional traders often find Structural conditions require strategy adaptation rather than simple defensive positioning. Confidence and what it tells you The Confidence reading indicates internal model consistency. High confidence means all four underlying components agree in their assessment. Low confidence means components diverge significantly. High confidence combined with Extreme state represents the clearest signal. The model is both indicating fragility and agreeing with itself about that assessment. Retail and professional traders alike should treat this combination with maximum seriousness. Low confidence in any state reduces signal reliability. For retail traders, low confidence suggests waiting for clearer conditions before making significant portfolio changes. For professional traders, low confidence warrants increased skepticism about the score and potentially reduced position sizing in either direction. Alignment and model health The Alignment indicator monitors whether the model's calibration remains valid relative to recent market behavior. Good alignment means recent score distributions match longer-term historical patterns. The model's percentile rankings remain meaningful. Both retail and professional traders can interpret scores at face value. Degraded alignment indicates that recent market behavior has shifted somewhat from historical norms. Scores remain interpretable but with reduced precision. Retail traders should apply wider uncertainty bands to their interpretation. Professional traders might reduce position sizing slightly or require additional confirmation before acting. Poor alignment signals significant distribution shift. The model may be comparing current conditions to an increasingly irrelevant historical baseline. Retail traders should rely more heavily on other information sources during Poor alignment periods. Professional traders typically reduce model weight in their decision frameworks until alignment recovers. Volatility regime context The volatility regime provides essential context for score interpretation. Low volatility combined with Extreme state creates maximum concern. Research consistently shows that low-volatility environments can precede significant market dislocations. The market's apparent calm masks underlying fragility. Retail traders should recognize that low volatility does not mean low risk; it often means compressed risk premiums that will eventually normalize, potentially violently. Professional traders typically maintain or increase defensive positioning despite the market's calm appearance. High volatility combined with Extreme state is actually less immediately concerning than low volatility. The market has already acknowledged stress; risk premiums have expanded; potential sellers may have already sold. Retail traders should resist the urge to panic sell during high-volatility extremes, as much of the adjustment may have already occurred. Professional traders recognize that high-volatility extremes often represent better entry points than low-volatility extremes. Normal volatility requires no regime adjustment to interpretation. Scores mean what they appear to mean. Cross-asset analysis When enabled, the model calculates scores for multiple asset classes simultaneously, enabling systemic versus idiosyncratic risk assessment. Systemic risk (multiple assets in Extreme with high correlation) indicates market-wide fragility. Diversification benefits are reduced precisely when most needed. Retail traders should recognize that their portfolio's apparent diversification may not protect them during systemic events. Professional traders implement cross-asset hedges and consider tail-risk protection. Broad risk (multiple assets in Extreme with low correlation) suggests widespread but potentially unrelated overextension. Diversification may still provide some protection. Retail traders can take modest comfort in genuine diversification. Professional traders analyze which assets might offer relative value. Isolated risk (single asset in Extreme while others remain Normal) indicates asset-specific rather than market-wide conditions. Retail traders holding the affected asset should evaluate their position specifically. Professional traders may find relative value opportunities going long unaffected assets against the extended one. Scattered risk represents a few assets showing elevation without clear pattern. This typically warrants monitoring rather than action for both retail and professional traders. Parameter guidance The Short Percentile parameter (default 504 bars, approximately two years) controls the shorter normalization window. Increasing this value makes the model more conservative, requiring more extreme readings to flag concern. Retail traders should generally leave this at default. Professional traders might increase it for assets with shorter reliable history. The Long Percentile parameter (default 1260 bars, approximately five years) controls the longer normalization window. This provides regime stability. Again, default settings suit most applications. The Critical Threshold (default 0.30) determines where the Extreme state boundary lies. Lowering this value makes the model less sensitive, flagging fewer Extreme conditions. Raising it increases sensitivity. Retail traders seeking fewer false alarms might lower this to 0.25. Professional traders seeking earlier warning might raise it to 0.35. The Structural Duration parameter (default 40 bars) determines when Tactical conditions become Structural. Shorter values provide earlier Structural classification. Longer values require more persistence before reclassification. The State Maturity and Transition Resistance parameters control how readily the model changes states. Higher values create more stable states with fewer transitions. Lower values create more responsive but potentially noisier state changes. Default settings balance responsiveness against stability. The Adaptive Smoothing parameters control how the model filters noise. In extreme zones, longer smoothing periods reduce whipsaws but increase lag. In normal zones, shorter periods maintain responsiveness. Most traders should leave these at defaults. What the model cannot do The model cannot predict when overextended conditions will resolve. Markets can remain irrational longer than any trader can remain solvent, as the saying goes. Extended Extreme readings may persist for months or even years before any correction materializes. The model cannot distinguish between healthy bull markets and dangerous bubbles in their early stages. Both initially appear as strong returns and positive momentum. The model begins flagging concern only when statistical extremity develops, which may occur well into an advance. The model cannot account for fundamental changes in market structure. If a new paradigm genuinely justifies higher valuations (rare but not impossible), the model will continue flagging extremity against historical norms that may no longer apply. The Alignment indicator provides partial protection against this failure mode but cannot eliminate it. The model cannot replace judgment. It provides systematic measurement of conditions that have historically preceded fragility. Whether and how to act on that measurement remains entirely the trader's responsibility. Retail traders must still evaluate their personal circumstances, time horizons, and risk tolerance. 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