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Morpheus Trade OffThe Morpheus Trade Off Indicator is a versatile macroeconomic oscillator designed to provide a clear, quantitative view of key economic data such as inflation and unemployment, helping traders and analysts anticipate central bank actions. Below is a detailed explanation of how to set up, read, and interpret the indicator for operational use. 1. Setting Up the Indicator Select the ticker: Choose the economic data you want to monitor. Common examples include: USIRYY – U.S. annual inflation (YoY) USUR – U.S. unemployment rate USNFP – Non-Farm Payrolls (absolute number) You can also select symbols from other economies (e.g., CAIRYY, CAUR, CA60) or even non-macro assets like GOLD to analyze correlated markets. Set the “Analyzed Data” period: This defines the lookback period for the indicator. Typical settings: 52 months – long-term, macro-scale analysis 12 months – annual perspective, capturing standard deviations relative to the past year Adjust threshold levels: Default operational thresholds are 20 and 80 on the percent rank scale. These define zones of attention: Values above 80 indicate historically high readings (inflation spikes, very low unemployment, etc.) Values below 20 indicate historically low readings (disinflation, weak labor market) 2. How the Indicator Works The indicator calculates the average of the last N releases (as set in “Analyzed Data”) and then measures the latest release relative to this history. It combines absolute levels with rate of change, highlighting rapid accelerations or decelerations in economic conditions. The result is normalized into a percent rank from 0 to 100: 0–20: very low readings 20–80: normal/mid-range readings 80–100: very high readings ⚠ Important : USNFP is an absolute number, not a percentage. The oscillator treats it differently from rate-based data. Only the actual flow of data is considered; expectations or forecasts are not included in the calculation. 3. Reading the Indicator Identify extreme zones: If unemployment > 80 and inflation < 20, this signals a likely shift toward expansionary (dovish) policy. If inflation > 80 and unemployment < 20, the signal points toward restrictive (hawkish) policy. Monitor speed and direction: Rapid changes, even if the absolute value is moderate, may indicate that central banks are about to react. Contextualize with accumulation phases: The indicator is particularly effective when underlying markets show gentle, orderly trends (e.g., equity accumulation phases), allowing traders to anticipate directional opportunities. 4. Operational Considerations Timeframe: Designed for monthly data, but symbols with continuous quotes can be used on lower timeframes for intermarket analysis. Cross-market application: You can monitor related assets (e.g., GOLD for macro signals that may influence SILVER or other correlated markets). Threshold alerts: Use the 20/80 percent rank thresholds to create visual or automated alerts for attention zones. Risk management: The indicator provides context, not trade signals. Proper position sizing, risk management, and execution discipline are essential when acting on its insights. 5. Key Takeaways The Morpheus Trade Off Indicator transforms raw macroeconomic releases into actionable, normalized signals. It allows a quantitative understanding of central bank pressures, combining both absolute levels and momentum of economic variables. It is flexible, applicable across economies, indices, and even correlated commodities, providing a bridge between macro trends and operational trading. Always interpret within the broader context: market structure, trend, and risk management remain critical to applying insights effectively. Important Reading Note : The Morpheus Trade Off Indicator must be read on the monthly timeframe when monitoring monthly macroeconomic data such as inflation (USIRYY/CAIRYY) or unemployment (USUR/CAUR). Using lower timeframes for these monthly releases will distort the calculation and the percent rank, producing misleading signals. Always ensure that the chart timeframe matches the frequency of the underlying economic data.
Pine Script® 인디케이터
Andrea_Burzichelli의
Lot Size CalculatorThis indicator shows, based on your entered risk and account balance, for different pip sizes the lot size, which needs to be entered. The lot size is calculated based on the current market price and the pip-distance to the SL. You can activate/deactivate up to 5 different pip sizes and configure them with own values. You can also select you account currency and the indicator converts the currency from the open asset into your account one. Please always check by yourself before use in Live-Trading!!!
Pine Script® 인디케이터
tobiasblock94의
Vector Liquidity ModelVector Liquidity Model (VLM) Smart Money Concepts with Plain English Explanations The Vector Liquidity Model represents a professional approach to detecting institutional manipulation patterns, combining sophisticated liquidity sweep analysis with dynamic trendline mechanics. What sets VLM apart is its unique "Signal Rationale" panel that explains every signal in simple, non-technical language - making institutional-grade trading accessible to all experience levels. Status: Professional Release | Use: All markets, all timeframes Design Philosophy: Transparency - know WHY every signal occurs, not just when ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 Dual-Strategy Architecture Strategy 1 - Liquidity Sweeps Detects stop-loss hunting at key levels, identifying when institutions trigger retail stops before reversing. The classic "trap and reverse" pattern that catches breakout traders off guard. Strategy 2 - Trendline Breakouts Captures momentum when price breaks dynamic support/resistance with volume confirmation. Clean breaks that signal continuation moves. Both strategies feature automatic signal explanations, quality scoring, and comprehensive market analysis. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ Quick Start Guide 1. Add to chart → Select your Market Type (Stocks/ETFs/Futures/Forex/Crypto) 2. Choose your instrument from the preset dropdown: • Stocks: AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA • ETFs: SPY, QQQ • Futures: ES, NQ • Forex: EURUSD, GBPUSD, USDJPY, AUDUSD • Crypto: BTC, ETH, XRP, SOL 3. Verify default settings: • Active Strategy: "Both" (recommended) • Min Signal Quality: 85 (safe threshold) • Show Signal Explanation: ON 4. Monitor the panels: • Dashboard (top-right): Real-time market metrics • Rationale Panel (bottom-right): Signal explanations 5. Wait for signals: Green ▲ (buy) or Red ▼ (sell) 6. Read the explanation - Understand exactly WHY the signal triggered ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Visual Components Support/Resistance Zones • Pivot Zones: Gold (weak) → Burgundy (strong after multiple touches) • Fair Value Gaps: Purple rectangles marking price imbalances • Order Blocks: Bronze zones at institutional entry points • Yesterday High: Red zone at prior day's high • Yesterday Low: Teal zone at prior day's low • Premarket H/L: Purple zones at pre-market levels (Stocks/ETFs only) Trading Signals • Buy Signal: Green triangle ▲ below bar • Sell Signal: Red triangle ▼ above bar • Conflict: Orange X when both directions trigger (no trade taken) • Quality Score: 0-100% displayed in dashboard Risk Management • Stop Loss: Red dotted line (ATR-based, dynamic) • Take Profit: Teal dotted line (risk-reward optimized) • VWAP: Blue line with optional standard deviation bands Trendlines • Support: Green dashed line (bullish bias) • Resistance: Red dashed line (bearish bias) • Auto-adjusting slope based on recent price action ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Signal Explanation Panel - The VLM Difference Every signal comes with clear, plain English explanations: Example Buy Signal: 🟢 BUY SIGNAL 📍 LIQUIDITY SWEEP Price swept below the 20-bar low, trapping sellers, then reversed. 📊 VOLUME: 1.8x average ━━━━━━━━━━━━━━━━━━━━ 📊 STRENGTH: STRONG (91%) 💰 Entry: $595.42 ⏱️ 3 bars ago No jargon. No confusion. Just clear reasoning anyone can understand. Perfect for: • Learning WHY professional setups work • Building pattern recognition skills • Gaining confidence through comprehension • Making better discretionary decisions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔧 Market-Specific Presets (22 Instruments) Each instrument has individually calibrated parameters for optimal performance: Magnificent 7 Stocks AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA • Tuned for each stock's unique volatility profile • VIX-based regime adaptation Major ETFs SPY, QQQ • Tighter parameters for index products • Institutional-level precision Futures Contracts ES (S&P 500), NQ (Nasdaq) • Optimized for futures volatility • Extended session support Forex Majors EURUSD, GBPUSD, USDJPY, AUDUSD • 24-hour market adaptations • Lower volatility thresholds Cryptocurrencies BTCUSD, ETHUSD, XRPUSD, SOLUSD • High volatility parameters • Extended target multipliers ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏰ Futures & Forex Session Handling Futures Sessions • RTH Only: Regular Trading Hours (9:30 AM - 4:00 PM ET) - Most liquid, cleanest signals • ETH Only: Extended/Overnight session (6:00 PM - 9:30 AM ET) - Lower volume, wider spreads • Full Session: 24-hour trading - All sessions combined Forex Sessions • All Sessions: 24-hour trading (default) • Asian (Tokyo): 7:00 PM - 4:00 AM ET - Lower volatility, range-bound • London: 3:00 AM - 12:00 PM ET - High volatility, trend starts • New York: 8:00 AM - 5:00 PM ET - Highest USD volume • London/NY Overlap: 8:00 AM - 12:00 PM ET - Peak liquidity, best setups Forex Volume Note ⚠️ Forex uses "tick volume" (number of price changes) rather than actual traded volume. This varies significantly between brokers and is less reliable than stock/futures volume. By default, VLM disables volume-based filtering for Forex. Enable "Use Volume for Forex" only if your broker provides reliable tick volume data. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 Strategy 1: Liquidity Sweep Detection Core Concept: Institutions need liquidity to fill large orders. They often push price through obvious levels (where retail stops cluster) to trigger those stops, then reverse. What VLM Detects: 1. Price sweeps beyond 20-bar high/low 2. Rejection wick forms (trap confirmation) 3. Volume anomaly present (institutional participation) 4. Price recovers back through the level 5. ADX confirms trend environment Zone Sweep Variant: Same concept, but at key S/R zones: • Pivot levels, Order Blocks, FVGs • Yesterday/Premarket H/L • Stronger signals when zones are "proven" (multiple touches) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📉 Strategy 2: Trendline Breakouts Core Concept: Clean breaks of established trendlines with volume confirmation signal momentum continuation. What VLM Detects: 1. Price breaks above/below dynamic trendline 2. Break exceeds threshold (0.15% default) 3. Volume spike OR consolidation breakout 4. ADX shows directional movement 5. Quality score validates setup Trendline Management: • Auto-drawn from pivot highs/lows • Dynamic slope adjustment • Extended projection for anticipation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🧠 Market Regime Detection VLM automatically adapts to market conditions: For Stocks/ETFs - VIX-Based: • VIX < 15: LOW VOL (tighter parameters) • VIX 15-20: NORMAL (balanced) • VIX 20-30: HIGH VOL (wider parameters) • VIX > 30: EXTREME (maximum caution) For Other Markets - ATR-Based: • Automatically calculates volatility percentile • Adjusts zone widths, volume thresholds, targets • No manual tuning required The dashboard shows current regime in real-time with color coding. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Dashboard Guide Header Section • VLM: Indicator identifier • Market/Instrument: Current configuration (e.g., "ETFs / SPY") Market Analysis • Strategy: Active mode (Liquidity/Trendline/Both/Off) • Session: Current session status - Stocks/ETFs: OPEN/CLOSED - Futures: RTH/ETH/24H - Forex: ASIA/LDN/NY/LDN-NY/24H • ADX: Trend strength with direction arrow (↑/↓) • Trend: BULLISH / BEARISH / NEUTRAL • VIX/ATR%: Volatility gauge • Regime: LOW VOL / NORMAL / HIGH VOL / EXTREME Zone Status • Volume: Current vs average (e.g., "1.5x") - Shows "N/A" for Forex when volume disabled • In Zone: YES/NO • Zone Type: Pivot Hi/Lo, FVG, OB, Yest H/L, PM H/L • Zones: Count of active zones Position & Quality • Position: LONG / SHORT / FLAT • Quality: Last signal score (0-100) • Mode: Conservative / Normal / Aggressive • Conflict: Warning if both signals triggered ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ Key Settings Configuration Strategy Configuration • Active Strategy: Both (use all detection methods) • Show Dashboard: ON (monitor market state) • Clean Zone Mode: ON (max 3 zones for clarity) • Show Signal Explanation: ON (learn from every trade) Signal Adaptation • Market Adaptation: ON (auto-adjust to volatility) • VIX Adaptation: ON for stocks/ETFs • Signal Sensitivity: Normal (Conservative = fewer signals, Aggressive = more) • Min Signal Quality: 85 (adjustable 50-100) ADX Filter • Use ADX Filter: ON (avoid choppy markets) • Minimum ADX: 20 (trend threshold) • ADX Boosts Quality: ON (reward strong trends) Zone Detection • Pivot Zones: ON • Fair Value Gaps: ON • Order Blocks: ON • Yesterday H/L: ON • Premarket H/L: ON (Stocks/ETFs only) Exit Management • Take Profit: 1.75 ATR (scales with instrument) • Stop Loss: 1.25 ATR (scales with instrument) • Volume Drop Exits: Auto-exit on momentum fade ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🛡️ Conflict Detection System When both BUY and SELL signals trigger simultaneously: • Orange X marker appears on chart • Dashboard shows "Conflict: YES ⚠️" • Rationale panel explains: "Both BUY and SELL triggered. No trade taken." • NO position is opened This prevents entering trades during genuine market indecision, protecting against whipsaws and false breakouts. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔔 Alert Configuration Four alert conditions available: • VLM Buy Signal: Triggers on valid buy setup • VLM Sell Signal: Triggers on valid sell setup • VLM Conflict: Triggers when both directions fire • VLM Any Signal: Triggers on any signal event All alerts include signal quality for filtering. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✅ Professional Trading Checklist Pre-Trade Validation: ☐ Market Type selected (not "None") ☐ Correct instrument preset chosen ☐ Market hours showing "OPEN" ☐ ADX above minimum (green/orange, not red) ☐ Signal quality meets your threshold (85+ recommended) ☐ Read and understand signal explanation ☐ No conflict warning active Trade Management: ☐ Honor plotted SL/TP levels ☐ Monitor volume ratio for momentum fade ☐ Watch for regime shifts in dashboard ☐ Note any new signals in opposite direction Post-Trade Review: ☐ Screenshot setup with explanation panel ☐ Log signal quality vs actual outcome ☐ Review weekly for pattern recognition ☐ Adjust minimum quality based on results ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📝 Best Practices • Best Timeframes: 5m for scalping, 15m-1H for day trading • Market Hours: Regular session recommended for stocks/ETFs • Combines Well With: Volume profile, market internals, order flow • Avoid: Major news releases, low liquidity periods, first 5 minutes of open • Learning Mode: Read every signal explanation to internalize the patterns ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 What Makes VLM Different Unlike black-box indicators that just show arrows: ✓ Transparency: Every signal explained in plain English ✓ Education: Learn WHY setups work, not just when ✓ Adaptation: Auto-adjusts to market conditions ✓ Quality Scoring: Filter signals by probability ✓ Conflict Protection: Prevents trades during indecision ✓ Multi-Asset: 22 pre-tuned instruments across 5 markets ✓ Professional Grade: Institutional concepts made accessible VLM doesn't just tell you WHAT to do - it teaches you WHY. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Risk Disclaimer: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss. Always use proper position sizing and never risk more than you can afford to lose. The signal explanations are for educational purposes and should not be construed as financial advice. Developed by: ayushpanda_25 | Version: 1.0 | Status: Professional Release
Pine Script® 인디케이터
ayushpanda_25의
X VFI (LB) w absorptiona variation of the On-Balance Volume (OBV) introduced by Markos Katsanos and further refined by LazyBear, is a robust volume-based momentum oscillator designed to measure the strength and direction of money flow. It utilizes advanced filtering mechanisms to enhance signal quality for active trading environments. This version has added an absorption feature. Core Functionality and Enhancements Filtered Volume Flow: The VFI is calculated using the Typical Price (HLC/3) and incorporates filters for Volatility (coef) and Excessive Volume (vcoef). This ensures the indicator responds only to price changes supported by sustained, relevant volume, filtering out market noise and anomalous spikes. Zero-Line Bias: VFI values above zero indicate net accumulation (bullish flow), while values below zero indicate net distribution (bearish flow). Signal Line Timing (vfima): The Exponential Moving Average (EMA) of the VFI acts as the Signal Line. Crossovers between the VFI (fast line) and the Signal Line are primary triggers for trade entries and exits. Absorption/Distribution Signals This customized version introduces unique features to visually isolate periods where underlying volume conviction contradicts immediate price action—the most powerful setups for reversals and strong continuations. Absorption/Distribution Highlighting: The histogram's color is dynamically changed to highlight hidden buying or selling pressure: (Absorption Signal): Indicates strong positive VFI momentum occurring on a bearish (down) candle. This signals aggressive buying absorption of supply, where large traders are accumulating positions despite brief selling pressure, often preceding a sharp upward move. (Distribution Signal): Indicates strong negative VFI momentum occurring on a bullish (up) candle. This signals aggressive selling distribution into demand, where large traders are offloading positions into brief rallies, often preceding a sharp downward move. Volume-Filtered Conviction: The visual intensity (transparency) of the signal color is adjusted based on a Volume Filter (minVolFilter). Darker, solid colors denote high-conviction signals supported by above-average volume, while transparent colors indicate lower-conviction signals. Histogram Magnification: The magnification input allows users to visually increase the height of the histogram bars (e.g., 2x). This enhances the immediate visual recognition of momentum acceleration or deceleration.
Pine Script® 인디케이터
KinetiCapital의
Super OrderBlock With FVG EQH/EQLThis indicator is designed for traders who apply Smart Money Concepts (SMC). It highlights critical market structures including Order Blocks (OB), Fair Value Gaps (FVG), and Equal Highs/Lows (EQH/EQL) to support liquidity‑based and institutional trading strategies. 🔹 Order Blocks Detects bullish and bearish order blocks. Differentiates between: Key OBs at swing highs/lows. Internal OBs within mid‑swing structures. Customizable box colors, border styles, transparency, and label text. Limits maximum number of boxes displayed to maintain chart clarity. 🔹 Fair Value Gaps Identifies both Breaking FVGs (interacting with structure) and Internal FVGs (pure 3‑candle gaps). Plots colored boxes with optional labels. Configurable border style, transparency, and maximum number of boxes. 🔹 Equal Highs & Equal Lows (EQH/EQL) Detects liquidity levels where price forms equal highs or equal lows. Plots dotted lines connecting the two pivots that form the level. Labels are displayed at the midpoint of the line: EQH below the line (red). EQL above the line (blue). Supports two modes: Historical: keeps all detected levels. Present: shows only the most recent EQH/EQL. ⚙️ Features Independent input groups for OB, FVG, and EQH/EQL. Customizable colors, styles, and label sizes. Alerts available for EQH/EQL detection. Optimized to avoid clutter by limiting active drawings. ✅ How to Use Use Order Blocks to identify institutional demand/supply zones. Use Fair Value Gaps to spot imbalances that may act as magnets for price. Use EQH/EQL to highlight liquidity pools often targeted by price. Combine these tools for a complete Smart Money Concepts workflow.
Pine Script® 인디케이터
TCMaster의
Kaufman Trend Navigator [QuantAlgo]🟢 Overview The Kaufman Trend Navigator is an adaptive trend following system that combines efficiency-weighted price smoothing with volatility-adjusted bands to identify and track directional market movements. The indicator dynamically adjusts its sensitivity based on market conditions, becoming more responsive during trending periods and more conservative during consolidation. This dual-layer approach provides traders and investors with a systematic framework for trend identification, entry timing, and risk management across multiple timeframes and asset classes. 🟢 How It Works The indicator employs an efficiency ratio mechanism that measures the directional movement of price relative to total price volatility over a defined lookback period. This ratio determines the adaptive response rate, allowing the system to distinguish between genuine directional moves and random market noise. When price exhibits strong directional characteristics, the internal smoothing accelerates to track the trend more closely. Conversely, during periods of low efficiency or choppy price action, the smoothing becomes more conservative to filter out false signals. Volatility bands are constructed using normalized range measurements, creating dynamic upper and lower boundaries around the adaptive trend calculation. These bands expand and contract based on recent market volatility, providing context-dependent thresholds for trend validation. The trend line itself updates through a band-following logic where it tracks the relevant boundary based on the current directional bias, creating a stepping mechanism that maintains trend persistence while allowing for validated reversals. The visual representation uses a gradient-weighted display to emphasize the primary trend line while maintaining clarity on price charts. Trend direction changes trigger when the internal logic confirms a boundary crossover, generating signals for potential position entries or exits. The system includes preset configurations calibrated for different trading timeframes, from responsive settings for scalping to smoother parameters suited for swing and position trading. 🟢 How to Use It ▶ Enter Long positions when the trend line transitions to Bullish (Green) coloring, which indicates upward directional bias has been established. Conversely, enter Short positions or exit Longs when the trend line shifts to Bearish (Red), which signals confirmed downward momentum. The trend line itself can be used as dynamic support during uptrends and resistance during downtrends, providing logical areas for position management and stop placement. Price remaining above the line during bullish phases or below during bearish phases can also be used as a confirmation of trend strength and continuation probability. ▶ Built-in alert functionality provides real-time notifications for trend changes without requiring continuous chart monitoring. Configure alerts for Bullish Trend Signal to capture upward reversals, Bearish Trend Signal for downward shifts, or the general Trend Change alert to monitor both directions simultaneously. These alerts trigger only on confirmed trend transitions, reducing noise from intrabar fluctuations. The indicator also includes six color presets (Classic, Aqua, Cosmic, Ember, Neon, Custom) to optimize visual clarity across different chart themes and lighting conditions. Select presets based on your monitor setup and background preference to ensure immediate trend recognition without visual strain. Bar coloring can be enabled to highlight trend direction directly on the price chart, eliminating the need to reference the trend line position during rapid market analysis. 🟢 Pro Tips for Trading and Investing ▶ Match the preset configuration (or your preferred settings) to your trading timeframe: use Fast Response for intraday charts (1-15 minutes), Default for swing trading (hourly to daily), and Smooth Trend for position trading (4-hour to weekly). ▶ Combine trend signals with volume analysis and market structure to filter lower-probability setups. During sideways markets, expect increased signal frequency with reduced reliability; consider waiting for the trend line to establish a clear slope before committing capital. ▶ Use the trend line as a trailing reference rather than a fixed stop level, allowing normal intrabar volatility while protecting against genuine reversals. ▶ For portfolio management, align position sizing with trend strength by observing the angle and consistency of the trend line progression.
Pine Script® 인디케이터
QuantAlgo의
3
EMA Crossover Signals with filtersCore Strategy: BUY Signal: When EMA 10 crosses above EMA 100 SELL Signal: When EMA 10 crosses below EMA 21 All signals displayed directly on the chart with labels Built-in Filters (All Optional - Can Toggle On/Off): 1. Time Filter ⏰ Trade only during specific hours (e.g., 9:30 AM - 4:00 PM) Prevents signals outside your preferred trading session Format: HHMM-HHMM 2. Trend Filter (ADX) 📈 Blocks signals in choppy/sideways markets Only allows trades when market is trending ADX Threshold: Set minimum trend strength (default: 25) Higher threshold = stronger trend required = fewer but better signals 3. Candle Pattern Confirmation 🕯️ Requires bullish candle for BUY signals Requires bearish candle for SELL signals Three strength levels: Any: Just needs green/red candle Moderate: Candle body ≥ 40% of range Strong: Candle body ≥ 60% of range (filters weak/indecision candles)
Pine Script® 인디케이터
gabhish4의
Pro Order Flow – NQ 5m/15mThis is a professional-grade order flow tool designed for scalpers and intraday futures traders (especially NQ 5m/15m, ES, SPY, BTC, and gold). Right-click indicator → Move to new pane below (recommended, so price is clean) It combines five high-probability institutional signals into one clean, fast indicator: What This Indicator Shows 1. Candle Delta Histogram (Buyer vs Seller Pressure) Each bar shows whether aggressive buyers (market orders lifting ask) or aggressive sellers (hitting bid) controlled that candle. Green = buying pressure Red = selling pressure 2.Session Cumulative Delta (True Direction) Tracks buyer/seller domination for the entire session. Rising cumDelta = buyers absorbing sellers Falling cumDelta = sellers absorbing buyers If price goes up but cumulative delta goes down → distribution (short signal) If price goes down but cumulative delta goes up → accumulation (long signal) This is one of the strongest institutional signals. 3 Big Delta Bars (Unusual Aggression) Highlights candles where delta is 2.2× larger than average volume. These mark: Institutional absorption Breakout pressure Stop-run attacks Failed breakout reversals Green = big buying aggression Red = big selling aggression 4 Smart-Money Wick Absorption (Absorb↑ / Absorb↓) Tracks wick length vs body size + delta. Used to detect: Stop hunts Liquidity grabs Reversals off trapped traders Absorb↓ (triangle up) = buyers absorbed sell-side liquidity (bullish) Absorb↑ (triangle down) = sellers absorbed buy-side liquidity (bearish) This is a high-confidence signal for NQ. 5 Real Delta Divergences (Δ Bull / Δ Bear) Not RSI divergences — order flow divergences: 🔻 Bearish Delta Divergence (Δ Bear) Price makes higher high Cumulative delta makes lower high → buyers weakening High-probability short 🔺 Bullish Delta Divergence (Δ Bull) Price makes lower low Cumulative delta makes higher low → sellers weakening High-probability long These are professional reversal points. How to Use (Trading Strategy) Recommended for: NQ 5m entries + 15m bias, ES, SPY, BTC, gold. 🟩 Long Setup (Buy) On 15m, session cumulative delta sloping UP Price in an uptrend (higher highs/lows) On 5m, look for ANY of these: Δ Bull divergence Absorb↓ tail after a stop-hunt wick Big positive delta bar at support Delta flips from red → green at VWAP Entry: Enter on close of the signal candle Stop: Below swing low or wick Targets: Next liquidity high, or 2R–3R 🟥 Short Setup (Sell) On 15m, session cumulative delta sloping DOWN Price in a downtrend On 5m, look for: Δ Bear divergence Absorb↑ tail above a high Big negative delta bar Delta flips from green → red at resistance Entry: Enter on close Stop: Above wick or structure Targets: Prior low, or 2R–3R Best Timeframes 15m = trend/bias 5m = signal + entry Works on: NQ, ES, SPY, QQQ, BTC, Gold, Oil Settings (Recommended) Avg Volume Length = 100 (best for NQ volatility) Big Delta Sensitivity = 2.2× Pivots = 3 left / 3 right (good for intraday swings) Included Alerts Bullish Delta Divergence Bearish Delta Divergence Big Positive Delta (aggressive buying) Big Negative Delta (aggressive selling) Perfect for scalpers who want real-time signals.
Pine Script® 인디케이터
helloaxr의
ORB + Fair Value Gaps (FVG/iFVG) Suite with Daily 50% MidlineA complete smart-money–focused price-action toolkit combining the New York Open Range Breakout (ORB), ICT-style Fair Value Gaps, Inverted FVGs, and a dynamic Daily 50% Midline. Designed for traders who want a clean, fast, and highly visual way to track liquidity, imbalances, and intraday directional bias. 📌 Key Features 1. NY Session ORB (09:30–09:45 New York Time) Automatically plots: ORB High ORB Low Labels for ORB high/low Optional 5-minute chart restriction Lines extend forward for easy reference Used to identify breakout conditions, liquidity sweeps, and directional bias into the morning session. 📌 2. ICT-Style Fair Value Gaps (FVGs) Full automated detection of bullish & bearish FVGs based on the classic 3-candle displacement structure: Bullish FVG: high < low Bearish FVG: low > high Each FVG is drawn as a box with: Custom colour Custom border style (solid, dashed, dotted) Automatic extension to the right until filled Optional size text showing the gap in points (font size/colour adjustable) Adjustable max lookback for performance 📌 3. Inverted FVGs (iFVGs) Once price fully fills an FVG, it automatically becomes an iFVG, shown with: Custom iFVG colour Custom border style Extension to the right Once price trades through the zone from the opposite side, the iFVG is considered “consumed” and: It stops extending And optionally auto-deletes based on user settings This makes it easy to track meaningful imbalances that turn into liquidity pockets. 📌 4. “Show Only After ORB” Filter Optionally hide all FVGs/iFVGs formed before the ORB completes. This is especially useful for intraday strategies focused on NY session structure only. 📌 5. Daily 50% Midline (OHLC Midpoint) A dynamic, always-updating midpoint of the current daily candle: Mid = (Daily High + Daily Low) / 2 Features: Custom colour Dashed styling Extends left and right as a horizontal ray Updates live as the daily candle forms Great for bias filters, mean reversion, and daily liquidity zones. 📌 6. Performance-Optimized (Fast!) Built with: Fully configurable max lookback Memory-efficient arrays Auto-cleaning of old FVG/iFVG objects Lightweight daily midline recalculation This allows extremely fast rendering even on 1-minute charts. 📌 7. Alerts Includes a clean alert condition: Price returned to a Fair Value Gap Works for both bullish and bearish FVG revisits. 🎯 Who This Indicator Is For This tool is ideal for traders who use: ICT / SMC concepts Liquidity-based trading ORB strategies Imbalance-driven price action Intraday or NY session-focused setups Futures, crypto, forex, and equities 🎁 Summary This indicator gives you: A clean ORB framework Automatic, dynamic FVG and iFVG analysis Real-time daily candle context Customizable visuals Powerful session filtering Efficient performance All in one clean, intuitive package built for real-time decision making.
Pine Script® 인디케이터
jiggggggerr의
New Day Opening Gaps (1m 3:29-9:15)Precise Day Opening Gap IndicatorThis custom Pine Script indicator is designed for traders who rely on high-precision gap analysis, particularly for markets like the NSE/BSE.🎯 Core Functionality: Precision Gap IdentificationThis indicator calculates and highlights the exact price gap between the previous day's close and the current day's open. Unlike standard gap analysis that relies on higher timeframes, this script ensures accuracy by strictly using 1-minute data:Previous Close: Captured from the 1-minute candle closing at 3:29 PM (15:29).Current Open: Captured from the 1-minute candle opening at 9:15 AM (09:15).The resulting gap zone is plotted and automatically extends to the right, serving as a critical level for current price interaction.✨ Key Features1. Cross-Timeframe PersistenceThe gap markings are calculated based on the 1-minute chart but are displayed correctly and persist across all timeframes, including 5-minute, 15-minute, 1-hour, and even the Daily chart, ensuring consistency no matter how you analyze the price action.2. Controlled Historical ViewAvoid chart clutter with the "Number of Gaps to Show" input. Easily control how many historical day-opening gaps you want to display on your chart, allowing you to focus only on the most recent and relevant levels.3. Full CustomizationCustomize the look and feel to fit your charting style:Gap Zone: Adjust the color and opacity (transparency) of the gap box.Date Label: Toggle the date label display on/off and control its color, background, opacity, and size. The label is optimally placed at the top-right of the gap zone for clear visibility.🛠️ Recommended UseThis tool is perfect for intraday traders looking to:Identify immediate support and resistance zones based on overnight price action.Track where price action reacts to prior day gaps (filling or holding the gap).Maintain a clear visual reference of daily market openings.
Pine Script® 인디케이터
Aviiinash의
Sessions Indicator(clean))🔰 True Day Open + Asian / London / NY Session Levels Intraday Market Structure & Session Liquidity Indicator This indicator automatically plots the True Day Opening Price, Asian Session High & Low, London Session High & Low, and New York Session Open — helping you understand daily directional bias, session liquidity zones, and potential reversal levels. 📌 What It Does ✔ Plots True Day Open (not broker-based — captures real market direction) ✔ Marks Asia Session High & Low – accumulation zone ✔ Marks London Session High & Low – expansion zone ✔ Shows New York Session Open – reversal / continuation zone ✔ Works on any chart: Forex, Indices, Crypto, Commodities ✔ Ideal for ICT / SMC / Day Trading / Scalping ✔ Fully customizable colors & display options 🧠 Why It’s Useful The market often moves in this sequence: ➡ Asia → consolidation ➡ London → expansion / breakout ➡ New York → reversal OR continuation This indicator helps you see this structure clearly, allowing traders to: Avoid fake breakouts Understand the daily bias earlier Trade liquidity grabs & session-to-session transitions Time entries with precision 📌 Best Timeframes M5 / M15 / M30 → Scalping & intraday setups H1 / H4 → Market structure view Daily → Session zone backtesting ⚙ Inputs & Customization Enable/Disable each session Change session time manually Color settings Line styles: solid / dashed / background zones 🧠 Suggested Trading Concepts Works great with: Smart Money Concepts (SMC) ICT Models Liquidity Sweep Strategy Session Breakout Trading Mean Reversion Trading 📈 How to Use Wait for Asian Range Trade break of Asian High/Low during London At NY Open, check for reversal or continuation Use True Day Open for directional bias Look for liquidity sweeps near session levels 📢 Coming Soon (Next Update) 🔔 Session Break Alerts 📊 Liquidity Sweep Labels
Pine Script® 인디케이터
moramsanjaykumar의
Dynamic Equity Allocation Model//@version=6 indicator('Dynamic Equity Allocation Model', shorttitle = 'DEAM', overlay = false, precision = 1, scale = scale.right, max_bars_back = 500) // DYNAMIC EQUITY ALLOCATION MODEL // Quantitative framework for dynamic portfolio allocation between stocks and cash. // Analyzes five dimensions: market regime, risk metrics, valuation, sentiment, // and macro conditions to generate allocation recommendations (0-100% equity). // // Uses real-time data from TradingView including fundamentals (P/E, ROE, ERP), // volatility indicators (VIX), credit spreads, yield curves, and market structure. // INPUT PARAMETERS group1 = 'Model Configuration' model_type = input.string('Adaptive', 'Allocation Model Type', options = , group = group1, tooltip = 'Conservative: Slower to increase equity, Aggressive: Faster allocation changes, Adaptive: Dynamic based on regime') use_crisis_detection = input.bool(true, 'Enable Crisis Detection System', group = group1, tooltip = 'Automatic detection and response to crisis conditions') use_regime_model = input.bool(true, 'Use Market Regime Detection', group = group1, tooltip = 'Identify Bull/Bear/Crisis regimes for dynamic allocation') group2 = 'Portfolio Risk Management' target_portfolio_volatility = input.float(12.0, 'Target Portfolio Volatility (%)', minval = 3, maxval = 20, step = 0.5, group = group2, tooltip = 'Target portfolio volatility (Cash reduces volatility: 50% Equity = ~10% vol, 100% Equity = ~20% vol)') max_portfolio_drawdown = input.float(15.0, 'Maximum Portfolio Drawdown (%)', minval = 5, maxval = 35, step = 2.5, group = group2, tooltip = 'Maximum acceptable PORTFOLIO drawdown (not market drawdown - portfolio with cash has lower drawdown)') enable_portfolio_risk_scaling = input.bool(true, 'Enable Portfolio Risk Scaling', group = group2, tooltip = 'Scale allocation based on actual portfolio risk characteristics (recommended)') risk_lookback = input.int(252, 'Risk Calculation Period (Days)', minval = 60, maxval = 504, group = group2, tooltip = 'Period for calculating volatility and risk metrics') group3 = 'Component Weights (Total = 100%)' w_regime = input.float(35.0, 'Market Regime Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_risk = input.float(25.0, 'Risk Metrics Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_valuation = input.float(20.0, 'Valuation Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_sentiment = input.float(15.0, 'Sentiment Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_macro = input.float(5.0, 'Macro Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) group4 = 'Crisis Detection Thresholds' crisis_vix_threshold = input.float(40, 'Crisis VIX Level', minval = 30, maxval = 80, group = group4, tooltip = 'VIX level indicating crisis conditions (COVID peaked at 82)') crisis_drawdown_threshold = input.float(15, 'Crisis Drawdown Threshold (%)', minval = 10, maxval = 30, group = group4, tooltip = 'Market drawdown indicating crisis conditions') crisis_credit_spread = input.float(500, 'Crisis Credit Spread (bps)', minval = 300, maxval = 1000, group = group4, tooltip = 'High yield spread indicating crisis conditions') group5 = 'Display Settings' show_components = input.bool(false, 'Show Component Breakdown', group = group5, tooltip = 'Display individual component analysis lines') show_regime_background = input.bool(true, 'Show Dynamic Background', group = group5, tooltip = 'Color background based on allocation signals') show_reference_lines = input.bool(false, 'Show Reference Lines', group = group5, tooltip = 'Display allocation percentage reference lines') show_dashboard = input.bool(true, 'Show Analytics Dashboard', group = group5, tooltip = 'Display comprehensive analytics table') show_confidence_bands = input.bool(false, 'Show Confidence Bands', group = group5, tooltip = 'Display uncertainty quantification bands') smoothing_period = input.int(3, 'Smoothing Period', minval = 1, maxval = 10, group = group5, tooltip = 'Smoothing to reduce allocation noise') background_intensity = input.int(95, 'Background Intensity (%)', minval = 90, maxval = 99, group = group5, tooltip = 'Higher values = more transparent background') // Styling Options color_scheme = input.string('EdgeTools', 'Color Theme', options = , group = 'Appearance', tooltip = 'Professional color themes') use_dark_mode = input.bool(true, 'Optimize for Dark Theme', group = 'Appearance') main_line_width = input.int(3, 'Main Line Width', minval = 1, maxval = 5, group = 'Appearance') // DATA RETRIEVAL // Market Data sp500 = request.security('SPY', timeframe.period, close) sp500_high = request.security('SPY', timeframe.period, high) sp500_low = request.security('SPY', timeframe.period, low) sp500_volume = request.security('SPY', timeframe.period, volume) // Volatility Indicators vix = request.security('VIX', timeframe.period, close) vix9d = request.security('VIX9D', timeframe.period, close) vxn = request.security('VXN', timeframe.period, close) // Fixed Income and Credit us2y = request.security('US02Y', timeframe.period, close) us10y = request.security('US10Y', timeframe.period, close) us3m = request.security('US03MY', timeframe.period, close) hyg = request.security('HYG', timeframe.period, close) lqd = request.security('LQD', timeframe.period, close) tlt = request.security('TLT', timeframe.period, close) // Safe Haven Assets gold = request.security('GLD', timeframe.period, close) usd = request.security('DXY', timeframe.period, close) yen = request.security('JPYUSD', timeframe.period, close) // Financial data with fallback values get_financial_data(symbol, fin_id, period, fallback) => data = request.financial(symbol, fin_id, period, ignore_invalid_symbol = true) na(data) ? fallback : data // SPY fundamental metrics spy_earnings_per_share = get_financial_data('AMEX:SPY', 'EARNINGS_PER_SHARE_BASIC', 'TTM', 20.0) spy_operating_earnings_yield = get_financial_data('AMEX:SPY', 'OPERATING_EARNINGS_YIELD', 'FY', 4.5) spy_dividend_yield = get_financial_data('AMEX:SPY', 'DIVIDENDS_YIELD', 'FY', 1.8) spy_buyback_yield = get_financial_data('AMEX:SPY', 'BUYBACK_YIELD', 'FY', 2.0) spy_net_margin = get_financial_data('AMEX:SPY', 'NET_MARGIN', 'TTM', 12.0) spy_debt_to_equity = get_financial_data('AMEX:SPY', 'DEBT_TO_EQUITY', 'FY', 0.5) spy_return_on_equity = get_financial_data('AMEX:SPY', 'RETURN_ON_EQUITY', 'FY', 15.0) spy_free_cash_flow = get_financial_data('AMEX:SPY', 'FREE_CASH_FLOW', 'TTM', 100000000) spy_ebitda = get_financial_data('AMEX:SPY', 'EBITDA', 'TTM', 200000000) spy_pe_forward = get_financial_data('AMEX:SPY', 'PRICE_EARNINGS_FORWARD', 'FY', 18.0) spy_total_debt = get_financial_data('AMEX:SPY', 'TOTAL_DEBT', 'FY', 500000000) spy_total_equity = get_financial_data('AMEX:SPY', 'TOTAL_EQUITY', 'FY', 1000000000) spy_enterprise_value = get_financial_data('AMEX:SPY', 'ENTERPRISE_VALUE', 'FY', 30000000000) spy_revenue_growth = get_financial_data('AMEX:SPY', 'REVENUE_ONE_YEAR_GROWTH', 'TTM', 5.0) // Market Breadth Indicators nya = request.security('NYA', timeframe.period, close) rut = request.security('IWM', timeframe.period, close) // Sector Performance xlk = request.security('XLK', timeframe.period, close) xlu = request.security('XLU', timeframe.period, close) xlf = request.security('XLF', timeframe.period, close) // MARKET REGIME DETECTION // Calculate Market Trend sma_20 = ta.sma(sp500, 20) sma_50 = ta.sma(sp500, 50) sma_200 = ta.sma(sp500, 200) ema_10 = ta.ema(sp500, 10) // Market Structure Score trend_strength = 0.0 trend_strength := trend_strength + (sp500 > sma_20 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_50 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_200 ? 2 : -2) trend_strength := trend_strength + (sma_50 > sma_200 ? 2 : -2) // Volatility Regime returns = math.log(sp500 / sp500 ) realized_vol_20d = ta.stdev(returns, 20) * math.sqrt(252) * 100 realized_vol_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 ewma_vol = ta.ema(math.pow(returns, 2), 20) realized_vol = math.sqrt(ewma_vol * 252) * 100 vol_premium = vix - realized_vol // Drawdown Calculation running_max = ta.highest(sp500, risk_lookback) current_drawdown = (running_max - sp500) / running_max * 100 // Regime Score regime_score = 0.0 // Trend Component (40%) if trend_strength >= 4 regime_score := regime_score + 40 regime_score else if trend_strength >= 2 regime_score := regime_score + 30 regime_score else if trend_strength >= 0 regime_score := regime_score + 20 regime_score else if trend_strength >= -2 regime_score := regime_score + 10 regime_score else regime_score := regime_score + 0 regime_score // Volatility Component (30%) if vix < 15 regime_score := regime_score + 30 regime_score else if vix < 20 regime_score := regime_score + 25 regime_score else if vix < 25 regime_score := regime_score + 15 regime_score else if vix < 35 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Drawdown Component (30%) if current_drawdown < 3 regime_score := regime_score + 30 regime_score else if current_drawdown < 7 regime_score := regime_score + 20 regime_score else if current_drawdown < 12 regime_score := regime_score + 10 regime_score else if current_drawdown < 20 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Classify Regime market_regime = regime_score >= 80 ? 'Strong Bull' : regime_score >= 60 ? 'Bull Market' : regime_score >= 40 ? 'Neutral' : regime_score >= 20 ? 'Correction' : regime_score >= 10 ? 'Bear Market' : 'Crisis' // RISK-BASED ALLOCATION // Calculate Market Risk parkinson_hl = math.log(sp500_high / sp500_low) parkinson_vol = parkinson_hl / (2 * math.sqrt(math.log(2))) * math.sqrt(252) * 100 garman_klass_vol = math.sqrt((0.5 * math.pow(math.log(sp500_high / sp500_low), 2) - (2 * math.log(2) - 1) * math.pow(math.log(sp500 / sp500 ), 2)) * 252) * 100 market_volatility_20d = math.max(ta.stdev(returns, 20) * math.sqrt(252) * 100, parkinson_vol) market_volatility_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 market_drawdown = current_drawdown // Initialize risk allocation risk_allocation = 50.0 if enable_portfolio_risk_scaling // Volatility-based allocation vol_based_allocation = target_portfolio_volatility / math.max(market_volatility_20d, 5.0) * 100 vol_based_allocation := math.max(0, math.min(100, vol_based_allocation)) // Drawdown-based allocation dd_based_allocation = 100.0 if market_drawdown > 1.0 dd_based_allocation := max_portfolio_drawdown / market_drawdown * 100 dd_based_allocation := math.max(0, math.min(100, dd_based_allocation)) dd_based_allocation // Combine (conservative) risk_allocation := math.min(vol_based_allocation, dd_based_allocation) // Dynamic adjustment current_equity_estimate = 50.0 estimated_portfolio_vol = current_equity_estimate / 100 * market_volatility_20d estimated_portfolio_dd = current_equity_estimate / 100 * market_drawdown vol_utilization = estimated_portfolio_vol / target_portfolio_volatility dd_utilization = estimated_portfolio_dd / max_portfolio_drawdown risk_utilization = math.max(vol_utilization, dd_utilization) risk_adjustment_factor = 1.0 if risk_utilization > 1.0 risk_adjustment_factor := math.exp(-0.5 * (risk_utilization - 1.0)) risk_adjustment_factor := math.max(0.5, risk_adjustment_factor) risk_adjustment_factor else if risk_utilization < 0.9 risk_adjustment_factor := 1.0 + 0.2 * math.log(1.0 / risk_utilization) risk_adjustment_factor := math.min(1.3, risk_adjustment_factor) risk_adjustment_factor risk_allocation := risk_allocation * risk_adjustment_factor risk_allocation else vol_scalar = target_portfolio_volatility / math.max(market_volatility_20d, 10) vol_scalar := math.min(1.5, math.max(0.2, vol_scalar)) drawdown_penalty = 0.0 if current_drawdown > max_portfolio_drawdown drawdown_penalty := (current_drawdown - max_portfolio_drawdown) / max_portfolio_drawdown drawdown_penalty := math.min(1.0, drawdown_penalty) drawdown_penalty risk_allocation := 100 * vol_scalar * (1 - drawdown_penalty) risk_allocation risk_allocation := math.max(0, math.min(100, risk_allocation)) // VALUATION ANALYSIS // Valuation Metrics actual_pe_ratio = spy_earnings_per_share > 0 ? sp500 / spy_earnings_per_share : spy_pe_forward actual_earnings_yield = nz(spy_operating_earnings_yield, 0) > 0 ? spy_operating_earnings_yield : 100 / actual_pe_ratio total_shareholder_yield = spy_dividend_yield + spy_buyback_yield // Equity Risk Premium (multi-method calculation) method1_erp = actual_earnings_yield - us10y method2_erp = actual_earnings_yield + spy_buyback_yield - us10y payout_ratio = spy_dividend_yield > 0 and actual_earnings_yield > 0 ? spy_dividend_yield / actual_earnings_yield : 0.4 sustainable_growth = spy_return_on_equity * (1 - payout_ratio) / 100 method3_erp = spy_dividend_yield + sustainable_growth * 100 - us10y implied_growth = spy_revenue_growth * 0.7 method4_erp = total_shareholder_yield + implied_growth - us10y equity_risk_premium = method1_erp * 0.35 + method2_erp * 0.30 + method3_erp * 0.20 + method4_erp * 0.15 ev_ebitda_ratio = spy_enterprise_value > 0 and spy_ebitda > 0 ? spy_enterprise_value / spy_ebitda : 15.0 debt_equity_health = spy_debt_to_equity < 1.0 ? 1.2 : spy_debt_to_equity < 2.0 ? 1.0 : 0.8 // Valuation Score base_valuation_score = 50.0 if equity_risk_premium > 4 base_valuation_score := 95 base_valuation_score else if equity_risk_premium > 3 base_valuation_score := 85 base_valuation_score else if equity_risk_premium > 2 base_valuation_score := 70 base_valuation_score else if equity_risk_premium > 1 base_valuation_score := 55 base_valuation_score else if equity_risk_premium > 0 base_valuation_score := 40 base_valuation_score else if equity_risk_premium > -1 base_valuation_score := 25 base_valuation_score else base_valuation_score := 10 base_valuation_score growth_adjustment = spy_revenue_growth > 10 ? 10 : spy_revenue_growth > 5 ? 5 : 0 margin_adjustment = spy_net_margin > 15 ? 5 : spy_net_margin < 8 ? -5 : 0 roe_adjustment = spy_return_on_equity > 20 ? 5 : spy_return_on_equity < 10 ? -5 : 0 valuation_score = base_valuation_score + growth_adjustment + margin_adjustment + roe_adjustment valuation_score := math.max(0, math.min(100, valuation_score * debt_equity_health)) // SENTIMENT ANALYSIS // VIX Term Structure vix_term_structure = vix9d > 0 ? vix / vix9d : 1 backwardation = vix_term_structure > 1.05 steep_backwardation = vix_term_structure > 1.15 // Safe Haven Flows gold_momentum = ta.roc(gold, 20) dollar_momentum = ta.roc(usd, 20) yen_momentum = ta.roc(yen, 20) treasury_momentum = ta.roc(tlt, 20) safe_haven_flow = gold_momentum * 0.3 + treasury_momentum * 0.3 + dollar_momentum * 0.25 + yen_momentum * 0.15 // Advanced Sentiment Analysis vix_percentile = ta.percentrank(vix, 252) vix_zscore = (vix - ta.sma(vix, 252)) / ta.stdev(vix, 252) vix_momentum = ta.roc(vix, 5) vvix_proxy = ta.stdev(vix_momentum, 20) * math.sqrt(252) risk_reversal_proxy = (vix - realized_vol) / realized_vol // Sentiment Score base_sentiment = 50.0 vix_adjustment = 0.0 if vix_zscore < -1.5 vix_adjustment := 40 vix_adjustment else if vix_zscore < -0.5 vix_adjustment := 20 vix_adjustment else if vix_zscore < 0.5 vix_adjustment := 0 vix_adjustment else if vix_zscore < 1.5 vix_adjustment := -20 vix_adjustment else vix_adjustment := -40 vix_adjustment term_structure_adjustment = backwardation ? -15 : steep_backwardation ? -30 : 5 vvix_adjustment = vvix_proxy > 2.0 ? -10 : vvix_proxy < 1.0 ? 10 : 0 sentiment_score = base_sentiment + vix_adjustment + term_structure_adjustment + vvix_adjustment sentiment_score := math.max(0, math.min(100, sentiment_score)) // MACRO ANALYSIS // Yield Curve yield_spread_2_10 = us10y - us2y yield_spread_3m_10 = us10y - us3m // Credit Conditions hyg_return = ta.roc(hyg, 20) lqd_return = ta.roc(lqd, 20) tlt_return = ta.roc(tlt, 20) hyg_duration = 4.0 lqd_duration = 8.0 tlt_duration = 17.0 hyg_log_returns = math.log(hyg / hyg ) lqd_log_returns = math.log(lqd / lqd ) hyg_volatility = ta.stdev(hyg_log_returns, 20) * math.sqrt(252) lqd_volatility = ta.stdev(lqd_log_returns, 20) * math.sqrt(252) hyg_yield_proxy = -math.log(hyg / hyg ) * 100 lqd_yield_proxy = -math.log(lqd / lqd ) * 100 tlt_yield = us10y hyg_spread = (hyg_yield_proxy - tlt_yield) * 100 lqd_spread = (lqd_yield_proxy - tlt_yield) * 100 hyg_distance = (hyg - ta.lowest(hyg, 252)) / (ta.highest(hyg, 252) - ta.lowest(hyg, 252)) lqd_distance = (lqd - ta.lowest(lqd, 252)) / (ta.highest(lqd, 252) - ta.lowest(lqd, 252)) default_risk_proxy = 2.0 - (hyg_distance + lqd_distance) credit_spread = hyg_spread * 0.5 + (hyg_volatility - lqd_volatility) * 1000 * 0.3 + default_risk_proxy * 200 * 0.2 credit_spread := math.max(50, credit_spread) credit_market_health = hyg_return > lqd_return ? 1 : -1 flight_to_quality = tlt_return > (hyg_return + lqd_return) / 2 // Macro Score macro_score = 50.0 yield_curve_score = 0 if yield_spread_2_10 > 1.5 and yield_spread_3m_10 > 2 yield_curve_score := 40 yield_curve_score else if yield_spread_2_10 > 0.5 and yield_spread_3m_10 > 1 yield_curve_score := 30 yield_curve_score else if yield_spread_2_10 > 0 and yield_spread_3m_10 > 0 yield_curve_score := 20 yield_curve_score else if yield_spread_2_10 < 0 or yield_spread_3m_10 < 0 yield_curve_score := 10 yield_curve_score else yield_curve_score := 5 yield_curve_score credit_conditions_score = 0 if credit_spread < 200 and not flight_to_quality credit_conditions_score := 30 credit_conditions_score else if credit_spread < 400 and credit_market_health > 0 credit_conditions_score := 20 credit_conditions_score else if credit_spread < 600 credit_conditions_score := 15 credit_conditions_score else if credit_spread < 1000 credit_conditions_score := 10 credit_conditions_score else credit_conditions_score := 0 credit_conditions_score financial_stability_score = 0 if spy_debt_to_equity < 0.5 and spy_return_on_equity > 15 financial_stability_score := 20 financial_stability_score else if spy_debt_to_equity < 1.0 and spy_return_on_equity > 10 financial_stability_score := 15 financial_stability_score else if spy_debt_to_equity < 1.5 financial_stability_score := 10 financial_stability_score else financial_stability_score := 5 financial_stability_score macro_score := yield_curve_score + credit_conditions_score + financial_stability_score macro_score := math.max(0, math.min(100, macro_score)) // CRISIS DETECTION crisis_indicators = 0 if vix > crisis_vix_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if vix > 60 crisis_indicators := crisis_indicators + 2 crisis_indicators if current_drawdown > crisis_drawdown_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if current_drawdown > 25 crisis_indicators := crisis_indicators + 1 crisis_indicators if credit_spread > crisis_credit_spread crisis_indicators := crisis_indicators + 1 crisis_indicators sp500_roc_5 = ta.roc(sp500, 5) tlt_roc_5 = ta.roc(tlt, 5) if sp500_roc_5 < -10 and tlt_roc_5 < -5 crisis_indicators := crisis_indicators + 2 crisis_indicators volume_spike = sp500_volume > ta.sma(sp500_volume, 20) * 2 sp500_roc_1 = ta.roc(sp500, 1) if volume_spike and sp500_roc_1 < -3 crisis_indicators := crisis_indicators + 1 crisis_indicators is_crisis = crisis_indicators >= 3 is_severe_crisis = crisis_indicators >= 5 // FINAL ALLOCATION CALCULATION // Convert regime to base allocation regime_allocation = market_regime == 'Strong Bull' ? 100 : market_regime == 'Bull Market' ? 80 : market_regime == 'Neutral' ? 60 : market_regime == 'Correction' ? 40 : market_regime == 'Bear Market' ? 20 : 0 // Normalize weights total_weight = w_regime + w_risk + w_valuation + w_sentiment + w_macro w_regime_norm = w_regime / total_weight w_risk_norm = w_risk / total_weight w_valuation_norm = w_valuation / total_weight w_sentiment_norm = w_sentiment / total_weight w_macro_norm = w_macro / total_weight // Calculate Weighted Allocation weighted_allocation = regime_allocation * w_regime_norm + risk_allocation * w_risk_norm + valuation_score * w_valuation_norm + sentiment_score * w_sentiment_norm + macro_score * w_macro_norm // Apply Crisis Override if use_crisis_detection if is_severe_crisis weighted_allocation := math.min(weighted_allocation, 10) weighted_allocation else if is_crisis weighted_allocation := math.min(weighted_allocation, 25) weighted_allocation // Model Type Adjustment model_adjustment = 0.0 if model_type == 'Conservative' model_adjustment := -10 model_adjustment else if model_type == 'Aggressive' model_adjustment := 10 model_adjustment else if model_type == 'Adaptive' recent_return = (sp500 - sp500 ) / sp500 * 100 if recent_return > 5 model_adjustment := 5 model_adjustment else if recent_return < -5 model_adjustment := -5 model_adjustment // Apply adjustment and bounds final_allocation = weighted_allocation + model_adjustment final_allocation := math.max(0, math.min(100, final_allocation)) // Smooth allocation smoothed_allocation = ta.sma(final_allocation, smoothing_period) // Calculate portfolio risk metrics (only for internal alerts) actual_portfolio_volatility = smoothed_allocation / 100 * market_volatility_20d actual_portfolio_drawdown = smoothed_allocation / 100 * current_drawdown // VISUALIZATION // Color definitions var color primary_color = #2196F3 var color bullish_color = #4CAF50 var color bearish_color = #FF5252 var color neutral_color = #808080 var color text_color = color.white var color bg_color = #000000 var color table_bg_color = #1E1E1E var color header_bg_color = #2D2D2D switch color_scheme // Apply color scheme 'Gold' => primary_color := use_dark_mode ? #FFD700 : #DAA520 bullish_color := use_dark_mode ? #FFA500 : #FF8C00 bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #C0C0C0 : #808080 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A00 : #FFFEF0 header_bg_color := use_dark_mode ? #2D2600 : #F5F5DC header_bg_color 'EdgeTools' => primary_color := use_dark_mode ? #4682B4 : #1E90FF bullish_color := use_dark_mode ? #4CAF50 : #388E3C bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #708090 : #696969 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0F1419 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A3A : #E6F3FF header_bg_color 'Behavioral' => primary_color := #808080 bullish_color := #00FF00 bearish_color := #8B0000 neutral_color := #FFBF00 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A1A : #F8F8F8 header_bg_color := use_dark_mode ? #2D2D2D : #E8E8E8 header_bg_color 'Quant' => primary_color := #808080 bullish_color := #FFA500 bearish_color := #8B0000 neutral_color := #4682B4 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0D0D0D : #FAFAFA header_bg_color := use_dark_mode ? #1A1A1A : #F0F0F0 header_bg_color 'Ocean' => primary_color := use_dark_mode ? #20B2AA : #008B8B bullish_color := use_dark_mode ? #00CED1 : #4682B4 bearish_color := use_dark_mode ? #FF4500 : #B22222 neutral_color := use_dark_mode ? #87CEEB : #2F4F4F text_color := use_dark_mode ? #F0F8FF : #191970 bg_color := use_dark_mode ? #001F3F : #F0F8FF table_bg_color := use_dark_mode ? #001A2E : #E6F7FF header_bg_color := use_dark_mode ? #002A47 : #CCF2FF header_bg_color 'Fire' => primary_color := use_dark_mode ? #FF6347 : #DC143C bullish_color := use_dark_mode ? #FFD700 : #FF8C00 bearish_color := use_dark_mode ? #8B0000 : #800000 neutral_color := use_dark_mode ? #FFA500 : #CD853F text_color := use_dark_mode ? #FFFAF0 : #2F1B14 bg_color := use_dark_mode ? #2F1B14 : #FFFAF0 table_bg_color := use_dark_mode ? #261611 : #FFF8F0 header_bg_color := use_dark_mode ? #3D241A : #FFE4CC header_bg_color 'Matrix' => primary_color := use_dark_mode ? #00FF41 : #006400 bullish_color := use_dark_mode ? #39FF14 : #228B22 bearish_color := use_dark_mode ? #FF073A : #8B0000 neutral_color := use_dark_mode ? #00FFFF : #008B8B text_color := use_dark_mode ? #C0FF8C : #003300 bg_color := use_dark_mode ? #0D1B0D : #F0FFF0 table_bg_color := use_dark_mode ? #0A1A0A : #E8FFF0 header_bg_color := use_dark_mode ? #112B11 : #CCFFCC header_bg_color 'Arctic' => primary_color := use_dark_mode ? #87CEFA : #4169E1 bullish_color := use_dark_mode ? #00BFFF : #0000CD bearish_color := use_dark_mode ? #FF1493 : #8B008B neutral_color := use_dark_mode ? #B0E0E6 : #483D8B text_color := use_dark_mode ? #F8F8FF : #191970 bg_color := use_dark_mode ? #191970 : #F8F8FF table_bg_color := use_dark_mode ? #141B47 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A5C : #E0F0FF header_bg_color // Transparency settings bg_transparency = use_dark_mode ? 85 : 92 zone_transparency = use_dark_mode ? 90 : 95 band_transparency = use_dark_mode ? 70 : 85 table_transparency = use_dark_mode ? 80 : 15 // Allocation color alloc_color = smoothed_allocation >= 80 ? bullish_color : smoothed_allocation >= 60 ? color.new(bullish_color, 30) : smoothed_allocation >= 40 ? primary_color : smoothed_allocation >= 20 ? color.new(bearish_color, 30) : bearish_color // Dynamic background var color dynamic_bg_color = na if show_regime_background if smoothed_allocation >= 70 dynamic_bg_color := color.new(bullish_color, background_intensity) dynamic_bg_color else if smoothed_allocation <= 30 dynamic_bg_color := color.new(bearish_color, background_intensity) dynamic_bg_color else if smoothed_allocation > 60 or smoothed_allocation < 40 dynamic_bg_color := color.new(primary_color, math.min(99, background_intensity + 2)) dynamic_bg_color bgcolor(dynamic_bg_color, title = 'Allocation Signal Background') // Plot main allocation line plot(smoothed_allocation, 'Equity Allocation %', color = alloc_color, linewidth = math.max(1, main_line_width)) // Reference lines (static colors for hline) hline_bullish_color = color_scheme == 'Gold' ? use_dark_mode ? #FFA500 : #FF8C00 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4CAF50 : #388E3C : color_scheme == 'Behavioral' ? #00FF00 : color_scheme == 'Quant' ? #FFA500 : color_scheme == 'Ocean' ? use_dark_mode ? #00CED1 : #4682B4 : color_scheme == 'Fire' ? use_dark_mode ? #FFD700 : #FF8C00 : color_scheme == 'Matrix' ? use_dark_mode ? #39FF14 : #228B22 : color_scheme == 'Arctic' ? use_dark_mode ? #00BFFF : #0000CD : #4CAF50 hline_bearish_color = color_scheme == 'Gold' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'EdgeTools' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'Behavioral' ? #8B0000 : color_scheme == 'Quant' ? #8B0000 : color_scheme == 'Ocean' ? use_dark_mode ? #FF4500 : #B22222 : color_scheme == 'Fire' ? use_dark_mode ? #8B0000 : #800000 : color_scheme == 'Matrix' ? use_dark_mode ? #FF073A : #8B0000 : color_scheme == 'Arctic' ? use_dark_mode ? #FF1493 : #8B008B : #FF5252 hline_primary_color = color_scheme == 'Gold' ? use_dark_mode ? #FFD700 : #DAA520 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4682B4 : #1E90FF : color_scheme == 'Behavioral' ? #808080 : color_scheme == 'Quant' ? #808080 : color_scheme == 'Ocean' ? use_dark_mode ? #20B2AA : #008B8B : color_scheme == 'Fire' ? use_dark_mode ? #FF6347 : #DC143C : color_scheme == 'Matrix' ? use_dark_mode ? #00FF41 : #006400 : color_scheme == 'Arctic' ? use_dark_mode ? #87CEFA : #4169E1 : #2196F3 hline(show_reference_lines ? 100 : na, '100% Equity', color = color.new(hline_bullish_color, 70), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 80 : na, '80% Equity', color = color.new(hline_bullish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 60 : na, '60% Equity', color = color.new(hline_bullish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(50, '50% Balanced', color = color.new(hline_primary_color, 50), linestyle = hline.style_solid, linewidth = 2) hline(show_reference_lines ? 40 : na, '40% Equity', color = color.new(hline_bearish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 20 : na, '20% Equity', color = color.new(hline_bearish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 0 : na, '0% Equity', color = color.new(hline_bearish_color, 70), linestyle = hline.style_dotted, linewidth = 1) // Component plots plot(show_components ? regime_allocation : na, 'Regime', color = color.new(#4ECDC4, 70), linewidth = 1) plot(show_components ? risk_allocation : na, 'Risk', color = color.new(#FF6B6B, 70), linewidth = 1) plot(show_components ? valuation_score : na, 'Valuation', color = color.new(#45B7D1, 70), linewidth = 1) plot(show_components ? sentiment_score : na, 'Sentiment', color = color.new(#FFD93D, 70), linewidth = 1) plot(show_components ? macro_score : na, 'Macro', color = color.new(#6BCF7F, 70), linewidth = 1) // Confidence bands upper_band = plot(show_confidence_bands ? math.min(100, smoothed_allocation + ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Upper Band') lower_band = plot(show_confidence_bands ? math.max(0, smoothed_allocation - ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Lower Band') fill(upper_band, lower_band, color = show_confidence_bands ? color.new(neutral_color, zone_transparency) : na, title = 'Uncertainty') // DASHBOARD if show_dashboard and barstate.islast var table dashboard = table.new(position.top_right, 2, 20, border_width = 1, bgcolor = color.new(table_bg_color, table_transparency)) table.clear(dashboard, 0, 0, 1, 19) // Header header_color = color.new(header_bg_color, 20) dashboard_text_color = text_color table.cell(dashboard, 0, 0, 'DEAM', text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) table.cell(dashboard, 1, 0, model_type, text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) // Core metrics table.cell(dashboard, 0, 1, 'Equity Allocation', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 1, str.tostring(smoothed_allocation, '##.#') + '%', text_color = alloc_color, text_size = size.small) table.cell(dashboard, 0, 2, 'Cash Allocation', text_color = dashboard_text_color, text_size = size.small) cash_color = 100 - smoothed_allocation > 70 ? bearish_color : primary_color table.cell(dashboard, 1, 2, str.tostring(100 - smoothed_allocation, '##.#') + '%', text_color = cash_color, text_size = size.small) // Signal signal_text = 'NEUTRAL' signal_color = primary_color if smoothed_allocation >= 70 signal_text := 'BULLISH' signal_color := bullish_color signal_color else if smoothed_allocation <= 30 signal_text := 'BEARISH' signal_color := bearish_color signal_color table.cell(dashboard, 0, 3, 'Signal', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 3, signal_text, text_color = signal_color, text_size = size.small) // Market Regime table.cell(dashboard, 0, 4, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_color_display = market_regime == 'Strong Bull' or market_regime == 'Bull Market' ? bullish_color : market_regime == 'Neutral' ? primary_color : market_regime == 'Crisis' ? bearish_color : bearish_color table.cell(dashboard, 1, 4, market_regime, text_color = regime_color_display, text_size = size.small) // VIX table.cell(dashboard, 0, 5, 'VIX Level', text_color = dashboard_text_color, text_size = size.small) vix_color_display = vix < 20 ? bullish_color : vix < 30 ? primary_color : bearish_color table.cell(dashboard, 1, 5, str.tostring(vix, '##.##'), text_color = vix_color_display, text_size = size.small) // Market Drawdown table.cell(dashboard, 0, 6, 'Market DD', text_color = dashboard_text_color, text_size = size.small) market_dd_color = current_drawdown < 5 ? bullish_color : current_drawdown < 10 ? primary_color : bearish_color table.cell(dashboard, 1, 6, '-' + str.tostring(current_drawdown, '##.#') + '%', text_color = market_dd_color, text_size = size.small) // Crisis Detection table.cell(dashboard, 0, 7, 'Crisis Detection', text_color = dashboard_text_color, text_size = size.small) crisis_text = is_severe_crisis ? 'SEVERE' : is_crisis ? 'CRISIS' : 'Normal' crisis_display_color = is_severe_crisis or is_crisis ? bearish_color : bullish_color table.cell(dashboard, 1, 7, crisis_text, text_color = crisis_display_color, text_size = size.small) // Real Data Section financial_bg = color.new(primary_color, 85) table.cell(dashboard, 0, 8, 'REAL DATA', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) table.cell(dashboard, 1, 8, 'Live Metrics', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) // P/E Ratio table.cell(dashboard, 0, 9, 'P/E Ratio', text_color = dashboard_text_color, text_size = size.small) pe_color = actual_pe_ratio < 18 ? bullish_color : actual_pe_ratio < 25 ? primary_color : bearish_color table.cell(dashboard, 1, 9, str.tostring(actual_pe_ratio, '##.#'), text_color = pe_color, text_size = size.small) // ERP table.cell(dashboard, 0, 10, 'ERP', text_color = dashboard_text_color, text_size = size.small) erp_color = equity_risk_premium > 2 ? bullish_color : equity_risk_premium > 0 ? primary_color : bearish_color table.cell(dashboard, 1, 10, str.tostring(equity_risk_premium, '##.##') + '%', text_color = erp_color, text_size = size.small) // ROE table.cell(dashboard, 0, 11, 'ROE', text_color = dashboard_text_color, text_size = size.small) roe_color = spy_return_on_equity > 20 ? bullish_color : spy_return_on_equity > 10 ? primary_color : bearish_color table.cell(dashboard, 1, 11, str.tostring(spy_return_on_equity, '##.#') + '%', text_color = roe_color, text_size = size.small) // D/E Ratio table.cell(dashboard, 0, 12, 'D/E Ratio', text_color = dashboard_text_color, text_size = size.small) de_color = spy_debt_to_equity < 0.5 ? bullish_color : spy_debt_to_equity < 1.0 ? primary_color : bearish_color table.cell(dashboard, 1, 12, str.tostring(spy_debt_to_equity, '##.##'), text_color = de_color, text_size = size.small) // Shareholder Yield table.cell(dashboard, 0, 13, 'Dividend+Buyback', text_color = dashboard_text_color, text_size = size.small) yield_color = total_shareholder_yield > 4 ? bullish_color : total_shareholder_yield > 2 ? primary_color : bearish_color table.cell(dashboard, 1, 13, str.tostring(total_shareholder_yield, '##.#') + '%', text_color = yield_color, text_size = size.small) // Component Scores component_bg = color.new(neutral_color, 80) table.cell(dashboard, 0, 14, 'Components', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 1, 14, 'Scores', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 0, 15, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_score_color = regime_allocation > 60 ? bullish_color : regime_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 15, str.tostring(regime_allocation, '##'), text_color = regime_score_color, text_size = size.small) table.cell(dashboard, 0, 16, 'Risk', text_color = dashboard_text_color, text_size = size.small) risk_score_color = risk_allocation > 60 ? bullish_color : risk_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 16, str.tostring(risk_allocation, '##'), text_color = risk_score_color, text_size = size.small) table.cell(dashboard, 0, 17, 'Valuation', text_color = dashboard_text_color, text_size = size.small) val_score_color = valuation_score > 60 ? bullish_color : valuation_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 17, str.tostring(valuation_score, '##'), text_color = val_score_color, text_size = size.small) table.cell(dashboard, 0, 18, 'Sentiment', text_color = dashboard_text_color, text_size = size.small) sent_score_color = sentiment_score > 60 ? bullish_color : sentiment_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 18, str.tostring(sentiment_score, '##'), text_color = sent_score_color, text_size = size.small) table.cell(dashboard, 0, 19, 'Macro', text_color = dashboard_text_color, text_size = size.small) macro_score_color = macro_score > 60 ? bullish_color : macro_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 19, str.tostring(macro_score, '##'), text_color = macro_score_color, text_size = size.small) // ALERTS // Major allocation changes alertcondition(smoothed_allocation >= 80 and smoothed_allocation < 80, 'High Equity Allocation', 'Equity allocation reached 80% - Bull market conditions') alertcondition(smoothed_allocation <= 20 and smoothed_allocation > 20, 'Low Equity Allocation', 'Equity allocation dropped to 20% - Defensive positioning') // Crisis alerts alertcondition(is_crisis and not is_crisis , 'CRISIS DETECTED', 'Crisis conditions detected - Reducing equity allocation') alertcondition(is_severe_crisis and not is_severe_crisis , 'SEVERE CRISIS', 'Severe crisis detected - Maximum defensive positioning') // Regime changes regime_changed = market_regime != market_regime alertcondition(regime_changed, 'Regime Change', 'Market regime has changed') // Risk management alerts risk_breach = enable_portfolio_risk_scaling and (actual_portfolio_volatility > target_portfolio_volatility * 1.2 or actual_portfolio_drawdown > max_portfolio_drawdown * 1.2) alertcondition(risk_breach, 'Risk Breach', 'Portfolio risk exceeds target parameters') // USAGE // The indicator displays a recommended equity allocation percentage (0-100%). // Example: 75% allocation = 75% stocks, 25% cash/bonds. // // The model combines market regime analysis (trend, volatility, drawdowns), // risk management (portfolio-level targeting), valuation metrics (P/E, ERP), // sentiment indicators (VIX term structure), and macro factors (yield curve, // credit spreads) into a single allocation signal. // // Crisis detection automatically reduces exposure when multiple warning signals // converge. Alerts available for major allocation shifts and regime changes. // // Designed for SPY/S&P 500 portfolio allocation. Adjust component weights and // risk parameters in settings to match your risk tolerance. View in Pine
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