Cross-Market Regime Scanner [BOSWaves]Cross-Market Regime Scanner - Multi-Asset ADX Positioning with Correlation Network Visualization
Overview
Cross-Market Regime Scanner is a multi-asset regime monitoring system that maps directional strength and trend intensity across correlated instruments through ADX-based coordinate positioning, where asset locations dynamically reflect their current trending versus ranging state and bullish versus bearish bias.
Instead of relying on isolated single-asset trend analysis or static correlation matrices, regime classification, spatial positioning, and intermarket relationship strength are determined through ADX directional movement calculation, percentile-normalized coordinate mapping, and rolling correlation network construction.
This creates dynamic regime boundaries that reflect actual cross-market momentum patterns rather than arbitrary single-instrument levels - visualizing trending assets in right quadrants when ADX strength exceeds thresholds, positioning ranging assets in left quadrants during consolidation, and incorporating correlation web topology to reveal which instruments move together or diverge during regime transitions.
Assets are therefore evaluated relative to ADX-derived regime coordinates and correlation network position rather than conventional isolated technical indicators.
Conceptual Framework
Cross-Market Regime Scanner is founded on the principle that meaningful market insights emerge from simultaneous multi-asset regime awareness rather than sequential single-instrument analysis.
Traditional trend analysis examines assets individually using separate chart windows, which often obscures the broader cross-market regime structure and correlation patterns that drive coordinated moves. This framework replaces isolated-instrument logic with unified spatial positioning informed by actual ADX directional measurements and correlation relationships.
Three core principles guide the design:
Asset positioning should be determined by ADX-based regime coordinates that reflect trending versus ranging state and directional bias simultaneously.
Spatial mapping must normalize ADX values to place assets within consistent quadrant boundaries regardless of instrument volatility characteristics.
Correlation network visualization reveals which assets exhibit coordinated behavior versus divergent regime patterns during market transitions.
This shifts regime analysis from isolated single-chart monitoring into unified multi-asset spatial awareness with correlation context.
Theoretical Foundation
The indicator combines ADX directional movement calculation, coordinate normalization methodology, quadrant-based regime classification, and rolling correlation network construction.
A Wilder's smoothing implementation calculates ADX, +DI, and -DI for each monitored asset using True Range and directional movement components. The ADX value relative to a configurable threshold determines X-axis positioning (ranging versus trending), while the difference between +DI and -DI determines Y-axis positioning (bearish versus bullish). Coordinate normalization caps values within fixed boundaries for consistent quadrant placement. Pairwise correlation calculations over rolling windows populate a network graph where line thickness and opacity reflect correlation strength.
Five internal systems operate in tandem:
Multi-Asset ADX Engine : Computes smoothed ADX, +DI, and -DI values for up to 8 configurable instruments using Wilder's directional movement methodology.
Coordinate Transformation System : Converts ADX strength and directional movement into normalized X/Y coordinates with threshold-relative scaling and boundary capping.
Quadrant Classification Logic : Maps coordinate positions to four distinct regime states—Trending Bullish, Trending Bearish, Ranging Bullish, Ranging Bearish—with color-coded zones.
Historical Trail Rendering : Maintains rolling position history for each asset, drawing gradient-faded trails that visualize recent regime trajectory and velocity.
Correlation Network Calculator : Computes pairwise return correlations across all enabled assets, rendering weighted connection lines in circular web topology with strength-based styling.
This design allows simultaneous cross-market regime awareness rather than reacting sequentially to individual instrument signals.
How It Works
Cross-Market Regime Scanner evaluates markets through a sequence of multi-asset spatial processes:
Data Request Processing : Security function retrieves high, low, and close values for up to 8 configurable symbols with lookahead offset to ensure confirmed bar data.
ADX Calculation Per Asset : True Range computed from high-low-close relationships, directional movement derived from up-moves versus down-moves, smoothed via Wilder's method over configurable period.
Directional Index Derivation : +DI and -DI calculated as smoothed directional movement divided by smoothed True Range, scaled to percentage values.
Coordinate Transformation : X-axis position equals (ADX - threshold) * 2, capped between -50 and +50; Y-axis position equals (+DI - -DI), capped between -50 and +50.
Quadrant Assignment : Positive X indicates trending (ADX > threshold), negative X indicates ranging; positive Y indicates bullish (+DI > -DI), negative Y indicates bearish.
Trail History Management : Configurable-length position history maintains recent coordinates for each asset, rendering gradient-faded lines connecting sequential positions.
Velocity Vector Calculation : 7-bar coordinate change converted to directional arrow overlays showing regime momentum and trajectory.
Return Correlation Processing : Bar-over-bar returns calculated for each asset, pairwise correlations computed over rolling window.
Network Graph Construction : Assets positioned in circular topology, correlation lines drawn between pairs exceeding threshold with thickness/opacity scaled by correlation strength, positive correlations solid green, negative correlations dashed red.
Risk Regime Scoring : Composite score aggregates bullish risk-on assets (equities, crypto, commodities) minus bullish risk-off assets (gold, dollar, VIX), generating overall market risk sentiment with colored candle overlay.
Together, these elements form a continuously updating spatial regime framework anchored in multi-asset momentum reality and correlation structure.
Interpretation
Cross-Market Regime Scanner should be interpreted as unified spatial regime boundaries with correlation context:
Top-Right Quadrant (TREND ▲) : Assets positioned here exhibit ADX above threshold with +DI exceeding -DI - confirmed bullish trending conditions with directional conviction.
Bottom-Right Quadrant (TREND ▼) : Assets positioned here exhibit ADX above threshold with -DI exceeding +DI - confirmed bearish trending conditions with directional conviction.
Top-Left Quadrant (RANGE ▲) : Assets positioned here exhibit ADX below threshold with +DI exceeding -DI - ranging consolidation with bullish bias but insufficient trend strength.
Bottom-Left Quadrant (RANGE ▼) : Assets positioned here exhibit ADX below threshold with -DI exceeding +DI - ranging consolidation with bearish bias but insufficient trend strength.
Position Trails : Gradient-faded lines connecting recent coordinate history reveal regime trajectory - curved paths indicate regime rotation, straight paths indicate sustained directional conviction.
Velocity Arrows : Directional vectors overlaid on current positions show 7-bar regime momentum - arrow length indicates speed of regime change, angle indicates trajectory direction.
Correlation Web : Circular network graph positioned left of main quadrant map displays pairwise asset relationships - solid green lines indicate positive correlation (moving together), dashed red lines indicate negative correlation (diverging moves), line thickness reflects correlation strength magnitude.
Asset Dots : Multi-layer glow effects with color-coded markers identify each asset on both quadrant map and correlation web-symbol labels positioned adjacent to current location.
Regime Summary Bar : Vertical boxes on right edge display condensed regime state for each enabled asset - box background color reflects quadrant classification, border color matches asset identifier.
Risk Regime Candles : Overlay candles on price chart colored by composite risk score - green indicates risk-on dominance (bullish equities/crypto exceeding bullish safe-havens), red indicates risk-off dominance (bullish gold/dollar/VIX exceeding bullish risk assets), gray indicates neutral balance.
Quadrant positioning, trail trajectory, correlation network topology, and velocity vectors outweigh isolated single-asset readings.
Signal Logic & Visual Cues
Cross-Market Regime Scanner presents spatial positioning insights rather than discrete entry signals:
Regime Clustering : Multiple assets congregating in same quadrant suggests broad market regime consensus - all assets in TREND ▲ indicates coordinated bullish momentum across instruments.
Regime Divergence : Assets splitting across opposing quadrants reveals intermarket disagreement - equities in TREND ▲ while safe-havens in TREND ▼ suggests healthy risk-on environment.
Quadrant Transitions : Assets crossing quadrant boundaries mark regime shifts - movement from left (ranging) to right (trending) indicates breakout from consolidation into directional phase.
Trail Curvature Patterns : Sharp curves in position trails signal rapid regime rotation, straight trails indicate sustained directional conviction, loops indicate regime uncertainty with back-and-forth oscillation.
Velocity Acceleration : Long arrows indicate rapid regime change momentum, short arrows indicate stable regime persistence, arrow direction reveals whether asset moving toward trending or ranging state.
Correlation Breakdown Events : Previously strong correlation lines (thick, opaque) suddenly thinning or disappearing indicates relationship decoupling - often precedes major regime transitions.
Correlation Inversion Signals : Assets shifting from positive correlation (solid green) to negative correlation (dashed red) marks structural market regime change - historically correlated assets beginning to diverge.
Risk Score Extremes : Composite score reaching maximum positive (all risk-on bullish, all risk-off bearish) or maximum negative (all risk-on bearish, all risk-off bullish) marks regime conviction extremes.
The primary value lies in simultaneous multi-asset regime awareness and correlation pattern recognition rather than isolated timing signals.
Strategy Integration
Cross-Market Regime Scanner fits within macro-aware and intermarket analysis approaches:
Regime-Filtered Entries : Use quadrant positioning as directional filter for primary trading instrument - favor long setups when asset in TREND ▲ quadrant, short setups in TREND ▼ quadrant.
Correlation Confluence Trading : Enter positions when target asset and correlated instruments occupy same quadrant - multiple assets in TREND ▲ provides conviction for long exposure.
Divergence-Based Reversal Anticipation : Monitor for regime divergence between correlated assets - if historically aligned instruments split to opposite quadrants, anticipate mean-reversion or regime rotation.
Breakout Confirmation via Cross-Asset Validation : Confirm primary instrument breakouts by verifying correlated assets simultaneously transitioning from ranging to trending quadrants.
Risk-On/Risk-Off Positioning : Use composite risk score and safe-haven positioning to determine overall market environment - scale risk exposure based on risk regime dominance.
Velocity-Based Timing : Enter during periods of high regime velocity (long arrows) when momentum carries assets decisively into new quadrants, avoid entries during low velocity regime uncertainty.
Multi-Timeframe Regime Alignment : Apply higher-timeframe regime scanner to establish macro context, use lower-timeframe price action for entry timing within aligned regime structure.
Correlation Web Pattern Recognition : Identify regime transitions early by monitoring correlation network topology changes - previously disconnected assets forming strong correlations suggests regime coalescence.
Technical Implementation Details
Core Engine : Wilder's smoothing-based ADX calculation with separate True Range and directional movement tracking per asset
Coordinate Model : Threshold-relative X-axis scaling (trending versus ranging) with directional movement differential Y-axis (bullish versus bearish)
Normalization System : Boundary capping at ±50 for consistent spatial positioning regardless of instrument volatility
Trail Rendering : Rolling array-based position history with gradient alpha decay and width tapering
Correlation Engine : Return-based pairwise correlation calculation over rolling window with configurable lookback
Network Visualization : Circular topology with trigonometric positioning, weighted line rendering based on correlation magnitude
Risk Scoring : Composite calculation aggregating directional states across classified risk-on and risk-off asset categories
Performance Profile : Optimized for 8 simultaneous security requests with efficient array management and conditional rendering
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-regime monitoring for intraday correlation shifts and short-term regime rotations
15 - 60 min : Intraday regime structure with meaningful ADX development and correlation stability
4H - Daily : Swing and position-level macro regime identification with sustained trend classification
Weekly - Monthly : Long-term regime cycle tracking with structural correlation pattern evolution
Suggested Baseline Configuration:
ADX Period : 14
ADX Smoothing : 14
Trend Threshold : 25.0
Trail Length : 15
Correlation Period : 50
Min |Correlation| to Show Line : 0.3
Web Radius : 30
Show Quadrant Colors : Enabled
Show Regime Summary Bar : Enabled
Show Velocity Arrows : Enabled
Show Correlation Web : Enabled
These suggested parameters should be used as a baseline; their effectiveness depends on the selected assets' volatility profiles, correlation characteristics, and preferred spatial sensitivity, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Assets clustering too tightly : Decrease Trend Threshold (e.g., 20) to spread ranging/trending separation, or increase ADX Period for smoother ADX calculation reducing noise.
Assets spreading too widely : Increase Trend Threshold (e.g., 30-35) to demand stronger ADX confirmation before classifying as trending, tightening quadrant boundaries.
Trail too short to show trajectory : Increase Trail Length (20-25) to visualize longer regime history, revealing sustained directional patterns.
Trail too cluttered : Decrease Trail Length (8-12) for cleaner visualization focusing on recent regime state, reducing visual complexity.
Unstable ADX readings : Increase ADX Period and ADX Smoothing (18-21) for heavier smoothing reducing bar-to-bar regime oscillation.
Sluggish regime detection : Decrease ADX Period (10-12) for faster response to directional changes, accepting increased sensitivity to noise.
Too many correlation lines : Increase Min |Correlation| threshold (0.4-0.6) to display only strongest relationships, decluttering network visualization.
Missing significant correlations : Decrease Min |Correlation| threshold (0.2-0.25) to reveal weaker but potentially meaningful relationships.
Correlation too volatile : Increase Correlation Period (75-100) for more stable correlation measurements, reducing network line flickering.
Correlation too stale : Decrease Correlation Period (30-40) to emphasize recent correlation patterns, capturing regime-dependent relationship changes.
Velocity arrows too sensitive : Modify 7-bar lookback in code to longer period (10-14) for smoother velocity representation, or increase magnitude threshold for arrow display.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Macro-aware trading approaches requiring cross-market regime context for directional bias
Intermarket analysis strategies monitoring correlation breakdowns and regime divergences
Portfolio construction decisions requiring simultaneous multi-asset regime classification
Risk management frameworks using safe-haven positioning and risk-on/risk-off scoring
Trend-following systems benefiting from cross-asset regime confirmation before entry
Mean-reversion strategies identifying regime extremes via clustering patterns and correlation stress
Reduced Effectiveness:
Single-asset focused strategies not incorporating cross-market context in decision logic
High-frequency trading approaches where multi-security request latency impacts execution
Markets with consistently weak correlations where network topology provides limited insight
Extremely low volatility environments where ADX remains persistently below threshold for all assets
Instruments with erratic or unreliable ADX characteristics producing unstable coordinate positioning
Integration Guidelines
Confluence : Combine with BOSWaves structure, volume analysis, or primary instrument technical indicators for entry timing within aligned regime
Quadrant Respect : Trust signals occurring when primary trading asset occupies appropriate quadrant for intended trade direction
Correlation Context : Prioritize setups where target asset exhibits strong correlation with instruments in same regime quadrant
Divergence Awareness : Monitor for safe-haven assets moving opposite to risk assets - regime divergence validates directional conviction
Velocity Confirmation : Favor entries during periods of strong regime velocity indicating decisive momentum rather than regime oscillation
Risk Score Alignment : Scale position sizing and exposure based on composite risk score - larger positions during clear risk-on/risk-off environments
Trail Pattern Recognition : Use trail curvature to identify regime stability (straight) versus rotation (curved) versus uncertainty (looped)
Multi-Timeframe Structure : Apply higher-timeframe regime scanner for macro filter, lower-timeframe for tactical positioning within established regime
Disclaimer
Cross-Market Regime Scanner is a professional-grade multi-asset regime visualization and correlation analysis tool. It uses ADX-based coordinate positioning and rolling correlation calculation but does not predict future regime transitions or guarantee relationship persistence. Results depend on selected assets' characteristics, parameter configuration, correlation stability, and disciplined interpretation. Security request timing may introduce minor latency in real-time data retrieval. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, fundamental macro awareness, and comprehensive risk management.
Forecasting
XAUUSD ELIRANTo build a professional and accurate description of your strategy, I have distilled the information you shared into a neat "Trading Plan". This strategy combines strict financial discipline with a desire for consistent growth in the Forex market.
Here is a suggestion for describing your strategy:
The "Safe Profit" Strategy: Capital Management and Growth in the Forex Market
The strategy focuses on preserving equity while creating cash flow for withdrawal and leveraging profits to purchase additional trading portfolios. The goal is to reduce personal risk and increase purchasing power in the market.
1. Capital and Withdrawal Goals
Starting Capital/Base: $2,250.
Periodic Profit Target: $1,000.
Withdrawal Policy: Upon reaching the profit target, the $1,000 is immediately withdrawn for "cash out" and reinvestment in additional trading portfolios.
2. Operational Logic (The Workflow)
The strategy operates in cycles of accumulation -> withdrawal -> expansion:
Accumulation phase: Focus on trading Forex assets with the aim of achieving a return of approximately 44% on the base portfolio.
Withdrawal phase: Defining the first $1,000 as net profit that leaves the market to ensure "money in your pocket".
Expansion phase: Using part of the profit that is withdrawn to purchase an additional trading portfolio, which allows for increased trading volume without increasing the risk on the original portfolio.
3. Advantages of the strategy
Psychological risk management: Knowing that you are withdrawing money "home" reduces mental stress and allows for cleaner decision-making.
Smart leverage: Purchasing additional portfolios creates diversification of risks between different accounts.
Self-discipline: Pre-defined profit and withdrawal targets prevent the "greed trap" that exists in Forex.
1of1 Trades Expected Ranges (Friday Close Calculator)Expected Ranges (Friday Close Calculator)
Expected Ranges is a simple, non-plotting calculator designed for weekly market preparation.
It uses the most recent Friday’s daily close as the base price and calculates an expected trading range for the upcoming week.
This indicator is intentionally built as a calculator only — it does not draw lines or zones on the chart. This ensures there is no bleed between symbols and allows traders to convert levels into permanent TradingView drawings (horizontal lines and shaded rectangles) that are stored per symbol in their account.
How It Works
Friday Close is automatically detected from the daily chart.
You input a single value for Expected Weekly Move.
The indicator calculates:
Upper Range = Friday Close + Expected Move
Lower Range = Friday Close − Expected Move
Values are displayed in a clean top-right panel for quick reference.
MAs+Engulfing O caminho das Criptos
This indicator overlays multiple moving averages (EMAs 12/20/50/100/200 and SMA 200) and highlights bullish/bearish engulfing candles by dynamically coloring the candle body. The EMA 12 (gray) provides short-term momentum insight, helping refine entry timing and micro pullbacks.
When a bullish engulfing is detected, the candle appears as a strong dark green; for bearish engulfing, a vivid red. Normal candles retain classic lime/red colors. Visual alerts and bar coloring make price-action patterns instantly visible.
Includes built-in alert conditions for both patterns, supporting both trading automation and education. The tool upgrades trend-following setups by combining macro structure (longer EMAs) with micro momentum (EMA 12) and automatic price-action insights.
Market Internals SPY[TP]# Market Internals SPY Dashboard - TradingView Publication
## 📊 Overview
**Market Internals SPY ** is a comprehensive multi-factor market sentiment dashboard designed specifically for SPY (S&P 500 ETF) traders. This indicator combines four powerful market breadth signals into one easy-to-read interface, helping traders identify high-probability setups and avoid false breakouts.
---
## 🎯 What Makes This Indicator Unique?
Unlike single-indicator tools, this dashboard synthesizes **multiple market internals** to provide confluence-based trading signals:
- **CPR (Central Pivot Range)** - Institutional pivot levels
- **VIX (Volatility Index)** - Fear gauge
- **Put/Call Ratio** - Options sentiment with dynamic crossover alerts
- ** USI:ADD (Advance/Decline Line)** - Market breadth strength
All presented in a clean, real-time dashboard with visual alerts directly on your chart.
---
## 📈 Key Features
### 1. **Static Daily CPR Levels**
- Automatically plots Top CPR, Pivot, and Bottom CPR
- Levels remain fixed throughout the trading day (no repainting)
- **Trend Bias Indicator**: Green = Current Pivot > Previous Pivot (Bullish structure)
### 2. **Put/Call Ratio Crossover System**
- 10-period SMA smoothing for cleaner signals
- **Bullish Signal** (Green background): Put/Call crosses below SMA
- Indicates decreasing hedging activity (bullish)
- **Bearish Signal** (Red background): Put/Call crosses above SMA
- Indicates increasing hedging activity (bearish)
### 3. **Price/Breadth Divergence Detection**
- **Yellow Candles**: Highlight when price and USI:ADD diverge
- Price rising but USI:ADD falling = Potential reversal
- Price falling but USI:ADD rising = Possible bottom
### 4. **Comprehensive Real-Time Dashboard**
A top-right table displaying:
- **CPR Trend Bias**: Bullish/Bearish structure
- **VIX Level**: Current value + directional bias
- **Put/Call Ratio**: Live value + trend arrows
- **AD Line**: Breadth strength with directional indicators
### 5. **Intelligent Bar Coloring**
- **Green bars**: USI:ADD rising (breadth improving)
- **Red bars**: USI:ADD falling (breadth deteriorating)
- **Yellow bars**: Divergence warning (potential reversal)
---
## 🔧 How to Use
### Setup Instructions
1. **Add to Chart**: Apply to SPY on your preferred intraday timeframe (5m, 15m, 30m, 1H)
2. **Configure Symbols** (if needed):
- Default settings work for most platforms
- If "PCC" doesn't load, try: `PCCR`, `INDEX:PCC`, `USI:PCC`, or `CBOE:PCC`
- Ensure you have market internals data access ( USI:ADD , VIX)
### Trading Signals
#### 🟢 **Bullish Confluence** (High-Probability Long Setup)
- CPR Trend = BULLISH
- VIX falling or low (<20)
- Put/Call below SMA (or green background crossover)
- USI:ADD rising (green bars)
- **Entry**: Look for bullish price action at support levels
#### 🔴 **Bearish Confluence** (High-Probability Short Setup)
- CPR Trend = BEARISH
- VIX rising or elevated (>25)
- Put/Call above SMA (or red background crossover)
- USI:ADD falling (red bars)
- **Entry**: Look for bearish rejection at resistance
#### ⚠️ **Divergence Warning**
- Yellow candles indicate mismatch between price and breadth
- Consider profit-taking or reversals when divergence appears at extremes
### Best Practices
- **Multi-Timeframe Confirmation**: Check higher timeframes (4H, Daily) for trend alignment
- **Volume Confirmation**: Combine with volume analysis for stronger signals
- **Risk Management**: Always use stop losses; no indicator is 100% accurate
- **News Awareness**: Be cautious around major economic releases
---
## 📚 Understanding the Components
### CPR (Central Pivot Range)
Traditional floor trader pivot levels calculated from previous day's High, Low, Close:
- **Pivot (PP)** = (High + Low + Close) / 3
- **Top CPR (TC)** = (PP - BC) + PP
- **Bottom CPR (BC)** = (High + Low) / 2
### VIX (Volatility Index)
- **< 15**: Complacency, potential for sudden moves
- **15-20**: Normal conditions
- **20-30**: Elevated uncertainty
- **> 30**: High fear, potential bottoming process
### Put/Call Ratio
- **< 0.7**: Excessive optimism (contrarian bearish)
- **0.7-1.0**: Balanced sentiment
- **> 1.0**: Defensive positioning (contrarian bullish potential)
### USI:ADD (NYSE Advance/Decline)
- **> 0**: More stocks advancing than declining (bullish breadth)
- **< 0**: More stocks declining than advancing (bearish breadth)
- **Extreme readings** (±2000+): Potential exhaustion
---
## ⚙️ Customization Options
### Input Parameters
- **AD Line Symbol**: Default "ADD" (try "ADVN" or "NYSE:ADD" if needed)
- **VIX Symbol**: Default "VIX" (try "CBOE:VIX" if needed)
- **Put/Call Symbol**: Default "PCC" (alternatives listed above)
### Color Scheme
- Blue: CPR levels
- Purple: Pivot point
- Green: Bullish signals/backgrounds
- Red: Bearish signals/backgrounds
- Yellow: Divergence warnings
---
## 💡 Pro Tips
1. **Wait for Confluence**: Don't trade on a single indicator - wait for 3+ signals to align
2. **Use CPR as Dynamic S/R**: Price tends to react at TC and BC levels
3. **Watch the Crossovers**: Put/Call crossovers often precede significant moves
4. **Monitor Divergences**: Yellow candles at key levels are high-value signals
5. **Combine with Price Action**: This tool confirms direction - you still need entry triggers
---
## ⚠️ Limitations & Disclaimers
- Requires **premium data** for USI:ADD and VIX on most platforms
- Best suited for **intraday SPY trading** (may adapt to other indices)
- **Not a standalone system** - use with proper risk management
- Past performance does not guarantee future results
- Always backtest before live trading
---
## 🎓 Example Scenario
**Bullish Setup**:
- 9:45 AM EST: Price pulls back to Bottom CPR
- Dashboard shows: ✅ Bullish CPR Bias, ✅ VIX 16.5 (falling), ✅ Put/Call 0.68 ⬇️ Bull, ✅ USI:ADD +850 ⬆️
- Green background flashes (Put/Call crossunder)
- **Action**: Enter long at BC with stop below TC of previous day
---
## 📊 Ideal Timeframes
- **Primary**: 5-minute, 15-minute (day trading)
- **Secondary**: 30-minute, 1-hour (swing entries)
- **Confirmation**: Daily chart for trend context
---
## 🔄 Updates & Support
This indicator is actively maintained. If you encounter symbol loading issues:
1. Check your data provider includes market internals
2. Try alternative symbols in inputs
3. Ensure you're using a premium TradingView plan (if required)
---
## 📝 Version Information
- **Version**: 5 (Pine Script v5)
- **Type**: Overlay Indicator
- **Author**: tapaspattanaik
- **Category**: Market Internals / Breadth Analysis
---
## 🏆 Final Thoughts
This indicator is designed for **serious traders** who understand that edge comes from confluence, not single signals. By combining institutional pivot levels with real-time market internals, you gain a significant advantage in reading market sentiment and timing entries with precision.
**Remember**: The best trades happen when multiple independent factors align. Use this dashboard to find those moments.
---
## 📌 How to Add This Indicator
1. Open TradingView and navigate to Pine Editor
2. Copy the complete script code
3. Click "Add to Chart"
4. Configure symbols if needed (see Setup Instructions above)
5. Adjust position/colors to your preference
---
**Happy Trading! 📈**
*This indicator is for educational purposes. Always manage risk appropriately and never risk more than you can afford to lose.*
---
### Tags
`#SPY` `#MarketInternals` `#CPR` `#VIX` `#PutCallRatio` `#BreadthAnalysis` `#DayTrading` `#SwingTrading` `#TechnicalAnalysis` `#PivotPoints`
Dynamic Risk and RewardThe Dynamic Equity Projection (DEP Map) is an institutional-grade visual execution tool designed to automate risk-to-reward mapping directly on your chart. Unlike standard drawing tools, it is context-aware—calculating volatility and trend bias in real-time to provide a "live" projection of your trade's potential.Core Logic & Intelligence1. Trend-Filtered SentimentThe indicator uses a 200-period Exponential Moving Average (EMA) as a directional filter.Bullish Map: If the current price is above the EMA, the DEP Map projects a green "Long" zone.Bearish Map: If the price is below the EMA, it instantly flips to a red "Short" zone.This helps traders stay aligned with the primary market momentum, avoiding the trap of "trading against the tide."2. Volatility-Adaptive Risk (ATR)Rather than using arbitrary point distances, the DEP Map utilizes the Average True Range (ATR).It measures the market's "noise" level over the last 14 bars.The Stop Loss is set at a multiplier (default 1.5x) of this volatility, ensuring your stop is wide enough to survive market breathing but tight enough to maintain a high R:R.Technical FeaturesFeatureDescriptionProfessional BenefitProjection BoxA dynamic rectangle that extends into the "future" (right-side offset).Keeps the current price action clear while providing a visual goalpost for the trade.Persistent LogicUses advanced var object handling to prevent "ghosting" or label stacking.Ensures a clean, high-performance chart interface without clutter.R:R Equity LadderSegments the profit zone into specific milestones: 1.0, 2.0, 3.0, and the "Equity Target" (5.0).Allows for precise partial profit-taking and psychological target setting.Dashed SL LineA high-contrast red dashed line indicating the invalidation point.Provides an immediate visual cue of the trade's total risk.How to Use the DEP MapIdentify the Bias: Observe the color of the box. A green box suggests looking for buying opportunities; a red box suggests selling.Verify the Levels: The labels on the right edge of the box provide the exact price points for your Stop Loss and Take Profit orders.Execute & Manage:R:R 1.0: The "Safety Point." Many traders move their stop to breakeven here.R:R 2.0 - 3.0: The "Standard Exit." This is where the bulk of the trade's profit is usually captured.Equity Target: The "Home Run." Reserved for high-conviction trend extensions.
Risk/Reward vs Win Rate HeatmapThis indicator overlays two decision-support tables on your main chart:
1. Reward:Risk vs Win Rate Heatmap
A matrix that shows whether a given combination of Win Rate (%) and Reward:Risk (R:R) is expected to be:
Profitable (green)
Break-even (amber)
Not Profitable (red)
The color is based on the standard expectancy concept:
E = p * R - (1 -p)
where p is win probability and R is Reward:Risk.
The diagonal amber cells represent the break-even boundary.
2. Drawdown Table
A quick reference table showing how much % gain is required to recover after a capital drawdown (e.g., -20% requires +25% to return to break-even). This is meant to anchor capital preservation and risk management decisions.
________________________________________
How to Use
Set your expected Win Rate and R:R in the inputs.
Enable Show highlight to display a status icon on the matching cell:
Profitable: 💰
Break-even: ⚠
Not profitable: 🚫
(All icons are customizable.)
Use the heatmap to sanity-check whether your strategy parameters make sense, and use the drawdown table as a reminder of why protecting capital matters.
________________________________________
Inputs & Customization
Position: Place each table anywhere on the chart (default layout provided).
Colors: Header colors and heatmap colors are customizable (defaults included).
Fonts: Title, headers, labels, legend, and icon font sizes are configurable.
Icons: Set your own symbols for Profitable / Break-even / Not profitable (with optional auto-contrast).
________________________________________
Notes
This script is educational and provides a visual framework to reason about expectancy and drawdowns.
It does not generate trade signals and does not guarantee profitability.
Results depend on the accuracy of your inputs and real-world execution (slippage, fees, market regime, etc.).
________________________________________
Disclaimer
This indicator is for educational purposes only and is not financial advice. You are responsible for any trading decisions and risk management.
Trend Candles - [EntryLab]
Trend Candles:
This indicator overrides or overlays standard chart candles with a color gradient that reflects a calculated trend bias (uptrend or downtrend), helping traders quickly assess the overall market direction.Features:Candles are colored using a gradient scale: stronger shades indicate higher-confidence trend direction based on the algorithm.
Two usage modes:
Full override: Disable and hide the chart's native ticker/symbol candles (via chart settings) so the indicator's colored candles take over completely.
Hover preview: Keep your preferred candle setup/colors intact; simply hover the mouse over the indicator name in the chart legend to temporarily display the trend-colored gradient candles for quick reference without altering your main view.
Customizable inputs (adjust in settings): gradient colors for up/down trends, intensity thresholds, etc.
How it works (high-level):
The trend bias is determined using a combination of multiple VWAP calculations, trend-following data, and momentum-based indicators. This multi-factor approach aims to provide a smoother, more reliable signal of whether the market is in an uptrend (bullish bias) or downtrend (bearish bias) compared to single-indicator methods.
How to use:
Apply the indicator to your chart and use the colored candles as a visual aid for trend bias decision-making. For example:In a strong uptrend (deeper bullish gradient), consider favoring long setups or avoiding shorts.
In a downtrend (deeper bearish gradient), consider short opportunities or caution on longs.
Combine with other tools (support/resistance, volume, etc.) for confluence rather than relying solely on candle color.
This script offers a unique way to visualize trend strength via candle recoloring with gradient feedback, which can provide a broader overview of directional bias without cluttering the chart with additional plots/lines.Best suited for any timeframe, especially higher ones for swing/position trading or lower ones for intraday confirmation. No repainting occurs once a bar closes. Not financial advice. Trading carries significant risk of loss of capital. Always backtest and use discretion; results are not guaranteed.
New York | Asia | London - Session Range + ORB - [EntryLab]Session Ranges & 15min ORB – Asia, London, New YorkShort Title
This indicator plots the high and low of the three major trading sessions (Asia, London, New York) as well as the Opening Range Breakout (ORB) levels based on the first 15 minutes of each session.
Features: Full session high/low ranges for Asia (00:00–09:00 UTC), London (07:00–16:00 UTC), and New York (~13:30–20:00 UTC). Times are approximate UTC and may need adjustment depending on broker timezone or DST.
ORB: high and low calculated from the first 15-minute period (or equivalent bars) at the start of each session.
Customizable: toggle sessions on/off, change ORB duration, line styles, colors.
How to use:
Traders often monitor price action around prior session highs and lows to identify potential liquidity grabs or sweeps. The ORB provides additional confluence for gauging the session's potential directional bias or breakout levels.For example:A sweep of a prior session high/low can signal liquidity being taken.
Price breaking above/below the session's ORB high/low may indicate momentum in that direction for the current session.
This script combines multi-session range visualization with per-session ORB levels in one tool, which can help assess where liquidity pools may exist and where price could be drawn to fill or sweep certain areas.Best used on lower timeframes (e.g., 1m–15m) for intraday analysis. Session times are fixed (no automatic DST handling); users can modify them in the code if needed.Not financial advice. Trading involves significant risk of loss. Use at your own discretion and always test thoroughly.
1k EMA Clouds1k EMA Clouds
This indicator is a multi-EMA cloud system designed to give clear trend structure, momentum context, and higher-timeframe bias directly on your chart.
It plots five EMA cloud pairs using short and long moving averages, allowing you to visually identify trend alignment, trend shifts, and areas of dynamic support and resistance. When the fast EMA is above the slow EMA, the cloud reflects bullish conditions. When it’s below, the cloud reflects bearish conditions.
The script also includes:
An optional VWAP with session anchoring and standard deviation bands for intraday mean-reversion and institutional reference
Optional 200 MA and 100 MA for higher-timeframe trend confirmation
Clean visual hierarchy so price action remains readable during scaling and chart movement
This tool is intended to be used alongside price action, structure, and risk management, not as a standalone signal generator.
Credits
EMA cloud logic is inspired by and credited to Ripster’s EMA Clouds
Modified and extended for personal workflow, visual clarity, and intraday trading use
Trend Signal GridTrend Signal Grid
Based on Trend Direction & Force Index - TDFI by Causecelebre, the TDFI Grid is a multi-timeframe momentum indicator that builds on the original TDFI concept. It calculates TDFI across three user-selectable timeframes using three different lookback periods, creating a 3×3 consensus grid (9 readings total).
Each cell is classified as bullish, bearish, or neutral based on configurable upper and lower thresholds. When a majority of the 9 readings align in the same direction (default 65%), the indicator triggers a directional signal — either GRID UP or GRID DOWN. Alerts fire automatically on new signals so you never miss a shift.
How it works
The indicator uses a smoothed EMA-based momentum calculation, normalises the output against its recent highest absolute value, and then maps it across your chosen timeframes and lookback lengths. The results are displayed in a clean on-chart table showing the state of each timeframe/lookback combination at a glance.
Settings:
Timeframe 1, 2, 3 — Choose any three timeframes (defaults to 1m, 5m, 15m).
LB1, LB2, LB3 — Lookback periods for each TDFI calculation.
UP / DOWN thresholds — Controls how far the TDFI must move before a cell registers as bullish or bearish.
Majority — The percentage of the 9 cells that must agree to trigger a signal.
Table position — Place the grid anywhere on your chart.
Best used for
Trading setups where you need to confirm momentum alignment across multiple timeframes before entering or scaling a position. Works well on forex and metals.
VWMA 200 (HTF) + Fibonacci BandsVWMA 200 (HTF) + Fibonacci Bands + VWAP Trend Dashboard
This indicator combines a Higher Timeframe VWMA (default: 200) with volatility bands and Fibonacci-based internal levels, plus anchored VWAPs (Daily / Weekly / Monthly) and an optional trend dashboard table.
It was designed to help you quickly spot:
where price is relative to a major HTF VWMA mean
whether price is trading in normal / extreme zones
how price is positioned versus session/period VWAPs
a simple “at-a-glance” trend bias across multiple anchors
What’s included
1) VWMA 200 (HTF)
VWMA is calculated on a locked timeframe (HTF) using request.security.
Default source is HLC3, but you can change the source.
2) Volatility bands + Fibonacci levels
The outer bands are based on a scaled standard deviation (mult * stdev) around the HTF VWMA.
Internal bands use Fibonacci ratios:
11.8% / 23.6% / 38.2% / 50% / 61.8% / 76.4% / 88.6% / 100%
Clean labels on the right side show each level as a percentage.
3) Extreme candle highlighting
Candles can be highlighted when the close is beyond the 76.4% band (upper or lower).
Helps identify potential stretched conditions / breakout zones.
4) Anchored VWAPs
Optional Daily / Weekly / Monthly VWAP (anchored by period change).
Optional VWAP labels on the right side (toggle separately).
5) Trend Dashboard Table
Optional table showing Bullish / Bearish / Neutral for:
VWAP D, VWAP W, VWAP M, and VWMA HTF
Displays distance from each reference in points (price units).
Includes a Confluence row:
Bullish if price is above all references
Bearish if price is below all references
Mixed otherwise
Table position can be customized (Top Right / Top Left / Bottom Right / Bottom Left).
How to use (quick guide)
Mean reversion / structure: Use the HTF VWMA as the central “mean” reference.
Zones: Fibonacci bands show progressively stronger deviation zones from the mean.
Extremes: Candle coloring beyond 76.4% can highlight stretched price action.
Trend bias: The dashboard helps confirm whether price is aligned above or below key anchors (VWAPs + VWMA HTF).
Confluence: When multiple anchors agree, trend conviction tends to be higher.
Notes / Disclaimer
This tool is intended for context and decision support, not as a standalone strategy.
VWAP behavior may differ across markets/sessions depending on symbol and exchange rules.
Always combine with your own risk management and confirmation tools.
ES SPX Pullback Engine (v1)this script is intended to provide clear long or short pullback entries, while /ES is leading the index
Peak Rejection LevelsPeak Rejection Levels is a price-action–based indicator designed to automatically identify strong rejection levels at swing highs and swing lows.
It highlights areas where price attempted to move further but was firmly rejected, often acting as key support or resistance zones.
The indicator is especially useful for :
Intraday and swing trading
Identifying high-probability rejection zones
Support/resistance mapping based on pure price action
Confluence with trend, structure, or indicator-based strategies
📈 What Is a “Peak Rejection”?
A peak rejection is defined using strict price-action rules:
🔺 Swing High Rejection (Resistance)
A swing high is marked as a rejection when:
The candle is a confirmed swing high
The candle has an upper wick
The upper wick is larger than the candle body
The wick represents the highest price of the swing
This indicates strong selling pressure and rejection from higher prices
🔻 Swing Low Rejection (Support)
A swing low is marked as a rejection when:
The candle is a confirmed swing low
The candle has a lower wick
The lower wick is larger than the candle body
The wick represents the lowest price of the swing
This indicates strong buying pressure and rejection from lower prices
When these conditions are met, the indicator draws a horizontal level at the rejection wick.
🧠 Key Features
✅ Works on any timeframe
✅ Non-repainting (uses confirmed swings)
✅ Automatically removes broken levels
✅ Automatically removes old levels based on time
✅ Clean and uncluttered chart output
✅ Pure price-action logic (no indicators, no lag)
Asia Range + Killzones (London/NY) + Liquidity Sweep AlertsGPT Asia Range + Killzones (London/NY) + Liquidity Sweep Alerts
NY PM Session Highlighter (For Hawaiian Traders)Purpose: This script is designed for traders targeting the New York PM Session (1:30 PM – 4:00 PM ET). Based on 5-year historical data for ES and NQ, this window represents a high-probability period for 2:1 Risk-to-Reward setups as institutional traders rebalance and drive price toward the daily close.
Key Features:
DST-Automated Tracking: Uses the America/New_York timezone to ensure the lines stay accurate during Daylight Saving transitions.
Visual Guidance: Draws a dashed vertical line at the 1:30 PM ET start and the 4:00 PM ET close.
Session Boxing: Highlights the background in a soft blue to define the "trading zone," helping you ignore the low-volume "lunch doldrums" that occur immediately before.
Hawaii-Friendly: Automatically adjusts to your local Hawaii Standard Time (HST) so you don't have to calculate the 5 or 6-hour offset manually.
Trade Logic:
Wait for the 1:30 PM ET (8:30 AM HST) line.
Look for a sweep of the 12:00 PM – 1:00 PM (Lunch) range.
Enter on a Market Structure Shift (MSS) or Fair Value Gap (FVG).
Target a 2:1 Reward-to-Risk ratio, aiming to exit by the 4:00 PM ET line.
Bitcoin Power Law Bottom PriceThis is a super simplified version of Bitcoin Rainbow Wave script.
I removed everything except the power law bottom band.
ES to SPX Lead (RTH Adaptive)Very simple script designed especially to trade CFD but also scalping.
Only RTH (you'll understand why)
Not a stand-alone indicator, e.g., an external event may hit the index and /ES leading nature will become meaningless. Same with a sudden crash on a Mag7 stock.
Uses Z Score to evaluate if /Es is leading SPX (or not) and /ES VWAP to establish bullish (+1) or bearish territory (-1). Histogram is the product of Z Score times VWAP status, red or green depending.
Z score goes from -2 to +2.
Zscore reading: 0.4 < |Z| < 1.2 is the trading zone.
|Z| <0.4 is sort of neutral shifting gears zone, a no-trade and may be transition moment.
Middle numbers show max. limits based on actual volatility (i.e. when to exit and when definitely not to enter a trade).
Grey stripes is NO TRADE zone.
Final number is the composite histogram value.
So:
Textbook bullish: /ES above VWAP and Z Score positive
Textbook negative: /ES below VWAP and Z score negative
If Green Histogram & negative Z Score, you may enter bearish pullback trades making sure Z score is in the sweet spot bracket.
If Red histogram & negative Z score, it's a conflict state, signals are not alined. Holds a bullish nature but it may be a warning sign.
Script produced by Chat GPT after several iterations.
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. Professional traders must still integrate model output with fundamental analysis, portfolio constraints, and client mandates.
References
Andrews, D.W.K. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61(4).
Brunnermeier, M.K., & Pedersen, L.H. (2009). Market Liquidity and Funding Liquidity. Review of Financial Studies, 22(6).
Campbell, J.Y., & Shiller, R.J. (1988). Stock Prices, Earnings, and Expected Dividends. Journal of Finance, 43(3).
Dietterich, T.G. (2000). Ensemble Methods in Machine Learning. Multiple Classifier Systems.
Dixit, A. (1989). Entry and Exit Decisions under Uncertainty. Journal of Political Economy, 97(3).
Engle, R.F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4).
Fama, E.F., & French, K.R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2).
Hamilton, J.D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2).
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1).
Kindleberger, C.P. (1978). Manias, Panics, and Crashes: A History of Financial Crises. Basic Books.
Lo, A.W., & MacKinlay, A.C. (1990). Data-Snooping Biases in Tests of Financial Asset Pricing Models. Review of Financial Studies, 3(3).
Longin, F., & Solnik, B. (2001). Extreme Correlation of International Equity Markets. Journal of Finance, 56(2).
Rabiner, L.R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2).
Shiller, R.J. (2000). Irrational Exuberance. Princeton University Press.
Taleb, N.N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
Taleb, N.N., & Douady, R. (2012). Mathematical Definition, Mapping, and Detection of (Anti)Fragility. Quantitative Finance, 13(11).
