UVXY Contango Rate - my quick and dirty indicatorFeel the force ... read the source.
Give you an idea of the contango headwind / backwardation tailwind that UVXY is experiencing.
Works on Daily time frame only unless you buy the VIX feed.
"vix" için komut dosyalarını ara
Synthetic Vix StochasticI noticed that this indicator was not in the public library, so I decided to share it. This is Larry Williams take on stochastics, based on his idea of synthetic vix. Thanks to Active trader magazine, his article on the idea shows us how this tool can be used as a timing instrument for his sythetic vix. The idea he relates is that the market becomes oversold at the height of volatility and the stochastic can highlight the periods when the panic may be over. This is evidenced by readings above 80 and below 20. He states that his indicator is less reliable at market tops rather than bottoms, and evidence suggests just that. Stochastics readings in this indicator have been adjusted to look and 'feel' like traditional readings. His suggested settings are the default, but I have included a more traditional line in the code that reads the WVF high and low in the calculation instead of just the WVF, just uncomment the appropriate lines and see for yourself. This indicator works really well with the Williams Vix Fix, inverted of course, coded by ChrisMoody.
Enjoy responsibly
ShirokiHeishi
see the notes on chart
CM_Williams_Vix_Fix_V3_Upper_Text PlotsWilliams Vix Fix Text Plots! Alert Capable!
Use With Lower Indicator or as Single Indicator!
Has Text Plots For All Plot Types Lower Indicator Uses.
To Get Lower Indicator:
Info On Lower Indicator - Discussion:
Volitility Overbought Oversold IndicatorVIX Overbought Oversold Indicator identifies when the Vix is nearing a top or bottom usually within 2 candles.
How it works? When the VIX moves more than 12% above or below its 10 DMA the indicator moves
outside the normal range band signaling that the move is overextended. Price action and normal VIX support/resistance level analysis can be used to verify signal.
When the indicator crosses from above 12% to below it can used as buy/sell signals, but is less reliable.
I am not the creator, I stumbled upon the indicator on a (professional) trading blog
VIX/VXV Ratio OscillatorThis script is meant to assist in determining optimal triggers for entering or exiting XIV and ZIV. Red area and above = go to cash.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
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Uptrick: Crypto Volatility Index** Crypto Volatility Index(VIX) **
Overview
The Crypto Volatility Index (VIX) is a specialized technical indicator designed to measure the volatility of cryptocurrency prices. Leveraging advanced statistical methods, including logarithmic returns and variance, the Crypto VIX offers a refined measure of market fluctuations. This approach makes it particularly useful for traders in the highly volatile cryptocurrency market, providing insights that traditional volatility indicators may not capture as effectively.
Purpose
The Crypto VIX aims to deliver a nuanced understanding of market volatility, tailored specifically for the cryptocurrency space. Unlike other volatility measures, the Crypto VIX employs sophisticated statistical methods to reflect the unique characteristics of cryptocurrency price movements. This makes it especially valuable for cryptocurrency traders, helping them navigate the inherent volatility of digital assets and manage their trading strategies and risk exposure more effectively.
Calculation
1. Indicator Declaration
The Crypto VIX is plotted in a separate pane below the main price chart for clarity:
indicator("Crypto Volatility Index (VIX)", overlay=false, shorttitle="Crypto VIX")
2. Input Parameters
Users can adjust the period length for volatility calculations:
length = input.int(14, title="Period Length")
3. Calculating Daily Returns
The daily returns are calculated using logarithmic returns:
returns = math.log(close / close )
- **Logarithmic Returns:** These returns provide a normalized measure of price changes, making it easier to compare returns over different periods and across different assets.
4. Average Return Calculation
The average return over the specified period is computed with a Simple Moving Average (SMA):
avg_return = ta.sma(returns, length)
5. Variance Calculation
Variance measures the dispersion of returns from the average:
variance = ta.sma(math.pow(returns - avg_return, 2), length)
- Variance : This tells us how much the returns deviate from the average, giving insight into how volatile the market is.
6. Standard Deviation (Volatility) Calculation
Volatility is derived as the square root of the variance:
volatility = math.sqrt(variance)
- Standard Deviation : This provides a direct measure of volatility, showing how much the price typically deviates from the mean return.
7. Plotting the Indicator
The volatility and average return are plotted:
plot(volatility, color=#21f34b, title="Volatility Index")
plot(avg_return, color=color.new(color.red, 80), title="Average Return", style=plot.style_columns)
Practical Examples
1. High Volatility Scenario
** Example :** During significant market events, such as major regulatory announcements or geopolitical developments, the Crypto VIX tends to rise sharply. For instance, if the Crypto VIX moves from a baseline level of 0.2 to 0.8, it indicates heightened market volatility. Traders might see this as a signal to adjust their strategies, such as reducing position sizes or setting tighter stop-loss levels to manage increased risk.
2. Low Volatility Scenario
** Example :** In a stable market, where prices fluctuate within a narrow range, the Crypto VIX will show lower values. For example, a drop in the Crypto VIX from 0.4 to 0.2 suggests lower volatility and stable market conditions. Traders might use this information to consider longer-term trades or take advantage of potential consolidation patterns.
Best Practices
1. Combining Indicators
- Moving Averages : Use the Crypto VIX with moving averages to identify trends and potential reversal points.
- Relative Strength Index (RSI): Combine with RSI to assess overbought or oversold conditions for better entry and exit points.
- Bollinger Bands : Pair with Bollinger Bands to understand volatility relative to price movements and spot potential breakouts.
2. Adjusting Parameters
- Short-Term Trading : Use a shorter period length (e.g., 7 days) to capture rapid volatility changes suitable for day trading.
- Long-Term Investing : A longer period length (e.g., 30 days) provides a smoother view of volatility, helping long-term investors navigate market trends.
Backtesting and Performance Insights
While specific backtesting data for the Crypto VIX is not yet available, the indicator is built on established principles of volatility measurement, such as logarithmic returns and standard deviation. These methods are well-regarded in financial analysis for accurately reflecting market volatility. The Crypto VIX is designed to offer insights similar to other effective volatility indicators, tailored specifically for the cryptocurrency markets. Its adaptation to digital assets and ability to provide precise volatility measures underscore its practical value for traders.
Originality and Uniqueness
The Crypto Volatility Index (VIX) distinguishes itself through its specialized approach to measuring volatility in the cryptocurrency markets. While the concepts of logarithmic returns and standard deviation are not new, the Crypto VIX integrates these methods into a unique framework designed specifically for digital assets.
- Tailored Methodology : Unlike generic volatility indicators, the Crypto VIX is adapted to the unique characteristics of cryptocurrencies, providing a more precise measure of price fluctuations that reflects the inherent volatility of digital markets.
- Enhanced Insights : By focusing on cryptocurrency-specific price behavior and incorporating advanced statistical techniques, the Crypto VIX offers insights that traditional volatility indicators might miss. This makes it a valuable tool for traders navigating the complex and fast-moving cryptocurrency landscape.
- Innovative Application : The Crypto VIX combines established financial metrics in a novel way, offering a fresh perspective on market volatility and contributing to more effective risk management and trading strategies in the cryptocurrency space.
Summary
The Crypto Volatility Index (VIX) is a specialized tool for measuring cryptocurrency market volatility. By utilizing advanced statistical methods such as logarithmic returns and standard deviation, it provides a detailed measure of price fluctuations. While not entirely original in its use of these methods, the Crypto VIX stands out through its tailored application to the unique characteristics of the cryptocurrency market. Traders can use the Crypto VIX to gauge market risk, adjust their strategies, and make informed trading decisions, supported by practical examples, best practices, and clear visual aids.
Yelober - Market Internal direction+ Key levelsYelober – Market Internals + Key Levels is a focused intraday trading tool that helps you spot high-probability price direction by anchoring decisions to structure that matters: yesterday’s RTH High/Low, today’s pre-market High/Low, and a fast Value Area/POC from the prior session. Paired with a compact market internals dashboard (NYSE/NASDAQ UVOL vs. DVOL ratios, VOLD slopes, TICK/TICKQ momentum, and optional VIX trend), it gives you a real-time read on breadth so you can choose which direction to trade, when to enter (breaks, retests, or fades at PMH/PML/VAH/VAL/POC), and how to plan exits as internals confirm or deteriorate. On top of these intraday decision benefits, it also allows traders—in a very subtle but powerful way—to keep an eye on the VIX and immediately recognize significant spikes or sharp decreases that should be factored in before entering a trade, or used as a quick signal to modify an existing position. In short: clear levels for the chart, live internals for the context, and a smarter, rules-based path to execution.
# Yelober – Market Internals + Key Levels
*A TradingView indicator for session key levels + real‑time market internals (NYSE/NASDAQ TICK, UVOL/DVOL/VOLD, and VIX).*
**Script name in Pine:** `Yelober - Market Internal direction+ Key levels` (Pine v6)
---
## 1) What this indicator does
**Purpose:** Help intraday traders quickly find high‑probability reaction zones and read market internals momentum without switching charts. It overlays yesterday/today’s **automatic price levels** on your active chart and shows a **market breadth table** that summarizes NYSE/NASDAQ buying pressure and TICK direction, with an optional VIX trend read.
### Key features at a glance
* **Automatic Price Levels (overlay on chart)**
* Yesterday’s High/Low of Day (**yHoD**, **yLoD**)
* Extended Hours High/Low (**yEHH**, **yEHL**) across yesterday AH + today pre‑market
* Today’s Pre‑Market High/Low (**PMH**, **PML**)
* Yesterday’s **Value Area High/Low** (**VAH/VAL**) and **Point of Control (POC)** computed from a volume profile of yesterday’s **regular session**
* Smart de‑duplication:
* Shows **only the higher** of (yEHH vs PMH) and **only the lower** of (yEHL vs PML) to avoid redundant bands
* **Market Breadth Table (on‑chart table)**
* **NYSE ratio** = UVOL/DVOL (signed) with **VOLD slope** from session open
* **NASDAQ ratio** = UVOLQ/DVOLQ (signed) with **VOLDQ slope** from session open
* **TICK** and **TICKQ**: live cumulative ratio and short‑term slope
* **VIX** (optional): current value + slope over a configurable lookback/timeframe
* Color‑coded trends with sensible thresholds and optional normalization
---
## 2) How to use it (trader workflow)
1. **Mark your reaction zones**
* Watch **yHoD/yLoD**, **PMH/PML**, and **VAH/VAL/POC** for first touches, break/retest, and failure tests.
* Expect increased responsiveness when multiple levels cluster (e.g., PMH ≈ VAH ≈ daily pivot).
2. **Read the breadth panel for context**
* **NYSE/NASDAQ ratio** (>1 = more up‑volume than down‑volume; <−1 = down‑dominant). Strong green across both favors long setups; red favors short setups.
* **VOLD slopes** (NYSE & NASDAQ): positive and accelerating → broadening participation; negative → persistent pressure.
* **TICK/TICKQ**: cumulative ratio and **slope arrows** (↗ / ↘ / →). Use the slope to gauge **near‑term thrust or fade**.
* **VIX slope**: rising VIX (red) often coincides with risk‑off; falling VIX (green) with risk‑on.
3. **Confluence = higher confidence**
* Example: Price reclaims **PMH** while **NYSE/NASDAQ ratios** print green and **TICK slopes** point ↗ — consider break‑and‑go; if VIX slope is ↘, that adds risk‑on confidence.
* Example: Price rejects **VAH** while **VOLD slopes** roll negative and VIX ↗ — consider fade/reversal.
4. **Risk management**
* Place stops just beyond key levels tested; if breadth flips, tighten or exit.
> **Timeframes:** Works best on 1–15m charts for intraday. Value Area is computed from **yesterday’s RTH**; choose a smaller calculation timeframe (e.g., 5–15m) for stable profiles.
---
## 3) Inputs & settings (what each option controls)
### Global Style
* **Enable all automatic price levels**: master toggle for yHoD/yLoD, yEHH/yEHL, PMH/PML, VAH/VAL/POC.
* **Line style/width**: applies to all drawn levels.
* **Label size/style** and **label color linking**: use the same color as the line or override with a global label color.
* **Maximum bars lookback**: how far the script scans to build yesterday metrics (performance‑sensitive).
### Value Area / Volume Profile
* **Enable Value Area calculations** *(on by default)*: computes yesterday’s **POC**, **VAH**, **VAL** from a simplified intraday volume profile built from yesterday’s **regular session bars**.
* **Max Volume Profile Points** *(default 50)*: lower values = faster; higher = more precise.
* **Value Area Calculation Timeframe** *(default 15)*: the security timeframe used when collecting yesterday’s highs/lows/volumes.
### Individual Level Toggles & Colors
* **yHoD / yLoD** (yesterday high/low)
* **yEHH / yEHL** (yesterday AH + today pre‑market extremes)
* **PMH / PML** (today pre‑market extremes)
* **VAH / VAL / POC** (yesterday RTH value area + point of control)
### Market Breadth Panel
* **Show NYSE / NASDAQ / VIX**: choose which series to display in the table.
* **Table Position / Size / Background Color**: UI placement and legibility.
* **Slope Averaging Periods** *(default 5)*: number of recent TICK/TICKQ ratio points used in slope calculation.
* **Candles for Rate** *(default 10)* & **Normalize Rate**: VIX slope calculation as % change between `now` and `n` candles ago; normalize divides by `n`.
* **VIX Timeframe**: optionally compute VIX on a higher TF (e.g., 15, 30, 60) for a smoother regime read.
* **Volume Normalization** (NYSE & NASDAQ): display VOLD slopes scaled to `tens/thousands/millions/10th millions` for readable magnitudes; color thresholds adapt to your choice.
---
## 4) Data sources & definitions
* **UVOL/VOLD (NYSE)** and **UVOLQ/DVOLQ/VOLDQ (NASDAQ)** via `request.security()`
* **Ratio** = `UVOL/DVOL` (signed; negative when down‑volume dominates)
* **VOLD slope** ≈ `(VOLD_now − VOLD_open) / bars_since_open`, then normalized per your setting
* **TICK/TICKQ**: cumulative sum of prints this session with **positives vs negatives ratio**, plus a simple linear regression **slope** of the last `N` ratio values
* **VIX**: value and slope across a user‑selected timeframe and lookback
* **Sessions (EST/EDT)**
* **Regular:** 09:30–16:00
* **Pre‑Market:** 04:00–09:30
* **After Hours:** 16:00–20:00
* **Extended‑hours extremes** combine **yesterday AH** + **today PM**
> **Note:** All session checks are done with TradingView’s `time(…,"America/New_York")` context. If your broker’s RTH differs (e.g., futures), adjust expectations accordingly.
---
## 5) How the algorithms work (plain English)
### A) Key Levels
* **Yesterday’s RTH High/Low**: scans yesterday’s bars within 09:30–16:00 and records the extremes + bar indices.
* **Extended Hours**: scans yesterday AH and today PM to get **yEHH/yEHL**. Script shows **either yEHH or PMH** (whichever is **higher**) and **either yEHL or PML** (whichever is **lower**) to avoid duplicate bands stacked together.
* **Value Area & POC (RTH only)**
* Build a coarse volume profile with `Max Volume Profile Points` buckets across the price range formed by yesterday’s RTH bars.
* Distribute each bar’s volume uniformly across the buckets it spans (fast approximation to keep Pine within execution limits).
* **POC** = bucket with max volume. **VA** expands from POC outward until **70%** of cumulative volume is enclosed → yields **VAH/VAL**.
### B) Market Breadth Table
* **NYSE/NASDAQ Ratio**: signed UVOL/DVOL with basic coloring.
* **VOLD Slopes**: from session open to current, normalized to human‑readable units; colors flip green/red based on thresholds that map to your normalization setting (e.g., ±2M for NYSE, ±3.5×10M for NASDAQ).
* **TICK/TICKQ Slope**: linear regression over the last `N` ratio points → **↗ / → / ↘** with the rounded slope value.
* **VIX Slope**: % change between now and `n` candles ago (optionally divided by `n`). Red when rising beyond threshold; green when falling.
---
## 6) Recommended presets
* **Stocks (liquid, intraday)**
* Value Area **ON**, `Max Volume Points` = **40–60**, **Timeframe** = **5–15**
* Breadth: show **NYSE & NASDAQ & VIX**, `Slope periods` = **5–8**, `Candles for rate` = **10–20**, **Normalize VIX** = **ON**
* **Index futures / very high‑volume symbols**
* If you see Pine timeouts, set `Max Volume Points` = **20–40** or temporarily **disable Value Area**.
* Keep breadth panel **ON** (it’s light). Consider **VIX timeframe = 15/30** for regime clarity.
---
## 7) Tips, edge cases & performance
* **Performance:** The volume profile is capped (`maxBarsToProcess ≤ 500` and bucketed) to keep it responsive. If you experience slowdowns, reduce `Max Volume Points`, `Maximum bars lookback`, or disable Value Area.
* **Redundant lines:** The script **intentionally suppresses** PMH/PML when yEHH/yEHL are more extreme, and vice‑versa.
* **Label visibility:** Use `Label style = none` if you only want clean lines and read values from the right‑end labels.
* **Futures/RTH differences:** Value Area is from **yesterday’s RTH** only; for 24h instruments the RTH period may not reflect overnight structure.
* **Session transitions:** PMH/PML tracking stops as soon as RTH starts; values persist as static levels for the session.
---
## 8) Known limitations
* Uses public TradingView symbols: `UVOL`, `VOLD`, `UVOLQ`, `DVOLQ`, `VOLDQ`, `TICK`, `TICKQ`, `VIX`. If your data plan or region limits any symbol, the corresponding table rows may show `na`.
* The VA/POC approximation assumes uniform distribution of each bar’s volume across its high–low. That’s fast but not a tick‑level profile.
* Works best on US equities with standard NY session; alternative sessions may need code changes.
---
## 9) Troubleshooting
* **“Script is too slow / timed out”** → Lower `Max Volume Points`, lower `Maximum bars lookback`, or toggle **OFF** `Enable Value Area calculations` for that instrument.
* **Missing breadth values** → Ensure the symbols above load on your account; try reloading chart or switching timeframes once.
* **Overlapping labels** → Set `Label style = none` or reduce label size.
---
## 10) Version / license / contribution
* **Version:** Initial public release (Pine v6).
* **Author:** © yelober
* **License:** Free for community use and enhancement. Please keep author credit.
* **Contributing:** Open PRs/ideas: presets, alert conditions, multi‑day VA composites, optional mid‑value (`(VAH+VAL)/2`), session filter for futures, and alertable state machine for breadth regime transitions.
---
## 11) Quick start (TL;DR)
1. Add the indicator and **keep default settings**.
2. Trade **reactions** at yHoD/yLoD/PMH/PML/VAH/VAL/POC.
3. Use the **breadth table**: look for **green ratios + ↗ slopes** (risk‑on) or **red ratios + ↘ slopes** (risk‑off). Check **VIX** slope for confirmation.
4. Manage risk around levels; when breadth flips against you, tighten or exit.
---
### Changelog (public)
* **v1.0:** First community release with automatic RTH levels, VA/POC approximation, breadth dashboard (NYSE/NASDAQ/TICK/TICKQ/VIX) with normalization and adaptive color thresholds.
CandelaCharts - Contango Slope Index 📝 Overview
The Contango Slope Index (CSI) is a volatility term structure analysis tool designed to quantify the slope of the VIX futures curve over time.
By measuring the rate of change in implied volatility across multiple tenors—such as VIX1D, VIX (1M), VIX3M, VIX6M, and VIX1Y—the CSI provides traders and analysts with real-time insights into market sentiment, risk appetite, and potential turning points in equity markets.
Developed by CandelaCharts, the CSI draws from established financial research on volatility term structures, particularly focusing on how contango (upward-sloping curve) and backwardation (downward-sloping curve) regimes correlate with future market behavior.
The index computes a normalized slope using linear regression across available VIX futures, offering a dynamic view of evolving market expectations. The core output a slope value expressed in annualized percentage points per year (%/yr)—represents the steepness of the volatility curve:
Positive slope: Contango regime, typically associated with market stability and complacency.
Negative slope: Backwardation, historically linked to fear, near-term uncertainty, and often preceding market rallies.
Slope crossing zero or key thresholds: Generates regime shifts and alert conditions.
📦 Features
The Contango Slope Index offers a comprehensive set of features for analyzing volatility dynamics:
Multi-Tenor Volatility Input: Users can select which VIX futures contracts to include in the slope calculation: VIX, VIX1D, etc
Dynamic Slope Calculation: The indicator calculates the slope of the VIX term structure using linear regression on time-to-maturity (TTM) vs. volatility levels.
Moving Average Overlay: A configurable moving average (SMA, EMA, RMA, WMA, VWMA) is applied to the smoothed slope to identify trend direction and momentum shifts.
Regime Classification: Based on the slope value and its relationship to the moving average, the CSI classifies current market conditions into distinct regimes.
Visual Enhancements: Color-coded slope line, background shading, etc
Real-Time Label & Tooltip: On the last bar, a dynamic label displays: Current regime, Slope value and direction, etc
⚙️ Settings
VIX: Toggles use of spot VIX index (CBOE_DLY:VIX).
VIX1D: Toggles use of 1-day VIX futures (CBOE_DLY:VIX1D).
VIX3M: Toggles use of 3-month VIX futures (CBOE_DLY:VIX3M).
VIX6M: Toggles use of 6-month VIX futures (CBOE_DLY:VIX6M).
VIX1Y: Toggles use of 1-year VIX futures (CBOE_DLY:VIX1Y).
MA: Enables moving average filter; options include type (SMA, etc.) and period length.
Slope: Defines slope calculation line thickness and colors.
Bg: Enables background shading with customizable colors.
⚡️ Showcase
Slope Line
Customizable Moving Average
Regime Shift Zones
📒 Usage
The CSI is plotted as a standalone oscillator beneath the price chart (non-overlay mode). Key interpretation guidelines:
Slope Direction
Slope < 0 - Backwardation: Indicates near-term volatility is higher than long-term expectations. Historically, this has preceded equity market rallies, as panic subsides and fear peaks.
Slope > 0 - Contango: Reflects normal market conditions where longer-dated volatility is priced higher. Persistent high contango may signal complacency.
Magnitude of Slope
Slope > 0.0232 (%/yr) - Elevated complacency: The term structure is steeper than historical average—caution advised ahead of potential corrections.
Slope near 0 - Neutral or transitioning regime: Markets may be at inflection points.
Slope vs. MA Crossover
Slope crosses above MA: Improving confidence, potential upside acceleration
Slope crosses below MA: Deteriorating structure, rising stress
🚨 Alerts
Six pre-configured alerts are available for integration into trading systems:
🚨 Backwardation Detected – Slope turns negative
🔚 Exit Backwardation – Slope crosses above zero
⚠️ Elevated Complacency – Slope exceeds 2.32%/yr
📈 Potential Bullish Setup – Slope crosses below zero
✅ Slope Crosses Above MA – Momentum improves
⚠️ Slope Crosses Below MA – Momentum deteriorates
⚠️ Disclaimer
These tools are exclusively available on the TradingView platform.
Our charting tools are intended solely for informational and educational purposes and should not be regarded as financial, investment, or trading advice. They are not designed to predict market movements or offer specific recommendations. Users should be aware that past performance is not indicative of future results and should not rely on these tools for financial decisions. By using these charting tools, the purchaser agrees that the seller and creator hold no responsibility for any decisions made based on information provided by the tools. The purchaser assumes full responsibility and liability for any actions taken and their consequences, including potential financial losses or investment outcomes that may result from the use of these products.
By purchasing, the customer acknowledges and accepts that neither the seller nor the creator is liable for any undesired outcomes stemming from the development, sale, or use of these products. Additionally, the purchaser agrees to indemnify the seller from any liability. If invited through the Friends and Family Program, the purchaser understands that any provided discount code applies only to the initial purchase of Candela's subscription. The purchaser is responsible for canceling or requesting cancellation of their subscription if they choose not to continue at the full retail price. In the event the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable.
We do not offer reimbursements, refunds, or chargebacks. Once these Terms are accepted at the time of purchase, no reimbursements, refunds, or chargebacks will be issued under any circumstances.
By continuing to use these charting tools, the user confirms their understanding and acceptance of these Terms as outlined in this disclaimer.
Rule of 16 - LowerThe "Rule of 16" is a simple guideline used by traders and investors to estimate the expected annualized volatility of the S&P 500 Index (SPX) based on the level of the CBOE Volatility Index (VIX). The VIX, often referred to as the "fear gauge" or "fear index," measures the market's expectations for future volatility. It is calculated using the implied volatility of a specific set of S&P 500 options.
The Rule of 16 provides a rough approximation of the expected annualized percentage change in the S&P 500 based on the VIX level. Here's how it works:
Find the VIX level: Look up the current value of the VIX. Let's say it's currently at 20.
Apply the Rule of 16: Divide the VIX level by 16. In this example, 20 divided by 16 equals 1.25.
Result: The result of this calculation represents the expected annualized percentage change in the S&P 500. In this case, 1.25% is the estimated annualized volatility.
So, according to the Rule of 16, a VIX level of 20 suggests an expected annualized volatility of approximately 1.25% in the S&P 500.
Here's how you can use the Rule of 16:
Market Sentiment: The VIX is often used as an indicator of market sentiment. When the VIX is high (above its historical average), it suggests that investors expect higher market volatility, indicating potential uncertainty or fear in the markets. Conversely, when the VIX is low, it suggests lower expected volatility and potentially more confidence in the markets.
Risk Management: Traders and investors can use the Rule of 16 to estimate the potential risk associated with their portfolios. For example, if you have a portfolio of S&P 500 stocks and the VIX is at 20, you can use the Rule of 16 to estimate that the annualized volatility of your portfolio may be around 1.25%. This information can help you make decisions about position sizing and risk management.
Option Pricing: Options traders may use the Rule of 16 to get a quick estimate of the implied annualized volatility priced into S&P 500 options. It can help them assess whether options are relatively expensive or cheap based on the VIX level.
It's important to note that the Rule of 16 is a simplification and provides only a rough estimate of expected volatility. Market conditions and the relationship between the VIX and the S&P 500 can change over time. Therefore, it should be used as a guideline rather than a precise forecasting tool. Traders and investors should consider other factors and use additional analysis to make informed decisions.
Daily Directional Bias Indicator (S&P 500)This indicator is designed to help you be on the right side of the trade.
Most traders who struggle to know which way price may move are only looking at part of the picture. This Directional Bias Indicator uses both the Accumulation/Distribution Line and VIX for directional confirmation.
The Accumulation/Distribution Line
The Accumulation/Distribution (ACC) line helps us gauge market momentum by showing the cumulative flow of money into or out of an asset. When the ACC line is rising, it suggests that buying pressure is dominating, indicating a bullish market. Conversely, when the ACC line is falling, it suggests that selling pressure is stronger, indicating a bearish market. By comparing the ACC line with the VWAP, traders can see if the price is moving in line with the overall market sentiment. If the ACC line is above the VWAP, it suggests the market is in a bullish phase; if it's below, it indicates a bearish phase.
The VIX
The VIX (Volatility Index) is often referred to as the "fear gauge" of the market. When the VIX is rising, it typically signals increased market fear and higher volatility, which can be a sign of bearish market conditions. Conversely, when the VIX is falling, it suggests lower volatility and a more stable, bullish market. Using the VIX with the VWAP helps us confirm market direction, particularly in relation to the S&P 500.
VWAP
For both the ACC Line and VIX, we use a VWAP line to gauge whether the ACC line or the VIX is above or below the average. When the ACC line is above the VWAP, we view it as a sign that price will go up. However, because the VIX has an inverse relationship, when the VIX falls below the VWAP, we take that as a sign to go long.
How to use
The yellow line represents the ACC Line.
The red line represents the VWAP based on the ACC line.
The triangles at the bottom simply show when the ACC line is above or below the VWAP.
The triangles at the top show whether the VIX is bullish or bearish.
If both triangles (top or bottom) are bullish, this confirms that the price of an asset like the S&P 500 will likely go up. If both triangles are pointing down, it suggests that price will fall.
As always, test for yourself.
Happy trading!
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125.
Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Integrated Market Analysis IndicatorThe Integrated Market Analysis Indicator is designed to provide traders with a macro perspective on market conditions, focusing on the S&P 500 (SPX) and market volatility (VIX), to assist in swing trading decisions. This script integrates various technical indicators and market health metrics to generate scores that help in assessing the overall market trend, potential breakout opportunities, and mean reversion scenarios. It is tailored for traders who wish to align their individual stock or index trades with broader market movements.
Functionality:
Trend Analysis: The script analyzes the trend of the S&P 500 using moving averages (5-day SMA, 10-day EMA, 20-day EMA) to determine whether the market is in an uptrend, downtrend, or neutral state. This provides a foundation for understanding the general market direction.
Volatility Assessment: It uses the VIX to gauge market volatility, which is crucial for risk management. The script calculates thresholds based on the 20-day SMA of the VIX to categorize the market volatility into low, medium, or high.
Market Breadth: The advance/decline ratio (A/D ratio) from the USI:ADVQ and USI:DECLQ indices gives an indication of market participation, helping to understand if the market movement is broad-based or led by a few stocks.
Scoring System: Three scores are calculated:
Trend Score: Evaluates the market trend in conjunction with volume, market breadth, and VIX to assign a grade from 'A' to 'D'.
Breakout Score: Assesses potential breakout conditions by looking at price action relative to dynamic support/resistance levels, short-term momentum, and volume.
Mean Reversion Score: Identifies conditions where mean reversion might occur, based on price movement, volume, and high VIX levels, indicating potential overbought or oversold conditions.
Risk Management: Position sizing recommendations are provided based on VIX levels and the calculated scores, aiming to adjust exposure according to market conditions.
How to Use the Script:
Application: Apply this indicator on any stock or index chart in TradingView. Since it uses data from SPX and VIX, the scores will reflect the macro environment regardless of the underlying chart.
Interpreting Scores:
Trend Score: Use this to gauge the overall market direction. An 'A' score might suggest a strong uptrend, making it a good time for bullish trades, while a 'D' could indicate a bearish environment.
Breakout Score: Look for 'A' scores when considering trades that aim to capitalize on breakouts. A 'B' might suggest a less certain breakout, requiring more caution.
Mean Reversion Score: A 'B' or 'A' here might be a signal to look for trades where you expect the price to revert to the mean after an extreme move.
Risk Management: Use the suggested position sizes ('Normal Size', '1/3 Size', '1/4 Size', '1/10 Size') to manage your risk exposure. Higher VIX levels or lower scores suggest reducing position sizes to mitigate risk.
Visual Cues: The script plots various SMAs, EMAs, and dynamic support/resistance levels, providing visual indicators of where the market might find support or resistance, aiding in entry and exit decisions.
How NOT to Use the Script:
Not for Intraday Trading: This indicator is designed for swing trading, focusing on daily or longer timeframes. Using it for intraday trading might not provide the intended insights due to its macro focus.
Avoid Over-reliance: While the script provides valuable insights, do not rely solely on it for trading decisions. Always consider additional analysis, news, and fundamental data.
Do Not Ignore Individual Stock Analysis: Although the script gives a macro view, individual stock analysis is crucial. The macro conditions might suggest a trend, but stock-specific factors could contradict this.
Not for High-Frequency Trading: The script's logic and the data it uses are not optimized for high-frequency trading strategies where microsecond decisions are made.
Misinterpretation of Scores: Do not misinterpret the scores as absolute signals. They are guidelines that should be part of a broader trading strategy.
Logic Explanation:
Moving Averages: The script uses different types of moving averages to smooth out price data, providing a clearer view of the trend over short to medium-term periods.
ATR for Volatility: The Average True Range (ATR) is used to calculate dynamic support and resistance levels, giving a sense of how much price movement can be expected, which helps in setting realistic expectations for price action.
VIX for Risk: By comparing current VIX levels to its 20-day SMA, the script assesses market fear or complacency, adjusting risk exposure accordingly.
Market Breadth: The A/D ratio helps to understand if the market movement is supported by a broad base of stocks or if it's narrow, which can influence the reliability of the trend.
This indicator should be used as part of a comprehensive trading strategy, providing a macro overlay to your trading decisions, ensuring you're not fighting against the broader market trends or volatility conditions. Remember, while it can guide your trading, always integrate it with other forms of analysis for a well-rounded approach.
Macro Context v1 - NobruzeraaaHMacro Context v1
Advanced Multi-Asset Correlation Analysis for Professional Trading
"In institutional trading, correlation is king. This panel puts the crown on your charts."
Overview
This is a sophisticated real-time market analysis tool that monitors critical institutional correlations across traditional and cryptocurrency markets. This indicator provides traders with actionable insights based on academic research and institutional trading patterns.
Features
- **Multi-Asset Correlation Engine**
- **13 Advanced Analysis Layers** covering macro, crypto, and institutional flows
- **Real-time Correlation Detection** between BTC, equities, bonds, and commodities
- **Institutional Divergence Alerts** for early trend identification
- **Risk Sentiment Analysis** using VIX, DXY, and yield curve data
**Professional Grade Analytics**
- **NDX/SPX vs BTC Correlation** - Critical tech-crypto relationship monitoring
- **VIX Breakout Detection** - Institutional panic (>30) and dangerous complacency (<15) alerts
- **Yield Curve Inversion Monitoring** - Recession signal detection via US10Y-US2Y spread
- **Institutional Flow Tracking** - Real proxies using MSTR/COIN performance
- **DXY Critical Levels** - USD dominance (>105) and weakness (<95) thresholds
**Smart Actionable Signals**
- **Opportunity Detection** in altcoins during confirmed risk-on periods
- **Divergence Warnings** when BTC-Tech correlations break down
- **Volatility Preparation** alerts during market complacency
- **Hedge Recommendations** during institutional flight to quality
Correlation Matrix Monitored
**Traditional Markets**
| Asset | Function | Institutional Significance |
|-------|----------|---------------------------|
| **SPX** | Equity benchmark | Risk-on/off sentiment |
| **NDX** | Tech growth proxy | Innovation capital flows |
| **VIX** | Volatility index | Fear/greed institutional gauge |
| **DXY** | Dollar strength | Global liquidity flows |
| **US10Y-US2Y** | Yield curve | Recession probability |
| **Gold** | Safe haven | Inflation hedge demand |
| **Copper** | Industrial metal | Growth expectations |
**Cryptocurrency Markets**
| Asset | Function | Institutional Significance |
|-------|----------|---------------------------|
| **BTC** | Digital store of value | Institutional adoption gauge |
| **ETH** | Smart contract platform | DeFi institutional interest |
| **BTC.D** | Bitcoin dominance | Crypto capital allocation |
| **USDT.D** | Stablecoin dominance | Risk-off crypto indicator |
| **TOTAL3** | Alt market cap | Retail vs institutional flow |
**Institutional Proxies**
| Asset | Function | Why It Matters |
|-------|----------|----------------|
| **MSTR** | MicroStrategy stock | Corporate BTC holdings proxy |
| **COIN** | Coinbase stock | Crypto institutional gateway |
---
Critical Correlations Detected
**1. Tech-Led Risk-On Confirmation**
**Trigger:** NDX outperforming SPX + BTC rising + VIX declining
**Signal:** Strong institutional appetite for growth assets
**Action:** Opportunity in tech and crypto momentum
**2. BTC-Tech Divergence Warning**
**Trigger:** NDX/SPX ratio positive + BTC declining significantly
**Signal:** Potential institutional crypto exit while maintaining tech exposure
**Action:** Monitor for broader crypto weakness
**3. Institutional Panic Mode**
**Trigger:** VIX > 30 + USDT.D rising + BTC/equities declining
**Signal:** Fear-driven liquidations across all risk assets
**Action:** Wait for clarity, prepare for volatility
**4. Dangerous Complacency**
**Trigger:** VIX < 15 + low volatility across assets
**Signal:** Market complacency reaching dangerous levels
**Action:** Prepare for sudden volatility spike
**5. Yield Curve Recession Signal**
**Trigger:** US10Y-US2Y spread deeply inverted (<-0.5%)
**Signal:** Bond market pricing in economic slowdown
**Action:** Defensive positioning, reduce risk exposure
**6. USD Super-Dominance**
**Trigger:** DXY > 105 + gold declining + risk assets under pressure
**Signal:** Extreme USD strength creating global liquidity stress
**Action:** Monitor emerging market stress, dollar-denominated debt concerns
**7. Altseason Confirmation**
**Trigger:** BTC.D declining + USDT.D declining + TOTAL3 outperforming + low VIX
**Signal:** Capital rotating from BTC to altcoins in risk-on environment
**Action:** Opportunity in alternative cryptocurrencies
---
Advanced Analytics Provided
**Risk Sentiment Classification**
- 🔴 **Fear in System** - Multiple fear indicators triggered
- 🟡 **Cautious Mode** - Mixed signals, proceed carefully
- 🟢 **Risk Appetite** - Confirmed risk-on environment
- 🟢 **Strong Risk-On** - Multiple bullish confirmations
- 🟠 **Dangerous Complacency** - Excessive optimism warning
**Macro Context Analysis**
- 💪 **Dollar Dominant** - USD strength driving global flows
- 🌍 **USD Weakening** - Emerging market and commodity positive
- ⚠️ **Market Stress** - Multiple stress indicators active
- 🚀 **Solid Bull Market** - Confirmed uptrend across assets
- 🏭 **Growth Acceleration** - Copper/Gold ratio signaling expansion
- 🛡️ **Defensive Rotation** - Flight to quality assets
**Actionable Intelligence**
- ✅ **Opportunity in Alts** - Multiple confirmations for altcoin exposure
- ⚠️ **Wait for Clarity** - High uncertainty, avoid new positions
- 🏛️ **Consider Hedge** - Defensive positioning recommended
- 📈 **Ride Momentum** - Trend continuation likely
- 🔍 **Monitor Divergence** - Correlation breakdown warning
- ⚠️ **Prepare for Volatility** - Complacency extreme reached
Technical Implementation
**Data Sources**
- **Traditional Markets:** TradingView real-time feeds
- **Cryptocurrency:** Binance spot prices and market cap data
- **Macro Data:** US Treasury yields, volatility indices
- **Update Frequency:** Every minute during market hours
**Calculation Methodology**
- **24-hour percentage changes** for all assets
- **Real-time price levels** for VIX and DXY thresholds
- **Spread calculations** for yield curve analysis
- **Ratio analysis** for relative performance metrics
**Multi-Language Support**
- 🇺🇸 **English** - Full professional terminology
- 🇪🇸 **Spanish** - Complete translation for Latin American markets
- 🇧🇷 **Portuguese** - Brazilian market terminology
---
Academic Foundation
This indicator is built upon peer-reviewed research and institutional trading patterns:
**Research-Based Correlations**
- **Bitcoin-NASDAQ correlation studies** (2024 academic papers)
- **VIX threshold analysis** from institutional trading desks
- **Yield curve inversion** recession prediction models
- **Dollar index breakout** historical analysis
- **Cryptocurrency dominance** flow studies
**Institutional Insights**
- **Fear & Greed Index** methodology adaptation
- **Professional volatility** threshold implementation
- **Corporate treasury** Bitcoin adoption tracking
- **Institutional proxy** correlation validation
---
Quick Start Guide
**Configuration**
- **Language Selection:** Choose your preferred language
- **Asset Selection:** Enable/disable specific asset monitoring
- **Timezone:** Set your preferred timezone for timestamp display
**Interpretation**
- **Green indicators:** Bullish/risk-on signals
- **Red indicators:** Bearish/risk-off signals
- **Yellow indicators:** Neutral/mixed signals
- **Orange indicators:** Warning/extreme conditions
---
Use Cases
**Traders**
- **Portfolio allocation** based on institutional flows
- **Risk management** through correlation monitoring
- **Market timing** using sentiment extremes
- **Divergence trading** opportunities
**Analysts**
- **Multi-asset correlation** research
- **Macro theme** identification
- **Risk sentiment** quantification
- **Flow analysis** across asset classes
**Cryptocurrency Investors**
- **Altseason timing** through dominance analysis
- **Macro correlation** understanding
- **Institutional adoption** tracking
- **Risk-on/off** positioning
---
Important Disclaimers
- **Not Financial Advice:** This tool provides analytical insights, not investment recommendations
- **Market Risk:** All trading involves substantial risk of loss
- **Correlation Changes:** Market correlations can shift rapidly during crisis periods
- **Supplementary Tool:** Should be used alongside other analysis methods
This indicator represents cutting-edge market analysis combining traditional finance and cryptocurrency insights. Regular updates ensure continued accuracy as market structures evolve.
**Version:** 1.0
**Last Updated:** 2025
**Compatibility:** Pine Script v6
**Category:** Multi-Asset Analysis
Combined EMA Technical AnalysisThis script is written in Pine Script (version 5) for TradingView and creates a comprehensive technical analysis indicator called "Combined EMA Technical Analysis." It overlays multiple technical indicators on a price chart, including Exponential Moving Averages (EMAs), VWAP, MACD, PSAR, RSI, Bollinger Bands, ADX, and external data from the S&P 500 (SPX) and VIX indices. The script also provides visual cues through colors, shapes, and a customizable table to help traders interpret market conditions.
Here’s a breakdown of the script:
---
### **1. Purpose**
- The script combines several popular technical indicators to analyze price trends, momentum, volatility, and market sentiment.
- It uses color coding (green for bullish, red for bearish, gray/white for neutral) and a table to display key information.
---
### **2. Custom Colors**
- Defines custom RGB colors for bullish (`customGreen`), bearish (`customRed`), and neutral (`neutralGray`) signals to enhance visual clarity.
---
### **3. User Inputs**
- **EMA Colors**: Users can customize the colors of five EMAs (8, 20, 9, 21, 50 periods).
- **MACD Settings**: Adjustable short length (12), long length (26), and signal length (9).
- **RSI Settings**: Adjustable length (14).
- **Bollinger Bands Settings**: Length (20), multiplier (2), and proximity threshold (0.1% of band width).
- **ADX Settings**: Adjustable length (14).
- **Table Settings**: Position (e.g., "Bottom Right") and text size (e.g., "Small").
---
### **4. Indicator Calculations**
#### **Exponential Moving Averages (EMAs)**
- Calculates five EMAs: 8, 20, 9, 21, and 50 periods based on the closing price.
- Used to identify short-term and long-term trends.
#### **Volume Weighted Average Price (VWAP)**
- Resets daily and calculates the average price weighted by volume.
- Color-coded: green if price > VWAP (bullish), red if price < VWAP (bearish), white if neutral.
#### **MACD (Moving Average Convergence Divergence)**
- Uses short (12) and long (26) EMAs to compute the MACD line, with a 9-period signal line.
- Displays "Bullish" (green) if MACD > signal, "Bearish" (red) if MACD < signal.
#### **Parabolic SAR (PSAR)**
- Calculated with acceleration factors (start: 0.02, increment: 0.02, max: 0.2).
- Indicates trend direction: green if price > PSAR (bullish), red if price < PSAR (bearish).
#### **Relative Strength Index (RSI)**
- Measures momentum over 14 periods.
- Highlighted in green if > 70 (overbought), red if < 30 (oversold), white otherwise.
#### **Bollinger Bands (BB)**
- Uses a 20-period SMA with a 2-standard-deviation multiplier.
- Color-coded based on price position:
- Green: Above upper band or close to it.
- Red: Below lower band or close to it.
- Gray: Neutral (within bands).
#### **Average Directional Index (ADX)**
- Manually calculates ADX to measure trend strength:
- Strong trend: ADX > 25.
- Very strong trend: ADX > 50.
- Direction: Bullish if +DI > -DI, bearish if -DI > +DI.
#### **EMA Crosses**
- Detects bullish (crossover) and bearish (crossunder) events for:
- EMA 9 vs. EMA 21.
- EMA 8 vs. EMA 20.
- Visualized with green (bullish) or red (bearish) circles.
#### **SPX and VIX Data**
- Fetches daily closing prices for the S&P 500 (SPX) and VIX (volatility index).
- SPX trend: Bullish if EMA 9 > EMA 21, bearish if EMA 9 < EMA 21.
- VIX levels: High (> 25, fear), Low (< 15, stability).
- VIX color: Green if SPX bullish and VIX low, red if SPX bearish and VIX high, white otherwise.
---
### **5. Visual Outputs**
#### **Plots**
- EMAs, VWAP, and PSAR are plotted on the chart with their respective colors.
- EMA crosses are marked with circles (green for bullish, red for bearish).
#### **Table**
- Displays a summary of indicators in a customizable position and size.
- Indicators shown (if enabled):
- EMA 8/20, 9/21, 50: Green dot if bullish, red if bearish.
- VWAP: Green if price > VWAP, red if price < VWAP.
- MACD: Green if bullish, red if bearish.
- MACD Zero: Green if MACD > 0, red if MACD < 0.
- PSAR: Green if price > PSAR, red if price < PSAR.
- ADX: Arrows for very strong trends (↑/↓), dots for weaker trends, colored by direction.
- Bollinger Bands: Arrows (↑/↓) or dots based on price position.
- RSI: Numeric value, colored by overbought/oversold levels.
- VIX: Numeric value, colored based on SPX trend and VIX level.
---
### **6. Alerts**
- Triggers alerts for EMA 8/20 crosses:
- Bullish: "EMA 8/20 Bullish Cross on Candle Close!"
- Bearish: "EMA 8/20 Bearish Cross on Candle Close!"
---
### **7. Key Features**
- **Flexibility**: Users can toggle indicators on/off in the table and adjust parameters.
- **Visual Clarity**: Consistent use of green (bullish), red (bearish), and neutral colors.
- **Comprehensive**: Combines trend, momentum, volatility, and market sentiment indicators.
---
### **How to Use**
1. Add the script to TradingView.
2. Customize inputs (colors, lengths, table position) as needed.
3. Interpret the chart and table:
- Green signals suggest bullish conditions.
- Red signals suggest bearish conditions.
- Neutral signals indicate indecision or consolidation.
4. Set up alerts for EMA crosses to catch trend changes.
This script is ideal for traders who want a multi-indicator dashboard to monitor price action and market conditions efficiently.
Market Sentiment Indicator with Adjustable Metric Smoothing
Market Sentiment Indicator for BTCUSDT: How It Works
This indicator analyzes market sentiment for BTCUSDT by combining data from various financial metrics like the VIX, DXY, SPX, GOLD, USOIL, US10Y, and BTC itself. It processes this data to assign scores that reflect bearish, bullish, or neutral conditions, ultimately providing a sentiment value. Here’s the step-by-step process:
1. Input Parameters
VIX Thresholds: Define levels (e.g., high, moderate, low) to identify risk-off (bearish) or risk-on (bullish) market conditions.
Change Thresholds: Set limits for "large" and "small" percentage changes in metrics like DXY (Dollar Index), SPX (S&P 500), and BTC, as well as other assets.
2. Data Fetching
Collect closing prices for:
VIX (volatility index)
DXY (U.S. Dollar Index)
SPX (S&P 500)
GOLD (gold price)
USOIL (crude oil price)
US10Y (10-year Treasury yield)
BTC (from the BTCUSDT chart)
These are typically retrieved using a function like request.security in a trading platform.
3. Smoothed Data
Apply Exponential Moving Averages (EMAs) to the closing prices of each metric to smooth out noise. The smoothing length (e.g., 10-day, 20-day) is customizable.
Calculate:
Smoothed percentage changes for DXY, SPX, BTC, GOLD, and USOIL.
Absolute change for US10Y (since it’s a yield, not a price).
4. Step Scores Calculation
The indicator assigns scores based on specific market conditions:
Step 1: Strong Risk-Off (Bearish)
High VIX, strongly rising DXY, strongly falling SPX and BTC.
Step 2: Moderate Risk-Off (Bearish)
Moderately high VIX, moderately rising DXY, moderately falling SPX and BTC.
Step 3: Neutral
VIX near neutral, small changes in DXY, SPX, and BTC.
Step 4: Moderate Risk-On (Bullish)
Moderately low VIX, moderately falling DXY, moderately rising SPX and BTC.
Step 5: Strong Risk-On (Bullish)
Low VIX, strongly falling DXY, strongly rising SPX and BTC.
Step 6: Inflation Concern
Rising GOLD, USOIL, US10Y, and falling DXY.
Step 7: Deflation Concern
Falling GOLD, USOIL, US10Y, and rising DXY.
5. Composite Scores
Bearish Composite: Sum of Step 1 (strong risk-off) and Step 2 (moderate risk-off) scores.
Bullish Composite: Sum of Step 4 (moderate risk-on) and Step 5 (strong risk-on) scores.
6. Sentiment Calculation
Compute a weighted average of all step scores:
Weights: -3 (Step 1), -2 (Step 2), 0 (Step 3), 2 (Step 4), 3 (Step 5), 1 (Step 6), -1 (Step 7).
This produces a sentiment score ranging from negative (bearish) to positive (bullish).
If the total score is zero, sentiment is set to zero to avoid issues.
7. Visualization
Bearish Composite: Plotted in red.
Bullish Composite: Plotted in green.
Sentiment: Plotted in blue.
Why It’s Useful
This indicator gives you a comprehensive view of market sentiment by blending traditional financial metrics (like VIX and SPX) with BTC-specific data. The use of EMAs and customizable thresholds makes it adaptable to different timeframes and market conditions, helping you gauge whether the market is leaning bearish, bullish, or neutral.
Market Internals & InfoThis script provides various information on Market Internals and other related info. It was a part of the Daily Levels script but that script was getting very large so I decided to separate this piece of it into its own indicator. I plan on adding some additional features in the near future so stay tuned for those!
The script provides customizability to show certain market internals, tickers, and even Market Profile TPO periods.
Here is a summary of each setting:
NASDAQ and NYSE Breadth Ratio
- Ratio between Up Volume and Down Volume for NASDAQ and NYSE markets. This can help inform about the type of volume flowing in and out of these exchanges.
Advance/Decline Line (ADL)
The ADL focuses specifically on the number of advancing and declining stocks within an index, without considering their trading volume.
Here's how the ADL works:
It tracks the daily difference between the number of stocks that are up in price (advancing) and the number of stocks that are down in price (declining) within a particular index.
The ADL is a cumulative measure, meaning each day's difference is added to the previous day's total.
If there are more advancing stocks, the ADL goes up.
If there are more declining stocks, the ADL goes down.
By analyzing the ADL, investors can get a sense of how many stocks are participating in a market move.
Here's what the ADL can tell you:
Confirmation of Trends: When the ADL moves in the same direction as the underlying index (e.g., ADL rising with a rising index), it suggests broad participation in the trend and potentially stronger momentum.
Divergence: If the ADL diverges from the index (e.g., ADL falling while the index is rising), it can be a warning sign. This suggests that fewer stocks are participating in the rally, which could indicate a weakening trend.
Keep in mind:
The ADL is a backward-looking indicator, reflecting past market activity.
It's often used in conjunction with other technical indicators for a more complete picture.
TRIN Arms Index
The TRIN index, also called the Arms Index or Short-Term Trading Index, is a technical analysis tool used in the stock market to gauge market breadth and sentiment. It essentially compares the number of advancing stocks (gaining in price) to declining stocks (losing price) along with their trading volume.
Here's how to interpret the TRIN:
High TRIN (above 1.0): This indicates a weak market where declining stocks and their volume are dominating the market. It can be a sign of a potential downward trend.
Low TRIN (below 1.0): This suggests a strong market where advancing stocks and their volume are in control. It can be a sign of a potential upward trend.
TRIN around 1.0: This represents a more balanced market, where it's difficult to say which direction the market might be headed.
Important points to remember about TRIN:
It's a short-term indicator, primarily used for intraday trading decisions.
It should be used in conjunction with other technical indicators for a more comprehensive market analysis. High or low TRIN readings don't guarantee future price movements.
VIX/VXN
VIX and VXN are both indexes created by the Chicago Board Options Exchange (CBOE) to measure market volatility. They differ based on the underlying index they track:
VIX (Cboe Volatility Index): This is the more well-known index and is considered the "fear gauge" of the stock market. It reflects the market's expectation of volatility in the S&P 500 index over the next 30 days.
VXN (Cboe Nasdaq Volatility Index): This is a counterpart to the VIX, but instead gauges volatility expectations for the Nasdaq 100 index over the coming 30 days. The tech-heavy Nasdaq can sometimes diverge from the broader market represented by the S&P 500, hence the need for a separate volatility measure.
Both VIX and VXN are calculated based on the implied volatilities of options contracts listed on their respective indexes. Here's a general interpretation:
High VIX/VXN: Indicates a high level of fear or uncertainty in the market, suggesting investors expect significant price fluctuations in the near future.
Low VIX/VXN: Suggests a more complacent market with lower expectations of volatility.
Important points to remember about VIX and VXN:
They are forward-looking indicators, reflecting market sentiment about future volatility, not necessarily current market conditions.
High VIX/VXN readings don't guarantee a market crash, and low readings don't guarantee smooth sailing.
These indexes are often used by investors to make decisions about portfolio allocation and hedging strategies.
Inside/Outside Day
This provides a quick indication of it we are still trading inside or outside of yesterdays range and will show "Inside Day" or "Outside Day" based upon todays range vs. yesterday's range.
Custom Ticker Choices
Ability to add up to 5 other tickers that can be tracked within the table
Show Market Profile TPO
This only shows on timeframes less than 30m. It will show both the current TPO period and the remaining time within that period.
Table Customization
Provided drop downs to change the text size and also the location of the table.
Market Traffic Light (redesigned)redesigned the market traffic light from funcharts, all honor to him, I just put a new design ;-) and some bugfixes
1. Section (Fear & Greed)
Approximation of the CNN Money Fear & Greed index based on code of user MagicEins. The index shows values between 0 (extreme fear, red) and 100 (extreme greed, green).
2. Section (warning signs)
VIX: Values above 20 are red and below green. The legend shows the value of the current bar including the change from the bar before. The average VIX is about 16. Values over 20 are a sign of stressed market.
Distribution days: A distribution day (loss to the day before > 0,2 % and higher volume ) is marked with a yellow dot. In case there are more than four distributions days within 25 markets days the dot is orange. When big players redistribute their investments distribution days can occur. If this is done often (more than four times within 25 market days) it is possible that the markets changes or that a sector rotation occurs. For calculation distribution days futures of S&P 500 ( ES1! ) and NASDAQ ( NQ1! ) are used because the volume for this calculation is needed. TradingView does not support volumes for S&P 500 or NASDAQ directly.
Markets: A green/red dot signals that the market is above/below its 25-Daily-EMA. A green/red square signals that the market is above/below its 25-Weekly-EMA. Markets can give as a feeling about where investors store their money. E.g. when markets are falling but DUX (Down Jones Utility Average) is rising this means that investors put their money into save haven. This can be a sign that the markets will fall more.
3. Section (panic signs, = signs of reaching a low within a correction of a crash)
VIX-Reversion: A VIX reversion day ( VIX > 20 & VIX high > VIX high of the day before & VIX high – VIX close > 3) is marked as a yellow dot
VVIX: A value equal or above 140 is marked with a yellow dot and shows absolute panic.
PCR Intra max: A value equal or above 1.4 is marked with a yellow dot.
New high/lows: New highs/lows are shown for AMEX, NYSE and NASDAQ. A yellow dot is shown if the ratio is less or equal than 0. 01 .
Down-Day: Down days are shown for AMEX, NYSE and NASDA. A yellow dot is shown if at least 90 % of the whole volume (up and down) is a down volume .
In Addition to the warning signs in the second section a check of the Advance Decline Line (NYSE and NASDAQ) for bullish and bearish divergences is useful. The whole set-up can be seen in the screenshot.
Only one signal normally does not give us a good prediction. Therefore we need to see these indication as a bundle. TradingView gives us the opportunity to check some striking market situations in the past. So feel free to test this indication for building up your own opinion.
Please feel free to comment in case of failures, improvements or experiences (good or bad).
Market Traffic LightThis indicator visualizes warning and panic signs, which are shown separately.
1. Section (Fear & Greed)
Approximation of the CNN Money Fear & Greed index based on code of user MagicEins. The index shows values between 0 (extreme fear, red) and 100 (extreme greed, green).
2. Section (warning signs)
VIX: Values above 20 are red and below green. The legend shows the value of the current bar including the change from the bar before. The average VIX is about 16. Values over 20 are a sign of stressed market.
Distribution days: A distribution day (loss to the day before > 0,2 % and higher volume) is marked with a yellow dot. In case there are more than four distributions days within 25 markets days the dot is orange. When big players redistribute their investments distribution days can occur. If this is done often (more than four times within 25 market days) it is possible that the markets changes or that a sector rotation occurs. For calculation distribution days futures of S&P 500 (ES1!) and NASDAQ (NQ1!) are used because the volume for this calculation is needed. TradingView does not support volumes for S&P 500 or NASDAQ directly.
Markets: A green/red dot signals that the market is above/below its 25-Daily-EMA. A green/red square signals that the market is above/below its 25-Weekly-EMA. Markets can give as a feeling about where investors store their money. E.g. when markets are falling but DUX (Down Jones Utility Average) is rising this means that investors put their money into save haven. This can be a sign that the markets will fall more.
3. Section (panic signs, = signs of reaching a low within a correction of a crash)
VIX-Reversion: A VIX reversion day (VIX > 20 & VIX high > VIX high of the day before & VIX high – VIX close > 3) is marked as a yellow dot
VVIX: A value equal or above 140 is marked with a yellow dot and shows absolute panic.
PCR Intra max: A value equal or above 1.4 is marked with a yellow dot.
New high/lows: New highs/lows are shown for AMEX, NYSE and NASDAQ. A yellow dot is shown if the ratio is less or equal than 0.01.
Down-Day: Down days are shown for AMEX, NYSE and NASDA. A yellow dot is shown if at least 90 % of the whole volume (up and down) is a down volume.
In Addition to the warning signs in the second section a check of the Advance Decline Line (NYSE and NASDAQ) for bullish and bearish divergences is useful. The whole set-up can be seen in the screenshot.
Only one signal normally does not give us a good prediction. Therefore we need to see these indication as a bundle. TradingView gives us the opportunity to check some striking market situations in the past. So feel free to test this indication for building up your own opinion.
Please feel free to comment in case of failures, improvements or experiences (good or bad).
Implied Volatility TestThought for 42sOverview of the "Implied Volatility Suite" Indicator
The "Implied Volatility Suite" is a custom TradingView indicator written in Pine Script (version 6) designed to estimate and visualize implied volatility (IV) for any stock or asset charted on TradingView. Unlike true implied volatility derived from options pricing (e.g., via Black-Scholes), this script provides a synthetic approximation based on historical price data. It offers flexibility by allowing users to choose between two calculation methods: "Model Implied Volatility" (a statistical projection based on log-normal assumptions) or "VixFix" (a historical volatility proxy inspired by Larry Williams' VIX Fix indicator). The output is plotted as an oscillating line, similar to the Relative Strength Index (RSI), making it easy to interpret overbought/oversold conditions or trends in volatility. Users can select what to plot: raw Implied Volatility, IV Rank, IV Percentile, or Volatility Skew Index, with color-coded visuals for quick analysis (e.g., red/green thresholds for ranks/percentiles).
This indicator is particularly useful for stocks without listed options, where real IV data isn't available, or for traders seeking a quick volatility gauge integrated into their charts.
What the Code Does
At its core, the script computes a volatility metric and transforms it into one of four plottable formats, then displays it as a line chart in a separate pane below the main price chart. Here's a breakdown:
User Inputs and Configuration:
Volatility Calculation Method: Choose "Model Implied Volatility" (default) or "VixFix".
Expiry Parameters (for Model method): Minutes, Hours, and Days until expiry (default 45 days). These are combined into Days (as a float for fractional days) and converted to years (Expiry = Days / 365).
Length Parameters: For Model IV rank/percentile (default 365), VixFix length (default 252, with recommendations like 9, 22, etc.), and VixFix rank/percentile length (default 252).
Output Choice: Select "Implied Volatility", "IV Rank", "IV Percentile" (default "IV Rank"), or "Volatility Skew Index".
The script uses spot = close as the reference price.
Core Calculations:
Model Implied Volatility:
Computes log returns: LogReturn = math.log(spot / spot ) (percentage change between prior bars).
Calculates the simple moving average (Average) and standard deviation (STDEV) of log returns over an integer-rounded Days period.
Projects a time-adjusted mean (Time_Average = Days * Average) and standard deviation (Time_STDEV = STDEV * math.sqrt(Days)), assuming a random walk scaled by time.
Derives upper and lower bounds for the price at expiry: upper = spot * math.exp(Time_Average + 1 * Time_STDEV) and lower = spot * math.exp(Time_Average - 1 * Time_STDEV), representing a 1-standard-deviation range under log-normal distribution.
Computes the width of this range (width = upper - lower), halves it to get standard_dev, and annualizes it to sigma: sigma = standard_dev / (spot * math.sqrt(Expiry)).
Applies an "optimizer": If sigma > 1, halve it (to prevent unrealistically high values).
Result: IV (a decimal, e.g., 0.25 for 25% IV).
VixFix (Synthetic VIX Proxy):
Based on Larry Williams' VIX Fix formula, which estimates fear/volatility without options data: (ta.highest(spot, VIXFixLength) - low) / ta.highest(spot, VIXFixLength) * 100.
The script extends this for "upside" and "downside" by shifting the spot and low prices by multiples of standard deviation (0 for base VixFix).
VixFix is the average of upside(0) and downside(0), which are identical, yielding the standard VIX Fix value.
Volatility Skew Index:
Measures asymmetry in volatility (e.g., higher downside vol indicating fear).
For Model: Averages "upside IV" (calculated on spot shifted up by 1,2,3 * stdev) minus "downside IV" (shifted down).
For VixFix: Similar, but using shifted VIX Fix formulas for upside/downside.
Positive skew might indicate upside bias; negative indicates downside.
Rank and Percentile:
IV Rank: Normalizes the current volatility: (Volatility - ta.lowest(Volatility, Len)) / (ta.highest(Volatility, Len) - ta.lowest(Volatility, Len)) * 100.
IV Percentile: Uses ta.percentrank(Volatility, Len) to show what percentage of past values are below the current.
Len depends on the chosen method (e.g., 365 for Model).
Plotting and Visualization:
Selects VolatilityData based on user choice (e.g., IV * 100 for percentage display).
Applies colors: Red (<50) or green (>=50) for rank/percentile; aqua for skew; yellow for raw IV.
Plots as a line: plot(VolatilityData, color=col, title="Volatility Data").
The script switches logic seamlessly via conditionals (e.g., Volatility = VolCalc == "VixFix" ? VixFix : IV), ensuring the chosen method and output are used.
How It Works (Step-by-Step Execution Flow)
Initialization: Reads user inputs and sets spot = close. Computes Days (float) and DaysInt = math.round(Days) for integer lengths in TA functions.
Log Returns and Base Stats: For Model, calculates log returns, then SMA and STDEV over DaysInt.
Projection and IV Derivation: Scales stats to expiry time, computes bounds, derives sigma/IV.
Skew Functions: Defines reusable functions Model_Upside(i) and Model_Downside(i) (or VIX equivalents) to shift prices and recompute IV/VIX on shifted series.
Aggregation: Computes skew as average difference; sets Volatility to IV or VixFix.
Rank/Percentile/Skew: Applies over user-defined lengths.
Output Logic: Determines what to plot and its color based on VolatilityChoice.
Rendering: Plots the line in TradingView's indicator pane, updating bar-by-bar.
This leverages Pine Script's built-in functions like ta.sma, ta.stdev, ta.highest/lowest, and math.exp/log for efficiency.
Pros
Accessibility: Provides IV estimates for non-optionable assets (e.g., individual stocks, ETFs without options), filling a gap in TradingView's native tools.
Customization: Multiple methods (Model for forward-looking, VixFix for historical) and outputs (raw, ranked, percentile, skew) allow tailored analysis. Expiry adjustments make it suitable for options-like thinking.
Visual Simplicity: Oscillates like RSI (0-100 for ranks/percentiles), with intuitive colors, aiding quick decisions (e.g., high IV Rank might signal options selling opportunities).
No External Data Needed: Relies solely on chart data (close, low), making it lightweight and real-time.
Educational Value: Exposes users to volatility concepts like skew and log-normal projections, potentially improving trading strategies.
Flexibility in Timeframes: Works on any chart interval, with adjustable lengths for short-term (e.g., 9-bar VixFix) or long-term (365-day ranks).
Limitations
Not True Implied Volatility: This is a historical or model-based proxy, not derived from actual options prices. It may overestimate/underestimate real market-implied vol, especially during events (e.g., earnings) where options premium spikes unpredictably.
Assumptions in Model Method: Relies on log-normal distribution and constant volatility, ignoring fat tails, jumps, or mean reversion in real markets. The "optimizer" (halving sigma >1) is arbitrary and may distort results.
VixFix Variant Limitations: While based on a proven indicator, the upside/downside shifts (by stdev of prices, not returns) could be inaccurate for skew, as stdev(prices) doesn't scale properly with returns. It's backward-looking, not predictive like true IV.
Data Requirements: Needs sufficient historical bars (e.g., 365 for ranks), failing on new listings or short charts. Rounding Days to integer may introduce minor inaccuracies for fractional expiries.
Computational Intensity: Functions like repeated ta.stdev and shifts for skew (called multiple times per bar) could slow performance on long histories or low-power devices.
No Real-Time Options Integration: Doesn't pull live options data; users must manually compare to actual IV (e.g., via CBOE VIX for indices).
Potential for Misinterpretation: Oscillating line might mislead (e.g., high IV Rank doesn't always mean "sell vol"), and skew calculation is non-standard, requiring user expertise.
Version Dependency: Built for Pine v6 (as of 2025); future TradingView updates could break it, though it's straightforward to migrate.
Overall, this script is a valuable tool for volatility-aware trading but should be used alongside other indicators (e.g., ATR, Bollinger Bands) and validated against real options data when available. For improvements, consider backtesting its signals or integrating alerts for thresholds.1.9sHow can Grok help?
EMI, RSI, Pivot based technical IndicatorsMulti-Indicator Trading Toolkit for Comprehensive Market Analysis
This Pine Script-based trading tool integrates multiple technical indicators to provide traders with a holistic view of market trends, volatility, and key price levels. Designed for flexibility, the script allows users to customize settings to match their trading strategy.
Key Features & Functionalities
1. Exponential Moving Averages (EMA)
Plots four configurable EMAs (default: 9, 21, 50, and 200) to identify trend direction and potential support/resistance zones.
2. Relative Strength Index (RSI)
Calculates RSI (default length: 14) to highlight overbought (70) and oversold (30) market conditions.
Customizable thresholds allow traders to adapt the RSI to different market conditions.
3. Pivot Points with Support & Resistance Levels
Computes pivot levels based on the previous day’s high, low, and close.
Includes dynamic support (S1-S5) and resistance (R1-R5) levels to assist in trade decision-making.
4. Volume-Weighted Average Price (VWAP)
Tracks the average price weighted by volume over a session, helping traders determine fair value zones.
5. Opening Range Breakout (ORB)
Identifies potential breakout opportunities based on the highest and lowest price within a user-defined opening range (5m, 15m, or 30m, etc.).
Highlights these levels to assist in trade entries and exits.
6. Multi-Timeframe RSI Analysis
Retrieves and displays RSI values from multiple timeframes (e.g., 1m, 5m, 15m, 1H, 1D).
Helps traders analyze RSI trends across different timeframes in a single view.
7. Volatility Index (VIX) Tracking
Monitors VIX data to gauge market volatility.
Displays daily percentage change and short-term variations (15 min, 1 hour), aiding in risk assessment.
8. Dynamic Table Display
Organizes key data (RSI values, VIX levels, and volatility changes) in customizable tables for an easy-to-read format.
Users can modify table position, colors, and display settings for personalized analysis.
9. Advanced Plotting for Visual Clarity
Clearly plots EMA, RSI, pivot levels, ORB levels, VWAP, and VIX movements on the chart.
Uses distinct colors and styles to enhance readability.
10. Customizable Inputs for Personalized Analysis
Provides a user-friendly input panel to modify settings, including EMA periods, RSI thresholds, ORB duration, table position, and plotting preferences.
Enables traders to tailor the script to their preferred trading style.
🧠 How the Indicators Work Together (Strategy Logic)
This script is not just a visual dashboard of common indicators — it’s a cohesive trading assistant designed to help traders make data-driven decisions through multi-layered confluence logic:
1. Trend Confirmation using EMAs:
The combination of 9/21/50/200 EMAs filters trades based on overall trend strength.
For example, long trades are considered only when the 21 EMA is above the 50 EMA and price is above the 9 EMA, confirming bullish momentum.
2. Momentum Alignment via Multi-Timeframe RSI:
Traditional RSI can give early or false signals. This script fetches RSI values from multiple timeframes (1m, 5m, 15m, 1H, 1D) to identify consistent strength or weakness across market structures.
3. Only when RSI is oversold/overbought across multiple timeframes does the signal become valid — reducing noise.
Breakout Confirmation with ORB + VWAP:
The script detects opening range breakouts (ORB) and filters them using VWAP and EMA zones to confirm whether breakouts are supported by volume and trend.
For instance, a long breakout above the ORB high is validated only when price is also above VWAP and EMAs align.
4. Volatility Context with VIX:
Incorporates VIX changes (live, 15m, 1h) to assess whether market conditions favor breakouts or consolidation.
If VIX is rising sharply, the script highlights caution zones — useful for adjusting position size or avoiding false breakouts.
5. Dynamic Signal Filtering:
Instead of triggering alerts directly, this script displays aligned conditions visually in a dynamic table.
Traders can see at a glance whether all confluence layers are in agreement, helping reduce over trading and enhance timing.
🌟 What Makes This Script Unique
✅ Strategic Integration of trend, momentum, volatility, and range-breakout concepts — not just plotted indicators.
✅ Multi-timeframe RSI logic presented in a table — letting traders spot alignment across timeframes without switching charts.
✅ Built-in volatility filter (VIX) for risk context, a feature rarely combined with ORB and trend indicators in a single script.
✅ Adaptive to all timeframes and instruments, especially useful for high-beta indices like Bank Nifty or Nasdaq.
✅ User-friendly customization, allowing traders to tailor everything from ORB window size to table position and indicator settings.
This all-in-one trading toolkit is designed for traders seeking a structured, data-driven approach to technical analysis. Whether you’re a short-term trader or a long-term investor, this script offers the flexibility and insight needed to navigate the markets effectively.
Internals Elite NYSE [Beta]Overview:
This indicator is designed to provide traders with a quick overview of key market internals and metrics in a single, easy-to-read table displayed directly on the chart. It incorporates a variety of metrics that help gauge market sentiment, momentum, and overall market conditions.
The table dynamically updates in real-time and uses color-coding to highlight significant changes or thresholds, allowing traders to quickly interpret the data and make informed trading decisions.
Features:
Market Internals:
TICK: Measures the difference between the number of stocks ticking up versus those ticking down on the NYSE. Green or red background indicates if it crosses a user-defined threshold.
Advance/Decline (ADD): Shows the net number of advancing versus declining stocks on the NYSE. Color-coded to show positive, negative, or neutral conditions.
Volatility Metrics:
VIX Change (%): Displays the percentage change in the Volatility Index (VIX), a key gauge of market fear or complacency. Color-coded for direction.
VIX Price: Displays the current VIX price with thresholds to indicate low, medium, or high volatility.
Other Market Metrics:
DXY Change (%): Percentage change in the US Dollar Index (DXY), indicating dollar strength or weakness.
VWAP Deviation (%): Percentage of stocks above VWAP (Volume Weighted Average Price), helping traders assess intraday buying and selling pressure.
Asset-Specific Metrics:
BTCUSD Change (%): Percentage change in Bitcoin (BTC) price, useful for monitoring cryptocurrency sentiment.
SPY Change (%): Percentage change in the S&P 500 ETF (SPY), a proxy for the overall stock market.
Current Ticker Change (%): Percentage change in the currently selected ticker on the chart.
US10Y Change (%): Percentage change in the yield of the 10-Year US Treasury Note (TVC:US10Y), an important macroeconomic indicator.
Customizable Appearance:
Adjustable text size to suit your chart layout.
User-defined thresholds for key metrics (e.g., TICK, ADD, VWAP, VIX).
Dynamic Table Placement:
You can position the table anywhere on the chart: top-right, top-left, bottom-right, bottom-left, middle-right, or middle-left.
How to Use:
Add the Indicator to Your Chart:
Apply the indicator to your chart from the Pine Script editor in TradingView.
Customize the Inputs:
Adjust the thresholds for TICK, ADD, VWAP, and VIX according to your trading style.
Enable or disable the metrics you want to see in the table by toggling the display options for each metric (e.g., Show TICK, Show BTC, Show SPY).
Set the table placement to your preferred position on the chart.
Interpret the Table:
Look for color-coded cells to quickly identify significant changes or breaches of thresholds.
Positive values are typically shown in green, negative values in red, and neutral/insignificant changes in gray.
Use metrics like TICK and ADD to gauge market breadth and momentum.
Refer to VWAP deviation to assess intraday buying or selling pressure.
Monitor the VIX and US10Y changes to stay aware of macroeconomic and volatility shifts.
Incorporate Into Your Strategy:
Use the indicator alongside technical analysis to confirm setups or identify areas of caution.
Keep an eye on correlated metrics (e.g., VIX and SPY) for broader market context.
Use BTCUSD or DXY as additional indicators of risk-on/risk-off sentiment.
Ideal Users:
Day Traders: Quickly gauge intraday market conditions and momentum.
Swing Traders: Identify broader sentiment shifts using metrics like ADD, DXY, and US10Y.
Macro Investors: Stay updated on key macroeconomic indicators like the 10-Year Treasury yield (US10Y) and the US Dollar Index (DXY).
This indicator serves as a comprehensive tool for understanding market conditions at a glance, enabling traders to act decisively based on the latest data.
Implied and Historical VolatilityAbstract
This TradingView indicator visualizes implied volatility (IV) derived from the VIX index and historical volatility (HV) computed from past price data of the S&P 500 (or any selected asset). It enables users to compare market participants' forward-looking volatility expectations (via VIX) with realized past volatility (via historical returns). Such comparisons are pivotal in identifying risk sentiment, volatility regimes, and potential mispricing in derivatives.
Functionality
Implied Volatility (IV):
The implied volatility is extracted from the VIX index, often referred to as the "fear gauge." The VIX represents the market's expectation of 30-day forward volatility, derived from options pricing on the S&P 500. Higher values of VIX indicate increased uncertainty and risk aversion (Whaley, 2000).
Historical Volatility (HV):
The historical volatility is calculated using the standard deviation of logarithmic returns over a user-defined period (default: 20 trading days). The result is annualized using a scaling factor (default: 252 trading days). Historical volatility represents the asset's past price fluctuation intensity, often used as a benchmark for realized risk (Hull, 2018).
Dynamic Background Visualization:
A dynamic background is used to highlight the relationship between IV and HV:
Yellow background: Implied volatility exceeds historical volatility, signaling elevated market expectations relative to past realized risk.
Blue background: Historical volatility exceeds implied volatility, suggesting the market might be underestimating future uncertainty.
Use Cases
Options Pricing and Trading:
The disparity between IV and HV provides insights into whether options are over- or underpriced. For example, when IV is significantly higher than HV, options traders might consider selling volatility-based derivatives to capitalize on elevated premiums (Natenberg, 1994).
Market Sentiment Analysis:
Implied volatility is often used as a proxy for market sentiment. Comparing IV to HV can help identify whether the market is overly optimistic or pessimistic about future risks.
Risk Management:
Institutional and retail investors alike use volatility measures to adjust portfolio risk exposure. Periods of high implied or historical volatility might necessitate rebalancing strategies to mitigate potential drawdowns (Campbell et al., 2001).
Volatility Trading Strategies:
Traders employing volatility arbitrage can benefit from understanding the IV/HV relationship. Strategies such as "long gamma" positions (buying options when IV < HV) or "short gamma" (selling options when IV > HV) are directly informed by these metrics.
Scientific Basis
The indicator leverages established financial principles:
Implied Volatility: Derived from the Black-Scholes-Merton model, implied volatility reflects the market's aggregate expectation of future price fluctuations (Black & Scholes, 1973).
Historical Volatility: Computed as the realized standard deviation of asset returns, historical volatility measures the intensity of past price movements, forming the basis for risk quantification (Jorion, 2007).
Behavioral Implications: IV often deviates from HV due to behavioral biases such as risk aversion and herding, creating opportunities for arbitrage (Baker & Wurgler, 2007).
Practical Considerations
Input Flexibility: Users can modify the length of the HV calculation and the annualization factor to suit specific markets or instruments.
Market Selection: The default ticker for implied volatility is the VIX (CBOE:VIX), but other volatility indices can be substituted for assets outside the S&P 500.
Data Frequency: This indicator is most effective on daily charts, as VIX data typically updates at a daily frequency.
Limitations
Implied volatility reflects the market's consensus but does not guarantee future accuracy, as it is subject to rapid adjustments based on news or events.
Historical volatility assumes a stationary distribution of returns, which might not hold during structural breaks or crises (Engle, 1982).
References
Black, F., & Scholes, M. (1973). "The Pricing of Options and Corporate Liabilities." Journal of Political Economy, 81(3), 637-654.
Whaley, R. E. (2000). "The Investor Fear Gauge." The Journal of Portfolio Management, 26(3), 12-17.
Hull, J. C. (2018). Options, Futures, and Other Derivatives. Pearson Education.
Natenberg, S. (1994). Option Volatility and Pricing: Advanced Trading Strategies and Techniques. McGraw-Hill.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2001). The Econometrics of Financial Markets. Princeton University Press.
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill.
Baker, M., & Wurgler, J. (2007). "Investor Sentiment in the Stock Market." Journal of Economic Perspectives, 21(2), 129-151.
