⚡ AbyssX Dip Hunter ⚡SP_Quant⚡AbyssX Dip Hunter ⚡ by SP_Quant
AbyssX Dip Hunter is a clean and effective indicator designed to help you identify potential dip-buying opportunities using Z-Score analysis and an optional RSI filter.
🔍 How It Works:
Z-Score Logic: Detects statistical price dips when the Z-Score drops below your set threshold.
Optional RSI Filter: Filters signals only when RSI is under a specified level — useful for avoiding false positives in strong trends.
Custom Color Mode: Choose your preferred signal color (Purple, Cyan, Green, or Orange).
Chart Visuals: Dip signals are shown with triangle markers and soft background highlights.
Live Info Panel: Displays real-time RSI, Z-Score, and signal status in a table on your chart.
⚙️ Settings:
Z-Score Length & Threshold
RSI Filter ON/OFF with custom RSI Length and Threshold
Visual Style (Color Mode)
✅ Best for traders who use oversold/mean-reversion strategies or look for extreme pullbacks.
⚠️ Disclaimer:
This indicator is for educational and informational purposes only and does not constitute financial advice, trading advice, or investment recommendations. Use it at your own risk. Always perform your own analysis before making any trading decisions. The author is not liable for any losses or damages resulting from the use of this script.
Fundamental Analysis
MK SpreadCalculates the spread between two instruments, with a primary use case of tracking the differential between real (inflation-adjusted) and nominal yields.
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.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
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. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
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.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
ZoneShift+StochZ+LRO + AI Breakout Bands [Combined]This composite Pine Script brings together four powerful trend and momentum tools into a single, easy-to-read overlay:
ZoneShift
Computes a dynamic “zone” around price via an EMA/HMA midpoint ± average high-low range.
Flags flips when price closes convincingly above or below that zone, coloring candles and drawing the zone lines in bullish or bearish hues.
Stochastic Z-Score
Converts your chosen price series into a statistical Z-score, then runs a Stochastic oscillator on it and HMA-smooths the result.
Marks momentum flips in extreme over-sold (below –2) or over-bought (above +2) territory.
Linear Regression Oscillator (LRO)
Builds a bar-indexed linear regression, normalizes it to standard deviations, and shows area-style up/down coloring.
Highlights local reversals when the oscillator crosses its own look-back values, and optionally plots LRO-colored candles on price.
AI Breakout Bands (Kalman + KNN)
Applies a Kalman filter to price, smooths it further with a KNN-weighted average, then measures mean-absolute-error bands around that smoothed line.
Colors the Kalman trend line and bands for bullish/bearish breaks, giving you a data-driven channel to trade.
Composite Signals & Alerts
Whenever the ZoneShift flip, Stoch Z-Score flip, and LRO reversal all agree and price breaks the AI bands in the same direction, the script plots a clear ▲ (bull) or ▼ (bear) on the chart and fires an alert. This triple-confirmation approach helps you zero in on high-probability reversal points, filtering out noise and combining trend, momentum, and statistical breakout criteria into one unified signal.
CVDD Z-ScoreCumulative Value Days Destroyed (CVDD) - The CVDD was created by Willy Woo and is the ratio of the cumulative value of Coin Days Destroyed in USD and the market age (in days). While this indicator is used to detect bottoms normally, an extension is used to allow detection of BTC tops. When the BTC price goes above the CVDD extension, BTC is generally considered to be overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator is triggered for a top when the BTC price is above the CVDD extension. For the bottoms, the CVDD is shifted upwards at a default value of 120%. The slope, intercept, and CVDD bottom shift can all be modified in the script.
Now with the automatic Z-Score calculation for ease of classification of Bitcoin's valuation according to this metric.
Created for TRW.
Leader-Lagger DashboardSummary:
The ultimate frustration for a trader: being right on the idea, but wrong on the asset.
You correctly predict a market move, develop a solid bullish or bearish thesis, but the instrument you choose fails to follow through. Meanwhile, a correlated asset makes the exact move you anticipated, leaving you with a losing trade or a missed opportunity.
This common pitfall is precisely what the Leader/Lagger Dashboard is designed to solve.
The Solution: Instant Clarity on Relative Strength
The Leader/Lagger Dashboard provides a clear, real-time verdict on the relative strength between two correlated assets, such as ES (S&P 500 futures) and NQ (Nasdaq 100 futures).
By instantly identifying the Leader (the stronger asset) and the Lagger (the weaker asset), it empowers you to focus your capital on the instrument with the highest probability of performing in line with your market view.
As shown in the example image, if your idea is to short the market, choosing the "Weak" asset (ES) results in a winning trade, while shorting the "Strong" asset (NQ) would have failed. This tool helps you make that critical distinction before you enter.
How It Works
The engine at the core of this dashboard analyzes the price action of two assets on a higher timeframe (defaulting to 90 minutes). It measures how the current bar's high and low are performing relative to the previous bar's range for each asset. By comparing these normalized values, it generates a score to determine which asset is exhibiting stronger momentum (the Leader) and which is showing weakness (the Lagger).
A tie-breaking mechanism using a lower timeframe ensures you always have a decisive verdict.
How to Use It
The principle is simple: Go long the leader, and short the lagger.
If you are Bullish: Look for the asset marked "Strong." This is the instrument most likely to lead the upward move.
If you are Bearish: Look for the asset marked "Weak." This is the instrument most likely to lead the downward move.
By aligning your trade execution with the market's internal momentum, you dramatically increase your odds of success and avoid the frustration of trading against underlying strength or weakness.
Key Features
Instant Verdict: A simple on-chart table displays a "Strong" or "Weak" verdict for each asset.
Focus on the Leader: Easily identify which asset is leading the move to align your trades with momentum.
Avoid the Lagger: Steer clear of the weaker asset that might chop around or reverse, even if your directional bias is correct.
Fully Customizable: Change the two assets to any symbols you trade (e.g., GOLD vs. SILVER, EURUSD vs. GBPUSD).
Adjustable Display: Control the table's position and font size to perfectly fit your chart layout. The table is designed to be visible on lower timeframes (5-minutes and under) to assist with day trading execution.
This tool is designed to be a crucial part of your decision-making process, providing an objective layer of confirmation for your trading ideas. so Stop guessing and start trading the right asset.
As always, use this indicator in conjunction with your own complete analysis and risk management strategy.
Bitcoin Logarithmic Growth Curve 2025 Z-Score"The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements.
The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity.
Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences.
The Bitcoin logarithmic growth curve has the following function:
y = 10^(a * log10(x) - b)
In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart.
How is it made (You can skip this section if you’re not a fan of math):
To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows:
113, 18.55
240, 1004.42
451, 19128.27
655, 65502.47
The same process was applied to the bear market low values:
103, 2.48
267, 211.03
471, 3192.87
676, 16255.15
Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values.
For the cycle peak (x,y) values:
2.053, 1.268
2.380, 3.002
2.654, 4.282
2.816, 4.816
And for the bear market low (x,y) values:
2.013, 0.394
2.427, 2.324
2.673, 3.504
2.830, 4.211
Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary).
Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship.
The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value.
Below are the results:
snapshot
snapshot
It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression.
These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063).
The final bull cycle function is:
y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324)
The final bear cycle function is:
y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063)
The main Criticisms of growth curve models:
The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak.
This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band.
Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post:
This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns."
Now with Z-Score calculation for easy and constant valuation classification of Bitcoin according to this metric.
Created for TRW
Regime Reaper | QuantEdgeB📊 Regime Reaper | QuantEdgeB
🔍 What is Regime Reaper?
Regime Reaper is QuantEdgeB’s premier regime detection engine, designed to quantify market behavior through a scientific blend of stationarity tests, trend diagnostics, and reversion signals.
Rather than guessing if a market is trending or mean-reverting, Regime Reaper mathematically determines it—blending econometrics with market momentum, volatility texture, and predictive correlation.
💡 Think of Regime Reaper as a financial MRI — probing deep statistical layers to tell you what kind of environment you're in before you make a move.
⚙️ Core Components
✅ Z-Blend Framework
At its core, Regime Reaper combines up to 15 independent signals, each normalized via Z-Scores, including:
• 🧪 Stationarity Tests: ADF, KPSS, PP Test — detecting mean-reverting pressure or randomness
• 🌀 Cycle Predictors: Hurst exponent, Fourier approximation
• 🔥 Trend Strength: ADX, Price Momentum Correlation (PMC), Relative Price Change
• 💣 Volatility Analysis: GARCH, BBW, VAM
• ⚡ Behavioral Texture: Choppiness Index, Wavelet Energy, Half-Life
Each signal is optionally enabled/disabled — allowing surgical custom blends tailored to your asset or timeframe.
✅ Z-Avg Value Engine
• All active signals are aggregated into a composite Z-Score (Z-Avg)
• This value forms the backbone of regime classification logic
• Combined with adaptive percentile thresholds for precision detection
🎯 Regime Classification Logic
🧭 Z-Avg-Based Threshold Model
Regime Reaper classifies markets into three states:
Z-Avg Score Market Regime
≥ Threshold + Percentile 🔺 Trending
≥ Threshold Only ⚖️ Neutral / Weak Trend
≤ Reversion Threshold 🔻 Mean-Reverting
These scores are colored, plotted, and displayed in a histogram view to make regime transitions immediately visible.
✅ Custom threshold values via:
• Trending Threshold
• Reverting Threshold
• Percentile Rank Comparison
📊 Dashboard Overlay (Optional)
Regime Reaper includes three live tables:
1. Metrics Panel (𝓡𝓮𝓰𝓲𝓶𝓮 𝓡𝓮𝓪𝓹𝓮𝓻)
o Displays the Z-Score of each active metric
o Highlights total Z-Blend Score
o Shows current regime stage (Trending, Reverting, Neutral)
2. Signal Scanner Table
o Explains current Z-Avg score & decision logic
o Displays thresholds for trend/revert neutrality
o Delivers a stage verdict with live updates
3. Info Panel
o Visual color-coded regime bars
o Snapshot of all 3 possible states
🎨 Visual Signal System
• Z-Avg Histogram — core value colored by regime state
• Background Coloring — lightly shades trending vs reverting periods
• Table Text Coloring — shows metric strength in live table updates
• User-Specified Color Themes — switch between Magic, Strategy, Cool, etc.
🧠 Why Use Regime Reaper?
Because knowing the market’s regime changes everything:
• Reversion strategies fail in strong trends
• Trend systems bleed during choppy reverts
• Random walks are dangerous to both
With Regime Reaper, you no longer have to guess — you measure.
💼 Ideal Use Cases
• Trend vs Mean-Reversion Filters
• System Mode Switching (Auto Toggle)
• Volatility Regime Adaptation
• Signal Confidence Boosting (by regime match)
• Portfolio Allocation Strategy Filters
🧬 Default Config
• Composite Model: All 15 metrics ON
• Trending Threshold: +0.15
• Reversion Threshold: −0.15
• Adaptive Filtering via Percentile Ranks
🧬 In Summary
Regime Reaper | QuantEdgeB is more than a filter — it's a regime recognition system built on powerful statistical indicators and dynamic Z-Score fusion. It doesn’t just observe behavior; it categorizes it.
Use it to confirm entries, time exits, suppress signals in bad regimes, or dynamically change your system logic.
📌 Trade the Right Logic in the Right Market | Powered by QuantEdgeB
🔹 Disclaimer: No indicator guarantees future performance.
🔹 Tip: Tune metric
15-Min ORB Box x SmartBlackGirlThe 15-Minute ORB (Opening Range Breakout) Box plots the first 15-minute high and low after the market opens, giving you a visual reference for potential breakout zones and **direction
Multi-Tool Nasdaq US100 IndikatorA combination of several tools such as moving averages (EMA 50, 100, 200), Fibonacci retracements, pivot points, RSI (Relative Strength Index), order blocks, fair value gaps, supply and demand zones, and a simple volume profile.
The indicator is designed to enable high profitability by combining various established technical analysis approaches into one tool, facilitating decision-making regarding entry and exit points.
The script can be integrated and used directly in TradingView by creating a new indicator script and pasting the code there.
Risk & Money Calculator / Fixed Losses This indicator is designed for people who want to control their losses as precisely as possible!
It allows you to quickly calculate the potential loss on a position, taking commission into account. It's designed so that you can have a fixed loss with different stop-loss lengths by adjusting the position size, expressed in currency!
Next to the Stop Loss price, you'll see the percentage distance to the stop and the actual loss, including the double commission (for opening and closing).
The indicator is very easy to use. You select the trade direction, enter the entry price, and the Stop Loss price. Optionally, you can set a Take Profit price to visualize the profit percentage! Since commission is charged both when opening and closing a position, you need to specify the size of your one-way commission.
Important!
• DON'T FORGET ABOUT LIQUIDATION, WHICH HAPPENS BEFORE THE CORRESPONDING STOP LOSS PERCENTAGE IS REACHED!
• YOU ARE SOLELY RESPONSIBLE FOR YOUR CALCULATIONS AND LOSSES!
• IF YOU HAVE ANY WISHES OR SUGGESTIONS RELATED TO THE INDICATOR'S OPERATION, I'M READY TO LISTEN AND POSSIBLY MAKE CHANGES TO ITS FUNCTIONALITY!
Bitcoin: Pi Cycle Top & Bottom Indicator Z ScoreIndicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs within 3 days.
It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
Note: The multiple is of the price values of the 350DMA, not the number of days.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking.
It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. However, in this instance, it does so with a high degree of accuracy over Bitcoin's adoption phase of growth.
Bitcoin Price Prediction Using This Tool
The Pi Cycle Top Indicator forecasts the cycle top of Bitcoin’s market cycles. It attempts to predict the point where Bitcoin price will peak before pulling back. It does this on major high time frames and has picked the absolute tops of Bitcoin’s major price moves throughout most of its history.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter-term moving average, which is the 111-day moving average, has reached an x2 multiple of the 350-day moving average. Historically, it has proved advantageous to sell Bitcoin around this time in Bitcoin's price cycles.
It is also worth noting that this indicator has worked during Bitcoin's adoption growth phase, the first 15 years or so of Bitcoin's life. With the launch of Bitcoin ETF's and Bitcoin's increased integration into the global financial system, this indicator may cease to be relevant at some point in this new market structure.
Added the Z-Score metric for easy classification of the value of Bitcoin according to this indicator.
Created for TRW
Opening Range Breakout (9:30 - 9:45 EST)Here's a Pine Script (v5) for TradingView that plots the Opening Range Breakout (ORB) lines from 9:30 AM to 9:45 AM EST on a 15-minute chart.
It draws a green line at the high of the opening range and a red line at the low, both extending through the rest of the day.
buysellsignal-Santhosh Buy Point and Sell PointThis indicator utilizes a custom signal engine based on price depth, deviation, and backstep algorithms to identify potential buy/sell zones. It marks critical swing points on the chart and triggers signals based on directional price shifts. Users can customize input parameters and visual styling, and alerts are integrated for seamless automation. Designed for traders seeking dynamic entry/exit signals and compatible with algorithmic trading strategies
STATEMAP | QuantEdgeBIntroducing STATEMAP by QuantEdgeB
🔍 Overview
STATEMAP | QuantEdgeB is a holistic Trend & Valuation dashboard for 16 key crypto and index assets. It distills each asset’s momentum regime and valuation state into two live averages—trend average and valuation average—and presents them via color-coded candles, dynamic plots, and a comprehensive bottom-right table. Switch effortlessly between Trend and Valuation views to spot regime shifts across the market.
✨ Key Features
• 🧭 Trend vs. Valuation Toggle
Flip between viewing the overall market’s trend average or its valuation average with a single input.
• 📊 Trend Average
Aggregates each asset’s directional bias (bullish vs. bearish) into one composite gauge that flags risk-on vs. risk-off regimes.
• 💎 Valuation Average
Blends each asset’s statistical valuation score to highlight extreme under-/over-valued conditions.
• 🎨 Color-Coded Visualization
Candles turn blue for bullish, red for bearish, and gray for neutral; fills and horizontal bands reinforce regime thresholds.
• 🗺️ Live Asset Matrix
A 16×8 table shows for each symbol:
• Current Trend (Bullish/Bearish)
• Score & Prior Score with direction arrows (⬆️/⬇️/🔄)
• RoC (momentum change)
• Valuation State label (e.g. “Strong UnderValued”)
• Valuation value & prior value with change indicator
• 🚦 Market Stage Banner
A top-row line combines both averages into a concise “Market Stage” message (e.g. “Trend ⟹ Bullish Value ⟹ Sli. OverValued”).
⚙️ How It Works
• Universal Trend Assessment
Uses a library of trend-following logic to classify each asset as bullish or bearish, then averages those signals into the trend average.
• Adaptive Valuation Mapping
Evaluates each asset’s statistical valuation band, maps it into intuitive labels (under-/fair-/over-valued), and averages into the valuation average.
• Regime Thresholds
• Trend average above +0.1 ⇒ risk-on (bullish); below –0.1 ⇒ risk-off (bearish); otherwise neutral.
• Valuation average buckets assign six states from “Strong UnderValued” to “Strong OverValued.”
• Dynamic Visualization
Color-coded candlesticks and filled plot areas highlight when the market crosses key levels, making regime changes immediately apparent.
🎯 Who Should Use It
• Systematic Traders looking for a unified regime filter across multiple assets
• Portfolio Managers wanting a pulse on market momentum vs. valuation before reallocations
• Swing & Position Traders confirming cross-asset alignment or spotting divergences
• Risk Managers monitoring broad contractions (trend bearish) vs. expansions (trend bullish) alongside valuation extremes
🧬 Default Settings
• View Mode: Trend
• Bar color: Yes
• Assets Covered: TOTAL market cap, TOTAL OTHERS, BTC, ETH, SOL, FET, RNDR, INJ, VRA, ORDI, PAAL, ONDO, TIA, AKT, PEPE, DOGE
📌 Conclusion
STATEMAP | QuantEdgeB delivers a clear, emoji-tagged map of where the crypto market stands in both momentum and valuation space. With an easy toggle and a rich dashboard, it empowers you to make regime-aware trading and allocation decisions at a glance.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Gold Killzone Bias Suite🟡 Gold Killzone Bias Suite
The Gold Killzone Bias Suite is an advanced institutional-grade tool designed to generate high-confidence directional bias for XAU/USD (Gold) during the London and New York killzones.
Built for traders using a structured, confluence-driven approach, this tool blends price action, smart money principles, momentum, and volume into a real-time bias engine with a clean, easy-to-read dashboard.
🔧 Key Features
🕰️ Session-Based Bias (London / New York)
Independent bias calculation per session
Killzone times customizable with timezone support
Background highlighting (blue/red) for each session
📊 VWAP Engine
Reclaim & rejection detection
VWAP deviation alerts
Daily HTF VWAP integration
Score impact based on VWAP behaviour
📉 Market Structure (CHoCH / BOS)
Detects swing highs/lows
Labels bullish/bearish CHoCHs
Structure score contributes to session bias
💧 Liquidity Grabs
Detects stop hunts above highs / below lows
Confirms with candle rejection (body % filter)
Plots labels and adds to bias scoring
⚡ Momentum Filters
RSI: Bullish >55, Bearish <45
MACD: Histogram + Signal Line crossovers
Combined momentum score used in bias
🧠 Smart Money Proximity
Optional FVG/OB score toggle (placeholder for custom logic)
Adds static confluence for proximity-based setups
⏫ Higher Time Frame Context
Daily VWAP comparison
4H high/low structure breaks
Adds trend score to current session bias
🧠 How Bias Works
The suite uses a scoring model. Each confluence adds or subtracts points:
VWAP reclaim/reject: ±30
CHoCH/BOS: ±30
Liquidity grab: ±20
RSI/MACD: ±10
FVG/OB Proximity: +10
Daily VWAP trend: ±10
H4 Trend Break: ±10
Final Bias:
Bullish if score ≥ +20
Bearish if score ≤ -20
Neutral if between -19 and +19
A confidence % (capped at 100) is also shown, along with the contributing confluences (VWAP, Structure, Liquidity, etc.).
📋 Dashboard
A real-time dashboard shows for each session:
Session name and time
Bias (Bullish / Bearish / Neutral)
Confidence (%)
Confluences used
Position can be moved (Top Left, Top Right, etc.). Designed to be unobtrusive yet informative.
🧪 Best Practices
Use on 15m / 5m charts for intraday setups
Confirm with D1 or H4 structure for directional context
Combine with OB/FVG zones or SMT for entries
Use Trading View alerts for bias flips or liquidity grabs (custom logic can be added)
Bar Replay compatible for back testing and journaling bias shifts
🔐 Notes
Does not generate trade signals or alerts by default
Focused on bias generation and confluence stacking
Compatible with funded account trading models
📈 Built for traders who want a systematic, score-based approach to identifying directional edge in high-volume gold sessions.
Year Dividers with LabelsDraws year start markers due visually show start of a year. Useful when looking at year seasonality and related factors
Fundamental Analysis & Economic-Based Stock ValuationFundamental Analysis & Economic-Based Stock Valuation
The Fundamental Analysis & Economic-Based Stock Valuation is a powerful tool designed to give traders and investors a quick, comprehensive overview of a company’s financial health. This horizontal, color-coded table includes live financial data, progress indicators, and smart health insights for informed decision-making. Below are the key financial metrics included in the table:
________________________________________
1. Market Capitalization (Market Cap)
Definition: Market Cap is calculated as the total number of outstanding shares multiplied by the current stock price.
Importance: This gives investors an idea of the company’s size and valuation.
How to Use:
• Large-cap stocks (> $10B) are typically stable, established companies.
• Small- or mid-cap stocks may offer higher growth but come with more volatility.
aiTrendview Feature: Progress bars visually represent the company's size. This helps users quickly gauge whether the stock is a micro-cap, mid-cap, or large-cap investment opportunity.
________________________________________
2. Earnings Yield (%)
Definition: Earnings Yield = (EPS / Price) × 100. It shows how much a company earns relative to its stock price.
Importance: It’s the inverse of the P/E ratio and is used to compare returns from equity with bond yields.
How to Use:
• A yield > 10% may indicate undervaluation.
• Lower yield (< 3%) may indicate an overpriced stock.
aiTrendview Feature: Health indicators like “STRONG”, “FAIR”, or “POOR” and a progress bar help investors assess return potential relative to risk.
________________________________________
3. Price-to-Book Ratio (P/B Ratio)
Definition: P/B Ratio = Market Price / Book Value per Share.
Importance: Measures market valuation relative to the company's net assets.
How to Use:
• A ratio < 1 can mean the stock is undervalued.
• 3 might indicate overvaluation unless justified by high ROE.
aiTrendview Feature: Color-coded health markers show if the company is UNDERVALUED, FAIR, or OVERVALUED, making valuation analysis visual.
________________________________________
4. Price-to-Earnings Ratio (P/E Ratio)
Definition: P/E = Price / Earnings per Share. It tells you how much investors are paying for each unit of earnings.
Importance: One of the most commonly used valuation metrics.
How to Use:
• A low P/E (< 15) might indicate undervaluation.
• High P/E (> 30) could mean overvaluation or growth expectations.
aiTrendview Feature: The health indicator ("CHEAP", "FAIR", "HIGH", "EXPENSIVE") with a visual bar helps judge sentiment and valuation instantly.
________________________________________
5. Price-to-Sales Ratio (P/S Ratio)
Definition: Market Cap / Revenue. Indicates how much investors pay per dollar of sales.
Importance: Useful for valuing companies with low or negative earnings.
How to Use:
• < 2 is attractive in most industries.
• Higher ratios need to be justified by strong growth.
aiTrendview Feature: P/S-based health tags and progress bars help traders decide whether the stock is reasonably priced on revenue.
________________________________________
6. EBITDA (Earnings Before Interest, Taxes, Depreciation & Amortization)
Definition: A measure of a company's core operational profitability.
Importance: Strips out non-operational costs and is used for comparative analysis.
How to Use:
• Positive EBITDA suggests financial strength.
• Compare year-over-year for growth consistency.
aiTrendview Feature: Visual score and health indicator classify profitability status as “PROFIT” or “LOSS”.
________________________________________
7. Total Revenue
Definition: The total income from sales before expenses.
Importance: Indicates the scale of business operations.
How to Use:
• Rising revenue over quarters = growth.
• Compare with competitors for market share insight.
aiTrendview Feature: Categorizes revenue scale as “MICRO”, “SMALL”, “MEDIUM”, or “LARGE” – useful for gauging company tier.
________________________________________
8. Net Income
Definition: Profit after all expenses, taxes, and interest.
Importance: Shows the company’s actual profitability.
How to Use:
• Positive Net Income = healthy bottom line.
• Use for EPS and ROE calculations.
aiTrendview Feature: Margin percentage + status label (“PROFIT” or “LOSS”) instantly convey financial strength.
________________________________________
9. Book Value Per Share (BVPS)
Definition: Total equity divided by the number of outstanding shares.
Importance: Indicates the liquidation value per share.
How to Use:
• Compare with current market price.
• Price < BVPS can mean undervaluation.
aiTrendview Feature: Shows whether the stock is trading at “DISCOUNT” or “PREMIUM” to its actual value.
________________________________________
10. Earnings Per Share (EPS)
Definition: Net income divided by outstanding shares.
Importance: Measures profitability on a per-share basis.
How to Use:
• Key input for valuation and dividend decisions.
• Positive EPS is essential for investment appeal.
aiTrendview Feature: Labeled “PROFIT” or “LOSS” and enhanced with visual status for clarity.
________________________________________
11. Symbol & Exchange Info
Definition: Displays the trading symbol and exchange (e.g., NSE, NYSE).
Importance: Ensures clarity when analyzing or sharing screenshots.
How to Use:
• Useful for verifying ticker and confirming data source.
aiTrendview Feature: Clearly displayed with "LIVE" tag for credibility.
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12. Fundamental Health Score
Definition: aiTrendview computes a composite score (0–100) based on 5 core metrics: Net Income, EPS, P/E, P/B, and EBITDA.
Importance: Provides a single summary score to assess the company's overall financial strength.
How to Use:
• Use this as a filter to shortlist strong candidates.
• Score > 80 = “EXCELLENT”; 60–80 = “GOOD”; < 40 = “POOR”.
aiTrendview Feature: A professional horizontal progress bar with color-coded grade makes it visually intuitive.
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⚠️ Disclaimer from aiTrendview
The information provided in this Fundamental Analysis dashboard is for educational and informational purposes only. While the data is sourced live and computed dynamically, it should not be interpreted as investment advice. Traders and investors must do their own due diligence and consider risk appetite, macroeconomic factors, and other indicators before making any financial decisions. aiTrendview.com or its affiliates shall not be held liable for any loss arising from the use of this tool. Markets are risky — trade wisely and responsibly.
Gold Power Queen StrategyTrade XAUUSD (Gold) or XAUEUR LIKE A CHAMP!!!! Only during the most volatile hours of the New York session, using momentum and trend confirmation, with session-specific risk/reward profiles.
✅ Strategy Rules
🕒 Valid Trading Times ("Power Hours"):
Trades are only taken during high-probability time windows on Tuesdays, Wednesdays, and Thursdays, corresponding to key New York session activity:
Morning Session:
08:00 – 12:00 (NY time)
Afternoon Session:
12:00 – 15:00
These times align with institutional activity and economic news releases.
📊 Technical Indicators:
50-period Simple Moving Average (SMA50):
Identifies the dominant market trend.
14-period Relative Strength Index (RSI):
Measures market momentum with session-adjusted thresholds.
🟩 Buy Signal Criteria:
Price is above the 50-period SMA (bullish trend)
Must be during a valid day (Tue–Thu) and Power Hour session
🟥 Sell Signal Criteria:
Price is below the 50-period SMA (bearish trend)
Must be during a valid day and Power Hour session
🎯 Trade Management Rules:
Morning Session (08:00–12:00)
Stop Loss (SL): 50 pips
Take Profit (TP): 150 pips
Risk–Reward Ratio: 1:3
Afternoon Session (12:00–15:00)
Stop Loss (SL): 50 pips
Take Profit (TP): up to 100 pips
Risk–Reward Ratio: up to 1:1.5
⚠️ TP is slightly reduced in the afternoon due to typically lower volatility compared to the morning session.
📺 Visuals & Alerts:
Buy signals: Green triangle plotted below the bar
Sell signals: Red triangle plotted above the bar
SMA50 line: Orange
Valid session background: Light pink
Alerts: Automatic alerts for buy/sell signals
Multi-Timeframe SMT Divergences [Zora]HI !
Here is a my first indicator who show you every SMT for ES or every symbol you want until 5 Timeframe different in the same time !
Tell me if you want something else or if you see some error ^^
Option CalculatorOption Calculator – Comprehensive Feature Guide
The aiTrendview Option Calculator is a feature-rich options trading dashboard built using Pine Script, designed for real-time market interpretation and strategy selection. It integrates Black-Scholes-based pricing models with dynamic market inputs to help traders evaluate directional bias, volatility, risk, and potential profitability in a structured, intuitive format. The tool supports both beginner and experienced options traders in making data-driven decisions.
Core Inputs and Pricing Foundations
Users can input the strike price, days to expiration, implied volatility (IV), interest rate, and option type (call or put). These values feed directly into calculations for the option's theoretical price, Greeks, and expected move. For example:
• Strike Price helps define moneyness, impacting delta and risk/reward balance.
• Days to Expiry determines the speed of time decay (theta).
• Risk-Free Rate adjusts for time value and interest rate impact (rho).
• Implied Volatility affects premium pricing and vega exposure.
• Option Type sets the directional foundation for strategy analysis.
Live Market Data Integration
The script pulls current underlying price, price change, and volume comparison against a moving average (e.g., current volume vs. 20-day average). This helps identify unusual trading activity or volume spikes. Volatility readings are also incorporated using ATR or external volatility indexes to enhance the realism of IV assessments.
Greek Calculations
The dashboard provides visual and numerical values for all five major Greeks:
• Delta shows directional sensitivity and is plotted with a visual bar.
• Gamma represents the rate of delta change, especially critical near-the-money.
• Theta measures time decay and is most impactful in the final weeks before expiration.
• Vega tracks sensitivity to volatility shifts, crucial for premium-selling strategies.
• Rho reflects sensitivity to interest rates, primarily relevant in long-dated options.
Each Greek is calculated based on real-time inputs, providing a statistical framework for assessing risk and return.
Market Sentiment & Risk Environment
A sentiment scoring system interprets the put-call ratio (PCR), volume trends, and price momentum (e.g., RSI). IV levels are color-coded (e.g., low, medium, high) to identify whether options are relatively cheap or expensive. These values support better timing decisions and help identify whether to be a buyer or seller of premium.
Strategy Recommendation Engine
The script dynamically evaluates six core strategies based on current data:
1. Long Call
2. Short Put
3. Long Put
4. Bull Call Spread
5. Long Straddle
6. Iron Butterfly
Each strategy is assigned a confidence score (0–100%) and updated in real-time. This system is designed to match the appropriate strategy to market conditions such as trend, volatility, and time to expiration.
Risk-Adjusted Trading Insights
The dashboard helps traders evaluate whether to initiate trades, reduce exposure, or wait:
• High Confidence (80%+): Favorable environment; standard sizing recommended.
• Moderate Confidence (60–80%): Trade with caution and reduced risk.
• Low Confidence (<60%): Consider avoiding the trade or waiting for better setup.
It also supports risk mitigation through defined-risk strategies and provides guidance on stop-loss, profit targets, and time-based exits (e.g., managing options with <21 days to expiry).
Real-Time Monitoring
The script continuously tracks:
• Changes in Greeks as price, volatility, or time evolve.
• Profit probability estimates using expected move and breakeven pricing.
• Volume activity and IV rank to spot institutional behavior.
This empowers traders to manage trades proactively, adjust exposure, or lock in profits based on changing market conditions.
Practical Use Case Flow
Step 1: Input Setup
Enter option-specific parameters (strike, expiry days, IV, etc.) and let the dashboard auto-calculate risk metrics.
Step 2: Analyze Market
Use sentiment analysis, IV level, and volume data to understand the environment.
Step 3: Select Strategy
Rely on the confidence score and recommendation engine to choose a suitable options strategy.
Step 4: Manage Risk
Apply size rules based on signal strength, adjust based on exposure, and set alerts if needed.
Step 5: Monitor Outcomes
Track Greeks, probability, and progress metrics to stay informed throughout the trade.
Trading Environment Adaptation
• Low IV: Favor long premium strategies (e.g., long straddles, long calls).
• High IV: Favor premium selling strategies (e.g., iron condors, credit spreads).
• Bullish Markets: Focus on call-based trades or bullish spreads.
• Sideways Markets: Use neutral setups like iron butterflies or calendar spreads.
Position sizing and stop-loss logic are aligned with industry practices (e.g., risk no more than 2% per trade, take profit at 50%, and cut losses at double the premium received).
Dashboard Interpretation Guide
• Green: High confidence strategy, favourable IV, and strong volume confirmation.
• Yellow: Mixed signals or moderate conviction – proceed with caution.
• Red: Low confidence, poor conditions – better to wait for clearer opportunities.
Disclaimer from aiTrendview
This script is intended for educational and informational use only. It does not offer financial advice or trading signals, nor does it guarantee results. aiTrendview and its affiliates are not responsible for any financial loss or decision made using this tool. Options trading involves substantial risk and is not suitable for all investors. Past performance of any strategy or metric does not guarantee future results. Users are encouraged to consult with a certified financial advisor and conduct independent research before making trading decisions.
Smoothed Increment MA RSI | MisinkoMasterThe Smoothed Increment MA RSI is my latest creation, being a versatile tool allowing traders and investors not only to catch reversals in Trend, but also catch high value and low value zones, working both as a Trend Following and Mean Reverting indicator for everyone's usage.
Use Cases:
1. Mean Reversion/Value Spotting:
This indicator, because of being based on the RSI, can catch high value and low value zones,
and if you experiment with conditions like:
Entry - values in the green zone
Exit - values in the red zone and downtrend
You will find very good trades:
2. Trend Reversals:
The main usage of this indicator, this is for what it has been intended, so it must excell at it!
When the line is green a reversal up is happening, when red, reversal down.
This indicator will provide you with fast reversals, no matter the asset - it will always do it's job.
Change the asset? No problem!
COINBASE:ETHUSD
But it also works on meme coins like CRYPTOCAP:DOGE
How it works?
The Smoothed Increment MA RSI works like so:
1. Calculate the RSI/Relative Strength Index, which will be the core of this all
2. Calculate the increment, this will be used by comparing the Moving Average, ATR/Average True Range and Volume values now to past values with different weights
3. Now it is time to check the RSI's ROC and apply the increment to it, making a much more volatile RSI
4. Now we just smooth the values using multiple Moving Averages over a smoothing period at base set to 6, but can be adjusted
5. Trend logic, this one depends on everyone, some may set it to crossing the value of 50, some may set it to something else, but for the purpose of this indicator I found the best working case to be just comparing if the value of the RSI has grown or decreased.
There will be also black dots with white borders plotted, this is for easier spotting of reversals.
This indicator also uses volume, so you have to check this on something with volume available.
I left all the settings available for changing, so you can adjust it to whatever you like and get the best out of this!
RatioSPX/Inverted Fed Funds RateHere is the Pine Script that compares the S&P 500 (SPX) to the inverse Fed Funds Rate, both normalized to a 0–1 scale for easy visual comparison:
RatioBTC/Inverted Fed Funds RateHere is the Pine Script that compares the S&P 500 (SPX) to the inverse Fed Funds Rate, both normalized to a 0–1 scale for easy visual comparison:
