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.
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3-1-3 PatternThis Pine Script indicator analyzes and visualizes a specific candlestick pattern called the "3-1-3 Pattern" across multiple timeframes. Here's what it does:
Core Functionality
Pattern Detection: The script looks for a 7-bar candlestick pattern:
Bearish 3-1-3: 3 red candles + 1 green candle + 3 red candles
Bullish 3-1-3: 3 green candles + 1 red candle + 3 green candles
Visual Output
When a 3-1-3 pattern is detected, the script:
Creates a colored box around the middle bar (bar 3) of the pattern
Adds a small label showing the pattern type ("Bear 1H" or "Bull 4H", etc.)
Extends the box forward until the price breaks above the pattern's high or below its low
Pattern Management
The script actively manages the patterns by:
Tracking active patterns for each timeframe separately
Removing expired patterns when price breaks the pattern's high/low levels
Extending boxes to the current time to keep them visible
Practical Use
This indicator helps traders:
Spot reversal patterns across multiple timeframes simultaneously
See confluence when patterns align on different timeframes
Track pattern validity (boxes disappear when invalidated by price action)
Essentially, it's a multi-timeframe pattern recognition tool that automatically identifies and tracks these specific 7-bar reversal patterns on your chart.
Volume Based Analysis V 1.00
Volume Based Analysis V1.00 – Multi-Scenario Buyer/Seller Power & Volume Pressure Indicator
Description:
1. Overview
The Volume Based Analysis V1.00 indicator is a comprehensive tool for analyzing market dynamics using Buyer Power, Seller Power, and Volume Pressure scenarios. It detects 12 configurable scenarios combining volume-based calculations with price action to highlight potential bullish or bearish conditions.
When used in conjunction with other technical tools such as Ichimoku, Bollinger Bands, and trendline analysis, traders can gain a deeper and more reliable understanding of the market context surrounding each signal.
2. Key Features
12 Configurable Scenarios covering Buyer/Seller Power convergence, divergence, and dominance
Advanced Volume Pressure Analysis detecting when both buy/sell volumes exceed averages
Global Lookback System ensuring consistency across all calculations
Dominance Peak Module for identifying strongest buyer/seller dominance at structural pivots
Real-time Signal Statistics Table showing bullish/bearish counts and volume metrics
Fully customizable inputs (SMA lengths, multipliers, timeframes)
Visual chart markers (S01 to S12) for clear on-chart identification
3. Usage Guide
Enable/Disable Scenarios: Choose which signals to display based on your trading strategy
Fine-tune Parameters: Adjust SMA lengths, multipliers, and lookback periods to fit your market and timeframe
Timeframe Control: Use custom lower timeframes for refined up/down volume calculations
Combine with Other Indicators:
Ichimoku: Confirm volume-based bullish signals with cloud breakouts or trend confirmation
Bollinger Bands: Validate divergence/convergence signals with overbought/oversold zones
Trendlines: Spot high-probability signals at breakout or retest points
Signal Tables & Peaks: Read buy/sell volume dominance at a glance, and activate the Dominance Peak Module to highlight key turning points.
4. Example Scenarios & Suggested Images
Image #1 – S01 Bullish Convergence Above Zero
S01 activated, Buyer Power > 0, both buyer power slope & price slope positive, above-average buy volume. Show S01 ↑ marker below bar.
Image #2 – Combined with Ichimoku
Display a bullish scenario where price breaks above Ichimoku cloud while S01 or S09 bullish signal is active. Highlight both the volume-based marker and Ichimoku cloud breakout.
Image #3 – Combined with Bollinger Bands & Trendlines
Show a bearish S10 signal at the upper Bollinger Band near a descending trendline resistance. Highlight the confluence of the volume pressure signal with the band touch and trendline rejection.
Image #4 – Dominance Peak Module
Pivot low with green ▲ Bull Peak and pivot high with red ▼ Bear Peak, showing strong dominance counts.
Image #5 – Statistics Table in Action
Bottom-left table showing buy/sell volume, averages, and bullish/bearish counts during an active market phase.
5. Feedback & Collaboration
Your feedback and suggestions are welcome — they help improve and refine this system. If you discover interesting use cases or have ideas for new features, please share them in the script’s comments section on TradingView.
6. Disclaimer
This script is for educational purposes only. It is not financial advice. Past performance does not guarantee future results. Always do your own analysis before making trading decisions.
Tip: Use this tool alongside trend confirmation indicators for the most robust signal interpretation.
FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator
1. Concept and Rationale
The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings.
The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much.
2. Data Pipeline
• Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes.
• Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer.
• User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study.
3. Math under the Hood
(1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent.
(2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite.
(3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive.
(4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers.
(5) Threshold Grid – Five horizontal guides create a structured regime map:
• Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.
• Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.
• Zero Line demarcates neutral alignment.
4. Chart Furniture and User Interface
• Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts.
• Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation.
• Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar.
• Everything—line width, thresholds, colours—can be customised so the indicator blends into any template.
5. Interpretation Guide
Macro Liquidity Pulse
• When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).
• Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety.
Regime Transition Signals
• Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.
• Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite.
Momentum Exhaustion and Mean-Reversion
• Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.
• Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold.
Pair Trading / Relative Value
• Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.
• Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader.
Event-Driven Tactics
• FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.
• CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common.
6. Alerts Suite
Pre-bundled conditions let you automate workflows:
• Bullish / Bearish zero crosses – queue spot or futures entries.
• Standard OB / OS – notify for first contact with actionable zones.
• Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions.
7. Parameter Playground
• Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors.
• Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC.
• Try HMA smoothing for responsive yet smooth curves on intraday charts.
• Colour-blind users can easily swap bull and bear palette selections for preferred contrasts.
8. Limitations and Best Practices
• The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals.
• Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily.
• Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps.
9. Final Thoughts
The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark.
Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
Momentum DivergenceOverview
The Momentum Divergence Oscillator is a valuable tool designed for traders who are familiar with basic charting but want to deepen their market insights. This indicator combines a momentum calculation with divergence detection, presenting the data in an intuitive way with a blue momentum line and colored divergence signals ("Bull" and "Bear"). It’s perfect for refining entry and exit points across various timeframes, especially for scalping or swing trading strategies.
Understanding the Concepts
What is Momentum?
Momentum measures the speed and strength of a price movement by comparing the current closing price to a previous close over a set period. In this indicator, it’s calculated as the difference between the current close and the close from a user-defined number of bars ago (default: 10). A rising momentum line indicates accelerating upward momentum, while a falling line suggests slowing momentum or a potential reversal. This helps you gauge whether a trend is gaining power or losing steam, making it a key indicator for spotting overbought or oversold conditions.
What is a Divergence?
A divergence occurs when the price action and the momentum indicator move in opposite directions, often signaling a potential trend reversal. The Momentum Divergence Oscillator highlights two types:
Bullish Divergence: When the price forms a lower low (indicating weakness), but the momentum shows a higher low (suggesting underlying strength). This can foreshadow an upward reversal.
Bearish Divergence: When the price reaches a higher high (showing strength), but the momentum records a lower high (indicating fading momentum). This may hint at an impending downward turn.
How the Indicator Works
The indicator plots a momentum line in a separate pane below your chart, giving you a clear view of price momentum over time. It also scans for divergences using adjustable lookback periods (default: 5 bars left and right) and a range window (default: 5-60 bars) to ensure relevance. When a divergence is detected, it’s visually highlighted, and you can customize the sensitivity through input settings like the momentum length and pivot lookback. Alerts are included to notify you of new divergence signals in real-time, saving you from constant monitoring.
How to Apply It
Identifying Opportunities: Use bullish divergences ("Bull") as a cue to consider long positions, especially when confirmed by support levels or a moving average crossover. Bearish divergences ("Bear") can signal short opportunities, particularly near resistance zones.
Combining with Other Tools: Pair this oscillator with indicators like the Relative Strength Index (RSI) or volume analysis to filter out false signals and increase confidence in your trades. For example, a bullish divergence with rising volume can be a stronger buy signal.
Timeframe Flexibility: Test it on shorter timeframes (e.g., 5-minute charts) for quick scalping trades or longer ones (e.g., 1-hour or 4-hour charts) for swing trading, adjusting the momentum length to suit the market’s pace.
Alert Setup: Enable the built-in alerts to get notified when a divergence forms, allowing you to react promptly without staring at the screen all day.
Strategy Example
Spot a bullish divergence on a 15-minute chart where the price hits a lower low, but the momentum rises.
Confirm with a break above a 20-period EMA and increasing volume.
Enter a long position with a stop-loss below the recent low and a take-profit near the next resistance level.
Customization Tips
Adjust the "Momentum Length" (default: 10) to make the oscillator more or less sensitive—shorter lengths react faster, while longer ones smooth out noise.
Tweak the "Pivot Lookback" settings to widen or narrow the divergence detection range based on your trading style.
Use the "Range Upper/Lower" inputs to focus on divergences within a specific timeframe that matches your strategy.
Important Considerations
b]This indicator is a technical analysis tool, not a guaranteed trading system. Always pair it with a solid strategy and strict risk management, such as setting stop-losses.
In strong trending markets, divergences can sometimes produce false signals. Consider adding a trend filter (e.g., ADX below 25) to avoid whipsaws.
Experiment with the settings on a demo account or backtest to find what works best for your preferred markets and timeframes.
HMM-Style Market RegimeVisual outputs rendered by the script
Background color bands (bgcolor)
Green when regime == Bull
Red when regime == Bear
Gray when regime == Sideways (uncertain)
Labels (label.new)
“Bull” in the top-left corner when entering a Bull regime
“Bear” when entering a Bear regime
“Sideways” when entering a Sideways regime
Regime-change arrows (plotshape)
▲ Up arrow when the regime flips to Bull
▼ Down arrow when the regime flips to Bear
Optional metric plots
A chart of the return Z-score and volatility Z-score (when debug mode is enabled)
Advanced ICT Theory - A-ICT📊 Advanced ICT Theory (A-ICT): The Institutional Manipulation Detector
Are you tired of being the liquidity? Stop chasing shadows and start tracking the architects of price movement.
This is not another lagging indicator. This is a complete framework for viewing the market through the lens of institutional traders. Advanced ICT Theory (A-ICT) is an all-in-one, military-grade analysis engine designed to decode the complex language of "Smart Money." It automates the core tenets of Inner Circle Trader (ICT) methodology, moving beyond simple patterns to build a dynamic, real-time narrative of market manipulation, liquidity engineering, and institutional order flow.
AIT provides a living blueprint of the market, identifying high-probability zones, tracking structural shifts, and scoring the quality of setups with a sophisticated, multi-factor algorithm. This is your X-ray into the market's true intentions.
🔬 THE CORE ENGINE: DECODING THE THEORY & FORMULAS
A-ICT is built upon a sophisticated, multi-layered logic system that interprets price action as a story of cause and effect. It does not guess; it confirms. Here is the foundational theory that drives the engine:
1. Market Structure: The Blueprint of Trend
The script first establishes a deep understanding of the market's skeleton through multi-level pivot analysis. It uses ta.pivothigh and ta.pivotlow to identify significant swing points.
Internal Structure (iBOS): Minor swings that show the short-term order flow. A break of internal structure is the first whisper of a potential shift.
External Structure (eBOS): Major swing points that define the primary trend. A confirmed break of external structure is a powerful statement of trend continuation. AIT validates this with optional Volume Confirmation (volume > volumeSMA * 1.2) and Candle Confirmation to ensure the break is driven by institutional force, not just a random spike.
Change of Character (CHoCH): This is the earthquake. A CHoCH occurs when a confirmed eBOS happens against the prevailing trend (e.g., a bearish eBOS in a clear uptrend). A-ICT flags this immediately, as it is the strongest signal that the primary trend is under threat of reversal.
2. Liquidity Engineering: The Fuel of the Market
Institutions don't buy into strength; they buy into weakness. They need liquidity. A-ICT maps these liquidity pools with forensic precision:
Buyside & Sellside Liquidity (BSL/SSL): Using ta.highest and ta.lowest, AIT identifies recent highs and lows where clusters of stop-loss orders (liquidity) are resting. These are institutional targets.
Liquidity Sweeps: This is the "manipulation" part of the detector. AIT has a specific formula to detect a sweep: high > bsl and close < bsl . This signifies that institutions pushed price just high enough to trigger buy-stops before aggressively selling—a classic "stop hunt." This event dramatically increases the quality score of subsequent patterns.
3. The Element Lifecycle: From Potential to Power
This is the revolutionary heart of A-ICT. Zones are not static; they have a lifecycle. AIT tracks this with its dynamic classification engine.
Phase 1: PENDING (Yellow): The script identifies a potential zone of interest based on a specific candle formation (a "displacement"). It is marked as "Pending" because its true nature is unknown. It is a question.
Phase 2: CLASSIFICATION: After the zone is created, AIT watches what happens next. The zone's identity is defined by its actions:
ORDER BLOCK (Blue): The highest-grade element. A zone is classified as an Order Block if it directly causes a Break of Structure (BOS) . This is the footprint of institutions entering the market with enough force to validate the new trend direction.
TRAP ZONE (Orange): A zone is classified as a Trap Zone if it is directly involved in a Liquidity Sweep . This indicates the zone was used to engineer liquidity, setting a "trap" for retail traders before a reversal.
REVERSAL / S&R ZONE (Green): If a zone is not powerful enough to cause a BOS or a major sweep, but still serves as a pivot point, it's classified as a general support/resistance or reversal zone.
4. Market Inefficiencies: Gaps in the Matrix
Fair Value Gaps (FVG): AIT detects FVGs—a 3-bar pattern indicating an imbalance—with a strict formula: low > high (for a bullish FVG) and gapSize > atr14 * 0.5. This ensures only significant, volatile gaps are shown. An FVG co-located with an Order Block is a high-confluence setup.
5. Premium & Discount: The Law of Value
Institutions buy at wholesale (Discount) and sell at retail (Premium). AIT uses a pdLookback to define the current dealing range and divides it into three zones: Premium (sell zone), Discount (buy zone), and Equilibrium. An element's quality score is massively boosted if it aligns with this principle (e.g., a bullish Order Block in a Discount zone).
⚙️ THE CONTROL PANEL: A COMPLETE GUIDE TO THE INPUTS MENU
Every setting is a lever, allowing you to tune the AIT engine to your exact specifications. Master these to unlock the script's full potential.
🎯 A-ICT Detection Engine
Min Displacement Candles: Controls the sensitivity of element detection. How it works: It defines the number of subsequent candles that must be "inside" a large parent candle. Best practice: Use 2-3 for a balanced view on most timeframes. A higher number (4-5) will find only major, more significant zones, ideal for swing trading. A lower number (1) is highly sensitive, suitable for scalping.
Mitigation Method: Defines when a zone is considered "used up" or mitigated. How it works: Cross triggers as soon as price touches the zone's boundary. Close requires a candle to fully close beyond it. Best practice: Cross is more responsive for fast-moving markets. Close is more conservative and helps filter out fake-outs caused by wicks, making it safer for confirmations.
Min Element Size (ATR): A crucial noise filter. How it works: It requires a detected zone to be at least this multiple of the Average True Range (ATR). Best practice: Keep this around 0.5. If you see too many tiny, irrelevant zones, increase this value to 0.8 or 1.0. If you feel the script is missing smaller but valid zones, decrease it to 0.3.
Age Threshold & Pending Timeout: These manage visual clutter. How they work: Age Threshold removes old, mitigated elements after a set number of bars. Pending Timeout removes a "Pending" element if it isn't classified within a certain window. Best practice: The default settings are optimized. If your chart feels cluttered, reduce the Age Threshold. If pending zones disappear too quickly, increase the Pending Timeout.
Min Quality Threshold: Your primary visual filter. How it works: It hides all elements (boxes, lines, labels) that do not meet this minimum quality score (0-100). Best practice: Start with the default 30. To see only A- or B-grade setups, increase this to 60 or 70 for an exceptionally clean, high-probability view.
🏗️ Market Structure
Lookbacks (Internal, External, Major): These define the sensitivity of the trend analysis. How they work: They set the number of bars to the left and right for pivot detection. Best practice: Use smaller values for Internal (e.g., 3) to see minor structure and larger values for External (e.g., 10-15) to map the main trend. For a macro, long-term view, increase the Major Swing Lookback.
Require Volume/Candle Confirmation: Toggles for quality control on BOS/CHoCH signals. Best practice: It is highly recommended to keep these enabled. Disabling them will result in more structure signals, but many will be false alarms. They are your filter against market noise.
... (Continue this detailed breakdown for every single input group: Display Configuration, Zones Style, Levels Appearance, Colors, Dashboards, MTF, Liquidity, Premium/Discount, Sessions, and IPDA).
📊 THE INTELLIGENCE DASHBOARDS: YOUR COMMAND CENTER
The dashboards synthesize all the complex analysis into a simple, actionable intelligence briefing.
Main Dashboard (Bottom Right)
ICT Metrics & Breakdown: This is your statistical overview. Total Elements shows how much structure the script is tracking. High Quality instantly tells you if there are any A/B grade setups nearby. Unmitigated vs. Mitigated shows the balance of fresh opportunities versus resolved price action. The breakdown by Order Blocks, Trap Zones, etc., gives you a quick read on the market's recent character.
Structure & Market Context: This is your core bias. Order Flow tells you the current script-determined trend. Last BOS shows you the most recent structural event. CHoCH Active is a critical warning. HTF Bias shows if you are aligned with the higher timeframe—the checkmark (✓) for alignment is one of the most important confluence factors.
Smart Money Flow: A volume-based sentiment gauge. Net Flow shows the raw buying vs. selling pressure, while the Bias provides an interpretation (e.g., "STRONG BULLISH FLOW").
Key Guide (Large Dashboard only): A built-in legend so you never have to guess. It defines every pattern, structure type, and special level visually.
📖 Narrative Dashboard (Bottom Left)
This is the "story" of the market, updated in real-time. It's designed to build your trading thesis.
Recent Elements Table: A live list of the most recent, high-quality setups. It displays the Type , its Narrative Role (e.g., "Bullish OB caused BOS"), its raw Quality percentage, and its final Trade Score grade. This is your at-a-glance opportunity scanner.
Market Narrative Section: This is the soul of A-ICT. It combines all data points into a human-readable story:
📍 Current Phase: Tells you if you are in a high-volatility Killzone or a consolidation phase like the Asian Range.
🎯 Bias & Alignment: Your primary direction, with a clear indicator of HTF alignment or conflict.
🔗 Events: A causal sequence of recent events, like "💧 Sell-side liquidity swept →
📊 Bullish BOS → 🎯 Active Order Block".
🎯 Next Expectation: The script's logical conclusion. It provides a specific, forward-looking hypothesis, such as "📉 Pullback expected to bullish OB at 1.2345 before continuation up."
🎨 READING THE BATTLEFIELD: A VISUAL INTERPRETATION GUIDE
Every color and line is a piece of information. Learn to read them together to see the full picture.
The Core Zones (Boxes):
Blue Box (Order Block): Highest probability zone for trend continuation. Look for entries here.
Orange Box (Trap Zone): A manipulation footprint. Expect a potential reversal after price interacts with this zone.
Green Box (Reversal/S&R): A standard pivot area. A good reference point but requires more confluence.
Purple Box (FVG): A market imbalance. Acts as a magnet for price. An FVG inside an Order Block is an A+ confluence.
The Structural Lines:
Green/Red Line (eBOS): Confirms the trend direction. A break above the green line is bullish; a break below the red line is bearish.
Thick Orange Line (CHoCH): WARNING. The previous trend is now in question. The market character has changed.
Blue/Red Lines (BSL/SSL): Liquidity targets. Expect price to gravitate towards these lines. A dotted line with a checkmark (✓) means the liquidity has been "swept" or "purged."
How to Synthesize: The magic is in the confluence. A perfect setup might look like this: Price sweeps below a red SSL line , enters a green Discount Zone during the NY Killzone , and forms a blue Order Block which then causes a green eBOS . This sequence, visible at a glance, is the story of a high-probability long setup.
🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY
A-ICT was forged from the frustration of using lagging indicators in a market that is forward-looking. Traditional tools are reactive; they tell you what happened. The vision for A-ICT was to create a proactive engine that could anticipate institutional behavior by understanding their objectives: liquidity and efficiency. The development process was centered on creating a "lifecycle" for price patterns—the idea that a zone's true meaning is only revealed by its consequence. This led to the post-breakout classification system and the narrative-building engine. It's designed not just to show you patterns, but to tell you their story.
⚠️ RISK DISCLAIMER & BEST PRACTICES
Advanced ICT Theory (A-ICT) is a professional-grade analytical tool and does not provide financial advice or direct buy/sell signals. Its analysis is based on historical price action and probabilities. All forms of trading involve substantial risk. Past performance is not indicative of future results. Always use this tool as part of a comprehensive trading plan that includes your own analysis and a robust risk management strategy. Do not trade based on this indicator alone.
観の目つよく、見の目よわく
"Kan no me tsuyoku, ken no me yowaku"
— Miyamoto Musashi, The Book of Five Rings
English: "Perceive that which cannot be seen with the eye."
— Dskyz, Trade with insight. Trade with anticipation.
Flexi MA Heat ZonesOverview
Flexi MA Heat Zones is a powerful multi-timeframe visualization tool that helps traders easily identify trend strength, direction, and potential zones of confluence using multiple moving averages and dynamic heatmaps. The indicator plots up to three pairs of customizable moving averages, with color-coded heat zones to highlight bullish and bearish conditions at a glance.
Whether you're a trend follower, mean-reversion trader, or looking for visual confirmation zones, this indicator is designed to offer deep insights with high customizability.
⚙️ Key Features
🔄 Supports multiple MA types: Choose from EMA, SMA, WMA, VWMA to suit your strategy.
🎯 Six moving averages: Three MA pairs (MA1-MA2, MA3-MA4, MA5-MA6), each with independent lengths and colors.
🌈 Heatmap Zones: Dynamic fills between MA pairs, changing color based on bullish or bearish alignment.
👁️🗨️ Full customization: Enable/disable any MA pair and its heatmap zone from the settings.
🪞 Transparency controls: Adjust the visibility of heat zones for clarity or stylistic preference.
🎨 Color-coded for clarity: Bullish and bearish colors for each heat zone pair, fully user-configurable.
🧩 Efficient layout: Smart use of grouped inputs for easier configuration and visibility management.
📈 How to Use
Use the MA1–MA2 and MA3–MA4 zones for longer-term trend tracking and confluence analysis.
Use the faster MA5–MA6 zone for short-term micro-trend identification or scalping.
When a faster MA is above the slower one within a pair, the fill turns bullish (user-defined color).
When the faster MA is below the slower one, the fill turns bearish.
Combine with price action or other indicators for entry/exit confirmation.
🧠 Pro Tips
For trend-following strategies, consider using EMA or WMA types.
For mean-reversion or support/resistance zones, SMA and VWMA may offer better zone clarity.
Overlay with RSI, MACD, or custom entry signals for higher confidence setups.
Use different heatmap transparencies to visually separate overlapping MA zones.
RV Indicator This Pine Script defines a custom Relative Volatility (RV) Indicator, which measures the ratio of directional price movement to volatility over a specified number of bars. Below is a full explanation of what this script does.
Title:
RV Indicator — Relative Volatility Oscillator
Purpose:
This indicator measures how aggressively price is moving compared to recent volatility, and smooths the result with a signal line. It can be used to gauge momentum shifts and trend strength.
How It Works – Step by Step
1. Measuring Price Momentum (v1)
It calculates the difference between the close and open prices of the last 4 candles.
A weighted average is applied:
The current candle and the one 3 bars ago get weight 1.
The two middle candles (1 and 2 bars ago) get weight 2.
This creates a smoothed momentum measure:
If close > open (bullish), v1 is positive.
If close < open (bearish), v1 is negative.
2. Measuring Volatility (v2)
Similarly, it calculates the high-low range for the last 4 candles.
The same weighting (1, 2, 2, 1) is applied.
This gives a smoothed volatility measure.
3. Combining Momentum and Volatility (RV Ratio)
For the past ti bars (default: 10), it sums up:
All v1 values (momentum sum)
All v2 values (volatility sum)
Then it divides them:
𝑅𝑉= sum of price momentum % sum of volatility
This produces the RV value:
RV > 0: Momentum is bullish (price is generally moving up relative to its volatility).
RV < 0: Momentum is bearish (price is moving down relative to its volatility).
4. Smoothed Signal Line (rvsig)
A smoothed version of the RV is created using a weighted average of the latest 4 RV values.
This acts like a signal line, similar to how MACD uses a signal line.
Crossovers between RV and this signal line can be used to detect shifts in momentum.
5. Visual Output
Orange Line (RV): Shows the raw momentum/volatility ratio.
Blue Line (Signal): A smoother line that follows RV more slowly.
Zero Line: Divides bullish vs. bearish momentum.
How to Use It in Trading
1. Look for Crossovers:
If RV crosses above its signal line → Possible buy signal (momentum turning bullish).
If RV crosses below its signal line → Possible sell signal (momentum turning bearish).
2. Check the Zero Line:
If both RV and Signal are above zero, momentum is bullish.
If both are below zero, momentum is bearish.
3. Filter False Signals:
Combine RV with a trend filter (like a 50 or 200 EMA) to avoid trading against the main trend.
Disclaimer: This script is for informational and educational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. All trading decisions are solely your responsibility. Use at your own risk.
Choch Pattern Levels [BigBeluga]🔵 OVERVIEW
The Choch Pattern Levels indicator automatically detects Change of Character (CHoCH) shifts in market structure — crucial moments that often signal early trend reversals or major directional transitions. It plots the structural break level, visualizes the pattern zone with triangle overlays, and tracks delta volume to help traders assess the strength behind each move.
🔵 CONCEPTS
CHoCH Pattern: A bullish CHoCH forms when price breaks a previous swing high after a swing low, while a bearish CHoCH appears when price breaks a swing low after a prior swing high.
Break Level Mapping: The indicator identifies the highest or lowest point between the pivot and the breakout, marking it with a clean horizontal level where price often reacts.
Delta Volume Tracking: Net bullish or bearish volume is accumulated between the pivot and the breakout, revealing the momentum and conviction behind each CHoCH.
Chart Clean-Up: If price later closes through the CHoCH level, the zone is automatically removed to maintain clarity and focus on active setups only.
🔵 FEATURES
Automatic CHoCH pattern detection using pivot-based logic.
Triangle shapes show structure break: pivot → breakout → internal high/low.
Horizontal level marks the structural zone with a ◯ symbol.
Optional delta volume label with directional sign (+/−).
Green visuals for bullish CHoCHs, red for bearish.
Fully auto-cleaning invalidated levels to reduce clutter.
Clean organization of all lines, labels, and overlays.
User-defined Length input to adjust pivot sensitivity.
🔵 HOW TO USE
Use CHoCH levels as early trend reversal zones or confirmation signals.
Treat bullish CHoCHs as support zones, bearish CHoCHs as resistance.
Look for high delta volume to validate the strength behind each CHoCH.
Combine with other BigBeluga tools like supply/demand, FVGs, or liquidity maps for confluence.
Adjust pivot Length based on your strategy — shorter for intraday, longer for swing trading.
🔵 CONCLUSION
Choch Pattern Levels highlights key structural breaks that can mark the start of new trends. By combining precise break detection with volume analytics and automatic cleanup, it provides actionable insights into the true intent behind price moves — giving traders a clean edge in spotting early reversals and key reaction zones.
Magnet Zones: Trap Detection & Flow Map [@darshakssc]This script detects potential bull and bear trap candles—price actions that may appear strong but are likely to reverse—based on:
🔺 Wick structure
📊 Volume spike behavior
💡 RSI confirmation logic
⏳ Signal cooldown filter to reduce false positives
The indicator then plots:
🟥 Red “🚨 Trap” labels above candles showing possible bull traps
🟩 Green “🧲 Trap” labels below candles showing possible bear traps
➖ Horizontal zone lines to mark these trap levels as “magnet zones,” which may act as future support or resistance
🧠 How It Works:
1. Volume Spike Detection
2. The script first checks for unusually high volume (1.5× the average volume over the last 20 candles).
3. Trap Candle Structure
4. A trap is suspected when there is a long wick opposite the direction of the candle body, signaling a failed breakout or price manipulation.
5. RSI Confirmation
6. Bull Traps: RSI must be above 60
7. Bear Traps: RSI must be below 40
✅ This helps validate whether the price was overbought or oversold.
✅ Cooldown Mechanism
✅ After a trap is detected, it waits for 10 bars before allowing another signal—this reduces noise and overfitting.
✅ How to Use It:
1. Apply on any timeframe, especially effective for intraday trading (e.g. 5m, 15m, 1h).
2. Use the trap signals as early warnings to avoid fake breakouts.
3. Combine with your own strategy or trend-following system for confirmation.
4. The trap lines (magnet zones) can be used as dynamic support/resistance levels for future pullbacks or reversals.
⚠️ Important Note:
This script is for educational purposes only and is not financial advice.
Always use traps in combination with your personal discretion, risk management, and other confluence tools.
The Kyber Cell's – TTM Squeeze ProThe Kyber Cell’s TTM Squeeze Pro
TTM Squeeze + ALMA + VWAP for Precision Trade Timing
⸻
1. Introduction
Kyber Cell’s Squeeze Pro is a comprehensive, all-in-one overlay indicator built on top of John Carter’s famous TTM Squeeze concept. It integrates advanced momentum and trend analysis using Arnaud Legoux Moving Averages (ALMA), a scroll-aware VWAP with optional deviation bands, and a clean, user-friendly visual system. The goal is simple: give traders a clear and configurable chart that identifies price compression, detects release moments, confirms direction, and helps manage risk and reward visually and effectively.
This tool is intended for traders of all styles — scalpers, swing traders, or intraday strategists — looking for cleaner signals, better visual cues, and more confidence in entry/exit timing.
⸻
2. Core Concepts
At its heart, the Squeeze Pro builds an in-chart visualization of the TTM Squeeze, a strategy that identifies when price volatility compresses inside a Bollinger Band that is narrower than a Keltner Channel. These moments often precede explosive breakouts. This version categorizes squeezes into three levels of compression:
• Blue Dot – Low Compression
• Orange Dot – Medium Compression
• Red Dot – High Compression
When the squeeze “fires” (i.e., the Bollinger Bands expand beyond all Keltner thresholds), the indicator flips to a Green Dot, signaling potential entry if confirmed by trend direction.
The indicator also includes a momentum model using linear regression on smoothed price deviation to determine directional bias. Momentum is further reinforced by a customizable trend engine, allowing you to switch between EMA-21 or HMA 34/144 logic.
An ALMA ribbon is plotted across the chart to represent smoothed trend strength with minimal lag, and a scroll-aware VWAP (Volume-Weighted Average Price) line, optionally with ±σ bands, helps confirm mean-reversion or momentum continuation setups.
⸻
3. Visual Components
Squeeze Pro replaces the traditional histogram with bar coloring logic based on your selected overlay mode:
• Momentum Mode colors bars based on whether momentum is rising or falling and in which direction (aqua/blue for bullish, red/yellow for bearish).
• Trend Mode colors bars using EMA or HMA logic to identify whether price is in a bullish, bearish, or neutral trend state.
A colored backdrop is triggered when a squeeze fires and momentum direction is confirmed. It remains green for bullish runs and red for bearish runs. The background disappears when the trend exhausts or reverses.
Each squeeze level (low, medium, high) is plotted as tiny dots above or below candles, with configurable colors. On the exact bar where the squeeze fires, the indicator optionally plots entry markers — either arrows or triangles — which can be placed with adjustable padding using ATR. These provide an at-a-glance signal of possible long or short entries.
EXPERIMENTAL : For risk and reward management, protective stop lines and limit targets can be toggled on. Stops are calculated using either recent swing highs/lows or a fixed ATR multiple, depending on user preference. Limit targets are calculated from entry price using ATR-based projections.
All colors are customizable.
⸻
4. Multi-Timeframe Squeeze Panel
An optional MTF Squeeze Panel appears in the top-right corner of the chart, displaying the squeeze status across multiple timeframes — from 1-minute to Monthly. Each timeframe is color-coded:
• Red for High Compression
• Orange for Medium Compression
• Blue for Low Compression
• Yellow for Open/No Compression
This provides rapid context for whether multiple timeframes are simultaneously compressing (a common precursor to explosive moves), helping traders align higher- and lower-timeframe signals. Colors are customizable.
The MTF panel dynamically adjusts to chart space and only renders the selected intervals for clarity and performance.
⸻
5. Inputs and Configuration Options
Squeeze Pro offers a rich configuration suite:
• Squeeze Settings: Control the Bollinger Band standard deviation, and three separate Keltner Channel multipliers (for low, medium, and high compression zones).
• ALMA Controls: Adjust the smoothing length, offset, and σ factor to control ribbon sensitivity.
• VWAP Options: Toggle VWAP on/off and optionally show ±σ bands for mean reversion signals.
• Entry Markers: Customize marker shape (arrow or triangle), size (tiny to huge), color, and padding using ATR multipliers.
• Stops and Targets:
• Choose between Swing High/Low or ATR-based stop logic.
• Define separate ATR lengths and multipliers for stops and targets.
• Independently toggle their visibility and color.
• Bar Coloring Mode: Select either Momentum or Trend logic for bar overlays.
• Trend Engine: Choose between EMA-21 or HMA 34/144 for identifying trend direction.
• Squeeze Dot Colors: Customize the colors for each compression level and release state.
• MTF Panel: Toggle visibility per timeframe — from 1m to Monthly.
This high degree of customization ensures that the indicator can adapt to nearly any trading style or preference.
⸻
6. Trade Workflow Suggestions
To get the most out of this tool, traders can follow a consistent workflow:
1. Watch Dot Progression: Blue → Orange → Red indicates increasing compression and likelihood of breakout.
2. Enter on Green Dot: When the squeeze fires (green dot), confirm entry direction with bar color and backdrop.
3. Use Confirmation Tools:
• ALMA should slope in the trade direction.
• VWAP should support the price move or confirm expansion away from mean.
4. Manage Risk and Reward (experimental):
• Respect stop-loss placements (Swing/ATR).
• Use ATR-based limit targets if enabled.
5. Exit:
• Consider exiting when momentum crosses zero.
• Or exit when the background color disappears, signaling potential trend exhaustion.
⸻
7. Alerts
Includes built-in alert conditions to notify you when a squeeze fires in either direction:
• “Squeeze Long”: Triggers when a green dot appears and momentum is bullish.
• “Squeeze Short”: Triggers when a green dot appears and momentum is bearish.
You can use these alerts for automation or to stay notified of new setups even when away from the screen.
⸻
8. Disclaimer
This indicator is designed for educational purposes only and should not be interpreted as financial advice. Trading is inherently risky, and any decisions based on this tool should be made with full awareness of personal risk tolerance and capital exposure.
OBR 15min Session Opening Range Breakout + Volume Trend DeltaQuick Overview
This Pine Script plots the opening range for London and New York sessions, highlights breakout levels, draws previous session pivots, and offers a live volume delta table for trend confirmation.
Session Opening Range
- Captures the high/low of the first 15 minutes (configurable) for both London & NY sessions.
- Fills the range area with adjustable semi‑transparent colors.
- Optional alerts fire on breakout above the high or below the low.
Previous Session Levels
- Automatically draws previous day’s High, Low, Open and previous 4‑hour High/Low.
- Helps identify key S/R zones as price approaches ORB breakouts.
Volume Trend Delta
- Uses a CMO‑weighted moving average and ATR bands to detect trend state.
- Accumulates bullish vs. bearish volume during each trend.
- Displays Bull Vol, Bear Vol, and Delta % in a movable table for quick strength checks.
How to Use
1. Let the opening range complete (first 15 min).
2. Look for price closing above/below the ORB—enter long on an upside break, short on a downside break.
3. Check the Volume Delta table: positive delta confirms buying strength; negative delta confirms selling pressure.
4. Use previous day/4h levels as additional support/resistance filters.
Settings & Customization
- ORB Duration & Session Times (London/NY), fill colors, and toggles.
- Enable/disable Previous Day & 4H levels.
- Trend Period, Momentum Window, and Delta table position/size.
- Pre‑built alert conditions for all ORB breakouts.
Developer Notes
- Fully commented for easy adjustments.
- Modular sections: ORB, previous levels, trend delta, and alerts.
- No external libraries—pure Pine Script v6.
Tip
Combine ORB breakouts with Volume Delta and prior session pivots to filter false signals and trade stronger, more reliable moves.
ATR Trailing Stop with ATR Targets [v6]What the Indicator Does
This custom TradingView indicator is designed for active traders who want to automate and visualize their trailing stop management and target setting, using true market volatility. It combines the Average True Range (ATR) with dynamic market structure logic to:
Trail a stop-loss behind major swings in real time, using 2×ATR (adjustable) from the highest high in uptrends or the lowest low in downtrends.
Flip trading bias between bullish and bearish when the stop is breached.
Identify and plot three profit targets (at 1, 2, and 3 ATR from the breakout/flip point) after every stop-flip, helping traders scale out or set take-profits objectively.
Maintain a visible presence on your chart every bar to avoid indicator errors, with color and labeling for clear distinction between long/short phases.
How the Indicator Works
1. ATR Calculation
ATR Period and Multiplier: You select your preferred ATR length (default is 14 bars) and a multiplier (default is 2.0).
Volatility Adjustment: ATR measures the average "true" bar range, so the trailing stop and targets adapt to current volatility.
2. Trailing Stop Logic
Uptrend (bullish bias): The indicator tracks the highest high made since the last bearish-to-bullish flip and sets the stop at - .
The stop only raises (never lowers) during an uptrend, protecting gains in strong moves.
Downtrend (bearish bias): Tracks the lowest low made since the last bullish-to-bearish flip, with stop at + .
The stop only lowers (never raises) in a downtrend.
Flip Point: If price closes through the trailing stop, the current bias “flips,” and the logic reverses (bullish to bearish or vice versa). At the new close, flip price and bar index are stored for target calculation.
3. ATR Targets after Flip
After each stop flip:
Three targets—based on the new close price—are calculated and plotted:
Long flip (new bull bias): Target1 = close + 1×ATR, Target2 = close + 2×ATR, Target3 = close + 3×ATR.
Short flip (new bear bias): Target1 = close - 1×ATR, Target2 = close - 2×ATR, Target3 = close - 3×ATR.
These targets help with scaling out, partial profit-taking, or setting automated orders.
4. Visual Feedback
Trailing stop line: Green for long bias, red for short bias.
Targets: Distinct color-coded circles at 1, 2, 3 ATR levels from the most recent flip.
Flip Labels: Mark the bar and price where bias flipped (“Long Flip” or “Short Flip”) for quick pattern recognition.
Subtle background shading: Ensures TradingView's requirement for “indicator output every bar.”
How to Use This Indicator
Parameter Setup
ATR Period and Multiplier: Adjust to match the timeframe and volatility of your instrument.
Lower periods/multipliers for short-term/volatile trading.
Higher values for smoother signals or higher timeframes.
Starting Trend: Set to match the expected initial bias if the instrument has strong trend characteristics.
Trading Application
1. Daily Bias Approach
Establish your bias in line with your trading plan (e.g., only trade long if price is above the previous day's high, short below the previous day's low).
Only look for trades in the indicator's current bias direction, as expressed by the stop and background color.
2. Entry
Use the indicator as a real-time confirmation or trailing stop for your entries.
Breakout: Enter when price establishes the current bias, using the trailing stop as your risk level.
Reversal: Wait for a bias flip after an extended move; enter in the direction of the new bias.
VWAP Rebound: Combine with a VWAP bounce—enter only if the indicator bias supports your direction.
3. Exits/Targets
Trailing stop management: Move your stop according to the plotted line; exit if your stop is hit.
Profit-taking: Scale out or take profits as price approaches each ATR-based target.
Use the dynamic labeling to identify reversal flips and reset your plan if stopped or the bias changes.
4. Market Context
Filter and frame setups by watching correlated indicators (DXY, VIX, AUDJPY, put/call ratio) and upcoming news; trade only in the daily bias direction for best consistency.
5. Practical Tips
Combine this indicator with your custom watchlist and alert settings to get notified on flips or targets.
Review the last label ("Long Flip"/"Short Flip") and targets to plan partial exits.
Remember: ATR adapts to volatility, so the stop and targets stay proportionate even when price action shifts.
7 EMA CloudThe "7 EMA Cloud" script was likely flagged because it reuses the core concept of EMA clouds (shading areas between multiple EMAs to visualize trends, support/resistance, and momentum) without crediting the original inventor, Ripster (author ripster47 on TradingView). This concept is prominently associated with Ripster's "EMA Clouds" indicator, which popularized filling spaces between EMA pairs for trading signals. TradingView's house rules require crediting authors when reusing open-source ideas or code, even if not a direct copy-paste, and mandate significant improvements where the original forms a small proportion of the script. Your version adds features like multiple color modes (Classic rainbow, Monochrome, Heatmap), customizable signal sizes, and crossover alerts between the first and last EMA, which are enhancements, but the foundational EMA ribbon/cloud idea needs explicit attribution in the description and ideally code comments to comply.
Additionally, the description might be seen as not fully self-contained (e.g., it uses promotional language like "Advanced" and "Adaptive Trend & Signal Suite" without deeply explaining calculations or use cases), potentially violating rules against relying on code or external references for clarity.
To fix this, republish a new version with proper credits, ensure the description is detailed and standalone, and emphasize your improvements (e.g., the 7 Fibonacci-based EMAs, color modes, and signals). Do not reuse the flagged script—create a fresh one. Here's a compliant description you can use:
7 EMA Cloud Indicator
Overview
The 7 EMA Cloud overlays seven exponential moving averages (EMAs) with Fibonacci-inspired periods and fills the spaces between them with customizable "clouds" to visually represent trend strength, direction, and convergence/divergence. It includes crossover signals between the shortest and longest EMAs for potential entry/exit points, with adjustable visual modes for different trading styles. This helps traders identify bullish/bearish momentum, support/resistance zones, and overextensions in trending or ranging markets.
This script builds on the EMA cloud concept popularized by Ripster (ripster47) in their "EMA Clouds" indicatortradingview.com, where areas between EMA pairs are shaded for trend analysis. Improvements include a fixed set of 7 Fibonacci EMAs, multiple color schemes (Classic rainbow, Monochrome grayscale, Heatmap for intensity), user-selectable signal sizes, and transparency controls. Released under the Mozilla Public License 2.0.
Key Features
7 EMAs with Clouds: EMAs at periods 8, 13, 21, 34, 55, 89, and 144; clouds filled between consecutive pairs to show alignment (tight clouds for consolidation, wide for trends).
Color Modes:
Classic: Rainbow gradients (blue to purple) for vibrant distinction.
Monochrome: Grayscale shades for minimalistic charts.
Heatmap: Red-to-blue spectrum to highlight "hot" (volatile) vs. "cool" (stable) areas.
Crossover Signals: Triangle markers (up for bullish, down for bearish) when the shortest EMA crosses the longest; sizes from Tiny to Huge.
Display Options: Toggle EMA lines on/off, adjust cloud transparency (0-100%), and enable alerts for crossovers.
Alerts: Notifications for "Bullish EMA Crossover" (EMA1 > EMA7) and "Bearish EMA Crossover" (EMA1 < EMA7).
How It Works
EMA Calculations: Each EMA is computed using ta.ema(close, period), with periods based on Fibonacci sequences for natural market rhythm alignment.
Clouds: Filled via fill() between plot pairs, with colors derived from the selected mode and transparency applied.
Signals: Detected with ta.crossover(ema1, ema7) and ta.crossunder(ema1, ema7), plotted as shapes with mode-specific colors (e.g., green/lime for bull, red for bear).
Customization: Inputs grouped into EMA Settings (periods), Display Settings (visibility, colors, transparency), and Signal Settings (size).
Customization Options
EMA Periods: Individually adjustable (defaults: 8, 13, 21, 34, 55, 89, 144).
Show EMAs: Toggle to hide lines and focus on clouds.
Cloud Transparency: 0% for solid fills, 100% for invisible (default 80%).
Color Mode: Switch between Classic, Monochrome, or Heatmap.
Signal Size: Tiny, Small, Normal, Large, or Huge for crossover markers.
Ideal Use Case
Suited for swing or trend-following on any timeframe (e.g., 15m-1h for intraday, daily for swings) and assets (stocks, forex, crypto, futures). Enter long on bullish crossovers above aligned clouds; exit on bearish signals or cloud widenings. Use Monochrome for clean charts or Heatmap for volatility emphasis. Combine with volume or RSI for confirmation.
Why It's Valuable
By expanding Ripster's EMA cloud idea with multi-mode visuals and integrated signals, this indicator provides a versatile, at-a-glance tool for trend assessment—reducing noise while highlighting key shifts. It's more adaptive than basic MA ribbons, with Fibonacci periods adding a layer of harmonic analysis.
Note: Test on historical data or demo accounts. Not financial advice—incorporate risk management. Optimized for Pine Script v5; some features may vary on non-overlay charts.
CQ_MTF Target Price Lines [BITCOIN HOY]Comprehensive Indicator Script Overview
Intraday, Four Hour, Daily, and Weekly Price Target Lines—A Versatile Tool for Traders
Welcome to a powerful and flexible indicator script designed to enhance your trading experience across multiple timeframes. This script empowers users to interactively set, visualize, and manage price targets, entries, and objectives for both short-term and long-term trading strategies. Whether you are a day trader seeking to mark crucial intraday levels or a long-term investor planning strategic entries, this tool offers an all-encompassing solution.
Key Features
• Multi-Time Frame Price Target Lines: Effortlessly input and display calculated price targets for Intraday, Four Hour, Daily, and Weekly periods, ensuring you always have a clear view of the market objectives at every scale.
• X-Axis Price Control: Set precise x-axis price points for each timeframe, granting you granular control over how and where your target lines appear on the chart.
• Weekly Price Objectives: Enter your calculated price objectives for the current week to remain aligned with your trading plan and adapt to evolving market conditions.
• Long-Term Investment Entry Events: Document and display significant entry events for long-term investments, helping you maintain a strategic perspective while navigating short-term fluctuations.
• Long-Term Price Objectives: Input and track price objectives for your long-term trades, supporting your investment decisions with clearly visualized milestones.
• Customizable Labels and Lines: Each price target is accompanied by clearly labeled lines, making it easy to distinguish between timeframes and targets at a glance.
Optional Price Gauge for Intraday Dynamics
For users who wish to monitor real-time market sentiment, the script includes an optional price gauge. This dynamic feature tracks intraday price movement, providing visual cues to quickly assess whether the prevailing tendency is bullish or bearish. The intuitive gauge aids in confirming your intraday strategies or alerting you to potential reversals.
User Experience and Customization
• Interactive Inputs: All key parameters—price targets, x-axis prices, entry events, and objectives—are entered manually by the user. This approach ensures the script adapts to your personal analysis and trading methodology.
• Easy Visualization: The clear display of lines, labels, and the optional gauge streamlines your chart, making it easier to make informed decisions at a glance.
• Flexible Application: Whether you’re trading short-term swings or building positions for the long haul, the indicator integrates seamlessly into your workflow.
How to Use
• Input your calculated price targets for each timeframe (Intraday, Four Hour, Daily, and Weekly).
• Specify the exact x-axis price points where you’d like the lines to appear for each timeframe.
• For the current week, enter your weekly price objectives for quick reference and planning.
• If you’re a long-term investor, document your key entry events and set long-term price objectives to track their progression.
• To monitor current market momentum, activate the price gauge and follow the visual cues for bullish or bearish trends.
Benefits
• Comprehensive Market Overview: Simultaneously track multiple timeframes and objectives, keeping all critical information at your fingertips.
• Improved Decision Making: Visual clarity and strategic labeling support faster, more confident trading decisions.
• Customizable and Adaptable: Tailor the script to your unique trading style and analytical approach.
Enjoy using the indicator, and happy trading! Let this versatile tool be your companion in navigating the ever-changing tides of the market.
Liquidity Spectrum Visualizer [BigBeluga]🔵 OVERVIEW
The Liquidity Spectrum Visualizer is a smart tool for exposing hidden liquidity zones by combining a dynamic volume profile, clear liquidity levels, and intuitive volume bubbles directly on your price chart. It shows you exactly where significant volume is clustering inside your chosen lookback period — highlighting where big market participants may be defending price or planning breakouts.
🔵 CONCEPTS
Volume Profile Bins: Breaks your custom lookback range into 100 fine price bins, calculating total volume per bin to create a precise vertical liquidity histogram.
Liquidity Levels: Bins with high relative volume automatically plot as horizontal lines — thicker and brighter lines signal stronger liquidity concentrations.
Dynamic Coloring: Profile bins and liquidity levels adjust their colors live based on whether current price is trading above (support) or below (resistance).
Volume Bubbles: Each candle displays a bubble at its HLC3 price —
- The bubble’s size shows relative candle volume.
- Its color gradient indicates bullish or bearish volume: greenish for bullish candles, orange for bearish.
Bubble Labels: The largest bubbles automatically label the actual volume amount, revealing big hidden flows.
Range Box High/Low: Marks the absolute swing high and low inside the lookback window, clearly framing the active liquidity zone.
🔵 FEATURES
Smart, auto-scaled volume profile up to 200 candles (or custom).
Liquidity levels with dynamic thickness and color based on real-time volume.
Bubbles sized and colored to show both volume magnitude and bullish/bearish bias.
Largest bubbles labeled for fast detection of high-impact bars.
High and low price labels clearly show the analyzed range.
Toggle Volume Profile, Liquidity Levels, and Bubbles independently.
🔵 HOW TO USE
Watch for thick, bright liquidity levels — these zones mark where large orders or stop clusters are likely hidden.
Use dynamic coloring: if price is above a level, it’s support; if below, it’s resistance.
Pay special attention to big bubbles: these mark sudden spikes in traded volume and can signal absorption, traps, breakouts or significant price levels.
Combine with your existing confluence tools to confirm breakouts or fakeouts around visible liquidity clusters.
🔵 CONCLUSION
The Liquidity Spectrum Visualizer transforms hidden order flow into an intuitive, color-coded map. You see at a glance where price is absorbing, consolidating, or ready to break — all powered by real-time volume behavior and smart visuals. It’s a must-have tool for traders who want to read liquidity and react ahead of the crowd.
Support and Resistance Levels with BreaksThis indicator identifies dynamic support and resistance levels using pivot point analysis and provides clear trading signals when these levels are broken with volume confirmation. Enhanced version with improved signal clarity for better trading decisions.
## 🔧 Key Features
### Support & Resistance Detection
- Automatically identifies key pivot high and low levels
- Draws clear visual lines (red for resistance, blue for support)
- Configurable sensitivity with left/right bar settings
### Enhanced Trading Signals
- **BUY** signals when resistance is broken with volume confirmation
- **SELL** signals when support is broken with volume confirmation
- **Bull Wick** alerts for potential reversals at resistance
- **Bear Wick** alerts for potential reversals at support
### Volume Confirmation
- Built-in volume oscillator using 5 and 10-period EMAs
- Filters out low-volume false breakouts
- Adjustable volume threshold (default: 20%)
### Complete Alert System
- Support Broken alerts
- Resistance Broken alerts
- Bull Wick reversal alerts
- Bear Wick reversal alerts
## ⚙️ Settings
- **Show Breaks**: Toggle signal display
- **Left Bars**: Pivot detection lookback (default: 15)
- **Right Bars**: Pivot detection lookforward (default: 15)
- **Volume Threshold**: Minimum volume increase for valid signals (default: 20%)
## 📈 Best For
- Swing trading strategies
- Breakout confirmation
- Support/resistance trading
- Volume-based entry signals
## 🔍 How It Works
1. Identifies pivot highs/lows using configurable periods
2. Calculates volume oscillator for confirmation
3. Generates BUY signals on resistance breaks with volume
4. Generates SELL signals on support breaks with volume
5. Detects wick patterns for potential reversals
## 📋 Updates in This Version
- Enhanced BUY/SELL signal clarity (replaced generic "B" labels)
- Added Bull Wick and Bear Wick alert conditions
- Updated to Pine Script v6 compatibility
- Improved signal filtering and accuracy
## ⚠️ Disclaimer
This indicator is for educational and informational purposes only. Always conduct your own analysis and risk management before making trading decisions. Past performance does not guarantee future results.
---
**Original Script**: "Support and Resistance Levels with Breaks" by LuxAlgo
**License**: CC BY-NC-SA 4.0
**Enhanced by**: profitgang
**Version**: Pine Script v6
RSI PotentialRSI Potential
This indicator does more than just track RSI; it measures the "energy" or "fuel" left in a trend. It answers a critical question: how much further can the price move before momentum is exhausted?
The key insight is that high momentum often means low potential, and vice versa. This inverse relationship is what allows the indicator to provide powerful, forward-looking signals about trend health and potential reversals.
Think of it like a race car:
Momentum is the car's current speed.
Potential is the amount of fuel left in the tank.
A car at top speed (high momentum) is burning fuel rapidly (potential is decreasing). A car just starting (low momentum) has a full tank of fuel (high potential). This indicator helps you see the fuel gauge, not just the speedometer.
This indicator plots three distinct components in a separate pane below your chart:
1. Upside Potential (Green Line)
What it shows: The percentage price increase required to hit the Overbought RSI Level. In other words, how much "fuel" is left for the upward trend.
How to interpret it:
Low Value (Approaching Zero): This is a warning sign. It means the price is already in high gear, and there is very little room left to run before hitting overbought exhaustion. Even if the price is rocketing up (high momentum), low potential signals the rally is likely on its last legs.
High Value: This indicates the market has a full tank of fuel for a rally. Even if the price is moving sideways or slowly (low momentum), the high potential suggests that if a new uptrend starts, it has the energy to be sustainable and significant.
2. Downside Potential (Red Line)
What it shows: The percentage price decrease required to hit the Oversold RSI Level—the "fuel" for a downtrend.
How to interpret it:
Low Value (Approaching Zero): A warning for bears. The price may be dropping fast (high momentum), but it's running out of energy to fall further. This signals seller exhaustion and increases the probability of a bounce or reversal.
High Value: The market has significant room to fall before becoming oversold. This can confirm the health of a new downtrend or suggest that a current downtrend has more to go.
3. Net Potential (Columns / Histogram)
What it shows: The net balance of energy: Upside Potential - Downside Potential. It answers, "Which side has more fuel in the tank?"
Daily 50‑ & 200‑SMA Ceiling Radar — EnhancedDescription:
This custom TradingView indicator, developed by Trader Malik and licensed under Trades Per Minute, is a powerful visual tool for identifying how price behaves relative to major daily moving averages — the 50-SMA and 200-SMA. It helps traders quickly understand key technical dynamics such as trend alignment, MA proximity, and short-term momentum sentiment — all displayed on a clean, minimal overlay with visual alerts and an adjustable data table.
FEATURES
1. Daily 50 & 200 Simple Moving Averages (SMA):**
- Displayed directly on the chart using distinct blue and orange lines.
- These serve as primary trend filters and support/resistance zones.
2. Price Highlighting:
- A red background flashes momentarily when the price crosses either the 50-SMA or 200-SMA.
- A green background fills the chart when price is above both MAs (bullish zone).
- A red background persists if price is below both MAs (bearish zone).
3. MA Gap Analysis Table:
- 50-SMA Row**: Shows % gap between 50-SMA and 200-SMA.
- 200-SMA Row**: Shows % gap between 200-SMA and 50-SMA.
- Sentiment Row**: Displays short-term trend bias based on the slope of the past 7 daily closes — Bullish, Neutral, or Bearish.
USER SETTINGS
Table Location: Choose between **Top Right** or **Bottom Right** of the chart.
Table Size: Select **Small**, **Medium**, or **Large** to suit screen preferences and layout aesthetics.
This script is **intellectual property of Trades Per Minute** and distributed by **Trader Malik** for use under licensing terms. Redistribution or repurposing without authorization is strictly prohibited.
First FVG📘 Indicator Description (English)
First FVG – NY Open is a TradingView indicator designed to automatically identify the first Fair Value Gap (FVG) that appears during the New York session, following the ICT (Inner Circle Trader) methodology.
It highlights institutional inefficiencies in price caused by imbalanced price action and helps traders spot high-probability entry zones, especially after the 9:30 AM EST (New York Open).
⚙️ How It Works
Session time: The indicator scans for FVGs starting at 9:32 AM (allowing 3 candles after the NY Open to form).
FVG Conditions:
Bullish FVG: When the high of 2 candles ago is lower than the low of the current candle and the middle candle is bullish.
Bearish FVG: When the low of 2 candles ago is higher than the high of the current candle and the middle candle is bearish.
Only the first FVG per session is drawn, as taught by ICT for setups like Judas Swing or NY Reversal models.
A colored box is drawn to represent the FVG zone.
A dotted horizontal line (CE) is drawn at the midpoint of the FVG box (Consequent Encroachment), a key level watched by smart money traders.
A dashed vertical line is drawn at 9:30 NY time to mark the open.
🧠 How to Use It
Wait for the NY Open (9:30 AM EST) – the indicator becomes active at 9:32 AM.
Watch for the first FVG box of the day. This is often a high-probability reaction zone.
Use the CE line (center of the FVG) as a reference for entries, rejections, or liquidity grabs.
Combine with market structure, PD Arrays, and liquidity concepts as taught by ICT for confluence.
The FVG box and CE line will extend forward for several candles for visual clarity.
🎛️ Customizable Settings
Session time (default: 09:32–16:00 NY)
FVG box color (up/down)
Text color
Max number of days to keep boxes on chart
Option to show or hide the 9:30 NY Open vertical line
Internal Candle Strength [LuxAlgo]The Internal Candle Strength tool allows traders to divide each chart bar into multiple rows of custom size and inspect the strength of the lower timeframes trends located within each row.
This tool effectively helps traders in identifying the power dynamic between bulls and bears within multiple areas within each bar, providing the ability to conduct LTF analysis.
🔶 USAGE
The strength displayed within each row ranges from 0% to 100%, with 0% being the most bearish and 100% being the most bullish.
Traders should be aware of the extreme probabilities located at the higher/lower end of the bars, as this can signal a change in strength and price direction.
Traders can select the lower timeframe to pull the data from or the row size in the scale of the chart. Selecting a lower timeframe will provide more data to evaluate an area's strength.
Do note that only a timeframe lower than the chart timeframe should be selected.
🔹 Row Size
Selecting a smaller row size will increase the number of rows per bar, allowing for a more detailed analysis. A lower value will also generally mean that less data will be considered when calculating the strength of a specific area.
As we can see on the chart above (all BTCUSD 30m), by selecting a different row size, traders can control how many rows are displayed per bar.
🔶 SETTINGS
Timeframe: Lower timeframe used to calculate the candle strength.
Row Size: Size of each row on the chart scale, expressed as a fraction of the candle range.
IU Inside/Harami candlestick patternDESCRIPTION
The IU Inside/Harami Candlestick Pattern indicator is designed to detect bullish and bearish inside bar formations, also known as Harami patterns. This tool gives users flexibility by allowing pattern detection based on candle wicks, bodies, or a combination of both. It highlights detected patterns using colored boxes and optional text labels on the chart, helping traders quickly identify areas of consolidation and potential reversals.
USER INPUTS :
Pattern Recognition Based on =
Choose between "Wicks", "Body", or "Both" to determine how the inside candle pattern is identified.
Show Box =
Toggle the appearance of colored boxes that highlight the pattern zone.
Show Text =
Toggle on-screen labels for "Bullish Inside" or "Bearish Inside" when patterns are detected.
INDICATOR LOGIC :
Bullish Inside Bar (Harami) is detected when:
* The current candle's high is lower and low is higher than the previous candle (wick-based),
* or the current candle’s open and close are inside the previous candle’s body (body-based),
* and the current candle is bullish while the previous is bearish.
Bearish Inside Bar (Harami) is detected when:
* The current candle's high is lower and low is higher than the previous candle (wick-based),
* or the current candle’s open and close are inside the previous candle’s body (body-based),
* and the current candle is bearish while the previous is bullish.
The user can choose wick-based, body-based, or both logics for pattern confirmation.
Boxes are drawn between the highs and lows of the pattern, and alert messages are generated upon confirmation.
Optional labels show the pattern name for quick visual identification.
WHY IT IS UNIQUE :
Offers three different logic modes: wick-based, body-based, or combined.
Highlights patterns visually with customizable boxes and labels.
Includes built-in alerts for immediate notifications.
Uses clean and transparent plotting without repainting.
HOW USER CAN BENEFIT FROM IT :
Receive real-time alerts when Inside/Harami patterns are formed.
Use the boxes and text labels to spot price compression zones and breakout potential.
Combine it with other tools like trendlines or support/resistance for enhanced accuracy.
Suitable for scalpers, swing traders, and price action traders looking to trade inside bar breakouts or reversals.
DISCLAIMER :
This indicator is not financial advice, it's for educational purposes only highlighting the power of coding( pine script) in TradingView, I am not a SEBI-registered advisor. Trading and investing involve risk, and you should consult with a qualified financial advisor before making any trading decisions. I do not guarantee profits or take responsibility for any losses you may incur.
