TCP | Market Session | Session Analyzer๐ TCP | Market Session Indicator | Crypto Version
A powerful, real-time market session visualization tool tailored for crypto traders. Track the heartbeat of Asia, Europe, and US trading hours directly on your chart with live session boxes, behavioral analysis, liquidity grab detection, and countdown timers. Know when the action starts, how the market behaves, and where the traps lie.
๐ฐ Introduction:
Trade the Right Hours with the Right Tools
Time matters in trading. Most significant moves happen during key sessionsโand knowing when and how each session unfolds can give you a sharp edge. The TCP Market Session Indicator, developed by Trade City Pro (TCP), puts professional session tracking and behavioral insights at your fingertips.
Whether you're a scalper or swing trader, this indicator gives you the timing context to enter and exit trades with greater confidence and clarity.
๐ Core Features
โข Live Session Boxes :
Highlight active ranges during Asia, Europe, and US sessions with dynamic high/low updates.
โข Session Start/End Labels :
Know exactly when each session begins and ends plotted clearly on your chart with context.
โข Session Behavior Analysis :
At the end of each session, the indicator classifies the price action as:
- Trend Up
- Trend Down
- Consolidation
- Manipulation
โข Liquidity Grab Detection: Automatically detects possible stop hunts (fake breakouts) and marks them on the chart with precision filters (volume, ATR, reversal).
โข Session Countdown Table: A live dashboard showing:
- Current active session
- Time left in session
- Upcoming session and how many minutes until it starts
- Utility time converter (e.g. 90 min = 01:30)
โข Vertical Session Lines: Visualize past and upcoming session boundaries with customizable history and future range.
โข Multi-Day Support: Draw session ranges for previous, current, and future days for better backtesting and forecasting.
โ๏ธ Settings Panel
Customize everything to fit your trading style and schedule:
โข Session Time Settings:
Set the opening and closing time for each session manually using UTC-based minute inputs.
โ For example, enter Asia Start: 0, Asia End: 480 for 00:00โ08:00 UTC.
This gives full flexibility to adjust session hours to match your preferred market behavior.
โข Enable or Disable Elements:
Toggle the visibility of each session (Asia, Europe, US), as well as:
- Session Boxes
- Countdown Table
- Session Lines
- Liquidity Grab Labels
โข Timezone Selection:
Choose between using UTC or your chartโs local timezone for session calculations.
โข Customization Options:
Select number of past and future days to draw session data
Adjust vertical line transparency
Fine-tune label offset and spacing for clean layout
๐ Smart Session Boxes
Each session box tracks high, low, open, and close in real time, providing visual clarity on market structure. Once a session ends, the box closes, and the behavior type is saved and labeled ideal for spotting patterns across sessions.
โข Asia: Green Box
โข Europe: Orange Box
โข US: Blue Box
๐ก Why Use This Tool?
โข Perfect Timing: Donโt get chopped in low-liquidity hours. Focus on sessions where volume and volatility align.
โข Pattern Recognition: Study how price behaves session-to-session to build better strategies.
โข Trap Detection: Spot manipulation moves (liquidity grabs) early and avoid common retail pitfalls.
โข Macro Session Mapping: Use as a foundational layer to align trades with market structure and news cycles.
๐ Example Use Case
You're watching BTC at 12:45 UTC. The indicator tells you:
The Asia session just ended (label shows โAsia Session End: Trend Upโ)
Europe session starts in 15 minutes
A liquidity grab just triggered at the previous highโlabel confirmed
Now you know whoโs active, what the market just did, and whatโs about to startโall in one glance.
โ
Why Traders Trust It
โข Visual & Intuitive: Fully chart-based, no clutter, no guessing
โข Crypto-Focused: Designed specifically for 24/7 crypto markets (not outdated forex models)
โข Non-Repainting: All labels and boxes stay as printedโno tricks
โข Reliable: Tested across multiple exchanges, pairs, and timeframes
๐งฉ Built by Trade City Pro (TCP)
The TCP Market Session Indicator is part of a suite of professional tools used by over 150,000 traders. Itโs coded in Pine Script v6 for full compatibility with TradingViewโs latest capabilities.
๐ Resources
โข Tutorial: Learn how to analyze sessions like a pro in our TradingView guide:
"TradeCityPro Academy: Session Mapping & Liquidity Traps"
โข More Tools: Explore our full library of indicators on
Cari dalam skrip untuk "liquidity"
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) = ฮฑโ ร T(t) ร W_regime(t) + ฮฑโ ร F(t) ร (1 - W_regime(t)) + ฮฑโ ร M(t) + ฮต(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 ฮต(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 ร 0.4) + (VIX_Adjustment ร 0.4) + (Macro_Adjustment ร 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.
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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.
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Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
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Leola Lens SignalPro๐ Leola Lens SignalPro โ Structure-Aware Momentum Overlay (Invite-Only)
This script is designed for traders who prioritize clear structure, liquidity trap zones, and momentum transitions. It provides adaptive visual overlays that align with key decision points โ emphasizing structure over lagging indicators.
________________________________________
โ๏ธ Core Operating Modes
โ
Momentum Shift Mode (Always Active)
Tracks microstructure shifts using volatility compression, imbalance reactions, and adaptive logic for directional bias.
โก Scalper Mode (Optional)
Activates fast-response overlays for 1mโ15m charts โ tuned for crypto, indices, and intraday setups.
๐ก Safeguard Mode (Optional)
Applies volume and exhaustion filters for higher timeframe or conservative entries, ideal for swing traders.
________________________________________
๐ฆ Liquidity Control Box (LCB) Logic
๐ต Blue Box = Bullish Control
โข Break above โ continuation likely
โข Break below โ caution for reversal
๐ง Orange Box = Bearish Control
โข Break below โ continuation likely
โข Break above โ caution for squeeze
Use the last visible box for bias.
Box edges = confluence zones.
Box overlaps = consolidation โ avoid impulsive trades.
________________________________________
๐ง Signal Logic & Concept
Built using a custom structural engine, not derived from public scripts like RSI, MACD, or WaveTrend.
The overlays aim to capture price behavior often aligned with institutional concepts, such as:
โข Order Blocks
โข Liquidity Sweeps
โข Trap Reversals
โข Mitigation Moves
Pairs well with SMC-style analysis and order-flow-based trading.
________________________________________
๐ก Visual Signal Layers
โข BUY / SELL Labels โ Appear near structure flips and trap zones
โข Yellow Label โ High-risk trend shift zone
โข LCB Boxes โ Real-time market control zones
โข Green/Red Liquidity Zones โ Absorption or rejection
โข MA Overlays โ Adaptive slope-based guidance (optional)
โข Pink Lines โ High-reactivity reversal zones
โข Yellow Line โ Soft S/R (psychological pivot)
________________________________________
๐ฏ Suggested Entry & Exit Cues (Educational Use Only)
โ
Entry
โข BUY near Blue LCB + liquidity reaction
โข SELL after extended rallies into Orange LCB + trap behavior
โข โ Avoid trades directly at Yellow Labels unless other context supports
โ
Exit
โข On opposite label after structure break
โข On formation of opposite LCB
โข Near major liquidity zones or pink levels
๐งช Always backtest label behavior to fit your strategy before use.
________________________________________
๐ Originality Justification
This script introduces a non-indicator-based approach to structure detection โ combining real-time volatility response, adaptive liquidity logic, and multi-mode filtering. It avoids conventional oscillators in favor of clarity-driven visual overlays, offering a novel experience especially useful to discretionary traders.
________________________________________
โ ๏ธ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a trading signal. Always validate performance with backtesting and forward testing before live use.
________________________________________
Weekend Trap# Weekend Trap Indicator - Advanced Low-Liquidity Range Analysis
## ORIGINALITY & UNIQUE VALUE PROPOSITION
This indicator introduces a **novel approach** to weekend market analysis by combining three distinct methodologies into a single, cohesive system:
1. **Timezone-Specific Range Detection**: Unlike generic weekend indicators, this script uses Australia/Perth timezone (GMT+8) for precise weekend period identification (Saturday 5:00 AM to Monday 5:00 AM), specifically designed for Asia-Pacific trading sessions.
2. **Proprietary PVSRA Implementation**: Features a custom volume analysis engine that extends traditional PVSRA (Price Volume Spread Range Analysis) with weighted volume calculations using the formula: `Volume ร (High - Low)` compared against 10-period moving averages and highest weighted volume peaks.
3. **Dynamic Range Cutoff System**: Introduces configurable range update cutoffs (default: Sunday 3:00 PM Perth time) to account for varying institutional re-entry patterns across different markets.
**What Makes This Different**: Existing weekend indicators either focus on simple range detection OR volume analysis. This script uniquely combines both with timezone precision and institutional behavior modeling, creating a comprehensive low-liquidity period analysis tool not available in other publications.
---
## TECHNICAL METHODOLOGY & CALCULATIONS
### Weekend Range Detection Engine
```
Weekend Period: Saturday 5:00 AM โ Monday 5:00 AM (Perth Time)
Range Calculation:
- High/Low tracking with wick or body-only options
- Real-time updates until Sunday cutoff hour
- Automatic finalization at Monday 5:00 AM
```
### Advanced PVSRA Volume Analysis
The indicator implements a sophisticated 4-tier volume classification system:
**Volume Thresholds:**
- **200% Bull/Bear**: `volume โฅ (10-period average ร 2.0)` OR `weighted_volume โฅ highest_10_period_weighted`
- **150% Bull/Bear**: `volume โฅ (10-period average ร 1.5)`
**Weighted Volume Formula:**
```
weighted_volume = current_volume ร (high - low)
institutional_signature = weighted_volume โฅ highest(weighted_volume, 10)
```
**Color Classification:**
- ๐ข Lime: 200% Bull volume (Peak institutional buying)
- ๐ด Red: 200% Bear volume (Peak institutional selling)
- ๐ต Blue: 150% Bull volume (Elevated buying pressure)
- ๐ฃ Fuchsia: 150% Bear volume (Elevated selling pressure)
### Range Analytics Engine
- **Absolute Range**: `weekend_high - weekend_low`
- **Percentage Range**: `((high - low) / low) ร 100`
- **Direction Classification**: Based on `((close - open) / open) ร 100` with 0.1% threshold
- **50% Midline**: `(weekend_high + weekend_low) / 2` with dynamic updating
---
## INSTITUTIONAL BEHAVIOR MODELING
### Why Weekend Analysis Matters
During weekend periods, institutional trading volume drops by 80-90%, creating:
- **Thin liquidity conditions** where retail sentiment dominates
- **Range-bound behavior** as major institutions are absent
- **Volume spikes** when institutions DO trade (our detection target)
### Market Maker Detection Logic
The indicator identifies institutional activity through:
1. **Volume Anomaly Detection**: Spikes above statistical norms during low-liquidity periods
2. **Price Impact Analysis**: High volume relative to price movement (manipulation signature)
3. **Timing Analysis**: Activity during traditionally quiet periods indicates institutional involvement
---
## COMPREHENSIVE USAGE GUIDE
### Setup Instructions
1. **Timeframe**: Recommended 1H-4H (works on all timeframes)
2. **Markets**: Best on liquid instruments (major FX pairs, crypto, indices)
3. **Lookback Period**: Set 4-52 weeks based on analysis needs
4. **Timezone**: Automatically uses Perth time - no adjustment needed
### Interpretation Framework
**Range Analysis:**
- **Tight Ranges** (<0.5%): Expect Monday breakout potential
- **Wide Ranges** (>2.0%): Indicates weekend volatility/news impact
- **50% Line**: Key support/resistance for Monday open
**Volume Signals:**
- **200% Markers**: Major institutional activity - expect follow-through
- **150% Markers**: Moderate institutional interest - monitor for continuation
- **Clustering**: Multiple markers suggest sustained institutional involvement
- **Absence**: Pure retail weekend - ranges likely to hold
**Pattern Recognition:**
- **Expanding Ranges**: Increasing weekend volatility (trend change signal)
- **Contracting Ranges**: Decreasing volatility (consolidation/breakout setup)
- **Direction Bias**: Weekend direction often reverses on Monday open
### Trading Applications
1. **Gap Trading**: Weekend ranges help predict Monday gap fills
2. **Breakout Trading**: Range boundaries become key levels for Monday
3. **Institutional Following**: 200% volume signals indicate smart money direction
4. **Risk Management**: Range size helps determine appropriate position sizing
---
## ALERT SYSTEM & AUTOMATION
**Built-in Alerts:**
- Weekend Trap Start: Automated detection of Saturday 5:00 AM Perth
- Weekend Trap End: Monday 5:00 AM Perth confirmation
- Market Maker Activity: Real-time 150%+ volume detection
**Real-time Features:**
- Live weekend range updates with current direction
- Dynamic 50% line adjustment
- Progressive range statistics display
### Real-Time Weekend Tracking in Action
---
## PERFORMANCE & OPTIMIZATION
### Object Management System
- **Dynamic Limits**: Automatic cleanup based on selected lookback period
- **Memory Efficiency**: Objects created only within backtest range
- **Performance Scaling**: Handles 1-52 week analysis without lag
### Visual Optimization
- **Clean Display**: Configurable elements prevent chart clutter
- **Color Coding**: Intuitive PVSRA color scheme for quick recognition
- **Scalable Markers**: Adjustable sizes for different screen resolutions
---
## EDUCATIONAL VALUE & MARKET CONCEPTS
This indicator teaches traders about:
- **Market Microstructure**: How liquidity affects price behavior
- **Institutional vs Retail**: Identifying professional trading patterns
- **Weekend Market Dynamics**: Understanding low-liquidity period characteristics
- **Volume Analysis**: Advanced PVSRA methodology for market maker detection
**Research Applications:**
- Historical weekend volatility analysis
- Institutional activity pattern recognition
- Cross-market liquidity comparison
- Weekend gap prediction modeling
---
## DISCLAIMER & EDUCATIONAL PURPOSE
This indicator is designed for educational analysis of market microstructure during low-liquidity periods. The PVSRA methodology is adapted from institutional trading analysis techniques and should be used in conjunction with proper risk management and market analysis.
**Not Financial Advice**: All signals and analysis are for educational purposes only.
Diamond Peaks [EdgeTerminal]The Diamond Peaks indicator is a comprehensive technical analysis tool that uses a few mathematical models to identify high-probability trading opportunities. This indicator goes beyond traditional support and resistance identification by incorporating volume analysis, momentum divergences, advanced price action patterns, and market sentiment indicators to generate premium-quality buy and sell signals.
Dynamic Support/Resistance Calculation
The indicator employs an adaptive algorithm that calculates support and resistance levels using a volatility-adjusted lookback period. The base calculation uses ta.highest(length) and ta.lowest(length) functions, where the length parameter is dynamically adjusted using the formula: adjusted_length = base_length * (1 + (volatility_ratio - 1) * volatility_factor). The volatility ratio is computed as current_ATR / average_ATR over a 50-period window, ensuring the lookback period expands during volatile conditions and contracts during calm periods. This mathematical approach prevents the indicator from using fixed periods that may become irrelevant during different market regimes.
Momentum Divergence Detection Algorithm
The divergence detection system uses a mathematical comparison between price series and oscillator values over a specified lookback period. For bullish divergences, the algorithm identifies when recent_low < previous_low while simultaneously indicator_at_recent_low > indicator_at_previous_low. The inverse logic applies to bearish divergences. The system tracks both RSI (calculated using Pine Script's standard ta.rsi() function with Wilder's smoothing) and MACD (using ta.macd() with exponential moving averages). The mathematical rigor ensures that divergences are only flagged when there's a clear mathematical relationship between price momentum and the underlying oscillator momentum, eliminating false signals from minor price fluctuations.
Volume Analysis Mathematical Framework
The volume analysis component uses multiple mathematical transformations to assess market participation. The Cumulative Volume Delta (CVD) is calculated as โ(buying_volume - selling_volume) where buying_volume occurs when close > open and selling_volume when close < open. The relative volume calculation uses current_volume / ta.sma(volume, period) to normalize current activity against historical averages. Volume Rate of Change employs ta.roc(volume, period) = (current_volume - volume ) / volume * 100 to measure volume acceleration. Large trade detection uses a threshold multiplier against the volume moving average, mathematically identifying institutional activity when relative_volume > threshold_multiplier.
Advanced Price Action Mathematics
The Wyckoff analysis component uses mathematical volume climax detection by comparing current volume against ta.highest(volume, 50) * 0.8, while price compression is measured using (high - low) < ta.atr(20) * 0.5. Liquidity sweep detection employs percentage-based calculations: bullish sweeps occur when low < recent_low * (1 - threshold_percentage/100) followed by close > recent_low. Supply and demand zones are mathematically validated by tracking subsequent price action over a defined period, with zone strength calculated as the count of bars where price respects the zone boundaries. Fair value gaps are identified using ATR-based thresholds: gap_size > ta.atr(14) * 0.5.
Sentiment and Market Regime Mathematics
The sentiment analysis employs a multi-factor mathematical model. The fear/greed index uses volatility normalization: 100 - min(100, stdev(price_changes, period) * scaling_factor). Market regime classification uses EMA crossover mathematics with additional ADX-based trend strength validation. The trend strength calculation implements a modified ADX algorithm: DX = |+DI - -DI| / (+DI + -DI) * 100, then ADX = RMA(DX, period). Bull regime requires short_EMA > long_EMA AND ADX > 25 AND +DI > -DI. The mathematical framework ensures objective regime classification without subjective interpretation.
Confluence Scoring Mathematical Model
The confluence scoring system uses a weighted linear combination: Score = (divergence_component * 0.25) + (volume_component * 0.25) + (price_action_component * 0.25) + (sentiment_component * 0.25) + contextual_bonuses. Each component is normalized to a 0-100 scale using percentile rankings and threshold comparisons. The mathematical model ensures that no single component can dominate the score, while contextual bonuses (regime alignment, volume confirmation, etc.) provide additional mathematical weight when multiple factors align. The final score is bounded using math.min(100, math.max(0, calculated_score)) to maintain mathematical consistency.
Vitality Field Mathematical Implementation
The vitality field uses a multi-factor scoring algorithm that combines trend direction (EMA crossover: trend_score = fast_EMA > slow_EMA ? 1 : -1), momentum (RSI-based: momentum_score = RSI > 50 ? 1 : -1), MACD position (macd_score = MACD_line > 0 ? 1 : -1), and volume confirmation. The final vitality score uses weighted mathematics: vitality_score = (trend * 0.4) + (momentum * 0.3) + (macd * 0.2) + (volume * 0.1). The field boundaries are calculated using ATR-based dynamic ranges: upper_boundary = price_center + (ATR * user_defined_multiplier), with EMA smoothing applied to prevent erratic boundary movements. The gradient effect uses mathematical transparency interpolation across multiple zones.
Signal Generation Mathematical Logic
The signal generation employs boolean algebra with multiple mathematical conditions that must simultaneously evaluate to true. Buy signals require: (confluence_score โฅ threshold) AND (divergence_detected = true) AND (relative_volume > 1.5) AND (volume_ROC > 25%) AND (RSI < 35) AND (trend_strength > minimum_ADX) AND (regime = bullish) AND (cooldown_expired = true) AND (last_signal โ buy). The mathematical precision ensures that signals only generate when all quantitative conditions are met, eliminating subjective interpretation. The cooldown mechanism uses bar counting mathematics: bars_since_last_signal = current_bar_index - last_signal_bar_index โฅ cooldown_period. This mathematical framework provides objective, repeatable signal generation that can be backtested and validated statistically.
This mathematical foundation ensures the indicator operates on objective, quantifiable principles rather than subjective interpretation, making it suitable for algorithmic trading and systematic analysis while maintaining transparency in its computational methodology.
* for now, we're planning to keep the source code private as we try to improve the models used here and allow a small group to test them. My goal is to eventually use the multiple models in this indicator as their own free and open source indicators. If you'd like to use this indicator, please send me a message to get access.
Advanced Confluence Scoring System
Each support and resistance level receives a comprehensive confluence score (0-100) based on four weighted components:
Momentum Divergences (25% weight)
RSI and MACD divergence detection
Identifies momentum shifts before price reversals
Bullish/bearish divergence confirmation
Volume Analysis (25% weight)
Cumulative Volume Delta (CVD) analysis
Volume Rate of Change monitoring
Large trade detection (institutional activity)
Volume profile strength assessment
Advanced Price Action (25% weight)
Supply and demand zone identification
Liquidity sweep detection (stop hunts)
Wyckoff accumulation/distribution patterns
Fair value gap analysis
Market Sentiment (25% weight)
Fear/Greed index calculation
Market regime classification (Bull/Bear/Sideways)
Trend strength measurement (ADX-like)
Momentum regime alignment
Dynamic Support and Resistance Detection
The indicator uses an adaptive algorithm to identify significant support and resistance levels based on recent market highs and lows. Unlike static levels, these zones adjust dynamically to market volatility using the Average True Range (ATR), ensuring the levels remain relevant across different market conditions.
Vitality Field Background
The indicator features a unique vitality field that provides instant visual feedback about market sentiment:
Green zones: Bullish market conditions with strong momentum
Red zones: Bearish market conditions with weak momentum
Gray zones: Neutral/sideways market conditions
The vitality field uses a sophisticated gradient system that fades from the center outward, creating a clean, professional appearance that doesn't overwhelm the chart while providing valuable context.
Buy Signals (๐ BUY)
Buy signals are generated when ALL of the following conditions are met:
Valid support level with confluence score โฅ 80
Bullish momentum divergence detected (RSI or MACD)
Volume confirmation (1.5x average volume + 25% volume ROC)
Bull market regime environment
RSI below 35 (oversold conditions)
Price action confirmation (Wyckoff accumulation, liquidity sweep, or large buying volume)
Minimum trend strength (ADX > 25)
Signal alternation check (prevents consecutive buy signals)
Cooldown period expired (default 10 bars)
Sell Signals (๐ป SELL)
Sell signals are generated when ALL of the following conditions are met:
Valid resistance level with confluence score โฅ 80
Bearish momentum divergence detected (RSI or MACD)
Volume confirmation (1.5x average volume + 25% volume ROC)
Bear market regime environment
RSI above 65 (overbought conditions)
Price action confirmation (Wyckoff distribution, liquidity sweep, or large selling volume)
Minimum trend strength (ADX > 25)
Signal alternation check (prevents consecutive sell signals)
Cooldown period expired (default 10 bars)
How to Use the Indicator
1. Signal Quality Assessment
Monitor the confluence scores in the information table:
Score 90-100: Exceptional quality levels (A+ grade)
Score 80-89: High quality levels (A grade)
Score 70-79: Good quality levels (B grade)
Score below 70: Weak levels (filtered out by default)
2. Market Context Analysis
Use the vitality field and market regime information to understand the broader market context:
Trade buy signals in green vitality zones during bull regimes
Trade sell signals in red vitality zones during bear regimes
Exercise caution in gray zones (sideways markets)
3. Entry and Exit Strategy
For Buy Signals:
Enter long positions when premium buy signals appear
Place stop loss below the support confluence zone
Target the next resistance level or use a risk/reward ratio of 2:1 or higher
For Sell Signals:
Enter short positions when premium sell signals appear
Place stop loss above the resistance confluence zone
Target the next support level or use a risk/reward ratio of 2:1 or higher
4. Risk Management
Only trade signals with confluence scores above 80
Respect the signal alternation system (no overtrading)
Use appropriate position sizing based on signal quality
Consider the overall market regime before taking trades
Customizable Settings
Signal Generation Controls
Signal Filtering: Enable/disable advanced filtering
Confluence Threshold: Adjust minimum score requirement (70-95)
Cooldown Period: Set bars between signals (5-50)
Volume/Momentum Requirements: Toggle confirmation requirements
Trend Strength: Minimum ADX requirement (15-40)
Vitality Field Options
Enable/Disable: Control background field display
Transparency Settings: Adjust opacity for center and edges
Field Size: Control the field boundaries (3.0-20.0)
Color Customization: Set custom colors for bullish/bearish/neutral states
Weight Adjustments
Divergence Weight: Adjust momentum component influence (10-40%)
Volume Weight: Adjust volume component influence (10-40%)
Price Action Weight: Adjust price action component influence (10-40%)
Sentiment Weight: Adjust sentiment component influence (10-40%)
Best Practices
Always wait for complete signal confirmation before entering trades
Use higher timeframes for signal validation and context
Combine with proper risk management and position sizing
Monitor the information table for real-time market analysis
Pay attention to volume confirmation for higher probability trades
Respect market regime alignment for optimal results
Basic Settings
Base Length (Default: 25)
Controls the lookback period for identifying support and resistance levels
Range: 5-100 bars
Lower values = More responsive, shorter-term levels
Higher values = More stable, longer-term levels
Recommendation: 25 for intraday, 50 for swing trading
Enable Adaptive Length (Default: True)
Automatically adjusts the base length based on market volatility
When enabled, length increases in volatile markets and decreases in calm markets
Helps maintain relevant levels across different market conditions
Volatility Factor (Default: 1.5)
Controls how much the adaptive length responds to volatility changes
Range: 0.5-3.0
Higher values = More aggressive length adjustments
Lower values = More conservative length adjustments
Volume Profile Settings
VWAP Length (Default: 200)
Sets the calculation period for the Volume Weighted Average Price
Range: 50-500 bars
Shorter periods = More responsive to recent price action
Longer periods = More stable reference line
Used for volume profile analysis and confluence scoring
Volume MA Length (Default: 50)
Period for calculating the volume moving average baseline
Range: 10-200 bars
Used to determine relative volume (current volume vs. average)
Shorter periods = More sensitive to volume changes
Longer periods = More stable volume baseline
High Volume Node Threshold (Default: 1.5)
Multiplier for identifying significant volume spikes
Range: 1.0-3.0
Values above this threshold mark high-volume nodes with diamond shapes
Lower values = More frequent high-volume signals
Higher values = Only extreme volume events marked
Momentum Divergence Settings
Enable Divergence Detection (Default: True)
Master switch for momentum divergence analysis
When disabled, removes divergence from confluence scoring
Significantly impacts signal generation quality
RSI Length (Default: 14)
Period for RSI calculation used in divergence detection
Range: 5-50
Standard RSI settings apply (14 is most common)
Shorter periods = More sensitive, more signals
Longer periods = Smoother, fewer but more reliable signals
MACD Settings
Fast (Default: 12): Fast EMA period for MACD calculation (5-50)
Slow (Default: 26): Slow EMA period for MACD calculation (10-100)
Signal (Default: 9): Signal line EMA period (3-20)
Standard MACD settings for divergence detection
Divergence Lookback (Default: 5)
Number of bars to look back when detecting divergences
Range: 3-20
Shorter periods = More frequent divergence signals
Longer periods = More significant divergence signals
Volume Analysis Enhancement Settings
Enable Advanced Volume Analysis (Default: True)
Master control for sophisticated volume calculations
Includes CVD, volume ROC, and large trade detection
Critical for signal accuracy
Cumulative Volume Delta Length (Default: 20)
Period for CVD smoothing calculation
Range: 10-100
Tracks buying vs. selling pressure over time
Shorter periods = More reactive to recent flows
Longer periods = Broader trend perspective
Volume ROC Length (Default: 10)
Period for Volume Rate of Change calculation
Range: 5-50
Measures volume acceleration/deceleration
Key component in volume confirmation requirements
Large Trade Volume Threshold (Default: 2.0)
Multiplier for identifying institutional-size trades
Range: 1.5-5.0
Trades above this threshold marked as large trades
Lower values = More frequent large trade signals
Higher values = Only extreme institutional activity
Advanced Price Action Settings
Enable Wyckoff Analysis (Default: True)
Activates simplified Wyckoff accumulation/distribution detection
Identifies potential smart money positioning
Important for high-quality signal generation
Enable Supply/Demand Zones (Default: True)
Identifies fresh supply and demand zones
Tracks zone strength based on subsequent price action
Enhances confluence scoring accuracy
Enable Liquidity Analysis (Default: True)
Detects liquidity sweeps and stop hunts
Identifies fake breakouts vs. genuine moves
Critical for avoiding false signals
Zone Strength Period (Default: 20)
Bars used to assess supply/demand zone strength
Range: 10-50
Longer periods = More thorough zone validation
Shorter periods = Faster zone assessment
Liquidity Sweep Threshold (Default: 0.5%)
Percentage move required to confirm liquidity sweep
Range: 0.1-2.0%
Lower values = More sensitive sweep detection
Higher values = Only significant sweeps detected
Sentiment and Flow Settings
Enable Sentiment Analysis (Default: True)
Master control for market sentiment calculations
Includes fear/greed index and regime classification
Important for market context assessment
Fear/Greed Period (Default: 20)
Calculation period for market sentiment indicator
Range: 10-50
Based on price volatility and momentum
Shorter periods = More reactive sentiment readings
Momentum Regime Length (Default: 50)
Period for determining overall market regime
Range: 20-100
Classifies market as Bull/Bear/Sideways
Longer periods = More stable regime classification
Trend Strength Length (Default: 30)
Period for ADX-like trend strength calculation
Range: 10-100
Measures directional momentum intensity
Used in signal filtering requirements
Advanced Signal Generation Settings
Enable Signal Filtering (Default: True)
Master control for premium signal generation system
When disabled, uses basic signal conditions
Highly recommended to keep enabled
Minimum Signal Confluence Score (Default: 80)
Required confluence score for signal generation
Range: 70-95
Higher values = Fewer but higher quality signals
Lower values = More frequent but potentially lower quality signals
Signal Cooldown (Default: 10 bars)
Minimum bars between signals of same type
Range: 5-50
Prevents signal spam and overtrading
Higher values = More conservative signal spacing
Require Volume Confirmation (Default: True)
Mandates volume requirements for signal generation
Requires 1.5x average volume + 25% volume ROC
Critical for signal quality
Require Momentum Confirmation (Default: True)
Mandates divergence detection for signals
Ensures momentum backing for directional moves
Essential for high-probability setups
Minimum Trend Strength (Default: 25)
Required ADX level for signal generation
Range: 15-40
Ensures signals occur in trending markets
Higher values = Only strong trending conditions
Confluence Scoring Settings
Minimum Confluence Score (Default: 70)
Threshold for displaying support/resistance levels
Range: 50-90
Levels below this score are filtered out
Higher values = Only strongest levels shown
Component Weights (Default: 25% each)
Divergence Weight: Momentum component influence (10-40%)
Volume Weight: Volume analysis influence (10-40%)
Price Action Weight: Price patterns influence (10-40%)
Sentiment Weight: Market sentiment influence (10-40%)
Must total 100% for balanced scoring
Vitality Field Settings
Enable Vitality Field (Default: True)
Controls the background gradient field display
Provides instant visual market sentiment feedback
Enhances chart readability and context
Vitality Center Transparency (Default: 85%)
Opacity at the center of the vitality field
Range: 70-95%
Lower values = More opaque center
Higher values = More transparent center
Vitality Edge Transparency (Default: 98%)
Opacity at the edges of the vitality field
Range: 95-99%
Creates smooth fade effect from center to edges
Higher values = More subtle edge appearance
Vitality Field Size (Default: 8.0)
Controls the overall size of the vitality field
Range: 3.0-20.0
Based on ATR multiples for dynamic sizing
Lower values = Tighter field around price
Higher values = Broader field coverage
Recommended Settings by Trading Style
Scalping (1-5 minutes)
Base Length: 15
Volume MA Length: 20
Signal Cooldown: 5 bars
Vitality Field Size: 5.0
Higher sensitivity for quick moves
Day Trading (15-60 minutes)
Base Length: 25 (default)
Volume MA Length: 50 (default)
Signal Cooldown: 10 bars (default)
Vitality Field Size: 8.0 (default)
Balanced settings for intraday moves
Swing Trading (4H-Daily)
Base Length: 50
Volume MA Length: 100
Signal Cooldown: 20 bars
Vitality Field Size: 12.0
Longer-term perspective for multi-day moves
Conservative Trading
Minimum Signal Confluence: 85
Minimum Confluence Score: 80
Require all confirmations: True
Higher thresholds for maximum quality
Aggressive Trading
Minimum Signal Confluence: 75
Minimum Confluence Score: 65
Signal Cooldown: 5 bars
Lower thresholds for more opportunities
Sweep Swing Screener [TradingFinder]๐ต Introduction
Understanding how liquidity forms and how price reacts around key structural levels is essential for identifying precise, low-risk entry points. The Sweep Swing Screener is a specialized tool developed to continuously monitor market activity and detect liquidity sweeps, reaction zones, and valid confirmation candles across various trading instruments and timeframes.
This tool can be applied both to scan multiple symbols at once and to analyze all timeframes of a specific asset for potential reversal points. It begins by identifying a clear swing point, whether a swing high or a swing low, and then outlines a reaction zone between that level and either the highest or lowest value of the swing candle's open or close.
If the price revisits this zone, performs a liquidity grab, and prints an indecision candle like a doji or a narrow-bodied bar that closes within the zone, this may indicate a rejection of the level and the failure of a breakout attempt. At that moment, depending on the context, the screener may identify a bullish or bearish reversal and generate a corresponding Long or Short signal.
By emphasizing accurate entry timing, alignment with institutional order flow, and avoidance of common traps, this approach highlights market areas where liquidity engineering, reversal probability, and price inefficiency come together. As a result, the Sweep Swing Screener becomes a valuable part of any traderโs toolkit, particularly for those who rely on price action and liquidity logic to drive their decisions. It allows traders to focus on clean, actionable setups without getting lost in noise or misleading breakouts.
๐ต How to Use
The Sweep Swing Screener is designed to track market structure in real time and alert users when conditions for a potential reversal are present. Its methodology combines liquidity behavior with swing analysis and candle confirmation, all within predefined reaction zones.
To better understand this logic, consider a basic market flow where a swing high or low forms, followed by a return to that level. If the price sweeps the previous extreme and forms a confirming candle within the reaction zone, a signal is issued.
๐ฃ Long Signal
To identify a long setup, the screener looks for a valid swing low, often a level below which sell-side liquidity is likely to be clustered. Once found, it defines a reaction zone from the swing low to the lowest point between the candleโs open and close.
If the price returns to this area and creates a lower wick that extends beneath the swing low, the tool checks whether the price manages to close back inside the range, rejecting the breakdown. This indicates absorption of selling pressure and failure to sustain the move lower.
The screener then waits for a confirmation candle to appear. Typically, this is a doji or other small-bodied candle that closes inside the zone. If these conditions are met, the screener records a Long signal for that asset and, if enabled, sends a notification to alert the user.
๐ฃ Short Signal
For bearish setups, the screener begins by identifying a valid swing high, which usually marks a level where buy-side liquidity is concentrated. It then creates a reaction zone from the swing high to the highest point between the candleโs open and close.
When price returns to this level, sweeps above the swing high, and then fails to close higher, it may signal the presence of a bull trap and early exhaustion in the upward move.
A confirmation candle, usually a doji or a rejection bar that closes back within the zone, is then required. Once that occurs, the screener marks the asset with a Short signal and optionally sends a real-time alert to the user.
This type of setup helps highlight potential institutional sell zones, offering insight into where price is likely to reverse following a liquidity event.
๐ต Settings
๐ฃ Logical settings
Swing period : You can set the swing detection period.
Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine.
Max Swing Back : You can set the number of swings that will go back for checking.
Maximum Distance Between Swing and Signal : The maximum number of candles allowed between the swing point and the potential signal. The default value is 50, ensuring that only recent and relevant price reactions are considered valid.
๐ฃ Display Settings
Table Size : Lets you adjust the tableโs visual size with options such as: auto, tiny, small, normal, large, huge.
Table Position : Sets the screen location of the table. Choose from 9 possible positions, combining vertical (top, middle, bottom) and horizontal (left, center, right) alignments.
๐ฃ Symbol Settings
Each of the 10 symbol slots comes with a full set of customizable parameters :
Symbol : Define or select the asset (e.g., XAUUSD, BTCUSD, EURUSD, etc.).
Timeframe : Set your desired timeframe for each symbol (e.g., 15, 60, 240, 1D).
๐ฃ Alert Settings
Alert : Enables alerts for SSS.
Message Frequency : Determines the frequency of alerts. Options include 'All' (every function call), 'Once Per Bar' (first call within the bar), and 'Once Per Bar Close' (final script execution of the real-time bar). Default is 'Once per Bar'.
Show Alert Time by Time Zone : Configures the time zone for alert messages. Default is 'UTC'.
๐ต Conclusion
The Sweep Swing Screener provides a systematic method for identifying potential reversal zones by combining price structure, liquidity behavior, and candle-based confirmation. In markets that are often noisy and full of failed breakouts, focusing on these three elements helps clarify directional bias and supports more confident decision-making.
With the ability to scan multiple symbols and timeframes efficiently, this tool allows traders to stay focused on high-quality setups without the need to manually sift through dozens of charts. The inclusion of optional alerts further enhances its utility by offering timely updates when criteria are met.
By moving away from reactive strategies and toward structural anticipation, this screener supports traders who align their methods with institutional logic and the mechanics of smart money.
Mech Model - monkertrades x {DeadCatCode}Mech Model - Multi-Timeframe ICT Liquidity & iFVG Trading System
Detailed Methodology & Underlying Concepts
This indicator automates the Inner Circle Trader (ICT) methodology by identifying institutional order flow through liquidity sweeps and inverse Fair Value Gap (iFVG) formations across multiple timeframes.
Core Logic & Calculations
1. Liquidity Level Identification The script tracks four key liquidity pools:
NY session dynamic LQ detection everytime it sweeps high/low Calculates high/low from 18:00-09:30 EST
Session Extremes: Monitors Asia (20:00-23:00), London (02:00-05:00) session highs/lows
Previous Day Levels: Requests PDH/PDL using request.security() with daily timeframe
Dynamic Updates: Liquidity levels update in real-time when swept during NY session
2. Market Structure Analysis
Uses pivot points logic to understand HH.HL parameters
Classifies pivots as Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), Lower Lows (LL)
Stores last 50 pivots for reference in custom PivotPoint type arrays, background calculations to identfy price legs after sweep
3. Fair Value Gap Detection
Bullish FVG: When low > high (gap between candles)
Bearish FVG: When high < low
Stores FVG data including top, bottom, direction, and bar index
Tracks "wicking" - when price touches but doesn't close through FVG
4. Price Leg Formation (Key Innovation) When liquidity is swept:
Bull Leg: Forms after low sweep, connects previous swing high to sweep point
Bear Leg: Forms after high sweep, connects previous swing low to sweep point
Leg remains "active" and extends with continued liquidity breaks
5. iFVG Signal Generation The signal fires when:
An active price leg exists (post-liquidity sweep)
An FVG within the leg range gets "closed through" (not just wicked)
This creates an inverse FVG (iFVG) - the key entry signal
Signal direction matches leg type (bull leg + bull iFVG = buy signal)
6. Multi-Timeframe Synchronization
Uses request.security() to run detection logic on 1m, 2m, 3m, 4m, 5m
All signals display on 1-minute chart via status table
How Traders Use This
Setup Phase: Script identifies when price sweeps overnight/session liquidity
Confirmation: Waits for FVG within the "price leg" to be violated
Entry Signal: iFVG formation provides precise entry point
Target: Typically the next unmitigated FVG on 5-minute timeframe
Key Parameters Users Can Adjust
Session times for different market hours
Visual elements (colors, transparency, line styles)
Timeframe selection (enable/disable 1m-5m)
Wick grace period (0-100 bars)
Signal display mode (triangles vs horizontal lines)
This script essentially automates the manual process ICT traders use to identify institutional footprints through liquidity raids and subsequent rebalancing via FVG mitigation.
Super Arma Institucional PRO v6.3Super Arma Institucional PRO v6.3
Description
Super Arma Institucional PRO v6.3 is a multifunctional indicator designed for traders looking for a clear and objective analysis of the market, focusing on trends, key price levels and high liquidity zones. It combines three essential elements: moving averages (EMA 20, SMA 50, EMA 200), dynamic support and resistance, and volume-based liquidity zones. This integration offers an institutional view of the market, ideal for identifying strategic entry and exit points.
How it Works
Moving Averages:
EMA 20 (orange): Sensitive to short-term movements, ideal for capturing fast trends.
SMA 50 (blue): Represents the medium-term trend, smoothing out fluctuations.
EMA 200 (red): Indicates the long-term trend, used as a reference for the general market bias.
Support and Resistance: Calculated based on the highest and lowest prices over a defined period (default: 20 bars). These dynamic levels help identify zones where the price may encounter barriers or supports.
Liquidity Zones: Purple rectangles are drawn in areas of significantly above-average volume, indicating regions where large market participants (institutional) may be active. These zones are useful for anticipating price movements or order absorption.
Purpose
The indicator was developed to provide a clean and institutional view of the market, combining classic tools (moving averages and support/resistance) with modern liquidity analysis. It is ideal for traders operating swing trading or position trading strategies, allowing to identify:
Short, medium and long-term trends.
Key support and resistance levels to plan entries and exits.
High liquidity zones where institutional orders can influence the price.
Settings
Show EMA 20 (true): Enables/disables the 20-period EMA.
Show SMA 50 (true): Enables/disables the 50-period SMA.
Show EMA 200 (true): Enables/disables the 200-period EMA.
Support/Resistance Period (20): Sets the period for calculating support and resistance levels.
Liquidity Sensitivity (20): Period for calculating the average volume.
Minimum Liquidity Factor (1.5): Multiplier of the average volume to identify high liquidity zones.
How to Use
Moving Averages:
Crossovers between the EMA 20 and SMA 50 may indicate short/medium-term trend changes.
The EMA 200 serves as a reference for the long-term bias (above = bullish, below = bearish).
Support and Resistance: Use the red (resistance) and green (support) lines to identify reversal or consolidation zones.
Liquidity Zones: The purple rectangles highlight areas of high volume, where the price may react (reversal or breakout). Consider these zones to place orders or manage risks.
Adjust the parameters according to the asset and timeframe to optimize the analysis.
Notes
The chart should be configured only with this indicator to ensure clarity.
Use on timeframes such as 1 hour, 4 hours or daily for better visualization of liquidity zones and support/resistance levels.
Avoid adding other indicators to the chart to keep the script output easily identifiable.
The indicator is designed to be clean, without explicit buy/sell signals, following an institutional approach.
This indicator is perfect for traders who want a visually clear and powerful tool to trade based on trends, key levels and institutional behavior.
Dealing rangeHi all!
This indicator will show you the current dealing range. The concept of dealing range comes from the inner circle trader (ICT) and gives you a range between an established swing high and an established swing low (the length of these pivots can be changed in settings parameter Length and defaults to 5/2 (left/right)). These swing points must have taken out liquidity to be considered "established". The liquidity that must be grabbed by the swing point has to be a pivot of left length of 1 and a right length of 1.
The dealing range that's created should be used in conjunction with market structure. This could be done through scripts (maybe the Market structure script that I published ()) or manually. It's a common approach to look for long opportunities when the trend is bullish and price is currently in the discount zone of the dealing range. If the trend is bearish then short opportunities are presented when the price is currently in the premium zone of the dealing range.
The zones within the dealing range are premium and discount that are split on the 50% level of the dealing range. These zones can be split into 3 zone with a Fair price (also called Fair value ) zone in between premium and discount. This makes the premium zone to be in the upper third of the dealing range, fair price in the middle third and discount in the lower third. This can be enabled in the settings through the Fair price parameter.
Enabled:
You can choose to enable/disable the visualisation of liquidity grabs and the External liquidity available above and below the swing points that created the dealing range.
Enabled:
Disabled:
Enabled on a higher timeframe (will display a box of the liquidity grab price instead of a label):
This dealing range is configurable to be created by a higher timeframe then the visible charts. Use the setting Higher timeframe to change this.
You can force candles to be closed (for liquidity and swing points). Please note that if you use a higher timeframe then the visible charts the candles must be closed on this timeframe.
Lastly you can also change the transparency of liquidity grabs and external liquidity outside of the dealing range. Use the Transparency setting to change this (a lower value will lead to stronger visuals).
If you have any input or suggestions on future features or bugs, don't hesitate to let me know!
Best of trading luck!
MBODDS GLOBAL - Enhanceden
MBODDS GLOBAL Indicator โ Detailed Interpretation
What does the indicator measure?
Liquidity preferences
Credit risk perception
Market stress levels
Interpreting the ODDS Value
ODDS Value Explanation
Positive ODDS (> 0) SOFR is higher than the T-Bill rate โ Interbank liquidity is more expensive โ Possible financial stress.
Negative ODDS (< 0) T-Bill rates are higher than SOFR โ The government pays more interest in the short term โ Liquidity abundance, normal market conditions.
ODDS โ 0 Neutral market state โ Low stress, market is stable.
Z-Score Interpretation (Extremity Analysis)
The Z-Score measures the standard deviation of ODDS, detecting extreme values:
Z-Score Meaning
> +1.0 Spread is unusually high โ Stress/crisis risk increases.
< -1.0 Spread is unusually low โ Liquidity could be abundant.
> +2.0 Extremely high spread โ Systemic risk (observed during 2008-2020 periods).
โ 0 Average level โ Normal conditions, no notable risk.
The Z-Score functions as an "anomaly detector" for this indicator.
SMA (Simple Moving Average) Interpretation
The 21-day SMA shows the trend of ODDS:
ODDS consistently above SMA: Rising stress and credit costs.
ODDS consistently below SMA: Easier liquidity and lower market concerns.
Threshold Bands (ยฑ0.5)
These thresholds are visual guides for alerts:
ODDS > +0.5: Rising stress, potential liquidity tightening โ Risky environment.
ODDS < -0.5: Low spread โ Abundant liquidity, low stress โ Comfortable environment.
Use Cases
Macro analysis (especially after Fed policy changes)
Direction determination in bond, equity, or credit markets
Early signal for stressful periods
Predicting liquidity crises
Conclusion:
This indicator acts as a macro-based "silent alarm." Specifically:
SOFR > T-Bill and Z-Score > 1: Stress and risk are increasing, protection strategies should be considered.
T-Bill > SOFR and Z-Score < -1: Liquidity is abundant, risk appetite may rise.
ICT Turtle Soup Ultimate V2๐ ICT Turtle Soup Ultimate V2 โ Advanced Liquidity Reversal System
Overview:
The ICT Turtle Soup Ultimate V2 is a next-generation liquidity reversal indicator built on the principles of smart money concepts (SMC) and the classic ICT Turtle Soup setup. It is designed to detect false breakouts (liquidity grabs) at key swing points, enhanced by proprietary logic that filters out low-quality signals using a combination of trend context, kill zone timing, candle wick behavior, and multi-timeframe imbalance zones.
This tool is ideal for intraday traders seeking high-probability entry signals near liquidity pools and imbalance zones โ where smart money makes its move.
๐ What This Script Does
๐ง Liquidity Grab Detection (Turtle Soup Core Logic)
The script scans for recent swing highs/lows using a user-defined lookback.
A signal is generated when price breaks above/below a previous swing level but closes back inside โ indicating a liquidity run and likely reversal.
A special Wick Trap Mode enhances this logic by detecting long-wick fakeouts โ where the wick grabs stops but the candle body closes opposite the breakout direction.
๐ Trend Filter with ATR Buffer
Optional trend filter uses a simple moving average (SMA) to gauge market direction.
Instead of hard filtering, it applies an ATR-based buffer to allow for entries near the trend line, reducing signal suppression from micro-fluctuations.
๐ฐ๏ธ Kill Zone Session Filtering
Only show signals during institutional trading hours:
London Session
New York AM
Or any custom user-defined session
Helps traders avoid low-volume hours and focus on where stop hunts and price expansions typically occur.
๐งฑ Multi-Timeframe FVG Confluence (Optional)
Signal validation is strengthened by checking if price is within a higher timeframe Fair Value Gap โ commonly used to identify imbalances or inefficiencies.
Filters out setups that lack underlying displacement or order flow justification.
๐จ Visual Feedback
Plots ๐บ bullish and ๐ป bearish markers at signal candles.
Optionally displays:
Swing High/Low Labels (SH / SL)
Reversal distance labels
Background color shading on valid signals
Includes built-in alerts for automated trade notification.
๐ Unique Benefits
Wick Trap Detection: A proprietary approach to detecting stop hunts via wick behavior, not just candle closes.
ATR-based trend filtering: Avoids unnecessary filtering while still maintaining directional bias.
All-in-one system: No need to stack multiple indicators โ swing detection, reversal logic, session filtering, and imbalance confirmation are all integrated.
๐ก How to Use
Enable Wick Trap Mode to detect stealthy liquidity grabs with strong wicks.
Use Kill Zone filters to trade only when institutions are active.
Optionally enable FVG confluence to improve confidence in reversal zones.
Watch for Bullish signals near SL levels and Bearish signals near SH levels.
Combine with your own execution strategy or other SMC tools for optimal results.
๐ Best Used With:
Maximize your edge by combining this script with complementary SMC-based tools:
โ
First FVG โ Opening Range Fair Value Gap Detector
โ
ICT SMC Liquidity Grabs + OB + Fibonacci OTE Levels
โ
Liquidity Levels โ Smart Swing Highs and Lows with horizontal line projections
SuperTrend Momentum OscillatorOverview
The SuperTrend Momentum Oscillator (SMO) is a powerful technical analysis tool designed to identify trend direction and strength in financial markets. It combines short-term and long-term oscillator calculations to provide traders with a comprehensive view of market conditions through an intuitive candle-based visualization system.
Key Features
Dual-period oscillator system (short-term and long-term)
Candle-based visualization showing trend direction and alignment
Color-coded trend direction based on the main (slower) trend line
Candle size reflecting alignment between fast and slow components
High-confidence "Super" signals (green diamonds for buys, purple diamonds for sells)
Market liquidity insights through oscillator readings
Understanding the Candle Visualization
Main Trend vs. Fast Money
The SMO uses two key components that work together:
Main Trend Line (Slower): The longer-period oscillator that acts as the primary trend indicator
Dictates the overall color of the candles (green for uptrend, red for downtrend)
Represents the dominant market direction
Fast Line (Quicker): The shorter-period oscillator that reacts more quickly to price changes
Helps determine the size of candles through its alignment with the main trend
Represents "fast money" or shorter-term price reactions
Candle Components and Their Meaning
1. Candle Color
The color of each candle is determined by the direction of the main trend line:
Green Candles: Main trend line is rising (bullish)
Indicates an overall uptrend regardless of short-term fluctuations
Remains green even when the fast line temporarily moves against the trend
Red Candles: Main trend line is falling (bearish)
Indicates an overall downtrend regardless of short-term fluctuations
Remains red even when the fast line temporarily moves against the trend
2. Candle Body Size
The body size of each candle represents the alignment between fast and main trend lines:
Large Bodies: Both fast and main trend lines are moving in the same direction
Trading Action: Strong confirmation of the trend direction
Confidence Level: High confidence signals
Small Bodies: Fast line is moving against the main trend line
Trading Action: Exercise caution; potential for temporary pullback or consolidation
Confidence Level: Lower confidence in immediate continuation
3. Wick Length
Wicks (shadows) provide additional information about price rejection and volatility:
Long Wicks: Indicate price rejection and potential volatility
Trading Action: Be cautious of trend continuation when long wicks appear
Confidence Level: Reduced confidence in immediate trend continuation
Short Wicks: Indicate strong directional control with minimal rejection
Trading Action: More confidence in trend continuation
Confidence Level: Higher confidence in the current trend direction
Candle Patterns Over Time
The progression of candles provides valuable trend information:
Large Green Candles: Main trend is up and fast line confirms (strong bullish)
Trading Action: Consider entering or adding to long positions
Confidence Level: High confidence in uptrend
Small Green Candles: Main trend is up but fast line is moving down (caution in uptrend)
Trading Action: Hold existing long positions but wait before adding
Confidence Level: Moderate confidence in uptrend, possible short-term pullback
Large Red Candles: Main trend is down and fast line confirms (strong bearish)
Trading Action: Consider entering or adding to short positions
Confidence Level: High confidence in downtrend
Small Red Candles: Main trend is down but fast line is moving up (caution in downtrend)
Trading Action: Hold existing short positions but wait before adding
Confidence Level: Moderate confidence in downtrend, possible short-term bounce
Super Signals - High Confidence Trading Opportunities
The SMO focuses exclusively on high-confidence "Super" signals:
Green Diamond Super Buy Signals
Meaning: Both short-term and long-term oscillators are generating buy signals simultaneously
Visual Indicator: Green diamond markers at the bottom of the indicator (0 level)
Trading Action: Strong entry signal for long positions
Confidence Level: High confidence signal, especially when accompanied by large green candles
Purple Diamond Super Sell Signals
Meaning: Both short-term and long-term oscillators are generating sell signals simultaneously
Visual Indicator: Purple diamond markers at the top of the indicator (100 level)
Trading Action: Strong entry signal for short positions or exit signal for long positions
Confidence Level: High confidence signal, especially when accompanied by large red candles
Market Liquidity Concept
The SMO provides a unique perspective on market conditions that goes beyond traditional oscillator interpretations:
Low Oscillator Readings (Below 20)
When the oscillator shows low readings (below 20), this indicates:
Traditional interpretation: Market is oversold, potential for upward reversal
Liquidity interpretation: Insufficient money in the market
This suggests thin trading conditions where large orders may have outsized impact
Price movements may be more erratic and less predictable
Breakouts may lack follow-through due to insufficient participation
High Oscillator Readings (Above 80)
When the oscillator shows high readings (above 80), this indicates:
Traditional interpretation: Market is overbought, potential for downward reversal
Liquidity interpretation: Abundant money in the market
This suggests deep trading conditions with high participation
Price movements tend to be more orderly and trend-based
Breakouts may have stronger follow-through due to high participation
Trading Strategies with SMO
Strategy 1: Main Trend with Alignment Confirmation
This strategy uses the main trend direction with alignment confirmation:
Entry Criteria:
Main trend direction is established (green or red candles)
Fast line aligns with main trend (large candles)
Super signal confirms (green or purple diamond)
Exit Criteria:
For long positions: When candles turn red or Super Sell signal appears
For short positions: When candles turn green or Super Buy signal appears
Stop Loss Placement:
For long positions: Below recent swing low
For short positions: Above recent swing high
Strategy 2: Counter-Trend Opportunity Detection
This strategy identifies potential counter-trend opportunities:
Entry Criteria:
Small candles appear (indicating disagreement between fast and main trend lines)
Oscillator reaches extreme levels (above 80 or below 20)
Wait for candle color change before entering
Position Sizing:
Use smaller position sizes for counter-trend trades
Increase size only when main trend confirms the new direction
Exit Criteria:
Take profit at the first sign of alignment in the opposite direction
Use tighter stops than with trend-following trades
Strategy 3: Market Liquidity Strategy
This strategy incorporates the market liquidity concept:
For Low Liquidity Conditions (Readings below 20):
Wait for Super Buy signals (green diamond)
Use smaller position sizes
Be prepared for potentially erratic price movements
Look for signs of increasing liquidity (expanding candle bodies) before adding to positions
For High Liquidity Conditions (Readings above 80):
Consider holding positions longer despite "overbought" readings
Use trailing stops to capture extended moves
Be aware that trends may persist longer than expected
Practical Trading Scenarios
Scenario 1: Strong Trend Confirmation
Candle Pattern: Series of large green candles (main trend up, fast line confirms)
Signal: Green diamond Super Buy marker at the bottom (0 level)
Background: Intensifying green gradient
Action: Enter long position with confidence
Stop Loss: Below recent swing low
Take Profit: When candles become small or turn red
Scenario 2: Trend Weakening Detection
Candle Pattern: Green candles becoming smaller (main trend still up, but fast line diverging)
Signal: No new signals
Background: Fading green gradient
Action: Tighten stops on long positions, prepare for potential reversal
Reasoning: Fast money is starting to move against the main trend
Scenario 3: Trend Reversal Identification
Candle Pattern: Transition from small green candles to red candles (main trend changing)
Signal: Appearance of purple diamond Super Sell marker at the top (100 level)
Background: Changing from green to red gradient
Action: Exit long positions and potentially enter short positions
Timing: Most effective when reversal occurs near overbought (80) level
Demand and Supply MTF with SMC By StockFusion - 3.0Demand and Supply MTF with SMC By StockFusion - 3.0 - Indicator Description
Concepts
What is Supply & Demand?
Supply and Demand are foundational forces driving market dynamics. Demand reflects the presence of buyers willing to purchase a security, while Supply indicates sellers offering it for sale. These forces create zones on the chart where price tends to reactโeither reversing or continuingโbased on the balance between buying and selling pressure. This indicator identifies these zones using price action patterns, focusing on impulsive moves (strong directional momentum) and retracement phases (consolidation or pullbacks).
What is SMC (Smart Money Concepts)?
Smart Money Concepts (SMC) revolve around tracking the behavior of institutional traders, often called "smart money." By analyzing price action, market structure shifts, and liquidity, SMC helps retail traders align with the moves of larger players. Key SMC signals like Change of Character (CHoCH), Break of Structure (BOS), liquidity sweeps, and swing points provide insights into potential trend changes or continuations.
Overview
Demand and Supply MTF with SMC By StockFusion - 3.0 is a sophisticated, price action-based indicator designed to plot real-time Supply and Demand zones across multiple timeframes (MTF) directly on your chart. It goes beyond simple zone plotting by integrating Smart Money Concepts (SMC) and Inside Candle detection, offering traders a powerful tool for spotting high-probability reversal or continuation areas. The indicator highlights zones with customizable boxes, labels them for clarity, and provides additional SMC-driven insights such as CHoCH, BOS, liquidity sweeps, and swing high/low levels. This combination of multi-timeframe analysis, SMC, and consolidation detection creates a unique and highly practical tool for traders seeking an edge in the markets.
How It Works
The indicator operates by analyzing price action across two user-defined timeframes (Higher TF and Lower TF) to detect Supply and Demand zones. It identifies these zones based on specific price patterns:
Rally Base Rally (RBR): A bullish impulsive move, followed by consolidation, then another bullish moveโindicating a Demand zone.
Drop Base Drop (DBD): A bearish impulsive move, consolidation, then another bearish moveโindicating a Supply zone.
Drop Base Rally (DBR): A bearish move, consolidation, then a bullish reversalโindicating a Demand zone.
Rally Base Drop (RBD): A bullish move, consolidation, then a bearish reversalโindicating a Supply zone.
These patterns are detected using criteria like explosive candle movements (based on range-to-body ratios and ATR multipliers), volume thresholds, and base candle counts (configurable from 1 to 5 candles). Zones are plotted as horizontal bands, with Higher TF zones taking precedence to avoid overlap with Lower TF zones, ensuring clarity on the chart.
Smart Money Integration:
The indicator enhances zone analysis with SMC features:
CHoCH (Change of Character): Detects shifts in market sentiment by comparing price action against recent swing highs/lows over a customizable period.
BOS (Break of Structure): Identifies when price breaks key structural levels, signaling a potential trend shift.
Liquidity Sweeps: Marks areas where price briefly exceeds swing points before reversing, often targeting stop-loss orders.
Swings: Highlights significant swing highs and lows to track momentum and structure.
Inside Candle Detection:
Inside Candlesโsmaller candles contained within the range of a prior candleโare plotted to indicate consolidation or indecision, often preceding breakouts. Optional lines can be drawn around these candles for better visibility.
Key Features & How to Use
Real-Time Zone Plotting:
Automatically identifies and marks Supply and Demand zones as they form, using the RBR, RBD, DBR, and DBD patterns. Zones are color-coded (e.g., green for Demand, red for Supply) and can extend rightward for visibility.
Multi-Timeframe Analysis:
Operates on all timeframes, with separate settings for Higher TF (e.g., weekly) and Lower TF (e.g., daily) zones. This allows traders to see both macro and micro levels of market structure.
Automatic Detection:
No manual input is requiredโzones are plotted based on price action, volume, and SMA trends. Live candle volume is displayed for context.
Tested Zone Management:
Optionally removes zones after theyโre tested (price revisits and reverses) or after a second leg-out move, keeping the chart uncluttered.
Customizable Display:
Choose which patterns to detect (RBR, RBD, etc.).
Adjust base candle counts (1-5), explosive candle parameters (Range-Body Ratio, Multiplier), and quality filters (SMA length, Volume Multiplier).
Customize colors for zones, borders, labels, and candles (boring, bullish explosive, bearish explosive).
Enable/disable labels and pattern names on boxes.
Alerts:
Set notifications for zone formation, CHoCH, BOS, and liquidity sweeps on your chosen timeframe.
Inside Candle Visualization:
Highlights consolidation phases with color-coded candles and optional lines, aiding breakout anticipation.
SMC Insights:
Visualizes CHoCH, BOS, liquidity sweeps, and swings with distinct lines and labels, helping traders follow institutional moves.
How to Use It:
Approaching Zones: When price nears a Supply or Demand zone, watch for reversal patterns (e.g., pin bars, engulfing candles) or SMC signals (e.g., BOS, liquidity sweeps) to confirm entries. Combine with your tested strategyโdonโt trade zones blindly.
SMC Signals: Use CHoCH for early trend reversal clues, BOS for trend continuation, and liquidity sweeps to gauge manipulation.
Inside Candles: Monitor for breakouts after consolidation periods marked by Inside Candles.
Why Itโs Unique & Valuable
This indicator stands out by blending multi-timeframe Supply and Demand analysis with Smart Money Concepts and Inside Candle detection into a single, cohesive tool. While it uses classic elements like price action and volume, its proprietary logicโcombining specific pattern detection (RBR, RBD, DBR, DBD), SMC signals (CHoCH, BOS, etc.), and consolidation trackingโoffers a fresh approach. Unlike generic trend-following or scalping tools, it provides actionable insights into market structure and institutional behavior, making it worth considering for traders willing to invest in a premium tool. The flexibility of customization and MTF functionality further enhances its utility across trading styles, from scalping to swing trading.
Pivot S/R with Volatility Filter## *๐ Indicator Purpose*
This indicator identifies *key support/resistance levels* using pivot points while also:
โ
Detecting *high-volume liquidity traps* (stop hunts)
โ
Filtering insignificant pivots via *ATR (Average True Range) volatility*
โ
Tracking *test counts and breakouts* to measure level strength
---
## *โ SETTINGS โ Detailed Breakdown*
### *1๏ธโฃ โ General Settings*
#### *๐น Pivot Length*
- *Purpose:* Determines how many bars to analyze when identifying pivots.
- *Usage:*
- *Low values (5-20):* More pivots, better for scalping.
- *High values (50-200):* Fewer but stronger levels for swing trading.
- *Example:*
- Pivot Length = 50 โ Only the most significant highs/lows over 50 bars are marked.
#### *๐น Test Threshold (Max Test Count)*
- *Purpose:* Sets how many times a level can be tested before being invalidated.
- *Example:*
- Test Threshold = 3 โ After 3 tests, the level is ignored (likely to break).
#### *๐น Zone Range*
- *Purpose:* Creates a price buffer around pivots (ยฑ0.001 by default).
- *Why?* Markets often respect "zones" rather than exact prices.
---
### *2๏ธโฃ โ Volatility Filter (ATR)*
#### *๐น ATR Period*
- *Purpose:* Smoothing period for Average True Range calculation.
- *Default:* 14 (standard for volatility measurement).
#### *๐น ATR Multiplier (Min Move)*
- *Purpose:* Requires pivots to show *meaningful price movement*.
- *Formula:* Min Move = ATR ร Multiplier
- *Example:*
- ATR = 10 pips, Multiplier = 1.5 โ Only pivots with *15+ pip swings* are valid.
#### *๐น Show ATR Filter Info*
- Displays current ATR and minimum move requirements on the chart.
---
### *3๏ธโฃ โ Volume Analysis*
#### *๐น Volume Change Threshold (%)*
- *Purpose:* Filters for *unusual volume spikes* (institutional activity).
- *Example:*
- Threshold = 1.2 โ Requires *120% of average volume* to confirm signals.
#### *๐น Volume MA Period*
- *Purpose:* Lookback period for "normal" volume calculation.
---
### *4๏ธโฃ โ Wick Analysis*
#### *๐น Wick Length Threshold (Ratio)*
- *Purpose:* Ensures rejection candles have *long wicks* (strong reversals).
- *Formula:* Wick Ratio = (Upper Wick + Lower Wick) / Candle Range
- *Example:*
- Threshold = 0.6 โ 60% of the candle must be wicks.
#### *๐น Min Wick Size (ATR %)*
- *Purpose:* Filters out small wicks in volatile markets.
- *Example:*
- ATR = 20 pips, MinWickSize = 1% โ Wicks under *0.2 pips* are ignored.
---
### *5๏ธโฃ โ Display Settings*
- *Show Zones:* Toggles support/resistance shaded areas.
- *Show Traps:* Highlights liquidity traps (โฒ/โผ symbols).
- *Show Tests:* Displays how many times levels were tested.
- *Zone Transparency:* Adjusts opacity of zones.
---
## *๐ฏ Practical Use Cases*
### *1๏ธโฃ Liquidity Trap Detection*
- *Scenario:* Price spikes *above resistance* then reverses sharply.
- *Requirements:*
- Long wick (Wick Ratio > 0.6)
- High volume (Volume > Threshold)
- *Outcome:* *Short Trap* signal (โผ) appears.
### *2๏ธโฃ Strong Support Level*
- *Scenario:* Price bounces *3 times* from the same level.
- *Indicator Action:*
- Labels the level with test count (3/5 = 3 tests out of max 5).
- Turns *red* if broken (Break Count > 0).
Deep Dive: How This Indicator Works*
This indicator combines *four professional trading concepts* into one powerful tool:
1. *Classic Pivot Point Theory*
- Identifies swing highs/lows where price previously reversed
- Unlike basic pivot indicators, ours uses *confirmed pivots only* (filtered by ATR)
2. *Volume-Weighted Validation*
- Requires unusual trading volume to confirm levels
- Filters out "phantom" levels with low participation
3. *ATR Volatility Filtering*
- Eliminates insignificant price swings in choppy markets
- Ensures only meaningful levels are plotted
4. *Liquidity Trap Detection*
- Spots institutional stop hunts where markets fake out traders
- Uses wick analysis + volume spikes for high-probability signals
---
Deep Dive: How This Indicator Works*
This indicator combines *four professional trading concepts* into one powerful tool:
1. *Classic Pivot Point Theory*
- Identifies swing highs/lows where price previously reversed
- Unlike basic pivot indicators, ours uses *confirmed pivots only* (filtered by ATR)
2. *Volume-Weighted Validation*
- Requires unusual trading volume to confirm levels
- Filters out "phantom" levels with low participation
3. *ATR Volatility Filtering*
- Eliminates insignificant price swings in choppy markets
- Ensures only meaningful levels are plotted
4. *Liquidity Trap Detection*
- Spots institutional stop hunts where markets fake out traders
- Uses wick analysis + volume spikes for high-probability signals
---
## *๐ Parameter Encyclopedia (Expanded)*
### *1๏ธโฃ Pivot Engine Settings*
#### *Pivot Length (50)*
- *What It Does:*
Determines how many bars to analyze when searching for swing highs/lows.
- *Professional Adjustment Guide:*
| Trading Style | Recommended Value | Why? |
|--------------|------------------|------|
| Scalping | 10-20 | Captures short-term levels |
| Day Trading | 30-50 | Balanced approach |
| Swing Trading| 50-200 | Focuses on major levels |
- *Real Market Example:*
On NASDAQ 5-minute chart:
- Length=20: Identifies levels holding for ~2 hours
- Length=50: Finds levels respected for entire trading day
#### *Test Threshold (5)*
- *Advanced Insight:*
Institutions often test levels 3-5 times before breaking them. This setting mimics the "probe and push" strategy used by smart money.
- *Psychology Behind It:*
Retail traders typically give up after 2-3 tests, while institutions keep testing until stops are run.
---
### *2๏ธโฃ Volatility Filter System*
#### *ATR Multiplier (1.0)*
- *Professional Formula:*
Minimum Valid Swing = ATR(14) ร Multiplier
- *Market-Specific Recommendations:*
| Market Type | Optimal Multiplier |
|------------------|--------------------|
| Forex Majors | 0.8-1.2 |
| Crypto (BTC/ETH) | 1.5-2.5 |
| SP500 Stocks | 1.0-1.5 |
- *Why It Matters:*
In EUR/USD (ATR=10 pips):
- Multiplier=1.0 โ Requires 10 pip swings
- Multiplier=1.5 โ Requires 15 pip swings (fewer but higher quality levels)
---
### *3๏ธโฃ Volume Confirmation System*
#### *Volume Threshold (1.2)*
- *Institutional Benchmark:*
- 1.2x = Moderate institutional interest
- 1.5x+ = Strong smart money activity
- *Volume Spike Case Study:*
*Before Apple Earnings:*
- Normal volume: 2M shares
- Spike threshold (1.2): 2.4M shares
- Actual volume: 3.1M shares โ STRONG confirmation
---
### *4๏ธโฃ Liquidity Trap Detection*
#### *Wick Analysis System*
- *Two-Filter Verification:*
1. *Wick Ratio (0.6):*
- Ensures majority of candle shows rejection
- Formula: (UpperWick + LowerWick) / Total Range > 0.6
2. *Min Wick Size (1% ATR):*
- Prevents false signals in flat markets
- Example: ATR=20 pips โ Min wick=0.2 pips
- *Trap Identification Flowchart:*
Price Enters Zone โ
Spikes Beyond Level โ
Shows Long Wick โ
Volume > Threshold โ
TRAP CONFIRMED
---
## *๐ก Master-Level Usage Techniques*
### *Institutional Order Flow Analysis*
1. *Step 1:* Identify pivot levels with โฅ3 tests
2. *Step 2:* Watch for volume contraction near levels
3. *Step 3:* Enter when trap signal appears with:
- Wick > 2รATR
- Volume > 1.5ร average
### *Multi-Timeframe Confirmation*
1. *Higher TF:* Find weekly/monthly pivots
2. *Lower TF:* Use this indicator for precise entries
3. *Example:*
- Weekly pivot at $180
- 4H shows liquidity trap โ High-probability reversal
---
## *โ Critical Mistakes to Avoid*
1. *Using Default Settings Everywhere*
- Crude oil needs higher ATR multiplier than bonds
2. *Ignoring Trap Context*
- Traps work best at:
- All-time highs/lows
- Major psychological numbers (00/50 levels)
3. *Overlooking Cumulative Volume*
- Check if volume is building over multiple tests
HMA PLANz1. High Liquidity Candle Detection:
The indicator looks for candles with high liquidity (identified by comparing the current candle's volume with the highest volume of the last 10 candles).
If a candle has high liquidity, it is highlighted in yellow.
2. Midpoint Calculation of the Candle:
The midpoint of the candle is calculated by averaging the High and Low prices of the candle:
Midpoint
=
High
+
Low
2
Midpoint=
2
High+Low
โ
3. Draw a Line at the Midpoint of the High Liquidity Candle:
A horizontal line is drawn at the calculated midpoint value of the high liquidity candle and continues for the next five candles.
4. Change Line Color Based on Price vs. Midpoint:
If the current price is above the midpoint, the line is drawn in green.
If the current price is below the midpoint, the line is drawn in red.
5. Moving Averages (MA):
In addition to liquidity analysis, the indicator calculates and plots two moving averages on the chart.
Users can choose between EMA, SMA, WMA, or HMA for each moving average.
Users can also select the source for the moving averages (Close, High, Low).
The length for each moving average is customizable.
6. Display Moving Averages with Labels:
The moving average lines are plotted on the chart.
Labels are displayed above each moving average to show its type and source (e.g., "MA - HMA (Close)").
Summary of Key Features:
High Liquidity Candle Detection: Highlighted in yellow.
Draw a Horizontal Line at the Midpoint of the high liquidity candle: The line color changes based on price relation to the midpoint.
Moving Averages: Allows customization of types and lengths.
Labels: Shows details of the moving averages.
EQS by SiriusProtected Script Description: "EQS by Sirius"
This indicator is protected and published as invite-only due to its original multi-timeframe structure, advanced visual logic, and proprietary handling of liquidity zones and equal high/low detection. The complexity of its designโfeaturing adaptive time-based plotting, contextual tooltips, and dynamic zone trackingโreflects a level of custom development intended for professional use, necessitating source protection.
Purpose and Core Logic
โEQS by Siriusโ is designed to detect and visualize Equal Highs and Equal Lows (EQS) across multiple timeframes. These levels are commonly interpreted as potential liquidity zones or key market structures, often used by traders for identifying breakout traps, stop hunts, or reversal points. The script applies a precision-based algorithm to identify these EQS levels, providing users with visual cues to support decision-making in various market contexts.
The detection logic is based on comparing the difference between two successive highs (or lows) relative to the high-low range of the bars, allowing the user to fine-tune sensitivity via a precision parameter. When valid EQS conditions are met, horizontal lines are drawn at the detected price level, accompanied by optional shadow trendlines to represent liquidity channels.
Visual Outputs and Features
The indicator provides a rich and customizable visual environment, including:
Multi-Timeframe EQS Detection: Configurable from 1-minute to 4-hour timeframes with automatic sequencing.
Zone Highlighting: Optional background shading for designated date intervals.
Dynamic Shadow Mode: Projects angled trendlines representing potential liquidity zones based on EQS formations.
Touch Counters: Real-time counting of price interactions with plotted EQS levels.
Tooltips: Each label includes a timestamp and price breakdown to provide contextual clarity.
Line Customization: Adjustable color, width, and transparency for each EQS type and its shadow projections.
Auto-zoom Scaling: Adapts visual density based on the active chartโs timeframe.
Visibility Filters: Adjustable proximity thresholds ensure only relevant lines are displayed based on current price action.
How to Use in Trading
Traders can use this tool to:
Identify liquidity targets where price may reverse or accelerate due to stop hunts or breakout traps.
Analyze multi-timeframe confluence by comparing EQS zones from higher timeframes with local market structure.
Monitor touch counts to assess the strength or weakening of support/resistance levels.
Visualize trendline-based liquidity zones using the โshadow modeโ to infer possible manipulation or price magnet areas.
Integrate with existing strategies for entry/exit timing, particularly in breakout and mean-reversion models.
Due to the high level of customizability and visual control, the script is suitable for discretionary traders, smart money concept practitioners, and those seeking to combine structural analysis with liquidity mapping.
Money Flow Divergence IndicatorOverview
The Money Flow Divergence Indicator is designed to help traders and investors identify key macroeconomic turning points by analyzing the relationship between U.S. M2 money supply growth and the S&P 500 Index (SPX). By comparing these two crucial economic indicators, the script highlights periods where market liquidity is outpacing or lagging behind stock market growth, offering potential buy and sell signals based on macroeconomic trends.
How It Works
1. Data Sources
S&P 500 Index (SPX500USD): Tracks the stock market performance.
U.S. M2 Money Supply (M2SL - Federal Reserve Economic Data): Represents available liquidity in the economy.
2. Growth Rate Calculation
SPX Growth: Percentage change in the S&P 500 index over time.
M2 Growth: Percentage change in M2 money supply over time.
Growth Gap (Delta): The difference between M2 growth and SPX growth, showing whether liquidity is fueling or lagging behind market performance.
3. Visualization
A histogram displays the growth gap over time:
Green Bars: M2 growth exceeds SPX growth (potential bullish signal).
Red Bars: SPX growth exceeds M2 growth (potential bearish signal).
A zero line helps distinguish between positive and negative growth gaps.
How to Use It
โ
Bullish Signal: When green bars appear consistently, indicating that liquidity is outpacing stock market growth. This suggests a favorable environment for buying or holding positions.
โ Bearish Signal: When red bars appear consistently, meaning stock market growth outpaces liquidity expansion, signaling potential overvaluation or a market correction.
Best Timeframes for Analysis
This indicator works best on monthly timeframes (M) since it is designed for long-term investors and macro traders who focus on broad economic cycles.
Who Should Use This Indicator?
๐ Long-term investors looking for macroeconomic trends.
๐ Swing traders who incorporate liquidity analysis in their strategies.
๐ฐ Portfolio managers assessing market liquidity conditions.
๐ Use this indicator to stay ahead of market trends and make informed investment decisions based on macroeconomic liquidity shifts! ๐
AlphaSync | QuantEdgeB๐ข Introducing AlphaSync by QuantEdgeB
๐ ๏ธ Overview
AlphaSync is a comprehensive medium-term market guidance system designed for major assets such as BTC, ETH, and SOL. This system helps traders determine the overall market direction by integrating three universal strategies (EvolveXSync, ApexSync, QBHV Sync) and a Hybrid strategy (HybridSync).
๐ What Makes AlphaSync Unique?
โ
Multi-Strategy Fusion โ A robust blend of technical, economic, on-chain, and volatility-driven insights.
โ
HybridSync Component (90% Non-Price Factors) โ Incorporates macro and liquidity signals to balance pure price-based models.
โ
Structured Decision-Making โ The Trend Confluence score aggregates all sub-strategies, providing a unified market signal.
__________________________________________________________________________________
โจ Key Features
๐น HybridSync (Hybrid Model)
Utilizes on-chain, economic, liquidity, and volatility factors to provide a fundamental market risk outlook. Unlike technical models, it derives signals primarily from macroeconomic indicators, risk appetite gauges, and capital flows.
๐น EvolveXSync, & ApexSync (Technical Strategies)
Both strategies are purely price-based, relying on volatility-adjusted trend models, adaptive moving averages, and statistical deviations to confirm bullish or bearish trends.
๐น QBHV Sync (Momentum & Deviation-Based System)
A fusion of momentum-deviation and a volatility-driven trend confirmation model, designed to detect shifts in momentum while filtering out market noise.
๐น Trend Confluence (Final Aggregated Signal)
A weighted combination of all four models, delivering a single, structured signal to eliminate conflicting indicators and refine decision-making.
__________________________________________________________________________________
๐ How It Works
1๏ธโฃ HybridSync โ Non-Price Market Structure Analysis
HybridSync is an economic and liquidity-based framework, integrating macro variables, credit spreads, volatility indices, capital flows, and on-chain dynamics to assess risk-on/risk-off conditions.
๐ Key Components:
โ On-Chain Metrics โ Tracks investor behavior, exchange flows, and market cap ratios.
โ Liquidity Indicators โ Monitors global money supply (M2), Federal Reserve balance sheet, credit markets, and capital flows.
โ Volatility & Risk Metrics โ Uses MOVE, VIX, VVIX ratios, and bond market stress indicators to identify risk sentiment shifts.
๐น Why HybridSync?
โข Price alone does not dictate the market; macro liquidity and risk factors are often leading indicators of price movement, especially when it comes to risk assets such as cryptocurrencies.
โข Improves decision-making in uncertain market environments, particularly during high-volatility or trendless conditions.
2๏ธโฃ EvolveXSync, & ApexSync โ Trend-Following & Volatility Models
Both EvolveXSync, & ApexSync are technical strategies, independently designed to capture trend strength and volatility dynamics.
๐ Core Mechanisms:
โ VIDYA-Based Trend Detection โ Adaptive moving averages adjust dynamically to price swings.
โ SD-Filtered EMA Models โ Uses normalized standard deviation levels to confirm trend validity.
โ ATR-Adjusted Breakout Filters โ Prevents false signals by incorporating dynamic volatility assessments.
๐น Why Two UniStrategies?
โข EvolveXSync, & ApexSync have different calculation methods, providing diverse perspectives on trend confirmation.
โข Ensures robustness by mitigating overfitting to a single price-based model.
3๏ธโฃ QBHV Sync โ Momentum Deviation & Trend Confirmation
This component blends Bollinger Momentum Deviation (BMD) with a percentile-based trend model to confirm trend shifts.
๐ Core Components:
โ Bollinger Momentum Deviation โ A normalized SMA-SD filter detects overbought/oversold conditions.
โ Percentile-Based Trend Confirmation โ Ensures trends align with long-term volatility structure.
โ Adaptive Signal Filtering โ Prevents unnecessary trade signals by refining thresholds dynamically.
๐น Why QBHV Sync?
โข Adds a statistical layer to trend assessment, preventing whipsaws in volatile conditions.
โข Complements HybridSync by ensuring price movements align with broader market forces.
4๏ธโฃ Trend Confluence โ The Final Aggregated Signal
AlphaSync blends HybridSync, EvolveXSync, ApexSync, and QBHV Sync into one final output.
๐ How Itโs Weighted ? Equal Weight to remove any bias and over-reliance on one input.
โ HybridSync (Macro & On-Chain Factors) โ 25% Weight
โ UniStrat V1 (Pure Trend) โ 25% Weight
โ UniStrat V2 (Trend + ATR) โ 25% Weight
โ QBHV Sync (Momentum & Deviation) โ 25% Weight
๐น Why Merge These Into One System?
The core philosophy behind AlphaSync is to create a holistic, structured decision-making framework that eliminates the weaknesses of single-method trading approaches. Instead of relying solely on technical indicators, which can lag or fail in macro-driven markets, AlphaSync blends price-based trend signals with macroeconomic, liquidity, and risk-adjusted models.
This multi-layered approach ensures that the system:
โ Adapts dynamically to different market environments.
โ Eliminates conflicting signals by creating a structured confluence score.
โ Prevents over-reliance on a single market model, improving robustness.
๐ Final Signal Interpretation:
โ
Long Signal โ AlphaSync Score > Long Threshold
โ Short Signal โ AlphaSync Score < Short Threshold
__________________________________________________________________________________
๐ฅ Who Should Use AlphaSync?
โ
Medium-Term Traders & Portfolio Managers โ Ideal for traders who require macro-confirmed trend signals.
โ
Systematic & Quantitative Traders โ Designed for algorithmic integration and structured decision-making.
โ
Long-Term Position Traders โ Helps identify major trend shifts and capital rotation opportunities.
โ
Risk-Conscious Investors โ Incorporates macro volatility assessments to minimize unnecessary risk exposure.
__________________________________________________________________________________
๐ Backtest Mode - Evaluating Historical Performance
AlphaSync includes a fully integrated backtest module, allowing traders to assess its historical performance metrics.
๐น Backtest Metrics Displayed:
โ Equity Max Drawdown โ Measures historical peak loss.
โ Profit Factor โ Evaluates profitability vs. loss ratio.
โ Sharpe & Sortino Ratios โ Risk-adjusted return metrics.
โ Total Trades & Win Rate โ Performance across different market cycles.
โ Half Kelly Criterion โ Optimal position sizing based on historical returns.
๐ Disclaimer:Backtest results are based on past performance and do not guarantee future success. Always incorporate real-time validation and risk management in live trading.
๐ Why This Matters?
โ
Strategy Validation โ See how AlphaSync performs across various market conditions.
โ
Customizable Analysis โ Adjust parameters and observe real-time backtest results.
โ
Risk Awareness โ Understand potential drawdowns before deploying capital.
Behavior Across Crypto Majors:
BTC
ETH
SOL
๐ Disclaimer: Backtest results are based on historical data and past market behavior. Performance is not indicative of future results and should not be considered financial advice. Always conduct your own backtests and research before making any investment decisions. ๐
__________________________________________________________________________________
๐ Customization & Default Settings
๐ AlphaSync Input Parameters & Default Values
๐น Strategy Configuration
โข Color Mode โ "Strategy"
โข Extra Plots โ true
โข Long/Cash Signal Label โ false
โข AlphaSync Dashboard โ true
โข Enable BackTest Table โ false
โข Enable Equity Curve โ false
โข Table Position โ "Bottom Left"
โข Start Date โ '01 Jan 2018 00:00'
โข AlphaSync Long Threshold โ 0.00
โข AlphaSync Short Threshold โ 0.00
๐น QBHV.Sync
โข DEMA Source โ close
โข DEMA Length โ 14
โข Percentile Length โ 35
โข ATR Length โ 14
โข Long Multiplier (ATR Up) โ 1.8
โข Short Multiplier (ATR Down) โ 2.5
โข Momentum Length โ 8
โข Momentum Source โ close
โข Base Length (SMA Calculation) โ 40
โข Source for BMD โ close
โข Standard Deviation Length โ 30
โข SD Multiplier โ 0.7
โข Long Threshold โ 72
โข Short Threshold โ 59
๐น EvolveXSync Configuration
โข VIDYA Loop Length โ 2
โข VIDYA Loop Hist Length โ 5
โข Vidya Loop Long Threshold โ 40
โข Vidya Loop Short Threshold โ 10
โข Dynamic EMA Length โ 12
โข Dynamic EMA SD Length โ 30
โข Dynamic EMA Upper SD Weight โ 1.032
โข Dynamic EMA Lower SD Weight โ 1.02
โข SD Median Length โ 12
โข Normalized Median Length โ 20
โข Median SD Length โ 30
โข Median Long SD Weight โ 0.98
โข Median Short SD Weight โ 1.04
๐นApexSync Configuration
โข DEMA Length โ 30
โข DEMA ATR Length โ 14
โข DEMA ATR Multiplier โ 1.0
โข G-VIDYA Length โ 9
โข G-VIDYA Hist Length โ 30
โข VIDYA ATR Length โ 14
โข VIDYA ATR Multiplier โ 1.7
โข SD Kijun Length โ 24
โข Normalized Kijun Length โ 50
โข KIJUN SD Length โ 32
โข KIJUN Long SD Weight โ 0.98
โข KIJUN Short SD Weight โ 1.02
๐น Risk Mosaic (Macro & Liquidity Component)
โข Risk Signal Smoothing Length (EMA) โ 8
๐ AlphaSync is fully customizable to match different market conditions and trading styles
๐ By default, AlphaSync is optimized for structured, medium-term market guidance.
__________________________________________________________________________________
๐ Conclusion
AlphaSync redefines medium-term trend analysis by merging technical, fundamental, and quantitative models into one unified system. Unlike traditional strategies that rely solely on price action, AlphaSync incorporates macroeconomic and liquidity factors, ensuring a more holistic market view.
๐น Key Takeaways:
1๏ธโฃ Hybrid + Technical Fusion โ Balances macro & price-based strategies for stronger decision-making.
2๏ธโฃ Multi-Factor Trend Aggregation โ Reduces false signals by merging independent methodologies.
3๏ธโฃ Structured, Data-Driven Approach โ Designed for quantitative trading and risk-aware portfolio allocation.
๐ Master the market with precision and confidence | QuantEdgeB
๐น Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
๐น Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
[TehThomas] - ICT SMT DivergencesIntroduction
SMT Divergences is a cutting-edge trading tool designed for traders who utilize Smart Money Techniques (SMT), a core concept in the Inner Circle Trader (ICT) methodology. This indicator is specifically built to detect SMT divergences by comparing price action across multiple correlated assets. It helps traders identify institutional activity, liquidity grabs, and inefficiencies in the market, offering valuable insights for high-probability trade setups.
Smart Money Techniques revolve around the idea that institutional traders and large market participants leave behind footprints in the form of price divergences. By analyzing multiple asset pairs simultaneously, this indicator helps traders pinpoint areas where one market structure contradicts another, revealing potential trade opportunities before the majority of retail traders notice them.
What is SMT Divergence?
Smart Money Divergence (SMT) occurs when correlated assets or markets behave differently in key areas of interest. These divergences often indicate market inefficiencies caused by liquidity grabs or institutional order flow. There are two main types of SMT divergences:
1. Bearish SMT Divergence (Smart Money Distribution) ๐ด
Occurs when:
One asset makes a higher high, while another correlated asset makes a lower high.
This signals underlying weakness in the price action of the first asset.
Institutions may be offloading positions, preparing for a downward move.
๐ Example: If GBP/USD makes a higher high, but EUR/USD makes a lower high, it indicates potential weakness in GBP/USD and a possible short opportunity.
2. Bullish SMT Divergence (Smart Money Accumulation) ๐ต
Occurs when:
One asset makes a lower low, while another correlated asset makes a higher low.
This suggests strength and potential accumulation by institutional traders.
Smart Money may be positioning for a bullish reversal.
๐ Example: If NASDAQ (US100) makes a lower low, but S&P 500 (US500) makes a higher low, it could indicate bullish strength in the stock market, suggesting a possible long trade.
How This Indicator Works
The SMT Divergences automatically identifies and plots SMT divergences on your chart, allowing you to spot hidden market imbalances at a glance.
๐ Key Features
โ
Compare Up to 4 Assets Simultaneously โ Select up to four correlated pairs to compare against the main charted asset.
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Automatic Detection of SMT Divergences โ The script finds divergences in swing highs and swing lows and visually marks them on the chart.
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Customizable Line Styles & Colors โ Adjust the appearance of the divergence lines and labels to suit your trading style.
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Smart Labeling System โ Displays which asset pairs are diverging, making it easy to analyze market conditions.
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Works Across Multiple Markets โ Use for Forex, Indices, Crypto, and Commodities, giving traders flexibility in different asset classes.
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Designed for ICT Traders โ Aligns perfectly with other ICT concepts such as Liquidity Zones, Order Blocks, and Fair Value Gaps (FVGs).
๐ Indicator Settings & Customization
The indicator provides various settings to tailor it to your trading preferences:
Pivot Lookback Length: Adjusts how many bars the indicator looks back to determine swing highs/lows.
Symbol Selection: Choose up to four additional assets to compare against your main trading pair.
Divergence Line Colors: Customize the color of bearish (red) and bullish (blue) divergences for better visibility.
Line Styles: Choose between solid, dotted, or dashed lines to highlight divergences in your preferred way.
Label Customization: Modify text color and display preferences for a clean and informative chart layout.
How to Use This Indicator in Your Trading Strategy
This indicator is best used in combination with other ICT concepts to improve confluence and increase trade accuracy. Hereโs how you can integrate it into your trading strategy:
๐น Step 1: Identify SMT Divergences
Wait for bullish or bearish SMT divergences to appear on your chart.
Check if the divergence aligns with key liquidity zones, fair value gaps (FVGs), or order blocks.
๐น Step 2: Confirm Institutional Activity
Look for liquidity sweeps (stop hunts) before a potential reversal.
If a bearish SMT divergence forms near a major resistance level, it may signal Smart Money selling.
If a bullish SMT divergence forms near a support zone, it could indicate accumulation.
๐น Step 3: Enter a Trade with Confluence
Combine SMT divergences with market structure shifts to time entries.
Use additional ICT tools like Premium & Discount Arrays, Volume Profile, and Market Maker Models for confirmation.
Set stop-losses above liquidity zones and aim for high-risk reward ratios.
๐น Step 4: Manage Risk & Take Profits
Always use proper risk management, keeping an eye on liquidity grabs and market sentiment.
Consider taking partial profits at key structural points and letting the rest of the trade run.
Why This Indicator is a Game-Changer for ICT Traders
Traditional retail traders often fail to spot Smart Money footprints, which is why many struggle with false breakouts and liquidity traps. The - ICT SMT Divergences indicator eliminates this problem by providing a clear, visual representation of SMT divergences, allowing traders to track institutional movements in real-time.
๐น Save Time โ No need to manually compare charts; the script does the work for you.
๐น Improve Accuracy โ Get high-probability trade setups by following institutional footprints.
๐น Enhance Your Trading Edge โ Use SMT divergences in combination with liquidity grabs, order blocks, and fair value gaps to refine your strategy.
๐น Universal Market Compatibility โ Works for Forex, Indices, Crypto, Commodities, and even Stocks, giving you flexibility in different markets.
Final Thoughts
The SMT Divergences is a must-have tool for traders who rely on Smart Money Techniques (SMT) and ICT methodologies. By identifying SMT divergences across multiple correlated markets, this indicator provides unparalleled insights into institutional trading behavior and enhances your ability to trade with Smart Money.
Whether you are a day trader, swing trader, or position trader, this indicator will help you make more informed decisions, avoid liquidity traps, and improve your overall profitability.
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Thanks for your support!
If you found this idea helpful or learned something new, drop a like ๐ and leave a comment, Iโd love to hear your thoughts! ๐
Make sure to follow me for more price action insights, free indicators, and trading strategies. Letโs grow and trade smarter together! ๐
WhaleTrackBITGET:BTCUSDT.P
WhaleTrack โ Volume Heatmap to Uncover Institutional Trading Activity
Overview
WhaleTrack is a volume-based heatmap indicator designed to reveal areas of high institutional trading activity. The indicator helps traders identify hidden support and resistance levels, analyze trend sustainability, and optimize stop-loss placements by displaying where significant market participants (whales) have historically traded in large volumes.
Institutions and large traders often push price into areas of historical liquidity to trigger retail stop-losses and fill their own large orders at optimal prices. WhaleTrack visualizes these critical areas, allowing traders to anticipate future price movements based on past institutional behavior.
How WhaleTrack Works
WhaleTrack analyzes historical trading volume and calculates a normalized volume intensity relative to the moving average (SMA). This data is then mapped onto a heatmap that highlights key liquidity zones.
1. Volume Normalization & SMA-Based Calculation
The script calculates the ratio of current volume to its SMA-based average.
Zones with significantly high volume spikes are identified as key liquidity areas where large traders may have accumulated or distributed assets.
The volume is quantized into different levels, ranging from Low to Extreme, creating a clear heatmap gradient.
2. Why Do Whales Manipulate Liquidity?
Large traders (whales) need liquidity to execute their orders.
They push price into historical high-volume areas to trigger stop-losses and force retail traders into selling.
This behavior allows them to accumulate at lower prices or distribute at higher prices before a major move.
Whale zones often act as support/resistance because institutions tend to protect their previous accumulation or distribution levels.
3. Heatmap Color Model & Zone Classification
WhaleTrack assigns volume intensity levels based on historical market participation:
Low โ Minimal volume, weak interest
Low-Mid โ Slightly increased volume
Mid โ Standard trading activity, no major anomalies
Mid-High โ Significant increase in volume, possible whale activity
High โ Strong liquidity pool, institutional interest
Extreme โ Highly concentrated volume, key reversal area
By observing these zones, traders can determine whether a price level is likely to hold as support or resistance , or if a breakout has the strength to sustain.
Trading Applications of WhaleTrack
WhaleTrack can be used to identify trade setups based on liquidity behavior:
1. Identifying Hidden Reversal Points (Support & Resistance)
Large Whale Zones below price โ Likely strong support.
Large Whale Zones above price โ Likely strong resistance.
These zones often lead to reversals, as large traders defend their previous positions.
2. Evaluating Trend Sustainability
A strong uptrend should leave multiple high-volume zones behind.
If no new high-volume zones form, the trend may be unsustainable.
High volume clusters in trend direction? โ Likely trend continuation.
3. Optimizing Stop-Loss Placement
Placing stops inside whale zones increases stop-out risk.
Setting stops below whale buy zones protects against premature liquidation.
Stops above whale sell zones help avoid fake breakouts.
Customization & Settings
WhaleTrack is designed with flexibility in mind, offering multiple customization options:
1. Layout & Color Models
WhaleTrack Default โ optimized for whale volume tracking
Model 1 & Model 2 โ alternative heatmap color schemes
Contrast Mode โ high visibility
White-Black & Black-White โ for different chart backgrounds
Custom 1 & Custom 2 โ user-defined color configurations
2. Advanced Options
Draw Full Candle Boxes โ display full candle height or a partial range
Legend Visibility & Positioning โ control placement of the heatmap legend
Exponential Color Model โ choose between logarithmic and linear volume representation
Max Transparency Settings โ adjust visibility of older zones
Number of Heatmap Colors โ set the gradient sensitivity
3. Data Optimization Settings
Lookback Period โ define how many bars are analyzed for volume normalization
Max Box Display โ limit the number of displayed volume zones
Data Saver Mode โ increase range at the expense of detail
Minimum Volume Threshold โ filter out insignificant volume clusters
Disclaimer
This indicator is for educational and informational purposes only. It does not provide financial advice or guarantee future performance. Trading is riskyโconduct your own research before making any investment decisions.
High Volume Points [BigBeluga]High Volume Points is a unique volume-based indicator designed to highlight key liquidity zones where significant market activity occurs. By visualizing high-volume pivots with dynamically sized markers and optional support/resistance levels, traders can easily identify areas of interest for potential breakouts, liquidity grabs, and trend reversals.
๐ต Key Features:
High Volume Points Visualization:
The indicator detects pivot highs and lows with exceptionally high trading volume.
Each high-volume point is displayed as a concentric circle, with its size dynamically increasing based on the volume magnitude.
The exact volume at the pivot is shown within the circle.
Dynamic Levels from Volume Pivots:
Horizontal levels are drawn from detected high-volume pivots to act as support or resistance.
Traders can use these levels to anticipate potential liquidity zones and market reactions.
Liquidity Grabs Detection:
If price crosses a high-volume level and grabs liquidity, the level automatically changes to a dashed line.
This feature helps traders track areas where institutional activity may have occurred.
Volume-Based Filtering:
Users can filter volume points by a customizable threshold from 0 to 6, allowing them to focus only on the most significant high-volume pivots.
Lower thresholds capture more volume points, while higher thresholds highlight only the most extreme liquidity events.
๐ต Usage:
Identify strong support/resistance zones based on high-volume pivots.
Track liquidity grabs when price crosses a high-volume level and converts it into a dashed line.
Filter volume points based on significance to remove noise and focus on key areas.
Use volume circles to gauge the intensity of market interest at specific price points.
High Volume Points is an essential tool for traders looking to track institutional activity, analyze liquidity zones, and refine their entries based on volume-driven market structure.
SL Hunting Detector๐ Step 1: Identify Liquidity Zones
The script plots high-liquidity zones (red) and low-liquidity zones (green).
These are areas where big players target stop-losses before reversing the price.
Example:
If price is near a red liquidity zone, expect a potential stop-loss hunt & reversal downward.
If price is near a green liquidity zone, expect a potential stop-loss hunt & reversal upward.
๐ Step 2: Watch for Stop-Loss Hunts (Fakeouts)
The indicator marks stop-loss hunts with red (bearish) or green (bullish) arrows.
When do stop-loss hunts occur?
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A long wick below support (with high volume) = Stop hunt before reversal upward.
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A long wick above resistance (with high volume) = Stop hunt before reversal downward.
Confirmation:
Volume must spike (volume > 1.5x the average volume).
ATR-based wicks must be longer than usual (showing a stop-hunt trap).
๐ Step 3: Enter a Trade After a Stop-Hunt
๐น Bullish Trade (Buying a Dip)
If a green arrow appears (stop-hunt below support):
โ
Enter a long (buy) trade at or just above the wickโs recovery level.
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Stop-loss: Below the wickโs low (avoid getting hunted again).
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Take-profit: Next resistance level or mid-range of the liquidity zone.
๐น Bearish Trade (Shorting a Fakeout)
If a red arrow appears (stop-hunt above resistance):
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Enter a short (sell) trade at or just below the wickโs rejection level.
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Stop-loss: Above the wickโs high (avoid getting stopped out).
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Take-profit: Next support level or mid-range of the liquidity zone.
๐ Step 4: Set Alerts & Automate
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The indicator triggers alerts when a stop-hunt is detected.
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You can set TradingView to notify you instantly when:
A bullish stop-hunt occurs โ Look for long entry.
A bearish stop-hunt occurs โ Look for short entry.
๐ Example Trade Setup
Example (BTC Long Trade on Stop-Hunt)
BTC is near $40,000 support (green liquidity zone).
A long wick drops to $39,800 with a green arrow (bullish stop-hunt signal).
Volume spikes, and price recovers quickly back above $40,000.
Trade entry: Buy at $40,050.
Stop-loss: Below wick ($39,700).
Take-profit: $41,500 (next resistance).
Result: BTC pumps, stop-loss remains safe, and trade profits.
๐ฅ Final Tips
Always wait for confirmation (donโt enter blindly on signals).
Use higher timeframes (15m, 1H, 4H) for better accuracy.
Combine with Order Flow tools (like Bookmap) to see real liquidity zones.
๐ Now try it on TradingView! Let me know if you need adjustments. ๐๐ฅ
ZenAlgo - Aggregated DeltaZenAlgo - Aggregated Delta is an advanced market analysis tool designed to provide traders with a holistic view of market sentiment by leveraging multi-exchange volume aggregation, cumulative delta analysis, and divergence detection. Unlike traditional indicators that rely on a single data source, this tool aggregates order flow data from multiple exchanges, reducing the impact of exchange-specific anomalies and liquidity disparities.
This indicator is ideal for traders looking to enhance their understanding of market dynamics, trend confirmations, and order flow patterns. By intelligently combining multiple analytical components, it eliminates the need for manually interpreting separate indicators and offers traders a streamlined approach to market analysis.
This indicator was inspired by aggregated volume analysis techniques. Independently developed with a focus on cumulative delta and divergence detection.
Key Features & Their Interaction
Multi-Exchange Volume Aggregation: Aggregates buy and sell volumes from up to nine major exchanges, including Binance, Bybit, Coinbase, and Kraken. Unlike traditional single-source indicators, this ensures a robust, diversified measure of market sentiment and smooths out exchange-specific volume fluctuations.
Cumulative Delta Analysis: Tracks the net difference between buy and sell volumes across all aggregated exchanges, helping traders identify true buying/selling pressure rather than misleading short-term volume spikes.
Advanced Divergence Detection: Unlike basic divergence indicators, this tool detects divergences not only between price and cumulative delta but also across multiple analytical layers, including moving averages and temperature zones, offering deeper confirmation signals.
Dynamic Market Temperature Zones: Unlike fixed overbought/oversold indicators, this feature applies adaptive standard deviation-based filtering to classify market conditions dynamically as "Extreme Hot," "Hot," "Neutral," "Cold," and "Extreme Cold."
Intelligent Market State Classification: Determines whether the market is in a Full Bull, Bearish, or Neutral state by analyzing multi-exchange volume flow, cumulative delta positioning, and market-wide liquidity trends.
Real-Time Alerts & Adaptive Visualization: Provides fully configurable real-time alerts for trend shifts, divergences, and market conditions, allowing traders to act immediately on high-confidence signals.
What Makes ZenAlgo - Aggregated Delta Unique?
Unlike free or open-source alternatives, ZenAlgo - Aggregated Delta applies a multi-layered data processing approach to smooth inconsistencies and improve signal reliability. Instead of using raw exchange feeds, the system incorporates adaptive volume aggregation and standard deviation-based market classification to ensure accuracy and reduce noise. These enhancements lead to more precise trend signals and a clearer representation of market sentiment.
Multi-Exchange Order Flow Validation: Unlike single-source indicators that rely on individual exchange feeds, this tool ensures cross-market consistency by aggregating volume data dynamically.
Fractal-Based Divergence Detection: Detects divergences using fractal logic rather than contextual volume trends, reducing false-positive divergence signals while maintaining accuracy.
Automated Sentiment Analysis: Classifies market sentiment into structured phases (Full Bull, Bearish, etc.), reducing the manual effort needed to interpret order flow trends.
How It Works (Technical Breakdown)
Multi-Exchange Volume Aggregation: The system fetches and validates buy/sell volume data from multiple exchanges, applying volume aggregation techniques to smooth out inconsistencies. It ensures that data from low-liquidity exchanges does not disproportionately influence the analysis.
Cumulative Delta Computation: Cumulative delta is computed as the net difference between buy and sell volumes over a given period. By summing up these values across multiple exchanges, traders can identify real accumulation or distribution zones, reducing false signals from isolated exchange anomalies.
Divergence Detection Methodology: The tool uses a fractal-based logic approach to detect high-confidence divergences across price, volume, and delta trends. This allows for a more structured detection process compared to simple peak/trough analysis, reducing noise in the signals.
Temperature Zones Filtering: Market conditions are dynamically classified using a rolling standard deviation model, ensuring that hot/cold states adjust automatically based on recent volatility levels. This means that instead of using arbitrary fixed thresholds, the tool adapts based on historical data behavior.
Market Sentiment State Calculation: The tool evaluates liquidity conditions, volume trends, and cumulative delta flow, categorizing the market into predefined states (Bullish, Bearish, Neutral). This helps traders assess the broader context of price movements rather than reacting to isolated signals.
Real-Time Adaptive Alerts: The system provides fully configurable alerts that notify traders about key trend shifts, high-confidence divergences, and changes in market conditions as they occur. This ensures that traders can make timely and well-informed decisions.
Why This Approach Works
By aggregating data from multiple exchanges, it reduces the impact of exchange-specific liquidity disparities and anomalies, leading to a more holistic view of order flow.
The cumulative delta analysis ensures that price movements are validated by actual buying/selling pressure, filtering out misleading short-term spikes.
Dynamic market classification adapts to current conditions rather than using outdated fixed thresholds, making it more relevant in different market regimes.
Fractal-based divergence detection avoids common pitfalls of traditional divergence analysis, reducing false signals while maintaining accuracy.
Combining real-time adaptive alerts with well-structured classification improves tradersโ ability to respond to market shifts efficiently.
Practical Use Cases
Identifying High-Probability Trend Reversals: If cumulative delta shows bullish divergence while the market is in an Extreme Cold zone, it signals a strong potential for reversal.
Confirming Trend Continuation: When bullish moving average crossovers align with a rising cumulative delta, traders can enter positions with higher confidence.
Detecting Exhaustion in Market Moves: If price enters an "Extreme Hot" zone but cumulative delta starts declining, this suggests trend exhaustion and a possible reversal.
Filtering False Breakouts: If price breaks a resistance level but aggregated buy volume fails to increase, this invalidates the breakout, helping traders avoid bad trades.
Cross-Exchange Sentiment Confirmation: If cumulative delta on aggregated exchanges contradicts price action on an individual exchange, traders can identify localized exchange-based distortions.
Customization & Settings Overview
Exchange Selection: Traders can fine-tune exchange sources for aggregation, allowing for custom market-specific insights.
Adaptive Divergence Settings: Configure detection thresholds, lookback periods, and divergence filtering options to reduce noise and focus on high-confidence signals.
Moving Average Adjustments: Select custom MA types, lengths, and visualization preferences to match different trading styles.
Market Temperature Thresholds: Adjust hot/cold sensitivity to align with preferred risk levels and volatility expectations.
Configurable Alerts & Theme Customization: Full control over notification triggers, color themes, and label formatting to enhance user experience.
Important Considerations
Market Context Dependency: This tool provides order flow analysis, which should be used in conjunction with broader market context and risk management.
Data Source Variability: While multi-exchange aggregation improves reliability, some exchanges may report inaccurate or delayed data.
Extreme Volatility Handling: Large price swings can temporarily distort delta readings, so traders should always validate with additional context.
Liquidity Limitations: In low-liquidity conditions, order flow signals may be less reliable due to fragmented market participation.
