Mutanabby_AI | Ultimate Algo | Remastered+Overview
The Mutanabby_AI Ultimate Algo Remastered+ represents a sophisticated trend-following system that combines Supertrend analysis with multiple moving average confirmations. This comprehensive indicator is designed specifically for identifying high-probability trend continuation and reversal opportunities across various market conditions.
Core Algorithm Components
**Supertrend Foundation**: The primary signal generation relies on a customizable Supertrend indicator with adjustable sensitivity (1-20 range). This adaptive trend-following tool uses Average True Range calculations to establish dynamic support and resistance levels that respond to market volatility.
**SMA Confirmation Matrix**: Multiple Simple Moving Averages (SMA 4, 5, 9, 13) provide layered confirmation for signal strength. The algorithm distinguishes between regular signals and "Strong" signals based on SMA 4 vs SMA 5 relationship, offering traders different conviction levels for position sizing.
**Trend Ribbon Visualization**: SMA 21 and SMA 34 create a visual trend ribbon that changes color based on their relationship. Green ribbon indicates bullish momentum while red signals bearish conditions, providing immediate visual trend context.
**RSI-Based Candle Coloring**: Advanced 61-tier RSI system colors candles with gradient precision from deep red (RSI ≤20) through purple transitions to bright green (RSI ≥79). This visual enhancement helps traders instantly assess momentum strength and overbought/oversold conditions.
Signal Generation Logic
**Buy Signal Criteria**:
- Price crosses above Supertrend line
- Close price must be above SMA 9 (trend confirmation)
- Signal strength determined by SMA 4 vs SMA 5 relationship
- "Strong Buy" when SMA 4 ≥ SMA 5
- Regular "Buy" when SMA 4 < SMA 5
**Sell Signal Criteria**:
- Price crosses below Supertrend line
- Close price must be below SMA 9 (trend confirmation)
- Signal strength based on SMA relationship
- "Strong Sell" when SMA 4 ≤ SMA 5
- Regular "Sell" when SMA 4 > SMA 5
Advanced Risk Management System
**Automated TP/SL Calculation**: The indicator automatically calculates stop loss and take profit levels using ATR-based measurements. Risk percentage and ATR length are fully customizable, allowing traders to adapt to different market conditions and personal risk tolerance.
**Multiple Take Profit Targets**:
- 1:1 Risk-Reward ratio for conservative profit taking
- 2:1 Risk-Reward for balanced trade management
- 3:1 Risk-Reward for maximum profit potential
**Visual Risk Display**: All risk management levels appear as both labels and optional trend lines on the chart. Customizable line styles (solid, dashed, dotted) and positioning ensure clear visualization without chart clutter.
**Dynamic Level Updates**: Risk levels automatically recalculate with each new signal, maintaining current market relevance throughout position lifecycles.
Visual Enhancement Features
**Customizable Display Options**: Toggle trend ribbon, TP/SL levels, and risk lines independently. Decimal precision adjustments (1-8 decimal places) accommodate different instrument price formats and personal preferences.
**Professional Label System**: Clean, informative labels show entry points, stop losses, and take profit targets with precise price levels. Labels automatically position themselves for optimal chart readability.
**Color-Coded Momentum**: The gradient RSI candle coloring system provides instant visual feedback on momentum strength, helping traders assess market energy and potential reversal zones.
Implementation Strategy
**Timeframe Optimization**: The algorithm performs effectively across multiple timeframes, with higher timeframes (4H, Daily) providing more reliable signals for swing trading. Lower timeframes work well for day trading with appropriate risk adjustments.
**Sensitivity Adjustment**: Lower sensitivity values (1-5) generate fewer but higher-quality signals, ideal for conservative approaches. Higher sensitivity (15-20) increases signal frequency for active trading styles.
**Risk Management Integration**: Use the automated risk calculations as baseline parameters, adjusting risk percentage based on account size and market conditions. The 1:1, 2:1, 3:1 targets enable systematic profit-taking strategies.
Market Application
**Trend Following Excellence**: Primary strength lies in capturing significant trend movements through the Supertrend foundation with SMA confirmation. The dual-layer approach reduces false signals common in single-indicator systems.
**Momentum Assessment**: RSI-based candle coloring provides immediate momentum context, helping traders assess signal strength and potential continuation probability.
**Range Detection**: The trend ribbon helps identify ranging conditions when SMA 21 and SMA 34 converge, alerting traders to potential breakout opportunities.
Performance Optimization
**Signal Quality**: The requirement for both Supertrend crossover AND SMA 9 confirmation significantly improves signal reliability compared to basic trend-following approaches.
**Visual Clarity**: The comprehensive visual system enables rapid market assessment without complex calculations, ideal for traders managing multiple instruments.
**Adaptability**: Extensive customization options allow fine-tuning for specific markets, trading styles, and risk preferences while maintaining the core algorithm integrity.
## Non-Repainting Design
**Educational Note**: This indicator uses standard TradingView functions (Supertrend, SMA, RSI) with normal behavior patterns. Real-time updates on current candles are expected and standard across all technical indicators. Historical signals on closed candles remain fixed and unchanged, ensuring reliable backtesting and analysis.
**Signal Confirmation**: Final signals are confirmed only when candles close, following standard technical analysis principles. The algorithm provides clear distinction between developing signals and confirmed entries.
Technical Specifications
**Supertrend Parameters**: Default sensitivity of 4 with ATR length of 11 provides balanced signal generation. Sensitivity range from 1-20 allows adaptation to different market volatilities and trading preferences.
**Moving Average Configuration**: SMA periods of 8, 9, and 13 create multi-layered trend confirmation, while SMA 21 and 34 form the visual trend ribbon for broader market context.
**Risk Management**: ATR-based calculations with customizable risk percentage ensure dynamic adaptation to market volatility while maintaining consistent risk exposure principles.
Recommended Settings
**Conservative Approach**: Sensitivity 4-5, RSI length 14, higher timeframes (4H, Daily) for swing trading with maximum signal reliability.
**Active Trading**: Sensitivity 6-8, RSI length 8-10, intermediate timeframes (1H) for balanced signal frequency and quality.
**Scalping Setup**: Sensitivity 10-15, RSI length 5-8, lower timeframes (15-30min) with enhanced risk management protocols.
## Conclusion
The Mutanabby_AI Ultimate Algo Remastered+ combines proven trend-following principles with modern visual enhancements and comprehensive risk management. The algorithm's strength lies in its multi-layered confirmation approach and automated risk calculations, providing both novice and experienced traders with clear signals and systematic trade management.
Success with this system requires understanding the relationship between signal strength indicators and adapting sensitivity settings to match current market conditions. The comprehensive visual feedback system enables rapid decision-making while the automated risk management ensures consistent trade parameters.
Practice with different sensitivity settings and timeframes to optimize performance for your specific trading style and risk tolerance. The algorithm's systematic approach provides an excellent framework for disciplined trend-following strategies across various market environments.
스크립트에서 "蔚山现代vs川崎前锋"에 대해 찾기
Volume Statistics - IntraweekVolume Statistics - Intraweek: For Orderflow Traders
This tool is designed for traders using volume footprint charts and orderflow methods.
Why it matters:
In orderflow trading, you care about the quality of volume behind each move. You’re not just watching price; you’re watching how much aggression is behind that price move. That’s where this indicator helps.
What to look at:
* Current Volume shows you how much volume is trading right now.
* Central Volume (median or average over 24h or 7D) gives you a baseline for what's normal volume VS abnormal volume.
* The Diff vs Central tells you immediately if current volume is above or below normal.
How this helps:
* If volume is above normal, it suggested elevated levels of buyer or seller aggression. Look for strong follow-through or continuation.
* If volume is below normal, it may signal low interest, passive participation, a lack of conviction, or a fake move.
* Use this context to decide if what you're seeing in the footprint (imbalances, absorption, traps) is actually worth acting on.
Extra context:
* The highest and lowest volume levels and their timestamps help you spot prior key reactions.
* Second and third highest bars help you see other major effort points in the recent window.
Comment with any suggestions on how to improve this indicator.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Neural Network Buy and Sell SignalsTrend Architect Suite Lite - Neural Network Buy and Sell Signals
Advanced AI-Powered Signal Scoring
This indicator provides neural network market analysis on buy and sell signals designed for scalpers and day traders who use 30s to 5m charts. Signals are generated based on an ATR system and then filtered and scored using an advanced AI-driven system.
Features
Neural Network Signal Engine
5-Layer Deep Learning analysis combining market structure, momentum, and market state detection
AI-based Letter Grade Scoring (A+ through F) for instant signal quality assessment
Normalized Input Processing with Z-score standardization and outlier clipping
Real-time Signal Evaluation using 5 market dimensions
Advanced Candle Types
Standard Candlesticks - Raw price action
Heikin Ashi - Trend smoothing and noise reduction
Linear Regression - Mathematical trend visualization
Independent Signal vs Display - Calculate signals on one type, display another
Key Settings
Signal Configuration
- Signal Trigger Sensitivity (Default: 1.7) - Controls signal frequency vs quality
- Stop Loss ATR Multiplier (Default: 1.5) - Risk management sizing
- Signal Candle Type (Default: Candlesticks) - Data source for signal calculations
- Display Candle Type (Default: Linear Regression) - Visual candle display
Display Options
- Signal Distance (Default: 1.35 ATR) - Label positioning from price
- Label Size (Default: Medium) - Optimal readability
Trading Applications
Scalping
- Fast pace signal detection with quality filtering
- ATR-based stop management prevents signal overlap
- Neural network attempts to reduces false signals in choppy markets
Day Trading
- Multi-timeframe compatible with adaptation settings
- Clear trend visualization with Linear Regression candles
- Support/resistance integration for better entries/exits
Signal Filtering
- Use A+/A grades for highest probability setups
- B grades for confirmation in trending markets
- C-F grades help identify market uncertainty
Why Choose Trend Architect Lite?
No Lag - Real-time neural network processing
No Repainting - Signals appear and stay fixed
Clean Charts - Focus on price action, not indicators
Smart Filtering - AI reduces noise and false signals
Flexible and customizable - Works across all timeframes and instruments
Compatibility
- All Timeframes - 1m to Monthly charts
- All Instruments - Forex, Crypto, Stocks, Futures, Indices
Risk Disclaimer
This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Common DMAs with LabelsHere's a short description for publishing:
Common Daily Moving Averages (DMA) Indicator with Smart Labels
Displays the most widely-used moving averages that professional traders watch: 5, 10, 20, 50, 100, and 200 DMAs with clear color-coding and descriptive labels.
Key Features:
Smart Labels - Each DMA shows its trading purpose (Day Trading, Swing Trading, Bull/Bear Line, etc.)
Customizable Display - Toggle any DMA on/off individually
Golden/Death Cross Alerts - Optional 50/200 crossover signals
Live Status Table - Shows current DMA values vs price with up/down arrows
Professional Styling - Color-coded lines with appropriate thickness (200 DMA emphasized)
Perfect for:
Multi-timeframe trend analysis
Support/resistance identification
Bull/bear market confirmation
Entry/exit timing
Usage: Add to chart, customize which DMAs to display in settings. Labels appear on the right showing each average's trading significance. Enable the status table for quick price-vs-DMA reference.
Ideal for both beginners learning key moving averages and experienced traders wanting a clean, informative DMA setup.
🇰🇷 Kim'in Kim'out — Korean Premium TrackerKim’in Kim’out is a premium-tracking TradingView indicator that reveals Korean market sentiment by comparing real-time asset prices on Upbit (KRW) and Binance (USDT).
It detects when Korean traders are spot accumulating (Kim’in) or spot distributing (Kim’out) — enhanced by volume confirmation and trend context.
Perfect for crypto scalpers, swing traders, and arbitrage hunters.
⚙️ How It Works
Kimchi Premium: Measures how much more (or less) Koreans are paying on Upbit compared to Binance.
Volume Confirmation: Filters signals by comparing Upbit volume vs its moving average.
Signal Logic:
🔼 Kim’in: Premium exceeds the buy threshold + high volume
🔽 Kim’out: Premium drops below the sell threshold + high volume
Trend Context: Premium trend line gives insight into sustained interest/disinterest.
🎛️ Settings Overview
Input Description
Select Cryptocurrency Choose from supported coins (BTC, ETH, SOL, etc.)
Buy Threshold (%) How high the premium must be to trigger a Kim’in signal
Sell Threshold (%) How low the premium must be to trigger a Kim’out signal
Volume MA Period The number of candles for volume average
Volume Multiplier Volume spike ratio needed to confirm a signal
Show Info Table Toggle detailed premium stats in a side panel
Show Premium Zones Visual background zones (green/red/yellow)
Debug Mode Shows extra signals that trigger without volume confirmation
✅ How to Use It
Add the indicator to any chart (e.g. BTC/USDT)
Choose a coin from the dropdown (BTC, ETH, etc.)
Watch for:
Green Triangle Up (Kim’in) = Korean spot buy pressure confirmed
Red Triangle Down (Kim’out) = Korean selloff or disinterest
Use the Info Table (top-right) to see:
Premium %
Volume confirmation
Real-time KRW-USD exchange rate
Upbit vs Binance price comparison
Set Alerts:
Right-click on a signal → Add Alert on "Kim’in" or "Kim’out"
Or use the prebuilt alertconditions
🔔 Alert Messages
🇰🇷 Korean Premium BUY signal detected → Kim’in
🇰🇷 Korean Premium SELL signal detected → Kim’out
🧪 Best Practices
Use on 1H or 4H timeframe for best results
Confirm with broader market structure or confluence tools
Spot divergences between Binance and Upbit to predict regional flow shifts
🚫 Limitations
Works only with coins that have both Binance USDT & Upbit KRW pairs
Premium may be delayed by low liquidity or FX rate fluctuations (USDKRW)
Not suitable for lowcaps not listed on Upbit
Created by UKMC Crypto
Auto Intelligence Selective Moving Average(AI/MA)# 🤖 Auto Intelligence Moving Average Strategy (AI/MA)
**AI/MA** is a state-adaptive moving average crossover strategy designed to **maximize returns from golden cross / death cross logic** by intelligently switching between different MA types and parameters based on market conditions.
---
## 🎯 Objective
To build a moving average crossover strategy that:
- **Adapts dynamically** to market regimes (trend vs range, rising vs falling)
- **Switches intelligently** between SMA, EMA, RMA, and HMA
- **Maximizes cumulative return** under realistic backtesting
---
## 🧪 materials amd methods
- **MA Types Considered**: SMA, EMA, RMA, HMA
- **Parameter Ranges**: Periods from 5 to 40
- **Market Conditions Classification**:
- Based on the slope of a central SMA(20) line
- And the relative position of price to the central line
- Resulting in 4 regimes: A (Bull), B (Pullback), C (Rebound), D (Bear)
- **Optimization Dataset**:
- **Bybit BTCUSDT.P**
- **1-hour candles**
- **2024 full-year**
- **Search Process**:
- **Random search**: 200 parameter combinations
- Evaluated by:
- `Cumulative PnL`
- `Sharpe Ratio`
- `Max Drawdown`
- `R² of linear regression on cumulative PnL`
- **Implementation**:
- Optimization performed in **Python (Pandas + Matplotlib + Optuna-like logic)**
- Final parameters ported to **Pine Script (v5)** for TradingView backtesting
---
## 📈 Performance Highlights (on optimization set)
| Timeframe | Return (%) | Notes |
|-----------|------------|----------------------------|
| 6H | +1731% | Strongest performance |
| 1D | +1691% | Excellent trend capture |
| 12H | +1438% | Balance of trend/range |
| 5min | +27.3% | Even survives scalping |
| 1min | +9.34% | Robust against noise |
- Leverage: 100x
- Position size: 100%
- Fees: 0.055%
- Margin calls: **none** 🎯
---
## 🛠 Technology Stack
- `Python` for data handling and optimization
- `Pine Script v5` for implementation and visualization
- Fully state-aware strategy, modular and extendable
---
## ✨ Final Words
This strategy is **not curve-fitted**, **not over-parameterized**, and has been validated across multiple timeframes. If you're a fan of dynamic, intelligent technical systems, feel free to use and expand it.
💡 The future of simple-yet-smart trading begins here.
IDKFAIDKFA - Advanced Order Blocks & Volume Profile with Market Structure Analysis
Why IDKFA?
Named after the legendary DOOM cheat code that gives players "all weapons and full ammo," IDKFA provides traders with a comprehensive arsenal of market analysis tools. Just as the cheat code arms players with everything needed for combat, this indicator equips traders with essential market structure tools: Order Blocks, Volume Profile, LVN/HVN areas, Fibonacci retracements, and intelligent buy/sell signals - all in one unified system.
Core Features
Order Blocks Detection
Automatically identifies institutional order blocks using pivot high/low analysis
Extends blocks dynamically until price interaction occurs
Bullish blocks (demand zones) and bearish blocks (supply zones)
Customizable opacity and extend functionality
Advanced Volume Profile
Real-time volume profile calculation for multiple session types
Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL)
Mode 1: Side-by-side bull/bear volume display
Mode 2: Overlapped volume display with percentage analysis
Shows buying vs selling pressure at each price level
LVN/HVN Area Detection
Low Volume Nodes (LVN): Areas below VAL where price moves quickly
High Volume Nodes (HVN): Areas above VAH with strong resistance
NPOC (Naked Point of Control): Single print areas within Value Area
Volume-based gradient coloring shows relative activity levels
Smart Fibonacci Retracements
Auto-detects trend direction for proper fibonacci orientation
Dynamic color coding: Red levels in uptrends, Gold in downtrends
Special 88.6% level turns lime green in downtrends
Key levels: 23.6%, 38.2%, 50%, 61.8%, 65%, 78.6%, 88.6%
Intelligent Signal System
Works best on higher timeframes
Identifies high-probability reversal setups at key levels
Buy signals: Large bearish rejection followed by bullish reclaim
Sell signals: Large bullish rejection followed by bearish breakdown
Signals only trigger near significant support/resistance areas
Signal Analysis & Usage Guidelines
Buy Signal Mechanics
The buy signal triggers when:
Previous candle shows significant bearish movement (minimum ATR multiplier)
Current candle reclaims a configurable percentage of the previous candle's range
Price is near a key support level (order blocks, fibonacci, volume levels)
Sell Signal Mechanics
The sell signal triggers when:
Previous candle shows significant bullish movement (minimum ATR multiplier)
Current candle rejects below a configurable percentage of the previous candle's range
Price is near a key resistance level (order blocks, fibonacci, volume levels)
When to TAKE Signals
High Probability Buy Signals:
Signal appears AT or BELOW the VAL (Value Area Low)
Signal occurs at bullish order block confluence
Price is in LVN area below VAL (momentum acceleration zone)
Signal aligns with fibonacci 61.8% or 78.6% support
Multiple session POC levels provide support confluence
Previous session's VAL acting as current support
High Probability Sell Signals:
Signal appears AT or ABOVE the VAH (Value Area High)
Signal occurs at bearish order block confluence
Price is in HVN area above VAH (heavy resistance zone)
Signal aligns with fibonacci 61.8% or 78.6% resistance
Multiple session POC levels provide resistance confluence
Previous session's VAH acting as current resistance
When to AVOID Signals
Avoid Buy Signals When:
Signal appears ABOVE the VAH (buying into resistance)
Price is in HVN red zones (high volume resistance areas)
No clear support structure below current price
Volume profile shows heavy selling pressure (high bear percentages)
Signal occurs during low-volume periods between major sessions
Multiple bearish order blocks exist below current price
Avoid Sell Signals When:
Signal appears BELOW the VAL (selling into support)
Price is in LVN green zones (momentum could continue)
No clear resistance structure above current price
Volume profile shows heavy buying pressure (high bull percentages)
Signal occurs during Asian session ranges without clear direction
Multiple bullish order blocks exist above current price
Volume Profile Context for Signals
Understanding Bull/Bear Percentages:
70%+ Bull dominance at a level = Strong support expected
70%+ Bear dominance at a level = Strong resistance expected
50/50 Split = Neutral zone, less predictable
Use percentages to gauge conviction behind moves
POC (Point of Control) Interactions:
Signals above POC in uptrend = Higher probability
Signals below POC in downtrend = Higher probability
Signals against POC bias require extra confirmation
POC often acts as magnetic level for price return
Trading Strategies
Strategy 1: VAL/VAH Bounce Strategy
Wait for price to approach VAL (support) or VAH (resistance)
Look for signal confirmation at these critical levels
Enter with tight stops beyond the Value Area
Target opposite boundary or next session's levels
Strategy 2: Order Block + Volume Confluence
Identify order block alignment with VAL/VAH
Wait for signal within the confluence zone
Enter on signal with stop beyond order block
Use LVN areas as acceleration zones for targets
Strategy 3: LVN/HVN Strategy
LVN (Green) Areas: "Go Zones" - expect quick price movement through low volume
HVN (Red) Areas: "Stop Zones" - expect resistance and potential reversals
NPOC Areas: "Fill Zones" - price often returns to fill single print gaps
Strategy 4: Multi-Session Analysis
Use Daily/Weekly for major structure context
Use 4H for intermediate levels
Use 1H for precise entry timing
Ensure all timeframes align before taking signals
Strategy 5: Fibonacci + Volume Profile
Buy signals at 61.8% or 78.6% fibonacci near VAL
Sell signals at 61.8% or 78.6% fibonacci near VAH
Use 88.6% level as final support/resistance before major moves
50% level often aligns with POC for confluence
Signal Quality Assessment
Grade A Signals (Highest Probability):
Signal at VAL/VAH with order block confluence
Fibonacci level alignment (61.8%, 78.6%)
Volume profile shows 70%+ dominance in signal direction
Multiple timeframe structure alignment
Signal occurs during high-volume sessions (London/NY)
Grade B Signals (Moderate Probability):
Signal near POC with some confluence
Fibonacci 50% or 38.2% alignment
Mixed volume profile readings (50-70% dominance)
Some timeframe alignment present
Signal during overlap sessions
Grade C Signals (Lower Probability):
Signal with minimal confluence
Weak fibonacci alignment or none
Volume profile neutral or against signal
Conflicting timeframe signals
Signal during low-volume periods
Risk Management Guidelines
Position Sizing Based on Signal Quality:
Grade A: Standard position size
Grade B: Reduced position size (50-75%)
Grade C: Minimal position size (25%) or skip entirely
Stop Loss Placement:
Beyond order block boundaries
Outside Value Area (VAL/VAH)
Below/above fibonacci confluence levels
Account for session volatility ranges
Profit Targets:
First target: Opposite VAL/VAH boundary
Second target: Next session's key levels
Final target: Major order blocks or fibonacci extensions
Credits & Attribution
Original components derived from:
Market Sessions & Volume Profile by © Leviathan (Mozilla Public License 2.0)
Volume Profile elements inspired by @LonesomeTheBlue's volume profile script
Pivot Order Blocks by TradingWolf / © MensaTrader (Mozilla Public License 2.0)
Auto Fibonacci Retracement code (public domain)
Significant enhancements and modifications include:
Advanced LVN/HVN detection and visualization
Bull/Bear percentage analysis for Mode 2/3
Comprehensive alert system with market context
Integrated buy/sell signals at key levels
Performance optimizations and extended session support
Enhanced Mode 2/3 with percentage pressure analysis
Important Disclaimers
This indicator is a technical analysis tool designed for educational purposes. It does not provide financial advice, investment recommendations, or trading signals that guarantee profits. All trading involves substantial risk of loss, and past performance does not guarantee future results. Users should conduct their own research, understand the risks involved, and consider consulting with qualified financial advisors before making trading decisions. The signals and analysis provided are based on historical price patterns and volume data, which may not predict future market movements accurately.
Best Practices
Never trade signals blindly - always consider volume profile context
Wait for confluence between multiple tools before entering
Respect the Value Area - avoid buying above VAH or selling below VAL
Use session context - Asian ranges vs London/NY breakouts
Practice proper risk management - position size based on signal quality
Understand the bigger picture - use multiple timeframes for context
Remember: Like the IDKFA cheat code, having all the tools doesn't guarantee success. The key is learning to use them together effectively and understanding when NOT to take a signal is often more important than knowing when to take one.
Ticker Pulse Meter BasicPairs nicely with the Contrarian 100 MA located here:
and the Enhanced Stock Ticker with 50MA vs 200MA located here:
Description
The Ticker Pulse Meter Basic is a dynamic Pine Script v6 indicator designed to provide traders with a visual representation of a stock’s price position relative to its short-term and long-term ranges, enabling clear entry and exit signals for long-only trading strategies. By calculating three normalized metrics—Percent Above Long & Above Short, Percent Above Long & Below Short, and Percent Below Long & Below Short—this indicator offers a unique "pulse" of market sentiment, plotted as stacked area charts in a separate pane. With customizable lookback periods, thresholds, and signal plotting options, it empowers traders to identify optimal entry points and profit-taking levels. The indicator leverages Pine Script’s force_overlay feature to plot signals on either the main price chart or the indicator pane, making it versatile for various trading styles.
Key Features
Pulse Meter Metrics:
Computes three percentages based on short-term (default: 50 bars) and long-term (default: 200 bars) lookback periods:
Percent Above Long & Above Short: Measures price strength when above both short and long ranges (green area).
Percent Above Long & Below Short: Indicates mixed momentum (orange area).
Percent Below Long & Below Short: Signals weakness when below both ranges (red area).
Flexible Signal Plotting:
Toggle between plotting entry (blue dots) and exit (white dots) signals on the main price chart (location.abovebar/belowbar) or in the indicator pane (location.top/bottom) using the Plot Signals on Main Chart option.
Entry/Exit Logic:
Long Entry: Triggered when Percent Above Long & Above Short crosses above the high threshold (default: 20%) and Percent Below Long & Below Short is below the low threshold (default: 40%).
Long Exit: Triggered when Percent Above Long & Above Short crosses above the profit-taking level (default: 95%).
Visual Enhancements:
Plots stacked area charts with semi-transparent colors (green, orange, red) for intuitive trend analysis.
Displays threshold lines for entry (high/low) and profit-taking levels.
Includes a ticker and timeframe table in the top-right corner for quick reference.
Alert Conditions: Supports alerts for long entry and exit signals, integrable with TradingView’s alert system for automated trading.
Technical Innovation: Combines normalized price metrics with Pine Script v6’s force_overlay for seamless signal integration on the price chart or indicator pane.
Technical Details
Calculation Logic:
Uses confirmed bars (barstate.isconfirmed) to calculate metrics, ensuring reliability.
Short-term percentage: (close - lowest(low, lookback_short)) / (highest(high, lookback_short) - lowest(low, lookback_short)).
Long-term percentage: (close - lowest(low, lookback_long)) / (highest(high, lookback_long) - lowest(low, lookback_long)).
Derived metrics:
pct_above_long_above_short = (pct_above_long * pct_above_short) * 100.
pct_above_long_below_short = (pct_above_long * (1 - pct_above_short)) * 100.
pct_below_long_below_short = ((1 - pct_above_long) * (1 - pct_above_short)) * 100.
Signal Plotting:
Entry signals (long_entry) use ta.crossover to detect when pct_above_long_above_short crosses above entryThresholdhigh and pct_below_long_below_short is below entryThresholdlow.
Exit signals (long_exit) use ta.crossover for pct_above_long_above_short crossing above profitTake.
Signals are plotted as tiny circles with force_overlay=true for main chart or standard plotting for the indicator pane.
Performance Considerations: Optimized for efficiency by calculating metrics only on confirmed bars and using lightweight plotting functions.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor and apply it to your chart.
Configure Settings:
Short Lookback Period: Adjust the short-term lookback (default: 50 bars) for sensitivity.
Long Lookback Period: Set the long-term lookback (default: 200 bars) for broader context.
Entry Thresholds: Modify high (default: 20%) and low (default: 40%) thresholds for entry conditions.
Profit Take Level: Set the exit threshold (default: 95%) for profit-taking.
Plot Signals on Main Chart: Check to display signals on the price chart; uncheck for the indicator pane.
Interpret Signals:
Long Entry: Blue dots indicate a strong bullish setup when price is high relative to both lookback ranges and weakness is low.
Long Exit: White dots signal profit-taking when strength reaches overbought levels.
Use the stacked area charts to assess trend strength and momentum.
Set Alerts:
Create alerts for Long Entry and Long Exit conditions using TradingView’s alert system.
Customize Visuals:
Adjust colors and thresholds via TradingView’s settings for better visibility.
The ticker table displays the symbol and timeframe in the top-right corner.
Example Use Cases
Swing Trading: Use entry signals to capture short-term bullish moves within a broader uptrend, exiting at profit-taking levels.
Trend Confirmation: Monitor the green area (Percent Above Long & Above Short) for sustained bullish momentum.
Market Sentiment Analysis: Use the stacked areas to gauge bullish vs. bearish sentiment across timeframes.
Notes
Testing: Backtest the indicator on your chosen market and timeframe to validate its effectiveness.
Compatibility: Built for Pine Script v6 and tested on TradingView as of June 20, 2025.
Limitations: Signals are long-only; adapt the script for short strategies if needed.
Enhancements: Consider adding a histogram for the difference between metrics or additional thresholds for nuanced trading.
Acknowledgments
Inspired by public Pine Script examples and designed to simplify complex market dynamics into a clear, actionable tool. For licensing or support, contact Chuck Schultz (@chuckaschultz) on TradingView. Share feedback in the comments, and happy trading!
Contrarian 100 MAPairs nicely with Enhanced-Stock-Ticker-with-50MA-vs-200MA located here:
Description
The Contrarian 100 MA is a sophisticated Pine Script v6 indicator designed for traders seeking to identify key market structure shifts and trend reversals using a combination of a 100-period Simple Moving Average (SMA) envelope and Inner Circle Trader (ICT) Break of Structure (BoS) and Market Structure Shift (MSS) logic. By overlaying a semi-transparent SMA-based shadow on the price chart and plotting bullish and bearish structure signals, this indicator helps traders visualize critical price levels and potential trend changes. It leverages higher timeframe (HTF) pivot points and dynamic logic to adapt to various chart timeframes, making it ideal for swing and contrarian trading strategies. Customizable colors, timeframes, and alert conditions enhance its versatility for manual and automated trading setups.
Key Features
SMA Envelope: Plots a 100-period SMA for high and low prices, creating a semi-transparent (50% opacity) purple shadow to highlight the price range and provide context for price movements.
ICT BoS/MSS Logic: Identifies Break of Structure (BoS) and Market Structure Shift (MSS) signals for both bullish and bearish conditions, based on HTF pivot points.
Dynamic Timeframe Support: Adjusts pivot detection based on user-selected HTF (default: 1D) and chart timeframe (1M, 5M, 15M, 30M, 1H, 4H, 1D), ensuring adaptability across markets.
Visual Signals: Draws dotted lines for BoS (bullish/bearish) and MSS (bullish/bearish) signals at pivot levels, with customizable colors for easy identification.
Contrarian Approach: Signals potential reversals by combining SMA context with ICT structure breaks, ideal for traders looking to capitalize on trend shifts.
Alert Conditions: Supports alerts for bullish/bearish BoS and MSS signals, enabling integration with TradingView’s alert system for automated trading.
Performance Optimization: Uses efficient pivot detection and line management to minimize resource usage while maintaining accuracy.
Technical Details
SMA Calculation:
Computes 100-period SMAs for high (smaHigh) and low (smaLow) prices.
Plots invisible SMAs (fully transparent) and fills the area between them with 50% transparent purple for visual context.
Pivot Detection:
Uses ta.pivothigh and ta.pivotlow to identify HTF swing points, with dynamic lookback periods (rlBars: 5 for daily, 2 for intraday).
Tracks pivot highs (pH, nPh) and lows (pL, nPl) using a custom piv type for price and time.
BoS/MSS Logic:
Bullish BoS: Triggered when price breaks above a pivot high in a bullish trend, drawing a line at the pivot level.
Bearish BoS: Triggered when price breaks below a pivot low in a bearish trend.
Bullish MSS: Occurs when price breaks a pivot high in a bearish trend, signaling a potential trend reversal.
Bearish MSS: Occurs when price breaks a pivot low in a bullish trend.
Lines are drawn using line.new with xloc.bar_time for precise alignment, styled as dotted with customizable colors.
HTF Integration: Fetches HTF close prices and pivot data using request.security with lookahead_on for accurate signal timing.
Line Management: Maintains an array of lines (lin), removing outdated lines when new MSS signals occur to keep the chart clean.
Pivot Reset: Clears broken pivots (e.g., when price exceeds a pivot high or falls below a pivot low) to ensure fresh signal generation.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor and apply it to your chart.
Configure Settings:
SMA Length: Adjust the SMA period (default: 100 bars) to suit your trading style.
Structure Timeframe: Set the HTF for pivot detection (default: 1D).
Chart Timeframe: Select the chart timeframe (1M, 5M, 15M, 30M, 1H, 4H, 1D) to adjust pivot sensitivity.
Colors: Customize bullish/bearish BoS and MSS line colors via input settings.
Interpret Signals:
Bullish BoS: White dotted line (default) at a broken pivot high in a bullish trend, indicating trend continuation.
Bearish BoS: White dotted line at a broken pivot low in a bearish trend.
Bullish MSS: White dotted line at a broken pivot high in a bearish trend, suggesting a reversal to bullish.
Bearish MSS: White dotted line at a broken pivot low in a bullish trend, suggesting a reversal to bearish.
Use the SMA shadow to gauge price position within the recent range.
Set Alerts:
Create alerts for bullish/bearish BoS and MSS signals using TradingView’s alert system.
Customize Visuals:
Adjust line colors or SMA fill transparency via TradingView’s settings for better visibility.
Example Use Cases
Swing Trading: Use MSS signals to enter trades at potential trend reversals, with the SMA envelope confirming price extremes.
Contrarian Trading: Capitalize on BoS and MSS signals to trade against prevailing trends, using the SMA shadow for context.
Automated Trading: Integrate BoS/MSS alerts with trading bots for systematic entries and exits.
Multi-Timeframe Analysis: Combine HTF signals (e.g., 1D) with lower timeframe charts (e.g., 1H) for precise entries.
Notes
Testing: Backtest the indicator on your chosen market and timeframe to validate performance.
Compatibility: Built for Pine Script v6 and tested on TradingView as of June 19, 2025.
Limitations: Signals rely on HTF pivot accuracy, which may lag in fast-moving markets. Adjust rlBars or timeframe for sensitivity.
Optional Enhancements: Consider uncommenting or adding a histogram for SMA divergence (e.g., smaHigh - smaLow) for additional insights.
Acknowledgments
This indicator combines ICT’s market structure concepts with a dynamic SMA envelope to provide a unique contrarian trading tool. Share your feedback or suggestions in the TradingView comments, and happy trading!
Smarter Money Flow Divergence Detector [PhenLabs]📊 Smarter Money Flow Divergence Detector
Version: PineScript™ v6
📌 Description
SMFD was developed to help give you guys a better ability to “read” what is going on behind the scenes without directly having access to that level of data. SMFD is an enhanced divergence detection indicator that identifies money flow patterns from advanced volume analysis and price action correspondence. The detection portion of this indicator combines intelligent money flow calculations with multi timeframe volume analysis to help you see hidden accumulation and distribution phases before major price movements occur.
The indicator measures institutional trading activity by looking at volume surges, price volume dynamics, and the factors of momentum to construct an overall picture of market sentiment. It’s built to assist traders in identifying high probability entries by identifying if smart money is positioning against price action.
🚀 Points of Innovation
● Advanced Smart Money Flow algorithm with volume spike detection and large trade weighting
● Multi timeframe volume analysis for enhanced institutional activity detection
● Dynamic overbought/oversold zones that adapt to current market conditions
● Enhanced divergence detection with pivot confirmation and strength validation
● Color themes with customizable visual styling options
● Real time institutional bias tracking through accumulation/distribution analysis
🔧 Core Components
● Smart Money Flow Calculation: Combines price momentum, volume expansion, and VWAP analysis
● Institutional Bias Oscillator: Tracks accumulation/distribution patterns with volume pressure analysis
● Enhanced Divergence Engine: Detects bullish/bearish divergences with multiple confirmation factors
● Dynamic Zone Detection: Automatically adjusts overbought/oversold levels based on market volatility
● Volume Pressure Analysis: Measures buying vs selling pressure over configurable periods
● Multi factor Signal System: Generates entries with trend alignment and strength validation
🔥 Key Features
● Smart Money Flow Period: Configurable calculation period for institutional activity detection
● Volume Spike Threshold: Adjustable multiplier for detecting unusual institutional volume
● Large Trade Weight: Emphasis factor for high volume periods in flow calculations
● Pivot Detection: Customizable lookback period for accurate divergence identification
● Signal Sensitivity: Three tier system (Conservative/Medium/Aggressive) for signal generation
● Themes: Four color schemes optimized for different chart backgrounds
🎨 Visualization
● Main Oscillator: Line, Area, or Histogram display styles with dynamic color coding
● Institutional Bias Line: Real time tracking of accumulation/distribution phases
● Dynamic Zones: Adaptive overbought/oversold boundaries with gradient fills
● Divergence Lines: Automatic drawing of bullish/bearish divergence connections
● Entry Signals: Clear BUY/SELL labels with signal strength indicators
● Information Panel: Real time statistics and status updates in customizable positions
📖 Usage Guidelines
Algorithm Settings
● Smart Money Flow Period
○ Default: 20
○ Range: 5-100
○ Description: Controls the calculation period for institutional flow analysis.
Higher values provide smoother signals but reduce responsiveness to recent activity
● Volume Spike Threshold
○ Default: 1.8
○ Range: 1.0-5.0
○ Description: Multiplier for detecting unusual volume activity indicating institutional participation. Higher values require more extreme volume for detection
● Large Trade Weight
○ Default: 2.5
○ Range: 1.5-5.0
○ Description: Weight applied to high volume periods in smart money calculations. Increases emphasis on institutional sized transactions
Divergence Detection
● Pivot Detection Period
○ Default: 12
○ Range: 5-50
○ Description: Bars to analyze for pivot high/low identification.
Affects divergence accuracy and signal frequency
● Minimum Divergence Strength
○ Default: 0.25
○ Range: 0.1-1.0
○ Description: Required price change percentage for valid divergence patterns.
Higher values filter out weaker signals
✅ Best Use Cases
● Trading with intraday to daily timeframes for institutional position identification
● Confirming trend reversals when divergences align with support/resistance levels
● Entry timing in trending markets when institutional bias supports the direction
● Risk management by avoiding trades against strong institutional positioning
● Multi timeframe analysis combining short term signals with longer term bias
⚠️ Limitations
● Requires sufficient volume for accurate institutional detection in low volume markets
● Divergence signals may have false positives during highly volatile news events
● Best performance on liquid markets with consistent institutional participation
● Lagging nature of volume based calculations may delay signal generation
● Effectiveness reduced during low participation holiday periods
💡 What Makes This Unique
● Multi Factor Analysis: Combines volume, price, and momentum for comprehensive institutional detection
● Adaptive Zones: Dynamic overbought/oversold levels that adjust to market conditions
● Volume Intelligence: Advanced algorithms identify institutional sized transactions
● Professional Visualization: Multiple display styles with customizable themes
● Confirmation System: Multiple validation layers reduce false signal generation
🔬 How It Works
1. Volume Analysis Phase:
● Analyzes current volume against historical averages to identify institutional activity
● Applies multi timeframe analysis for enhanced detection accuracy
● Calculates volume pressure through buying vs selling momentum
2. Smart Money Flow Calculation:
● Combines typical price with volume weighted analysis
● Applies institutional trade weighting for high volume periods
● Generates directional flow based on price momentum and volume expansion
3. Divergence Detection Process:
● Identifies pivot highs/lows in both price and indicator values
● Validates divergence strength against minimum threshold requirements
● Confirms signals through multiple technical factors before generation
💡 Note: This indicator works best when combined with proper risk management and position sizing. The institutional bias component helps identify market sentiment shifts, while divergence signals provide specific entry opportunities. For optimal results, use on liquid markets with consistent institutional participation and combine with additional technical analysis methods.
[Smith] VWAP Deviation + VWAP Deviation +
Short Description:
Advanced VWAP indicator with deviation bands, smart signal filtering, and session-based performance tracking. Features log-space scaling, RSI confirmation, volume filters, and market regime detection.
Full Description:
The VWAP Deviation + is a comprehensive trading indicator that combines Volume Weighted Average Price (VWAP) analysis with advanced signal filtering to identify high-probability trade opportunities. This indicator goes beyond basic VWAP by incorporating multiple confirmation layers and intelligent market analysis.
🎯 Key Features
Core VWAP Analysis:
- Custom volume-weighted mean calculation with deviation bands (2σ and 3σ)
- Optional log-space scaling for proportional price movements
- Real-time VWAP line with customizable visibility
Smart Signal Detection:
- RSI confirmation for all trade signals
- Volume filter requiring above-average trading activity
- Market regime detection (trending vs ranging markets)
- Optional RSI divergence analysis
Advanced Filtering:
- Multi-condition signal validation
- Session-based performance tracking (Asian, London, NY)
- Real-time win rate calculation
- Strong vs regular signal classification
Visual Features:
- Clean, professional interface with customizable colors
- Optional signal shapes and annotations
- Performance statistics table
- Filled deviation bands for easy visualization
📊 How It Works
The indicator identifies trade opportunities when:
1. Price touches VWAP deviation bands (2σ or 3σ)
2. RSI confirms oversold/overbought conditions
3. Volume exceeds the specified threshold
4. Market regime conditions are favorable
Signal Types:
- LONG : Price at lower bands + RSI oversold + volume confirmation
- SHORT : Price at upper bands + RSI overbought + volume confirmation
- STRONG : Same conditions but at 3σ bands for higher conviction trades
⚙️ Customization Options
Core Settings:
- VWAP length and source selection
- Adjustable deviation multipliers
- Log-space scaling toggle
Signal Filters:
- RSI length and threshold levels
- Volume filter with customizable multiplier
- Market type filtering options
Advanced Features:
- Session statistics tracking
- RSI divergence detection
- Market regime analysis
Visual Controls:
- Show/hide individual components
- Custom color schemes
- Signal display toggles
🔔 Alert System
Built-in alerts for:
- Long and short trade opportunities
- Strong signal confirmations
- RSI divergence signals
💡 Best Practices
- Use higher timeframes (15m+) for more reliable signals
- Combine with additional confirmation indicators
- Pay attention to session statistics for timing optimization
- Monitor market regime indicators for context
This indicator is suitable for day traders, swing traders, and anyone looking to improve their VWAP-based trading strategies with advanced filtering and market analysis.
1 Candle SMT Divergence (Nephew_Sam_)📊 1 Candle SMT Divergence Detector
3-Way Smart Money Theory (SMT) Divergence Scanner for Multi-Symbol Analysis
This indicator identifies 1-candle SMT divergences by comparing one primary symbol against up to 2 correlation symbols across multiple timeframes simultaneously. Perfect for detecting institutional smart money moves and market inefficiencies.
🎯 Key Features:
3-Way Comparison: Compare 1 "From" symbol vs 2 "To" symbols (configurable)
5 Symbol Pairs: Pre-configure up to 5 different symbol combinations
Multi-Timeframe: Scan 5 timeframes simultaneously (Chart, 1H, 4H, Daily, Weekly)
Smart Filtering: Only displays timeframes equal to or higher than your chart
Real-Time Detection: Compares current vs previous candle highs/lows
Visual Alerts: Clean table display with color-coded divergence status
Line Drawing: Optional trend lines connecting divergence points
Replay Compatible: Works with TradingView's replay mode
📈 How It Works:
Detects when one symbol makes a higher high while correlated symbols make lower highs (and vice versa for lows). This creates SMT divergence signals that often precede significant market moves.
Volume pressure by GSK-VIZAG-AP-INDIA🔍 Volume Pressure by GSK-VIZAG-AP-INDIA
🧠 Overview
“Volume Pressure” is a multi-timeframe, real-time table-based volume analysis tool designed to give traders a clear and immediate view of buying and selling pressure across custom-selected timeframes. By breaking down buy volume, sell volume, total volume, and their percentages, this indicator helps traders identify demand/supply imbalances and volume momentum in the market.
🎯 Purpose / Trading Use Case
This indicator is ideal for intraday and short-term traders who want to:
Spot aggressive buying or selling activity
Track volume dynamics across multiple timeframes *1 min time frame will give best results*
Use volume pressure as a confirming tool alongside price action or trend-based systems
It helps determine when large buying/selling activity is occurring and whether such behavior is consistent across timeframes—a strong signal of institutional interest or volume-driven trend shifts.
🧩 Key Features & Logic
Real-Time Table Display: A clean, dynamic table showing:
Buy Volume
Sell Volume
Total Volume
Buy % of total volume
Sell % of total volume
Multi-Time frame Analysis: Supports 8 user-selectable custom time frames from 1 to 240 minutes, giving flexibility to analyze volume pressure at various granularities.
Color-Coded Volume Bias:
Green for dominant Buy pressure
Red for dominant Sell pressure
Yellow for Neutral
Intensity-based blinking for extreme values (over 70%)
Dynamic Data Calculation:
Uses volume * (close > open) logic to estimate buy vs sell volumes bar-by-bar, then aggregates by timeframe.
⚙️ User Inputs & Settings
Timeframe Selectors (TF1 to TF8): Choose any 8 timeframes you want to monitor volume pressure across.
Text & Color Settings:
Customize text colors for Buy, Sell, Total volumes
Choose Buy/Sell bias colors
Enable/disable blinking for visual emphasis on extremes
Table Appearance:
Set header color, metric background, and text size
Table positioning: top-right, bottom-right, etc.
Blinking Highlight Toggle: Enable this to visually highlight when Buy/Sell % exceeds 70%—a sign of strong pressure.
📊 Visual Elements Explained
The table has 6 rows and 10 columns:
Row 0: Headers for Today and TF1 to TF8
Rows 1–3: Absolute values (Buy Vol, Sell Vol, Total Vol)
Rows 4–5: Relative percentages (Buy %, Sell %), with dynamic background color
First column shows the metric names (e.g., “Buy Vol”)
Cells blink using alternate background colors if volume pressure crosses thresholds
💡 How to Use It Effectively
Use Buy/Sell % rows to confirm potential breakout trades or identify volume exhaustion zones
Look for multi-timeframe confluence: If 5 or more TFs show >70% Buy pressure, buyers are in control
Combine with price action (e.g., breakouts, reversals) to increase conviction
Suitable for equities, indices, futures, crypto, especially on lower timeframes (1m to 15m)
🏆 What Makes It Unique
Table-based MTF Volume Pressure Display: Most indicators only show volume as bars or histograms; this script summarizes and color-codes volume bias across timeframes in a tabular format.
Customization-friendly: Full control over colors, themes, and timeframes
Blinking Alerts: Rare visual feature to capture user attention during extreme pressure
Designed with performance and readability in mind—even for fast-paced scalping environments.
🚨 Alerts / Extras
While this script doesn’t include TradingView alert functions directly, the visual blinking serves as a strong real-time alert mechanism.
Future versions may include built-in alert conditions for buy/sell bias thresholds.
🔬 Technical Concepts Used
Volume Dissection using close > open logic (to estimate buyer vs seller pressure)
Simple aggregation of volume over custom timeframes
Table plotting using Pine Script table.new, table.cell
Dynamic color logic for bias identification
Custom blinking logic using na(bar_index % 2 == 0 ? colorA : colorB)
⚠️ Disclaimer
This indicator is a tool for analysis, not financial advice. Always backtest and validate strategies before using any indicator for live trading. Past performance is not indicative of future results. Use at your own risk and apply proper risk management.
✍️ Author & Signature
Indicator Name: Volume Pressure
Author: GSK-VIZAG-AP-INDIA
TradingView Username: prowelltraders
My scriptImplied Volatility vs Historical Volatility
**Uncheck Plot box**
IV > HV = Overvalued
IV = HV = Fair Value
IV > HV = Undervalued
1. Pair with IV Rank: Use IV vs HV to confirm the setup, but IV Rank (50+, 70+) tells you how “high” IV is relative to its own history.
2. Timeframe: Use daily charts — IV is not meaningful on intraday timeframes.
3. Avoid noise: Use a smoothed HV (e.g., 20-day) and don’t chase small crossovers — look for clear divergence.
Correlation Drift📈 Correlation Drift
The Correlation Drift indicator is designed to detect shifts in market momentum by analyzing the relationship between correlation and price lag. It combines the principles of correlation analysis and lag factor measurement to provide a unique perspective on trend alignment and momentum shifts.
🔍 Core Concept:
The indicator calculates the Correlation vs PLF Ratio, which measures the alignment between an asset’s price movement and a chosen benchmark (e.g., BTCUSD). This ratio reflects how well the asset’s momentum matches the market trend while accounting for price lag.
📊 How It Works:
Correlation Calculation:
The script calculates the correlation between the asset and the selected benchmark over a specified period.
A higher correlation indicates that the asset’s price movements are in sync with the benchmark.
Price Lag Factor (PLF) Calculation:
The PLF measures the difference between long-term and short-term price momentum, dynamically scaled by recent volatility.
It highlights potential overextensions or lags in the asset’s price movements.
Combining Correlation and PLF:
The Correlation vs PLF Ratio combines these metrics to detect momentum shifts relative to the trend.
The result is a dynamic, smoothed histogram that visualizes whether the asset is leading or lagging behind the trend.
💡 How to Interpret:
Positive Values (Green/Aqua Bars):
Indicates bullish alignment with the trend.
Aqua: Rising bullish momentum, suggesting continuation.
Teal: Decreasing bullish momentum, signaling caution.
Negative Values (Purple/Fuchsia Bars):
Indicates bearish divergence from the trend.
Fuchsia: Falling bearish momentum, indicating increasing pressure.
Purple: Rising bearish momentum, suggesting potential reversal.
Clipping for Readability:
Values are clipped between -3 and +3 to prevent outliers from compressing the histogram.
This ensures clear visualization of typical momentum shifts while still marking extreme cases.
🚀 Best Practices:
Use Correlation Drift as a confirmation tool in conjunction with trend indicators (e.g., moving averages) to identify momentum alignment or divergence.
Look for transitions from positive to negative (or vice versa) as signals of potential trend shifts.
Combine with volume analysis to strengthen confidence in breakout or breakdown signals.
⚠️ Key Features:
Customizable Settings: Adjust the correlation length, PLF length, and smoothing factor to fine-tune the indicator for different market conditions.
Visual Gradient: The histogram changes color based on the strength and direction of the ratio, making it easy to identify shifts at a glance.
Zero Line Reference: Clearly distinguishes between bullish and bearish momentum zones.
🔧 Recommended Settings:
Correlation Length: 14 (for short to medium-term analysis)
PLF Length: 50 (to smooth out noise while capturing trend shifts)
Smoothing Factor: 3 (for enhanced clarity without excessive lag)
Benchmark Symbol: BTCUSD (or another relevant market indicator)
By providing a quantitative measure of trend alignment while accounting for price lag, the Correlation Drift indicator helps traders make more informed decisions during periods of momentum change. Whether you are trading crypto, forex, or equities, this tool can be a powerful addition to your momentum-based trading strategies.
⚠️ Disclaimer:
The Correlation Drift indicator is a technical analysis tool designed to aid in identifying potential shifts in market momentum and trend alignment. It is intended for informational and educational purposes only and should not be considered as financial advice or a recommendation to buy, sell, or hold any financial instrument.
Trading financial instruments, including cryptocurrencies, involves significant risk and may result in the loss of your capital. Past performance is not indicative of future results. Always conduct thorough research and seek advice from a certified financial professional before making any trading decisions.
The developer (RWCS_LTD) is not responsible for any trading losses or adverse outcomes resulting from the use of this indicator. Users are encouraged to test and validate the indicator in a simulated environment before applying it to live trading. Use at your own risk.
Market Warning Dashboard Enhanced📊 Market Warning Dashboard Enhanced
A powerful macro risk dashboard that tracks and visualizes early signs of market instability across multiple key indicators—presented in a clean, professional layout with a real-time thermometer-style danger gauge.
🔍 Included Macro Signals:
Yield Curve Inversion: 10Y-2Y and 10Y-3M spreads
Credit Spreads: High-yield (HYG) vs Investment Grade (LQD)
Volatility Structure: VIX/VXV ratio
Breadth Estimate: SPY vs 50-day MA (as a proxy)
🔥 Features:
Real-time Danger Score: 0 (Safe) to 100 (Extreme Risk)
Descriptive warnings for each signal
Color-coded thermometer gauge
Alert conditions for each macro risk
Background shifts on rising systemic risk
⚠️ This dashboard can save your portfolio by alerting you to macro trouble before it hits the headlines—ideal for swing traders, long-term investors, and anyone who doesn’t want to get blindsided by systemic risk.
Aggressive Volume 📊 Indicator: Aggressive Volume – Simulated Buy/Sell Pressure
Aggressive Volume estimates delta volume using candle data to simulate the market’s internal buy/sell pressure. It helps visualize how aggressive buyers or sellers are moving the price without needing full order flow access.
⚙️ How It Works:
Calculates simulated delta volume based on candle direction and volume.
Bullish candles (close > open) suggest dominance by buyers.
Bearish candles (close < open) suggest dominance by sellers.
Delta is the difference between simulated buying and selling pressure.
🔍 Key Features:
Visual bars showing aggressive buyer vs seller dominance
Helps spot trend strength, momentum bursts, and potential reversals
Simple, effective, and compatible with any timeframe
Lightweight and ideal for scalping, day trading, and swing trading
💡 How to Use:
Look for strong positive delta during bullish trends for confirmation.
Watch for delta weakening or divergence as potential reversal signals.
Combine with trend indicators or price action for enhanced accuracy.
📊 Indicador: Volume Agressivo – Pressão de Compra/Venda Simulada
Volume Agressivo estima o delta de volume utilizando dados dos candles para simular a pressão interna de compra/venda do mercado. Ele ajuda a visualizar como os compradores ou vendedores agressivos estão movendo o preço, sem precisar de acesso completo ao fluxo de ordens.
⚙️ Como Funciona:
Calcula o delta de volume simulado com base na direção do candle e no volume.
Candles de alta (fechamento > abertura) indicam predominância de compradores.
Candles de baixa (fechamento < abertura) indicam predominância de vendedores.
O delta é a diferença entre a pressão de compra e venda simulada.
🔍 Principais Funcionalidades:
Barras visuais mostrando a dominância de compradores vs vendedores agressivos
Ajuda a identificar a força da tendência, explosões de momentum e possíveis reversões
Simples, eficaz e compatível com qualquer período de tempo
Leve e ideal para scalping, day trading e swing trading
💡 Como Usar:
Procure por delta positivo forte durante tendências de alta para confirmação.
Observe o delta enfraquecendo ou divergências como sinais de possível reversão.
Combine com indicadores de tendência ou price action para maior precisão.
Volumetric Tensegrity🧮 Volumetric Tensegrity unifies two of the Leading Indicator suite's critical engines — ZVOL ( volume anomaly detection ) and OBVX ( directional conviction ). Originally designed as a structural economizer for traders navigating strict indicator limits (e.g. < 10 slots per chart), it was forced to evolve beyond that constraint simply to fulfill it, albeit with a difference. The fatal flaw of traditional fusion, where two metrics are blended mathematically, is that they lose scale integrity (i.e. meaning). VTense encodes optical tensegrity to scale the amplitude of the ZVOL histogram and the slope of the OBVX spread independently, so that expansion and direction may coexist without either dominating the frame.
🧬 Tensegrity , by definition, is an intelligent design principle where elements in compression are suspended within a network of continuous tension, forming a stable, self-supporting structure . Originally conceived in esoteric biomorphology (c.f. Da Vinci, Snelson, Casteneda), tensegrity balances force through opposition, not rigidity. Applied to financial markets, Volumetric Tensegrity captures this same principle: price compresses, volume expands, conviction builds or fades — yet structure holds through the interplay. The result is not a prediction engine, but a pressure field — one that visualizes where structure might bend, break, or rebound based on how volume breathes.
🗜️ Rather than layering multiple indicators and consuming precious chart space, VTense frees up room for complementary overlays like momentum mapping, liquidity tiers, or volatility phase detection — making it ideal for modular traders operating in tight technical real estate.
🧠 Core Logic - VTense separates and preserves two essential structural forces:
• ZVOL Histogram : A Z-score-based expansion map that measures current volume deviation from its historical average. It reveals buildup zones, dormant stretches, and breakout pressure — regardless of price behavior.
• OBVX Spread : A directional conviction curve that tracks the difference between On-Balance Volume and its volume-weighted fast trend. It shows whether the crowd is leaning in (accumulation/distribution) or backing off.
🔊 ZVOL controls the amplitude of the histogram, while OBVX controls the curvature and slope of the spread. Without sacrificing breathing behavior or analytical depth, VTense provides a compact yet dynamic lens to track both expansion pressure and directional bias within a single footprint.
🌊 Volumetric Tensegrity forecasts breakout readiness, trend fatigue, and compression zones by measuring the volatility within volume . Unlike traditional tools that track volatility of price, this indicator reveals when effort becomes unstable — signaling inflection points before price reacts. Designed to decode rhythm shifts at the volume level, it operates as a pre-ignition scanner that thrives on low-timeframe charts (15m and under) while scaling effectively to 1H for validation.
🪖 From Generals to Scouts
👀 When used jointly, ZVOL + OBVX act as the general : deep-field analysts confirming stress, commitment, or exhaustion. VTense , by contrast, functions as a scout — capturing subtle buildup and alignment before structure fully reveals itself. The indicator aims to be a literal vanguard, establishing a position that can be confirmed or flexibly abandoned when the higher authority arrives to evaluate.
🥂 Use the ZVOL + OBVX pair when :
• You need independent axis control and manual dissection
• You’re building long-form confluence setups
• You have more indicator slots than you need
🔎 Use VTense when :
• You need compact clarity across multiple instruments
• You’re prioritizing confluence _detection_ over granular separation
• You’re building efficient multi-layered systems under slot constraints
🏗️ Structural Behavior and Interpretation
🫁 Z VOL Respiration Histogram : Structural Effort vs Baseline
🔵 Compression Coil – volume volatility is low and stable; the market is coiling
🟢 Steady Rhythm – volume is healthy but unremarkable; balanced participation
🟡 Passive/Absorbed Effort – expansion failing to manifest; watch for reversal
🟠 Clean Expansion – actionable volatility rise backed by structure
🔴 Volatile Blowout – chaos, climax; likely end-phase or fakeout
⚖️ ZVOL Respiration measures how hard the crowd is pressing — not just that volume is rising, but how statistically abnormal the surge is. Because it is rescaled proportionally to OBVX, the amplitude of the histogram reflects structural urgency without overwhelming the visual field.
🖐️ OBVX Spread : Real-Time Directional Conviction Behind Price Moves
🔑 The curvature of the spread reveals not just directional bias but crowd temp o: sharp slopes = urgent transitions; gradual slopes = building structural shifts. Curvature is key: sharp OBVX slope = urgency; gentle arcs = controlled drift or indecision.
• Green Rising : Accumulation — upward pressure from real buyers
• Red Falling : Distribution — sell pressure, downward slope
• Flat Curves : Transitional → uncertainty, microstructure digestion
🎭 Synchronized vs Divergent Behavior
⏱️ Synchronized (high-confluence) : often precedes structural breakouts, with internal conviction clearly visible before price resolves.
• ZVOL expands (yellow/orange/red) and OBVX climbs steeply green = strong bullish pressure
• ZVOL expands while OBVX steepens red = growing sell-side intent
🪤 Divergent (conflict tension) : flags potential traps, fakeouts, and liquidity sweeps.
• ZVOL expands sharply, but OBVX flattens or opposes → reactive expansion without crowd commitment
⛔️ Latent Drift + Structural Holding Patterns : tensegrity in action — the market holds tension without directional release.
• ZVOL compresses (blue) + OBVX meanders near zero → structure is resting, building up energy
• After prolonged drift, expect violent asymmetry when balance finally breaks
📚 Phase Interpretation: Dynamic Structural Read
• 1️⃣ Quiet Coil : Histogram flat, OBVX flat → no urgency
• 2️⃣ Initial Pulse : Yellow bars, OBVX slope builds → actionable tension
• 3️⃣ Structural Breath : Synchronized expansion and slope → directional commitment
• 4️⃣ Disagreement : Spike in ZVOL, flattening OBVX → exhaustion risk or false signal
💡 Suggested Use
• Run on 15m charts for breakout anticipation and 1H for validation
• Pair with ZVOL + OBVX to confirm crowd conviction behind the tension phase
• Use as a rhythm filter for the suite's trend indicators (e.g., RDI , SUPeR TReND 2.718 , et. al.)
• Ideal during low-volume regimes to detect pressure buildup before triggers
🧏🏻 Volumetric Tensegrity doesn’t signal. It breathes , and listens to pressure shifts before they speak in price. As a scout, it lets you see structural posture before signals align — helping you front-run resolution with clarity, not prediction.
Dskyz Options Flow Flux (OFF) - FuturesDskyz Options Flow Flux (OFF) - Futures
*This is a repost due to moderator intervention on use of ™ in my scripts. I'm in the process of getting this rectified. This was originally posted around mid-night CDT.
🧠 The Dskyz Options Flow Flux (OFF) - Futures indicator is a game changer for futures traders looking to tap into institutional activity with limited resources. Designed for TradingView this tool simulates options flow data (call/put volume and open interest) for futures contracts like MNQ MES NQ and ES giving u actionable insights through volume spike detection volatility adjustments and stunning visuals like aurora flux bands and round number levels. Whether u’re a beginner learning the ropes or a pro hunting for an edge this indicator delivers real time market sentiment and key price levels to boost ur trading game
Key Features
⚡ Simulated Options Flow: Breaks down call/put volume and open interest using market momentum and volatility
📈 Spike Detection: Spots big moves in volume and open interest with customizable thresholds
🧠 Volatility Filter: Adapts to market conditions using ATR for smarter spike detection
✨ Aurora Flux Bands: Glows with market sentiment showing u bullish or bearish vibes at a glance
🎯 Round Number Levels: Marks key psychological levels where big players might step in
📊 Interactive Dashboard: Real time metrics like sentiment score and volatility factor right on ur chart
🚨 Alerts: Get notified of bullish or bearish spikes so u never miss a move
How It Works
🧠 This indicator is built to make complex options flow analysis simple even with the constraints of Pine Script. Here’s the step by step:
Simulated Volume Data (Dynamic Split):
Pulls daily volume for ur chosen futures contract (MNQ1! MES1! NQ1! ES1!)
Splits it into call and put volume based on momentum (ta.mom) and volatility (ATR vs its 20 period average)
Estimates open interest (OI) for calls and puts (1.15x for calls 1.1x for puts)
Formula: callRatio = 0.5 + (momentum / close) * 10 + (volatility - 1) * 0.1 capped between 0.3 and 0.7
Why It Matters: Mimics how big players might split their trades giving u a peek into institutional sentiment
Spike Detection:
Compares current volume/OI to short term (lookbackShort) and long term (lookbackLong) averages
Flags spikes when volume/OI exceeds the average by ur set threshold (spikeThreshold for regular highConfidenceThreshold for strong)
Adjusts for volatility so u’re not fooled by choppy markets
Output: optionsSignal (2 for strong bullish -2 for strong bearish 1 for bullish -1 for bearish 0 for neutral)
Why It Matters: Pinpoints where big money might be stepping in
Volatility Filter:
Uses ATR (10 periods) and its 20 period average to calculate a volatility factor (volFactor = ATR / avgAtr)
Scales spike thresholds based on market conditions (volAdjustedThreshold = spikeThreshold * max(1 volFactor * volFilter))
Why It Matters: Keeps ur signals reliable whether the market is calm or wild
Sentiment Score:
Calculates a call/put ratio (callVolume / putVolume) and adjusts for volatility
Converts it to a 0 to 100 score (higher = bullish lower = bearish)
Formula: sentimentScore = min(max((volAdjustedSentiment - 1) * 50 0) 100)
Why It Matters: Gives u a quick read on market bias
Round Number Detection:
Finds the nearest round number (e.g. 100 for MNQ1! 50 for MES1!)
Checks for volume spikes (volume > 3 period SMA * spikeThreshold) and if price is close (within ATR * atrMultiplier)
Updates the top activity level every 15 minutes when significant activity is detected
Why It Matters: Highlights psychological levels where price often reacts
Visuals and Dashboard:
Combines aurora flux bands glow effects round number lines and a dashboard to make insights pop (see Visual Elements below)
Plots triangles for call/put spikes (green/red for strong lime/orange for regular)
Sets up alerts for key market moves
Why It Matters: Makes complex data easy to read at a glance
Inputs and Customization
⚙️ Beginners can tweak these settings to match their trading style while pros can dig deeper for precision:
Futures Symbol (symbol): Pick ur contract (MNQ1! MES1! NQ1! ES1!). Default: MNQ1!
Short Lookback (lookbackShort): Days for short term averages. Smaller = more sensitive. Range: 1+. Default: 5
Long Lookback (lookbackLong): Days for long term averages. Range: 5+. Default: 10
Spike Threshold (spikeThreshold): How big a spike needs to be (e.g. 1.1 = 10% above average). Range: 1.0+. Default: 1.1
High Confidence Threshold (highConfidenceThreshold): For strong spikes (e.g. 3.0 = 3x average). Range: 2.0+. Default: 3.0
Volatility Filter (volFilter): Adjusts for market volatility (e.g. 1.2 = 20% stricter in volatile markets). Range: 1.0+. Default: 1.2
Aurora Flux Transparency (glowOpacity): Controls band transparency (0 = solid 100 = invisible). Range: 0 to 100. Default: 65
Show Show OFF Dashboard (showDashboard): Toggles the dashboard with key metrics. Default: true
Show Nearest Round Number (showRoundNumbers): Displays round number levels. Default: true
ATR Multiplier for Proximity (atrMultiplier): How close price needs to be to a round number (e.g. 1.5 = within 1.5x ATR). Range: 0.5+. Default: 1.5
Functions and Logic
🧠 Here’s the techy stuff pros will love:
Simulated Volume Data : Splits daily volume into call/put volume and OI using momentum and volatility
Volatility Filter: Scales thresholds with volFactor = atr / avgAtr for adaptive detection
Spike Detection: Flags spikes and assigns optionsSignal (2, -2, 1, -1, 0) for sentiment
Sentiment Score: Converts call/put ratio into a 0-100 score for quick bias reads
Round Number Detection: Identifies key levels and significant activity for trading zones
Dashboard Display: Updates real time metrics like sentiment score and volatility factor
Visual Elements
✨ These visuals make data come alive:
Gradient Background: Green (bullish) red (bearish) or yellow (neutral/choppy) at 95% transparency to show trend
Aurora Flux Bands: Stepped bands (linewidth 3) around a 14 period EMA ± ATR * 1.8. Colors shift with sentiment (green red lime orange gray) with glow effects at 85% transparency
Round Number Visualization: Stepped lines (linewidth 2) at key levels (solid if active dashed if not) with labels (black background white text size.normal)
Visual Signals: Triangles above/below bars for spikes (size.small for strong size.tiny for regular)
Dashboard: Bottom left table (2 columns 10 rows) with a black background (29% transparency) gray border and metrics:
⚡ Round Number Activity: “Detected” or “None”
📈 Trend: “Bullish” “Bearish” or “Neutral” (colored green/red/gray)
🧠 ATR: Current 10 period ATR
📊 ATR Avg: 20 period SMA of ATR
📉 Volume Spike: “YES” (green) or “NO” (red)
📋 Call/Put Ratio: Current ratio
✨ Flux Signal: “Strong Bullish” “Strong Bearish” “Bullish” “Bearish” or “Neutral” (colored green/red/gray)
⚙️ Volatility Factor: Current volFactor
📈 Sentiment Score: 0-100 score
Usage and Strategy Recommendations
🎯 For Beginners: Use high confidence spikes (green/red triangles) for easy entries. Check the dashboard for a quick market read (sentiment score above 60 = bullish below 40 = bearish). Watch round number levels for support/resistance
💡 For Pros: Combine flux signals with round number activity for high probability setups. Adjust lookbackShort/lookbackLong for trending vs choppy markets. Use volFactor for position sizing (higher = smaller positions)
HMA Breakdown [NLR]Hull Moving Average (HMA) is a great trend-following tool — it's smooth, fast, and more responsive than traditional MAs like EMA or SMA. But the problem?
Sometimes it gives signals that look sharp but are misleading — especially in noisy markets or when the price is chopping around.
This script breaks down the inner mechanics of the HMA to give you more context:
Are the inputs to HMA actually diverging?
Is there momentum behind the move?
Is this trend about to reverse or just a pullback?
By plotting the components of HMA — and layering signal bands, color logic, and optional trend overlays — it gives you a visual breakdown of what's really going on under the hood.
What this indicator shows
HMA Trend (Main Line)
The Hull Moving Average (HMA) is plotted in bold, colored red or green based on internal conditions (not just price slope).
WMA Components (Fast & Slow)
Shows the two WMAs used inside the HMA calculation:
WMA Half (faster) — reacts quickly to price
WMA Full (slower) — smoother, less reactive
Their difference and crossover gives you clues on whether momentum is building or fading.
Signal & Band Structure
A calculated upper band and lower band are used to track when HMA is leading or lagging momentum.
When the HMA is below the band, trend may be weakening - this helps you filter false signals.
TMA Candle (Optional Visual)
A synthetic candle shows whether the smoothed average is rising or falling compared to a few candles back.
This is a simple visual cue to spot inflection points in the trend.
EMA Trend Overlay (Optional)
A pair of short-term EMAs built on a smoothed source helps you catch micro-trends or align your entries with the bigger trend.
Can act as an early heads-up or a trend confirmation layer.
What problem it solves
Noisy Signals: Helps filter out weak or fake trend signals often seen in HMA-only systems.
Visual Breakdown: Lets you see how each component of the HMA is behaving — so you’re not flying blind.
Momentum Confirmation: Adds layered confirmation using fast-vs-slow WMA cross, signal bands, and mini trend overlays.
Best Used For:
Trend-following traders who use HMA or WMA strategies
Filtering entries/exits in momentum-based systems
Visually confirming when a trend is real vs fake
Probability Grid [LuxAlgo]The Probability Grid tool allows traders to see the probability of where and when the next reversal would occur, it displays a 10x10 grid and/or dashboard with the probability of the next reversal occurring beyond each cell or within each cell.
🔶 USAGE
By default, the tool displays deciles (percentiles from 0 to 90), users can enable, disable and modify each percentile, but two of them must always be enabled or the tool will display an error message alerting of it.
The use of the tool is quite simple, as shown in the chart above, the further the price moves on the grid, the higher the probability of a reversal.
In this case, the reversal took place on the cell with a probability of 9%, which means that there is a probability of 91% within the square defined by the last reversal and this cell.
🔹 Grid vs Dashboard
The tool can display a grid starting from the last reversal and/or a dashboard at three predefined locations, as shown in the chart above.
🔶 DETAILS
🔹 Raw Data vs Normalized Data
By default the tool displays the normalized data, this means that instead of using the raw data (price delta between reversals) it uses the returns between each reversal, this is useful to make an apples to apples comparison of all the data in the dataset.
This can be seen in the left side of the chart above (BTCUSD Daily chart) where normalize data is disabled, the percentiles from 0 to 40 overlap and are indistinguishable from each other because the tool uses the raw price delta over the entire bitcoin history, with normalize data enabled as we can see in the right side of the chart we can have a fair comparison of the data over the entire history.
🔹 Probability Beyond or Within Each Cell
Two different probability modes are available, the default mode is Probability Beyond Each Cell, the number displayed in each cell is the probability of the next reversal to be located in the area beyond the cell, for example, if the cell displays 20%, it means that in the area formed by the square starting from the last reversal and ending at the cell, there is an 80% probability and outside that square there is a 20% probability for the location of the next reversal.
The second probability mode is the probability within each cell, this outlines the chance that the next reversal will be within the cell, as we can see on the right chart above, when using deciles as percentiles (default settings), each cell has the same 1% probability for the 10x10 grid.
🔶 SETTINGS
Swing Length: The maximum length in bars used to identify a swing
Maximum Reversals: Maximum number of reversals included in calculations
Normalize Data: Use returns between swings instead of raw price
Probability: Choose between two different probability modes: beyond and inside each cell
Percentiles: Enable/disable each of the ten percentiles and select the percentile number and line style
🔹 Dashboard
Show Dashboard: Enable or disable the dashboard
Position: Choose dashboard location
Size: Choose dashboard size
🔹 Style
Show Grid: Enable or disable the grid
Size: Choose grid text size
Colors: Choose grid background colors
Show Marks: Enable/disable reversal markers
AltSeasonality - MTFAltSeason is more than a brief macro market cycle — it's a condition. This indicator helps traders identify when altcoins are gaining strength relative to Bitcoin dominance, allowing for more precise entries, exits, and trade selection across any timeframe.
The key for altcoin traders is that the lower the timeframe, the higher the alpha.
By tracking the TOTAL3/BTC.D ratio — a real-time measure of altcoin strength versus Bitcoin — this tool highlights when capital is rotating into or out of altcoins. It works as a bias filter, helping traders avoid low-conviction setups, especially in chop or during BTC-led conditions.
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It works well on the 1D chart to validate swing entries during strong altcoin expansion phases — especially when TOTAL3/BTC.D breaks out while BTCUSD consolidates.
On the 4H or 1D chart, rising TOTAL3/BTC.D + a breakout on your altcoin = high-conviction setup. If BTC is leading, fade the move or reduce size. Consider pairing with the Accumulation - Distribution Candles, optimized for the 1D (not shown).
🔍 Where this indicator really excels, however, is on the 1H and 15M charts, where short-term traders need fast bias confirmation before committing to a move. Designed for scalpers, intraday momentum traders, and tactical swing setups.
Use this indicator to confirm whether an altcoin breakout is supported by broad market flow — or likely to fail due to hidden BTC dominance pressure.
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🧠 How it works:
- TOTAL3 = market cap of altcoins (excl. BTC + ETH)
- BTC.D = Bitcoin dominance as % of total market cap
- TOTAL3 / BTC.D = a normalized measure of altcoin capital strength vs Bitcoin
- BTCUSD = trend baseline and comparison anchor
The indicator compares these forces side-by-side, using a normalized dual-line ribbon. There is intentionally no "smoothing".
When TOTAL3/BTC.D is leading, the ribbon shifts to an “altseason active” phase. When BTCUSD regains control, the ribbon flips back into BTC dominance — signaling defensive posture.
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💡 Strategy Example:
On the 1H chart, a crossover into altseason → check the 15M chart for confirmation. Consider adding the SUPeR TReND 2.718 for confirmation (not shown). If both align, you have trend + flow confluence. If BTCUSD is leading or ribbon is mixed, reduce exposure or wait for confirmation. Further confirmation via Volume breakouts in your specific coin.
⚙️ Features:
• MTF source selection (D, 1H, 15M)
• Normalized ribbon (TOTAL3/BTC.D vs BTCUSD)
• Cross-aware fill shading
• Custom color and transparency controls
• Optional crossover markers
• Midline + zone guides (0.2 / 0.5 / 0.8)
