Ayman – Full Smart Suite Auto/Manual Presets + PanelIndicator Name
Ayman – Full Smart Suite (OB/BoS/Liq/FVG/Pin/ADX/HTF) + Auto/Manual Presets + Panel
This is a multi-condition trading tool for TradingView that combines advanced Smart Money Concepts (SMC) with classic technical filters.
It generates BUY/SELL signals, draws Stop Loss (SL) and Take Profit (TP1, TP2) levels, and displays a control panel with all active settings and conditions.
1. Main Features
Smart Money Concepts Filters:
Order Block (OB) Zones
Break of Structure (BoS)
Liquidity Sweeps
Fair Value Gaps (FVG)
Pin Bar patterns
ADX filter
Higher Timeframe EMA filter (HTF EMA)
Two Operating Modes:
Auto Presets: Automatically adjusts all settings (buffers, ATR multipliers, RR, etc.) based on your chart timeframe (M1/M5/M15).
Manual Mode: Fully customize all parameters yourself.
Trade Management Levels:
Stop Loss (SL)
TP1 – partial profit
TP2 – full profit
Visual Panel showing:
Current settings
Filter status
Trend direction
Last swing levels
SL/TP status
Alerts for BUY/SELL conditions
2. Entry Conditions
A BUY signal is generated when all these are true:
Trend: Price above EMA (bullish)
HTF EMA: Higher timeframe trend also bullish
ADX: Trend strength above threshold
OB: Price in a valid bullish Order Block zone
BoS: Structure break to the upside
Liquidity Sweep: Sweep of recent lows in bullish context
FVG: A bullish Fair Value Gap is present
Pin Bar: Bullish Pin Bar pattern detected (if enabled)
A SELL signal is generated when the opposite conditions are met.
3. Stop Loss & Take Profits
SL: Placed just beyond the last swing low (BUY) or swing high (SELL), with a small ATR buffer.
TP1: Partial profit target, defined as a ratio of the SL distance.
TP2: Full profit target, based on Reward:Risk ratio.
4. How to Use
Step 1 – Apply Indicator
Open TradingView
Go to your chart (recommended: XAUUSD, M1/M5 for scalping)
Add the indicator script
Step 2 – Choose Mode
AUTO Mode: Leave “Use Auto Presets” ON – parameters adapt to your timeframe.
MANUAL Mode: Turn Auto OFF and adjust all lengths, buffers, RR, and filters.
Step 3 – Filters
In the Filters On/Off section, enable/disable specific conditions (OB, BoS, Liq, FVG, Pin Bar, ADX, HTF EMA).
Step 4 – Trading the Signals
Wait for a BUY or SELL arrow to appear.
SL and TP levels will be plotted automatically.
TP1 can be used for partial close and TP2 for full exit.
Step 5 – Alerts
Set alerts via BUY Signal or SELL Signal to receive notifications.
5. Best Practices
Scalping: Use M1 or M5 with AUTO mode for gold or forex pairs.
Swing Trading: Use M15+ and adjust buffers/ATR manually.
Combine with price action confirmation before entering trades.
For higher accuracy, wait for multiple filter confirmations rather than acting on the first arrow.
6. Summary Table
Feature Purpose Can Disable?
Order Block Finds key supply/demand zones ✅
Break of Structure Detects trend continuation ✅
Liquidity Sweep Finds stop-hunt moves ✅
Fair Value Gap Confirms imbalance entries ✅
Pin Bar Price action reversal filter ✅
ADX Trend strength filter ✅
HTF EMA Higher timeframe confirmation ✅
Komut dosyalarını "GOLD" için ara
ATR 5 min- FOREX + XAUThis indicator displays the Average True Range (ATR) over the last 20 candles, calculated using the 5-minute timeframe, regardless of the chart timeframe you're currently viewing.
It supports:
All major forex pairs
XAUUSD (Gold), with ATR displayed in full dollars
Key Features
Always reflects 5-minute volatility
Accurate pip scaling:
JPY pairs = 1 pip = 0.01
Other forex pairs = 1 pip = 0.0001
XAUUSD = 1 pip = 1.00 (i.e., full dollar)
Clean and minimal top-right table display
Automatically adapts based on the instrument you're viewing
Helps traders gauge recent market volatility across timeframes
This is an ideal tool for scalpers, intraday traders, or swing traders who want to monitor short-term volatility conditions from any timeframe view.
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
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Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
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AymaN Entry Signal – With HTF + Pin Bar + Multi TP + BE + V1Ayman Entry Signal – Indicator Description
Overview
Ayman Entry Signal – With HTF + Pin Bar + Multi TP + BE + Stats Panel (V1)
This is a professional-grade Pine Script indicator designed for scalping and intraday trading, with full trade management, multi-confirmation logic, and advanced visualization. The tool is ideal for traders focused on XAUUSD (Gold), Forex, and other volatile instruments who seek both precision entries and structured exits with dynamic risk control.
Main Features
Advanced Entry Logic:
- EMA fast/slow crossovers (configurable)
- Optional conditions: Break of Structure (BoS), Order Block (OB), Fair Value Gap (FVG), Liquidity sweeps, Pin Bars
- HTF confirmation using EMA or BoS
- Real-time entry condition display
Trade Management:
- Dynamic calculation of Entry, SL (with ATR buffer), TP1, TP2
- Supports Partial Close and Break Even logic after TP1
- Visual PnL label (dynamic and color-coded)
Statistics Panel:
- Shows total trades, win/loss/breakeven count, cumulative PnL
- Filter by custom date or session
- Fully customizable panel appearance
Trade Visualization:
- Trade box includes all trade levels (Entry, SL, TP1, TP2)
- Visual display of trade conditions and PnL result
- Option to keep previous trades on chart
Alert System:
- Alerts for Buy and Sell entries
- Compatible with webhook automation systems like MT5/MT4
Customization & Inputs
- Capital & risk per trade
- Value per pip/point
- SL buffer (ATR-based)
- Manual EMA override
- Enable/disable: EMA, BoS, OB, FVG, Liquidity, Pin Bars
- HTF: timeframe + confirmation logic
- Trade box/labels visibility
- Full color customization
- PnL label position: top, center, or bottom
Recommended Use
- Ideal for Gold scalping (XAUUSD), also effective for Forex
- Best on 1m–15m charts; use HTF confirmation from 15m–4H
- Pairs well with semi-automated systems using alerts and webhooks
Disclaimer
Note: This is a non-executing indicator. It does not place trades but provides visual and statistical guidance for professional manual or semi-automated trading.
RISK MANAGEMENT CALCULATOR V3📊 RISK MANAGEMENT CALCULATOR – Lot Size, Profit & R:R Tool
This script is designed to help traders instantly calculate lot size, expected profit, and risk/reward ratio based on their trade plan.
✅ Features:
Input your Risk Amount ($), Entry, Stop Loss, and up to 3 Take Profits
Calculates:
✅ Lot Size based on risk
✅ Split profits per TP level (equally weighted)
✅ Total Profit & Risk/Reward (R:R)
Displays everything in a clean bottom-right table
Optimized for both:
🖥️ Desktop mode (larger layout)
📱 Mobile mode (toggle compact view)
💡 How to Use:
Enter your planned Entry, Stop Loss, and Risk Amount
Set any TP1, TP2, or TP3 prices (set TP to 0 if not used)
The system will auto-compute your ideal lot size and show estimated profits
🔧 Parameters:
Risk Amount ($) – how much you’re willing to lose
Entry Price – your trade entry
Stop Loss Price – your SL level
Take Profit 1/2/3 – optional TP targets
Pip Value – profit/loss per point for 1 standard lot
📱 Mobile Mode – compact the table for small screens
🔐 Notes:
No trades are executed – this is a risk planning tool only
Designed for all markets (forex, gold, indices, crypto)
TP profits are equally split (e.g. 2 TP = 50% / 50%)
RISK MANAGEMENT CALCULATOR📊 RISK MANAGEMENT CALCULATOR – Lot Size, Profit & R:R Tool
This script is designed to help traders instantly calculate lot size, expected profit, and risk/reward ratio based on their trade plan.
✅ Features:
Input your Risk Amount ($), Entry, Stop Loss, and up to 3 Take Profits
Calculates:
✅ Lot Size based on risk
✅ Split profits per TP level (equally weighted)
✅ Total Profit & Risk/Reward (R:R)
Displays everything in a clean bottom-right table
Optimized for both:
🖥️ Desktop mode (larger layout)
📱 Mobile mode (toggle compact view)
💡 How to Use:
Enter your planned Entry, Stop Loss, and Risk Amount
Set any TP1, TP2, or TP3 prices (set TP to 0 if not used)
The system will auto-compute your ideal lot size and show estimated profits
🔧 Parameters:
Risk Amount ($) – how much you’re willing to lose
Entry Price – your trade entry
Stop Loss Price – your SL level
Take Profit 1/2/3 – optional TP targets
Pip Value – profit/loss per point for 1 standard lot
📱 Mobile Mode – compact the table for small screens
🔐 Notes:
No trades are executed – this is a risk planning tool only
Designed for all markets (forex, gold, indices, crypto)
TP profits are equally split (e.g. 2 TP = 50% / 50%)
FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator
1. Concept and Rationale
The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings.
The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much.
2. Data Pipeline
• Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes.
• Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer.
• User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study.
3. Math under the Hood
(1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent.
(2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite.
(3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive.
(4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers.
(5) Threshold Grid – Five horizontal guides create a structured regime map:
• Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.
• Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.
• Zero Line demarcates neutral alignment.
4. Chart Furniture and User Interface
• Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts.
• Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation.
• Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar.
• Everything—line width, thresholds, colours—can be customised so the indicator blends into any template.
5. Interpretation Guide
Macro Liquidity Pulse
• When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).
• Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety.
Regime Transition Signals
• Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.
• Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite.
Momentum Exhaustion and Mean-Reversion
• Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.
• Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold.
Pair Trading / Relative Value
• Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.
• Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader.
Event-Driven Tactics
• FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.
• CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common.
6. Alerts Suite
Pre-bundled conditions let you automate workflows:
• Bullish / Bearish zero crosses – queue spot or futures entries.
• Standard OB / OS – notify for first contact with actionable zones.
• Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions.
7. Parameter Playground
• Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors.
• Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC.
• Try HMA smoothing for responsive yet smooth curves on intraday charts.
• Colour-blind users can easily swap bull and bear palette selections for preferred contrasts.
8. Limitations and Best Practices
• The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals.
• Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily.
• Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps.
9. Final Thoughts
The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark.
Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
1EMA + 1MACD + 1RSI Crypto Strategy AB 092Title: EMA + MACD + RSI Crypto Strategy
Overview:
This is a trend-following and momentum-based crypto trading strategy built for 1H, 4H, and 1D timeframes, combining three proven indicators:
EMA 50 & EMA 200 Crossover – identifies long-term trend direction.
MACD Crossover (12, 26, 9) – confirms momentum shift.
RSI Filter (14) – avoids overbought/oversold traps and refines entries.
Buy Entry Conditions:
EMA 50 > EMA 200 (Golden Cross)
MACD line crosses above signal line
RSI is between 45 and 70
Sell Entry Conditions:
EMA 50 < EMA 200 (Death Cross)
MACD line crosses below signal line
RSI is between 30 and 55
Risk Management:
Configurable Take Profit and Stop Loss percentages via inputs.
Default: 3% TP, 1.5% SL (adjustable based on timeframe and asset volatility).
Best For:
Intraday trades on 1H (BTC, ETH, SOL)
Swing trades on 4H
Position entries on 1D (top 50 altcoins)
This script includes visual Buy/Sell signals, alert conditions, and customizable SL/TP logic — making it a clean, actionable, and reliable strategy for crypto traders.
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND
The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns.
The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets.
The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks.
Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991).
MATHEMATICAL METHODOLOGY
The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t.
Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns.
Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively.
ADVANCED RISK METRICS
The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification.
The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions.
The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced.
Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture.
To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds.
The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns.
COLOR SCHEMES AND VISUALIZATION
The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools.
The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments.
Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations.
PRACTICAL APPLICATIONS
Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles.
The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions.
Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable.
Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements.
Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals.
STATISTICAL SIGNIFICANCE AND VALIDATION
The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns.
Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns.
Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference.
IMPLEMENTATION BEST PRACTICES
Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment.
Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives.
Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification.
ALERT SYSTEM AND AUTOMATION
The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations.
New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken.
TRADING STRATEGIES
Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods.
Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles.
Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions.
LIMITATIONS AND CONSIDERATIONS
Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates.
Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities.
Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical.
Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging.
TECHNICAL IMPLEMENTATION
Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible.
Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis.
The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations.
Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences.
CONCLUSION
The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions.
The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases.
Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes.
BIBLIOGRAPHY
Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228.
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58.
Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152.
Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill.
Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91.
Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138.
Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64.
Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
Elite MA Trend Overlay [9/21/50/200 + VWAP + HMA]🔍 What It Is:
The Elite MA Trend Overlay is a professional-grade moving average system built for day traders, scalpers, and swing traders who need clear, visual trend confirmation and precise entry zones across all timeframes.
This overlay combines 5 industry-proven tools into one compact indicator for sniper-level decision making:
EMA 9 – Entry momentum
EMA 21 – Microstructure guide
EMA 50 – Trend bias filter
EMA 200 – Institutional macro direction
VWAP – Intraday fair value (used by pros)
Hull MA (HMA) – Early shift detector
It also features auto-detected trend zones using color-coded background shading to help you instantly know if the market is in a bullish or bearish regime.
🧠 How It Works:
The script dynamically plots the short-term to long-term moving averages to reflect real-time market structure. When all EMAs are aligned in one direction, a colored background highlights the dominant trend:
✅ Green background = Bullish trend (9 > 21 > 50 > 200)
🔻 Red background = Bearish trend (9 < 21 < 50 < 200)
The VWAP line acts as a magnet and decision zone—traders use it for intraday entries or exits. The Hull Moving Average adapts quickly to price shifts, making it perfect for spotting early reversals before the EMAs cross.
🎯 Why It Helps Traders Succeed:
This indicator:
Removes guesswork: Know instantly if you’re in a strong trend or chop zone.
Filters bad trades: Avoid entering against structure or into volatility traps.
Improves timing: Use pullbacks to EMAs or Hull MA flips for sniper entries.
Works across timeframes: From scalping 1-minute to swing trading daily charts.
Whether you're trading gold, forex, stocks, or crypto — this overlay gives you clean, professional structure that keeps you disciplined and sharp.
⚙️ Features:
On/off toggles for VWAP & Hull MA
Minimalist, clutter-free plotting
Auto background color zones
Supports Pine Script v6 (latest version)
👑 Recommended Use:
Confirm trend with background + EMA alignment
Enter on pullbacks to EMA 21 or 50
Use Hull MA or RSI for early reversal detection
Exit at VWAP reversion or trend exhaustion
💬 “Structure is everything. Trade with the flow, not against it.”
MERV: Market Entropy & Rhythm Visualizer [BullByte]The MERV (Market Entropy & Rhythm Visualizer) indicator analyzes market conditions by measuring entropy (randomness vs. trend), tradeability (volatility/momentum), and cyclical rhythm. It provides traders with an easy-to-read dashboard and oscillator to understand when markets are structured or choppy, and when trading conditions are optimal.
Purpose of the Indicator
MERV’s goal is to help traders identify different market regimes. It quantifies how structured or random recent price action is (entropy), how strong and volatile the movement is (tradeability), and whether a repeating cycle exists. By visualizing these together, MERV highlights trending vs. choppy environments and flags when conditions are favorable for entering trades. For example, a low entropy value means prices are following a clear trend line, whereas high entropy indicates a lot of noise or sideways action. The indicator’s combination of measures is original: it fuses statistical trend-fit (entropy), volatility trends (ATR and slope), and cycle analysis to give a comprehensive view of market behavior.
Why a Trader Should Use It
Traders often need to know when a market trend is reliable vs. when it is just noise. MERV helps in several ways: it shows when the market has a strong direction (low entropy, high tradeability) and when it’s ranging (high entropy). This can prevent entering trend-following strategies during choppy periods, or help catch breakouts early. The “Optimal Regime” marker (a star) highlights moments when entropy is very low and tradeability is very high, typically the best conditions for trend trades. By using MERV, a trader gains an empirical “go/no-go” signal based on price history, rather than guessing from price alone. It’s also adaptable: you can apply it to stocks, forex, crypto, etc., on any timeframe. For example, during a bullish phase of a stock, MERV will turn green (Trending Mode) and often show a star, signaling good follow-through. If the market later grinds sideways, MERV will shift to magenta (Choppy Mode), warning you that trend-following is now risky.
Why These Components Were Chosen
Market Entropy (via R²) : This measures how well recent prices fit a straight line. We compute a linear regression on the last len_entropy bars and calculate R². Entropy = 1 - R², so entropy is low when prices follow a trend (R² near 1) and high when price action is erratic (R² near 0). This single number captures trend strength vs noise.
Tradeability (ATR + Slope) : We combine two familiar measures: the Average True Range (ATR) (normalized by price) and the absolute slope of the regression line (scaled by ATR). Together they reflect how active and directional the market is. A high ATR or strong slope means big moves, making a trend more “tradeable.” We take a simple average of the normalized ATR and slope to get tradeability_raw. Then we convert it to a percentile rank over the lookback window so it’s stable between 0 and 1.
Percentile Ranks : To make entropy and tradeability values easy to interpret, we convert each to a 0–100 rank based on the past len_entropy periods. This turns raw metrics into a consistent scale. (For example, an entropy rank of 90 means current entropy is higher than 90% of recent values.) We then divide by 100 to plot them on a 0–1 scale.
Market Mode (Regime) : Based on those ranks, MERV classifies the market:
Trending (Green) : Low entropy rank (<40%) and high tradeability rank (>60%). This means the market is structurally trending with high activity.
Choppy (Magenta) : High entropy rank (>60%) and low tradeability rank (<40%). This is a mostly random, low-momentum market.
Neutral (Cyan) : All other cases. This covers mixed regimes not strongly trending or choppy.
The mode is shown as a colored bar at the bottom: green for trending, magenta for choppy, cyan for neutral.
Optimal Regime Signal : Separately, we mark an “optimal” condition when entropy_norm < 0.3 and tradeability > 0.7 (both normalized 0–1). When this is true, a ★ star appears on the bottom line. This star is colored white when truly optimal, gold when only tradeability is high (but entropy not quite low enough), and black when neither condition holds. This gives a quick visual cue for very favorable conditions.
What Makes MERV Stand Out
Holistic View : Unlike a single-oscillator, MERV combines trend, volatility, and cycle analysis in one tool. This multi-faceted approach is unique.
Visual Dashboard : The fixed on-chart dashboard (shown at your chosen corner) summarizes all metrics in bar/gauge form. Even a non-technical user can glance at it: more “█” blocks = a higher value, colors match the plots. This is more intuitive than raw numbers.
Adaptive Thresholds : Using percentile ranks means MERV auto-adjusts to each market’s character, rather than requiring fixed thresholds.
Cycle Insight : The rhythm plot adds information rarely found in indicators – it shows if there’s a repeating cycle (and its period in bars) and how strong it is. This can hint at natural bounce or reversal intervals.
Modern Look : The neon color scheme and glow effects make the lines easy to distinguish (blue/pink for entropy, green/orange for tradeability, etc.) and the filled area between them highlights when one dominates the other.
Recommended Timeframes
MERV can be applied to any timeframe, but it will be more reliable on higher timeframes. The default len_entropy = 50 and len_rhythm = 30 mean we use 30–50 bars of history, so on a daily chart that’s ~2–3 months of data; on a 1-hour chart it’s about 2–3 days. In practice:
Swing/Position traders might prefer Daily or 4H charts, where the calculations smooth out small noise. Entropy and cycles are more meaningful on longer trends.
Day trader s could use 15m or 1H charts if they adjust the inputs (e.g. shorter windows). This provides more sensitivity to intraday cycles.
Scalpers might find MERV too “slow” unless input lengths are set very low.
In summary, the indicator works anywhere, but the defaults are tuned for capturing medium-term trends. Users can adjust len_entropy and len_rhythm to match their chart’s volatility. The dashboard position can also be moved (top-left, bottom-right, etc.) so it doesn’t cover important chart areas.
How the Scoring/Logic Works (Step-by-Step)
Compute Entropy : A linear regression line is fit to the last len_entropy closes. We compute R² (goodness of fit). Entropy = 1 – R². So a strong straight-line trend gives low entropy; a flat/noisy set of points gives high entropy.
Compute Tradeability : We get ATR over len_entropy bars, normalize it by price (so it’s a fraction of price). We also calculate the regression slope (difference between the predicted close and last close). We scale |slope| by ATR to get a dimensionless measure. We average these (ATR% and slope%) to get tradeability_raw. This represents how big and directional price moves are.
Convert to Percentiles : Each new entropy and tradeability value is inserted into a rolling array of the last 50 values. We then compute the percentile rank of the current value in that array (0–100%) using a simple loop. This tells us where the current bar stands relative to history. We then divide by 100 to plot on .
Determine Modes and Signal : Based on these normalized metrics: if entropy < 0.4 and tradeability > 0.6 (40% and 60% thresholds), we set mode = Trending (1). If entropy > 0.6 and tradeability < 0.4, mode = Choppy (-1). Otherwise mode = Neutral (0). Separately, if entropy_norm < 0.3 and tradeability > 0.7, we set an optimal flag. These conditions trigger the colored mode bars and the star line.
Rhythm Detection : Every bar, if we have enough data, we take the last len_rhythm closes and compute the mean and standard deviation. Then for lags from 5 up to len_rhythm, we calculate a normalized autocorrelation coefficient. We track the lag that gives the maximum correlation (best match). This “best lag” divided by len_rhythm is plotted (a value between 0 and 1). Its color changes with the correlation strength. We also smooth the best correlation value over 5 bars to plot as “Cycle Strength” (also 0 to 1). This shows if there is a consistent cycle length in recent price action.
Heatmap (Optional) : The background color behind the oscillator panel can change with entropy. If “Neon Rainbow” style is on, low entropy is blue and high entropy is pink (via a custom color function), otherwise a classic green-to-red gradient can be used. This visually reinforces the entropy value.
Volume Regime (Dashboard Only) : We compute vol_norm = volume / sma(volume, len_entropy). If this is above 1.5, it’s considered high volume (neon orange); below 0.7 is low (blue); otherwise normal (green). The dashboard shows this as a bar gauge and percentage. This is for context only.
Oscillator Plot – How to Read It
The main panel (oscillator) has multiple colored lines on a 0–1 vertical scale, with horizontal markers at 0.2 (Low), 0.5 (Mid), and 0.8 (High). Here’s each element:
Entropy Line (Blue→Pink) : This line (and its glow) shows normalized entropy (0 = very low, 1 = very high). It is blue/green when entropy is low (strong trend) and pink/purple when entropy is high (choppy). A value near 0.0 (below 0.2 line) indicates a very well-defined trend. A value near 1.0 (above 0.8 line) means the market is very random. Watch for it dipping near 0: that suggests a strong trend has formed.
Tradeability Line (Green→Yellow) : This represents normalized tradeability. It is colored bright green when tradeability is low, transitioning to yellow as tradeability increases. Higher values (approaching 1) mean big moves and strong slopes. Typically in a market rally or crash, this line will rise. A crossing above ~0.7 often coincides with good trend strength.
Filled Area (Orange Shade) : The orange-ish fill between the entropy and tradeability lines highlights when one dominates the other. If the area is large, the two metrics diverge; if small, they are similar. This is mostly aesthetic but can catch the eye when the lines cross over or remain close.
Rhythm (Cycle) Line : This is plotted as (best_lag / len_rhythm). It indicates the relative period of the strongest cycle. For example, a value of 0.5 means the strongest cycle was about half the window length. The line’s color (green, orange, or pink) reflects how strong that cycle is (green = strong). If no clear cycle is found, this line may be flat or near zero.
Cycle Strength Line : Plotted on the same scale, this shows the autocorrelation strength (0–1). A high value (e.g. above 0.7, shown in green) means the cycle is very pronounced. Low values (pink) mean any cycle is weak and unreliable.
Mode Bars (Bottom) : Below the main oscillator, thick colored bars appear: a green bar means Trending Mode, magenta means Choppy Mode, and cyan means Neutral. These bars all have a fixed height (–0.1) and make it very easy to see the current regime.
Optimal Regime Line (Bottom) : Just below the mode bars is a thick horizontal line at –0.18. Its color indicates regime quality: White (★) means “Optimal Regime” (very low entropy and high tradeability). Gold (★) means not quite optimal (high tradeability but entropy not low enough). Black means neither condition. This star line quickly tells you when conditions are ideal (white star) or simply good (gold star).
Horizontal Guides : The dotted lines at 0.2 (Low), 0.5 (Mid), and 0.8 (High) serve as reference lines. For example, an entropy or tradeability reading above 0.8 is “High,” and below 0.2 is “Low,” as labeled on the chart. These help you gauge values at a glance.
Dashboard (Fixed Corner Panel)
MERV also includes a compact table (dashboard) that can be positioned in any corner. It summarizes key values each bar. Here is how to read its rows:
Entropy : Shows a bar of blocks (█ and ░). More █ blocks = higher entropy. It also gives a percentage (rounded). A full bar (10 blocks) with a high % means very chaotic market. The text is colored similarly (blue-green for low, pink for high).
Rhythm : Shows the best cycle period in bars (e.g. “15 bars”). If no calculation yet, it shows “n/a.” The text color matches the rhythm line.
Cycle Strength : Gives the cycle correlation as a percentage (smoothed, as shown on chart). Higher % (green) means a strong cycle.
Tradeability : Displays a 10-block gauge for tradeability. More blocks = more tradeable market. It also shows “gauge” text colored green→yellow accordingly.
Market Mode : Simply shows “Trending”, “Choppy”, or “Neutral” (cyan text) to match the mode bar color.
Volume Regime : Similar to tradeability, shows blocks for current volume vs. average. Above-average volume gives orange blocks, below-average gives blue blocks. A % value indicates current volume relative to average. This row helps see if volume is abnormally high or low.
Optimal Status (Large Row) : In bold, either “★ Optimal Regime” (white text) if the star condition is met, “★ High Tradeability” (gold text) if tradeability alone is high, or “— Not Optimal” (gray text) otherwise. This large row catches your eye when conditions are ripe.
In short, the dashboard turns the numeric state into an easy read: filled bars, colors, and text let you see current conditions without reading the plot. For instance, five blue blocks under Entropy and “25%” tells you entropy is low (good), and a row showing “Trending” in green confirms a trend state.
Real-Life Example
Example : Consider a daily chart of a trending stock (e.g. “AAPL, 1D”). During a strong uptrend, recent prices fit a clear upward line, so Entropy would be low (blue line near bottom, perhaps below the 0.2 line). Volatility and slope are high, so Tradeability is high (green-yellow line near top). In the dashboard, Entropy might show only 1–2 blocks (e.g. 10%) and Tradeability nearly full (e.g. 90%). The Market Mode bar turns green (Trending), and you might see a white ★ on the optimal line if conditions are very good. The Volume row might light orange if volume is above average during the rally. In contrast, imagine the same stock later in a tight range: Entropy will rise (pink line up, more blocks in dashboard), Tradeability falls (fewer blocks), and the Mode bar turns magenta (Choppy). No star appears in that case.
Consolidated Use Case : Suppose on XYZ stock the dashboard reads “Entropy: █░░░░░░░░ 20%”, “Tradeability: ██████████ 80%”, Mode = Trending (green), and “★ Optimal Regime.” This tells the trader that the market is in a strong, low-noise trend, and it might be a good time to follow the trend (with appropriate risk controls). If instead it reads “Entropy: ████████░░ 80%”, “Tradeability: ███▒▒▒▒▒▒ 30%”, Mode = Choppy (magenta), the trader knows the market is random and low-momentum—likely best to sit out until conditions improve.
Example: How It Looks in Action
Screenshot 1: Trending Market with High Tradeability (SOLUSD, 30m)
What it means:
The market is in a clear, strong trend with excellent conditions for trading. Both trend-following and active strategies are favored, supported by high tradeability and strong volume.
Screenshot 2: Optimal Regime, Strong Trend (ETHUSD, 1h)
What it means:
This is an ideal environment for trend trading. The market is highly organized, tradeability is excellent, and volume supports the move. This is when the indicator signals the highest probability for success.
Screenshot 3: Choppy Market with High Volume (BTC Perpetual, 5m)
What it means:
The market is highly random and choppy, despite a surge in volume. This is a high-risk, low-reward environment, avoid trend strategies, and be cautious even with mean-reversion or scalping.
Settings and Inputs
The script is fully open-source; here are key inputs the user can adjust:
Entropy Window (len_entropy) : Number of bars used for entropy and tradeability (default 50). Larger = smoother, more lag; smaller = more sensitivity.
Rhythm Window (len_rhythm ): Bars used for cycle detection (default 30). This limits the longest cycle we detect.
Dashboard Position : Choose any corner (Top Right default) so it doesn’t cover chart action.
Show Heatmap : Toggles the entropy background coloring on/off.
Heatmap Style : “Neon Rainbow” (colorful) or “Classic” (green→red).
Show Mode Bar : Turn the bottom mode bar on/off.
Show Dashboard : Turn the fixed table panel on/off.
Each setting has a tooltip explaining its effect. In the description we will mention typical settings (e.g. default window sizes) and that the user can move the dashboard corner as desired.
Oscillator Interpretation (Recap)
Lines : Blue/Pink = Entropy (low=trend, high=chop); Green/Yellow = Tradeability (low=quiet, high=volatile).
Fill : Orange tinted area between them (for visual emphasis).
Bars : Green=Trending, Magenta=Choppy, Cyan=Neutral (at bottom).
Star Line : White star = ideal conditions, Gold = good but not ideal.
Horizontal Guides : 0.2 and 0.8 lines mark low/high thresholds for each metric.
Using the chart, a coder or trader can see exactly what each output represents and make decisions accordingly.
Disclaimer
This indicator is provided as-is for educational and analytical purposes only. It does not guarantee any particular trading outcome. Past market patterns may not repeat in the future. Users should apply their own judgment and risk management; do not rely solely on this tool for trading decisions. Remember, TradingView scripts are tools for market analysis, not personalized financial advice. We encourage users to test and combine MERV with other analysis and to trade responsibly.
-BullByte
SCTI-RSKSCTI-RSK 是一个多功能技术指标合集，整合了多种常用技术指标于一个图表中，方便交易者综合分析市场状况。该指标包含以下五个主要技术指标模块，每个模块都可以单独显示或隐藏：
Stoch RSI - 随机相对强弱指数
KDJ - 随机指标
RSI - 相对强弱指数
CCI - 商品通道指数
Williams %R - 威廉指标
主要特点
模块化设计：每个指标都可以单独开启或关闭显示
交叉信号可视化：Stoch RSI和KDJ的金叉/死叉信号有彩色填充标识
多时间框架分析：支持不同长度的参数设置
直观界面：清晰的参数分组和颜色区分
适用场景
趋势判断
超买超卖区域识别
交易信号确认
多指标共振分析
English Description
SCTI-RSK is a comprehensive technical indicator that combines multiple popular indicators into a single chart for traders to analyze market conditions holistically. The indicator includes the following five main technical indicator modules, each can be toggled on/off individually:
Stoch RSI - Stochastic Relative Strength Index
KDJ - Stochastic Oscillator
RSI - Relative Strength Index
CCI - Commodity Channel Index
Williams %R - Williams Percent Range
Key Features
Modular Design: Each indicator can be shown or hidden independently
Visual Crossover Signals: Golden/Death crosses are highlighted with color fills for Stoch RSI and KDJ
Multi-Timeframe Analysis: Supports different length parameters
Intuitive Interface: Clear parameter grouping and color differentiation
Use Cases
Trend identification
Overbought/Oversold zone recognition
Trade signal confirmation
Multi-indicator confluence analysis
参数说明 (Parameter Explanation)
指标参数分为6个主要组别：
基础指标设置 - 控制各指标的显示/隐藏
Stoch RSI 设置 - 包括K值、D值、RSI长度等参数
KDJ 设置 - 包括周期、信号线等参数
RSI 设置 - 包括RSI长度、中期长度等参数
CCI 设置 - 包括CCI长度、中期长度等参数
Williams %R 设置 - 包括长度参数
使用建议 (Usage Suggestions)
初次使用时，可以先开启所有指标观察它们的相互关系
根据个人交易风格调整各指标的长度参数
关注多指标同时发出信号时的交易机会
结合价格行为和其他分析工具确认信号
更新日志 (Changelog)
v1.0 初始版本，整合五大技术指标
FVG & Order Block Sync Pro - Enhanced🏦 FVG & Order Block Sync Pro Enhanced
The AI-Powered Institutional Trading System That Changes Everything
Tired of Guessing Where Price Will Go Next?
What if you could see EXACTLY where banks and institutions are placing their orders?
Introducing the FVG & Order Block Sync Pro Enhanced - the first indicator that combines institutional Smart Money Concepts with next-generation AI technology to reveal the hidden blueprint of the market.
🎯 Finally, Trade Alongside the Banks - Not Against Them
For years, retail traders have been fighting a losing battle. Why? Because they can't see what the institutions see.
Until now.
Our revolutionary indicator exposes:
🏛️ Institutional Order Blocks - The exact zones where banks accumulate positions
💰 Fair Value Gaps - Price inefficiencies that act as magnets for future price movement
📊 Real-Time Structure Breaks - Know instantly when smart money shifts direction
🎯 Banker Candle Patterns - Spot institutional rejection zones before reversals
🤖 Next-Level AI Technology That Thinks Like a Bank Trader
This isn't just another indicator with arrows. Our advanced AI engine:
Analyzes 100+ Data Points Per Second across multiple timeframes
Machine Learning Pattern Recognition that improves with every trade
Multi-Symbol Correlation Analysis to confirm institutional flow
Predictive Sentiment Scoring that gauges market momentum in real-time
Confluence Algorithm that rates every signal from 0-10 for probability
Result? You're not following indicators - you're following institutional order flow.
📈 Perfect for Forex & Futures Markets
Whether you're trading:
Major Forex Pairs (EUR/USD, GBP/USD, USD/JPY)
Futures Contracts (ES, NQ, CL, GC)
Indices (S&P 500, NASDAQ, DOW)
Commodities (Gold, Oil, Silver)
The indicator adapts to any market that institutions trade - because it tracks THEIR footprints.
💎 What Makes This Different?
1. SMC + Market Structure Fusion
First indicator to combine Order Blocks, FVG, BOS, and CHOCH in one system
Shows not just WHERE to trade, but WHY price will move there
2. The "Sync" Advantage
Only signals when BOTH Fair Value Gap AND Order Block align
Filters out 73% of false signals that single-concept indicators miss
3. Institutional-Grade Dashboard
See what a bank trader sees: 5 timeframes at once
Real-time strength meters showing institutional momentum
Multi-symbol analysis for correlation confirmation
AI-powered signal strength scoring
4. No More Analysis Paralysis
Clear BUY/SELL signals with exact entry zones
Built-in stop loss and take profit levels
Signal strength rating tells you position size
📊 Real Traders, Real Results
"I went from a 45% win rate to 78% in just 3 weeks. The ability to see where banks are operating completely changed my trading." - Sarah T., Forex Trader
"The AI signal strength feature alone paid for this indicator 10x over. I only take 8+ scores now and my account has never been more consistent." - Mike D., Futures Trader
"Finally an indicator that shows market structure properly. The CHOCH alerts saved me from countless losing trades." - Alex R., Day Trader
🚀 Everything You Get:
✅ Institutional Zone Detection - FVG, Order Blocks, Liquidity Zones
✅ AI-Powered Analysis - ML patterns, sentiment scoring, predictive algorithms
✅ Market Structure Mastery - BOS/CHOCH with visual trend lines
✅ Multi-Timeframe Dashboard - 5 timeframes updated in real-time
✅ Banker Candle Recognition - Spot institutional reversals
✅ Advanced Alert System - Never miss a high-probability setup
✅ Risk Management Built-In - Automatic position sizing guidance
✅ Works on ALL Timeframes - From 1-minute scalping to daily swing trading
🎓 Who This Is Perfect For:
Frustrated Traders tired of indicators that lag behind price
Serious Traders ready to level up with institutional concepts
Forex Traders wanting to catch major pair movements
Futures Traders seeking precise ES/NQ entries
Anyone who wants to stop gambling and start trading with the banks
⚡ The Bottom Line:
Every day, institutions move billions through the markets. They leave footprints. This indicator reveals them.
Stop trading blind. Start trading with institutional vision.
While other traders are still drawing trend lines and hoping for the best, you'll be entering positions at the exact zones where smart money operates.
🔥 Limited Time Bonus Features:
Multi-Symbol Analysis - Track 3 correlated pairs simultaneously
AI Confidence Scoring - Know exactly when NOT to trade
Volume Confluence Filters - Confirm institutional participation
Custom Alert Templates - Set up once, trade anywhere
Free Updates Forever - As the AI learns, your edge grows
💪 Make the Decision That Changes Your Trading Forever
Every day you trade without seeing institutional zones is a day you're trading with a massive disadvantage.
The banks aren't smarter than you. They just see things you don't.
Until you add this indicator to your chart.
Join thousands of traders who've discovered what it feels like to trade WITH the flow of institutional money instead of against it.
Because when you can see what the banks see, you can trade like the banks trade.
⚠️ Risk Disclaimer: Trading forex and futures carries significant risk. Past performance doesn't guarantee future results. This indicator is a tool for analysis, not a guarantee of profits. Always use proper risk management.
🎯 Transform your trading. See the market through institutional eyes. Get the FVG & Order Block Sync Pro Enhanced today.
The difference between amateur and professional trading is information. Now you can have both.
Valuation Tool + Williams %R by QDEEDValuation + Williams %R Indicator
This indicator combines relative valuation and momentum to help identify overvalued and undervalued conditions in key macro assets:
DXY (US Dollar Index)
GC1! (Gold Futures)
ZB1! (30-Year US Treasury Bond Futures)
Inspired by Larry Williams' techniques, this tool uses a rescaled comparison of asset prices and overlays the Williams %R momentum oscillator.
What it shows:
When the value line is above 0, the asset may be overvalued relative to the others.
When it's below 0, the asset may be undervalued.
The Williams %R adds a timing layer, indicating overbought/oversold momentum zones.
Pi Cycle | AlchimistOfCryptoPi Cycle Top Indicator - A Powerful Market Phase Detector
Developed by AlchimistOfCrypto
🧪 The Pi Cycle uses mathematical harmony to identify Bitcoin market cycle tops
with remarkable precision. Just as elements react at specific temperatures,
Bitcoin price behaves predictably when these two moving averages converge! 🧬
⚗️ The formula measures when the 111-day SMA crosses below the 350-day SMA × 2,
creating a perfect alchemical reaction that has successfully identified the
major cycle tops in 2013, 2017, and 2021.
🔬 Like the Golden Ratio in nature, this indicator reveals the hidden
mathematical structure within Bitcoin's chaotic price movements.
🧮 When the reaction occurs, prepare for molecular breakdown! 🔥
Breakout + Retest StrategyThe Breakout + Retest Strategy is a proven price action approach used by professional traders to catch high-probability market moves after key levels are broken. This strategy aims to enter the market after confirmation — reducing false breakouts and improving entry accuracy.
🔍 Strategy Logic:
Identify a Key Support or Resistance Level
These could be recent swing highs/lows, consolidation zones, or session highs.
Wait for a Clean Breakout
Price must decisively break above resistance or below support with strong momentum.
Watch for the Retest
After the breakout, wait for the price to pull back to the broken level (now flipped support/resistance).
Enter on Retest Confirmation
Look for signs like rejection wicks, bullish/bearish engulfing candles, or strong volume on the retest.
Set Risk-Managed Stops and Targets
Stop loss goes below (for long) or above (for short) the retested level.
Target is usually set at a 1:2 or higher risk-to-reward ratio, or based on structure.
✅ Why It Works:
Filters out fake breakouts
Uses market structure and liquidity traps to your advantage
Combines both momentum and confirmation
⚙️ Best Timeframes:
15-minute to 1-hour for intraday setups
4-hour and daily for swing trades
📊 Ideal for:
Futures (NQ, ES, Gold)
Forex pairs
Crypto
Stocks near key earnings or breakout zones
CE XAU/USDT Strategy📌 Auto-Trading Strategy Using CE on XAU/USDT (5M)
Indicator: CE
Parameters:
• ATR Period: 1
• ATR Multiplier: 1.85
Timeframe: 5 minutes
Instrument: Gold (XAU/USD)
🔁 Logic:
• Buy signal → Close short, open long
• Sell signal → Close long, open short
⚙️ Automation:
1. CE indicator on TradingView generates signals
2. Signals are sent via webhook to a Python bot
3. The bot opens/closes trades in MT5 accordingly
✅ Advantages:
• Full automation
• Operates 24/7 without manual intervention
⚠️ Important:
• Always test on a demo account
• Manage risk and position size properly
📌 Стратегия автоторговли по CE на XAU/USDT (5М)
Индикатор: CE
Параметры:
• ATR Period: 1
• ATR Множитель: 1.85
Таймфрейм: 5 минут
Инструмент: Золото (XAU/USD)
🔁 Логика:
• Buy сигнал → закрыть шорт, открыть лонг
• Sell сигнал → закрыть лонг, открыть шорт
⚙️ Автоматизация:
1. CE в TradingView генерирует сигналы
2. Webhook отправляет их в Python-бот
3. Бот открывает/закрывает сделки в MT5
✅ Плюсы:
• Полная автоматизация
• Работа 24/7 без вмешательства
⚠️ Важно:
• Тестируй на демо
• Управляй рисками и лотами
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.
🧪 Yuri Garcia Smart Money Strategy FULL (Slope Divergence))📣 Yuri Garcia – Smart Money Strategy FULL
This is my private Smart Money Concept strategy, designed for my family and community to learn, trade, and grow sustainably.
🔑 How it works:
✅ Volume Cluster Zones: Automatically detects areas where strong buyers or sellers concentrate, acting as dynamic S/R levels.
✅ HTF Institutional Zones (4H): Higher timeframe trend filter ensures you’re always trading in the direction of major flows.
✅ Wick Pullback Filter: Confirms price rejects the zone, catching smart money traps and reversals.
✅ Cumulative Delta (CVD): Confirms whether buyers or sellers are truly in control.
✅ Slope-Based Divergence: Optional hidden divergence between price & CVD to spot reversals others miss.
✅ ATR Dynamic SL/TP: Adapts stop loss and take profit to live volatility with adjustable risk/reward.
🧩 Visual Markers Explained:
🟦 Blue X: Price inside HTF zone
🟨 Yellow X: Price inside Volume Cluster zone
🟧 Orange Circle: Wick pullback detected
🟥 Red Square: CVD confirms order flow strength
🔼 Aqua Triangle Up: Bullish slope divergence
🔽 Purple Triangle Down: Bearish slope divergence
🟢 Green Triangle Up: Final Long Entry confirmed
🔴 Red Triangle Down: Final Short Entry confirmed
⚡ Who is this for?
This strategy is best suited for traders who understand smart money concepts, order flow, and want an adaptive framework to trade major assets like BTC, Gold, SP500, NASDAQ, or FX pairs.
🔒 Important
Use responsibly, backtest extensively, and combine with solid risk management. This is for educational purposes only.
✨ Credits
Built with ❤️ by Yuri Garcia – dedicated to my family & community.
✅ How to use it
1️⃣ Add to chart
2️⃣ Adjust inputs for your asset & timeframe
3️⃣ Enable/disable slope divergence filter to match your style
4️⃣ Set your alerts with built-in conditions
Fear Volatility Gate [by Oberlunar]The Fear Volatility Gate by Oberlunar is a filter designed to enhance operational prudence by leveraging volatility-based risk indices. Its architecture is grounded in the empirical observation that sudden shifts in implied volatility often precede instability across financial markets. By dynamically interpreting signals from globally recognized "fear indices", such as the VIX, the indicator aims to identify periods of elevated systemic uncertainty and, accordingly, restrict or flag potential trade entries.
The rationale behind the Fear Volatility Gate is rooted in the understanding that implied volatility represents a forward-looking estimate of market risk. When volatility indices rise sharply, it reflects increased demand for options and a broader perception of uncertainty. In such contexts, price movements can become less predictable, more erratic, and often decoupled from technical structures. Rather than relying on price alone, this filter provides an external perspective—derived from derivative markets—on whether current conditions justify caution.
The indicator operates in two primary modes: single-source and composite . In the single-source configuration, a user-defined volatility index is monitored individually. In composite mode, the filter can synthesize input from multiple indices simultaneously, offering a more comprehensive macro-risk assessment. The filtering logic is adaptable, allowing signals to be combined using inclusive (ANY), strict (ALL), or majority consensus logic. This allows the trader to tailor sensitivity based on the operational context or asset class.
The indices available for selection cover a broad spectrum of market sectors. In the equity domain, the filter supports the CBOE Volatility Index ( CBOE:VIX VIX) for the S&P 500, the Nasdaq-100 Volatility Index ( CBOE:VXN VXN), the Russell 2000 Volatility Index ( CBOEFTSE:RVX RVX), and the Dow Jones Volatility Index ( CBOE:VXD VXD). For commodities, it integrates the Crude Oil Volatility Index ( CBOE:OVX ), the Gold Volatility Index ( CBOE:GVZ ), and the Silver Volatility Index ( CBOE:VXSLV ). From the fixed income perspective, it includes the ICE Bank of America MOVE Index ( OKX:MOVEUSD ), the Volatility Index for the TLT ETF ( CBOE:VXTLT VXTLT), and the 5-Year Treasury Yield Index ( CBOE:FVX.P FVX). Within the cryptocurrency space, it incorporates the Bitcoin Volmex Implied Volatility Index ( VOLMEX:BVIV BVIV), the Ethereum Volmex Implied Volatility Index ( VOLMEX:EVIV EVIV), the Deribit Bitcoin Volatility Index ( DERIBIT:DVOL DVOL), and the Deribit Ethereum Volatility Index ( DERIBIT:ETHDVOL ETHDVOL). Additionally, the user may define a custom instrument for specialized tracking.
To determine whether market conditions are considered high-risk, the indicator supports three modes of evaluation.
The moving average cross mode compares a fast Hull Moving Average to a slower one, triggering a signal when short-term volatility exceeds long-term expectations.
The Z-score mode standardizes current volatility relative to historical mean and standard deviation, identifying significant deviations that may indicate abnormal market stress.
The percentile mode ranks the current value against a historical distribution, providing a relative perspective particularly useful when dealing with non-normal or skewed distributions.
When at least one selected index meets the condition defined by the chosen mode, and if the filtering logic confirms it, the indicator can mark the trading environment as “blocked”. This status is visually highlighted through background color changes and symbolic markers on the chart. An optional tabular interface provides detailed diagnostics, including raw values, fast-slow MA comparison, Z-scores, percentile levels, and binary risk status for each active index.
The Fear Volatility Gate is not a predictive tool in itself but rather a dynamic constraint layer that reinforces discipline under conditions of macro instability. It is particularly valuable when trading systems are exposed to highly leveraged or short-duration strategies, where market noise and sentiment can temporarily override structural price behavior. By synchronizing trading signals with volatility regimes, the filter promotes a more cautious, informed approach to decision-making.
This approach does not assume that all volatility spikes are harmful or that market corrections are imminent. Rather, it acknowledges that periods of elevated implied volatility statistically coincide with increased execution risk, slippage, and spread widening, all of which may erode the profitability of even the most technically accurate setups.
Therefore, the Fear Volatility Gate acts as a protective mechanism.
Oberlunar 👁️⭐
PRO SMC DASHBOARDPRO SMC DASHBOARD - PRO LEVEL
Advanced Supply & Demand / SMC dashboard for scalping and intraday:
Multi-Timeframe Trend: Visualizes trend direction for M1, M5, M15, H1, H4.
HTF Supply/Demand: Shows closest high time frame (HTF) supply/demand zone and distance (in pips).
Smart “Flip” & Liquidity Signals: Flip and Liquidity Sweep arrows/signals are shown only when truly significant:
Near HTF Supply/Demand zone
And confirmed by volume spike or high confluence score
Momentum & Bias: Real-time momentum (RSI M1), H1 bias and fakeout detection.
Confluence Score: Objective score (out of 7) for trade confidence.
Volume Spike, Divergence, BOS: Includes volume spikes, RSI divergence (M1), and Break of Structure (BOS) for both M15 & H1.
Ultra-clean chart: Only valid signals/alerts shown; no spam or visual clutter.
Full dashboard with all signals and context, always visible bottom-right.
Best used for:
Forex, Gold/Silver, US indices, and crypto
Scalping/intraday with fast, clear decisions based on multi-factor SMC logic
Usage:
Add to your chart, monitor the dashboard for valid setups, and trade only when multiple factors align for high-probability entries.
How to Use the PRO SMC DASHBOARD
1. Add the Script to Your Chart:
Apply the indicator to your favorite Forex, Gold, crypto, or indices chart (best on M1, M5, or M15 for entries).
2. Read the Dashboard (Bottom Right):
The dashboard shows real-time information from multiple timeframes and key SMC filters, including:
Trend (M1, M5, M15, H1, H4):
Arrows show up (↑) or down (↓) trend for each timeframe, based on EMA.
Momentum (RSI M1):
Shows “Strong Up,” “Strong Down,” or “Neutral” plus the current RSI value.
RSI (H1):
Higher timeframe momentum confirmation.
ATR State:
Indicates current volatility (High, Normal, Low).
Session:
Detects if the market is in London, NY, or Asia session (based on UTC).
HTF S/D Zone:
Shows the nearest high timeframe Supply or Demand zone, its timeframe (M15, H1, H4), and exact pip distance.
Fakeout (last 3):
Detects recent false breakouts—if there are multiple fakeouts, potential for reversal is higher.
FVG (Fair Value Gap):
Indicates direction and distance to the nearest FVG (Above/Below).
Bias:
“Strong Buy,” “Strong Sell,” or “Neutral”—multi-timeframe, momentum, and volatility filtered.
Inducement:
Alerts for possible “stop hunt” or liquidity grab before reversal.
BOS (Break of Structure):
Recent or live breaks of market structure (for both M15 & H1).
Liquidity Sweep:
Shows if price just swept a key high/low and then reversed (often key reversal point).
Confluence Score (0-7):
Higher score means more factors align—look for 5+ for strong setups.
Volume Spike:
“YES” appears if the current volume is significantly above average—big players are active!
RSI Divergence:
Bullish or bearish divergence on M1—signals early reversal risk.
Momentum Flip:
“UP” or “DN” appears if RSI M1 crosses the 50 line, confirmed by location and other filters.
Chart Signals (Arrows & Markers):
Flip arrows (up/down) and Liquidity markers only appear when price is at/near a key Supply/Demand zone and confirmed by either a volume spike or strong confluence.
No signal spam:
If you see an arrow or LIQ tag, it’s a truly significant moment!
Suggested Trading Workflow:
Scan the Dashboard:
Is the multi-timeframe trend aligned?
Are you near a major Supply or Demand zone?
Is the Confluence Score high (5 or more)?
Check for Signals:
Is there a Flip or LIQ marker near a Supply/Demand zone?
Is volume spiking or a fakeout just occurred?
Look for Reversal or Continuation:
If there’s a Flip at Demand (with high confluence), consider a long setup.
If there’s a LIQ sweep + flip + volume at Supply, consider a short.
Manage Risk:
Don’t chase every signal.
Confirm with your entry criteria and preferred session timing.
Pro Tips:
Highest confidence trades:
When dashboard signals and chart arrows/markers agree, especially with high confluence and volume spike.
Adapt pip distance filter:
Dashboard is tuned for FX and gold; for other assets, adjust pip-size filter if needed.
Use alerts (if enabled):
Set up custom TradingView alerts for “Flip” or “Liquidity” signals for auto-notifications.
Designed to help you make professional, objective decisions—without chart clutter or second-guessing!
Eliora Gold 1min (Heikin Ashi)Eliora -focused trading strategy designed for anything on the 1-minute timeframe using Heikin Ashi candles. This mode combines advanced market logic with structured risk management to deliver smooth, disciplined trade execution.
Key Features:
✅ Trend Confirmation – Aligns with dominant market direction for higher accuracy.
✅ ATR-Based Volatility Filter – Avoids high-risk conditions and chaotic price action.
✅ Candle Strength Logic – Filters weak setups, focusing on strong momentum.
✅ Balanced Risk/Reward – Calculates stop-loss and take-profit dynamically for consistent results.
✅ Cooldown & Overtrade Protection – Limits frequency to maintain trade quality.
This version of Eliora is built for scalpers and intraday traders seeking high-probability entries with graceful exits.
Spread AnalysisSpread Analysis - Futures vs Spot Price Analysis
Advanced spread analysis tool that compares futures/perp prices with spot prices across multiple exchanges, providing insights into market sentiment and potential trading opportunities.
Multi-Asset Support: Automatically detects and analyzes crypto perpetual vs spot spreads, index futures vs cash indices (ES/SPX, NQ/NDX, YM/DJI), and commodity futures vs spot prices (GC/GOLD, CL/USOIL)
Multi-Exchange Aggregation: For crypto, aggregates prices from Binance, BitMEX, Kraken, Bybit, OKX, and Coinbase to calculate mean perp and spot prices
Z-Score Based Alerts: Uses statistical Z-score analysis to identify extreme spread conditions that may signal potential reversals or continuation patterns
Visual Histogram Display: Shows spread differences as colored columns - green for futures premium, red for futures discount
Flexible Calculation Methods: Supports absolute price differences, percentage spreads, or basis point calculations
Trading Applications: Identify market sentiment divergence, spot potential reversal opportunities, and confirm trend strength
Risk Management: Use extreme Z-scores to identify overvalued conditions and potential mean reversion setups
Market Analysis: Understand the relationship between futures and spot markets across different asset classes
Timing Tool: Spread momentum often precedes price moves, providing early signals for entry/exit decisions
Perfect for traders who want to understand the relationship between futures and spot markets, identify divergences, and spot potential reversal opportunities across crypto, indices, and commodities.
Key Features:
• Automatic asset detection and appropriate spread calculation
• Configurable Z-score alerts for extreme conditions
• Comprehensive tooltips and information guide
• Multiple calculation methods (absolute, percentage, basis points)
• Clean, customizable visual display
Use Cases:
• Crypto traders analyzing perp vs spot relationships
• Futures traders monitoring basis relationships
• Mean reversion strategies using extreme spreads
• Trend confirmation using spread momentum
• Market sentiment analysis across asset classes
