US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.
Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408.
Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180.
Cochrane, J. H. (2005). Asset pricing. Princeton University Press.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.
Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119.
Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120.
Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.
Fisher, I. (1930). The theory of interest. Macmillan.
Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley.
Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin.
Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.
Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14.
Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson.
Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50.
Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson.
Nielsen, J. (1993). Usability engineering. Academic Press.
Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing.
Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall.
Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution.
Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill.
Sommerville, I. (2016). Software engineering. Pearson.
Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351-394.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
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Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
ค้นหาในสคริปต์สำหรับ "如何用wind搜索股票的发行价和份数"
Currency Weekend - shading weekend trading// ─────────────────────────────────────────────────────────────────────────────
// © 2025, Steve / Steven Anthony – "Currency Weekend"
// This script highlights the low-liquidity weekend window that often affects
// both fiat currency markets and cryptocurrencies like Bitcoin.
//
// ╭─────────────────────────────── DESCRIPTION ───────────────────────────────╮
// | This indicator shades a customizable time window on your chart, |
// | originally set to highlight the **forex weekend lull** from |
// | **Friday 21:00 UTC to Sunday 21:00 UTC**, when traditional fiat |
// | currency markets close. |
// | |
// | Traders who observe Bitcoin, Ethereum, or other crypto assets may |
// | notice reduced liquidity or increased erratic moves during this time, |
// | due to overlapping behaviors from professional forex traders who |
// | trade both markets. |
// ╰──────────────────────────────────────────────────────────────────────────╯
//
// 🔧 Flexible Configuration:
// - Define your own start and end **day + time** for shading
// - Useful for shading other custom quiet periods or session transitions
//
// 💡 Use Cases:
// - Avoid trading during low-liquidity periods
// - Spot potential weekend traps or price gaps
// - Align crypto behavior with fiat market hours
//
// 📍 Default Settings:
// - Start: Friday 21:00 UTC
// - End: Sunday 21:00 UTC
//
// Timezone is normalized to the chart’s timezone for seamless integration.
//
// ─────────────────────────────────────────────────────────────────────────────
Canonical Momenta Indicator [T1][T69]📌 Overview
The Canonical Momenta Indicator models trend pressure using a Lagrangian-based momentum engine combined with reflexivity theory to detect bursts in price movement influenced by herd behavior and volume acceleration.
🧠 Features
Lagrangian-based kinetic model combining velocity and acceleration
Reflexivity burst detection with directional scoring
Adaptive momentum-weighted output (adaptiveCMI)
Buy 🐋 / Sell 🐻 labels when reflexivity confirms direction
Fully parameterized for customization
⚙️ How to Use
This indicator helps traders:
Detect reflexive bursts in market activity driven by sharp price movement + volume spikes
Capture herd-driven directional moves early.
Gauge market pressure using a kinetic-potential energy model.
Suggested signals:
🐋 Reflexive Up: Strong bullish momentum spike confirmed by volume and positive lagrangian pressure
🐻 Reflexive Down: Strong bearish dump confirmed by volume and negative lagrangian burst
🔧 Configuration
MA Lookback Length - Smoothing for baseline price & energy calculation
Reflexivity Momentum Threshold - Price momentum trigger for burst detection
Reflexivity Lookback - Period over which bursts are counted
Reflexivity Window - Minimum burst sum to trigger signal label
Volume Spike Threshold - % above average volume to qualify as burst
📊 Behavior Description
The indicator computes a Lagrangian energy:
Kinetic Energy = (velocity² + 0.5 * acceleration²)
Potential Energy = deviation from moving average (distance²)
Lagrangian = Potential − Kinetic (higher = overextension)
Then, reflexive bursts are triggered when:
Price is rising or falling over short window (burstMvmnt)
Volume is above average by a user-defined multiple
Each bar gets a burst score:
+1 for up-burst
−1 for down-burst
0 otherwise
⚠️ Risk Profile Based on Lookback Settings
Risk Level | Description | Recommended Lookback
🟥 High | Extremely sensitive to bursts, prone to false signals | 7–10
🟨 Moderate | Balanced reflexivity with trend confirmation | 11–20
🟩 Low | Filters out most noise, slower to react | 21+
🧪 Advanced Tips
Combine with moving average slope for trend filtering
Use divergence between adaptiveCMI and price to detect exhaustion
Works well in crypto, commodities, and volatile assets
⚠️ Limitations
Sensitive to high volatility noise if volMult is too low
Designed for higher timeframes (1H, 4H, Daily) for reliability
Doesn’t confirm direction in sideways markets — pair with other filters
📝 Disclaimer
This tool is provided for educational and informational purposes. Always do your own backtesting and use proper risk management.
Trigonometric StochasticTrigonometric Stochastic - Mathematical Smoothing Oscillator
Overview
A revolutionary approach to stochastic oscillation using sine wave mathematical smoothing. This indicator transforms traditional stochastic calculations through trigonometric functions, creating an ultra-smooth oscillator that reduces noise while maintaining sensitivity to price changes.
Mathematical Foundation
Unlike standard stochastic oscillators, this version applies sine wave smoothing:
• Raw Stochastic: (close - lowest_low) / (highest_high - lowest_low) × 100
• Trigonometric Smoothing: 50 + 50 × sin(2π × raw_stochastic / 100)
• Result: Naturally smooth oscillator with mathematical precision
Key Features
Advanced Smoothing Technology
• Sine Wave Filter: Eliminates choppy movements while preserving signal integrity
• Natural Boundaries: Mathematically constrained between 0-100
• Reduced False Signals: Trigonometric smoothing filters market noise effectively
Traditional Stochastic Levels
• Overbought Zone: 80 level (dashed line)
• Oversold Zone: 20 level (dashed line)
• Midline: 50 level (dotted line) - equilibrium point
• Visual Clarity: Clean oscillator panel with clear level markings
Smart Signal Generation
• Anti-Repaint Logic: Uses confirmed previous bar values
• Buy Signals: Generated when crossing above 30 from oversold territory
• Sell Signals: Generated when crossing below 70 from overbought territory
• Crossover Detection: Precise entry/exit timing
Professional Presentation
• Separate Panel: Dedicated oscillator window (overlay=false)
• Price Format: Formatted as price indicator with 2-decimal precision
• Theme Adaptive: Automatically matches your chart color scheme
Parameters
• Cycle Length (5-200): Period for highest/lowest calculations
- Shorter periods = more sensitive, more signals
- Longer periods = smoother, fewer but stronger signals
Trading Applications
Momentum Analysis
• Overbought/Oversold: Clear visual identification of extreme levels
• Momentum Shifts: Early detection of momentum changes
• Trend Strength: Monitor oscillator position relative to midline
Signal Trading
• Long Entries: Buy when crossing above 30 (oversold bounce)
• Short Entries: Sell when crossing below 70 (overbought rejection)
• Confirmation Tool: Use with trend indicators for higher probability trades
Divergence Detection
• Bullish Divergence: Price makes lower lows, oscillator makes higher lows
• Bearish Divergence: Price makes higher highs, oscillator makes lower highs
• Early Warning: Spot potential trend reversals before they occur
Trading Strategies
Scalping (5-15min timeframes)
• Use cycle length 10-14 for quick signals
• Focus on 20/80 level bounces
• Combine with price action confirmation
Swing Trading (1H-4H timeframes)
• Use cycle length 20-30 for reliable signals
• Wait for clear crossovers with momentum
• Monitor divergences for reversal setups
Position Trading (Daily+ timeframes)
• Use cycle length 50+ for major signals
• Focus on extreme readings (below 10, above 90)
• Combine with fundamental analysis
Advantages Over Standard Stochastic
1. Smoother Action: Sine wave smoothing reduces whipsaws
2. Mathematical Precision: Trigonometric functions provide consistent behavior
3. Maintained Sensitivity: Smoothing doesn't compromise signal quality
4. Reduced Noise: Cleaner signals in volatile markets
5. Visual Appeal: More aesthetically pleasing oscillator movement
Best Practices
• Market Context: Consider overall trend direction
• Multiple Timeframe: Confirm signals on higher timeframes
• Risk Management: Always use proper position sizing
• Backtesting: Test parameters on your preferred instruments
• Combination: Works excellently with trend-following indicators
Built-in Alerts
• Buy Alert: Trigonometric stochastic oversold crossover
• Sell Alert: Trigonometric stochastic overbought crossunder
Technical Specifications
• Pine Script Version: v6
• Panel: Separate oscillator window
• Format: Price indicator with 2-decimal precision
• Performance: Optimized for all timeframes
• Compatibility: Works with all instruments
Free and open-source indicator. Modify, improve, and share with the community!
Educational Value: Perfect for traders wanting to understand how mathematical smoothing improves oscillators and trigonometric applications in technical analysis.
ORB Range Indicator with Fibonacci Targets
This script plots the Opening Range (ORB) high and low based on a configurable time window (5–45 minutes from the U.S. session open at 9:30 AM EST).
Once the ORB window closes, the indicator draws horizontal lines marking:
ORB High and Low
The size of the range in price and %
Fibonacci-based price targets above and below the range (0.382, 0.618, 1.000, 1.618, 2.000)
You can control:
Which Fibonacci levels to display
Whether to show long targets, short targets, or both
All drawings are automatically cleared at the start of each trading day.
Ideal for breakout traders using ORB and Fibonacci extensions for target planning.
Midnight 30min High/LowMidnight 30min High/Low — Overnight Liquidity Range Tracker
Capture the Overnight Session: A Strategic Level Identification Tool from Professional Trading Methodology
This indicator captures the high and low prices during the critical 30-minute midnight session (12:00-12:30 AM EST) and projects these levels forward as key support and resistance zones. These overnight ranges often contain significant liquidity and serve as crucial reference points for intraday price action, representing areas where institutional activity may have established important levels.
🔍 What This Script Does:
Identifies Critical Overnight Session Levels
- Automatically detects the 12:00-12:30 AM EST session window
- Captures the highest and lowest prices during this 30-minute period
- Projects these levels forward for multiple trading days
Creates Dynamic Support/Resistance Zones
- Extends midnight high/low levels as horizontal lines with customizable projection periods
- Fills the area between high and low to create a visual trading range
- Updates automatically each trading day with new overnight levels
Provides Clear Visual Reference Points
- Optional session start markers (●) highlight when the midnight session begins
- Color-coded lines distinguish between high and low levels
- Transparent fill area creates an easy-to-identify trading zone
Real-Time Level Tracking
- Updates levels in real-time during the active midnight session
- Maintains historical levels for reference and backtesting
- Compatible with data window for precise level values
⚙️ Customization Options:
Extend Days (1-30):** Control how many days forward the levels are projected (default: 5 days)
High Line Color:** Customize the midnight high line color (default: blue)
Low Line Color:** Customize the midnight low line color (default: orange)
Fill Color:** Adjust the transparency and color of the range area (default: light aqua, 80% transparency)
Show Session Markers:** Toggle yellow session start indicators on/off (default: enabled)
💡 How to Use:
Deploy on lower timeframes (1m-15m) for precise level identification and reaction monitoring**
Watch for key price interactions:
- Rejection at midnight high levels (potential resistance)
- Bounce from midnight low levels (potential support)
- Range-bound trading between the high and low levels
Combine with liquidity concepts:
- Monitor for stop hunts above/below these levels
- Look for false breakouts that snap back into the range
- Use as confluence with other ICT concepts like FVGs and Order Blocks
Strategic Applications:
- Range trading between midnight levels
- Breakout confirmation when price closes decisively outside the range
- Support/resistance validation for entry and exit planning
🔗 Combine With These Tools for Complete Market Structure Analysis:
✅ First FVG — Opening Range Fair Value Gap Detector.
✅ ICT Turtle Soup (Liquidity Reversal)— Spot stop hunts and false breakout scenarios
✅ ICT Macro Zones (Grey Box Version)- It tracks real-time highs and lows for each Silver Bullet session
✅ ICT SMC Liquidity Grabs and OBs- Liquidity Grabs, Order Block Zones, and Fibonacci OTE Levels, allowing traders to identify institutional entry models with clean, rule-based visual signals.
Together, these tools create a comprehensive Smart Money Concepts (SMC) framework — helping traders identify, anticipate, and capitalize on institutional-level price movements with precision and confidence during critical overnight sessions.
Initial Balance Wave MapThis indicator visualizes the Initial Balance (IB) range for any session, marking the first hour's high and low. It includes optional midpoints, extensions (e.g. 1.5x IB, 2x IB), and customizable time windows. Additional features allow users to display session open, high, low, close, and VWAP reference points. Designed to support price action and session structure analysis, it adapts to various global futures and FX market opens. All display elements are optional and fully configurable.
This updated indicator builds upon the open-source foundation by @noop-noop with enhancements and user-facing labels tailored for Auction Market Theory, scalping, and structure-based trade setups.
Key updated Featured: Multiple previous day's IB levels carry forward into the current day's chart, as opposed to just the previous day's levels carrying forward to the new IB time.
🙌 Credits:
This script builds upon the excellent open-source work by @noop-noop. Original script available here .
Alt Szn Oracle - Institutional GradeThe Alt Szn Oracle is a macro-level indicator built to help traders front-run altseason by tracking liquidity, dominance rotation, sentiment, and capital flows—all in one signal. It’s designed for those who don’t just chase pumps, but want to understand when the tide is turning and why. This tool doesn't predict specific coin breakouts—it tells you when the market as a whole is gearing up to rotate into higher beta assets like altcoins, including memes and microcaps.
The index consolidates ten macro inputs into a normalized, smoothed score from 0–100. These include Bitcoin and Ethereum dominance, ETH/BTC, altcoin market cap (Total3), relative volume flows, and stablecoin supply (USDT, USDC, DAI)—which act as proxies for risk-on appetite and dry powder entering the system. It also incorporates manually updated sentiment metrics from Google Trends and the Fear & Greed Index, giving it a behavioral edge that most indicators lack.
The logic is simple but powerful: when BTC dominance is falling, ETH/BTC is rising, altcoin volume increases relative to BTC/ETH, and stablecoins start moving—you're likely in the early innings of rotation. The index is also filtered through a volatility threshold and smoothed with an EMA to eliminate chop and fakeouts.
Use this indicator on macro charts like TOTAL3, TOTAL2, or ETHBTC to gauge market health, or overlay it on specific coins like PEPE, DOGE, or SOL to confirm if the tide is in your favor. Interpreting the score is straightforward: readings above 80 suggest euphoria and signal it’s time to de-risk, 60–80 indicates expansion and confirms altseason is underway, 40–60 is neutral, and 20–40 is a capitulation zone where smart money accumulates.
What sets this apart is that it doesn’t just track price—it reflects the flow of capital, the positioning of liquidity, and the sentiment of the crowd. Most altseason indicators are lagging, overfitted, or too simplistic. This one is modular, forward-looking, and grounded in real capital rotation theory.
If you're a trader who wants to time the cycle, not guess it, this is your tool. Refine it, fork it, or expand it to your niche—DeFi, NFTs, meme coins, or L1s. It’s a framework for reading the macro winds, not a signal service. Use it with discipline, and you’ll catch the wave while others drown in noise.
Staccked SMA - Regime Switching & Persistance StatisticsThis indicator is designed to identify the prevailing market regime by analyzing the behavior of a "stack" of Simple Moving Averages (SMAs). It helps you understand whether the market is currently trending, mean-reverting, or moving randomly.
Core Concept: SMA Correlation
At its heart, the indicator examines the relationship between a set of nine SMAs with different lengths (3, 5, 8, 13, 21, 34, 55, 89, 144) and the lengths themselves.
In a strong trending market (either up or down), the SMAs will be neatly "stacked" in order of their length. The shortest SMA will be furthest from the longest SMA, creating a strong, almost linear visual pattern. When we measure the statistical correlation between the SMA values and their corresponding lengths, we get a value close to +1 (perfect uptrend stack) or -1 (perfect downtrend stack). The absolute value of this correlation will be very high (close to 1).
In a mean-reverting or sideways market, the SMAs will be tangled and crisscrossing each other. There is no clear order, and the relationship between an SMA's length and its price value is weak. The correlation will be close to 0.
This indicator calculates this Pearson correlation on every bar, giving a continuous measure of how ordered or "trendy" the SMAs are. An absolute correlation above 0.8 is considered strongly trending, while a value between 0.4 and 0.8 suggests a mean-reverting character. Below 0.4, the market is likely random or choppy.
Regime Classification and Statistics
The indicator doesn't just look at the current correlation; it analyzes its behavior over a user-defined lookback window (default is 252 bars) to classify the overall market "regime."
It presents its findings in a clear table:
📊 |SMA Correlation| Regime Table: This main table provides a snapshot of the current market character.
Median: Shows the median absolute correlation over the lookback period, giving a central tendency of the market's behavior.
% > 0.80: The percentage of time the market was in a strong trend during the lookback period.
% < 0.80 & > 0.40: The percentage of time the market showed mean-reverting characteristics.
🧠 Regime: The final classification. It's labeled "📈 Trend-Dominant" if the median correlation is high and it has spent a significant portion of the time trending. It's labeled "🔄 Mean-Reverting" if the median is in the middle range and it has spent significant time in that state. Otherwise, it's considered "⚖️ Random/ Choppy".
📐 Regime Significance: This tells you how statistically confident you can be in the current regime classification, using a Z-score to compare its occurrence against random chance. ⭐⭐⭐ indicates high confidence (99%), while "❌ Not Significant" means the pattern could be random.
Regime Transition Probabilities
Optionally, a second table can be displayed that shows the historical probability of the market transitioning from one regime to another over different time horizons (t+5, t+10, t+15, and t+20 bars).
📈 → 🔄 → ⚖️ Transition Table: This table answers questions like, "If the market is trending now (From: 📈), what is the probability it will be mean-reverting (→ 🔄) in 10 bars?"
This provides powerful insights into the market's cyclical nature, helping you anticipate future behavior based on past patterns. For example, you might find that after a period of strong trending, a transition to a choppy state is more likely than a direct switch to a mean-reverting
Indicator Settings
Lookback Window for Regime Classification: This sets the number of recent bars (default is 252) the script analyzes to determine the current market regime (Trending, Mean-Reverting, or Random). A larger number provides a more stable, long-term view, while a smaller number makes the classification more sensitive to recent price action.
Show Regime Transition Table: A simple toggle (on/off) to show or hide the table that displays the probabilities of the market switching from one regime to another.
Lookback Offset for Starting Regime: This determines the "starting point" in the past for calculating regime transitions. The default is 20 bars ago. The script looks at the regime at this point and then checks what it became at later points.
Step 1, 2, 3, 4 Offset (bars): These define the future time intervals (5, 10, 15, and 20 bars by default) for the transition probability table. For example, the script checks the regime at the "Lookback Offset" and then sees what it transitioned to 5, 10, 15, and 20 bars later.
Significance Filter Settings
Use Regime Significance Filter: When enabled, this filter ensures that the regime transition statistics only count transitions that were "statistically significant." This helps to filter out noise and focus on more reliable patterns.
Min Stars Required (1=90%, 2=95%, 3=99%): This sets the minimum confidence level required for a regime to be included in the transition statistics when the significance filter is on.
1 ⭐: Requires at least 90% confidence.
2 ⭐⭐: Requires at least 95% confidence (default).
3 ⭐⭐⭐: Requires at least 99% confidence.
Time Period Highlighter V2This indicator highlights custom time periods on any intraday chart in TradingView, making it easier to visualize your preferred trading sessions.
You can define up to three separate time ranges per day, each with precise start and end times down to the minute (e.g., 08:30 - 12:15, 14:00 - 16:45, and 20:00 - 22:30). The indicator shades the background of your chart during these periods, helping you quickly identify when you're most active or when specific market conditions occur.
Key Features:
Set start and end times (hours and minutes) for up to three trading sessions.
Automatically highlights these periods across any intraday timeframe.
Uses 24-hour time format aligned with your TradingView chart timezone.
Perfect for day traders, scalpers, or anyone needing clear visual cues for their trading windows.
This tool is especially useful for reviewing trading strategies, backtesting, or ensuring you're focusing on high-probability market hours.
Tip: Double-check that your chart timezone matches your desired session times for accurate highlighting.
Greer Value Yields Line📈 Greer Value Yields Line – Valuation Signal Without the Clutter
Part of the Greer Financial Toolkit, this streamlined indicator tracks four valuation-based yield metrics and presents them clearly via the Data Window, GVY Score badge, and an optional Yield Table:
Earnings Yield (EPS ÷ Price)
FCF Yield (Free Cash Flow ÷ Price)
Revenue Yield (Revenue per Share ÷ Price)
Book Value Yield (Book Value per Share ÷ Price)
✅ Each yield is compared against its historical average
✅ A point is scored for each metric above average (0–4 total)
✅ Color-coded GVY Score badge highlights valuation strength
✅ Yield trend-lines Totals (TVAVG & TVPCT) help assess direction
✅ Clean layout: no chart clutter – just actionable insights
🧮 GVY Score Color Coding (0–4):
⬜ 0 = None (White)
⬜ 1 = Weak (Gray)
🟦 2 = Neutral (Aqua)
🟩 3 = Strong (Green)
🟨 4 = Gold Exceptional (All metrics above average)
Total Value Average Line Color Coding:
🟥 Red – Average trending down
🟩 Green – Average trending up
Ideal for long-term investors focused on fundamental valuation, not short-term noise.
Enable the table and badge for a compact yield dashboard — or keep it minimal with just the Data Window and trend-lines.
Range Bar Gaps DetectorRange Bar Gaps Detector
Overview
The Range Bar Gaps Detector identifies price gaps across multiple range bar sizes (12, 24, 60, and 120) on any trading instrument, helping traders spot potential support/resistance zones or breakout opportunities. Designed for Pine Script v6, this indicator detects gaps on range bars and exports data for use in companion scripts like Range Bar Gaps Overlap, making it ideal for multi-timeframe gap analysis.
Key Features
Multi-Range Gap Detection: Identifies gaps on 12, 24, 60, and 120-range bars, capturing both bullish (gap up) and bearish (gap down) price movements.
Customizable Sensitivity: Includes a user-defined minimum deviation (default: 10% of 14-period SMA) for 12-range gaps to filter out noise.
7-Day Lookback: Automatically prunes gaps older than 7 days to focus on recent, relevant price levels.
Data Export: Serializes up to 10 gaps per range (tops, bottoms, start bars, highest/lowest prices, and age) for seamless integration with overlap analysis scripts.
Debugging Support: Plots gap counts and aggregation data in the Data Window for easy verification of detected gaps.
How It Works
The indicator aggregates price movements to simulate higher range bars (24, 60, 120) from a base range bar chart. It detects gaps when the price jumps significantly between bars, ensuring gaps meet the minimum deviation threshold for 12-range bars. Gaps are stored in arrays, serialized for external use, and pruned after 7 days to maintain efficiency.
Usage
Add to your range bar chart (e.g., 12-range) to detect gaps across multiple ranges.
Use alongside the Range Bar Gaps Overlap indicator to visualize gaps and their overlaps as boxes on the chart.
Check the Data Window to confirm gap counts and sizes for each range (12, 24, 60, 120).
Adjust the "Minimal Deviation (%) for 12-Range" input to control gap detection sensitivity.
Settings
Minimal Deviation (%) for 12-Range: Set the minimum gap size for 12-range bars (default: 10% of 14-period SMA).
Range Sizes: Fixed at 24, 60, and 120 for higher range bar aggregation.
Notes
Ensure the script is published under your TradingView username (e.g., GreenArrow2005) for use with companion scripts.
Best used on range bar charts to maintain consistent gap detection.
For advanced overlap analysis, pair with the Range Bar Gaps Overlap indicator to highlight zones where gaps from different ranges align.
Ideal For
Traders seeking to identify key price levels for support/resistance or breakout strategies.
Multi-timeframe analysts combining gap data across various range bar sizes.
Developers building custom indicators that leverage gap data for advanced charting.
Frahm Factor Position Size CalculatorThe Frahm Factor Position Size Calculator is a powerful evolution of the original Frahm Factor script, leveraging its volatility analysis to dynamically adjust trading risk. This Pine Script for TradingView uses the Frahm Factor’s volatility score (1-10) to set risk percentages (1.75% to 5%) for both Margin-Based and Equity-Based position sizing. A compact table on the main chart displays Risk per Trade, Frahm Factor, and Average Candle Size, making it an essential tool for traders aligning risk with market conditions.
Calculates a volatility score (1-10) using true range percentile rank over a customizable look-back window (default 24 hours).
Dynamically sets risk percentage based on volatility:
Low volatility (score ≤ 3): 5% risk for bolder trades.
High volatility (score ≥ 8): 1.75% risk for caution.
Medium volatility (score 4-7): Smoothly interpolated (e.g., 4 → 4.3%, 5 → 3.6%).
Adjustable sensitivity via Frahm Scale Multiplier (default 9) for tailored volatility response.
Position Sizing:
Margin-Based: Risk as a percentage of total margin (e.g., $175 for 1.75% of $10,000 at high volatility).
Equity-Based: Risk as a percentage of (equity - minimum balance) (e.g., $175 for 1.75% of ($15,000 - $5,000)).
Compact 1-3 row table shows:
Risk per Trade with Frahm score (e.g., “$175.00 (Frahm: 8)”).
Frahm Factor (e.g., “Frahm Factor: 8”).
Average Candle Size (e.g., “Avg Candle: 50 t”).
Toggles to show/hide Frahm Factor and Average Candle Size rows, with no empty backgrounds.
Four sizes: XL (18x7, large text), L (13x6, normal), M (9x5, small, default), S (8x4, tiny).
Repositionable (9 positions, default: top-right).
Customizable cell color, text color, and transparency.
Set Frahm Factor:
Frahm Window (hrs): Pick how far back to measure volatility (e.g., 24 hours). Shorter for fast markets, longer for chill ones.
Frahm Scale Multiplier: Set sensitivity (1-10, default 9). Higher makes the score jumpier; lower smooths it out.
Set Margin-Based:
Total Margin: Enter your account balance (e.g., $10,000). Risk auto-adjusts via Frahm Factor.
Set Equity-Based:
Total Equity: Enter your total account balance (e.g., $15,000).
Minimum Balance: Set to the lowest your account can go before liquidation (e.g., $5,000). Risk is based on the difference, auto-adjusted by Frahm Factor.
Customize Display:
Calculation Method: Pick Margin-Based or Equity-Based.
Table Position: Choose where the table sits (e.g., top_right).
Table Size: Select XL, L, M, or S (default M, small text).
Table Cell Color: Set background color (default blue).
Table Text Color: Set text color (default white).
Table Cell Transparency: Adjust transparency (0 = solid, 100 = invisible, default 80).
Show Frahm Factor & Show Avg Candle Size: Check to show these rows, uncheck to hide (default on).
AZ Dynamic Trend Indicator with Heikin-Ashi### Dynamic Trend Indicator with Heikin-Ashi (v2.7)
**Effortlessly identify trends and reversals** with this versatile tool combining multi-timeframe analysis, adaptive moving averages, and Heikin-Ashi smoothing. Here's what it offers:
#### 🔍 **Core Features**
1. **Dual Timeframe Analysis**:
- Track trends on higher timeframes (e.g., 1H/D) while viewing signals on your current chart.
- Toggle between **Heikin-Ashi** or standard candles for cleaner trend visualization.
2. **8 Customizable MAs**:
- Choose from **ALMA, HMA, SMA, SWMA, VWMA, WMA, ZLEMA, or EMA** with adjustable periods.
- Unique "Trend Strength" metric: `(MA_Close - MA_Open) / (MA_High - MA_Low)` highlights momentum direction.
3. **Smart Signals**:
- **Entry/Exit**: Triangles mark crossovers between MA Close/Open.
- **Reversal Alerts**: Detects counter-trend moves within a user-defined window (default: 3 bars) after signals.
- Color-coded plots: Bullish (🟢), Bearish (🔴), Reversal Bull (🔵), Reversal Bear (🟠).
#### 🎨 **Visual Customization**
- Toggle **High/Low MA lines**, **Close line**, and **fill colors**.
- Adjust colors for all elements to match your chart theme.
- Hide signals or reversal markers as needed.
#### ⚙️ **Practical Use**
- **Trend Following**: Use the MA Close/Open crossover with trend fill colors to confirm direction.
- **Reversal Trading**: Capitalize on pullbacks with reversal signals (e.g., after a bearish signal, watch for Bull Reversal markers).
- **Multi-Timeframe Confirmation**: Avoid false signals by aligning higher-timeframe trends with your entries.
*Ideal for swing traders and trend riders!*
**Note**: Adjust `MA Period`, `Reversal Window`, and `Trend Timeframe` for your strategy. Disable Heikin-Ashi in choppy markets for faster reactions.
---
*Code v2.7 updates: Optimized reversal logic, added ALMA/ZLEMA support, and enhanced visual controls.*
BK AK-SILENCER🚨 Introducing BK AK-SILENCER — Volume Footprint Warfare, Right on the Price Bars 🚨
This isn’t a traditional indicator.
This is a tactical weapon — engineered to expose institutional behavior directly in the bar data, using volume logic, CVD divergence, and spike detection to pinpoint who’s really in control of the tape.
No panels. No clutter.
Just silent execution — built directly into price itself.
🔥 Why "SILENCER"?
Because real power moves in silence.
Institutions don’t chase — they build positions quietly, in size, beneath the surface.
BK AK-SILENCER gives you a real-time edge by visually revealing their footprints through color-coded bar behavior, divergence signals, and volume spike alerts — all directly on your chart.
🔹 “AK” honors my mentor A.K., whose training forged my trading discipline.
🔹 “SILENCER” represents the institutional mindset — high impact, low visibility. This tool lets you trade like them: without noise, without hesitation, with deadly clarity.
🧠 What Is BK AK-SILENCER?
A bar-level institutional detection tool, purpose-built to:
✅ Color-code bars based on volume aggression and close-location inside range
✅ Detect real-time bullish and bearish divergences between price and volume delta
✅ Tag volume spikes with a $ symbol to expose potential traps or silent position builds
✅ Overlay VWAP for real-time mean-reversion biasing
No extra windows.
No indicators talking over each other.
Just pure volume-logic weaponry embedded into price.
⚙️ What This Weapon Deploys
🔸 Bar Coloring Logic (Volume Footprint)
🟢 Power Buy = Strong close near highs on elevated volume
🟩 Accumulation = Weak close but still heavy volume
🔴 Power Sell = Strong close near lows on heavy selling
🟥 Distribution / Weakness = Low close without commitment
❗ Extreme Volume Spikes marked with $ — using standard deviation to highlight institutional bursts
🔸 CVD Divergence Detection
→ Tracks cumulative volume delta and compares it to price pivot behavior
Bullish Divergence = Price makes lower lows, CVD makes higher lows → hidden accumulation
Bearish Divergence = Price makes higher highs, CVD makes lower highs → hidden distribution
All plotted directly on bars with triangle markers.
🔸 VWAP Overlay (Optional)
→ Anchored VWAP gives immediate context for intraday bias — above VWAP = demand, below = supply
🎯 How to Use BK AK-SILENCER
🔹 Silent Reversal Detection
Bullish divergence + Power Buy bar + VWAP reclaim = sniper entry
Bearish divergence + Power Sell bar + VWAP rejection = trap confirmation
🔹 Volume-Based Entry Triggers
Look for Power Buy + $ spike after a pullback → watch for quiet reversal
Accumulation colors clustering? Institutions are likely loading silently
🔹 Institutional Trap Warnings
$ spike + red distribution bar at highs = time to exit or flip
Weakness bar below VWAP? Don’t chase the long.
🛡️ Why It Matters
✅ Clean — it integrates into price action, no separate panels
✅ Silent — tracks institutions who build without alerts or indicators
✅ Tactical — no fluff, no lag, just real-time behavior recognition
This tool is ideal for:
🔸 Scalpers reading bar-by-bar
🔸 Intraday swing traders using VWAP and structure
🔸 Professionals who need volume behavior decoded in real-time
🔸 Anyone who wants signal without clutter
🙏 Final Thoughts
This tool isn’t just about trading — it’s about tactical awareness.
🔹 Dedicated to my mentor A.K., whose wisdom runs deep in every logic tree.
🔹 Above all, I give thanks to Gd, the source of clarity, courage, and conviction.
Without Him, even the sharpest system is blind.
With Him, we execute with structure, purpose, and divine alignment.
⚡ No noise. No clutter. No delay. Just raw, silent execution.
🔥 BK AK-SILENCER — Bar-Level Volume Footprint Precision 🔥
Gd bless every step you take in this market.
Trade with clarity, move with intention. 🙏
Opening Range Breakout (ORB) with Fib RetracementOverview
“ORB with Fib Retracement” is a Pine Script indicator that anchors a full Fibonacci framework to the first minutes of the trading day (the opening-range breakout, or ORB).
After the ORB window closes the script:
Locks-in that session’s high and low.
Calculates a complete ladder of Fibonacci retracement levels between them (0 → 100 %).
Projects symmetric extension levels above and below the range (±1.618, ±2.618, ±3.618, ±4.618 by default).
Sub-divides every extension slice with additional 23.6 %, 38.2 %, 50 %, 61.8 % and 78.6 % mid-lines so each “zone” has its own inner fib grid.
Plots the whole structure and—optionally—extends every line into the future for ongoing reference.
**Session time / timezone** – Defines the ORB window (defaults 09:30–09:45 EST).
**Show All Fib Levels** – Toggles every retracement and extension line on or off.
**Show Extended Lines** – Draws dotted, extend-right projections of every level.
**Color group** – Assigns colors to buy-side (green), sell-side (red), and internal fibs (gray).
**Extension value inputs** – Allows custom +/- 1.618 to 4.618 fib levels for personalized projection zones.
