Z-Score Mean Reversion StrategyBased on Indicator "Rolling Z- Score trend" by QuantAlgo
The Z-Score Mean Reversion Strategy is a statistical trading approach that exploits price extremes and their tendency to return to average levels. It uses the Z-Score indicator to identify when an asset has deviated significantly from its statistical mean, creating high-probability reversal opportunities.
Core Concept:
Z-Score measures how many standard deviations price is from its moving average
When Z-Score reaches extreme levels (±1.5 or more), price is statistically "stretched"
The strategy trades the expected "snap back" to the mean
Works best in ranging or mean-reverting markets
How It Works:
LONG Entry: When price becomes oversold (Z-Score < -1.5), expect upward reversion
SHORT Entry: When price becomes overbought (Z-Score > +1.5), expect downward reversion
Exit: When price returns closer to the mean or reaches opposite extreme
Risk Management: Stop loss at -3% and take profit at +5% by default
🎯 Best Settings by Market & Timeframe
Cryptocurrency (High Volatility)
Preset: Scalping
Timeframe: 15m - 1H
Lookback: 10-15 periods
Entry Threshold: 1.0 - 1.5
Stop Loss: 2-3%
Take Profit: 3-5%
Notes: Crypto moves fast; use tighter parameters for quicker signals
Forex (Medium Volatility)
Preset: Default or Swing Trading
Timeframe: 1H - 4H
Lookback: 20-25 periods
Entry Threshold: 1.5 - 2.0
Stop Loss: 1-2%
Take Profit: 2-4%
Notes: Works well on major pairs during normal market conditions
Stocks (Lower Volatility)
Preset: Swing Trading
Timeframe: 4H - Daily
Lookback: 25-30 periods
Entry Threshold: 1.5 - 1.8
Stop Loss: 2-4%
Take Profit: 4-8%
Notes: Best on liquid stocks; avoid during earnings or major news
Indices (Trend + Ranging)
Preset: Trend Following
Timeframe: Daily - Weekly
Lookback: 35-50 periods
Entry Threshold: 2.0 - 2.5
Stop Loss: 3-5%
Take Profit: 5-10%
Notes: Higher threshold reduces false signals; captures major reversals
⚙️ Optimal Configuration Guide
Conservative (Lower Risk, Fewer Trades)
Lookback Period: 30-40
Entry Threshold: 2.0-2.5
Exit Threshold: 0.8-1.0
Stop Loss: 3-4%
Take Profit: 6-10%
Momentum Filter: ON
Balanced (Recommended Starting Point)
Lookback Period: 20-25
Entry Threshold: 1.5-1.8
Exit Threshold: 0.5-0.6
Stop Loss: 2-3%
Take Profit: 4-6%
Momentum Filter: OFF
Aggressive (Higher Risk, More Trades)
Lookback Period: 10-15
Entry Threshold: 1.0-1.2
Exit Threshold: 0.3-0.4
Stop Loss: 1-2%
Take Profit: 2-4%
Momentum Filter: OFF
💡 Pro Tips for Best Results
When the Strategy Works Best:
✅ Ranging markets with clear support/resistance
✅ High liquidity assets (major pairs, large-cap stocks)
✅ Normal market conditions (avoid during crashes or parabolic runs)
✅ Mean-reverting assets (avoid strong trending stocks)
When to Avoid:
❌ Strong trending markets (price won't revert)
❌ Low liquidity / low volume periods
❌ Major news events (earnings, FOMC, NFP)
❌ Market crashes or euphoria phases
Optimization Process:
Start with "Default" preset on your chosen timeframe
Backtest 6-12 months to see performance
Adjust Entry Threshold first (lower = more trades, higher = fewer but stronger signals)
Fine-tune Stop Loss/Take Profit based on average trade duration
Consider Momentum Filter if getting too many false signals
Key Metrics to Monitor:
Win Rate: Target 50-60% (mean reversion typically has moderate win rate)
Profit Factor: Aim for >1.5
Average Trade Duration: Should match your timeframe (scalping: minutes/hours, swing: days)
Max Drawdown: Keep under 20% of capital
📈 Quick Start Recommendation
For most traders, start here:
Timeframe: 1H or 4H
Preset: Default (Lookback 20, Threshold 1.5)
Stop Loss: 3%
Take Profit: 5%
Momentum Filter: OFF (turn ON if too many false entries)
Test on BTCUSD, EURUSD, or SPY first, then adapt to your preferred instruments!
In den Scripts nach "weekly" suchen
Gap Zones Pro - Price Action Confluence Indicator with Alerts█ OVERVIEW
Gap Zones Pro identifies and tracks price gaps - crucial areas where institutional interest and market imbalance create high-probability reaction zones. These gaps represent areas of strong initial buying/selling pressure that often act as magnets when price returns.
█ WHY GAPS MATTER IN TRADING
- Gaps reveal institutional footprints and areas of market imbalance
- When price returns to a gap, it often reaffirms the original directional bias
- Failed gap reactions can signal powerful reversals in the opposite direction
- Gaps provide excellent confluence when aligned with your trading narrative
- They act as natural support/resistance zones with clear risk/reward levels
█ KEY FEATURES
- Automatically detects and visualizes all gap zones on your chart
- Extends gaps to the right edge for easy monitoring
- Customizable number of gaps displayed (manage chart clarity)
- Minimum gap size filter to focus on significant gaps only
- Real-time alerts when price enters gap zones
- Color-coded visualization (green for gap ups, red for gap downs)
- Clean, professional appearance with adjustable transparency
█ HOW TO USE
1. Add to chart and adjust maximum gaps displayed based on your timeframe
2. Set minimum gap size % to filter out noise (0.5-1% recommended for stocks)
3. Watch for price approaching gap zones for potential reactions
4. Use gaps as confluence with other technical factors:
- Support/resistance levels
- Fibonacci retracements
- Supply/demand zones
- Trend lines and channels
5. Set alerts to notify you when price enters key gap zones
█ TRADING TIPS
- Gaps with strong contextual stories (earnings, news, breakouts) are most reliable
- Multiple gaps in the same area create stronger zones
- Unfilled gaps above price can act as resistance targets
- Unfilled gaps below price can act as support targets
- Watch for "gap and go" vs "gap fill" scenarios based on market context
█ SETTINGS
- Maximum Number of Gaps: Control how many historical gaps to display
- Minimum Gap Size %: Filter out insignificant gaps
- Colors: Customize gap up and gap down zone colors
- Transparency: Adjust visibility while maintaining chart readability
- Show Borders: Toggle gap zone borders on/off
- Alerts: Automatic notifications when price crosses gap boundaries
█ BEST TIMEFRAMES
Works on all timeframes but most effective on:
- Daily charts for swing trading
- 4H for intraday position trading
- 1H for day trading key levels
- Weekly for long-term investing
Remember: Gaps are most powerful when they align with your overall market thesis and other technical confluences. They should confirm your narrative, not define it.
---
Updates: Real-time gap detection | Alert system | Extended visualization | Performance optimized
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
DashBoard 2.3.1📌 Indicator Name:
DashBoard 2.3 – Smart Visual Market Overlay
📋 Description:
DashBoard 2.3 is a clean, efficient, and highly informative market overlay, designed to give you real-time context directly on your chart — without distractions. Whether you're swing trading or investing long-term, this tool keeps critical market data at your fingertips.
🔍 Key Features:
Symbol + Timeframe + Market Cap
Shows the current ticker and timeframe, optionally with real-time market cap.
ATR 14 with Volatility Signal
Displays ATR with color-coded risk levels:
🟢 Low
🟡 Moderate
🔴 High
⚫️ Extreme
You can choose between Daily ATR or timeframe-based ATR (auto-adjusted to chart resolution).
Adaptive Labeling
The ATR label updates to reflect the resolution:
ATR 14d (daily)
ATR 14W (weekly)
ATR 14H (hourly), etc.
Moving Average Tracker
Instantly shows whether price is above or below your selected moving average (e.g., 150 MA), with green/red indication.
Earnings Countdown
Clearly shows how many days remain until the next earnings report.
Industry & Sector Info (optional)
Useful for thematic or sector-based trading strategies.
Fully Customizable UI
Choose positioning, padding, font size, and which data to show. Designed for minimalism and clarity.
✅ Smart Logic:
Color dots appear only in relevant conditions (e.g., ATR color signals shown only on daily when enabled).
ATR display automatically reflects your time frame, if selected.
Clean chart integration – the overlay sits quietly in a corner, enhancing your analysis without intruding.
🧠 Ideal for:
Swing traders, position traders, and investors who want fast, high-impact insights directly from the chart.
Anyone looking for a compact, beautiful, and informative dashboard while they trade.
Mongoose Global Conflict Risk Index v1Overview
The Mongoose Global Conflict Risk Index v1 is a multi-asset composite indicator designed to track the early pricing of geopolitical stress and potential conflict risk across global markets. By combining signals from safe havens, volatility indices, energy markets, and emerging market equities, the index provides a normalized 0–10 score with clear bias classifications (Neutral, Caution, Elevated, High, Shock).
This tool is not predictive of headlines but captures when markets are clustering around conflict-sensitive assets before events are widely recognized.
Methodology
The indicator calculates rolling rate-of-change z-scores for eight conflict-sensitive assets:
Gold (XAUUSD) – classic safe haven
US Dollar Index (DXY) – global reserve currency flows
VIX (Equity Volatility) – S&P 500 implied volatility
OVX (Crude Oil Volatility Index) – energy stress gauge
Crude Oil (CL1!) – WTI front contract
Natural Gas (NG1!) – energy security proxy, especially Europe
EEM (Emerging Markets ETF) – global risk capital flight
FXI (China ETF) – Asia/China proxy risk
Rules:
Safe havens and vol indices trigger when z-score > threshold.
Energy triggers when z-score > threshold.
Risk assets trigger when z-score < –threshold.
Each trigger is assigned a weight, summed, normalized, and scaled 0–10.
Bias classification:
0–2: Neutral
2–4: Caution
4–6: Elevated
6–8: High
8–10: Conflict Risk-On
How to Use
Timeframes:
Daily (1D) for strategic signals and early warnings.
4H for event shocks (missiles, sanctions, sudden escalations).
Weekly (1W) for sustained trends and macro build-ups.
What to Look For:
A single trigger (for example, Gold ON) may be noise.
A cluster of 2–3 triggers across Gold, USD, VIX, and Energy often marks early stress pricing.
Elevated readings (>4) = caution; High (>6) = rotation into havens; Shock (>8) = market conviction of conflict risk.
Practical Application:
Monitor as a heatmap of global stress.
Combine with fundamental or headline tracking.
Use alert conditions at ≥4, ≥6, ≥8 for systematic monitoring.
Notes
This indicator is for informational and educational purposes only.
It is not financial advice and should be used in conjunction with other analysis methods.
US Net Liquidity + M2 / US Debt (FRED)US Net Liquidity + M2 / US Debt
🧩 What this chart shows
This indicator plots the ratio of US Net Liquidity + M2 Money Supply divided by Total Public Debt.
US Net Liquidity is defined here as the Federal Reserve Balance Sheet (WALCL) minus the Treasury General Account (TGA) and the Overnight Reverse Repo facility (ON RRP).
M2 Money Supply represents the broad pool of liquid money circulating in the economy.
US Debt uses the Federal Government’s total outstanding debt.
By combining net liquidity with M2, then dividing by total debt, this chart provides a structural view of how much monetary “fuel” is in the system relative to the size of the federal debt load.
🧮 Formula
Ratio
=
(
Fed Balance Sheet
−
(
TGA
+
ON RRP
)
)
+
M2
Total Public Debt
Ratio=
Total Public Debt
(Fed Balance Sheet−(TGA+ON RRP))+M2
An optional normalization feature scales the ratio to start at 100 on the first valid bar, making long-term trends easier to compare.
🔎 Why it matters
Liquidity vs. Debt Growth: The numerator (Net Liquidity + M2) captures the monetary resources available to markets, while the denominator (Debt) reflects the expanding obligation of the federal government.
Market Signal: Historically, shifts in net liquidity and money supply relative to debt have coincided with major turning points in risk assets like equities and Bitcoin.
Context: A rising ratio may suggest that liquidity conditions are improving relative to debt expansion, which can be supportive for risk assets. Conversely, a falling ratio may highlight tightening conditions or debt outpacing liquidity growth.
⚙️ How to use it
Overlay this chart against S&P 500, Bitcoin, or gold to analyze correlations with asset performance.
Watch for trend inflections—does the ratio bottom before equities rally, or peak before risk-off periods?
Use normalization for long historical comparisons, or raw values to see the absolute ratio.
📊 Data sources
This indicator pulls from FRED (Federal Reserve Economic Data) tickers available in TradingView:
WALCL: Fed balance sheet
RRPONTSYD: Overnight Reverse Repo
WTREGEN: Treasury General Account
M2SL: M2 money stock
GFDEBTN: Total federal public debt
⚠️ Notes
Some FRED series are updated weekly, others monthly—set your chart timeframe accordingly.
If any ticker is unavailable in your plan, replace it with the equivalent FRED symbol provided in TradingView.
This indicator is intended for macro analysis, not short-term trading signals.
Dynamic Levels: Mon + D/W/M/Y (O/H/L/C/Mid)Purpose!
This Pine Script plots key reference levels (Open,High,Low,Close,Mid) for Monday,Daily,Weekly, Monthly, and Yearly timeframes.
All levels update live while the bar is forming. ( intrabar updates).
USAGE
Add the script to Pine Editor on TradingView (desktop Web)
Save - Add to chart
On mobile app: Find it under indicators - My scripts.
Great for identifying key reaction zones (opens,mids,previous closes).
Sean Trades Style IndicatorThe Sean Trades Style Indicator is a powerful, user-friendly trading tool designed for swing traders who want to trade like Sean from the Options Cartel. It identifies high-probability buy and sell signals based on pivot points, trend confirmations, and price action patterns, helping traders enter and exit trades with precision. Compatible with multiple timeframes, it allows you to set up on daily and weekly charts while executing entries on lower timeframes like 15-minute and 5-minute charts, aligning perfectly with Sean’s strategy. Whether you’re looking to simplify decision-making or follow a proven swing trading approach, this indicator gives you clear visual cues to trade with confidence and consistency.
EMA 50 & 200 (TF-specific)This script plots EMA 50 and EMA 200 only on the timeframes where they matter most:
EMA 50 (gray): visible on 1H, 4H, and 12H charts – often used by intraday traders.
EMA 200 (black): visible on Daily and Weekly charts – a classic long-term trend indicator.
🔹 Why use it?
Avoids clutter by showing each EMA only on the relevant timeframe.
Helps align intraday trading with higher timeframe trends.
Simple, clean, and effective for both swing and day trading.
Volume Bubbles & Liquidity Heatmap [LuxAlgo]The Volume Bubbles & Liquidity Heatmap indicator highlights volume and liquidity clearly and precisely with its volume bubbles and liquidity heat map, allowing to identify key price areas.
Customize the bubbles with different time frames and different display modes: total volume, buy and sell volume, or delta volume.
🔶 USAGE
The primary objective of this tool is to offer traders a straightforward method for analyzing volume on any selected timeframe.
By default, the tool displays buy and sell volume bubbles for the daily timeframe over the last 2,000 bars. Traders should be aware of the difference between the timeframe of the chart and that of the bubbles.
The tool also displays a liquidity heat map to help traders identify price areas where liquidity accumulates or is lacking.
🔹 Volume Bubbles
The bubbles have three possible display modes:
Total Volume: Displays the total volume of trades per bubble.
Buy & Sell Volume: Each bubble is divided into buy and sell volume.
Delta Volume: Displays the difference between buy and sell volume.
Each bubble represents the trading volume for a given period. By default, the timeframe for each bubble is set to daily, meaning each bubble represents the trading volume for each day.
The size of each bubble is proportional to the volume traded; a larger bubble indicates greater volume, while a smaller bubble indicates lower volume.
The color of each bubble indicates the dominant volume: green for buy volume and red for sell volume.
One of the tool's main goals is to facilitate simple, clear, multi-timeframe volume analysis.
The previous chart shows Delta Volume bubbles with various chart and bubble timeframe configurations.
To correctly visualize the bubbles, traders must ensure there is a sufficient number of bars per bubble. This is achieved by using a lower chart timeframe and a higher bubble timeframe.
As can be seen in the image above, the greater the difference between the chart and bubble timeframes, the better the visualization.
🔹 Liquidity Heatmap
The other main element of the tool is the liquidity heatmap. By default, it divides the chart into 25 different price areas and displays the accumulated trading volume on each.
The image above shows a 4-hour BTC chart displaying only the liquidity heatmap. Traders should be aware of these key price areas and observe how the price behaves in them, looking for possible opportunities to engage with the market.
The main parameters for controlling the heatmap on the settings panel are Rows and Cell Minimum Size. Rows modifies the number of horizontal price areas displayed, while Cell Minimum Size modifies the minimum size of each liquidity cell in each row.
As can be seen in the above BTC hourly chart, the cell size is 24 at the top and 168 at the bottom. The cells are smaller on top and bigger on the bottom.
The color of each cell reflects the liquidity size with a gradient; this reflects the total volume traded within each cell. The default colors are:
Red: larger liquidity
Yellow: medium liquidity
Blue: lower liquidity
🔹 Using Both Tools Together
This indicator provides the means to identify directional bias and market timing.
The main idea is that if buyers are strong, prices are likely to increase, and if sellers are strong, prices are likely to decrease. This gives us a directional bias for opening long or short positions. Then, we combine our directional bias with price rejection or acceptance of key liquidity levels to determine the timing of opening or closing our positions.
Now, let's review some charts.
This first chart is BTC 1H with Delta Weekly Bubbles. Delta Bubbles measure the difference between buy and sell volume, so we can easily see which group is dominant (buyers or sellers) and how strong they are in any given week. This, along with the key price areas displayed by the Liquidity Heatmap, can help us navigate the markets.
We divided market behavior into seven groups, and each group has several bubbles, numbered from 1 to 17.
Bubbles 1, 2, and 3: After strong buyers market consolidates with positive delta, prices move up next week.
Bubbles 3, 4, and 5: Strength changes from buyers to sellers. Next week, prices go down.
Bubbles 6 and 7: The market trades at higher prices, but with negative delta. Next week, prices go down.
Bubbles 7, 8, and 9: Strength changes from sellers to buyers. Next weeks (9 and 10), prices go up.
Bubbles 10, 11, and 12: After strong buyers prices trade higher with a negative delta. Next weeks (12 and 13) prices go down.
Bubbles 12, 14, and 15: Strength changes from sellers to buyers; next week, prices increase.
Bubbles 15 and 16: The market trades higher with a very small positive delta; next week, prices go down.
Current bubble/week 17 is not yet finished. Right now, it is trading lower, but with a smaller negative delta than last week. This may signal that sellers are losing strength and that a potential reversal will follow, with prices trading higher.
This is the same BTC 1H chart, but with price rejections from key liquidity areas acting as strong price barriers.
When prices reach a key area with strong liquidity and are rejected, it signals a good time to take action.
By observing price behavior at certain key price levels, we can improve our timing for entering or exiting the markets.
🔶 DETAILS
🔹 Bubbles Display
From the settings panel, traders can configure the bubbles with four main parameters: Mode, Timeframe, Size%, and Shape.
The image above shows five-minute BTC charts with execution over the last 3,500 bars, different display modes, a daily timeframe, 100% size, and shape one.
The Size % parameter controls the overall size of the bubbles, while the Shape parameter controls their vertical growth.
Since the chart has two scales, one for time and one for price, traders can use the Shape parameter to make the bubbles round.
The chart above shows the same bubbles with different size and shape parameters.
You can also customize data labels and timeframe separators from the settings panel.
🔶 SETTINGS
Execute on last X bars: Number of bars for indicator execution
🔹 Bubbles
Display Bubbles: Enable/Disable volume bubbles.
Bubble Mode: Select from the following options: total volume, buy and sell volume, or the delta between buy and sell volume.
Bubble Timeframe: Select the timeframe for which the bubbles will be displayed.
Bubble Size %: Select the size of the bubbles as a percentage.
Bubble Shape: Select the shape of the bubbles. The larger the number, the more vertical the bubbles will be stretched.
🔹 Labels
Display Labels: Enable/Disable data labels, select size and location.
🔹 Separators
Display Separators: Enable/Disable timeframe separators and select color.
🔹 Liquidity Heatmap
Display Heatmap: Enable/Disable liquidity heatmap.
Heatmap Rows: select number of rows to be displayed.
Cell Minimum Size: Select the minimum size for each cell in each row.
Colors.
🔹 Style
Buy & Sell Volume Colors.
MTF Levels [OmegaTools]📖 Introduction
The Ω Levels Indicator is a complete market structure and level-mapping framework designed to help traders identify key zones where price is likely to react.
It blends classic technical anchors (VWAP, pivots, means, standard deviations) with modern statistical pattern recognition to dynamically project areas of manipulation, extension, and equilibrium.
At its core, Ω Levels creates an evolving map of market balance vs. imbalance, showing traders where liquidity is most likely to build and where price could pivot or accelerate.
But what makes it truly unique is the Pivot Forecaster — an embedded predictive engine that applies machine-learning inspired logic to recognize conditions that historically precede market turning points.
🔎 Key Features
Customizable Levels Framework
Define up to three levels (manipulation, extensions, VWAP, pivots, stdev bands, or prior extremes).
Choose mean references such as Open, VWAP, Pivot Mean, or Previous Session Mean.
Style controls (solid, dotted, dashed) and fill modes (internal, external, ranges) allow you to adapt the chart to your visual workflow.
Dynamic Zone Highlighting
Automatic fills between internal/external levels, or between specific level pairs (1–2, 1–3, 2–3).
Makes it easy to visualize value areas, expansions, and compression zones at a glance.
Multi-Timeframe Anchoring
Works on any timeframe, but calculations can be anchored to a higher timeframe (e.g., show daily VWAP & pivots on a 15m chart).
This allows traders to align intraday execution with higher timeframe context.
Pivot Forecaster (Machine Learning / Pattern Recognition)
This is the advanced predictive component.
The algorithm collects historical conditions observed around pivot highs and lows (volume state, ATR state, % candle expansion, oscillator conditions).
It then builds statistical “profiles” of typical pivot behavior and compares them in real-time against current market conditions.
When conditions match the “signature” of a pivot, the indicator highlights a Forecast Pivot High or Forecast Pivot Low (displayed as small diamond markers).
This functions as a pattern-recognition system, effectively learning from past pivots to anticipate where the next turning point is more likely to occur.
⚡ How Traders Can Use It
Intraday Execution: Use VWAP, manipulation, and extension levels to frame trades around liquidity zones.
Swing Context: Overlay higher timeframe pivots and means to guide medium-term positioning.
Fade Setups: Forecasted pivots often coincide with exhaustion zones where fading momentum carries edge.
Breakout Validation: When price breaks a structural level but the forecaster does not confirm a pivot, continuation probability is higher.
Risk Management: Levels provide natural stop/target placements, while pivot forecasts serve as warning signals for potential reversals.
⚙️ Settings Overview
Timeframe: Choose the anchor timeframe for calculations (default: Daily).
Means: Two selectable mean references (Open, VWAP, Pivot Point, Previous Mean).
Levels: Three levels can be customized (Manipulation, Extension, 1–2 StDev, Pivot Point, VWAP, Previous Extremes).
Fill Modes: Highlight zones between internal/external levels or custom ranges.
Visual Customization: Colors, line styles, fill opacity, and toggle for old levels.
Pivot Forecaster: Fully automated — no settings required, it adapts to instrument and timeframe.
🧭 Best Practices
Align Levels With Market Profile: Treat the levels as dynamic S/R zones and watch how price interacts with them.
Use Forecaster as Confirmation: The diamonds are not standalone signals; they are context filters that help you decide whether a move has higher reversal odds.
Higher Timeframe Anchoring: On intraday charts, set the timeframe to Daily or Weekly to trade with institutional levels.
Combine With ATR: Pair with the Ω ATR Indicator to size positions according to volatility while Ω Levels provides the structural roadmap.
📌 Summary
The Ω Levels Indicator is more than a level plotter — it’s a market map + predictive engine.
By combining traditional levels with an intelligent pivot forecaster, it gives traders both the static structure of where price should react, and the dynamic signal of where it is likely to react next.
This dual-layer approach — structural + predictive — makes it an invaluable tool for discretionary intraday traders, swing traders, and anyone who wants to anticipate price behavior instead of just reacting to it.
TRAM 37Tram37 represents the 377-day simple moving average, a key indicator for assessing long-term market trends. It provides significant insights on both daily and weekly timeframes, making it an effective tool for investors seeking to evaluate market direction and optimize long-term holding strategies.
Harmonic Super GuppyHarmonic Super Guppy – Harmonic & Golden Ratio Trend Analysis Framework
Overview
Harmonic Super Guppy is a comprehensive trend analysis and visualization tool that evolves the classic Guppy Multiple Moving Average (GMMA) methodology, pioneered by Daryl Guppy to visualize the interaction between short-term trader behavior and long-term investor trends. into a harmonic and phase-based market framework. By combining harmonic weighting, golden ratio phasing, and multiple moving averages, it provides traders with a deep understanding of market structure, momentum, and trend alignment. Fast and slow line groups visually differentiate short-term trader activity from longer-term investor positioning, while adaptive fills and dynamic coloring clearly illustrate trend coherence, expansion, and contraction in real time.
Traditional GMMA focuses primarily on moving average convergence and divergence. Harmonic Super Guppy extends this concept, integrating frequency-aware harmonic analysis and golden ratio modulation, allowing traders to detect subtle cyclical forces and early trend shifts before conventional moving averages would react. This is particularly valuable for traders seeking to identify early trend continuation setups, preemptive breakout entries, and potential trend exhaustion zones. The indicator provides a multi-dimensional view, making it suitable for scalping, intraday trading, swing setups, and even longer-term position strategies.
The visual structure of Harmonic Super Guppy is intentionally designed to convey trend clarity without oversimplification. Fast lines reflect short-term trader sentiment, slow lines capture longer-term investor alignment, and fills highlight compression or expansion. The adaptive color coding emphasizes trend alignment: strong green for bullish alignment, strong red for bearish, and subtle gray tones for indecision. This allows traders to quickly gauge market conditions while preserving the granularity necessary for sophisticated analysis.
How It Works
Harmonic Super Guppy uses a combination of harmonic averaging, golden ratio phasing, and adaptive weighting to generate its signals.
Harmonic Weighting : Each moving average integrates three layers of harmonics:
Primary harmonic captures the dominant cyclical structure of the market.
Secondary harmonic introduces a complementary frequency for oscillatory nuance.
Tertiary harmonic smooths higher-frequency noise while retaining meaningful trend signals.
Golden Ratio Phase : Phases of each harmonic contribution are adjusted using the golden ratio (default φ = 1.618), ensuring alignment with natural market rhythms. This reduces lag and allows traders to detect trend shifts earlier than conventional moving averages.
Adaptive Trend Detection : Fast SMAs are compared against slow SMAs to identify structural trends:
UpTrend : Fast SMA exceeds slow SMA.
DownTrend : Fast SMA falls below slow SMA.
Frequency Scaling : The wave frequency setting allows traders to modulate responsiveness versus smoothing. Higher frequency emphasizes short-term moves, while lower frequency highlights structural trends. This enables adaptation across asset classes with different volatility characteristics.
Through this combination, Harmonic Super Guppy captures micro and macro market cycles, helping traders distinguish between transient noise and genuine trend development. The multi-harmonic approach amplifies meaningful price action while reducing false signals inherent in standard moving averages.
Interpretation
Harmonic Super Guppy provides a multi-dimensional perspective on market dynamics:
Trend Analysis : Alignment of fast and slow lines reveals trend direction and strength. Expanding harmonics indicate momentum building, while contraction signals weakening conditions or potential reversals.
Momentum & Volatility : Rapid expansion of fast lines versus slow lines reflects short-term bullish or bearish pressure. Compression often precedes breakout scenarios or volatility expansion. Traders can quickly gauge trend vigor and potential turning points.
Market Context : The indicator overlays harmonic and structural insights without dictating entry or exit points. It complements order blocks, liquidity zones, oscillators, and other technical frameworks, providing context for informed decision-making.
Phase Divergence Detection : Subtle divergence between harmonic layers (primary, secondary, tertiary) often signals early exhaustion in trends or hidden strength, offering preemptive insight into potential reversals or sustained continuation.
By observing both structural alignment and harmonic expansion/contraction, traders gain a clear sense of when markets are trending with conviction versus when conditions are consolidating or becoming unpredictable. This allows for proactive trade management, rather than reactive responses to lagging indicators.
Strategy Integration
Harmonic Super Guppy adapts to various trading methodologies with clear, actionable guidance.
Trend Following : Enter positions when fast and slow lines are aligned and harmonics are expanding. The broader the alignment, the stronger the confirmation of trend persistence. For example:
A fast line crossover above slow lines with expanding fills confirms momentum-driven continuation.
Traders can use harmonic amplitude as a filter to reduce entries against prevailing trends.
Breakout Trading : Periods of line compression indicate potential volatility expansion. When fast lines diverge from slow lines after compression, this often precedes breakouts. Traders can combine this visual cue with structural supports/resistances or order flow analysis to improve timing and precision.
Exhaustion and Reversals : Divergences between harmonic components, or contraction of fast lines relative to slow lines, highlight weakening trends. This can indicate liquidity exhaustion, trend fatigue, or corrective phases. For example:
A flattening fast line group above a rising slow line can hint at short-term overextension.
Traders may use these signals to tighten stops, take partial profits, or prepare for contrarian setups.
Multi-Timeframe Analysis : Overlay slow lines from higher timeframes on lower timeframe charts to filter noise and trade in alignment with larger market structures. For example:
A daily bullish alignment combined with a 15-minute breakout pattern increases probability of a successful intraday trade.
Conversely, a higher timeframe divergence can warn against taking counter-trend trades in lower timeframes.
Adaptive Trade Management : Harmonic expansion/contraction can guide dynamic risk management:
Stops may be adjusted according to slow line support/resistance or harmonic contraction zones.
Position sizing can be modulated based on harmonic amplitude and compression levels, optimizing risk-reward without rigid rules.
Technical Implementation Details
Harmonic Super Guppy is powered by a multi-layered harmonic and phase calculation engine:
Harmonic Processing : Primary, secondary, and tertiary harmonics are calculated per period to capture multiple market cycles simultaneously. This reduces noise and amplifies meaningful signals.
Golden Ratio Modulation : Phase adjustments based on φ = 1.618 align harmonic contributions with natural market rhythms, smoothing lag and improving predictive value.
Adaptive Trend Scaling : Fast line expansion reflects short-term momentum; slow lines provide structural trend context. Fills adapt dynamically based on alignment intensity and harmonic amplitude.
Multi-Factor Trend Analysis : Trend strength is determined by alignment of fast and slow lines over multiple bars, expansion/contraction of harmonic amplitudes, divergences between primary, secondary, and tertiary harmonics and phase synchronization with golden ratio cycles.
These computations allow the indicator to be highly responsive yet smooth, providing traders with actionable insights in real time without overloading visual complexity.
Optimal Application Parameters
Asset-Specific Guidance:
Forex Majors : Wave frequency 1.0–2.0, φ = 1.618–1.8
Large-Cap Equities : Wave frequency 0.8–1.5, φ = 1.5–1.618
Cryptocurrency : Wave frequency 1.2–3.0, φ = 1.618–2.0
Index Futures : Wave frequency 0.5–1.5, φ = 1.618
Timeframe Optimization:
Scalping (1–5min) : Emphasize fast lines, higher frequency for micro-move capture.
Day Trading (15min–1hr) : Balance fast/slow interactions for trend confirmation.
Swing Trading (4hr–Daily) : Focus on slow lines for structural guidance, fast lines for entry timing.
Position Trading (Daily–Weekly) : Slow lines dominate; harmonics highlight long-term cycles.
Performance Characteristics
High Effectiveness Conditions:
Clear separation between short-term and long-term trends.
Moderate-to-high volatility environments.
Assets with consistent volume and price rhythm.
Reduced Effectiveness:
Flat or extremely low volatility markets.
Erratic assets with frequent gaps or algorithmic dominance.
Ultra-short timeframes (<1min), where noise dominates.
Integration Guidelines
Signal Confirmation : Confirm alignment of fast and slow lines over multiple bars. Expansion of harmonic amplitude signals trend persistence.
Risk Management : Place stops beyond slow line support/resistance. Adjust sizing based on compression/expansion zones.
Advanced Feature Settings :
Frequency tuning for different volatility environments.
Phase analysis to track divergences across harmonics.
Use fills and amplitude patterns as a guide for dynamic trade management.
Multi-timeframe confirmation to filter noise and align with structural trends.
Disclaimer
Harmonic Super Guppy is a trend analysis and visualization tool, not a guaranteed profit system. Optimal performance requires proper wave frequency, golden ratio phase, and line visibility settings per asset and timeframe. Traders should combine the indicator with other technical frameworks and maintain disciplined risk management practices.
Multi-Exchange VWAP Aggregator (Crypto)Description:
This advanced VWAP indicator aggregates volume data from up to 9 cryptocurrency exchanges simultaneously, providing a more accurate volume-weighted average price than single-exchange VWAP calculations.
Key Features:
Multi-Exchange Aggregation - Combines volume from Binance, Coinbase, Bybit, Bitfinex, Bitstamp, Deribit, OKEx, Phemex, and FTX
Flexible Currency Pairs - Supports both spot (USD, USDT, EUR, USDC, BUSD, DAI) and perpetual futures contracts
Standard Deviation Bands - Includes customizable 1σ, 2σ, and 3σ bands for identifying overbought/oversold levels
Multiple Reset Periods - Daily, Weekly, Monthly, or Session-based VWAP calculations
Volume Calculation Options - Choose between SUM, AVG, MEDIAN, or VARIANCE for volume aggregation
Why Use This?
Traditional VWAP indicators only use volume from a single exchange, which can be misleading in fragmented crypto markets. This indicator provides a comprehensive market-wide VWAP by aggregating volume across major exchanges, giving you a more reliable benchmark for entries, exits, and institutional price levels.
Perfect for traders who want to see where the real volume-weighted price sits across the entire crypto market, not just one exchange.
Price Between Tenkan & KijunThis is developed to find stocks on a weekly basis that are potentially breaking out or breaking down
MTF QFG (Quarter Fib Grid)The MTF QFG (Quarter Fib Grid) calculates quarter Fibonacci levels based on the previous daily, weekly, or monthly high/low. These levels act as potential support and resistance zones. Suitable for scalping, swing trading, or identifying key price reactions.
VCP-Minervini v2This indicator is based on the famous Volatility Contraction Pattern (VCP) developed by Mark Minervini. It is designed to detect breakout setups in stocks and indices and provides a complete framework for identifying and managing them.
VCP Detection: Uses ATR to measure contracting volatility and highlights potential setups with yellow markers.
Pivot Level: Once a VCP is detected, the script automatically fixes a pivot resistance level and plots it as a red line.
Breakout Signal: When price closes above the pivot and remains above the EMA, a green marker confirms that a breakout is active.
Exit Logic: If momentum fades (volume drops below average and True Range contracts), the green marker disappears, signaling it may be time to secure profits or adjust stops.
EMA Filter: Ensures that setups are only valid in the direction of the prevailing trend by requiring price to trade above the selected EMA.
How to use it
This tool is best suited for swing traders and position traders looking for clean breakout opportunities. It is not a simple mashup of indicators but an original combination specifically built for VCP setups.
Use primarily on daily or weekly charts of liquid stocks.
Build a watchlist from yellow markers (potential setups).
Watch for price closing above the pivot line to confirm the breakout.
Manage trades while the green marker is active, and take caution when it disappears.
Adjust the parameters to fine-tune detection sensitivity for your market or trading style.
Weinstein Stage Analyzer — Table Only (more padding)What it does
This indicator applies Stan Weinstein’s Stage Analysis (Stages 1–4) and presents the result in a clean, compact table only—no lines, labels, or overlays. It shows:
• Previous Stage
• Current Stage (with Early / Mature / Late tag)
• Duration (how long price has been in the current stage, in HTF bars)
• Sentiment (Bullish / Bearish / Balanced / Cautious, derived from stage & maturity)
Timeframe-aware logic
• Weekly charts: classic 30-period MA (Weinstein’s original 30-week concept).
• Daily & Intraday: computed on Daily 150 as a practical daily translation of the 30-week idea.
• Monthly: ~7-period MA (~30 weeks ≈ 7 months).
The stage classification itself is evaluated on this HTF context and then displayed on your active chart.
EMA/SMA toggle
Choose EMA (default) or SMA for the trend line used in stage detection.
How stages are decided (practical rules)
• Stage 2 (Advance): MA rising with price above an upper band.
• Stage 4 (Decline): MA falling with price below a lower band.
• Flat MA zones become Stage 1 (Base) or Stage 3 (Top) depending on the prior trend.
“Maturity” tags (Early/Mature/Late) come from run length and extension beyond the band.
Inputs you can tweak
• MA Type: EMA / SMA
• Price Band (±%) and Slope Threshold to tighten/loosen stage flips
• Maturity thresholds: min/max bars & late-extension %
Notes
• Duration is for the entire current stage (e.g., total time in Stage 4), not just the maturity slice.
• A Top Padding Rows input is included to nudge the table lower if it overlaps your OHLC readout.
Disclaimer
For educational use only. Not financial advice. Always confirm with your own analysis, risk management, and market context.
Measured Move Volume XIndicator Description
The "Measured Move Volume X" indicator, developed for TradingView using Pine Script version 6, projects potential price targets based on the measured move concept, where the magnitude of a prior price leg (Leg A) is used to forecast a subsequent move. It overlays translucent boxes on the chart to visualize bullish (green) or bearish (red) price projections, extending them to the right for a user-specified number of bars. The indicator integrates volume analysis (relative to a simple moving average), RSI for momentum, and VWAP for price-volume weighting, combining these into a confidence score to filter entry signals, displayed as triangles on breakouts. Horizontal key level lines (large, medium, small) are drawn at significant price points derived from the measured moves, with customizable thresholds, colors, and styles. Exhaustion hints, shown as orange labels near box extremes, indicate potential reversal points. Anomalous candles, marked with diamond shapes, are identified based on volume spikes and body-to-range ratios. Optional higher timeframe candle coloring enhances context. The indicator is fully customizable through input groups for lookback periods, transparency, and signal weights, making it adaptable to various assets and timeframes.
Adjustment Tips for Optimization
To optimize the "Measured Move Volume X" indicator for specific assets or timeframes, adjust the following input parameters:
Leg A Lookback (default: 14 bars): Increase to 20-30 for volatile markets (e.g., cryptocurrencies) to capture larger price swings; decrease to 5-10 for intraday charts (e.g., stocks) for faster signals.
Extend Box to the Right (default: 30 bars): Extend to 50+ for daily or weekly charts to project further targets; shorten to 10-20 for lower timeframes to reduce clutter.
Volume SMA Length (default: 20) and Relative Volume Threshold (default: 1.5): Lower the threshold to 1.2-1.3 for low-volume assets (e.g., commodities) to detect subtler spikes; raise to 2.0+ for high-volume equities to filter noise. Match SMA length to RSI length for consistency.
RSI Parameters (default: length 14, overbought 70, oversold 30): Set overbought to 80 and oversold to 20 in trending markets to reduce premature exit signals; shorten length to 7-10 for scalping.
Key Level Thresholds (default: large 10%, medium 5%, small 5%): Increase thresholds (e.g., large to 15%) for volatile assets to focus on significant moves; disable medium or small lines to declutter charts.
Confidence Score Weights (default: volume 0.5, VWAP 0.3, RSI 0.2): Increase volume weight (e.g., 0.7) for volume-driven markets like futures; emphasize RSI (e.g., 0.4) for momentum-focused strategies.
Anomaly Detection (default: volume multiplier 1.5, small body ratio 0.2, large body ratio 0.75): Adjust the volume multiplier higher for stricter anomaly detection in noisy markets; fine-tune body-to-range ratios based on asset-specific candle patterns.
Use TradingView’s replay feature to test adjustments on historical data, ensuring settings suit the chosen market and timeframe.
Tips for Using the Indicator
Interpreting Signals: Green upward triangles indicate bullish breakout entries when price exceeds the prior high with a confidence score ≥40; red downward triangles signal bearish breakouts. Use these to identify potential entry points aligned with the projected box targets.
Box Projections: Bullish boxes project upward targets (top of box) equal to the prior leg’s height added to the breakout price; bearish boxes project downward. Monitor price action near box edges for target completion or reversal.
Exhaustion Hints: Orange labels near box tops (bullish) or bottoms (bearish) suggest potential exhaustion when price deviates within the set percentage (default: 5%) and RSI or volume conditions are met. Use these as cues to watch for reversals.
Key Level Lines: Large, medium, and small lines mark significant price levels from box tops/bottoms. Use these as potential support/resistance zones, especially when drawn with high volume (colored differently).
Anomaly Candles: Orange diamonds highlight candles with unusual volume/body characteristics, indicating potential reversals or pauses. Combine with box levels for context.
Higher Timeframe Coloring: Enable to color bars based on higher timeframe candle closures (e.g., 1, 2, 5, or 15 minutes) for added trend context.
Customization: Toggle "Only Show Bullish Moves" to focus on bullish setups. Adjust transparency and line styles for visual clarity. Test settings to balance signal frequency and chart readability.
Inputs: Organized into groups (e.g., "Measured Move Settings") using input.int, input.float, input.color, and input.bool for user customization, with tooltips for clarity.
Calculations: Computes relative volume (ta.sma(volume, volLookback)), VWAP (ta.vwap(hlc3)), RSI (ta.rsi(close, rsiLength)), and prior leg extremes (ta.highest/lowest) using prior bar data ( ) to prevent repainting.
Boxes and Lines: Creates boxes (box.new) for bullish/bearish projections and lines (line.new) for key levels. The f_addLine function manages line arrays (array.new_line), capping at maxLinesCount to avoid clutter.
Confidence Score: Combines volume, VWAP distance, and RSI into a weighted score (confScore), filtering entries (≥40). Rounded for display.
Exhaustion Hints: Functions like f_plotBullExitHint assess price deviation, RSI, and volume decrease, using label.new for dynamic orange labels.
Entry Signals and Plots: plotshape displays triangles for breakouts; plot and hline show VWAP and RSI levels; request.security handles higher timeframe coloring.
Anomaly Detection: Identifies candles with small-body high-volume or large-body average-volume patterns via ratios, plotted as diamonds.
Swing Oracle Stock 2.0- Gradient Enhanced# 🌈 Swing Oracle Pro - Advanced Gradient Trading Indicator
**Transform your technical analysis with stunning gradient visualizations that make market trends instantly recognizable.**
## 🚀 **What Makes This Indicator Special?**
The **Swing Oracle Pro** revolutionizes traditional technical analysis by combining advanced NDOS (Normalized Distance from Origin of Source) calculations with a sophisticated gradient color system. This isn't just another indicator—it's a complete visual trading experience that adapts colors based on market strength, making trend identification effortless and intuitive.
## 🎨 **10 Professional Gradient Themes**
Choose from carefully crafted color schemes designed for optimal visual clarity:
- **🌅 Sunset** - Warm oranges and purples for classic elegance
- **🌊 Ocean** - Cool blues and teals for calm analysis
- **🌲 Forest** - Natural greens and browns for organic feel
- **✨ Aurora** - Ethereal greens and magentas for mystique
- **⚡ Neon** - Vibrant electric colors for high-energy trading
- **🌌 Galaxy** - Deep purples and cosmic hues for night sessions
- **🔥 Fire** - Intense reds and golds for volatile markets
- **❄️ Ice** - Cool whites and blues for clear-headed decisions
- **🌈 Rainbow** - Full spectrum for comprehensive analysis
- **⚫ Monochrome** - Professional grays for focused trading
## 📊 **Core Features**
### **Advanced NDOS System**
- Normalized Distance from Origin of Source calculation with 231-period length
- Smoothed with customizable EMA for reduced noise
- Multi-timeframe confirmation with H1 filter option
- Dynamic gradient coloring based on oscillator position
### **Intelligent Visual Feedback**
- **Primary Gradient Line** - Main NDOS plot with dynamic color transitions
- **Gradient Fill Zones** - Beautiful color-coded areas for bullish, neutral, and bearish regions
- **Smart Transparency** - Colors adjust intensity based on market volatility
- **Dynamic Backgrounds** - Subtle gradient backgrounds that respond to market conditions
### **Enhanced EMA Projection System**
- 75/760 period EMA normalization with 50-period lookback
- Gradient-colored projection line for trend forecasting
- Toggleable display with advanced gradient controls
- Price tracking for precise level identification
### **Multi-Timeframe Analysis Table**
- Real-time trend analysis across 6 timeframes (1m, 3m, 5m, 15m, 1H, 4H)
- Gradient-colored cells showing trend strength
- Customizable table size and position
- Professional emoji indicators (🚀 UP, 📉 DOWN, ➡️ FLAT)
### **Signal System**
- **Gradient Buy Signals** - Triangle up arrows with intensity-based coloring
- **Gradient Sell Signals** - Triangle down arrows with strength indicators
- **Alert Conditions** - Built-in alerts for all signal types
- **7-Day Cycle Tracking** - Tuesday-to-Tuesday weekly cycle visualization
## ⚙️ **Customization Controls**
### **🎨 Gradient Controls**
- **Gradient Intensity** - Adjust color vibrancy (0.1-1.0)
- **Gradient Smoothing** - Control color transition smoothness (1-10 periods)
- **Dynamic Background** - Toggle animated background gradients
- **Advanced Gradients** - Enable/disable EMA projection and enhanced features
### **🛠️ Custom Color System**
- **Bullish Colors** - Define custom start/end colors for bull markets
- **Bearish Colors** - Set personalized bear market gradients
- **Full Theme Override** - Create completely custom color schemes
- **Real-time Preview** - See changes instantly on your chart
## 📈 **How to Use**
1. **Choose Your Theme** - Select from 10 professional gradient themes
2. **Configure Levels** - Adjust high/low levels (default 60/40) for your timeframe
3. **Set Smoothing** - Fine-tune gradient smoothing for your trading style
4. **Enable Features** - Toggle background gradients, candlestick coloring, and advanced EMA projection
5. **Monitor Signals** - Watch for gradient buy/sell arrows and multi-timeframe confirmations
## 🎯 **Trading Applications**
- **Swing Trading** - Perfect for identifying medium-term trend changes
- **Scalping** - Multi-timeframe table provides quick trend confirmation
- **Position Sizing** - Gradient intensity shows signal strength for risk management
- **Market Analysis** - Beautiful visualizations make complex data instantly understandable
- **Education** - Ideal for learning market dynamics through visual feedback
## ⚡ **Performance Optimized**
- **Smart Rendering** - Colors update only on significant changes
- **Efficient Calculations** - Optimized algorithms for smooth performance
- **Memory Management** - Minimal resource usage even with complex gradients
- **Real-time Updates** - Responsive to market changes without lag
## 🚨 **Alert System**
Built-in alert conditions notify you when:
- NDOS crosses above high level (Buy Signal)
- NDOS crosses below low level (Sell Signal)
- Multi-timeframe confirmations align
- Customizable alert messages with emoji indicators
## 🔧 **Technical Specifications**
- **PineScript Version**: v6 (Latest)
- **Overlay**: True (plots on main chart)
- **Calculations**: NDOS, EMA normalization, volatility-based transparency
- **Timeframes**: Compatible with all timeframes
- **Markets**: Stocks, Forex, Crypto, Commodities, Indices
## 💡 **Why Choose Swing Oracle Pro?**
This isn't just another technical indicator—it's a complete visual transformation of your trading experience. The gradient system provides instant visual feedback that traditional indicators simply can't match. Whether you're a beginner learning to read market trends or an experienced trader seeking clearer signals, the Swing Oracle Pro delivers professional-grade analysis with unprecedented visual clarity.
**Experience the future of technical analysis. Your charts will never look the same.**
---
*⚠️ Disclaimer: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own research and consider risk management before making trading decisions.*
**🔔 Like this indicator? Please leave a comment and boost! Your feedback helps improve future updates.**
---
**📝 Tags:** #GradientTrading #SwingTrading #NDOS #MultiTimeframe #TechnicalAnalysis #VisualTrading #TrendAnalysis #ColorCoded #ProfessionalCharts #TradingToo
Phoenix Pattern Scanner v1.3.2 - Multi-Pattern, Score & PresetsAdvanced multi-pattern scanner with intelligent presets and heuristic scoring system.
🎯 KEY FEATURES
- 5 Trading Style Presets: Conservative, Balanced, Aggressive, Swing, Scalp
- 4 Core Patterns: RVOL (unusual volume), Momentum breakout, RSI bounce, Gap & Go
- Heuristic Score (0-100): Visual ranking system for signal quality
- Per-Pattern Anti-Noise: Prevents signal spam with configurable minimum distance
- Relative Strength %: Compare performance vs benchmark (default SPY)
- Squeeze Detection: Identifies low volatility compression (BB inside Keltner)
📊 SMART FILTERS
- Minimum price and average dollar volume gates
- Weekly trend confirmation (optional)
- Separate lookback periods for each pattern
- Configurable RSI length and Gap parameters
⚙️ CUSTOMIZATION
- All parameters adjustable via settings
- Toggle individual components on/off
- Clean info panel with real-time metrics
- Color-coded score visualization
📍 BEST USED ON
- Daily timeframe (primary design)
- Liquid stocks above $5
- As a screening tool alongside your analysis
⚠️ IMPORTANT NOTES
- Educational/informational tool only
- NOT financial advice or trade signals
- Heuristic score is diagnostic, not predictive
- Past pattern behavior ≠ future results
💡 QUICK START
1. Select a preset matching your style
2. Adjust filters for your market
3. Set alerts for patterns you want to track
4. Use score as relative ranking, not absolute signal
Version 1.3.2 - Stable release
Open source - Free to use and modify
Feedback and improvements welcome
Percentage Change per 5 Candles
🔎 What this indicator does
This indicator calculates and displays the percentage change of each candlestick directly on the chart.
• If a candle closed higher than it opened (bullish candle), it shows a positive % change (green).
• If a candle closed lower than it opened (bearish candle), it shows a negative % change (red).
• Small moves below your chosen threshold (e.g., 0.1%) are ignored to avoid clutter.
• The labels are placed above, below, or in the center of the candle (you choose).
So essentially, every candle “tells you in numbers” exactly how much it changed relative to its opening price.
________________________________________
⚙️ How it operates (the logic inside)
1. Calculate the change
o Formula:
\text{% Change} = \frac{(\text{Close} - \text{Open})}{\text{Open}} \times 100
o Example: If a candle opens at 100 and closes at 105, that’s a +5% change.
2. Round it nicely
o You can control decimals (e.g., show 2 decimals → +5.23%).
3. Filter out noise
o If a candle barely moved (say 0.02%), the label won’t appear unless you reduce the threshold.
4. Style the labels
o Bullish = green text, slightly transparent green background.
o Bearish = red text, slightly transparent red background.
o Neutral (0%) = gray.
5. Place the labels
o Options: above the candle, below the candle, or centered.
o Small vertical offset is applied so labels don’t overlap the candle itself.
________________________________________
📊 How this helps traders
This indicator turns visual candles into quantifiable numbers at a glance. Instead of guessing whether a move was “big” or “small,” you see it clearly.
Key Benefits:
1. Quick volatility analysis
o You can instantly see if candles are making big % swings or just small moves.
o This is especially useful on higher timeframes (daily/weekly) where moves can be large.
2. Pattern confirmation
o For example, you might spot a strong bullish engulfing candle — the % change label helps confirm whether it was truly significant (e.g., +4.5%) or just modest (+0.7%).
3. Noise filtering
o By setting a minimum % threshold, you only see labels when moves are meaningful (say > 0.5%). This keeps focus on important candles.
4. Backtesting & comparison
o You can compare moves across time:
“How strong was this breakout candle compared to the last one?”
“Are today’s bearish candles weaker or stronger than yesterday’s bullish candles?”
5. Better decision-making
o If you’re trading breakouts, reversals, or trend-following, knowing the % size of each candle helps confirm if the move has enough momentum.
________________________________________
✅ In short:
This indicator quantifies price action. Instead of just seeing “green” or “red” candles, you now know exactly how much the price changed in percentage terms, directly on the chart, in real time. It helps you distinguish between strong and weak moves and makes your analysis more precise.
________________________________________
