Ultimate Gold Confluence Score – Validator v6.1 By M.Lolas“Ultimate Gold Confluence Score Validator — multi-indicator add-on for a 15-minute, 20× long strategy with a very high win rate. Supports the strategy’s main indicator.”
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15m Continuation — prev → new (v6, styled)This indicator gives you backtested statistics on how often reversals vs continuations occur on 15 minute candles on any pair you want to trade. This is great for 15m binary markets like on Polymarket.
open 5 min range 09:00/15:30the indicator will remove himself after 2h. it´s for trading in the 1min chart. wait for breakout, than retest and after that trade away from the boxes if u see price action.
1H FVG Zones Only (5m & 1h)new uses trend anaylosis. takes 15 min chart and breaks into 1hr chart fvg gaps
15m FVG Inversion + Order BlockThe indicator finds the inversion of the FVG 15 minutes and the order block, after which it gives an entry signal.
FlowSpike ES — BB • RSI • VWAP + AVWAP + News MuteThis indicator is purpose-built for E-mini S&P 500 (ES) futures traders, combining volatility bands, momentum filters, and session-anchored levels into a streamlined tool for intraday execution.
Key Features:
• ES-Tuned Presets
Automatically optimized settings for scalping (1–2m), daytrading (5m), and swing trading (15–60m) timeframes.
• Bollinger Band & RSI Signals
Entry signals trigger only at statistically significant extremes, with RSI filters to reduce false moves.
• VWAP & Anchored VWAPs
Session VWAP plus anchored VWAPs (RTH open, weekly, monthly, and custom) provide high-confidence reference levels used by professional order-flow traders.
• Volatility Filter (ATR in ticks)
Ensures signals are only shown when the ES is moving enough to offer tradable edges.
• News-Time Mute
Suppresses signals around scheduled economic releases (customizable windows in ET), helping traders avoid whipsaw conditions.
• Clean Alerts
Long/short alerts are generated only when all conditions align, with optional bar-close confirmation.
Why It’s Tailored for ES Futures:
• Designed around ES tick size (0.25) and volatility structure.
• Session settings respect RTH hours (09:30–16:00 ET), the period where most liquidity and institutional flows concentrate.
• ATR thresholds and RSI bands are pre-tuned for ES market behavior, reducing the need for manual optimization.
⸻
This is not a generic indicator—it’s a futures-focused tool created to align with the way ES trades day after day. Whether you scalp the open, manage intraday swings, or align to weekly/monthly anchored flows, FlowSpike ES gives you a clear, rules-based signal framework.
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.
Long Multi-TimeframeTo be used on a 30 minute time frame with Market Bias changing from red to light red or green, 4 or more consecutive red dots on the 15 minute and 30 minute frames inside the market bias, and a red to green Bx-Trender, backed up with good flow (real-time plus green net cumulative flow).
童貞２_MACDUp and down arrows will appear to let you know which way to place it. It is important to be able to analyze the chart before using this indicator. We recommend using our homemade MACD at 15 minutes.
Advanced Trading System - [WOLONG X DBG]Advanced Multi-Timeframe Trading System
Overview
This technical analysis indicator combines multiple established methodologies to provide traders with market insights across various timeframes. The system integrates SuperTrend analysis, moving average clouds, MACD-based candle coloring, RSI analysis, and multi-timeframe trend detection to suggest potential entry and exit opportunities for both swing and day trading approaches.
Methodology
The indicator employs a multi-layered analytical approach based on established technical analysis principles:
Core Signal Generation
SuperTrend Engine: Utilizes adaptive SuperTrend calculations with customizable sensitivity (1-20) combined with SMA confirmation filters to identify potential trend changes and continuations
Braid Filter System: Implements moving average filtering using multiple MA types (McGinley Dynamic, EMA, DEMA, TEMA, Hull, Jurik, FRAMA) with percentage-based strength filtering to help reduce false signals
Multi-Timeframe Analysis: Analyzes trend conditions across 10 different timeframes (1-minute to Daily) using EMA-based trend detection for broader market context
Advanced Features
MACD Candle Coloring: Applies dynamic 4-level candle coloring system based on MACD histogram momentum and signal line relationships for visual trend strength assessment
RSI Analysis: Identifies potential reversal areas using RSI oversold/overbought conditions with SuperTrend confirmation
Take Profit Analysis: Features dual-mode TP detection using statistical slope analysis and Parabolic SAR integration for exit timing analysis
Key Components
Signal Types
Primary Signals: Green ▲ for potential long entries, Red ▼ for potential short entries with trend and SMA alignment
Reversal Signals: Small circular indicators for RSI-based counter-trend possibilities
Take Profit Markers: X-cross symbols indicating statistical TP analysis zones
Pullback Signals: Purple arrows for potential trend continuation entries using Parabolic SAR
Visual Elements
8-Layer MA Cloud: Customizable moving average cloud system with 3 color themes for trend visualization
Real-Time Dashboard: Multi-timeframe trend analysis table showing bullish/bearish status across all timeframes
Dynamic Candle Colors: 4-intensity MACD-based coloring system (ranging from light to strong trend colors)
Entry/SL/TP Labels: Automatic calculation and display of suggested entry points, stop losses, and multiple take profit levels
Usage Instructions
Basic Configuration
Sensitivity Setting: Start with default value 6
Increase (7-15) for more frequent signals in volatile markets
Decrease (3-5) for higher quality signals in trending markets
MA Filter Type: McGinley Dynamic recommended for smoother signals
Filter Strength: Set to 80% for balanced filtering, adjust based on market conditions
Signal Interpretation
Long Entry: Green ▲ suggests when price crosses above SuperTrend with bullish SMA alignment
Short Entry: Red ▼ suggests when price crosses below SuperTrend with bearish SMA alignment
Reversal Opportunities: Small circles indicate RSI-based counter-trend analysis
Take Profit Zones: X-crosses mark statistical TP areas based on slope analysis
Dashboard Analysis
Green Cells: Bullish trend detected on that timeframe
Red Cells: Bearish trend detected on that timeframe
Multi-Timeframe Confluence: Look for alignment across multiple timeframes for stronger signal confirmation
Risk Management Features
Automatic Calculations
ATR-Based Stop Loss: Dynamic stop loss calculation using ATR multiplier (default 1.9x)
Multiple Take Profit Levels: Three TP targets with 1:1, 1:2, and 1:3 risk-reward ratios
Position Sizing Guidance: Entry labels display suggested price levels for order placement
Confirmation Requirements
Trend Alignment: Requires SuperTrend and SMA confirmation before signal generation
Filter Validation: Braid filter must show sufficient strength before signals activate
Multi-Timeframe Context: Dashboard provides broader market context for decision making
Optimal Settings
Timeframe Recommendations
Scalping: 1M-5M charts with sensitivity 8-12
Day Trading: 15M-1H charts with sensitivity 6-8
Swing Trading: 4H-Daily charts with sensitivity 4-6
Market Conditions
Trending Markets: Reduce sensitivity, increase filter strength
Ranging Markets: Increase sensitivity, enable reversal signals
High Volatility: Adjust ATR risk factor to 2.0-2.5
Advanced Features
Customization Options
MA Cloud Periods: 8 customizable periods for cloud layers (default: 2,6,11,18,21,24,28,34)
Color Themes: Three professional color schemes plus transparent option
Dashboard Position: 9 positioning options with 4 size settings
Signal Filtering: Individual toggle controls for each signal type
Technical Specifications
Moving Average Types: 21 different MA calculations including advanced types (Jurik, FRAMA, VIDA, CMA)
Pullback Detection: Parabolic SAR with customizable start, increment, and maximum values
Statistical Analysis: Linear regression slope calculation for trend-based TP analysis
Important Limitations
Lagging Nature: Some signals may appear after potential entry points due to confirmation requirements
Ranging Markets: May produce false signals during extended sideways price action
High Volatility: Requires parameter adjustment during news events or unusual market conditions
Computational Load: Multiple timeframe analysis may impact performance on slower devices
No Guarantee: All signals are suggestions based on technical analysis and may be incorrect
Educational Disclaimers
This indicator is designed for educational and analytical purposes only. It represents a technical analysis tool based on mathematical calculations of historical price data and should not be considered as financial advice or trading recommendations.
Risk Warning: Trading involves substantial risk of loss and is not suitable for all investors. Past performance of any trading system or methodology is not necessarily indicative of future results. The high degree of leverage can work against you as well as for you.
Important Notes:
Always conduct your own analysis before making trading decisions
Use appropriate position sizing and risk management strategies
Never risk more than you can afford to lose
Consider your investment objectives, experience level, and risk tolerance
Seek advice from qualified financial professionals when needed
Performance Disclaimer: Backtesting results do not guarantee future performance. Market conditions change constantly, and what worked in the past may not work in the future. Always paper trade new strategies before risking real capital.
Price Persistence ScreenerPrice Persistence Screener
Pine Script v6 | Inspired by @pradeepbonde on X
This indicator, inspired by the insights of @pradeepbonde , is designed to identify stocks with high price persistence—stocks that consistently close higher than the previous day's close over various lookback periods. As described by Pradeep Bonde, stocks with high persistence are strong candidates for trading pullbacks or consolidations, as they often resume their upward trend due to aggressive buying and low selling pressure. This tool helps traders screen for such stocks and visualize their persistence across multiple timeframes.
Features:
Measures price persistence by counting bars where the closing price exceeds the previous bar’s close for fixed periods: 499, 252, 126, 60, 40, 20, 15, 10, and 5 bars.
Includes a customizable lookback period (1 to 499 bars) for flexible analysis.
Allows users to set a custom persistence threshold (0% to 100%) to highlight strong bullish trends.
How It Works:
For each lookback period, the indicator calculates how many times the closing price is higher than the previous bar’s close.
A higher count indicates stronger bullish persistence, signaling stocks with sustained upward momentum.
Usage:
This screener is aimed to be used on pine screener to see data in columns. Add this indicator to you favorites and in pine screener scan on your watchlist of up to 1000 stocks
Adjust the custom lookback period and threshold via input settings.
Sort columns to compare persistence across timeframes and identify stocks with high persistence for swing trading or long-term holding.
Settings:
Custom Lookback Period (Bars): Set the number of bars for the custom persistence calculation (default: 100).
Custom Persistence Threshold (%): Define the percentage threshold for highlighting high persistence in the custom period (default: 70%).
Credits:
This indicator is based on the price persistence concept shared by @pradeepbonde
in his YouTube video (www.youtube.com). He explains that stocks with high persistence—those consistently closing higher day after day—are strong candidates for trading pullbacks, as they tend to resume their upward trend. This screener automates and visualizes that concept, making it easier for traders to identify such stocks.
Note:
Ensure sufficient historical data is available for accurate calculations, especially for longer periods like 499 bars. if stock is less than 499 bars.
High persistence stocks may eventually lose momentum, signaling potential reversals or shorting opportunities, as noted by @pradeepbonde
.
Use this indicator as part of a broader trading strategy to screen strong trends with custom lookback scan, combining it with other technical or fundamental analysis.
Custom Linear Regression Candles with Real-Time PriceHii this is great indicator to build by chatgpt.
How to use------------
1. It is based on the linear regression formula which gives you accurate market conditions.
2. You can do this with a RSI indicator so you can know overbought and oversell label.
3.If you want to get good accuracy then you can use chart type Heikin Ashi.
Input--------------
1. You can take linear regression length on different timeframes, in my backtest it was
5 to 15 min----30 and 1hour to 4hour---20 and Day---10 you can keep it.
2. You can pinpoint the highs and lows of the linear regression line.
--Please use it and give your feedback.
ORB Pro w/ Filters + Debug Overlay Update with Reason box fixThis indicator is designed to highlight high-probability reversal setups for intraday traders.
It focuses on the cleanest, most reliable candlestick reversal patterns and combines them with trend, VWAP/EMA confluence, and a time-based filter to reduce noise.
🛠️ How It Works
The script scans each bar for well-known reversal signals:
Doji Reversal – small body, long wicks showing indecision.
Hammer / Shooting Star – long wick ≥ 2× body, showing exhaustion.
Engulfing Reversal – full body engulf of the prior candle.
Additional filters include:
✅ VWAP/EMA Confluence (optional) – confirms reversals near key intraday levels.
✅ Time Window (default 9:30–10:30 NY) – avoids false signals later in the session.
✅ Trend Exhaustion Check – requires a short-term directional push before reversal.
✅ Signal Cooldown – limits to one clean signal per move.
When conditions align, the script plots:
🟢 “Bull Rev” label below the bar for bullish reversals.
🔴 “Bear Rev” label above the bar for bearish reversals.
⚙️ Recommended Settings
For the tightest, most reliable signals:
Doji Body % → 25–30
Hammer Wick Multiple → 2.0
Confluence Tolerance % → 0.2–0.3
Time Filter → ON (9:30–10:30 NY)
VWAP/EMA Filter → ON
Cooldown Bars → 10–15
These settings minimize false positives and focus on the strongest reversals.
📈 Use Case
This tool is best for:
Intraday traders (stocks, ETFs, futures, crypto).
Traders who use Opening Range Breakout (ORB) or similar systems but want a secondary tool for catching reversals.
Anyone looking to filter out weak reversal patterns and focus on textbook setups.
⚠️ Disclaimer
This script is for educational purposes only and should not be considered financial advice. Always test in simulation/paper trading before applying live
🚀 Catch textbook reversals with confidence.
This indicator filters out noise and only plots high-probability reversal signals based on proven candlestick patterns + VWAP/EMA confluence.
🔥 Key Features:
✅ Detects Doji, Hammer/Shooting Star, and Engulfing Reversals
✅ VWAP & EMA confluence filter (optional)
✅ Time window filter (default 9:30–10:30 NY for max edge)
✅ Signal cooldown to avoid clutter
✅ Clean chart labels + alert conditions
🎯 Who’s It For?
Day traders who want precision reversal entries
ORB traders looking for secondary setups
Intraday scalpers who value quality over quantity
👉 Designed for traders who want fewer, cleaner, higher-probability signals.
⚠️ Not financial advice. For educational use only
_____
🎯 ORB SET-UP DESCRIPTIONS:
🔧 Exact settings I’d recommend (to avoid that mess):
requireClose = true
requireRetest = true with retestPct = 0.2%
minRangePct = 0.3%, maxRangePct = 1.5%
volumeFilter = true, volumeLength = 20
trendFilter = true, emaLength = 20
cooldownBars = 6 (on 5m chart → 30 minutes)
🔑 ORB Range Settings
Default sweet spot: 0.2% – 0.3%
→ This usually balances enough signals with reduced false breakouts.
High volatility days (CPI, FOMC, big gaps): 0.3% – 0.5%
→ Prevents fake outs.
Low volatility days (tight overnight range, slow open): 0.15% – 0.2%
→ Keeps you from sitting on hands all day.
📌 Filters you already added help you avoid noise
EMA alignment
Volume confirmation
Optional stop/target logic
This means you don’t have to shrink the box to 0.1% — the filters will keep you in higher-probability trades
✅ Why You Might NOT See a Signal
Check box for reason signal to turn it off, updated coloring so that candles are more visable.
ORB Box Too Wide
If the opening range is large, price has to move much further to trigger a clean breakout.
Wide box = fewer signals (but higher quality).
No Clean Break + Hold
Script waits for a candle to break above/below ORB and close strong enough.
A wick poke doesn’t count.
VWAP / EMA Filter Not Aligned
If price breaks but VWAP/EMA trend filter disagrees → no signal.
Keeps you out of fake moves against the trend.
Confirmation Candle Missing (if enabled)
Even if price breaks, the script may want the next bar to confirm direction before signaling.
Cooldown / One-Signal-Per-Break Rule
Some filters prevent back-to-back spam signals.
Only the first clean setup is alerted.
Tape Speed Pulse (Pace + Direction) [v6 + Climax]Tape Speed Pulse (Pace + Direction)
One-liner:
A lightweight “tape pulse” that turns intraday bursts of buying/selling into an easy-to-read histogram, with surge, slowdown, and climax (exhaustion) markers for fast decision-making. Use on sec and min charts.
What it measures
Pace (RVOL): current bar volume vs the recent average (smoothed).
Direction proxy: uptick/downtick by comparing close to close .
Pulse (histogram): direction × pace, so you see who’s pushing and how fast.
Colors
- Lime = Buy surge (pace ≥ threshold & upticking)
- Red = Sell surge (pace ≥ threshold & downticking)
- Teal = Buy pressure, sub-threshold
- Orange = Sell pressure, sub-threshold
- Faded/gray = Near-neutral pace (below the Neutral Band)
Lines (toggleable)
-White = Pace (RVOL)
- Yellow = Slowdown line = a drop of X% from the last 30-bar peak pace
Background tint mirrors the current state so you can glance risk: greenish for buy pressure, reddish for sell pressure.
Signals & alerts
- BUY surge – fires when pace crosses above the surge threshold with uptick direction (optional acceleration & uptick streak filters; cooldown prevents spam).
- SELL surge – mirror logic to downside.
- Slowdown – fires when pace crosses below the yellow slowdown line while direction ≤ 0 (early fade warning).
Climax (exhaustion)
- Buy Climax: previous bar was a buy surge with a large upper wick; current bar slows (below slowdown line) and direction ≤ 0.
- Sell Climax: mirror (large lower wick → slowdown → direction ≥ 0).
- Great for trimming/tight stops or fade setups at obvious spikes.
- Create alerts via Add alert → Condition: this indicator → choose the specific alert (BUY surge, SELL surge, Slowdown, Buy Climax, Sell Climax).
How to use it (playbook)
- Longs (e.g., VWAP reclaim / micro pullback)
- Only take entries when the pulse is teal→lime (buy pressure to buy surge).
- Into prior highs/VWAP bands, take partials on lime spikes.
- If you get a Slowdown dot and bars turn orange/red, tighten or exit.
Shorts (failed reclaim / lower-high)
- Look for teal→orange→red with rising pace at a level.
- Add confidence if a Buy Climax printed right before (exhaustion).
- Risk above the spike; don’t fight true ignitions out of bases.
Simple guardrails
- Avoid new longs when the histogram is orange/red; avoid new shorts when teal/lime.
- Use with VWAP + 9/20 EMA or your levels. The pulse is confirmation, not the whole thesis.
Inputs (what they do & when to tweak)
- Pace lookback (bars) – window for average volume. Lower = faster; higher = steadier.
Too jumpy? raise it. Missing quick bursts? lower it.
- Smoothing EMA (bars) – smooths pace. Higher = calmer.
Use 4–6 during the open; 3–4 midday.
- Surge threshold (× RVOL) – how fast counts as a surge.
Too many surges? raise it. Too late? lower it slightly.
- Slowdown drop from 30-bar max (%) – how far below the recent peak pace to call a slowdown.
Higher % = later slowdown; lower % = earlier warning.
- Neutral band (× RVOL) – paces below this fade to gray.
Raise to clean up noise; lower to see subtle pressure.
- Min seconds between signals – cooldown to prevent spam.
Increase in chop; reduce if you want more pings.
- BUY/SELL: min consecutive upticks/downticks – tiny streak filter.
Raise to avoid wiggles; lower for earlier signals.
Require pace accelerating into signal – ON = avoid stall breakouts; OFF = earlier pings.
Climax options: wick % threshold & “require slowdown cross”.
Raise wick% / require cross to be stricter; lower to catch more fades.
Quick presets
- Low-float runner, 5–10s chart
- Lookback 20, Smoothing 3–4, Surge 2.2–2.8, Slowdown 35–45, Neutral 1.0–1.2, Cooldown 15–25s, Streaks 2–3, Accel ON.
- Thick large-cap, 1-min
- Lookback 20–30, Smoothing 5–7, Surge 1.5–1.9, Slowdown 25–35, Neutral 0.8–1.0, Cooldown 30–60s, Streaks 2, Accel ON.
- Open vs Midday vs Power Hour
- Open: higher Surge, more Smoothing, longer Cooldown.
- Midday: lower Surge, less Smoothing to catch subtler pushes.
- Power hour: moderate Surge; keep Slowdown on for exits.
Reading common patterns
- Ignition (likely continuation): lime spike out of a base that holds above a level while pace stays above yellow.
- Exhaustion (likely fade): lime spike late in a run with upper wick → Slowdown → orange/red. The Buy Climax diamond is your tell.
Limits / notes
This is an OHLCV-based proxy (TradingView Pine can’t read raw tape/DOM). It won’t match Bookmap/Jigsaw tick-for-tick, but it’s fast and objective.
Use with levels and a risk plan. Past performance ≠ future results. Educational only.
Nirvana True Duel전략 이름
열반의 진검승부 (영문: Nirvana True Duel)
컨셉과 철학
“열반의 진검승부”는 시장 소음은 무시하고, 확실할 때만 진입하는 전략입니다.
EMA 리본으로 추세 방향을 확인하고, 볼린저 밴드 수축/확장으로 변동성 돌파를 포착하며, OBV로 거래량 확인을 통해 가짜 돌파를 필터링합니다.
전략 로직
매수 조건 (롱)
20EMA > 50EMA (상승 추세)
밴드폭 수축 후 확장 시작
종가가 상단 밴드 돌파
OBV 상승 흐름 유지
매도 조건 (숏)
20EMA < 50EMA (하락 추세)
밴드폭 수축 후 확장 시작
종가가 하단 밴드 이탈
OBV 하락 흐름 유지
진입·청산
손절: ATR × 1.5 배수
익절: 손절폭의 1.5~2배에서 부분 청산
시간 청산: 설정한 최대 보유 봉수 초과 시 강제 청산
장점
✅ 추세·변동성·거래량 3중 필터 → 노이즈 최소화
✅ 백테스트·알람 지원 → 기계적 매매 가능
✅ 5분/15분 차트에 적합 → 단타/스윙 트레이딩 활용 가능
주의점
⚠ 횡보장에서는 신호가 적거나 실패 가능
⚠ 수수료·슬리피지 고려 필요
📜 Nirvana True Duel — Strategy Description (English)
Name:
Nirvana True Duel (a.k.a. Nirvana Cross)
Concept & Philosophy
The “Nirvana True Duel” strategy focuses on trading only meaningful breakouts and avoiding unnecessary noise.
Nirvana: A calm, patient state — waiting for the right opportunity without emotional trading.
True Duel: When the signal appears, enter decisively and let the market reveal the outcome.
In short: “Ignore market noise, trade only high-probability breakouts.”
🧩 Strategy Components
Trend Filter (EMA Ribbon): Stay aligned with the main market trend.
Volatility Squeeze (Bollinger Band): Detect volatility contraction & expansion to catch explosive moves early.
Volume Confirmation (OBV): Filter out false breakouts by confirming with volume flow.
⚔️ Entry & Exit Conditions
Long Setup:
20 EMA > 50 EMA (uptrend)
BB width breaks out from recent squeeze
Close > Upper Bollinger Band
OBV shows positive flow
Short Setup:
20 EMA < 50 EMA (downtrend)
BB width breaks out from recent squeeze
Close < Lower Bollinger Band
OBV shows negative flow
Risk Management:
Stop Loss: ATR × 1.5 below/above entry
Take Profit: 1.5–2× stop distance, partial take-profit allowed
Time Stop: Automatically closes after max bars held (e.g. 8h on 5m chart)
✅ Strengths
Triple Filtering: Trend + Volatility + Volume → fewer false signals
Mechanical & Backtestable: Ideal for objective trading & performance validation
Adaptable: Works well on Bitcoin, Nasdaq futures, and other high-volatility markets (5m/15m)
⚠️ Things to Note
Low signal frequency or higher failure rate in sideways/range markets
Commission & slippage should be factored in, especially on lower timeframes
ATR multiplier and R:R ratio should be optimized per asset
[delta2win] Liquidity Zone Map🔥 Liquidity Zone Map — Volume‑normalized pivot zones with adaptive ATR scaling
📊 What it does:
• Detects potential liquidity/liquidation zones above confirmed highs and below confirmed lows
• Draws dynamic zones whose height scales with ATR and whose color intensity scales with volume
• Zones extend right and terminate on rule‑based events (midline cross)
🔬 How it works (core formulas):
• Pivot detection: ta.pivothigh/ta.pivotlow with length L
• Zone height: H = max(ATR(T) × M, MinTicks)
• Intensity (volume‑normalized):
– Z‑Score mode: I = clamp((V − μ) / (σ + ε), 0..1)
– Piecewise mode: I = clamp(V ≤ μ ? V/μ : (V − μ) / (Vmax − μ + ε), 0..1)
• Gradient color: col = Gradient(I) (start → mid → end)
• Zone life‑cycle:
– Creation on new pivot (top/bottom)
– Right edge follows bar_index
– Termination when with Mid = (Top+Bottom)/2, or optional TTL timeout
• Analysis range: global or constrained (Bars Back or ±% price window). Color scaling can be global or range‑scoped.
🆕 What’s new/different:
• Selectable volume normalization (Z‑Score or Piecewise)
• Timeframe‑adaptive ATR multiplier
• Range‑scoped vs. global color scaling
• Optional midlines, borders, info legend, scale legend
• Optional TTL termination for zones (lifetime in bars)
• Object management (cleanup/pooling) for performance
🧭 How to use (suggested presets):
• 1–5m: L=2, T=200, M=0.25, Range=Bars Back 1000, Intensity=Piecewise
• 15–60m: L=3, T=200, M=0.20, Range=Bars Back 1500, Intensity=Piecewise
• 4h+: L=4, T=200, M=0.20, Range=Off, Intensity=Z‑Score
⚙️ Settings:
• Pivot length L, ATR length T, multiplier M, MinTicks
• Opacity: base/auto (min/max)
• Range: Bars Back | Price Range ±% | Off
• Scaling: global vs. range‑scoped
• Midlines/borders/legends on/off
💡 Usage notes:
• Smaller L → more reactive, less robust
• Larger M → longer‑lasting zones
• On higher TFs, constrain "Bars Back" for performance
⚠️ Limitations:
• Non‑predictive; regime/volatility dependent
• Data quality impacts intensity computation
oi + funding oscillator cryptosmartThe oi + funding oscillator cryptosmart is an advanced momentum tool designed to gauge sentiment in the crypto derivatives market. It combines Open Interest (OI) changes with Funding Rates, normalizes them into a single oscillator using a z-score, and identifies potential market extremes.
This provides traders with a powerful visual guide to spot when the market is over-leveraged (overheated) or when a significant deleveraging event has occurred (oversold), signaling potential reversals.
How It Works
Combined Data: The indicator tracks the rate of change in Open Interest and the value of Funding Rates.
Oscillator: It blends these two data points into a single, smoothed oscillator line that moves above and below a zero line.
Extreme Zones:
Overheated (Red Zone): When the oscillator enters the upper critical zone, it suggests excessive greed and high leverage, increasing the risk of a sharp correction (long squeeze). A cross below this level generates a potential sell signal.
Oversold (Green Zone): When the oscillator enters the lower critical zone, it indicates panic, liquidations, and a potential market bottom. A cross above this level generates a potential buy signal.
Trading Strategy & Timeframes
This oscillator is designed to be versatile, but its effectiveness can vary depending on the timeframe.
Optimal Timeframes (1H and 4H): The indicator has shown its highest effectiveness on the 1-hour and 4-hour charts. These timeframes are ideal for capturing significant shifts in market sentiment reflected in OI and funding data, filtering out short-term noise while still providing timely reversal signals.
Lower Timeframes (e.g., 1-min, 5-min, 15-min): On shorter timeframes, the oscillator is still a highly effective tool, but it is best used as a confluence factor within a broader trading system. Due to the increased noise on these charts, it is not recommended to use its signals in isolation. Instead, use it as a final argument for entry. For example, if your primary scalping strategy gives you a buy signal, you can check if the oscillator is also exiting the oversold (green) zone to add a powerful layer of confirmation to your trade.
Heikin Ashi Overlay SuiteHeikin Ashi Overlay Suite is designed to give traders more control and clarity when working with Heikin Ashi candles — whether you're analyzing trend strength, reducing chart noise, or simply improving your visual read of market momentum. It works by layering multiple types of HA overlays and color systems on top of your standard candlestick chart — without switching chart types. With dynamic gradient coloring, smoothing options, and a predictive line tool, this script helps you see not just what the current trend is, but how strong it is, and what it would take to reverse it.
Heikin Ashi candles help reduce noise but this script goes further by:
➡️adding color intelligence that shows trend strength using a streak counter
➡️uses smoothing logic to clean up chop and whipsaws
➡️introduces a predictive close line — a subtle but powerful guide for anticipating trend flips before they happen
Everything is configurable: colors, candle sources, overlays, predictive tools, and line styles. It’s built for traders who want visual speed, but don’t want to sacrifice signal quality.
At its core, the script offers two powerful dropdown controls:
💥HA Color Scheme (Colors Regular Candles) — Applies Heikin Ashi-derived coloring to your regular candles based on trend direction or streak strength. This gives you instant visual context without switching to a separate chart type.
💥HA Candle Overlay Mode — Overlays actual Heikin Ashi-style candles directly on top of your chart, using your preferred source:
➡️Custom HA candles using internal formula logic
➡️TradingView’s built-in Heikin Ashi source with your own colors
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
🎨 Custom + Gradient HA Coloring🎨
See trend strength at a glance:
➡️1–4 bar streaks → lighter tone
➡️5–8 bars → medium tone
➡️9+ bars → bold tone, ideal for momentum-based entries, exits, or scaling strategies
→ Choose from:
➡️Your own custom color set
➡️A simple 2-color base mode
➡️Or a 3-level gradient for progressive trend analysis (using the streak counter)
🏛️ TradingView Official Heikin Ashi Overlay
Prefer native HA candles but want your own colors?
This mode plots TradingView's Heikin Ashi source, with your personal bullish/bearish color scheme.
➡️Ensures consistency with built-in charts while still leveraging your visual style.
🌊 Smoothed Heikin Ashi Candles — Clarity in Chaos🌊
These aren’t your standard HA candles. Smoothed Heikin Ashi uses a two-step EMA process to transform chaotic price action into a cleaner, slower-moving trend structure:
🔹 First, it smooths the raw OHLC data using EMA — filtering out minor price fluctuations.
🔹 Then, it applies the Heikin Ashi transformation on top of the smoothed data.
🔹 Finally, it applies a second EMA smoothing pass to the HA values — creating ultra-smooth candles.
📈 What You See:
Trends appear more fluid and consistent.
Choppy ranges and fakeouts are visually suppressed.
Minor pullbacks within a trend are de-emphasized, helping you avoid premature exits.
🎯 Best For:
Swing traders looking to stay in positions longer.
Intraday traders dealing with volatile or noisy instruments.
Anyone who wants a "trend map" overlay without the distractions of raw price action.
✅ Reduces whipsaws
✅ Delivers high-contrast trend zones
✅ Makes reversals more visually apparent (but with a slight lag)
📍 Predictive Close Line📍
Shows where the real close must land to flip the current HA candle's color.
✅ Use it like predictive support/resistance
✅ Know if the trend is actually at risk
✅Visualize potential fakeouts or confirmation
Color-coded based on current HA direction (bullish, bearish, or neutral).
📈 Tick by tick & bar-to-bar Plots📈
Provides 2 plot types:
1)1 plot that tracks a bar tick by tick
2)another plot that tracks the close from bar to bar
For the bar to bar plot, you can choose between 2 options:
✅Full Plot — continuous line colored by HA trend
✅Recent Segments — color just the last few bars (configurable) to reduce chart clutter
✅ Customize width, number of bars, and visibility
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
📘 How to Use this script📘
Imagine you're watching a choppy 15-minute chart on a volatile crypto pair — price action is messy, and it’s hard to tell if a trend is forming or just noise.
Here’s how to cut through the chaos using Heikin Ashi Overlay Suite:
🔹 Step 1: Enable "Smoothed HA Candles"
Start by turning on the smoothed candles. You’ll immediately notice the noise fades, and broader directional moves become easier to follow. It's like switching from static to clean trend zones.
🧠 Why: Smoothed HA uses a double EMA process that filters out small reversals and lets larger moves stand out. Perfect for sideways or jittery charts.
🔹 Step 2: Watch the Color Gradient Build
As the smoothed candles begin to align in one direction, the gradient coloring (1–4, 5–8, 9+ streaks) gives you an at-a-glance visual of how strong the trend is.
✅ If you see 9+ same-colored candles? You’re likely in a mature trend.
✅ If it resets often? You’re in chop — consider staying out.
🔹 Step 3: Use the Predictive Close Line for Anticipation
Now here’s the edge — this line tells you where the candle would have to close to flip colors.
📉 If price is hovering just above it during a bullish run — momentum may be weakening.
📈 If price bounces off it — the trend may be strengthening.
This is excellent for confirming entries, exits, or spotting early warning signs.
🔹 Step 4: Switch Between Candle Modes as Needed
You can flip between:
✅ Custom HA: Gradient candles with your colors
✅ TradingView HA: The official source with your styling
✅ None: Just color regular candles using the HA logic
Use what fits your style — everything is modular.
🔹 Step 5: Tune It to Your Chart
Lastly, tweak streak thresholds (currently only can do this within the source code), smoothing lengths, and line styles to match your timeframe and strategy.
🎯 Tailor The Settings to Fit Your Trading Style🎯
🔹 🧪 Scalper (1–5 min charts)
If you’re trading fast intraday moves, you want quicker responsiveness and less lag.
Try these settings:
🔸Smoothing Lengths: Use lower values (e.g. len = 3, len2 = 5)
🔸Candle Mode: Use Custom HA or TV’s HA for real-time color flips
🔸Predictive Close Line: Great for ultra-fast anticipation of color reversals
🔸Line Mode: Use Recent Segments mode to track short bursts of trend
🔸Colors: Use high-contrast, opaque colors for clarity
✅ These settings help you catch micro-trends and flip signals faster, while still filtering out the worst of the noise.
🔹 🧪 Swing Trader (30m–4h charts and beyond)
If you’re looking for multi-hour or multi-day trend confirmation, prioritize clarity and staying in moves longer.
Recommended setup:
🔸Smoothing Lengths: Medium to high values (e.g. len = 8, len2 = 21)
🔸Candle Mode: Use Smoothed HA Candles to block out intrabar chop
🔸Gradient Colors: Enable to visualize trend maturity and strength
🔸Predictive Close Line: Helps confirm trend continuation or spot early reversals
🔸Line Mode: Use Full Plot Line for clean HA-based trend tracking
✅ These settings give you a calm, clean view of the bigger picture — ideal for holding positions longer and avoiding early exits.
🔧 This script isn’t just a chart overlay — it’s a visual trend engine.🔧
Ideal For:
🔶 Trend-followers who want clean, color-coded confirmation
🔶 Reversal traders spotting exhaustion via predictive flips
🔶 Scalpers filtering noise with lighter smoothing
🔶 Swing traders using smoothed visuals to hold longer
📌 Final Note
Heikin Ashi Overlay Pro is designed to help you see momentum, trend shifts, and market structure with greater clarity — not to predict price on its own. For best results:
✔️ Combine with support/resistance, moving averages, or price action patterns
✔️ Use Predictive Close as a confirmation tool, not a signal generator
✔️ Pair gradient colors with structure to gauge trend maturity
✔️ Always zoom out and check higher timeframes for context
🧠 Use this as part of a layered approach — not a standalone system.
🙏 Credits🙏
⚡HA logic based on SimpleCryptoLife
⚡Smoothed HA concept adapted from a script by Jackvmk
💡💡💡Turn logic into clarity. Structure into trades. And uncertainty into confidence.💡💡💡
Smarter Money Concepts Dashboard [PhenLabs]📊Smarter Money Concepts Dashboard
Version: PineScript™v6
📌Description
The Smarter Money Concepts Dashboard is a comprehensive institutional trading analysis tool that combines six of our most powerful smarter money concepts indicators into one unified suite. This advanced system automatically detects and visualizes Fair Value Gaps, Inverted FVGs, Order Blocks, Wyckoff Springs/Upthrusts, Wick Rejection patterns, and ICT Market Structure analysis.
Built for serious traders who need institutional-grade market analysis, this dashboard eliminates subjective interpretation by automatically identifying where smart money is likely positioned. The integrated real-time dashboard provides instant status updates on all active patterns, making it easy to monitor market conditions at a glance.
🚀Points of Innovation
● Multi-Module Integration: Six different SMC concepts unified in one comprehensive system
● Real-Time Dashboard Display: Live tracking of all active patterns with customizable positioning
● Advanced Volume Filtering: Institutional volume confirmation across all pattern types
● Automated Pattern Management: Smart memory system prevents chart clutter while maintaining relevant zones
● Probability-Based Wyckoff Detection: Mathematical probability calculations for spring/upthrust patterns
● Dual FVG System: Both standard and inverted Fair Value Gap detection with equilibrium analysis
🔧Core Components
● Fair Value Gap Engine: Detects standard FVGs with volume confirmation and equilibrium line analysis
● Inverted FVG Module: Advanced IFVG detection using RVI momentum filtering for inversion confirmation
● Order Block System: Institutional order block identification with customizable mitigation methods
● Wyckoff Pattern Recognition: Automated spring and upthrust detection with probability scoring
● Wick Rejection Analysis: High-probability reversal patterns based on wick-to-body ratios
● ICT Market Structure: Simplified institutional concepts with commitment tracking
🔥Key Features
● Comprehensive Pattern Detection: All major SMC concepts in one indicator with automatic identification
● Volume-Confirmed Signals: Multiple volume filters ensure only institutional-grade patterns are highlighted
● Interactive Dashboard: Real-time status display with active pattern counts and module status
● Smart Memory Management: Automatic cleanup of old patterns while preserving relevant market zones
● Full Alert System: Complete notification coverage for all pattern types and signal generations
● Customizable Display Options: Adjustable colors, transparency, and positioning for all visual elements
🎨Visualization
● Color-Coded Zones: Distinct color schemes for bullish/bearish patterns across all modules
● Dynamic Box Extensions: Automatically extending zones until mitigation or invalidation
● Equilibrium Lines: Fair Value Gap midpoint analysis with dotted line visualization
● Signal Markers: Clear spring/upthrust signals with directional arrows and probability indicators
● Dashboard Table: Professional-grade status panel with module activation and pattern counts
● Candle Coloring: Wick rejection highlighting with transparency-based visual emphasis
📖Usage Guidelines
Fair Value Gap Settings
● Days to Analyze: Default 15, Range 1-100 - Controls historical FVG detection period
● Volume Filter: Enables institutional volume confirmation for gap validity
● Min Volume Ratio: Default 1.5 - Minimum volume spike required for gap recognition
● Show Equilibrium Lines: Displays FVG midpoint analysis for precise entry targeting
Order Block Configuration
● Scan Range: Default 25 bars - Lookback period for structure break identification
● Volume Filter: Institutional volume confirmation for order block validation
● Mitigation Method: Wick or Close-based invalidation for different trading styles
● Min Volume Ratio: Default 1.5 - Volume threshold for significant order block formation
Wyckoff Analysis Parameters
● S/R Lookback: Default 20 - Support/resistance calculation period for spring/upthrust detection
● Volume Spike Multiplier: Default 1.5 - Required volume increase for pattern confirmation
● Probability Threshold: Default 0.7 - Minimum probability score for signal generation
● ATR Recovery Period: Default 5 - Price recovery calculation for pattern strength assessment
Market Structure Settings
● Auto-Detect Zones: Automatic identification of high-volume thin zones
● Proximity Threshold: Default 0.20% - Price proximity requirements for zone interaction
● Test Window: Default 20 bars - Time period for zone commitment calculation
Display Customization
● Dashboard Position: Four corner options for optimal chart layout
● Text Size: Scalable from Tiny to Large for different screen configurations
● Pattern Colors: Full customization of all bullish and bearish zone colors
✅Best Use Cases
● Swing Trading: Identify major institutional zones for multi-day position entries
● Day Trading: Precise intraday entries at Fair Value Gaps and Order Block boundaries
● Trend Analysis: Market structure confirmation for directional bias establishment
● Risk Management: Clear invalidation levels provided by all pattern boundaries
● Multi-Timeframe Analysis: Works across all timeframes from 1-minute to monthly charts
⚠️Limitations
● Market Condition Dependency: Performance varies between trending and ranging market environments
● Volume Data Requirements: Requires accurate volume data for optimal pattern confirmation
● Lagging Nature: Some patterns confirmed after initial price movement has begun
● Pattern Density: High-volatility markets may generate excessive pattern signals
● Educational Tool: Requires understanding of smart money concepts for effective application
💡What Makes This Unique
● Complete SMC Integration: First indicator to combine all major smart money concepts comprehensively
● Real-Time Dashboard: Instant visual feedback on all active institutional patterns
● Advanced Volume Analysis: Multi-layered volume confirmation across all detection modules
● Probability-Based Signals: Mathematical approach to Wyckoff pattern recognition accuracy
● Professional Memory Management: Sophisticated pattern cleanup without losing market relevance
🔬How It Works
1. Pattern Detection Phase:
● Multi-timeframe scanning for institutional footprints across all enabled modules
● Volume analysis integration confirms patterns meet institutional trading criteria
● Real-time pattern validation ensures only high-probability setups are displayed
2. Signal Generation Process:
● Automated zone creation with precise boundary definitions for each pattern type
● Dynamic extension system maintains relevance until mitigation or invalidation occurs
● Alert system activation provides immediate notification of new pattern formations
3. Dashboard Update Cycle:
● Live status monitoring tracks all active patterns and module states continuously
● Pattern count updates provide instant feedback on current market condition density
● Commitment tracking for market structure analysis shows institutional engagement levels
💡Note:
This indicator represents institutional trading concepts and should be used as part of a comprehensive trading strategy. Pattern recognition accuracy improves with understanding of smart money principles. Combine with proper risk management and multiple confirmation methods for optimal results.
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.
HeatCandleHeatCandle - AOC Indicator
✨ Features
📊 Heat-Map Candles: Colors candles based on the price’s deviation from a Triangular Moving Average (TMA), creating a heat-map effect to visualize price zones.
📏 Zone-Based Coloring: Assigns colors to 20 distinct zones (Z0 to Z19) based on the percentage distance from the TMA, with customizable thresholds.
⚙️ Timeframe-Specific Zones: Tailored zone thresholds for 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, and 4-hour timeframes for precise analysis.
🎨 Customizable Visuals: Gradient color scheme from deep blue (oversold) to red (overbought) for intuitive price movement interpretation.
🛠️ Adjustable Parameters: Configure TMA length and threshold multiplier to fine-tune sensitivity.
🛠️ How to Use
Add to Chart: Apply the "HeatCandle - AOC" indicator on TradingView.
Configure Inputs:
TMA Length: Set the period for the Triangular Moving Average (default: 150).
Threshold Multiplier: Adjust the multiplier to scale zone sensitivity (default: 1.0).
Analyze: Observe colored candles on the chart, where colors indicate the price’s deviation from the TMA:
Dark blue (Z0) indicates strong oversold conditions.
Red (Z19) signals strong overbought conditions.
Track Trends: Use the color zones to identify potential reversals, breakouts, or trend strength based on price distance from the TMA.
🎯 Why Use It?
Visual Clarity: The heat-map candle coloring simplifies identifying overbought/oversold conditions at a glance.
Timeframe Flexibility: Zone thresholds adapt to the selected timeframe, ensuring relevance across short and long-term trading.
Customizable Sensitivity: Adjust TMA length and multiplier to match your trading style or market conditions.
Versatile Analysis: Ideal for scalping, swing trading, or trend analysis when combined with other indicators.
📝 Notes
Ensure sufficient historical data for accurate TMA calculations, especially with longer lengths.
The indicator is most effective on volatile markets where price deviations are significant.
Pair with momentum indicators (e.g., RSI, MACD) or support/resistance levels for enhanced trading strategies.
Happy trading! 🚀📈
