Multi-Symbol RSI/ADX Monitor# 📊 Multi-Symbol RSI/ADX Monitor + EMA Trend Analyzer
### 🔹 Smart Trend Analyzer with Golden/Death Cross Signals + Multi-Symbol Scanner
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## 📌 Overview
The **Multi-Symbol RSI/ADX Monitor + EMA Trend Analyzer** combines **trend detection**, **crossover signals**, and a **multi-asset strength scanner** into a single tool.
- 🔹 **EMA Trend Analyzer** → Detects strong/weak bullish & bearish phases based on price vs EMAs, slope, and crossovers.
- 🔹 **RSI/ADX Scanner** → Monitors up to **10 custom tickers** in a dynamic table for relative strength & momentum.
- 🔹 **Alerts** → Catch **Strong Trends** or **Golden/Death Crosses** instantly.
Perfect for traders who want to track **trend bias** on their main chart while scanning **other assets for confirmation**.
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## ✨ Key Features
### 🔹 EMA Trend Analyzer
- ✅ Plots **Fast EMA (20)** & **Slow EMA (50)**.
- ✅ Main **Trend EMA (100)** with slope confirmation.
- ✅ Detects **5 Market States**:
- 🟢 Strong Bullish (Green)
- 🟢 Moderate Bullish (Lime)
- 🟠 Moderate Bearish (Orange)
- 🔴 Strong Bearish (Red)
- ⚪ Neutral / Sideways (Gray)
- ✅ Highlights **Golden Cross** & **Death Cross**:
- 🎯 Golden Cross → Fast EMA crosses above Slow EMA (Green dot + label)
- 🎯 Death Cross → Fast EMA crosses below Slow EMA (Red dot + label)
- ✅ Dynamic **trend label** on the right edge (shows trend + crossover info).
- ✅ Optional **background shading** by trend strength.
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### 🔹 Multi-Symbol RSI/ADX Monitor
- ✅ Track up to **10 tickers** simultaneously.
- ✅ Calculates **RSI & ADX** per symbol on the current chart’s timeframe.
- ✅ **Table display** with flexible position (top, middle, bottom).
- ✅ Highlights assets meeting both **RSI ≥ Threshold** & **ADX ≥ Threshold**.
- ✅ Handles empty slots gracefully → `"No symbols selected"`.
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### 🔹 Alerts
- 📢 **Strong Bullish Trend**
- 📢 **Strong Bearish Trend**
- 📢 **Golden Cross (EMA Fast > Slow)**
- 📢 **Death Cross (EMA Fast < Slow)**
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## 📖 How to Use
1. **EMA Analyzer**
- Enable *“Show Trend Direction”* to see EMA-based market bias.
- Look for **color-coded labels** & **background shading** to guide bias.
- Watch for **Golden/Death Cross dots** as entry/exit signals.
2. **RSI/ADX Scanner**
- Enter up to **10 tickers** (e.g., `NASDAQ:AAPL`, `BINANCE:BTCUSDT`).
- Adjust **RSI/ADX Lengths & Thresholds** to match your strategy.
- Monitor the **table panel** for which markets show **strong trend confirmation**.
3. **Alerts**
- Add alerts to catch **trend shifts** or **crossovers** without watching charts 24/7.
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## 🎯 Best For
- ✅ Trend traders
- ✅ Swing traders
- ✅ Multi-asset confluence trading
- ✅ Traders using **EMA + RSI + ADX confirmation**
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## ⚠️ Disclaimer
This script is for **educational purposes only**.
It is **not financial advice**. Please trade responsibly.
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Buscar en scripts para "GOLD"
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.
DynamoSent DynamoSent Pro+ — Professional Listing (Preview)
— Adaptive Macro Sentiment (v6)
— Export, Adaptive Lookback, Confidence, Boxes, Heatmap + Dynamic OB/OS
Preview / Experimental build. I’m actively refining this tool—your feedback is gold.
If you spot edge cases, want new presets, or have market-specific ideas, please comment or DM me on TradingView.
⸻
What it is
DynamoSent Pro+ is an adaptive, non-repainting macro sentiment engine that compresses VIX, DXY and a price-based activity proxy (e.g., SPX/sector ETF/your symbol) into a 0–100 sentiment line. It scales context by volatility (ATR%) and can self-calibrate with rolling quantile OB/OS. On top of that, it adds confidence scoring, a plain-English Context Coach, MTF agreement, exportable sentiment for other indicators, and a clean Light/Dark UI.
Why it’s different
• Adaptive lookback tracks regime changes: when volatility rises, we lengthen context; when it falls, we shorten—less whipsaw, more relevance.
• Dynamic OB/OS (quantiles) self-calibrates to each instrument’s distribution—no arbitrary 30/70 lines.
• MTF agreement + Confidence gate reduce false positives by highlighting alignment across timeframes.
• Exportable output: hidden plot “DynamoSent Export” can be selected as input.source in your other Pine scripts.
• Non-repainting rigor: all request.security() calls use lookahead_off + gaps_on; signals wait for bar close.
Key visuals
• Sentiment line (0–100), OB/OS zones (static or dynamic), optional TF1/TF2 overlays.
• Regime boxes (Overbought / Oversold / Neutral) that update live without repaint.
• Info Panel with confidence heat, regime, trend arrow, MTF readout, and Coach sentence.
• Session heat (Asia/EU/US) to match intraday behavior.
• Light/Dark theme switch in Inputs (auto-contrasted labels & headers).
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How to use (examples & recipes)
1) EURUSD (swing / intraday blend)
• Preset: EURUSD 1H Swing
• Chart: 1H; TF1=1H, TF2=4H (default).
• Proxies: Defaults work (VIX=D, DXY=60, Proxy=D).
• Dynamic OB/OS: ON at 20/80; Confidence ≥ 55–60.
• Playbook:
• When sentiment crosses above 50 + margin with Δ ≥ signalK and MTF agreement ≥ 0.5, treat as trend breakout.
• In Oversold with rising Coach & TF agreement, take fade longs back toward mid-range.
• Alerts: Enable Breakout Long/Short and Fade; keep cooldown 8–12 bars.
2) SPY (daytrading)
• Preset: SPY 15m Daytrade; Chart: 15m.
• VIX (D) matters more; preset weights already favor it.
• Start with static 30/70; later try dynamic 25/75 for adaptive thresholds.
• Use Coach: in US session, when it says “Overbought + MTF agree → sell rallies / chase breakouts”, lean momentum-continuation after pullbacks.
3) BTCUSD (crypto, 24/7)
• Preset: BTCUSD 1H; Chart: 1H.
• DXY and BTC.D inform macro tone; keep Carry-forward ON to bridge sparse ticks.
• Prefer Dynamic OB/OS (15/85) for wider swings.
• Fade signals on weekend chop; Breakout when Confidence > 60 and MTF ≥ 1.0.
4) XAUUSD (gold, macro blend)
• Preset: XAUUSD 4H; Chart: 4H.
• Weights tilt to DXY and US10Y (handled by preset).
• Coach + MTF helps separate trend legs from news pops.
⸻
Best practices
• Theme: Switch Light/Dark in Inputs; the panel adapts contrast automatically.
• Export: In another script → Source → DynamoSent Pro+ → DynamoSent Export. Build your own filters/strategies atop the same sentiment.
• Dynamic vs Static OB/OS:
• Static 30/70: fast, universal baseline.
• Dynamic (quantiles): instrument-aware; use 20/80 (default) or 15/85 for choppy markets.
• Confidence gate: Start at 50–60% to filter noise; raise when you want only A-grade setups.
• Adaptive Lookback: Keep ON. For ultra-liquid indices, you can switch it OFF and set a fixed lookback.
⸻
Non-repainting & safety notes
• All request.security() calls use lookahead=barmerge.lookahead_off and gaps=barmerge.gaps_on.
• No forward references; signals & regime flips are confirmed on bar close.
• History-dependent funcs (ta.change, ta.percentile_linear_interpolation, etc.) are computed each bar (not conditionally).
• Adaptive lookback is clamped ≥ 1 to avoid lowest/highest errors.
• Missing-data warning triggers only when all proxies are NA for a streak; carry-forward can bridge small gaps without repaint.
⸻
Known limits & tips
• If a proxy symbol isn’t available on your plan/exchange, you’ll see the NA warning: choose a different symbol via Symbol Search, or keep Carry-forward ON (it defaults to neutral where needed).
• Intraday VIX is sparse—using Daily is intentional.
• Dynamic OB/OS needs enough history (see dynLenFloor). On short histories it gracefully falls back to static levels.
Thanks for trying the preview. Your comments drive the roadmap—presets, new proxies, extra alerts, and integrations.
Double Median SD Bands | MisinkoMasterThe Double Median SD Bands (DMSDB) is a trend-following tool designed to capture market direction in a way that balances responsiveness and smoothness, filtering out excessive noise without introducing heavy lag.
Think of it like a house:
A jail (too restrictive) makes you miss opportunities.
No house at all (too unsafe) leaves you exposed to false signals.
DMSDB acts like a comfortable house with windows—protecting you from the noise while still letting you see what’s happening in the market.
🔎 Methodology
The script works in the following steps:
Standard Deviation (SD) Calculation
Computes the standard deviation of the selected price source (ohlc4 by default).
The user can choose whether to use biased (sample) or unbiased (population) standard deviation.
Raw Bands Construction
Upper Band = source + (SD × multiplier)
Lower Band = source - (SD × multiplier)
The multiplier can be adjusted for tighter or looser bands.
First Median Smoothing
Applies a median filter over half of the length (len/2) to both bands.
This reduces noise without creating excessive lag.
Second Median Smoothing
Applies another median filter over √len to the already smoothed bands.
This produces a balance:
Cutting the length → maintains responsiveness.
Median smoothing → reduces whipsaws.
The combination creates a fast yet clean band system ideal for trend detection.
📈 Trend Logic
The trend is detected based on price crossing the smoothed bands:
Long / Bullish (Purple) → when price crosses above the upper band.
Short / Bearish (Gold) → when price crosses below the lower band.
Neutral → when price remains between the bands.
🎨 Visualization
Upper and lower bands are plotted as colored lines.
The area between the bands is filled with a transparent zone that reflects the current bias:
Purple shading = Bullish zone.
Golden shading = Bearish zone.
This creates a visual tunnel for trend confirmation, helping traders quickly identify whether price action is trending or consolidating.
⚡ Features
Adjustable Length parameter (len) for dynamic control.
Adjustable Band Multiplier for volatility adaptation.
Choice between biased vs. unbiased standard deviation.
Double median smoothing for clarity + responsiveness.
Works well on cryptocurrencies (e.g., BTCUSD) but is flexible enough for stocks, forex, and indices.
✅ Use Cases
Trend Following → Ride trends by staying on the correct side of the bands.
Entry Timing → Use crossovers above/below bands for entry triggers.
Filter for Other Strategies → Can serve as a directional filter to avoid trading against the trend.
⚠️ Limitations & Notes
This is a trend-following tool, so it will perform best in trending conditions.
In sideways or choppy markets, whipsaws may still occur (although smoothing reduces them significantly).
The indicator is not a standalone buy/sell system. For best results, combine with volume, momentum, or higher-timeframe confluence.
All of this makes for a really unique & original tool, as it removes noise but keeps good responsitivity, using methods from many different principles which make for a smooth a very useful tool
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.
RenKagi Fusion: Aura & SMA Clash IndicatorRenKagi Fusion: Aura & SMA Clash Indicator
Welcome to the RenKagi Fusion Indicator – a powerful, customizable tool that blends the strengths of Renko and Kagi charts to provide noise-filtered trend insights, enhanced with visual Aura effects and SMA (Simple Moving Average) crossover signals. Designed for traders seeking a unique edge in trend detection and reversal identification, this indicator combines traditional charting techniques with modern visualizations to help you navigate markets more effectively. Whether you're trading stocks, forex, or crypto, RenKagi Fusion offers a clean, actionable overview of market dynamics.
Key Features
RenKagi Line (Weighted Fusion of Renko and Kagi): The core of the indicator is the RenKagi line, a weighted average of Renko (brick-based trend filtering) and Kagi (reversal-focused line charts). Users can adjust the weight (default: 60% Renko, 40% Kagi) to prioritize stability or sensitivity. This fusion reduces market noise while highlighting key price movements.
Trend Scoring System: Calculates strength scores for Renko, Kagi, and RenKagi (capped at 20 points, converted to percentages). Scores increase with trend continuation and reset on reversals, giving a quantitative measure of momentum.
Aura Effects (Optional): Visual "glow" around lines based on score percentage – higher scores mean more opaque and thicker auras, adding a dynamic layer to trend visualization.
SMA Clash (Crossover Detection): Monitors daily SMA50, SMA100, and SMA200 for golden/death crosses (SMA50 crossing above/below longer SMAs) and RenKagi-SMA crossovers. These are displayed in a persistent info table for quick reference.
Customizable Visuals: Toggle lines, boxes, shapes, auras, and labels. Background coloring based on selected source (Renko, Kagi, or RenKagi) for intuitive trend bias.
Info Table: A configurable table (position and colors adjustable) summarizing scores, directions, cross states, brick size (with type), Kagi reversal (with type), and weights. No clutter – all in one place.
Alert Conditions: Built-in alerts for direction changes (Renko, Kagi, RenKagi), SMA crossovers, and golden/death crosses – perfect for real-time notifications.
How It Works
Renko Logic: Builds bricks based on user-selected type (Traditional fixed size, ATR dynamic, or Percentage). Scores build as trends persist, resetting on reversals.
Kagi Logic: Line reverses on thresholds (Traditional, ATR, or Percentage), scoring continuous moves.
RenKagi Calculation: Weighted average: (renkoPrice * renkoWeight + kagiLine * (100 - renkoWeight)) / 100. Score is a blend of individual scores.
SMA Integration: Daily timeframe SMAs for reliable long-term signals. Crossovers trigger alerts and update table states persistently until reversed.
Advantages for Traders
Noise Reduction: By fusing Renko's block structure with Kagi's reversal focus, it filters out minor fluctuations, helping identify strong trends early.
Versatility: Fully customizable – adjust weights, types, and visuals to fit any market or timeframe. Ideal for swing trading, trend following, or scalping.
Visual Clarity: Aura and background coloring provide at-a-glance insights, while the table consolidates data without overwhelming the chart.
Actionable Signals: Golden/Death crosses and direction changes offer clear entry/exit points, backed by alerts for timely execution.
Performance Optimization: Limits on lines/labels/boxes (500 each) ensure smooth operation on large datasets.
Usage Tips
Start with default settings for balanced performance.
Use in higher timeframes for trend confirmation or lower for intraday signals.
Combine with your favorite strategies – e.g., buy on RenKagi upward cross with SMA50 and golden cross confirmation.
Test on historical data to optimize weights and thresholds.
Note: This indicator is for educational and informational purposes only. Past performance is not indicative of future results. Always conduct your own analysis and use risk management. No financial advice is provided.
If you find this useful, please like, comment, or share your feedback!
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.**
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*⚠️ 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.**
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**📝 Tags:** #GradientTrading #SwingTrading #NDOS #MultiTimeframe #TechnicalAnalysis #VisualTrading #TrendAnalysis #ColorCoded #ProfessionalCharts #TradingToo
HUll Dynamic BandEducational Hull Moving Average Wave Analysis Tool
**MARS** is an innovative educational indicator that combines multiple Hull Moving Average timeframes to create a comprehensive wave analysis system, similar in concept to Ichimoku Cloud but with enhanced smoothness and responsiveness.
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🎯 Key Features
**Triple Wave System**
- **Peak Wave (34-period)**: Fast momentum signals, similar to Ichimoku's Conversion Line
- **Primary Wave (89-period)**: Main trend identification with retest detection
- **Swell Wave (178-period)**: Long-term trend context and major wave analysis
**Visual Wave Analysis**
- **Wave Power Fill**: Dynamic area between primary and swell waves showing trend strength
- **Peak Power Fill**: Short-term momentum visualization
- **Smooth Curves**: Hull MA-based calculations provide cleaner signals than traditional moving averages
**Intelligent Signal System**
- **Trend Shift Signals**: Clear visual markers when trend changes occur
- **Retest Detection**: Identifies potential retest opportunities with specific conditions
- **Correction Alerts**: Early warning signals for market corrections
---
📊 How It Works
The indicator uses **Hull Moving Averages** with **Fibonacci-based periods** (34, 89, 178) and a **Golden Ratio multiplier (1.64)** to create natural market rhythm analysis.
**Key Signal Types:**
- 🔵 **Circles**: Major trend shifts (primary wave crossovers)
- 💎 **Diamonds**: Retest opportunities with multi-wave confirmation
- ❌ **X-marks**: Correction signals and structural breaks
- 🌊 **Wave Fills**: Visual trend strength and direction
---
🎓 Educational Purpose
This indicator demonstrates:
- Advanced moving average techniques using Hull MA
- Multi-timeframe analysis in a single view
- Wave theory application in technical analysis
- Dynamic support/resistance concept visualization
**Similar to Ichimoku but Different:**
- Ichimoku uses price-based calculations → Angular cloud shapes
- MARS uses weighted averages → Smooth, flowing wave patterns
- Both identify trend direction, but MARS offers faster signals with cleaner visualization
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⚙️ Customizable Settings
- **Wave Periods**: Adjust primary wave length (default: 89)
- **Multipliers**: Fine-tune wave sensitivity (default: 1.64 Golden Ratio)
- **Visual Style**: Customize line widths and signal displays
- **Peak Analysis**: Independent fast signal system (default: 34)
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🔍 Usage Tips
1. **Trend Identification**: Watch wave fill colors and line positions
2. **Entry Timing**: Look for retest diamonds after trend shift circles
3. **Risk Management**: Use wave boundaries as dynamic support/resistance
4. **Confirmation**: Combine with price action and market structure analysis
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⚠️ Important Notes
- **Educational Tool**: Designed for learning wave analysis concepts
- **Not Financial Advice**: Always use proper risk management
- **Backtesting Recommended**: Test on historical data before live trading
- **Combine with Analysis**: Works best with additional confirmation methods
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🚀 Innovation
MARS represents a unique approach to wave analysis by:
- Combining Hull MA smoothness with Ichimoku-style visualization
- Providing multi-timeframe analysis without chart clutter
- Offering retest detection with specific wave conditions
- Creating an educational bridge between different analytical methods
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*This indicator is shared for educational purposes to help traders understand advanced moving average techniques and wave analysis concepts. Always practice proper risk management and combine with your own analysis.*
8MA Compass — HTF map + GC/DC cues8MA Compass provides a clean trend context by combining strict 4-of-4 confluence (Current TF vs Higher TF) with SMA200 repainting on Golden/Death Cross (GC/DC).
What it shows
4-of-4 background (context): compares EMA10, EMA20, SMA50, SMA200 on the Current TF against the same four MAs on the Higher TF (HTF).
All 4 above their HTF values → bullish background.
All 4 below their HTF values → bearish background.
SMA200 color on GC/DC (Current TF):
Last signal is DC and price below SMA200 → SMA200 turns red.
Price above SMA200 but the last signal is DC (no GC afterward) → SMA200 stays base color.
Last signal is GC and price above SMA200 → SMA200 turns green #089981.
Why “8MA” ? The 4-of-4 logic uses 8 moving averages in total: 4 on the Current TF and 4 on the HTF (EMA10/20 and SMA50/200 on both frames). HTF EMAs are used in calculations but are not plotted by default—hence the name 8MA Compass.
Auto HTF mapping
Current 1H → HTF 4H
Current 4H → HTF 1D
Current 1D → HTF 1W
All other timeframes: HTF defaults to Current TF (4-of-4 will typically be neutral).
Manual mode: choose any HTF. If Manual HTF equals Current TF, HTF SMAs are hidden to avoid overlap.
Settings
1. Display
Show CURRENT TF — plot EMA10/20, SMA50/200 on Current TF.
Show HARD TF — plot SMA50/200 on HTF (hidden if HTF == Current TF).
HTF mode — Auto / Manual, with Hard TF (Manual) selector.
2. Filter
Show base background (4-of-4) — enable/disable confluence shading.
Epsilon (in ticks) — small tolerance in Cur vs HTF comparisons to reduce flicker.
3. Golden/Death
Color SMA200 on GC/DC (Cur TF) — repaint SMA200 on GC/DC per rules above (enabled by default).
Alerts
GC/DC (Current TF, SMA50/200): Golden Cross / Death Cross (on bar close).
EMA10/20 (Current TF): “Bull regime ON” / “Bear regime ON” on crossovers.
Optional HTF GC/DC alerts (SMA50/200 on chosen HTF).
Visual details
HTF SMA50/200 are drawn first; Current TF lines are drawn on top for clarity.
SMA200 (Current TF) is drawn last (and slightly thicker) to remain readable.
HTF EMAs are used in 4-of-4 logic but not plotted by design.
Usage
1. Use the 4-of-4 background as inter-timeframe momentum context.
2. Use SMA200 color to gauge long-term regime confirmation:
Prefer longs when last GC and price holds above SMA200 (#089981 line).
Avoid longs when last DC and price is below SMA200 (red line).
Disclaimer : For educational purposes only. Not financial advice. Trading involves risk.
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action
1. Introduction
1.1 Overview
ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active.
The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured:
import TradingView/ta/10 as tvta
// Determine lower timeframe string based on user choice and chart resolution
string lower_tf_breakout = use_custom_tf_input ? custom_tf_input :
timeframe.isseconds ? "1S" :
timeframe.isintraday ? "1" :
timeframe.isdaily ? "5" : "60"
// Request up/down volume (both positive)
= tvta.requestUpAndDownVolume(lower_tf_breakout)
Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity.
Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate.
Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history.
Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”)
2. Price‑Structure Backbone
Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations.
2.1 What it draws
• Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded.
• Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly.
• Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels.
• Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick.
2.2 Key settings
• length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels.
• pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings.
• point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded.
• label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price.
• Styling inputs for trend lines, horizontal lines and labels (color, width and line style).
Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots.
These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle.
The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable.
3. Breakout Module
3.1 Concept
This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled.
3.2 Inputs
• lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly.
• vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout.
• rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution.
• ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases).
• use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe.
• Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width.
3.3 Detection logic
1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume.
2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window.
3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive.
4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history.
5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1).
6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector.
Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move.
Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold.
4. Market Phase Module (Volume Only)
4.1 Concept
Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table.
4.2 Inputs
• phase_period_len: Number of bars over which to compute sums and ratios for phase detection.
• phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume.
• strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation).
• Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles.
• show_phase_module: Toggles the entire phase detection subsystem.
• show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected.
4.3 Detection logic
The module computes three families of statistics over the volume window defined by phase_period_len:
1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends.
2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction.
3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues.
The algorithm then combines these signals:
• For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case.
• For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category.
If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” .
4.4 Outputs
• Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral).
• On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences.
Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began.
Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window.
5. OB/OS Spike Module
5.1 What overbought/oversold means here
In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate.
5.2 Inputs
• minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print.
• syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals.
• Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike.
• Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable.
• Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width.
5.3 Detection logic
1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos.
2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS.
3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold).
4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought.
5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line.
Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels.
Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow.
6. Buyer/Seller Trap Module
6.1 Concept
A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps.
6.2 Inputs
• trap_lookback_len: Window length used to rank extremes and detect sweeps.
• trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep.
• trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.)
• trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed.
• Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps.
• Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart.
6.3 Scoring logic
The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped):
1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick.
2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick.
3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand.
4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa.
5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average.
The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars.
Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label.
Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range.
7. Sharp Move Module
7.1 Concept
Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation.
7.2 Inputs
• sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels.
• sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window.
• sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax.
• sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks.
• sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range).
• sharpScoreRisk: Minimum number of conditions that must be true to print a risk label.
• sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation.
• sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution).
• Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves.
• Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn.
7.3 Conditions (six per side)
For each side, the indicator computes six boolean conditions and sums them to form a score:
1. Dominance (instant and cumulative):
– Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume.
– Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks).
2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish).
3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window.
4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range).
5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile.
6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed.
Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores.
7.4 Scoring and labels
• Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar.
• Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1).
• Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled.
Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move.
Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window.
8. Market‑Maker / Spread‑Capture Module
8.1 Concept
Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets.
8.2 Inputs
• scalpLookback: Window length used to compute volume averages.
• scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple.
• scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement.
• scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%.
• Label settings: ATR length, multiplier, size, colors, connector style and width.
• Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels.
8.3 Signal
When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled.
Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window.
9. Money Flow Module
9.1 Concept
To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow.
9.2 Inputs
• mf_period_len_mf: Number of bars used for summing buy and sell dollars.
• Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width.
• Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed.
9.3 Calculations
• Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $.
• Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum().
• Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate.
• Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences.
Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context.
In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period.
10. Info Table
10.1 Purpose
When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active.
10.2 Symbols
• ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money.
• MA — Moving average. In the table it shows the average value of a series over the lookback period.
• Σ (Sigma) — Cumulative sum over the chosen lookback period.
• Δ (Delta) — Difference between buy and sell values.
• B / S — Buyer and seller share of total volume, expressed as percentages.
• Ref. Price — Reference price for breakout calculations, based on the latest pivot.
• Status — Indicates whether a breakout condition is currently active (True) or has failed.
10.3 Row definitions
1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period.
2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity.
3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period.
4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume.
5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period.
6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume.
7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more.
8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed.
9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral.
10. Money Flow – The final rows display dollar amounts and status:
– ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period.
– ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum.
– Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively.
– Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral.
– Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral.
The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out).
11. Conclusion & Final Remarks
This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart.
A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window.
Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
Perfect Price-Anchored % Fib Grid This indicator generates support and resistance levels anchored to a fixed price of your choice.
You can also specify a percentage for the indicator to calculate potential highs and lows.
Commonly used values are 3.5% or 7%, as well as smaller decimal versions like 0.35% or 0.7%, depending on the volatility you expect.
In addition, the indicator can highlight potential stop-run levels in multiples of 27 — ranging from 0 up to 243. This automatically places the 243 GB range directly onto your chart.
The tool is versatile and can be applied not only to equities, but also to ES futures and Forex markets.
FibADX MTF Dashboard — DMI/ADX with Fibonacci DominanceFibADX MTF Dashboard — DMI/ADX with Fibonacci Dominance (φ)
This indicator fuses classic DMI/ADX with the Fibonacci Golden Ratio to score directional dominance and trend tradability across multiple timeframes in one clean panel.
What’s unique
• Fibonacci dominance tiers:
• BULL / BEAR → one side slightly stronger
• STRONG when one DI ≥ 1.618× the other (φ)
• EXTREME when one DI ≥ 2.618× (φ²)
• Rounded dominance % in the +DI/−DI columns (e.g., STRONG BULL 72%).
• ADX column modes: show the value (with strength bar ▂▃▅… and slope ↗/↘) or a tier (Weak / Tradable / Strong / Extreme).
• Configurable intraday row (30m/1H/2H/4H) + D/W/M toggles.
• Threshold line: color & width; Extended (infinite both ways) or Not extended (historical plot).
• Theme presets (Dark / Light / High Contrast) or full custom colors.
• Optional panel shading when all selected TFs are strong (and optionally directionally aligned).
How to use
1. Choose an intraday TF (30/60/120/240). Enable D/W/M as needed.
2. Use ADX ≥ threshold (e.g., 21 / 34 / 55) to find tradable trends.
3. Read the +DI/−DI labels to confirm bias (BULL/BEAR) and conviction (STRONG/EXTREME).
4. Prefer multi-TF alignment (e.g., 4H & D & W all strong bull).
5. Treat EXTREME as a momentum regime—trail tighter and scale out into spikes.
Alerts
• All selected TFs: Strong BULL alignment
• All selected TFs: Strong BEAR alignment
Notes
• Smoothing selectable: RMA (Wilder) / EMA / SMA.
• Percentages are whole numbers (72%, not 72.18%).
• Shorttitle is FibADX to comply with TV’s 10-char limit.
Why We Use Fibonacci in FibADX
Traditional DMI/ADX indicators rely on fixed numeric thresholds (e.g., ADX > 20 = “tradable”), but they ignore the relationship between +DI and −DI, which is what really determines trend conviction.
FibADX improves on this by introducing the Fibonacci Golden Ratio (φ ≈ 1.618) to measure directional dominance and classify trend strength more intelligently.
⸻
1. Fibonacci as a Natural Strength Threshold
The golden ratio φ appears everywhere in nature, growth cycles, and fractals.
Since financial markets also behave fractally, Fibonacci levels reflect natural crowd behavior and trend acceleration points.
In FibADX:
• When one DI is slightly larger than the other → BULL or BEAR (mild advantage).
• When one DI is at least 1.618× the other → STRONG BULL or STRONG BEAR (trend conviction).
• When one DI is 2.618× or more → EXTREME BULL or EXTREME BEAR (high momentum regime).
This approach adds structure and consistency to trend classification.
⸻
2. Why 1.618 and 2.618 Instead of Random Numbers
Other traders might pick thresholds like 1.5 or 2.0, but φ has special mathematical properties:
• φ is the most irrational ratio, meaning proportions based on φ retain structure even when scaled.
• Using φ makes FibADX naturally adaptive to all timeframes and asset classes — stocks, crypto, forex, commodities.
⸻
3 . Trading Advantages
Using the Fibonacci Golden Ratio inside DMI/ADX has several benefits:
• Better trend filtering → Avoid false DI crossovers without conviction.
• Catch early momentum shifts → Spot when dominance ratios approach φ before ADX reacts.
• Consistency across markets → Because φ is scalable and fractal, it works everywhere.
⸻
4. How FibADX Uses This
FibADX combines:
• +DI vs −DI ratio → Measures directional dominance.
• φ thresholds (1.618, 2.618) → Classifies strength into BULL, STRONG, EXTREME.
• ADX threshold → Confirms whether the move is tradable or just noise.
• Multi-timeframe dashboard → Aligns bias across 4H, D, W, M.
⸻
Quick Blurb for TradingView
FibADX uses the Fibonacci Golden Ratio (φ ≈ 1.618) to classify trend strength.
Unlike classic DMI/ADX, FibADX measures how much one side dominates:
• φ (1.618) = STRONG trend conviction
• φ² (2.618) = EXTREME momentum regime
This creates an adaptive, fractal-aware framework that works across stocks, crypto, forex, and commodities.
⚠️ Disclaimer : This script is provided for educational purposes only.
It does not constitute financial advice.
Use at your own risk. Always do your own research before making trading decisions.
Created by @nomadhedge
[GrandAlgo] Moving Averages Cross LevelsMoving Averages Cross Levels
Many traders watch for moving average crossovers – such as the golden cross (50 MA crossing above 200 MA) or death cross – as signals of changing trends. However, once a crossover happens, the exact price level where it occurred often fades from view, even though that level can be an important reference point. Moving Averages Cross Levels is an indicator that keeps those crossover price levels visible on your chart, helping you track where momentum shifts occurred and how price behaves relative to those key levels.
This tool plots horizontal line segments at the price where each pair of selected moving averages crossed within a recent window of bars. Each level is labeled with the moving average lengths (for example, “21×50” for a 21/50 MA cross) and is color-coded – green for bullish crossovers (short-term MA crossing above long-term MA) and red for bearish crossunders (short-term crossing below). By visualizing these crossover levels, you can quickly identify past trend change points and use them as potential support/resistance or decision levels in your trading. Importantly, this indicator is non-repainting – once a crossover level is plotted, it remains fixed at the historical price where the cross occurred, allowing you to continually monitor that level going forward. (As with any moving average-based analysis, crossover signals are lagging, so use these levels in conjunction with other tools for confirmation.)
Key Features:
✅ Multiple Moving Averages: Track up to 7 different MAs (e.g. 5, 8, 21, 50, 64, 83, 200 by default) simultaneously. You can enable/disable each MA and set its length, allowing flexible combinations of short-term and long-term averages.
✅ Selectable MA Type: Each average can be calculated as a Simple (SMA), Exponential (EMA), Volume-Weighted (VWMA), or Smoothed (RMA) moving average, giving you flexibility to match your preferred method.
✅ Auto Crossover Detection: The script automatically detects all crosses between any enabled MA pairs, so you don’t have to specify pairs manually. Whether it’s a fast cross (5×8) or a long-term cross (50×200), every crossover within the lookback period will be identified and marked.
✅ Horizontal Level Markers: For each detected crossover, a horizontal line segment is drawn at the exact price where the crossover occurred. This makes it easy to glance at your chart and see precisely where two moving averages intersected in the recent past.
✅ Labeled and Color-Coded: Each crossover line is labeled with the two MA lengths that crossed (e.g. “50×200”) for clear identification. Colors indicate crossover direction – by default green for bullish (positive) crossovers and red for bearish (negative) crossovers – so you can tell at a glance which way the trend shifted. (You can customize these colors in the settings.)
✅ Adjustable Lookback: A “Crosses with X candles” input lets you control how far back the script looks for crossovers to plot. This prevents your chart from getting cluttered with too many old levels – for example, set X = 100 to show crossovers from roughly the last 100 bars. Older crossover lines beyond this lookback window will automatically clear off the chart.
✅ Optional MA Plots: You can toggle the display of each moving average line on the chart. This means you can either view just the crossover levels alone for a clean look, or also overlay the MA curves themselves for additional context (to see how price and MAs were moving around the crossover).
✅ No Repainting or Hindsight Bias: Once a crossover level is plotted, it stays at that fixed price. The indicator doesn’t move levels around after the fact – each line is a true historical event marker. This allows you to backtest visually: see how price acted after the crossover by observing if it retested or respected that level later.
How It Works:
1️⃣ Add to Chart & Configure – Simply add the indicator to your chart. In the settings, choose which moving averages you want to include and set their lengths. For example, you might enable 21, 50, 200 to focus on medium and long-term crosses (including the golden cross), or turn on shorter MAs like 5 and 8 for quick momentum shifts. Adjust the lookback (number of bars to scan for crosses) if needed.
2️⃣ Visualization – The script continuously checks the latest X bars for any points where one MA crossed above or below another. Whenever a crossover is found, it calculates the exact price level at which the two moving averages intersected. On the last bar of your chart, it will draw a horizontal line segment extending from the crossover bar to the current bar at that price level, and place a label to the right of the line with the MA lengths. Green lines/labels signify bullish crossovers (where the first MA crossed above the second), and red lines indicate bearish crossunders.
3️⃣ On Your Chart – You will see these labeled levels aligned with the price scale. For example, if a 50 MA crossed above a 200 MA (bullish) 50 bars ago at price $100, there will be a green “50×200” line at $100 extending to the present, showing you exactly where that golden cross happened. You might notice price pulling back near that level and bouncing, or if price falls back through it, it could signal a failed crossover. The indicator updates in real-time: if a new crossover happens on the latest bar, a new line and label will instantly appear, and if any old cross moves out of the lookback range, its line is removed to keep the chart focused.
4️⃣ Customization – You can fine-tune the appearance: toggle any MA’s visibility, change line colors or label styles, and modify the lookback length to suit different timeframes. For instance, on a 1-hour chart you might use a lookback of 500 bars to see a few weeks of cross history, whereas on a daily chart 100 bars (about 4–5 months) may be sufficient. Adjust these settings based on how many crossover levels you find useful to display.
Ideal for Traders Who:
Use MA Crossovers in Strategy: If your strategy involves moving average crossovers (for trend confirmation or entry/exit signals), this indicator provides an extra layer of insight by keeping the price of those crossover events in sight. For example, trend-followers can watch if price stays above a bullish crossover level as a sign of trend strength, or falls below it as a sign of weakness.
Identify Support/Resistance from MA Events: Crossover levels often coincide with pivot points in market sentiment. A crossover can act like a regime change – the level where it happened may turn into support or resistance. This tool helps you mark those potential S/R levels automatically. Rather than manually noting where a golden cross occurred, you’ll have it highlighted, which can be useful for setting stop-losses (e.g. below the crossover price in a bullish scenario) or profit targets.
Track Multiple Averages at Once: Instead of focusing on just one pair of moving averages, you might be interested in the interaction of several (short, medium, and long-term trends). This indicator caters to that by plotting all relevant crossovers among your chosen MAs. It’s great for multi-timeframe thinkers as well – e.g. you could apply it on a higher timeframe chart to mark major cross levels, then drill down to lower timeframes knowing those key prices.
Value Clean Visualization: There are no flashing signals or arrows – just simple lines and labels that enhance your chart’s storytelling. It’s ideal if you prefer to make trading decisions based on understanding price interaction with technical levels rather than following automatic trade calls. Moving Averages Cross Levels gives you information to act on, without imposing any bias or strategy – you interpret the crossover levels in the context of your own trading system.
Rolling Correlation BTC vs Hedge AssetsRolling Correlation BTC vs Hedge Assets
Overview
This indicator calculates and plots the rolling correlation between Bitcoin (BTC) returns and several key hedge assets:
• XAUUSD (Gold)
• EURUSD (proxy for DXY, U.S. Dollar Index)
• VIX (Volatility Index)
• TLT (20y U.S. Treasury Bonds ETF)
By monitoring these dynamic correlations, traders can identify whether BTC is moving in sync with risk assets or decoupling as a hedge, and adjust their trading strategy accordingly.
How it works
1. Computes returns for BTC and each asset using percentage change.
2. Uses the rolling correlation function (ta.correlation) over a configurable window length (default = 12 bars).
3. Plots each correlation as a separate colored line (Gold = Yellow, EURUSD = Blue, VIX = Red, TLT = Green).
4. Adds threshold levels at +0.3 and -0.3 to help classify correlation regimes.
How to use it
• High positive correlation (> +0.3): BTC is moving together with the asset (risk-on behavior).
• Near zero (-0.3 to +0.3): BTC is showing little to no correlation — neutral/independent moves.
• Negative correlation (< -0.3): BTC is moving in the opposite direction — potential hedge opportunity.
Practical strategies:
• Watch BTC vs VIX: a spike in volatility (VIX ↑) usually coincides with BTC selling pressure.
• Track BTC vs EURUSD: stronger USD often puts downside pressure on BTC.
• Observe BTC vs Gold: during “flight to safety” events, gold rises while BTC weakens.
• Monitor BTC vs TLT: rising yields (falling TLT) often align with BTC weakness.
Inputs
• Window Length (bars): Number of bars used to calculate rolling correlations (default = 12).
• Comparison Timeframe: Default = 5m. Can be changed to align with your intraday or swing trading style.
Notes
• Works best on intraday charts (1m, 5m, 15m) for scalping and short-term setups.
• Use correlations as context, not standalone signals — combine with volume, VWAP, and price action.
• Correlations are dynamic; they can switch regimes quickly during macro events (CPI, NFP, FOMC).
This tool is designed for traders who want to manage risk exposure by monitoring whether BTC is behaving as a risk-on asset or hedge, and to exploit opportunities during decoupling phases.
[blackcat] L1 Value Trend IndicatorOVERVIEW
The L1 Value Trend Indicator is a sophisticated technical analysis tool designed for TradingView users seeking advanced market trend identification and trading signals. This comprehensive indicator combines multiple analytical techniques to provide traders with a holistic view of market dynamics, helping identify potential entry and exit points through various signal mechanisms. 📈 It features a main Value Trend line along with a lagged version, golden cross and dead cross signals, and multiple technical indicators including RSI, Williams %R, Stochastic %K/D, and Relative Strength calculations. The indicator also includes reference levels for support and resistance analysis, making it a versatile tool for both short-term and long-term trading strategies. ✅
FEATURES
📈 Primary Value Trend Line: Calculates a smoothed value trend using a combination of SMA and custom smoothing techniques
🔍 Value Trend Lag: Implements a lagged version of the main trend line for cross-over analysis
🚀 Golden Cross & Dead Cross Signals: Identifies buy/sell opportunities when the main trend line crosses its lagged version
💸 Multi-Indicator Integration: Combines multiple technical analysis tools for comprehensive market view
📊 RSI Calculations: Includes 6-period, 7-period, and 13-period RSI calculations for momentum analysis
📈 Williams %R: Provides overbought/oversold conditions using the Williams %R formula
📉 Stochastic Oscillator: Implements both Stochastic %K and %D calculations for momentum confirmation
📋 Relative Strength: Calculates relative strength based on highest highs and current price
✅ Visual Labels: Displays BUY and SELL labels on chart when crossover conditions are met
📣 Alert Conditions: Provides automated alert conditions for golden cross and dead cross events
📌 Reference Levels: Plots entry (25) and exit (75) reference lines for support/resistance analysis
HOW TO USE
Copy the Script: Copy the complete Pine Script code from the original file
Open TradingView: Navigate to TradingView website or application
Access Pine Editor: Go to the Pine Script editor (usually found in the chart toolbar)
Paste Code: Paste the copied script into the editor
Save Script: Save the script with a descriptive name like " L1 Value Trend Indicator"
Select Chart: Choose the chart where you want to apply the indicator
Add Indicator: Apply the indicator to your chart
Configure Parameters: Adjust input parameters to customize behavior
Monitor Signals: Watch for golden cross (BUY) and dead cross (SELL) signals
Use Reference Levels: Monitor entry (25) and exit (75) lines for support/resistance levels
LIMITATIONS
⚠️ Potential Repainting: The script may repaint due to lookahead bias in some calculations
📉 Lookahead Bias: Some calculations may reference future values, potentially causing repainting issues
🔄 Parameter Sensitivity: Results may vary significantly with different parameter settings
📉 Computational Complexity: May impact chart performance with heavy calculations on large datasets
📊 Resource Usage: Requires significant processing power for multiple indicator calculations
🔄 Data Sensitivity: Results may be affected by data quality and market conditions
NOTES
📈 Signal Timing: Cross-over signals may lag behind actual price movements
📉 Parameter Optimization: Optimal parameters may vary by market conditions and asset type
📋 Market Conditions: Performance may vary significantly across different market environments
📈 Multi-Indicator: Combine signals with other technical indicators for confirmation
📉 Timeframe Analysis: Use multiple timeframes for enhanced signal accuracy
📋 Volume Analysis: Incorporate volume data for additional confirmation
📈 Strategy Integration: Consider using this indicator as part of a broader trading strategy
📉 Risk Management: Use signals as part of a comprehensive risk management approach
📋 Backtesting: Test parameter combinations with historical data before live trading
THANKS
🙏 Original Creator: blackcat1402 creates the L1 Value Trend Indicator
📚 Community Contributions: Recognition to TradingView community for continuous improvements and contributions
📈 Collaborative Development: Appreciation for collaborative efforts in enhancing technical analysis tools
📉 TradingView Community: Special thanks to TradingView community members for their ongoing support and feedback
📋 Educational Resources: Recognition of educational resources that helped in understanding technical analysis principles
AI Fib Strategy (Full Trade Plan)This indicator automatically plots Fibonacci retracements and a Golden Zone box (61.8%–65% retracement) based on the 4H candle body high/low.
Features:
Auto-detects session breaks or daily breaks (configurable).
Draws standard Fib retracement levels (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%).
Highlights the Golden Zone for high-probability trade entries.
Optional Take Profit extensions (TP1, TP2, TP3).
Fully compatible with Pine Script v6.
Usage:
Best applied on intraday charts (15m, 30m, 1H).
Use the Golden Zone for entry confirmations.
Combine with candlestick patterns, order blocks, or volume for stronger signals.
Jimb0ws Strategy Trending Info PanelsJimb0ws Strategy — Golden Candles + Bubble Zones
A price-action/EMA strategy built for FX scalping and intraday swings. It colors Golden Candles when strong bodies touch/skim EMA20/50 in trend (“bubble”) and optionally highlights Robin Candles (break of the prior golden body). Signals are throttled per bubble and filtered by multiple higher-timeframe conditions.
How it trades
Trend bubbles: Uses EMA20/50/100/200 alignment on the chart timeframe; also reads 1H & 4H bubbles for context.
Entries: BUY/SELL labels appear only when a golden setup aligns with fractal/structure checks and all active filters pass.
Stops/Targets (strategy mode):
• Longs: SL = EMA100 if EMA200 > EMA100, else SL = EMA200.
• Shorts: SL = EMA100 if EMA200 < EMA100, else SL = EMA200.
• TP = RR × risk (default 2R).
An on-chart SL/TP info label prints the exact prices at each signal.
Risk filter options: disable beyond 1H EMA50, proximity band around 1H EMA50, wick overdrive veto, session filter (toggle on/off), max signals per bubble.
Visuals & tools
Colored EMAs (20/50/100/200), bubble zone background.
4H info panel (state, start time, duration); Prev-Day ATR panel sits above it.
Optional 1H info panel and consolidation warning.
Fractal markers (size selectable).
Alerts
1H bubble state change (Long/Short/Consolidation).
BUY/SELL signals.
Inputs worth checking
Session & timezone, min body size, pip tolerances, proximity/WOD filters, max signals per bubble, RR, SL/TP label offset.
Notes
Best on FX pairs; pip = mintick × 10. Backtest and adjust to your instrument and session. This is not financial advice.
EMA Pullback Entry SignalsEMA Pullback Entry Signals is a tool designed to help traders identify trend continuation opportunities by detecting price pullbacks toward a slow EMA (Exponential Moving Average) during trending conditions.
This indicator combines moving average crossovers, price interaction with EMAs, and optional filtering to improve the timing and quality of trend entries.
Core Features:
Golden Cross / Death Cross Detection
Golden Cross: Fast EMA crossing above Slow EMA
Death Cross: Fast EMA crossing below Slow EMA
Optional X-shaped markers for crossover visualization
Pullback Signal on Slow EMA
Green triangle: Price crosses up through the slow EMA during a bullish trend
Red triangle: Price crosses down through the slow EMA during a bearish trend
Designed to capture continuation entries after a trend pullback
Optional Fast EMA Signals
Green arrow: Price crosses above fast EMA in a bull trend
Red arrow: Price crosses below fast EMA in a bear trend
Helps confirm minor retracements or short-term momentum shifts
Sideways Market Filter
Suppresses signals when the fast and slow EMAs are too close
Prevents entries during low-trend or choppy price action
Cooldown Timer
Enforces a minimum bar interval between signals to reduce overtrading
Helps avoid multiple entries from clustered signals
Custom Alerts
Alerts available for all signal types
Include ticker and timeframe in each alert message
Configurable Settings:
Fast and slow EMA lengths1
Toggle individual signal types (pullbacks, fast EMA crosses, crossovers)
Enable/disable cooldown logic and set bar duration
Sideways market detection sensitivity (EMA proximity threshold)
Primary Use Case
This script is most useful for trend-following traders seeking to enter pullbacks after a trend is established. When the price retraces to the slow EMA and then resumes in the trend direction, it can offer high-quality continuation setups. Works well across timeframes and markets.
MACD Overlay In main chart# MACD Overlay Indicator
## Overview
This indicator displays MACD (Moving Average Convergence Divergence) signals directly on the price chart without creating a separate window. It shows the momentum and trend changes through simple + and - symbols positioned relative to candlesticks.
## Features
- **Overlay Display**: Shows MACD information on the main price chart
- **Clean Interface**: Uses minimal + and - symbols instead of complex charts
- **Position Logic**: Symbol placement indicates MACD position relative to zero line
- **Energy Analysis**: Symbols represent histogram energy changes (absolute value)
- **Color Coding**: Different colors for golden cross and death cross signals
## Symbol Meaning
### Position Logic
- **Above Candlesticks**: MACD is above zero line (bullish territory)
- **Below Candlesticks**: MACD is below zero line (bearish territory)
### Symbol Meaning
- **+ Symbol**: MACD histogram absolute value is increasing (momentum strengthening)
- **- Symbol**: MACD histogram absolute value is decreasing (momentum weakening)
### Color Coding
- **Yellow**: Golden cross (MACD line above signal line)
- **Red**: Death cross (MACD line below signal line)
## Settings
- **Fast Length**: Default 12 (EMA fast period)
- **Slow Length**: Default 26 (EMA slow period)
- **Signal Smoothing**: Default 9 (Signal line period)
- **Oscillator MA Type**: EMA or SMA for MACD calculation
- **Signal Line MA Type**: EMA or SMA for signal line
## How to Use
1. **Trend Identification**:
- Symbols above candlesticks = Bullish MACD territory
- Symbols below candlesticks = Bearish MACD territory
2. **Momentum Analysis**:
- + symbols = Momentum is strengthening
- - symbols = Momentum is weakening
3. **Signal Confirmation**:
- Yellow symbols = MACD above signal line (bullish signal)
- Red symbols = MACD below signal line (bearish signal)
## Advantages
- **Space Efficient**: No separate indicator window required
- **Clean Chart**: Maintains price chart clarity
- **Quick Analysis**: Instant visual feedback on MACD status
- **Non-Intrusive**: Doesn't alter candlestick colors or backgrounds
## Best Practices
- Use in conjunction with price action analysis
- Combine with other technical indicators for confirmation
- Pay attention to symbol color changes for trend shifts
- Monitor symbol position changes for momentum shifts
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*This indicator provides a simplified way to monitor MACD signals without cluttering your chart with additional windows.*
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.
Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408.
Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180.
Cochrane, J. H. (2005). Asset pricing. Princeton University Press.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.
Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119.
Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120.
Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.
Fisher, I. (1930). The theory of interest. Macmillan.
Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley.
Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin.
Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.
Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14.
Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson.
Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50.
Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson.
Nielsen, J. (1993). Usability engineering. Academic Press.
Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing.
Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall.
Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution.
Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill.
Sommerville, I. (2016). Software engineering. Pearson.
Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351-394.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.
Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
caracalla ema long short signal📌 Indicator Name
caracalla ema long short signal
This script generates long and short trading signals using multiple technical indicators: EMAs, MACD, RSI, Stochastic, and volume.
🔧 Indicators Used
1. Exponential Moving Averages (EMA)
ema5, ema20, ema60, ema120 — used to determine overall trend direction.
2. Trend Confirmation (MA Alignment)
Bullish alignment: ema5 > ema20 > ema60 > ema120
Bearish alignment: ema5 < ema20 < ema60 < ema120
3. Crossover Signals
Golden Cross: ema5 crosses above ema20
Dead Cross: ema5 crosses below ema20
4. MACD
Standard parameters: 12, 26, 9
MACD Long: MACD line crosses above signal line
MACD Short: MACD line crosses below signal line
5. RSI & Stochastic
RSI(14): checks momentum
Stochastic (%K, %D)
Bullish: RSI > 50 and Stochastic %K crosses above %D
Bearish: RSI < 50 and Stochastic %K crosses below %D
6. Volume Filter
20-period simple average volume
Volume Up: Current volume > 120% of average
Volume Down: Current volume < 80% of average
✅ Signal Logic
📈 Long Signal (longSignal)
Triggered when 3 or more of the following are true:
EMA bullish alignment
Golden cross
MACD bullish crossover
RSI > 50 and Stochastic bullish crossover
High volume
📉 Short Signal (shortSignal)
Triggered when 3 or more of the following are true:
EMA bearish alignment
Dead cross
MACD bearish crossover
RSI < 50 and Stochastic bearish crossover
Low volume
📊 Visual Elements
Long Signal: Green “롱” label below the candle
Short Signal: Red “숏” label above the candle
EMA Lines:
EMA5 (Blue)
EMA20 (Orange)
EMA60 (Green)
EMA120 (Red)
Hunting Bollinger Bands for scalping📌 Bollinger Band Reversal BUY/SELL Indicator
Name: Hunting Bollinger Bands for scalping
Purpose: Displays reversal signals for short-term scalping in range-bound markets.
Target Users: Scalpers and day traders, especially for trading Gold (XAU/USD).
Recommended Target: Works well for scalping approximately $3 price movements on Gold.
Core Logic:
Detects excessive price deviation using Bollinger Bands (±2σ).
Filters out excessive signals with a bar interval limiter.
Displays clear and simple BUY/SELL labels for entry timing.
📌 Signal Conditions
BUY
Price closes below the Lower Bollinger Band.
At least the specified number of bars has passed since the previous signal.
Displays a “BUY” label below the bar.
SELL
Price closes above the Upper Bollinger Band.
At least the specified number of bars has passed since the previous signal.
Displays a “SELL” label above the bar.
📌 Parameters
Parameter Description Default
Bollinger Band Length (bbLength) Period for Bollinger Band calculation 20
Standard Deviation (bbStdDev) Standard deviation multiplier for band width 2.0
Signal Interval (barLimit) Minimum bar interval to avoid repeated signals 10
📌 How to Use
Add the indicator to your chart; Bollinger Bands and BUY/SELL labels will appear.
When a signal appears, confirm price reaction and enter a scalp trade (around $3 for Gold is recommended).
Adjust the “Signal Interval (barLimit)” to control signal frequency.
Avoid using it during high-impact news events or strong trending markets.
📌 Best Market Conditions
Range-bound markets
Scalping small price movements (~$3)
Low-volatility sessions (e.g. Asian session for Gold)
📌 Notes
May generate frequent signals during strong trends, leading to potential losses.
Can be combined with other indicators (e.g. 200 MA, RSI, VWAP) for higher accuracy.
Signals are for reference only and should not be used as the sole trading decision factor.
📌 ボリンジャーバンド逆張りBUY/SELL インジケーター解説
名前：Hunting Bollinger Bands for scalping
目的：レンジ相場での短期的な反発を狙った逆張りシグナルを表示
対象ユーザー：スキャルピングやデイトレードで、特にゴールド(XAU/USD)での小幅な値動きを狙うトレーダー
推奨利幅：ゴールドでおよそ 3ドル前後 を目安にスキャルピングを行うと有効
メインロジック：
ボリンジャーバンド（±2σ）で過剰な価格乖離を検出
バー間隔フィルターで過剰シグナルを制御
BUY/SELLラベルで視覚的にシンプルなエントリーポイントを表示
📌 シグナル条件
BUY（買いシグナル）
現在価格が ボリンジャーバンド下限（Lower Band）を下回った時
前回シグナルから指定したバー数以上経過
この条件を満たした場合、ローソク足下に「BUY」ラベルを表示します。
SELL（売りシグナル）
現在価格が ボリンジャーバンド上限（Upper Band）を上回った時
前回シグナルから指定したバー数以上経過
この条件を満たした場合、ローソク足上に「SELL」ラベルを表示します。
📌 パラメータ
項目 説明 初期値
ボリンジャーバンド期間 (bbLength) ボリンジャーバンド計算の期間 20
標準偏差 (bbStdDev) バンド幅を決める標準偏差 2.0
シグナル間隔 (barLimit) シグナルの連続表示を防止する最小バー間隔 10
📌 使い方
インジケーターをチャートに追加すると、ボリンジャーバンドとBUY/SELLラベルが表示されます
シグナルが出たら、反発確認後にスキャルピングエントリー（ゴールドなら約3ドルを目安に）
「シグナル間隔（barLimit）」を調整して、シグナルの過剰表示を防ぐ
経済指標発表や強いトレンド発生時は使用を控える
📌 このインジケーターが向いている相場
レンジ相場
小さな値幅（約3ドル前後）を狙うスキャルピング
トレンドが弱い横ばいの時間帯（例：アジア時間のゴールドなど）
📌 注意点
強いトレンド相場では、逆張りシグナルが連続的に発生し、損切りが増える可能性あり
200MAやRSI、VWAPなど他の指標と組み合わせることで精度を高められる
シグナルは参考用であり、単独での売買判断は推奨されない
トレンドフォローBUY&SELL ver1.1Indicator Description
This indicator displays three moving averages (MAs) and generates buy and sell signals based on their crossovers. It’s designed to help traders easily follow the trend and avoid counter-trend trades.
1. Three Moving Averages
MA1 (Default: 7) – Short-term trend (Yellow)
MA2 (Default: 50) – Medium-term trend (Blue)
MA3 (Default: 200) – Long-term trend (Red), also used as a filter
2. Signal Types
(A) MA1 and MA3 Crossovers (Yellow Signals)
Golden Cross (BUY): MA1 crosses above MA3
Dead Cross (SELL): MA1 crosses below MA3
→ Helps identify shifts between short-term and long-term trends.
(B) MA1 and MA2 Crossovers (Green & Red Signals)
BUY (Green): MA1 and MA2 cross, and both are above MA3
SELL (Red): MA1 and MA2 cross, and both are below MA3
→ Only trend-aligned signals are shown (buy only above MA3, sell only below MA3).
(C) Gray Signals (Filtered-Out Signals)
If MA1 and MA2 cross but don’t meet the MA3 condition, a gray signal is displayed.
Example: “BUY” below MA3 or “SELL” above MA3 appears as gray.
→ This feature is ON by default but can be turned OFF in the settings.
3. Alerts
Alerts can be triggered for:
MA1 × MA3 Golden Cross / Dead Cross
MA1 × MA2 BUY / SELL (with MA3 filter)
This allows you to receive notifications when valid trade setups occur.
4. Key Benefits
Visualize short-, medium-, and long-term trends at the same time
Trade only in the direction of the 200MA trend using the built-in filter
Optionally view filtered-out (gray) signals for extra context
Set alerts to avoid missing trading opportunities
With this indicator, you can focus on trading with the trend—buying above the 200MA and selling below it—while staying informed of all crossover events.
このインジケーターは 3本の移動平均線（MA） と、
それらのクロスに基づいた 売買シグナル を表示するツールです。
1. 3本の移動平均線
MA1（デフォルト7）：短期のトレンドを把握するための線（黄色）
MA2（デフォルト50）：中期のトレンドを把握するための線（青）
MA3（デフォルト200）：長期のトレンド（赤）。フィルターとしても使用
2. シグナルの種類
(A) MA1とMA3のクロス（黄色シグナル）
ゴールデンクロス（BUY）：MA1がMA3を上抜け
デッドクロス（SELL）：MA1がMA3を下抜け
→ 長期トレンドと短期の変化を確認するための参考シグナル
(B) MA1とMA2のクロス（緑・赤シグナル）
BUY（緑）：MA1とMA2がクロスし、両方がMA3より上にある
SELL（赤）：MA1とMA2がクロスし、両方がMA3より下にある
→ 200MAを基準に「上なら買い、下なら売り」のトレンド方向に沿ったシグナルだけを表示
(C) グレーシグナル（フィルター除外）
MA1とMA2がクロスしたが、MA3の条件を満たさなかった場合にグレー表示
例えば「MA3より下でBUY」「MA3より上でSELL」はグレー
→ 初期設定ではONになっていますが、オフにすることも可能
逆張りの指標や、トレンド転換のサインにもなる
3. アラート機能
MA1×MA3のゴールデンクロス/デッドクロス
MA1×MA2のBUY/SELL（MAフィルターあり）
→ これらが発生したタイミングでTradingViewのアラートを出せる
4. 使い方のポイント
短期・中期・長期のトレンドを同時に把握できる
200MAを基準にフィルターすることで「逆張りシグナル」を排除
フィルターで外れたシグナルもグレーで確認できる（任意）
アラートを設定すれば、チャンスを逃さずにエントリー可能
このインジケーターを使うことで、「200MAの上では買いのみ」「下では売りのみ」というシンプルでトレンドに沿ったトレードができるようになります。
