Alt buy signal 1H Entry + 4H Confirm (MACD + Stoch RSI + HMA)This indicator is a multi-timeframe (MTF) analysis tool designed for the ALT trading , capturing entry signals on the 1-hour (1H) timeframe and confirming trends on the 4-hour (4H) timeframe. It combines MACD, Stoch RSI, and Hull Moving Average (HMA) to identify precise buy opportunities, particularly at reversal points after a downtrend or during trend shifts. It visually marks both past and current BUY signals for easy reference.
Key Features:
1H Entry Signal (Early Ping): Triggers on a MACD golden cross (below 0) combined with a Stoch RSI oversold cross (below 20), offering an initial buy opportunity.
4H Trend Confirmation (Entry Ready): Validates the trend with a 4H MACD histogram rising (in negative territory) or a golden cross, plus a Stoch RSI turn-up (above 30).
Past BUY Display: Labels past data points where these conditions were met as "1H BUY" or "FULL BUY," facilitating backtesting.
HMA Filter: Optional HMA(16) to confirm price breakouts, enhancing trend validation.
Purpose: Ideal for short-term scalping and swing trading. Supports a two-step strategy: initial partial entry on 1H signals, followed by additional entry on 4H confirmation.
Usage Instructions
Installation: Add the indicator to an IMX/USDT 1H chart on TradingView.
Signal Interpretation:
lime "1H BUY": 1H conditions met, consider initial entry (stop-loss: 3-5% below recent low).
green "FULL BUY": 1H+4H conditions met, confirm trend for additional entry (take-profit: 10% below recent swing high).
Customization: Adjust TF (1H/4H), MACD/Stoch RSI parameters, and HMA usage via the input settings.
Alert Setup: Enable alerts for "ENTRY READY" (1H+4H) or "EARLY PING" (1H only) conditions.
Advantages
Accuracy: Reduces false signals by combining MACD golden cross below 0 with Stoch RSI oversold conditions.
Dual Confirmation: 1H for quick timing and 4H for trend validation, improving risk management.
Visualization: Past BUY points enable easy backtesting and pattern recognition.
Flexibility: 4H confirmation mode adjustable (histogram rise or golden cross).
Limitations
Timeframe Dependency: Optimized for 1H charts; may not work on other timeframes.
Market Conditions: Potential whipsaws in sideways markets; additional filters (e.g., RSI > 50) recommended.
Manual Management: Stop-loss and take-profit require user discretion.
在脚本中搜索"GOLD"
Foxbrady D/G CrossFoxbrady D/G Cross - Golden Cross & Death Cross Indicator**
A clean and simple indicator that identifies Golden Cross and Death Cross events using the classic 50-day and 200-day simple moving averages.
Features:
- Blue line: 50-day SMA (fast moving average)
- Red line: 200-day SMA (slow moving average)
- Green "GC" label appears at the exact crossover point when a Golden Cross occurs (bullish signal)
- Red "DC" label appears at the exact crossover point when a Death Cross occurs (bearish signal)
- Built-in alert conditions for both events
- Customizable MA periods to suit your trading style
How to Use:
The Golden Cross (50 MA crossing above 200 MA) is traditionally viewed as a bullish long-term signal, while the Death Cross (50 MA crossing below 200 MA) is considered a bearish indicator. This indicator makes it easy to spot these events historically and receive alerts when they occur in real-time.
Perfect for swing traders and long-term investors looking to identify major trend changes.
3MA/EMA Alerts指标名称（中文/英文）
中文名：多均线趋势指标（带上穿与金叉提醒）
英文名：Multi MA/EMA Trend Indicator (with Price & Golden Cross Alerts)
指标功能介绍（中文）
多均线趋势指标（带上穿与金叉提醒） 是一个可自定义的均线工具，适用于趋势分析和交易信号提醒。
核心功能：
多均线显示
默认显示 EMA20，EMA80/200 可选择显示
每条均线可独立选择 EMA 或 SMA
自定义颜色和线宽
价格上穿均线提醒
当价格向上突破任意开启的均线时触发提醒
可用于捕捉短线趋势启动点
金叉提醒
当短期均线向上穿过中长期均线时触发提醒
可用于捕捉潜在的趋势反转或加速
中文 UI
参数和提醒信息均为中文，便于快速理解和使用
适用场景
趋势确认
趋势反转捕捉
短线入场和长期持仓参考
Indicator Description (English)
Multi MA/EMA Trend Indicator (with Price & Golden Cross Alerts) is a customizable moving average tool for trend analysis and trading alerts.
Key Features:
Multiple Moving Averages
Default display: EMA20; EMA80/200 optional
Each MA can be set as EMA or SMA individually
Customizable colors and line widths
Price Cross Alerts
Alerts when price crosses above any active MA
Helps identify short-term trend initiation points
Golden Cross Alerts
Alerts when a short-term MA crosses above a mid/long-term MA
Useful for detecting trend acceleration or reversal signals
User-Friendly Interface
Parameters and alerts are labeled in Chinese (can be translated)
Applications
Trend confirmation
Trend reversal detection
Short-term entries and long-term position guidance
MA Pack + Cross Signals (Short vs Long)Overview
A flexible moving average pack that lets you switch between short-term trend detection and long-term trend confirmation .
Short-term mode: plots 5, 10, 20, and 50 MAs with early crossovers (10/50, 20/50).
Long-term mode: plots 50, 100, 200 MAs with Golden Cross and Death Cross signals.
Choice of SMA or EMA .
Alerts included for all crossovers.
Why Use It
Catch early trend shifts in short-term mode.
Confirm institutional trend levels in long-term mode.
Visual signals (triangles + labels) make spotting setups easy.
Alert-ready for automated trade monitoring.
Usage
Add to chart.
In settings, choose Short-term or Long-term .
Watch for markers:
Green triangles = bullish cross
Red triangles = bearish cross
Green label = Golden Cross
Red label = Death Cross
Optional: enable alerts for notifications.
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.
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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.
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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.
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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.
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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
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📊 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
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🎓 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.
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.
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)
MA Crossover Detector
The Moving Average Crossover Detector is a custom indicator that visually shows buy and sell signals clearly on the chart. based on the crossing of two moving averages — a popular and beginner-friendly tool in technical analysis.
It plots two moving averages — One fast (short period) and one slow (long period) — and highlights crossover points:
✅ Buy Signal (Golden Cross) – When the fast MA crosses above the slow MA.
❌ Sell Signal (Death Cross) – When the fast MA crosses below the slow MA.
✅ Features
Visual: Clearly shows crossovers on the chart.
Customizable: Choose periods, types, styles, etc.
Alert-ready: You can set alerts for crossovers.
The Moving Average (MA) Crossover Strategy is one of the simplest and most widely used strategies in technical analysis for trading stocks, forex, crypto, and other markets. It relies on the interaction between two moving averages to generate buy and sell signals.
Core Components
Short-Term Moving Average (Fast MA) : Reacts quickly to price changes (e.g., 9-period or 20-period).
Long-Term Moving Average (Slow MA) : Reacts more slowly to price changes (e.g., 21-period or 200-period).
How the Strategy Works
Bullish Crossover (Golden Cross):
Occurs when the fast MA crosses above the slow MA. Interpreted as a buy signal, indicating a potential uptrend.
Bearish Crossover (Death Cross):
Occurs when the fast MA crosses below the slow MA. Interpreted as a sell signal, indicating a potential downtrend.
Common Variants
Short-term trading
9 EMA
21 EMA
Swing trading
20 SMA
50 SMA
Long-term investing
50 SMA
200 SMA
Pros
Easy to understand and implement
Works well in trending markets
Can be automated for backtesting and execution
Cons
Lagging indicator: MAs are based on past prices, so signals come after the move has started.
Choppy markets = whipsaws: Generates false signals in sideways/range-bound conditions.
May underperform in volatile or mean-reverting environments
Tips for Improvement
Use confirmation tools : e.g., RSI, MACD, volume analysis, price action
Add filters : Trend filter (ADX), volatility filter (ATR), or time filter (session-based)
Combine with price structure : Support/resistance, breakouts, pullbacks
