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.
Cari skrip untuk "Divergence"
SMT SwiftEdge PowerhouseSMT SwiftEdge Powerhouse: Precision Trading with Divergence, Liquidity Grabs, and OTE Zones
The SMT SwiftEdge Powerhouse is a powerful trading tool designed to help traders identify high-probability entry points during the most active market sessions—London and New York. By combining Smart Money Technique (SMT) Divergence, Liquidity Grabs, and Optimal Trade Entry (OTE) Zones, this script provides a unique and cohesive strategy for capturing market reversals with precision. Whether you're a scalper or a swing trader, this indicator offers clear visual signals to enhance your trading decisions on any timeframe.
What Does This Script Do?
This script integrates three key concepts to identify potential trading opportunities:
SMT Divergence:
SMT Divergence compares the price action of two correlated assets (e.g., Nasdaq and S&P 500 futures) to detect hidden market reversals. When one asset makes a higher high while the other makes a lower high (bearish divergence), or one makes a lower low while the other makes a higher low (bullish divergence), it signals a potential reversal. This technique leverages institutional "smart money" behavior to anticipate market shifts.
Liquidity Grabs:
Liquidity Grabs occur when price breaks above recent highs or below recent lows on higher timeframes (5m and 15m), often triggering stop-loss orders from retail traders. These breakouts are identified using pivot points and confirm institutional activity, setting the stage for a reversal. The script focuses on liquidity grabs during the London and New York sessions for maximum market activity.
Optimal Trade Entry (OTE) Zones:
OTE Zones are Fibonacci-based retracement areas (e.g., 61.8%) calculated after a liquidity grab. These zones highlight where price is likely to retrace before continuing in the direction of the reversal, offering a high-probability entry point. The script adjusts the width of these zones using the Average True Range (ATR) to adapt to market volatility.
By combining these components, the script identifies when institutional activity (liquidity grabs) aligns with market reversals (SMT divergence) and pinpoints precise entry points (OTE zones) during high-liquidity sessions.
Why Combine These Components?
The integration of SMT Divergence, Liquidity Grabs, and OTE Zones creates a robust trading system for several reasons:
Synergy of Institutional Signals: SMT Divergence and Liquidity Grabs both reflect "smart money" behavior—divergence shows hidden reversals, while liquidity grabs confirm institutional intent to trap retail traders. Together, they provide a strong foundation for identifying high-probability setups.
Session-Based Precision: Focusing on the London and New York sessions ensures signals occur during periods of high volatility and liquidity, increasing their reliability.
Precision Entries with OTE: After confirming a setup with divergence and liquidity grabs, OTE zones provide a clear entry area, reducing guesswork and improving trade accuracy.
Adaptability: The script works on any timeframe, with adjustable settings for signal sensitivity, session times, and Fibonacci levels, making it versatile for different trading styles.
This combination makes the script unique by aligning institutional insights with actionable entry points, tailored to the most active market hours.
How to Use the Script
Setup:
Add the script to your chart (works on any timeframe, e.g., 1m, 5m, 15m).
Configure the settings in the indicator's inputs:
Session Settings: Adjust the start/end times for London and New York sessions (default: London 8-11 UTC, New York 13-16 UTC). You can disable session restrictions if desired.
Asset Settings: Set the primary and secondary assets for SMT Divergence (default: NQ1! and ES1!). Ensure the assets are correlated.
Signal Settings: Adjust the lookback period, ATR period, and signal sensitivity (Low/Medium/High) to control the frequency of signals.
OTE Settings: Choose the Fibonacci level for OTE zones (default: 61.8%).
Visual Settings: Enable/disable OTE zones, SMT labels, and debug labels for troubleshooting.
Interpreting Signals:
Blue Circles: Indicate a liquidity grab (price breaking a 5m or 15m pivot high/low), marking the start of a potential setup.
Blue OTE Zones: Appear after a liquidity grab, showing the retracement area (e.g., 61.8% Fibonacci level) where price is likely to enter for a reversal trade. The label "OTE Trigger 5m/15m" confirms the direction (Short/Long) and session.
Green/Red Entry Boxes: Mark precise entry points when price enters the OTE zone and confirms the SMT Divergence. Green boxes indicate a long entry, red boxes a short entry.
Trading Example:
On a 1m chart, a blue circle appears when price breaks a 5m pivot high during the London session.
A blue OTE zone forms, showing a retracement area (e.g., 61.8% Fibonacci level) with the label "OTE Trigger 5m/15m (Short, London)".
Price retraces into the OTE zone, and a red "Short Entry" box appears, confirming a bearish SMT Divergence.
Enter a short trade at the red box, with a stop-loss above the OTE zone and a take-profit at the next support level.
Originality and Utility
The SMT SwiftEdge Powerhouse stands out by merging SMT Divergence, Liquidity Grabs, and OTE Zones into a single, session-focused indicator. Unlike traditional indicators that focus on one aspect of price action, this script combines institutional reversal signals with precise entry zones, tailored to the most active market hours. Its adaptability across timeframes, customizable settings, and clear visual cues make it a versatile tool for traders seeking to capitalize on smart money movements with confidence.
Tips for Best Results
Use on correlated assets like NQ1! (Nasdaq futures) and ES1! (S&P 500 futures) for accurate SMT Divergence.
Test on lower timeframes (1m, 5m) for scalping or higher timeframes (15m, 1H) for swing trading.
Adjust the "Signal Sensitivity" to "High" for more signals or "Low" for fewer, high-quality setups.
Enable "Show Debug Labels" if signals are not appearing as expected, to troubleshoot pivot points and liquidity grabs.
Enhanced CCI with Trend FiltersThis indicator combines the power of the Commodity Channel Index (CCI) with adaptive trend filters and divergence detection to identify high-probability trading opportunities. It's designed to reduce false signals by incorporating trend direction and divergence confirmation.
🔍 How It Works:
1. Trend Detection:
- Uses an adaptive Simple Moving Average (SMA) that automatically adjusts between daily and weekly timeframes
- Daily mode: Default 200 SMA for longer-term trend
- Weekly mode: Default 40 SMA for broader market perspective
- Trend direction is determined by comparing current SMA value to its previous value
2. CCI Component:
- Traditional CCI calculation with customizable length (default: 20)
- Adjustable overbought (default: +100) and oversold (default: -100) levels
- Color-coded CCI line for quick visual reference:
* Green: Overbought and rising
* Red: Oversold and falling
* Gray: Neutral zone
3. Signal Generation:
- Buy Signal (Green Background):
* CCI crosses above oversold level AND
* Main trend is confirmed bullish (rising SMA)
- Sell Signal (Red Background):
* CCI crosses below overbought level AND
* Main trend is confirmed bearish (falling SMA)
4. Divergence Detection:
- Automatically identifies bullish and bearish divergences
- Bullish Divergence: Price makes lower low while CCI makes higher low
- Bearish Divergence: Price makes higher high while CCI makes lower high
- Visualized with distinct markers on the CCI line
- Can be used to set up alerts for divergence confirmations
⚙️ Customization Options:
- CCI calculation length and price source
- Overbought/Oversold levels
- SMA lengths for daily and weekly modes
- Divergence lookback period
- Signal colors and visualization settings
🔔 Alert Capabilities:
- Set alerts for CCI crossovers of overbought/oversold levels
- Create divergence detection alerts
- Configure trend change notifications
- Combine multiple conditions for complex alert strategies
💡 Trading Tips:
- Combine divergence signals with trend direction for better accuracy
- Wait for signal confirmation before entering trades
- Use additional indicators or price action for exit decisions
This indicator helps traders identify potential trend reversals while keeping them aligned with the main market trend. Perfect for both trend following and counter-trend strategies when used appropriately.
Tags: CCI, trend following, divergence, momentum, signals, SMA, multi-timeframe, technical analysis, trend filter, oversold, overbought
Note: Past performance does not guarantee future results. Always combine with proper risk management.
MACD All In One Screener [ChartPrime]INTRODUCTION
MACD All In One Screener (ChartPrime) is a multi instrument, multi timeframe indicator designed to provide traders with a comprehensive solution to monitoring the market. This indicator is designed to be easy to use and visually appealing while also being highly flexible and feature rich. Users can pick up to 10 symbols not including the chart's symbol and set up alerts for many different signals that the MACD produces. One standout feature of this indicator is its ability to display not only each symbol individually as a MACD but you can also view its chart from within this indicator. This removes the need to flip between symbols to see the price action for your basket.
On top of that we have designed this indicator to be friendly with "indicator on indicator" by providing outputs for all of the standards of price that users may want. Included is an overview section that shows all of the symbols signals symbolically over time. Additionally we have included a table for easy monitoring. This table includes the symbol, its timeframe, the current alert, and its histogram state. To make things as user friendly as possible we have also included rich error handling that tells you exactly what is wrong with your configuration.
HOW TO USE
To use this indicator, simply add it to your chart and navigate to the settings. From there select the symbols you want to monitor and the timeframes you want to use. Next you want to navigate down to the alerts section to select the what alerts you want to receive, and what symbols you want to get alerts for. Finally, you wan to create your alert using "Any alert() function call". Now your screener is all set up!
OVERVIEW OF INPUTS
View allows you to select what the indicator currently displays. You can pick from any one of the selected symbols, an overview of all of the symbols, or simply nothing. If you want to only use the table, "None" is provided so you can move the indicator into the chart panel.
View Toggle lets you pick from displaying the MACD for the selected symbol or the Price Action as a candle chart. To see your "indicator on indicator" you will have to select a symbol from the view list. There is a bug where if you select "Overview" while you are using "indicator on indicator" your added indicator will see the last symbol you viewed. To fix this, simply change the setting of your overlaid indicator and it will correct its self.
History Length is the number of historical bars to calculate over. This feature is here to prevent the indicator from breaking due to uneven historical data between the symbols.
Show Price Line toggles a dotted line that follows the current symbols closing price when "Price" is selected under the "View Toggle" dropdown.
Show Symbol Label toggles a label that displays the current symbols name and timeframe. This only impacts the single symbol view.
Overview Label Color adjusts the color of the symbol labels for both overview and single symbol view.
MA Type lets you pick what kind of moving average you want to use for the oscillator or signal. You can pick from the standard SMA or EMA.
Fast Length is a standard input for MACD. This lets you pick the period of the fast MA.
Slow Length , just like Fast Lenght, is a standard input for MACD. This lets you pick the period of the slow MA.
Signal Length is another standard input for MACD. This lets you configure the period of the signal MA.
MACD Cross Overlay Icon is a toggle to display MACD crosses when viewing a single symbol's MACD. When the MACD has a bullish cross it will plot a bullish dot, and when it has a bearish cross it will plot a bearish dot. This is purely visual.
Regular Bullish and Bearish toggles the visual display of the divergences on the single symbol view. This does not effect the indicators ability do send alerts.
Divergence Look Right adjusts the number of bars into the future to look for confirmation of a signal. This directly impacts lag but enhances stability.
Divergence Look Left adjusts the number of bars into the past to check for a signal. A longer period will filter out smaller moves
Maximum Lookback adjusts the maximum size of a divergence.
Minimum Lookback adjusts the minimum size of a divergence.
Divergence Drawings picks how you want to visualize the divergence. You can pick from displaying it as a line, a label, or both.
Enable Table toggles the overview table. When enabled it will show you the enabled symbols and their current state. From left to right: symbol name, timeframe, current alert, and histogram state.
Position picks where on the chart you want the table to be.
Text Color adjusts the text color of the table.
BG Color adjusts the background color of the table.
Frame Color adjust the frame color of the table.
Current Symbol Time Frame adjusts the timeframe of the chart's symbol.
Symbol 1 - 10 pick "Symbol's" symbol and timeframe. To use higher timeframes, the symbol's have to be the same type. You can't have a crypto and a stock using HTF at the same time as they don't have the same sessions and will result in an error. You can use unsafe mode (as described below) to potentially get around this.
Enable Symbol when enabled it will give you alerts for the symbol. This also enables the symbol in the overview. If this is disabled it won't send alerts, and it will not show up in overview, or the table.
Wait for Close enables waiting for the bar to close before printing an alert.
Alert Symbol Size picks what size you want the overview symbols to be.
Enable Cross Over 0 Alert: MACD crosses over the 0 line.
Enable Cross Under 0 Alert: MACD crosses under the 0 line.
Enable MACD Cross Bullish Alert: Bullish MACD cross.
Enable MACD Cross Bearish Alert: Bearish MACD cross.
Enable Histogram Bullish Turn Alert: MACD begins to turn bullish but hasn't crossed.
Enable Histogram Bearish Turn Alert: MACD begins to turn bearish but hasn't crossed.
Enable Histogram Bullish Continuation Alert: MACD is in a bullish cross state and it was declining but began rising again.
Enable Histogram Bearish Continuation Alert: MACD is in a bearish cross state and it was rising but began falling again.
Enable Bullish/Bearish Divergence Alert enables divergence alerts. Divergences are lagging, especially on a higher timeframe. These alerts will also tell you the time in the past when the divergence occurred.
Color Section is provided to allow for personalization of the indicator. Everything can be adjusted here.
Disable Error Checking: Only enable this if you want to bypass the built in error checking. This will enable 'Safe Requesting'. Safe Requesting will only request enabled symbols and you will not be able to view symbols that are not enabled in this mode. Only use this if you want to mix symbol types and you know it will work. (An example would be viewing stocks and SPY at the same time.)
CONCLUSION
The MACD All In One Screener (ChartPrime) is a versatile indicator designed to monitor multiple symbols across various timeframes. The flexibility in customization, from MACD settings to visual alerts and table presentations, allows users to tailor the screener to their needs and preferences. We hope you find this as useful and interesting as we do and wish you good luck in the market!
Enjoy
RSI Supreme Multi-Method [MyTradingCoder]Introducing the "RSI Supreme Multi-Method" indicator, a powerful tool that combines the Relative Strength Index (RSI) with selectable manipulation methods to identify overbought and oversold conditions in the market, along with the ability to detect divergences for enhanced trading insights.
The indicator features four distinct manipulation methods for the RSI, each providing valuable insights into market conditions:
1. Standard RSI Method: The indicator uses the traditional RSI calculation to identify overbought and oversold areas.
2. Volatility Weighted RSI Method: This method applies a volatility formula to the RSI calculation, allowing for a more responsive indication of market conditions during periods of heightened volatility. Users can adjust the length of the volatility formula to fine-tune this method.
3. Smoothed RSI Method: The smoothed RSI method utilizes a smoothing algorithm to reduce noise in the RSI values, presenting a clearer representation of overbought and oversold conditions. The length of the smoothing can be adjusted to match your trading preferences.
4. Session Weighted RSI Method: With this innovative method, users can specify multipliers for different time sessions throughout the day to manipulate the base RSI. Each session can be customized with start and end times, enabling or disabling specific sessions, and specifying the multiplier for each session. This feature allows traders to adapt the RSI to different market sessions dynamically.
Additionally, the "RSI Supreme Multi-Method" indicator draws divergences on the oscillator, providing an extra layer of analysis for traders. Divergences occur when the direction of the RSI differs from the direction of the price movement, potentially signaling trend reversals.
Key Settings:
RSI Length: Adjust the length of the base RSI before applying any manipulation.
RSI Source: Determine the data source for the base RSI calculation.
Overbought Value: Set the RSI value at which overbought conditions are indicated.
Oversold Value: Set the RSI value at which oversold conditions are indicated.
RSI Type: Choose from four options: Standard, Smoothed, Volatility Manipulated, or Session Manipulated.
Volatility Manipulated Settings: Adjust the length of the volatility formula (applicable to Volatility Manipulated method).
Smoothed Settings: Adjust the length of the smoothing (applicable to Smoothed method).
Session Manipulated Settings: Customize six different time sessions with start and end times, enable or disable specific sessions, and specify multipliers for each session.
Divergence Color: Adjust the color of the drawn divergences to suit your chart's aesthetics.
Divergence Tuning: Fine-tune the sensitivity of the divergence detection for more accurate signals.
The "RSI Supreme Multi-Method" indicator is a versatile and comprehensive tool that can be used to identify overbought and oversold areas, as well as to spot potential trend reversals through divergences. However, like all technical analysis tools, it should be used in conjunction with other indicators and analysis methods to make well-informed trading decisions.
Enhance your trading insights with the "RSI Supreme Multi-Method" indicator and gain an edge in identifying critical market conditions and divergences with precision.
taLibrary "ta"
This library is a Pine Script™ programmer’s tool containing calcs for my oscillators and some helper functions.
buoyancy(src, targetPeriod, maxLookback)
Calculates buoyancy using a target of `src` summed over `targetPeriod` bars, not searching back farther than `maxLookback` bars. See:
Parameters:
src : (series float) The source value that is summed to constitute the target.
targetPeriod : (series int) The qty of bars to sum `src` for in order to calculate the target.
maxLookback : (simple int) The maximum number of bars back the function will search.
Returns: (series float) Buoyancy: the gap between the avg distance of past up and dn bars added to reach the target, divided by the max distance reached. Returns zero when an error condition occurs.
efficientWork(length)
Calculates Efficient Work on `length` bars. See:
Parameters:
length : (simple int) The length of the ALMA used to calculate the result.
Returns: (series float) A -1 to +1 value representing the efficiency of price travel, bar to bar.
ma(type, src, length)
Returns the `type` MA of the `src` over the `length`.
Parameters:
type : (simple string) The type of MA required (uses constants that must be defined earlier in the script).
src : (series float) The source value used to calculate the MA.
length : (simple int) The length value used to calculate the MA.
Returns: (series float) The MA value.
divergenceChannel(divergence, hiSrc, loSrc, breachHiSrc, breachLoSrc)
Calculates the levels and states of divergence channels, which are created when divergences occur.
Parameters:
divergence : (series bool) `true` on divergences, which can be defined any way. On breached channels it creates a new channel, otherwise, channel levels are expanded.
hiSrc : (series float) The price source used to set the channel's hi level when a divergence occurs.
loSrc : (series float) The price source used to set the channel's lo level when a divergence occurs.
breachHiSrc : (series float) The price source that must breach over the channel's `channelHi` level for a breach to occur.
breachLoSrc : (series float) The price source that must breach under the channel's `channelLo` level for a breach to occur.
Returns: A tuple containing the following values:
sourceStrToFloat(srcString)
Converts the name of a source in the `srcString` to its numerical equivalent.
Parameters:
srcString : (series string) The string representing the name of the source value to be returned.
Returns: (series float) The source's value.
Volume CloudsI know it isn't much, but it is my first time coding an indicator!
You might need to modify distance to fit the volatility and time frame of the chart you are looking at (if candles stay on top of cloud in an ugly mess, increase distance until signals can be seen). Sometimes heikin ashi candles can really help clear the picture up as well.
How to use:
The lines are the Volume Weighted Moving Average (green) and Simple Moving Average (red).
Cloud is green when VWMA is above SMA ( bullish ) and red when SMA is above VWMA ( bearish ).
The thicker the cloud, the stronger the signal. Also, the farther price strays from the cloud, the more pressure there is for it to return.
Look for divergences:
When price passes below a green cloud you have a bullish divergence (or if cloud turns green at crossing point)
Price pass above red cloud = bearish divergence.
Or confirmations:
Price passes below red cloud = bearish confirmation.
Price pass above green cloud = bullish confirmation.
I hope this helps you at least a little! Twitter: GarrettZ
Cypto Oscillator with Sortino-like VolatilityEnhanced Inverted Ultimate Oscillator with Sortino-like Volatility
This indicator combines the power of the Ultimate Oscillator with a unique Sortino-like volatility calculation to provide a comprehensive view of market dynamics. It's designed to help traders identify potential turning points and assess the risk associated with price movements.
**Core Components:**
* **Ultimate Oscillator (UO):** The UO is a momentum indicator that incorporates short, medium, and long-term price action to identify overbought and oversold conditions. This indicator inverts and normalizes the UO to a 0-10 scale, providing a clear view of momentum shifts.
* **Sortino-like Volatility:** Instead of a standard deviation, this indicator uses a downside deviation calculation. This focuses specifically on *negative* price movements, offering a more relevant measure of risk for most traders. By not penalizing upside volatility, it avoids giving false signals during strong bull runs. The downside deviation is scaled as a percentage of the closing price for cross-asset comparability.
* **Volatility Signal:** The inverted UO is multiplied by the downside deviation to create a combined volatility signal. This signal reflects both momentum and downside risk, providing a more nuanced market perspective.
**Key Features and Uses:**
* **Identifying Potential Turning Points:** Divergences between the UO and price action can signal potential trend reversals. Look for the UO to make higher lows while price makes lower lows (bullish divergence) or the UO to make lower highs while price makes higher highs (bearish divergence).
* **Assessing Downside Risk:** The Sortino-like volatility component helps traders gauge the potential for downside price swings. Higher volatility suggests greater risk.
* **Dynamic Volatility Thresholds:** The indicator includes adjustable upper and lower volatility thresholds, based on a moving average of the volatility signal. These thresholds can be used to identify periods of unusually high or low volatility.
* **Customizable Lookback Periods:** Traders can adjust the lookback periods for the UO and the standard deviation calculation to fine-tune the indicator to their specific trading style and market conditions.
* **Visualizations:** The indicator provides several visual aids, including:
* A histogram of the volatility signal, colored dynamically based on its relationship to the moving average of volatility. Red indicates volatility above the upper bound, orange between the bounds and green below the lower bound.
* A line plot of the volatility signal.
* An optional moving average of the volatility signal.
* Optional upper and lower volatility threshold lines with a filled range for visual clarity.
* **Alerts:** The indicator includes alert conditions for when the volatility signal crosses above the upper threshold (high volatility) or below the lower threshold (low volatility).
**How to Use:**
1. **Inputs:** Adjust the input parameters to optimize the indicator for your chosen asset and timeframe.
2. **Divergences:** Look for divergences between the UO and price to identify potential trend reversals.
3. **Volatility:** Use the volatility signal and thresholds to assess downside risk.
4. **Alerts:** Enable alerts to be notified of high or low volatility events.
**Disclaimer:** This indicator is for informational purposes only and should not be considered financial advice. Always conduct your own thorough analysis before making any trading decisions.
Key improvements in this description:
Clear and concise language: Easy for traders to understand.
Focus on benefits: Highlights how the indicator can help traders.
Detailed explanation of features: Covers all the important aspects.
How-to-use section: Provides practical guidance.
Disclaimer: Includes a necessary disclaimer.
Emphasis on the Sortino-like approach: This is a unique selling point of your indicator.
Well-structured and formatted: Easy to read and digest.
This description should be a great starting point for sharing your indicator with the TradingView community. You can further customize it by adding screenshots of the indicator in action or linking to a chart where it's being used. Remember to respond to comments and questions from other users to build engagement and improve your indicator over time.
GOLDEN RSI by @thejamiulGOLDEN RSI thejamiul is a versatile Relative Strength Index (RSI)-based tool designed to provide enhanced visualization and additional insights into market trends and potential reversal points. This indicator improves upon the traditional RSI by integrating gradient fills for overbought/oversold zones and divergence detection features, making it an excellent choice for traders who seek precise and actionable signals.
Source of this indicator : This indicator is based on @TradingView original RSI indicator with a little bit of customisation to enhance overbought and oversold identification.
Key Features
1. Customizable RSI Settings:
RSI Length: Adjust the RSI calculation period to suit your trading style (default: 14).
Source Selection: Choose the price source (e.g., close, open, high, low) for RSI calculation.
2. Gradient-Filled RSI Zones:
Overbought Zone (80-100): Gradient fill with shades of green to indicate strong bullish conditions.
Oversold Zone (0-20): Gradient fill with shades of red to highlight strong bearish conditions.
3. Support and Resistance Levels:
Upper Band: 80
Middle Bands: 60 (bullish) and 40 (bearish)
Lower Band: 20
These levels help identify overbought, oversold, and neutral zones.
4. Divergence Detection:
Bullish Divergence: Detects lower lows in price with corresponding higher lows in RSI, signaling potential upward reversals.
Bearish Divergence: Detects higher highs in price with corresponding lower highs in RSI, indicating potential downward reversals.
Visual Indicators:
Bullish divergence is marked with green labels and line plots.
Bearish divergence is marked with red labels and line plots.
5. Alert Functionality:
Custom Alerts: Set up alerts for bullish or bearish divergences to stay notified of potential trading opportunities without constant chart monitoring.
6. Enhanced Chart Visualization:
RSI Plot: A smooth and visually appealing RSI curve.
Color Coding: Gradient and fills for better distinction of trading zones.
Pivot Labels: Clear identification of divergence points on the RSI plot.
DMI Delta by 0xjcfOverview
This indicator integrates the Directional Movement Index (DMI), Average Directional Index (ADX), and volume analysis into an Oscillator designed to help traders identify divergence-based trading signals. Unlike typical volume or momentum indicators, this combination provides insight into directional momentum and volume intensity, allowing traders to make well-informed decisions based on multiple facets of market behavior.
Purpose and How Components Work Together
By combining DMI and ADX with volume analysis, this indicator helps traders detect when momentum diverges from price action—a common precursor to potential reversals or significant moves. The ADX filter enhances this by distinguishing trending from range-bound conditions, while volume analysis highlights moments of extreme sentiment, such as solid buying or selling. Together, these elements provide traders with a comprehensive view of market strength, directional bias, and volume surges, which help filter out weaker signals.
Key Features
DMI Delta and Oscillator: The DMI indicator measures directional movement by comparing DI+ and DI- values. This difference (DMI Delta) is calculated and displayed as a histogram, visualizing changes in directional bias. When combined with ADX filtering, this histogram helps traders gauge the strength of momentum and spot directional shifts early. For instance, a rising histogram in a bearish price trend might signal a potential bullish reversal.
Volume Analysis with Extremes: Volume is monitored to reveal when market participation is unusually high, using a customizable multiplier to highlight significant volume spikes. These extreme levels are color-coded directly on the histogram, providing visual cues on whether buying or selling interest is particularly strong. Volume analysis adds depth to the directional insights from DMI, allowing traders to differentiate between regular and powerful moves.
ADX Trending Filter: The ADX component filters trends by measuring the overall strength of a price move, with a default threshold of 25. When ADX is above this level, it suggests that the market is trending strongly, making the DMI Delta readings more reliable. Below this threshold, the market is likely range-bound, cautioning traders that signals might not have as much follow-through.
Using the Indicator in Divergence Strategies
This indicator excels in divergence strategies by highlighting moments when price action diverges from directional momentum. Here’s how it aids in decision-making:
Bullish Divergence: If the price is falling to new lows while the DMI Delta histogram rises, it can indicate weakening bearish momentum and signal a potential price reversal to the upside.
Bearish Divergence: Conversely, if prices are climbing but the DMI Delta histogram falls, it may point to waning bullish momentum, suggesting a bearish reversal.
Visual Cues and Customization
The color-coded output enhances usability:
Bright Green/Red: Extreme volume with strong bullish or bearish signals, often at points of high potential for trend continuation or reversal.
Green/Red Shades: These shades reflect trending conditions (bullish or bearish) based on ADX, factoring in volume. Green signals a bullish trend, and red is a bearish trend.
Blue/Orange Shades: Indicates non-trending or weaker conditions, suggesting a more cautious approach in range-bound markets.
Customizable for Diverse Trading Styles
This indicator allows users to adjust settings like the ADX threshold and volume multiplier to optimize performance for various timeframes and strategies. Whether a trader prefers swing trading or intraday scalping, these parameters enable fine-tuning to enhance signal reliability across different market contexts.
Practical Usage Tips
Entry and Exit Signals: Use this indicator in conjunction with price action. Divergences between the price and DMI Delta histogram can reinforce entry or exit decisions.
Adjust Thresholds: Based on backtesting, customize the ADX Trending Threshold and Volume Multiplier to ensure optimal performance on different timeframes or trading styles.
In summary, this indicator is tailored for traders seeking a multi-dimensional approach to market analysis. It blends momentum, trend strength, and volume insights to support divergence-based strategies, helping traders confidently make informed decisions. Remember to validate signals through backtesting and use it alongside price action for the best results.
Stochastic Momentum Index (SMI) of Money Flow Index (MFI)"He who does not know how to make predictions and makes light of his opponents, underestimating his ability, will certainly be defeated by them."
(Sun Tzu - The Art of War)
▮ Introduction
The Stochastic Momentum Index (SMI) is a technical analysis indicator that uses the difference between the current closing price and the high or low price over a specific time period to measure price momentum.
On the other hand, the Money Flow Index (MFI) is an indicator that uses volume and price to measure buying and selling pressure.
When these two indicators are combined, they can provide a more comprehensive view of price direction and market strength.
▮ Improvements
By combining SMI with MFI, we can gain even more insights into the market. One way to do this is to use the MFI as an input to the SMI, rather than just using price.
This means we are measuring momentum based on buying and selling pressure rather than just price.
Another way to improve this indicator is to adjust the periods to suit your specific trading needs.
▮ What to look
When using the SMI MFI indicator, there are a few things to look out for.
First, look at the SMI signal line.
When the line crosses above -40, it is considered a buy signal, while the crossing below +40 is considered a sell signal.
Also, pay attention to divergences between the SMI MFI and the price.
If price is rising but the SMI MFI is showing negative divergence, it could indicate that momentum is waning and a reversal could be in the offing.
Likewise, if price is falling but the SMI MFI is showing positive divergence, this could indicate that momentum is building and a reversal could also be in the offing.
In the examples below, I show the use in conjunction with the price SMI, in which the MFI SMI helps to anticipate divergences:
In summary, the SMI MFI is a useful indicator that can provide valuable insights into market direction and price strength.
By adjusting the timeframes and paying attention to divergences and signal line crossovers, traders can use it as part of a broader trading strategy.
However, remember that no indicator is a magic bullet and should always be used in conjunction with other analytics and indicators to make informed trading decisions.
[blackcat] L1 New TRIX ScalperNOTE: Because the originally released script failed to comply with the House Rule in the description, it was banned. After revising and reviewing the description, it is republished again. Please forgive the inconvenience caused.
Level: 1
Background
The Triple Exponential Moving Average (TRIX) indicator is a strong technical analysis tool. It can help investors determine the price momentum and identify oversold and overbought signals in a financial asset. Jack Hutson is the creator of the TRIX indicator . He created it in the early 1980s to show the rate of change in a triple exponentially smoothed moving average.
When used as an oscillator, it shows a potential peak and trough price zones. A positive value tells traders that there is an overbought market while a negative value means an oversold market. When traders use TRIX as a momentum indicator, it filters spikes in the price that are vital to the general dominant trend.
A positive value means momentum is rising while a negative value means that momentum is reducing. A lot of analysts believe that when the TRIX crosses above the zero line it produces a buy signal, and when it closes below the zero line, it produces a sell signal.The indicator has three major components:
Zero line
TRIX line (or histograms)
Percentage Scale
Function
The TRIX indicator determines overbought and oversold markets, and it can also be a momentum indicator. Just as it is with most oscillators, TRIX oscillates around a zero line. Additionally, divergences between price and TRIX can mean great turning points in the market. TRIX calculates a triple exponential moving average of the log of the price input. It calculates this based on the time specified by the length input for the current bar.
Trading TRIX indicator signals
Zero line cross
TRIX can help determine the impulse of the market. With the 0 value acting as a centerline, if it crosses from below, it will be mean that the impulse is growing in the market.Traders can, therefore, look for opportunities to place buy orders in the market. Similarly, a cross of the centerline from above will mean a shrinking impulse in the market. Traders can, therefore, look for opportunities to sell in the market.
Signal line cross
To select the best entry points, investors add a signal line on the TRIX indicator. The signal line is a moving average of the TRIX indicator, and due to this, it will lag behind the TRIX.A signal to place a buy order will occur when the TRIX crosses the signal line from below. In the same way, a signal to place a sell order will come up when the TRIX crosses the signal line from above. This is applicable in both trending and ranging markets.In trending markets, a signal line cross will indicate an end of the price retracement, and the main trend will resume. In ranging markets, a signal line confirms that resistance and support zones have been upheld in the market.
Divergences
Traders can use the Triple Exponential Average can to identify when important turning points can happen in the market. They can achieve this by looking at divergences. Divergences happen when the price is moving in the opposite direction as the TRIX indicator.When price makes higher highs but the TRIX makes lower highs, it means that the up-trend is weakening, and a bearish reversal is about to form. When the price makes lower lows, but the TRIX makes higher lows, it means that a bullish reversal is about to happen. Bullish and bearish divergences happen when the security and the indicator do not confirm themselves. A bullish divergence can happen when the security makes a lower low, but the indicator forms a higher low. This higher low means less downside momentum that may foreshadow a bullish reversal. A bearish divergence happens when the commodity makes a higher low, but the indicator forms a lower high. This lower high indicates weak upside momentum that can foreshadow a bearish reversal sometimes. Bearish divergences do not work well in strong uptrends. Even though momentum appears to be weakening due to the indicator is making lower highs, momentum still has a bullish bias as long as it is above its centerline.When bullish and bearish divergences work, they work very well. The secret is to separate the bad signals from the good signals.
Key Signal
RXval --> new TRIX indicator.
AvgTRX --> linear regression average of new TRIX indicator.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
MACD Trend CandlesThe script combines 2 indicators (MACD and Stoch-RSI) and puts them visually directly on the candles - can be used with normal OHLC candles or Heiken Ashi candles. Furthermore, you can derive divergences exremely easy directly visually from the candles as well. Lastly, a SMA 20 high and a SMA 20 low line build a trend channel.
Script is best used in trending markets to trade with the trend.
1) SMA trend channel:
* uptrend: close above
* downtrend: close below
* aggressive entry (uptrend) closing inside channel from below
* conservative entry (uptrend) closing above channel from inside
* hold (uptrend) until close below channel
* can be used accordingly for the downtrend
2) MACD candles
* visualization of the MACD histogram directly on the candles
* dark blue: histogram > 0 and histogram > histogram of previous candle
* light blue: histogram > 0 and histogram < histogram of previous candle
* orange: histogram < 0 and histogram < histogram of previous candle
* light blue: histogram < 0 and histogram > histogram of previous candle
* hold uptrend (dark/light blue candles) - combined with trend channel (above channel)
* hold downtrend (orange /yellow candles) - combined with trend channel (below channel)
* Color divergence: light blue candle > dark blue candle (price and MACD show divergence (bearish)
* Color divergence: yellow candle < orange candle (price and MACD show divergence (bullish)
* Trend change (0 line cross to upside) yellow or orange to dark blue
* Trend change (0 line cross to downside) dark or light blue to orange
3) Stoch RSI diamonds
* visualization of the STOCH-RSI as diamonds above or below the candle
* k, d line > 80: diamond above the candle
* k, d line < 20: diamond below the candle
* divergence caldle without diamond above > candle with diamond above (bearish divergence)
* divergence caldle without diamond below < candle with diamond below (bullish divergence)
Feel free to test each part individually and combine it with other indicators, e.g. BBands and Ichimoku Cloud - you will see it is a powerful visualization script
HAVE FUN
Keltner Channel Enhanced [DCAUT]█ Keltner Channel Enhanced
📊 ORIGINALITY & INNOVATION
The Keltner Channel Enhanced represents an important advancement over standard Keltner Channel implementations by introducing dual flexibility in moving average selection for both the middle band and ATR calculation. While traditional Keltner Channels typically use EMA for the middle band and RMA (Wilder's smoothing) for ATR, this enhanced version provides access to 25+ moving average algorithms for both components, enabling traders to fine-tune the indicator's behavior to match specific market characteristics and trading approaches.
Key Advancements:
Dual MA Algorithm Flexibility: Independent selection of moving average types for middle band (25+ options) and ATR smoothing (25+ options), allowing optimization of both trend identification and volatility measurement separately
Enhanced Trend Sensitivity: Ability to use faster algorithms (HMA, T3) for middle band while maintaining stable volatility measurement with traditional ATR smoothing, or vice versa for different trading strategies
Adaptive Volatility Measurement: Choice of ATR smoothing algorithm affects channel responsiveness to volatility changes, from highly reactive (SMA, EMA) to smoothly adaptive (RMA, TEMA)
Comprehensive Alert System: Five distinct alert conditions covering breakouts, trend changes, and volatility expansion, enabling automated monitoring without constant chart observation
Multi-Timeframe Compatibility: Works effectively across all timeframes from intraday scalping to long-term position trading, with independent optimization of trend and volatility components
This implementation addresses key limitations of standard Keltner Channels: fixed EMA/RMA combination may not suit all market conditions or trading styles. By decoupling the trend component from volatility measurement and allowing independent algorithm selection, traders can create highly customized configurations for specific instruments and market phases.
📐 MATHEMATICAL FOUNDATION
Keltner Channel Enhanced uses a three-component calculation system that combines a flexible moving average middle band with ATR-based (Average True Range) upper and lower channels, creating volatility-adjusted trend-following bands.
Core Calculation Process:
1. Middle Band (Basis) Calculation:
The basis line is calculated using the selected moving average algorithm applied to the price source over the specified period:
basis = ma(source, length, maType)
Supported algorithms include EMA (standard choice, trend-biased), SMA (balanced and symmetric), HMA (reduced lag), WMA, VWMA, TEMA, T3, KAMA, and 17+ others.
2. Average True Range (ATR) Calculation:
ATR measures market volatility by calculating the average of true ranges over the specified period:
trueRange = max(high - low, abs(high - close ), abs(low - close ))
atrValue = ma(trueRange, atrLength, atrMaType)
ATR smoothing algorithm significantly affects channel behavior, with options including RMA (standard, very smooth), SMA (moderate smoothness), EMA (fast adaptation), TEMA (smooth yet responsive), and others.
3. Channel Calculation:
Upper and lower channels are positioned at specified multiples of ATR from the basis:
upperChannel = basis + (multiplier × atrValue)
lowerChannel = basis - (multiplier × atrValue)
Standard multiplier is 2.0, providing channels that dynamically adjust width based on market volatility.
Keltner Channel vs. Bollinger Bands - Key Differences:
While both indicators create volatility-based channels, they use fundamentally different volatility measures:
Keltner Channel (ATR-based):
Uses Average True Range to measure actual price movement volatility
Incorporates gaps and limit moves through true range calculation
More stable in trending markets, less prone to extreme compression
Better reflects intraday volatility and trading range
Typically fewer band touches, making touches more significant
More suitable for trend-following strategies
Bollinger Bands (Standard Deviation-based):
Uses statistical standard deviation to measure price dispersion
Based on closing prices only, doesn't account for intraday range
Can compress significantly during consolidation (squeeze patterns)
More touches in ranging markets
Better suited for mean-reversion strategies
Provides statistical probability framework (95% within 2 standard deviations)
Algorithm Combination Effects:
The interaction between middle band MA type and ATR MA type creates different indicator characteristics:
Trend-Focused Configuration (Fast MA + Slow ATR): Middle band uses HMA/EMA/T3, ATR uses RMA/TEMA, quick trend changes with stable channel width, suitable for trend-following
Volatility-Focused Configuration (Slow MA + Fast ATR): Middle band uses SMA/WMA, ATR uses EMA/SMA, stable trend with dynamic channel width, suitable for volatility trading
Balanced Configuration (Standard EMA/RMA): Classic Keltner Channel behavior, time-tested combination, suitable for general-purpose trend following
Adaptive Configuration (KAMA + KAMA): Self-adjusting indicator responding to efficiency ratio, suitable for markets with varying trend strength and volatility regimes
📊 COMPREHENSIVE SIGNAL ANALYSIS
Keltner Channel Enhanced provides multiple signal categories optimized for trend-following and breakout strategies.
Channel Position Signals:
Upper Channel Interaction:
Price Touching Upper Channel: Strong bullish momentum, price moving more than typical volatility range suggests, potential continuation signal in established uptrends
Price Breaking Above Upper Channel: Exceptional strength, price exceeding normal volatility expectations, consider adding to long positions or tightening trailing stops
Price Riding Upper Channel: Sustained strong uptrend, characteristic of powerful bull moves, stay with trend and avoid premature profit-taking
Price Rejection at Upper Channel: Momentum exhaustion signal, consider profit-taking on longs or waiting for pullback to middle band for reentry
Lower Channel Interaction:
Price Touching Lower Channel: Strong bearish momentum, price moving more than typical volatility range suggests, potential continuation signal in established downtrends
Price Breaking Below Lower Channel: Exceptional weakness, price exceeding normal volatility expectations, consider adding to short positions or protecting against further downside
Price Riding Lower Channel: Sustained strong downtrend, characteristic of powerful bear moves, stay with trend and avoid premature covering
Price Rejection at Lower Channel: Momentum exhaustion signal, consider covering shorts or waiting for bounce to middle band for reentry
Middle Band (Basis) Signals:
Trend Direction Confirmation:
Price Above Basis: Bullish trend bias, middle band acts as dynamic support in uptrends, consider long positions or holding existing longs
Price Below Basis: Bearish trend bias, middle band acts as dynamic resistance in downtrends, consider short positions or avoiding longs
Price Crossing Above Basis: Potential trend change from bearish to bullish, early signal to establish long positions
Price Crossing Below Basis: Potential trend change from bullish to bearish, early signal to establish short positions or exit longs
Pullback Trading Strategy:
Uptrend Pullback: Price pulls back from upper channel to middle band, finds support, and resumes upward, ideal long entry point
Downtrend Bounce: Price bounces from lower channel to middle band, meets resistance, and resumes downward, ideal short entry point
Basis Test: Strong trends often show price respecting the middle band as support/resistance on pullbacks
Failed Test: Price breaking through middle band against trend direction signals potential reversal
Volatility-Based Signals:
Narrow Channels (Low Volatility):
Consolidation Phase: Channels contract during periods of reduced volatility and directionless price action
Breakout Preparation: Narrow channels often precede significant directional moves as volatility cycles
Trading Approach: Reduce position sizes, wait for breakout confirmation, avoid range-bound strategies within channels
Breakout Direction: Monitor for price breaking decisively outside channel range with expanding width
Wide Channels (High Volatility):
Trending Phase: Channels expand during strong directional moves and increased volatility
Momentum Confirmation: Wide channels confirm genuine trend with substantial volatility backing
Trading Approach: Trend-following strategies excel, wider stops necessary, mean-reversion strategies risky
Exhaustion Signs: Extreme channel width (historical highs) may signal approaching consolidation or reversal
Advanced Pattern Recognition:
Channel Walking Pattern:
Upper Channel Walk: Price consistently touches or exceeds upper channel while staying above basis, very strong uptrend signal, hold longs aggressively
Lower Channel Walk: Price consistently touches or exceeds lower channel while staying below basis, very strong downtrend signal, hold shorts aggressively
Basis Support/Resistance: During channel walks, price typically uses middle band as support/resistance on minor pullbacks
Pattern Break: Price crossing basis during channel walk signals potential trend exhaustion
Squeeze and Release Pattern:
Squeeze Phase: Channels narrow significantly, price consolidates near middle band, volatility contracts
Direction Clues: Watch for price positioning relative to basis during squeeze (above = bullish bias, below = bearish bias)
Release Trigger: Price breaking outside narrow channel range with expanding width confirms breakout
Follow-Through: Measure squeeze height and project from breakout point for initial profit targets
Channel Expansion Pattern:
Breakout Confirmation: Rapid channel widening confirms volatility increase and genuine trend establishment
Entry Timing: Enter positions early in expansion phase before trend becomes overextended
Risk Management: Use channel width to size stops appropriately, wider channels require wider stops
Basis Bounce Pattern:
Clean Bounce: Price touches middle band and immediately reverses, confirms trend strength and entry opportunity
Multiple Bounces: Repeated basis bounces indicate strong, sustainable trend
Bounce Failure: Price penetrating basis signals weakening trend and potential reversal
Divergence Analysis:
Price/Channel Divergence: Price makes new high/low while staying within channel (not reaching outer band), suggests momentum weakening
Width/Price Divergence: Price breaks to new extremes but channel width contracts, suggests move lacks conviction
Reversal Signal: Divergences often precede trend reversals or significant consolidation periods
Multi-Timeframe Analysis:
Keltner Channels work particularly well in multi-timeframe trend-following approaches:
Three-Timeframe Alignment:
Higher Timeframe (Weekly/Daily): Identify major trend direction, note price position relative to basis and channels
Intermediate Timeframe (Daily/4H): Identify pullback opportunities within higher timeframe trend
Lower Timeframe (4H/1H): Time precise entries when price touches middle band or lower channel (in uptrends) with rejection
Optimal Entry Conditions:
Best Long Entries: Higher timeframe in uptrend (price above basis), intermediate timeframe pulls back to basis, lower timeframe shows rejection at middle band or lower channel
Best Short Entries: Higher timeframe in downtrend (price below basis), intermediate timeframe bounces to basis, lower timeframe shows rejection at middle band or upper channel
Risk Management: Use higher timeframe channel width to set position sizing, stops below/above higher timeframe channels
🎯 STRATEGIC APPLICATIONS
Keltner Channel Enhanced excels in trend-following and breakout strategies across different market conditions.
Trend Following Strategy:
Setup Requirements:
Identify established trend with price consistently on one side of basis line
Wait for pullback to middle band (basis) or brief penetration through it
Confirm trend resumption with price rejection at basis and move back toward outer channel
Enter in trend direction with stop beyond basis line
Entry Rules:
Uptrend Entry:
Price pulls back from upper channel to middle band, shows support at basis (bullish candlestick, momentum divergence)
Enter long on rejection/bounce from basis with stop 1-2 ATR below basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Downtrend Entry:
Price bounces from lower channel to middle band, shows resistance at basis (bearish candlestick, momentum divergence)
Enter short on rejection/reversal from basis with stop 1-2 ATR above basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Trend Management:
Trailing Stop: Use basis line as dynamic trailing stop, exit if price closes beyond basis against position
Profit Taking: Take partial profits at opposite channel, move stops to basis
Position Additions: Add to winners on subsequent basis bounces if trend intact
Breakout Strategy:
Setup Requirements:
Identify consolidation period with contracting channel width
Monitor price action near middle band with reduced volatility
Wait for decisive breakout beyond channel range with expanding width
Enter in breakout direction after confirmation
Breakout Confirmation:
Price breaks clearly outside channel (upper for longs, lower for shorts), channel width begins expanding from contracted state
Volume increases significantly on breakout (if using volume analysis)
Price sustains outside channel for multiple bars without immediate reversal
Entry Approaches:
Aggressive: Enter on initial break with stop at opposite channel or basis, use smaller position size
Conservative: Wait for pullback to broken channel level, enter on rejection and resumption, tighter stop
Volatility-Based Position Sizing:
Adjust position sizing based on channel width (ATR-based volatility):
Wide Channels (High ATR): Reduce position size as stops must be wider, calculate position size using ATR-based risk calculation: Risk / (Stop Distance in ATR × ATR Value)
Narrow Channels (Low ATR): Increase position size as stops can be tighter, be cautious of impending volatility expansion
ATR-Based Risk Management: Use ATR-based risk calculations, position size = 0.01 × Capital / (2 × ATR), use multiples of ATR (1-2 ATR) for adaptive stops
Algorithm Selection Guidelines:
Different market conditions benefit from different algorithm combinations:
Strong Trending Markets: Middle band use EMA or HMA, ATR use RMA, capture trends quickly while maintaining stable channel width
Choppy/Ranging Markets: Middle band use SMA or WMA, ATR use SMA or WMA, avoid false trend signals while identifying genuine reversals
Volatile Markets: Middle band and ATR both use KAMA or FRAMA, self-adjusting to changing market conditions reduces manual optimization
Breakout Trading: Middle band use SMA, ATR use EMA or SMA, stable trend with dynamic channels highlights volatility expansion early
Scalping/Day Trading: Middle band use HMA or T3, ATR use EMA or TEMA, both components respond quickly
Position Trading: Middle band use EMA/TEMA/T3, ATR use RMA or TEMA, filter out noise for long-term trend-following
📋 DETAILED PARAMETER CONFIGURATION
Understanding and optimizing parameters is essential for adapting Keltner Channel Enhanced to specific trading approaches.
Source Parameter:
Close (Most Common): Uses closing price, reflects daily settlement, best for end-of-day analysis and position trading, standard choice
HL2 (Median Price): Smooths out closing bias, better represents full daily range in volatile markets, good for swing trading
HLC3 (Typical Price): Gives more weight to close while including full range, popular for intraday applications, slightly more responsive than HL2
OHLC4 (Average Price): Most comprehensive price representation, smoothest option, good for gap-prone markets or highly volatile instruments
Length Parameter:
Controls the lookback period for middle band (basis) calculation:
Short Periods (10-15): Very responsive to price changes, suitable for day trading and scalping, higher false signal rate
Standard Period (20 - Default): Represents approximately one month of trading, good balance between responsiveness and stability, suitable for swing and position trading
Medium Periods (30-50): Smoother trend identification, fewer false signals, better for position trading and longer holding periods
Long Periods (50+): Very smooth, identifies major trends only, minimal false signals but significant lag, suitable for long-term investment
Optimization by Timeframe: 1-15 minute charts use 10-20 period, 30-60 minute charts use 20-30 period, 4-hour to daily charts use 20-40 period, weekly charts use 20-30 weeks.
ATR Length Parameter:
Controls the lookback period for Average True Range calculation, affecting channel width:
Short ATR Periods (5-10): Very responsive to recent volatility changes, standard is 10 (Keltner's original specification), may be too reactive in whipsaw conditions
Standard ATR Period (10 - Default): Chester Keltner's original specification, good balance between responsiveness and stability, most widely used
Medium ATR Periods (14-20): Smoother channel width, ATR 14 aligns with Wilder's original ATR specification, good for position trading
Long ATR Periods (20+): Very smooth channel width, suitable for long-term trend-following
Length vs. ATR Length Relationship: Equal values (20/20) provide balanced responsiveness, longer ATR (20/14) gives more stable channel width, shorter ATR (20/10) is standard configuration, much shorter ATR (20/5) creates very dynamic channels.
Multiplier Parameter:
Controls channel width by setting ATR multiples:
Lower Values (1.0-1.5): Tighter channels with frequent price touches, more trading signals, higher false signal rate, better for range-bound and mean-reversion strategies
Standard Value (2.0 - Default): Chester Keltner's recommended setting, good balance between signal frequency and reliability, suitable for both trending and ranging strategies
Higher Values (2.5-3.0): Wider channels with less frequent touches, fewer but potentially higher-quality signals, better for strong trending markets
Market-Specific Optimization: High volatility markets (crypto, small-caps) use 2.5-3.0 multiplier, medium volatility markets (major forex, large-caps) use 2.0 multiplier, low volatility markets (bonds, utilities) use 1.5-2.0 multiplier.
MA Type Parameter (Middle Band):
Critical selection that determines trend identification characteristics:
EMA (Exponential Moving Average - Default): Standard Keltner Channel choice, Chester Keltner's original specification, emphasizes recent prices, faster response to trend changes, suitable for all timeframes
SMA (Simple Moving Average): Equal weighting of all data points, no directional bias, slower than EMA, better for ranging markets and mean-reversion
HMA (Hull Moving Average): Minimal lag with smooth output, excellent for fast trend identification, best for day trading and scalping
TEMA (Triple Exponential Moving Average): Advanced smoothing with reduced lag, responsive to trends while filtering noise, suitable for volatile markets
T3 (Tillson T3): Very smooth with minimal lag, excellent for established trend identification, suitable for position trading
KAMA (Kaufman Adaptive Moving Average): Automatically adjusts speed based on market efficiency, slow in ranging markets, fast in trends, suitable for markets with varying conditions
ATR MA Type Parameter:
Determines how Average True Range is smoothed, affecting channel width stability:
RMA (Wilder's Smoothing - Default): J. Welles Wilder's original ATR smoothing method, very smooth, slow to adapt to volatility changes, provides stable channel width
SMA (Simple Moving Average): Equal weighting, moderate smoothness, faster response to volatility changes than RMA, more dynamic channel width
EMA (Exponential Moving Average): Emphasizes recent volatility, quick adaptation to new volatility regimes, very responsive channel width changes
TEMA (Triple Exponential Moving Average): Smooth yet responsive, good balance for varying volatility, suitable for most trading styles
Parameter Combination Strategies:
Conservative Trend-Following: Length 30/ATR Length 20/Multiplier 2.5, MA Type EMA or TEMA/ATR MA Type RMA, smooth trend with stable wide channels, suitable for position trading
Standard Balanced Approach: Length 20/ATR Length 10/Multiplier 2.0, MA Type EMA/ATR MA Type RMA, classic Keltner Channel configuration, suitable for general purpose swing trading
Aggressive Day Trading: Length 10-15/ATR Length 5-7/Multiplier 1.5-2.0, MA Type HMA or EMA/ATR MA Type EMA or SMA, fast trend with dynamic channels, suitable for scalping and day trading
Breakout Specialist: Length 20-30/ATR Length 5-10/Multiplier 2.0, MA Type SMA or WMA/ATR MA Type EMA or SMA, stable trend with responsive channel width
Adaptive All-Conditions: Length 20/ATR Length 10/Multiplier 2.0, MA Type KAMA or FRAMA/ATR MA Type KAMA or TEMA, self-adjusting to market conditions
Offset Parameter:
Controls horizontal positioning of channels on chart. Positive values shift channels to the right (future) for visual projection, negative values shift left (past) for historical analysis, zero (default) aligns with current price bars for real-time signal analysis. Offset affects only visual display, not alert conditions or actual calculations.
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Keltner Channel Enhanced provides improvements over standard implementations while maintaining proven effectiveness.
Response Characteristics:
Standard EMA/RMA Configuration: Moderate trend lag (approximately 0.4 × length periods), smooth and stable channel width from RMA smoothing, good balance for most market conditions
Fast HMA/EMA Configuration: Approximately 60% reduction in trend lag compared to EMA, responsive channel width from EMA ATR smoothing, suitable for quick trend changes and breakouts
Adaptive KAMA/KAMA Configuration: Variable lag based on market efficiency, automatic adjustment to trending vs. ranging conditions, self-optimizing behavior reduces manual intervention
Comparison with Traditional Keltner Channels:
Enhanced Version Advantages:
Dual Algorithm Flexibility: Independent MA selection for trend and volatility vs. fixed EMA/RMA, separate tuning of trend responsiveness and channel stability
Market Adaptation: Choose configurations optimized for specific instruments and conditions, customize for scalping, swing, or position trading preferences
Comprehensive Alerts: Enhanced alert system including channel expansion detection
Traditional Version Advantages:
Simplicity: Fewer parameters, easier to understand and implement
Standardization: Fixed EMA/RMA combination ensures consistency across users
Research Base: Decades of backtesting and research on standard configuration
When to Use Enhanced Version: Trading multiple instruments with different characteristics, switching between trending and ranging markets, employing different strategies, algorithm-based trading systems requiring customization, seeking optimization for specific trading style and timeframe.
When to Use Standard Version: Beginning traders learning Keltner Channel concepts, following published research or trading systems, preferring simplicity and standardization, wanting to avoid optimization and curve-fitting risks.
Performance Across Market Conditions:
Strong Trending Markets: EMA or HMA basis with RMA or TEMA ATR smoothing provides quicker trend identification, pullbacks to basis offer excellent entry opportunities
Choppy/Ranging Markets: SMA or WMA basis with RMA ATR smoothing and lower multipliers, channel bounce strategies work well, avoid false breakouts
Volatile Markets: KAMA or FRAMA with EMA or TEMA, adaptive algorithms excel by automatic adjustment, wider multipliers (2.5-3.0) accommodate large price swings
Low Volatility/Consolidation: Channels narrow significantly indicating consolidation, algorithm choice less impactful, focus on detecting channel width contraction for breakout preparation
Keltner Channel vs. Bollinger Bands - Usage Comparison:
Favor Keltner Channels When: Trend-following is primary strategy, trading volatile instruments with gaps, want ATR-based volatility measurement, prefer fewer higher-quality channel touches, seeking stable channel width during trends.
Favor Bollinger Bands When: Mean-reversion is primary strategy, trading instruments with limited gaps, want statistical framework based on standard deviation, need squeeze patterns for breakout identification, prefer more frequent trading opportunities.
Use Both Together: Bollinger Band squeeze + Keltner Channel breakout is powerful combination, price outside Bollinger Bands but inside Keltner Channels indicates moderate signal, price outside both indicates very strong signal, Bollinger Bands for entries and Keltner Channels for trend confirmation.
Limitations and Considerations:
General Limitations:
Lagging Indicator: All moving averages lag price, even with reduced-lag algorithms
Trend-Dependent: Works best in trending markets, less effective in choppy conditions
No Direction Prediction: Indicates volatility and deviation, not future direction, requires confirmation
Enhanced Version Specific Considerations:
Optimization Risk: More parameters increase risk of curve-fitting historical data
Complexity: Additional choices may overwhelm beginning traders
Backtesting Challenges: Different algorithms produce different historical results
Mitigation Strategies:
Use Confirmation: Combine with momentum indicators (RSI, MACD), volume, or price action
Test Parameter Robustness: Ensure parameters work across range of values, not just optimized ones
Multi-Timeframe Analysis: Confirm signals across different timeframes
Proper Risk Management: Use appropriate position sizing and stops
Start Simple: Begin with standard EMA/RMA before exploring alternatives
Optimal Usage Recommendations:
For Maximum Effectiveness:
Start with standard EMA/RMA configuration to understand classic behavior
Experiment with alternatives on demo account or paper trading
Match algorithm combination to market condition and trading style
Use channel width analysis to identify market phases
Combine with complementary indicators for confirmation
Implement strict risk management using ATR-based position sizing
Focus on high-quality setups rather than trading every signal
Respect the trend: trade with basis direction for higher probability
Complementary Indicators:
RSI or Stochastic: Confirm momentum at channel extremes
MACD: Confirm trend direction and momentum shifts
Volume: Validate breakouts and trend strength
ADX: Measure trend strength, avoid Keltner signals in weak trends
Support/Resistance: Combine with traditional levels for high-probability setups
Bollinger Bands: Use together for enhanced breakout and volatility analysis
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Keltner Channel Enhanced has limitations and should not be used as the sole basis for trading decisions. While the flexible moving average selection for both trend and volatility components provides valuable adaptability across different market conditions, algorithm performance varies with market conditions, and past characteristics do not guarantee future results.
Key considerations:
Always use multiple forms of analysis and confirmation before entering trades
Backtest any parameter combination thoroughly before live trading
Be aware that optimization can lead to curve-fitting if not done carefully
Start with standard EMA/RMA settings and adjust only when specific conditions warrant
Understand that no moving average algorithm can eliminate lag entirely
Consider market regime (trending, ranging, volatile) when selecting parameters
Use ATR-based position sizing and risk management on every trade
Keltner Channels work best in trending markets, less effective in choppy conditions
Respect the trend direction indicated by price position relative to basis line
The enhanced flexibility of dual algorithm selection provides powerful tools for adaptation but requires responsible use, thorough understanding of how different algorithms behave under various market conditions, and disciplined risk management.
Enhanced Std Dev Oscillator (Z-Score)Enhanced Std Dev Oscillator (Z-Score)
Overview
The Enhanced Std Dev Oscillator (ESDO) is a refined Z-Score indicator that normalizes price deviations from a moving mean using standard deviation, smoothed for clarity and equipped with divergence detection. This oscillator shines in identifying extreme overbought/oversold conditions and potential reversals, making it ideal for mean-reversion strategies in stocks, forex, or crypto. By highlighting when prices stray too far from the norm, it helps traders avoid chasing trends and focus on high-probability pullbacks.
Key Features
Customisable Mean & Deviation: Choose SMA or EMA for the mean (default: SMA, length 14); opt for Population or Sample standard deviation for precise statistical accuracy.
Smoothing for Clarity: Apply a simple moving average (default: 3) to the raw Z-Score, reducing noise without lagging signals excessively.
Zone Highlighting: Background colours flag extreme zones—red tint above +2 (overbought), green below -2 (oversold)—for quick visual scans.
Divergence Alerts: Automatically detects bullish (price lows lower, Z-Score higher) and bearish (price highs higher, Z-Score lower) divergences using pivot points (default length: 5), with labeled shapes for easy spotting.
Built-in Alerts: Notifications for Z-Score crossovers into OB/OS zones and divergence events to keep you informed without constant monitoring.
How It Works
Core Calculation: Computes the mean (SMA/EMA) over the specified length, then standard deviation (Population or adjusted Sample formula for N>1). Z-Score = (Source - Mean) / Std Dev, handling edge cases like zero deviation.
Smoothing: Averages the Z-Score with an SMA to create a cleaner plot oscillating around zero.
Levels & Zones: Plots horizontal lines at ±1 (orange dotted) and ±2 (red dashed) for reference; backgrounds activate in extreme zones.
Divergence Logic: Scans for pivot highs/lows in price and Z-Score; flags divergences when price extremes diverge from oscillator extremes (looking back 2 pivots for confirmation).
Visualisation: Blue line for the smoothed Z-Score; green/red labels for bull/bear divergences.
Usage Tips
Buy Signal: Z-Score crosses below -2 (oversold) or bullish divergence forms—pair with volume spike for confirmation.
Sell Signal: Z-Score crosses above +2 (overbought) or bearish divergence—watch for resistance alignment.
Customisation: Use EMA mean for trendier assets; enable Sample std dev for smaller datasets. Increase pivot length (7-10) in volatile markets to filter false signals.
Timeframes: Excels on daily/4H for swing trades; test smoothing on lower frames to avoid over-smoothing. Always combine with trend filters like a 200-period MA.
This open-source script is licensed under Mozilla Public License 2.0. Backtest thoroughly—past performance isn't indicative of future results. Trade with discipline! 📈
© HighlanderOne
Ratio-Adjusted McClellan Summation Index RASI NASIRatio-Adjusted McClellan Summation Index (RASI NASI)
In Book "The Complete Guide to Market Breadth Indicators" Author Gregory L. Morris states
"It is the author’s opinion that the McClellan indicators, and in particular, the McClellan Summation Index, is the single best breadth indicator available. If you had to pick just one, this would be it."
What It Does: The Ratio-Adjusted McClellan Summation Index (RASI) is a market breadth indicator that tracks the cumulative strength of advancing versus declining issues for a user-selected exchange (NASDAQ, NYSE, or AMEX). Derived from the McClellan Oscillator, it calculates ratio-adjusted net advances, applies 19-day and 39-day EMAs, and sums the oscillator values to produce the RASI. This indicator helps traders assess market health, identify bullish or bearish trends, and detect potential reversals through divergences.
Key features:
Exchange Selection : Choose NASDAQ (USI:ADVN.NQ, USI:DECL.NQ), NYSE (USI:ADVN.NY, USI:DECL.NY), or AMEX (USI:ADVN.AM, USI:DECL.AM) data.
Trend-Based Coloring : RASI line displays user-defined colors (default: black for uptrend, red for downtrend) based on its direction.
Customizable Moving Average: Add a moving average (SMA, EMA, WMA, VWMA, or RMA) with user-defined length and color (default: EMA, 21, green).
Neutral Line at Zero: Marks the neutral level for trend interpretation.
Alerts: Six custom alert conditions for trend changes, MA crosses, and zero-line crosses.
How to Use
Add to Chart: Apply the indicator to any TradingView chart. Ensure access to advancing and declining issues data for the selected exchange.
Select Exchange: Choose NASDAQ, NYSE, or AMEX in the input settings.
Customize Settings: Adjust EMA lengths, RASI colors, MA type, length, and color to match your trading style.
Interpret the Indicator :
RASI Line: Black (default) indicates an uptrend (RASI rising); red indicates a downtrend (RASI falling).
Above Zero: Suggests bullish market breadth (more advancing issues).
Below Zero : Indicates bearish breadth (more declining issues).
MA Crosses: RASI crossing above its MA signals bullish momentum; crossing below signals bearish momentum.
Divergences: Compare RASI with the market index (e.g., NASDAQ Composite) to identify potential reversals.
Large Moves : A +3,600-point move from a low (e.g., -1,550 to +1,950) may signal a significant bull run.
Set Alerts:
Add the indicator to your chart, open the TradingView alert panel, and select from six conditions (see Alerts section).
Configure notifications (e.g., email, webhook, or popup) for each condition.
Settings
Market Selection:
Exchange: Select NASDAQ, NYSE, or AMEX for advancing/declining issues data.
EMA Settings:
19-day EMA Length: Period for the shorter EMA (default: 19).
39-day EMA Length: Period for the longer EMA (default: 39).
RASI Settings:
RASI Uptrend Color: Color for rising RASI (default: black).
RASI Downtrend Color: Color for falling RASI (default: red).
RASI MA Settings:
MA Type: Choose SMA, EMA, WMA, VWMA, or RMA (default: EMA).
MA Length: Set the MA period (default: 21).
MA Color: Color for the MA line (default: green).
Alerts
The indicator uses alertcondition() to create custom alerts. Available conditions:
RASI Trend Up: RASI starts rising (based on RASI > previous RASI, shown as black line).
RASI Trend Down: RASI starts falling (based on RASI ≤ previous RASI, shown as red line).
RASI Above MA: RASI crosses above its moving average.
RASI Below MA: RASI crosses below its moving average.
RASI Bullish: RASI crosses above zero (bullish market breadth).
RASI Bearish: RASI crosses below zero (bearish market breadth).
To set alerts, add the indicator to your chart, open the TradingView alert panel, and select the desired condition.
Notes
Data Requirements: Requires access to advancing/declining issues data (e.g., USI:ADVN.NQ, USI:DECL.NQ for NASDAQ). Some symbols may require a TradingView premium subscription.
Limitations: RASI is a medium- to long-term indicator and may lag in volatile or range-bound markets. Use alongside other technical tools for confirmation.
Data Reliability : Verify the selected exchange’s data accuracy, as inconsistencies can affect results.
Debugging: If no data appears, check symbol validity (e.g., try $ADVN/Q, $DECN/Q for NASDAQ) or contact TradingView support.
Credits
Based on the Ratio-Adjusted McClellan Summation Index methodology by McClellan Financial Publications. No external code was used; the implementation is original, inspired by standard market breadth concepts.
Disclaimer
This indicator is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Conduct your own research and combine with other tools for informed trading decisions.
IDX - 5UPThe UDX-5UP is a custom indicator designed to assist traders in identifying trends, entry and exit signals, and market reversal moments with greater accuracy. It combines price analysis, volume, and momentum (RSI) to provide clear buy ("Buy") and sell ("Sell") signals across any asset and timeframe, whether you're a scalper on the 5M chart or a swing trader on the 4H chart. Inspired by robust technical analysis strategies, the UDX-5UP is ideal for traders seeking a reliable tool to operate in volatile markets such as cryptocurrencies, forex, stocks, and futures.
Components of the UDX-5UP
The UDX-5UP consists of three main panels that work together to provide a comprehensive view of the market:
Main Panel (Price):
Pivot Supertrend: A dynamic line that changes color to indicate the trend. Green for an uptrend (look for buys), red for a downtrend (look for sells).
SMAs (Simple Moving Averages): Two SMAs (8 and 21 periods) to confirm the trend direction. When the SMA 8 crosses above the SMA 21, it’s a bullish signal; when it crosses below, it’s a bearish signal.
Entry/Exit Signals: "Buy" (green) and "Sell" (red) labels are plotted on the chart when entry or exit conditions are met.
Volume Panel:
Colored Volume Bars: Green bars indicate dominant buying volume, while red bars indicate dominant selling volume.
Volume Moving Average (MA 20): A blue line that helps identify whether the current volume is above or below the average, confirming the strength of the movement.
RSI Panel:
RSI (Relative Strength Index): Calculated with a period of 14, with overbought (70) and oversold (30) lines to identify momentum extremes.
Divergences: The indicator detects divergences between the RSI and price, plotting signals for potential reversals.
How the UDX-5UP Works
The UDX-5UP uses a combination of rules to generate buy and sell signals:
Buy Signal ("Buy"):
The Pivot Supertrend changes from red to green.
The SMA 8 crosses above the SMA 21.
The volume is above the MA 20, with green bars (indicating buying pressure).
The RSI is rising and, ideally, below 70 (not overbought).
Example: On the 4H chart, the price of Tether (USDT) is at 0.05515. The Pivot Supertrend turns green, the SMA 8 crosses above the SMA 21, the volume shows green bars above the MA 20, and the RSI is at 46. The UDX-5UP plots a "Buy".
Sell Signal ("Sell"):
The Pivot Supertrend changes from green to red.
The SMA 8 crosses below the SMA 21.
The volume is above the MA 20, with red bars (indicating selling pressure).
The RSI is falling and, ideally, above 70 (overbought).
Example: On the 4H chart, the price of Tether rises to 0.05817. The Pivot Supertrend turns red, the SMA 8 crosses below the SMA 21, the volume shows red bars, and the RSI is above 70. The UDX-5UP plots a "Sell".
RSI Divergences:
The indicator identifies bullish divergences (price makes a lower low, but RSI makes a higher low) and bearish divergences (price makes a higher high, but RSI makes a lower high), plotting alerts for potential reversals.
Adjustable Settings
The UDX-5UP is highly customizable to suit your trading style:
Pivot Supertrend Period: Default is 2. Increase to 3 or 4 for more conservative signals (fewer false positives, but more lag).
SMA Periods: Default is 8 and 21. Adjust to 5 and 13 for smaller timeframes (e.g., 5M) or 13 and 34 for larger timeframes (e.g., 1D).
RSI Period: Default is 14. Reduce to 10 for greater sensitivity or increase to 20 for smoother signals.
Overbought/Oversold Levels: Default is 70/30. Adjust to 80/20 in volatile markets.
Display Panels: You can enable/disable the volume and RSI panels to simplify the chart.
How to Use the UDX-5UP
Identify the Trend:
Use the Pivot Supertrend and SMAs to determine the market direction. Uptrend: look for buys. Downtrend: look for sells.
Confirm with Volume and RSI:
For buys: Volume above the MA 20 with green bars, RSI rising and below 70.
For sells: Volume above the MA 20 with red bars, RSI falling and above 70.
Enter the Trade:
Enter a buy when the UDX-5UP plots a "Buy" and all conditions are aligned.
Enter a sell when the UDX-5UP plots a "Sell" and all conditions are aligned.
Plan the Exit:
Use Fibonacci levels or support/resistance on the price chart to set targets.
Exit the trade when the UDX-5UP plots an opposite signal ("Sell" after a buy, "Buy" after a sell).
Tips for Beginners
Start with Larger Timeframes: Use the 4H or 1D chart for more reliable signals and less noise.
Combine with Other Indicators: Use the UDX-5UP with tools like Fibonacci or the Candles RSI (another powerful indicator) to confirm signals.
Practice in Demo Mode: Test the indicator in a demo account before using real money.
Manage Risk: Always use a stop-loss and don’t risk more than 1-2% of your capital per trade.
Why Use the UDX-5UP?
Simplicity: Clear "Buy" and "Sell" signals make trading accessible even for beginners.
Versatility: Works on any asset (crypto, forex, stocks) and timeframe.
Multiple Confirmations: Combines price, volume, and momentum to reduce false signals.
Customizable: Adjust the settings to match your trading style.
Author’s Notes
The UDX-5UP was developed based on years of trading and technical analysis experience. It is an evolution of tested strategies, designed to help traders navigate volatile markets with confidence. However, no indicator is infallible. Always combine the UDX-5UP with proper risk management and fundamental analysis, especially in unpredictable markets. Feedback is welcome – leave a comment or reach out with suggestions for improvements!
lib_divergenceLibrary "lib_divergence"
offers a commonly usable function to detect divergences. This will take the default RSI or other symbols / indicators / oscillators as source data.
divergence(osc, pivot_left_bars, pivot_right_bars, div_min_range, div_max_range, ref_low, ref_high, min_divergence_offset_fraction, min_divergence_offset_dev_len, min_divergence_offset_atr_mul)
Detects Divergences between Price and Oscillator action. For bullish divergences, look at trend lines between lows. For bearish divergences, look at trend lines between highs. (strong) oscillator trending, price opposing it | (medium) oscillator trending, price trend flat | (weak) price opposite trending, oscillator trend flat | (hidden) price trending, oscillator opposing it. Pivot detection is only properly done in oscillator data, reference price data is only compared at the oscillator pivot (speed optimization)
Parameters:
osc (float) : (series float) oscillator data (can be anything, even another instrument price)
pivot_left_bars (simple int) : (simple int) optional number of bars left of a confirmed pivot point, confirming it is the highest/lowest in the range before and up to the pivot (default: 5)
pivot_right_bars (simple int) : (simple int) optional number of bars right of a confirmed pivot point, confirming it is the highest/lowest in the range from and after the pivot (default: 5)
div_min_range (simple int) : (simple int) optional minimum distance to the pivot point creating a divergence (default: 5)
div_max_range (simple int) : (simple int) optional maximum amount of bars in a divergence (default: 50)
ref_low (float) : (series float) optional reference range to compare the oscillator pivot points to. (default: low)
ref_high (float) : (series float) optional reference range to compare the oscillator pivot points to. (default: high)
min_divergence_offset_fraction (simple float) : (simple float) optional scaling factor for the offset zone (xDeviation) around the last oscillator H/L detecting following equal H/Ls (default: 0.01)
min_divergence_offset_dev_len (simple int) : (simple int) optional lookback distance for the deviation detection for the offset zone around the last oscillator H/L detecting following equal H/Ls. Used as well for the ATR that does the equal H/L detection for the reference price. (default: 14)
min_divergence_offset_atr_mul (simple float) : (simple float) optional scaling factor for the offset zone (xATR) around the last price H/L detecting following equal H/Ls (default: 1)
@return A tuple of deviation flags.
On Balance Volume Oscillator of Trading Volume TrendOn Balance Volume Oscillator of Trading Volume Trend
Introduction
This indicator, the "On Balance Volume Oscillator of Trading Volume Trend," is a technical analysis tool designed to provide insights into market momentum and potential trend reversals by combining the On Balance Volume (OBV) and Relative Strength Index (RSI) indicators.
Calculation and Methodology
* OBV Calculation: The indicator first calculates the On Balance Volume, which is a cumulative total of the volume of up days minus the volume of down days. This provides a running tally of buying and selling pressure.
* RSI of OBV: The RSI is then applied to the OBV values to smooth the data and identify overbought or oversold conditions.
* Exponential Moving Averages (EMAs): Two EMAs are calculated on the RSI of OBV. A shorter-term EMA (9-period in this case) and a longer-term EMA (100-period) are used to generate signals.
Interpretation and Usage
* EMA Crossovers: When the shorter-term EMA crosses above the longer-term EMA, it suggests increasing bullish momentum. Conversely, a downward crossover indicates weakening bullish momentum or increasing bearish pressure.
* RSI Divergences: Divergences between the price and the indicator can signal potential trend reversals. For example, if the price is making new highs but the indicator is failing to do so, it could be a bearish divergence.
* Overbought/Oversold Conditions: When the RSI of OBV is above 70, it suggests the market may be overbought and a potential correction could be imminent. Conversely, when it is below 30, it suggests the market may be oversold.
Visual Representation
The indicator is plotted on a chart with multiple lines and filled areas:
* Two EMAs: The shorter-term EMA and longer-term EMA are plotted to show the trend of the OBV.
* Filled Areas: The area between the two EMAs is filled with a color to indicate the strength of the trend. The color changes based on whether the shorter-term EMA is above or below the longer-term EMA.
* RSI Bands: Horizontal lines at 30 and 70 mark the overbought and oversold levels for the RSI of OBV.
Summary
The On Balance Volume Oscillator of Trading Volume Trend provides a comprehensive view of market momentum and can be a valuable tool for traders. By combining the OBV and RSI, this indicator helps identify potential trend reversals, overbought and oversold conditions, and the strength of the current trend.
Note: This indicator should be used in conjunction with other technical analysis tools and fundamental analysis to make informed trading decisions.
[blackcat] L2 Double EMA Convergence and Diverence (DEMACD)Introduction:
The " L2 Double EMA Convergence and Divergence (DEMACD)" is a custom technical indicator designed for use in TradingView. It's based on the concept of Double Exponential Moving Averages (DEMA) and incorporates elements from the well-known Moving Average Convergence Divergence (MACD). This guide aims to provide an understanding of its definition, history, calculation, operations, usage, input settings, and style.
1. Definition:
The DEMACD indicator is designed to detect changes in price trends using a modified approach of the traditional MACD, with a focus on reducing lag. It does this by comparing two DEMAs of different lengths, providing traders with signals of converging and diverging trends.
2. History:
The concept of DEMA was introduced by Patrick Mulloy in 1994 to reduce the lag inherent in traditional EMAs. MACD, developed by Gerald Appel in the 1970s, is a trend-following momentum indicator that shows the relationship between two moving averages of a security's price. The DEMACD combines the quick response feature of DEMA with the reliable trend analysis of MACD.
3. Calculation Method:
DEMACD is calculated through several steps:
Smoothed price S is first computed as (3 * close + high + low + open) / 6.
DAYLINE is calculated as 2 * EMA(S, len_ema) - EMA(EMA(S, 5), len_ema).
The mainTrendLine is the EMA of the EMA of the closing price over len_dema periods.
DIF is the difference between the DAYLINE and mainTrendLine.
DEA is the EMA of DIF over len_smooth periods.
Finally, DEMACD is calculated as (DIF - DEA) * 2.
4. Basic Operations and Comparison with MACD:
DEMACD's key feature is its reduced lag compared to the traditional MACD. While MACD uses EMA, DEMACD uses DEMA, providing a faster and more accurate response to price changes. This makes it particularly useful in volatile market conditions where traditional MACD may lag.
5. Usage:
Similar to MACD, DEMACD is used for trend confirmation, crossover signals, and divergences:
Trend confirmation is observed when the DIF line is above or below the DEA line.
Crossover signals are generated when the DIF line crosses the DEA line.
Divergences between the DEMACD and price action can signal potential trend reversals.
6. Input Settings:
Users can configure the following settings in TradingView:
len_ema: Length of the EMA for DAYLINE.
len_dema: Length of the EMA for the main trend line.
len_smooth: Smoothing length for DEA.
Adjusting these settings allows traders to tailor the indicator to different trading styles and market conditions.
7. Style:
The DEMACD in TradingView is represented with different colors and line thicknesses:
DIF is plotted in red with a line thickness of 2.
DEA is plotted in gray, also with a line thickness of 2.
DEMACD histogram changes color based on its value relative to its previous value and zero.
Conclusion:
The " L2 Double EMA Convergence and Divergence (DEMACD)" is a versatile indicator that combines the rapid response of DEMA with the trend-following abilities of MACD. Its reduced lag makes it a valuable tool for traders looking for timely market signals. Proper understanding and application of its settings can enhance its effectiveness in various trading strategies.
RSI Stochastic AlignmentRSI Stochastic Alignment input RSI and Stochastic into 1 windows and align them to find bullish and bearish divergence.
A. The Line display in windows:
1. Fast RSI (green line) is RSI(close,3)
2. Slow Rsi (red line) is Linear Regession of Fast RSI with 5 period and offset 0 = linreg(rsi,5,0)
3. Fast Stochastic (blue line) is %K of Stochastic
4. Slow Stochastic (aqua line) is %D of Stochastic
B. Alignment and Divergence Detect
1. Bearish Divergence:
* Slow RSI at top
* Fast Stochastic at bottom
* Fast RSI over overbought level (default = 70)
* Slow Stochastic under overbought level minus a constant value (Divergence Power value, default this value = 1)
2. Bullish Divergence:
* Fast Stochastic at top
* Slow RSI at bottom
* Fast RSI under oversold level (default = 30)
* Slow Stochastic over oversold level plus a constant value (Divergence Power value, default this value = 1)
C. Script Option
1. RSI value adjustable
2. Stochastic value adjustable
3. Overbought and Oversold Level adjustable
4. Enable/Disable Level line
5. Enable/Disable Divergence Column
6. Enable/Disable Key Bar Colored
RSI+Bollinger BandsThis is a simple RSI with applied Bollinger Bands. The BB may help spotting "hidden" divergences or "fake" divergences. For instance: in an uptrend we have a high followed by a higher high, the plain RSI shows a divergence, adding BB we see that the first (higher) RSI high is inside the BB, while the second (lower) RSI high is outside the bands, this would be a fake divergence.
The use of the indicator should be straightforward, for any questions feel free to write me a message
Tunç ŞatıroğluTunç Şatıroğlu's Technical Analysis Suite
Description:
This comprehensive Pine Script indicator, inspired by the technical analysis teachings of Tunç Şatıroğlu, integrates six powerful TradingView indicators into a single, user-friendly suite for robust trend, momentum, and divergence analysis. Each component has been carefully selected and enhanced by beytun to improve functionality, performance, and visual clarity, aligning with Şatıroğlu's approach to technical analysis. The default configuration is meticulously set to match the exact settings of the individual indicators as used by Tunç Şatıroğlu in his training, ensuring authenticity and ease of use for followers of his methodology. Whether you're a beginner or an experienced trader, this suite provides a versatile toolkit for analyzing markets across multiple timeframes.
Included Indicators:
1. WaveTrend with Crosses (by LazyBear, modified): A momentum oscillator that identifies overbought/oversold conditions and trend reversals with clear buy/sell signals via crosses and bar color highlights.
2. Kaufman Adaptive Moving Average (KAMA) (by HPotter, modified): A dynamic moving average that adapts to market volatility, offering a smoother trend-following signal.
3. SuperTrend (by Alex Orekhov, modified): A trend-following indicator that plots dynamic support/resistance levels with buy/sell signals and optional wicks for enhanced accuracy.
4. Nadaraya-Watson Envelope (by LuxAlgo, modified): A non-linear envelope that highlights potential reversals with customizable repainting options for smoother outputs.
5. Divergence for Many Indicators v4 (by LonesomeTheBlue, modified): Detects regular and hidden divergences across multiple indicators (MACD, RSI, Stochastic, CCI, Momentum, OBV, VWMA, CMF, MFI, and more) for early reversal signals.
6. Ichimoku Cloud (TradingView built-in, modified): A multi-faceted indicator for trend direction, support/resistance, and momentum, with enhanced visuals for the Kumo Cloud.
Key Features:
- Authentic Default Settings : Pre-configured to mirror the exact parameters used by Tunç Şatıroğlu for each indicator, ensuring alignment with his proven technical analysis approach.
- Customizable Settings : Enable/disable individual indicators and fine-tune parameters to suit your trading style while retaining the option to revert to Şatıroğlu’s defaults.
- Enhanced User Experience : Modifications improve visual clarity, performance, and usability, with options like repainting smoothing for Nadaraya-Watson and adjustable Ichimoku projection periods.
- Multi-Timeframe Analysis : Combines trend-following, momentum, and divergence tools for a holistic view of market dynamics.
- Alert Conditions : Built-in alerts for SuperTrend direction changes, buy/sell signals, and divergence detections to keep you informed.
- Visual Clarity : Overlays (KAMA, SuperTrend, Nadaraya-Watson, Ichimoku) and pane-based indicators (WaveTrend, Divergences) are clearly distinguished, with customizable colors and styles.
Notes:
- The Nadaraya-Watson Envelope and Ichimoku Cloud may repaint in their default modes. Use the "Repainting Smoothing" option for Nadaraya-Watson or adjust Ichimoku settings to mitigate repainting if preferred.
- Published under the MIT License, with components licensed under GPL-3.0 (SuperTrend), CC BY-NC-SA 4.0 (Nadaraya-Watson), MPL 2.0 (Divergence), and TradingView's terms (Ichimoku Cloud).
Usage:
Add this indicator to your TradingView chart to leverage Tunç Şatıroğlu’s exact indicator configurations out of the box. Customize settings as needed to align with your strategy, and use the combined signals to identify trends, reversals, and divergences. Ideal for traders following Şatıroğlu’s methodologies or anyone seeking a powerful, all-in-one technical analysis tool.
Credits:
Original authors: LazyBear, HPotter, Alex Orekhov, LuxAlgo, LonesomeTheBlue, and TradingView.
Modifications and integration by beytun .
License:
Published under the MIT License, incorporating code under GPL-3.0, CC BY-NC-SA 4.0, MPL 2.0, and TradingView’s terms where applicable.
