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.
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Cardwell RSI by TQ📌 Cardwell RSI – Enhanced Relative Strength Index
This indicator is based on Andrew Cardwell’s RSI methodology , extending the classic RSI with tools to better identify bullish/bearish ranges and trend dynamics.
In uptrends, RSI tends to hold between 40–80 (Cardwell bullish range).
In downtrends, RSI tends to stay between 20–60 (Cardwell bearish range).
Key Features :
Standard RSI with configurable length & source
Fast (9) & Slow (45) RSI Moving Averages (toggleable)
Cardwell Core Levels (80 / 60 / 40 / 20) – enabled by default
Base Bands (70 / 50 / 30) in dotted style
Optional custom levels (up to 3)
Alerts for MA crosses and level crosses
Data Window metrics: RSI vs Fast/Slow MA differences
How to Use :
Monitor RSI behavior inside Cardwell’s bullish (40–80) and bearish (20–60) ranges
Watch RSI crossovers with Fast (9) and Slow (45) MAs to confirm momentum or trend shifts
Use levels and alerts as confluence with your trading strategy
Default Settings :
RSI Length: 14
MA Type: WMA
Fast MA: 9 (hidden by default)
Slow MA: 45 (hidden by default)
Cardwell Levels (80/60/40/20): ON
Base Bands (70/50/30): ON
ST-Stochastic DashboardST-Stochastic Dashboard: User Manual & Functionality
1. Introduction
The ST-Stochastic Dashboard is a comprehensive tool designed for traders who utilize the Stochastic Oscillator. It combines two key features into a single indicator:
A standard, fully customizable Stochastic Oscillator plotted directly on your chart.
A powerful Multi-Timeframe (MTF) Dashboard that shows the status of the Stochastic %K value across three different timeframes of your choice.
This allows you to analyze momentum on your current timeframe while simultaneously monitoring for confluence or divergence on higher or lower timeframes, all without leaving your chart.
Disclaimer: In accordance with TradingView's House Rules, this document describes the technical functionality of the indicator. It is not financial advice. The indicator provides data based on user-defined parameters; all trading decisions are the sole responsibility of the user. Past performance is not indicative of future results.
2. How It Works (Functionality)
The indicator is divided into two main components:
A. The Main Stochastic Indicator (Chart Pane)
This is the visual representation of the Stochastic Oscillator for the chart's current timeframe.
%K Line (Blue): This is the main line of the oscillator. It shows the current closing price in relation to the high-low range over a user-defined period. A high value means the price is closing near the top of its recent range; a low value means it's closing near the bottom.
%D Line (Black): This is the signal line, which is a moving average of the %K line. It is used to smooth out the %K line and generate trading signals.
Overbought Zone (Red Area): By default, this zone is above the 75 level. When the Stochastic lines are in this area, it indicates that the asset may be "overbought," meaning the price is trading near the peak of its recent price range.
Oversold Zone (Blue Area): By default, this zone is below the 25 level. When the Stochastic lines are in this area, it indicates that the asset may be "oversold," meaning the price is trading near the bottom of its recent price range.
Crossover Signals:
Buy Signal (Blue Up Triangle): A blue triangle appears below the candles when the %K line crosses above the Oversold line (e.g., from 24 to 26). This suggests a potential shift from bearish to bullish momentum.
Sell Signal (Red Down Triangle): A red triangle appears above the candles when the %K line crosses below the Overbought line (e.g., from 76 to 74). This suggests a potential shift from bullish to bearish momentum.
B. The Multi-Timeframe Dashboard (Table on Chart)
This is the informational table that appears on your chart. Its purpose is to give you a quick, at-a-glance summary of the Stochastic's condition on other timeframes.
Function: The script uses TradingView's request.security() function to pull the %K value from three other timeframes that you specify in the settings.
Efficiency: The table is designed to update only on the last (most recent) bar (barstate.islast) to ensure the script runs efficiently and does not slow down your chart.
Columns:
Timeframe: Displays the timeframe you have selected (e.g., '5', '15', '60').
Stoch %K: Shows the current numerical value of the %K line for that specific timeframe, rounded to two decimal places.
Status: Interprets the %K value and displays a clear status:
OVERBOUGHT (Red Background): The %K value is above the "Upper Line" setting.
OVERSOLD (Blue Background): The %K value is below the "Lower Line" setting.
NEUTRAL (Black/Dark Background): The %K value is between the Overbought and Oversold levels.
3. Settings / Parameters in Detail
You can access these settings by clicking the "Settings" (cogwheel) icon on the indicator name.
Stochastic Settings
This group controls the behavior and appearance of the main Stochastic indicator plotted in the pane.
Stochastic Period (length)
Description: This is the lookback period used to calculate the Stochastic Oscillator. It defines the number of past bars to consider for the high-low range.
Default: 9
%K Smoothing (smoothK)
Description: This is the moving average period used to smooth the raw Stochastic value, creating the %K line. A higher value results in a smoother, less sensitive line.
Default: 3
%D Smoothing (smoothD)
Description: This is the moving average period applied to the %K line to create the %D (signal) line. A higher value creates a smoother signal line that lags further behind the %K line.
Default: 6
Lower Line (Oversold) (ul)
Description: This sets the threshold for the oversold condition. When the %K line is below this value, the dashboard will show "OVERSOLD". It is also the level the %K line must cross above to trigger a Buy Signal triangle.
Default: 25
Upper Line (Overbought) (ll)
Description: This sets the threshold for the overbought condition. When the %K line is above this value, the dashboard will show "OVERBOUGHT". It is also the level the %K line must cross below to trigger a Sell Signal triangle.
Default: 75
Dashboard Settings
This group controls the data and appearance of the multi-timeframe table.
Timeframe 1 (tf1)
Description: The first timeframe to be displayed in the dashboard.
Default: 5 (5 minutes)
Timeframe 2 (tf2)
Description: The second timeframe to be displayed in the dashboard.
Default: 15 (15 minutes)
Timeframe 3 (tf3)
Description: The third timeframe to be displayed in the dashboard.
Default: 60 (1 hour)
Dashboard Position (table_pos)
Description: Allows you to select where the dashboard table will appear on your chart.
Options: top_right, top_left, bottom_right, bottom_left
Default: bottom_right
4. How to Use & Interpret
Configuration: Adjust the Stochastic Settings to match your trading strategy. The default values (9, 3, 6) are common, but feel free to experiment. Set the Dashboard Settings to the timeframes that are most relevant to your analysis (e.g., your entry timeframe, a medium-term timeframe, and a long-term trend timeframe).
Analysis with the Dashboard: The primary strength of this tool is confluence. Look for situations where multiple timeframes align. For example:
If the dashboard shows OVERSOLD on the 15-minute, 60-minute, and your current 5-minute chart, a subsequent Buy Signal on your 5-minute chart may carry more weight.
Conversely, if your 5-minute chart shows OVERSOLD but the 60-minute chart is strongly OVERBOUGHT, it could indicate that you are looking at a minor pullback in a larger downtrend.
Interpreting States:
Overbought is not an automatic "sell" signal. It simply means momentum has been strong to the upside, and the price is near its recent peak. It could signal a potential reversal, but the price can also remain overbought for extended periods in a strong uptrend.
Oversold is not an automatic "buy" signal. It means momentum has been strong to the downside. While it can signal a potential bounce, prices can remain oversold for a long time in a strong downtrend.
Use the signals and dashboard states as a source of information to complement your overall trading strategy, which should include other forms of analysis such as price action, support/resistance levels, or other indicators.
MirPapa_Library_ICTLibrary "MirPapa_Library_ICT"
GetHTFoffsetToLTFoffset(_offset, _chartTf, _htfTf)
GetHTFoffsetToLTFoffset
@description Adjust an HTF offset to an LTF offset by calculating the ratio of timeframes.
Parameters:
_offset (int) : int The HTF bar offset (0 means current HTF bar).
_chartTf (string) : string The current chart’s timeframe (e.g., "5", "15", "1D").
_htfTf (string) : string The High Time Frame string (e.g., "60", "1D").
@return int The corresponding LTF bar index. Returns 0 if the result is negative.
IsConditionState(_type, _isBull, _level, _open, _close, _open1, _close1, _low1, _low2, _low3, _low4, _high1, _high2, _high3, _high4)
IsConditionState
@description Evaluate a condition state based on type for COB, FVG, or FOB.
Overloaded: first signature handles COB, second handles FVG/FOB.
Parameters:
_type (string) : string Condition type ("cob", "fvg", "fob").
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_level (int) : int Swing level (only used for COB).
_open (float) : float Current bar open price (only for COB).
_close (float) : float Current bar close price (only for COB).
_open1 (float) : float Previous bar open price (only for COB).
_close1 (float) : float Previous bar close price (only for COB).
_low1 (float) : float Low 1 bar ago (only for COB).
_low2 (float) : float Low 2 bars ago (only for COB).
_low3 (float) : float Low 3 bars ago (only for COB).
_low4 (float) : float Low 4 bars ago (only for COB).
_high1 (float) : float High 1 bar ago (only for COB).
_high2 (float) : float High 2 bars ago (only for COB).
_high3 (float) : float High 3 bars ago (only for COB).
_high4 (float) : float High 4 bars ago (only for COB).
@return bool True if the specified condition is met, false otherwise.
IsConditionState(_type, _isBull, _pricePrev, _priceNow)
IsConditionState
@description Evaluate FVG or FOB condition based on price movement.
Parameters:
_type (string) : string Condition type ("fvg", "fob").
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_pricePrev (float) : float Previous price (for FVG/FOB).
_priceNow (float) : float Current price (for FVG/FOB).
@return bool True if the specified condition is met, false otherwise.
IsSwingHighLow(_isBull, _level, _open, _close, _open1, _close1, _low1, _low2, _low3, _low4, _high1, _high2, _high3, _high4)
IsSwingHighLow
@description Public wrapper for isSwingHighLow.
Parameters:
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_level (int) : int Swing level (1 or 2).
_open (float) : float Current bar open price.
_close (float) : float Current bar close price.
_open1 (float) : float Previous bar open price.
_close1 (float) : float Previous bar close price.
_low1 (float) : float Low 1 bar ago.
_low2 (float) : float Low 2 bars ago.
_low3 (float) : float Low 3 bars ago.
_low4 (float) : float Low 4 bars ago.
_high1 (float) : float High 1 bar ago.
_high2 (float) : float High 2 bars ago.
_high3 (float) : float High 3 bars ago.
_high4 (float) : float High 4 bars ago.
@return bool True if swing condition is met, false otherwise.
AddBox(_left, _right, _top, _bot, _xloc, _colorBG, _colorBD)
AddBox
@description Draw a rectangular box on the chart with specified coordinates and colors.
Parameters:
_left (int) : int Left bar index for the box.
_right (int) : int Right bar index for the box.
_top (float) : float Top price coordinate for the box.
_bot (float) : float Bottom price coordinate for the box.
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_colorBG (color) : color Background color for the box.
_colorBD (color) : color Border color for the box.
@return box Returns the created box object.
Addline(_x, _y, _xloc, _color, _width)
Addline
@description Draw a vertical or horizontal line at specified coordinates.
Parameters:
_x (int) : int X-coordinate for start (bar index).
_y (int) : float Y-coordinate for start (price).
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_color (color) : color Line color.
_width (int) : int Line width.
@return line Returns the created line object.
Addline(_x, _y, _xloc, _color, _width)
Parameters:
_x (int)
_y (float)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (int)
_x2 (int)
_y2 (int)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (int)
_x2 (int)
_y2 (float)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (float)
_x2 (int)
_y2 (int)
_xloc (string)
_color (color)
_width (int)
Addline(_x1, _y1, _x2, _y2, _xloc, _color, _width)
Parameters:
_x1 (int)
_y1 (float)
_x2 (int)
_y2 (float)
_xloc (string)
_color (color)
_width (int)
AddlineMid(_type, _left, _right, _top, _bot, _xloc, _color, _width)
AddlineMid
@description Draw a midline between top and bottom for FVG or FOB types.
Parameters:
_type (string) : string Type identifier: "fvg" or "fob".
_left (int) : int Left bar index for midline start.
_right (int) : int Right bar index for midline end.
_top (float) : float Top price of the region.
_bot (float) : float Bottom price of the region.
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_color (color) : color Line color.
_width (int) : int Line width.
@return line or na Returns the created line or na if type is not recognized.
GetHtfFromLabel(_label)
GetHtfFromLabel
@description Convert a Korean HTF label into a Pine Script timeframe string via handler library.
Parameters:
_label (string) : string The Korean label (e.g., "5분", "1시간").
@return string Returns the corresponding Pine Script timeframe (e.g., "5", "60").
IsChartTFcomparisonHTF(_chartTf, _htfTf)
IsChartTFcomparisonHTF
@description Determine whether a given HTF is greater than or equal to the current chart timeframe.
Parameters:
_chartTf (string) : string Current chart timeframe (e.g., "5", "15", "1D").
_htfTf (string) : string HTF timeframe (e.g., "60", "1D").
@return bool True if HTF ≥ chartTF, false otherwise.
CreateBoxData(_type, _isBull, _useLine, _top, _bot, _xloc, _colorBG, _colorBD, _offset, _htfTf, htfBarIdx, _basePoint)
CreateBoxData
@description Create and draw a box and optional midline for given type and parameters. Returns success flag and BoxData.
Parameters:
_type (string) : string Type identifier: "fvg", "fob", "cob", or "sweep".
_isBull (bool) : bool Direction flag: true for bullish, false for bearish.
_useLine (bool) : bool Whether to draw a midline inside the box.
_top (float) : float Top price of the box region.
_bot (float) : float Bottom price of the box region.
_xloc (string) : string X-axis location type (e.g., xloc.bar_index).
_colorBG (color) : color Background color for the box.
_colorBD (color) : color Border color for the box.
_offset (int) : int HTF bar offset (0 means current HTF bar).
_htfTf (string) : string HTF timeframe string (e.g., "60", "1D").
htfBarIdx (int) : int HTF bar_index (passed from HTF request).
_basePoint (float) : float Base point for breakout checks.
@return tuple(bool, BoxData) Returns a boolean indicating success and the created BoxData struct.
ProcessBoxDatas(_datas, _useMidLine, _closeCount, _colorClose)
ProcessBoxDatas
@description Process an array of BoxData structs: extend, record volume, update stage, and finalize boxes.
Parameters:
_datas (array) : array Array of BoxData objects to process.
_useMidLine (bool) : bool Whether to update the midline endpoint.
_closeCount (int) : int Number of touches required to close the box.
_colorClose (color) : color Color to apply when a box closes.
@return void No return value; updates are in-place.
BoxData
Fields:
_isActive (series bool)
_isBull (series bool)
_box (series box)
_line (series line)
_basePoint (series float)
_boxTop (series float)
_boxBot (series float)
_stage (series int)
_isStay (series bool)
_volBuy (series float)
_volSell (series float)
_result (series string)
LineData
Fields:
_isActive (series bool)
_isBull (series bool)
_line (series line)
_basePoint (series float)
_stage (series int)
_isStay (series bool)
_result (series string)
MirPapa_Handler_HTFLibrary "MirPapa_Handler_HTF"
High Time Frame Handler Library:
Provides utilities for working with High Time Frame (HTF) and chart (LTF) conversions and data retrieval.
IsChartTFcomparisonHTF(_chartTf, _htfTf)
IsChartTFcomparisonHTF
@description
Determine whether the given High Time Frame (HTF) is greater than or equal to the current chart timeframe.
Parameters:
_chartTf (string) : The current chart’s timeframe string (examples: "5", "15", "1D").
_htfTf (string) : The High Time Frame string to compare (examples: "60", "1D").
@return
Returns true if HTF minutes ≥ chart minutes, false otherwise or na if conversion fails.
GetHTFrevised(_tf, _case)
GetHTFrevised
@description
Retrieve a specific bar value from a Higher Time Frame (HTF) series.
Supports current and historical OHLC values, based on a case identifier.
Parameters:
_tf (string) : The target HTF string (examples: "60", "1D").
_case (string) : A case string determining which OHLC value and bar offset to request:
"b" → HTF bar_index
"o" → HTF open
"h" → HTF high
"l" → HTF low
"c" → HTF close
"o1" → HTF open one bar ago
"h1" → HTF high one bar ago
"l1" → HTF low one bar ago
"c1" → HTF close one bar ago
… up to "o5", "h5", "l5", "c5" for five bars ago.
@return
Returns the requested HTF value or na if _case does not match any condition.
GetHTFfromLabel(_label)
GetHTFfromLabel
@description
Convert a Korean HTF label into a Pine Script-recognizable timeframe string.
Examples:
"5분" → "5"
"1시간" → "60"
"일봉" → "1D"
"주봉" → "1W"
"월봉" → "1M"
"연봉" → "12M"
Parameters:
_label (string) : The Korean HTF label string (examples: "5분", "1시간", "일봉").
@return
Returns the Pine Script timeframe string corresponding to the label, or "1W" if no match is found.
GetHTFoffsetToLTFoffset(_offset, _chartTf, _htfTf)
GetHTFoffsetToLTFoffset
@description
Adjust an HTF bar index and offset so that it aligns with the current chart’s bar index.
Useful for retrieving historical HTF data on an LTF chart.
Parameters:
_offset (int) : The HTF bar offset (0 means current HTF bar, 1 means one bar ago, etc.).
_chartTf (string) : The current chart’s timeframe string (examples: "5", "15", "1D").
_htfTf (string) : The High Time Frame string to align (examples: "60", "1D").
@return
Returns the corresponding LTF bar index after applying HTF offset. If result is negative, returns 0.
Ergodic Market Divergence (EMD)Ergodic Market Divergence (EMD)
Bridging Statistical Physics and Market Dynamics Through Ensemble Analysis
The Revolutionary Concept: When Physics Meets Trading
After months of research into ergodic theory—a fundamental principle in statistical mechanics—I've developed a trading system that identifies when markets transition between predictable and unpredictable states. This indicator doesn't just follow price; it analyzes whether current market behavior will persist or revert, giving traders a scientific edge in timing entries and exits.
The Core Innovation: Ergodic Theory Applied to Markets
What Makes Markets Ergodic or Non-Ergodic?
In statistical physics, ergodicity determines whether a system's future resembles its past. Applied to trading:
Ergodic Markets (Mean-Reverting)
- Time averages equal ensemble averages
- Historical patterns repeat reliably
- Price oscillates around equilibrium
- Traditional indicators work well
Non-Ergodic Markets (Trending)
- Path dependency dominates
- History doesn't predict future
- Price creates new equilibrium levels
- Momentum strategies excel
The Mathematical Framework
The Ergodic Score combines three critical divergences:
Ergodic Score = (Price Divergence × Market Stress + Return Divergence × 1000 + Volatility Divergence × 50) / 3
Where:
Price Divergence: How far current price deviates from market consensus
Return Divergence: Momentum differential between instrument and market
Volatility Divergence: Volatility regime misalignment
Market Stress: Adaptive multiplier based on current conditions
The Ensemble Analysis Revolution
Beyond Single-Instrument Analysis
Traditional indicators analyze one chart in isolation. EMD monitors multiple correlated markets simultaneously (SPY, QQQ, IWM, DIA) to detect systemic regime changes. This ensemble approach:
Reveals Hidden Divergences: Individual stocks may diverge from market consensus before major moves
Filters False Signals: Requires broader market confirmation
Identifies Regime Shifts: Detects when entire market structure changes
Provides Context: Shows if moves are isolated or systemic
Dynamic Threshold Adaptation
Unlike fixed-threshold systems, EMD's boundaries evolve with market conditions:
Base Threshold = SMA(Ergodic Score, Lookback × 3)
Adaptive Component = StDev(Ergodic Score, Lookback × 2) × Sensitivity
Final Threshold = Smoothed(Base + Adaptive)
This creates context-aware signals that remain effective across different market environments.
The Confidence Engine: Know Your Signal Quality
Multi-Factor Confidence Scoring
Every signal receives a confidence score based on:
Signal Clarity (0-35%): How decisively the ergodic threshold is crossed
Momentum Strength (0-25%): Rate of ergodic change
Volatility Alignment (0-20%): Whether volatility supports the signal
Market Quality (0-20%): Price convergence and path dependency factors
Real-Time Confidence Updates
The Live Confidence metric continuously updates, showing:
- Current opportunity quality
- Market state clarity
- Historical performance influence
- Signal recency boost
- Visual Intelligence System
Adaptive Ergodic Field Bands
Dynamic bands that expand and contract based on market state:
Primary Color: Ergodic state (mean-reverting)
Danger Color: Non-ergodic state (trending)
Band Width: Expected price movement range
Squeeze Indicators: Volatility compression warnings
Quantum Wave Ribbons
Triple EMA system (8, 21, 55) revealing market flow:
Compressed Ribbons: Consolidation imminent
Expanding Ribbons: Directional move developing
Color Coding: Matches current ergodic state
Phase Transition Signals
Clear entry/exit markers at regime changes:
Bull Signals: Ergodic restoration (mean reversion opportunity)
Bear Signals: Ergodic break (trend following opportunity)
Confidence Labels: Percentage showing signal quality
Visual Intensity: Stronger signals = deeper colors
Professional Dashboard Suite
Main Analytics Panel (Top Right)
Market State Monitor
- Current regime (Ergodic/Non-Ergodic)
- Ergodic score with threshold
- Path dependency strength
- Quantum coherence percentage
Divergence Metrics
- Price divergence with severity
- Volatility regime classification
- Strategy mode recommendation
- Signal strength indicator
Live Intelligence
- Real-time confidence score
- Color-coded risk levels
- Dynamic strategy suggestions
Performance Tracking (Left Panel)
Signal Analytics
- Total historical signals
- Win rate with W/L breakdown
- Current streak tracking
- Closed trade counter
Regime Analysis
- Current market behavior
- Bars since last signal
- Recommended actions
- Average confidence trends
Strategy Command Center (Bottom Right)
Adaptive Recommendations
- Active strategy mode
- Primary approach (mean reversion/momentum)
- Suggested indicators ("weapons")
- Entry/exit methodology
- Risk management guidance
- Comprehensive Input Guide
Core Algorithm Parameters
Analysis Period (10-100 bars)
Scalping (10-15): Ultra-responsive, more signals, higher noise
Day Trading (20-30): Balanced sensitivity and stability
Swing Trading (40-100): Smooth signals, major moves only Default: 20 - optimal for most timeframes
Divergence Threshold (0.5-5.0)
Hair Trigger (0.5-1.0): Catches every wiggle, many false signals
Balanced (1.5-2.5): Good signal-to-noise ratio
Conservative (3.0-5.0): Only extreme divergences Default: 1.5 - best risk/reward balance
Path Memory (20-200 bars)
Short Memory (20-50): Recent behavior focus, quick adaptation
Medium Memory (50-100): Balanced historical context
Long Memory (100-200): Emphasizes established patterns Default: 50 - captures sufficient history without lag
Signal Spacing (5-50 bars)
Aggressive (5-10): Allows rapid-fire signals
Normal (15-25): Prevents clustering, maintains flow
Conservative (30-50): Major setups only Default: 15 - optimal trade frequency
Ensemble Configuration
Select markets for consensus analysis:
SPY: Broad market sentiment
QQQ: Technology leadership
IWM: Small-cap risk appetite
DIA: Blue-chip stability
More instruments = stronger consensus but potentially diluted signals
Visual Customization
Color Themes (6 professional options):
Quantum: Cyan/Pink - Modern trading aesthetic
Matrix: Green/Red - Classic terminal look
Heat: Blue/Red - Temperature metaphor
Neon: Cyan/Magenta - High contrast
Ocean: Turquoise/Coral - Calming palette
Sunset: Red-orange/Teal - Warm gradients
Display Controls:
- Toggle each visual component
- Adjust transparency levels
- Scale dashboard text
- Show/hide confidence scores
- Trading Strategies by Market State
- Ergodic State Strategy (Primary Color Bands)
Market Characteristics
- Price oscillates predictably
- Support/resistance hold
- Volume patterns repeat
- Mean reversion dominates
Optimal Approach
Entry: Fade moves at band extremes
Target: Middle band (equilibrium)
Stop: Just beyond outer bands
Size: Full confidence-based position
Recommended Tools
- RSI for oversold/overbought
- Bollinger Bands for extremes
- Volume profile for levels
- Non-Ergodic State Strategy (Danger Color Bands)
Market Characteristics
- Price trends persistently
- Levels break decisively
- Volume confirms direction
- Momentum accelerates
Optimal Approach
Entry: Breakout from bands
Target: Trail with expanding bands
Stop: Inside opposite band
Size: Scale in with trend
Recommended Tools
- Moving average alignment
- ADX for trend strength
- MACD for momentum
- Advanced Features Explained
Quantum Coherence Metric
Measures phase alignment between individual and ensemble behavior:
80-100%: Perfect sync - strong mean reversion setup
50-80%: Moderate alignment - mixed signals
0-50%: Decoherence - trending behavior likely
Path Dependency Analysis
Quantifies how much history influences current price:
Low (<30%): Technical patterns reliable
Medium (30-50%): Mixed influences
High (>50%): Fundamental shift occurring
Volatility Regime Classification
Contextualizes current volatility:
Normal: Standard strategies apply
Elevated: Widen stops, reduce size
Extreme: Defensive mode required
Signal Strength Indicator
Real-time opportunity quality:
- Distance from threshold
- Momentum acceleration
- Cross-validation factors
Risk Management Framework
Position Sizing by Confidence
90%+ confidence = 100% position size
70-90% confidence = 75% position size
50-70% confidence = 50% position size
<50% confidence = 25% or skip
Dynamic Stop Placement
Ergodic State: ATR × 1.0 from entry
Non-Ergodic State: ATR × 2.0 from entry
Volatility Adjustment: Multiply by current regime
Multi-Timeframe Alignment
- Check higher timeframe regime
- Confirm ensemble consensus
- Verify volume participation
- Align with major levels
What Makes EMD Unique
Original Contributions
First Ergodic Theory Trading Application: Transforms abstract physics into practical signals
Ensemble Market Analysis: Revolutionary multi-market divergence system
Adaptive Confidence Engine: Institutional-grade signal quality metrics
Quantum Coherence: Novel market alignment measurement
Smart Signal Management: Prevents clustering while maintaining responsiveness
Technical Innovations
Dynamic Threshold Adaptation: Self-adjusting sensitivity
Path Memory Integration: Historical dependency weighting
Stress-Adjusted Scoring: Market condition normalization
Real-Time Performance Tracking: Built-in strategy analytics
Optimization Guidelines
By Timeframe
Scalping (1-5 min)
Period: 10-15
Threshold: 0.5-1.0
Memory: 20-30
Spacing: 5-10
Day Trading (5-60 min)
Period: 20-30
Threshold: 1.5-2.5
Memory: 40-60
Spacing: 15-20
Swing Trading (1H-1D)
Period: 40-60
Threshold: 2.0-3.0
Memory: 80-120
Spacing: 25-35
Position Trading (1D-1W)
Period: 60-100
Threshold: 3.0-5.0
Memory: 100-200
Spacing: 40-50
By Market Condition
Trending Markets
- Increase threshold
- Extend memory
- Focus on breaks
Ranging Markets
- Decrease threshold
- Shorten memory
- Focus on restores
Volatile Markets
- Increase spacing
- Raise confidence requirement
- Reduce position size
- Integration with Other Analysis
- Complementary Indicators
For Ergodic States
- RSI divergences
- Bollinger Band squeezes
- Volume profile nodes
- Support/resistance levels
For Non-Ergodic States
- Moving average ribbons
- Trend strength indicators
- Momentum oscillators
- Breakout patterns
- Fundamental Alignment
- Check economic calendar
- Monitor sector rotation
- Consider market themes
- Evaluate risk sentiment
Troubleshooting Guide
Too Many Signals:
- Increase threshold
- Extend signal spacing
- Raise confidence minimum
Missing Opportunities
- Decrease threshold
- Reduce signal spacing
- Check ensemble settings
Poor Win Rate
- Verify timeframe alignment
- Confirm volume participation
- Review risk management
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results.
The ergodic framework provides unique market insights but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
This tool should complement, not replace, comprehensive trading strategies and sound judgment. Markets remain inherently unpredictable despite advanced analysis techniques.
Transform market chaos into trading clarity with Ergodic Market Divergence.
Created with passion for the TradingView community
Trade with insight. Trade with anticipation.
— Dskyz , for DAFE Trading Systems
IU Higher Timeframe MA Cross StrategyIU Higher Timeframe MA Cross Strategy
The IU Higher Timeframe MA Cross Strategy is a versatile trading tool designed to identify trend by utilizing two customizable moving averages (MAs) across different timeframes and types. This strategy includes detailed entry and exit rules with fully configurable inputs, offering flexibility to suit various trading styles.
Key Features:
- Two moving averages (MA1 and MA2) with customizable types, lengths, sources, and timeframes.
- Both long and short trade setups based on MA crossovers.
- Integrated risk management with adjustable stop-loss and take-profit levels based on a user-defined risk-to-reward (RTR) ratio.
- Clear visualization of MAs, entry points, stop-loss, and take-profit zones.
Inputs:
1. Risk-to-Reward Ratio (RTR):
- Defines the take-profit level in relation to the stop-loss distance. Default is 2.
2. MA1 Settings:
- Source: Select the data source for calculating MA1 (e.g., close, open, high, low). Default is close.
- Timeframe: Specify the timeframe for MA1 calculation. Default is 60 (60-minute chart).
- Length: Set the lookback period for MA1 calculation. Default is 20.
- Type: Choose the type of moving average (options: SMA, EMA, SMMA, WMA, VWMA). Default is EMA.
- Smooth: Option to enable or disable smoothing of MA1 to merge gaps. Default is true.
3. MA2 Settings:
- Source: Select the data source for calculating MA2 (e.g., close, open, high, low). Default is close.
- Timeframe: Specify the timeframe for MA2 calculation. Default is 60 (60-minute chart).
- Length: Set the lookback period for MA2 calculation. Default is 50.
- Type: Choose the type of moving average (options: SMA, EMA, SMMA, WMA, VWMA). Default is EMA.
- Smooth: Option to enable or disable smoothing of MA2 to merge gaps. Default is true.
Entry Rules:
- Long Entry:
- Triggered when MA1 crosses above MA2 (crossover).
- Entry is confirmed only when the bar is closed and no existing position is active.
- Short Entry:
- Triggered when MA1 crosses below MA2 (crossunder).
- Entry is confirmed only when the bar is closed and no existing position is active.
Exit Rules:
- Stop-Loss:
- For long positions: Set at the low of the bar preceding the entry.
- For short positions: Set at the high of the bar preceding the entry.
- Take-Profit:
- For long positions: Calculated as (Entry Price - Stop-Loss) * RTR + Entry Price.
- For short positions: Calculated as Entry Price - (Stop-Loss - Entry Price) * RTR.
Visualization:
- Plots MA1 and MA2 on the chart with distinct colors for easy identification.
- Highlights stop-loss and take-profit levels using shaded zones for clear visual representation.
- Displays the entry level for active positions.
This strategy provides a robust framework for traders to identify and act on trend reversals while maintaining strict risk management. The flexibility of its inputs allows for seamless customization to adapt to various market conditions and trading preferences.
libTFLibrary "libTF"
libTF: Find higher/lower TF automatically
This library to find higher/lower TF from current timeframe(timeframe.period) for Pine Script version6(or higher).
Basic Algorithm
Using a timeframe scale Array and timeframe.in_seconds() function to find higher/lower timeframe.
Return value is na if could not find TF in the timeframe scale.
The timeframe scale could be changed by the parameter 'scale'(CSV).
How to use
1. Set higher/lower TF
higher()/lower() function returns higher/lower TF.
Default timeframe scale is "1, 5, 15, 60, 240, 1D, 1M, 3M, 12M".
example:
htf1 = higher()
htf2 = higher(htf1)
ltf1 = lower()
ltf2 = lower(ltf1)
2. Set higher/lower TF using your timeframe scale
The timeframe scale could be changed by the parameter.
example:
myscale="1,60,1D,1M,12M"
htf1 = higher(timeframe.period,myscale)
htf2 = higher(htf1,myscale)
ltf1 = lower(timeframe.period,myscale)
ltf2 = lower(ltf1,myscale)
3. How to use with request.*() function
na value is set if no higher/lower TF in timeframe scale.
It returns current timeframe's value, when na value as timeframe parameter in request.*().
As bellow, if it should be na when timeframe is na.
example:
return_value_request_htf1 = na(htf1)?na:request.security(syminfo.tickerid,htf1,timeframe.period)
return_value_request_ltf1 = na(ltf1)?na:request.security(syminfo.tickerid,ltf1,timeframe.period)
higher(tf, scale)
higher: find higher TF from TF string.
Parameters:
tf (string) : default value is timeframe.period.
scale (string) : TF scale in CSV. default is "1,5,15,60,240,1D,1W,1M,3M,12M".
Returns: higher TF string.
lower(tf, scale)
lower: find lower TF from TF string.
Parameters:
tf (string) : default value is timeframe.period.
scale (string) : TF scale in CSV. defalut is "1,5,15,60,240,1D,1W,1M,3M,12M".
Returns: lower TF string.
Bitcoin Cycle Master [InvestorUnknown]The "Bitcoin Cycle Master" indicator is designed for in-depth, long-term analysis of Bitcoin's price cycles, using several key metrics to track market behavior and forecast potential price tops and bottoms. The indicator integrates multiple moving averages and on-chain metrics, offering a comprehensive view of Bitcoin’s historical and projected performance. Each of its components plays a crucial role in identifying critical cycle points:
Top Cap: This is a multiple of the Average Cap, which is calculated as the cumulative sum of Bitcoin’s price (price has a longer history than Market Cap) divided by its age in days. Top Cap serves as an upper boundary for speculative price peaks, multiplied by a factor of 35.
Time_dif() =>
date = ta.valuewhen(bar_index == 0, time, 0)
sec_r = math.floor(date / 1000)
min_r = math.floor(sec_r / 60)
h_r = math.floor(min_r / 60)
d_r = math.floor(h_r / 24)
// Launch of BTC
start = timestamp(2009, 1, 3, 00, 00)
sec_rb = math.floor(start / 1000)
min_rb = math.floor(sec_rb / 60)
h_rb = math.floor(min_rb / 60)
d_rb = math.floor(h_rb / 24)
difference = d_r - d_rb
AverageCap() =>
ta.cum(btc_price) / (Time_dif() + btc_age)
TopCap() =>
// To calculate Top Cap, it is first necessary to calculate Average Cap, which is the cumulative sum of Market Cap divided by the age of the market in days.
// This creates a constant time-based moving average of market cap.
// Once Average cap is calculated, those values are multiplied by 35. The result is Top Cap.
// For AverageCap the BTC price was used instead of the MC because it has more history
// (the result should have minimal if any deviation since MC would have to be divided by Supply)
AverageCap() * 35
Delta Top: Defined as the difference between the Realized Cap and the Average Cap, this metric is further multiplied by a factor of 7. Delta Top provides a historically reliable signal for Bitcoin market cycle tops.
DeltaTop() =>
// Delta Cap = Realized Cap - Average Cap
// Average Cap is explained in the Top Cap section above.
// Once Delta Cap is calculated, its values over time are then multiplied by 7. The result is Delta Top.
(RealizedPrice() - AverageCap()) * 7
Terminal Price: Derived from Coin Days Destroyed, Terminal Price normalizes Bitcoin’s historical price behavior by its finite supply (21 million bitcoins), offering an adjusted price forecast as all bitcoins approach being mined. The original formula for Terminal Price didn’t produce expected results, hence the calculation was adjusted slightly.
CVDD() =>
// CVDD stands for Cumulative Value Coin Days Destroyed.
// Coin Days Destroyed is a term used for bitcoin to identify a value of sorts to UTXO’s (unspent transaction outputs). They can be thought of as coins moving between wallets.
(MCR - TV) / 21000000
TerminalPrice() =>
// Theory:
// Before Terminal price is calculated, it is first necessary to calculate Transferred Price.
// Transferred price takes the sum of > Coin Days Destroyed and divides it by the existing supply of bitcoin and the time it has been in circulation.
// The value of Transferred Price is then multiplied by 21. Remember that there can only ever be 21 million bitcoin mined.
// This creates a 'terminal' value as the supply is all mined, a kind of reverse supply adjustment.
// Instead of heavily weighting later behavior, it normalizes historical behavior to today. By normalizing by 21, a terminal value is created
// Unfortunately the theoretical calculation didn't produce results it should, in pinescript.
// Therefore the calculation was slightly adjusted/improvised
TransferredPrice = CVDD() / (Supply * math.log(btc_age))
tp = TransferredPrice * 210000000 * 3
Realized Price: Calculated as the Market Cap Realized divided by the current supply of Bitcoin, this metric shows the average value of Bitcoin based on the price at which coins last moved, giving a market consensus price for long-term holders.
CVDD (Cumulative Value Coin Days Destroyed): This on-chain metric analyzes Bitcoin’s UTXOs (unspent transaction outputs) and the velocity of coins moving between wallets. It highlights key market dynamics during prolonged accumulation or distribution phases.
Balanced Price: The Balanced Price is the difference between the Realized Price and the Terminal Price, adjusted by Bitcoin's supply constraints. This metric provides a useful signal for identifying oversold market conditions during bear markets.
BalancedPrice() =>
// It is calculated by subtracting Transferred Price from Realized Price
RealizedPrice() - (TerminalPrice() / (21 * 3))
Each component can be toggled individually, allowing users to focus on specific aspects of Bitcoin’s price cycle and derive meaningful insights from its long-term behavior. The combination of these models provides a well-rounded view of both speculative peaks and long-term value trends.
Important consideration:
Top Cap did historically provide reliable signals for cycle peaks, however it may not be a relevant indication of peaks in the future.
RSI with Swing Trade by Kelvin_VAlgorithm Description: "RSI with Swing Trade by Kelvin_V"
1. Introduction:
This algorithm uses the RSI (Relative Strength Index) and optional Moving Averages (MA) to detect potential uptrends and downtrends in the market. The key feature of this script is that it visually changes the candle colors based on the market conditions, making it easier for users to identify potential trend swings or wave patterns.
The strategy offers flexibility by allowing users to enable or disable the MA condition. When the MA condition is enabled, the strategy will confirm trends using two moving averages. When disabled, the strategy will only use RSI to detect potential market swings.
2. Key Features of the Algorithm:
RSI (Relative Strength Index):
The RSI is used to identify potential market turning points based on overbought and oversold conditions.
When the RSI exceeds a predefined upper threshold (e.g., 60), it suggests a potential uptrend.
When the RSI drops below a lower threshold (e.g., 40), it suggests a potential downtrend.
Moving Averages (MA) - Optional:
Two Moving Averages (Short MA and Long MA) are used to confirm trends.
If the Short MA crosses above the Long MA, it indicates an uptrend.
If the Short MA crosses below the Long MA, it indicates a downtrend.
Users have the option to enable or disable this MA condition.
Visual Candle Coloring:
Green candles represent a potential uptrend, indicating a bullish move based on RSI (and MA if enabled).
Red candles represent a potential downtrend, indicating a bearish move based on RSI (and MA if enabled).
3. How the Algorithm Works:
RSI Levels:
The user can set RSI upper and lower bands to represent potential overbought and oversold levels. For example:
RSI > 60: Indicates a potential uptrend (bullish move).
RSI < 40: Indicates a potential downtrend (bearish move).
Optional MA Condition:
The algorithm also allows the user to apply the MA condition to further confirm the trend:
Short MA > Long MA: Confirms an uptrend, reinforcing a bullish signal.
Short MA < Long MA: Confirms a downtrend, reinforcing a bearish signal.
This condition can be disabled, allowing the user to focus solely on RSI signals if desired.
Swing Trade Logic:
Uptrend: If the RSI exceeds the upper threshold (e.g., 60) and (optionally) the Short MA is above the Long MA, the candles will turn green to signal a potential uptrend.
Downtrend: If the RSI falls below the lower threshold (e.g., 40) and (optionally) the Short MA is below the Long MA, the candles will turn red to signal a potential downtrend.
Visual Representation:
The candle colors change dynamically based on the RSI values and moving average conditions, making it easier for traders to visually identify potential trend swings or wave patterns without relying on complex chart analysis.
4. User Customization:
The algorithm provides multiple customization options:
RSI Length: Users can adjust the period for RSI calculation (default is 4).
RSI Upper Band (Potential Uptrend): Users can customize the upper RSI level (default is 60) to indicate a potential bullish move.
RSI Lower Band (Potential Downtrend): Users can customize the lower RSI level (default is 40) to indicate a potential bearish move.
MA Type: Users can choose between SMA (Simple Moving Average) and EMA (Exponential Moving Average) for moving average calculations.
Enable/Disable MA Condition: Users can toggle the MA condition on or off, depending on whether they want to add moving averages to the trend confirmation process.
5. Benefits of the Algorithm:
Easy Identification of Trends: By changing candle colors based on RSI and MA conditions, the algorithm makes it easy for users to visually detect potential trend reversals and trend swings.
Flexible Conditions: The user has full control over the RSI and MA settings, allowing them to adapt the strategy to different market conditions and timeframes.
Clear Visualization: With the candle color changes, users can quickly recognize when a potential uptrend or downtrend is forming, enabling faster decision-making in their trading.
6. Example Usage:
Day traders: Can apply this strategy on short timeframes such as 5 minutes or 15 minutes to detect quick trends or reversals.
Swing traders: Can use this strategy on longer timeframes like 1 hour or 4 hours to identify and follow larger market swings.
Trend and RSI Bias FusionTrend and RSI Bias Fusion Indicator
This is my first ever indicator. I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share this because I believe in learning and earing together as a community. I will later share the rest of the indicators I have created. This is my first time ever sharing any indicator so if you guys have any questions or suggestions write them.
Overview
The "Trend and RSI Bias Fusion" indicator is a versatile tool designed to help traders identify key market trends, potential reversals, momentum shifts, and RSI-based pullbacks. This indicator fuses trend analysis and RSI bias into a single, comprehensive visual, making it easier to make informed trading decisions across various timeframes and market conditions.
Features
Dual Timeframe Analysis: Combines trend analysis on a higher timeframe (e.g., Daily) with RSI analysis on a lower timeframe (e.g., 4-Hour), providing a more granular view of market conditions. You can, however, choose any timeframe you want for instance 12hr with trend and 2hr RSI analysis.
Trend and Momentum Visualization: The indicator uses Exponential Moving Averages (EMAs) to determine trend direction and colors the chart background to reflect bullish or bearish trends, along with momentum strength.
RSI Bias Detection: Automatically identifies overbought and oversold conditions using the RSI, providing a clear indication of potential market reversals or continuations.
Color-Coded Bars: Optionally color codes bars based on either trend direction or RSI bias, giving you a quick visual cue of the market's state.
Reversal Markers: Displays trend reversal markers on the chart when the short-term EMA crosses over or under the long-term EMA.
Calculation Details
Exponential Moving Averages (EMAs): The indicator calculates short-term and long-term EMAs using the closing prices.
The crossover between these EMAs is used to determine the trend direction:
Short-Term EMA: Typically a 14-period EMA.
Long-Term EMA: Typically a 50-period EMA.
Momentum: Calculated using the RSI and then centered around zero by subtracting 50. This allows the indicator to distinguish between positive and negative momentum.
RSI Bias: The RSI is calculated on a lower timeframe to detect overbought (above 60) and oversold (below 40) conditions, which are used to determine the bias:
RSI Above 60: Indicates potential overbought conditions (bearish bias).
RSI Below 40: Indicates potential oversold conditions (bullish bias).
How to Use the Indicator
Select Your Timeframes: Choose your preferred trend timeframe (e.g., Daily) and RSI timeframe (e.g., 4-2 Hour) in the indicator settings. These should match your trading strategy and the asset class you're analyzing.
Interpret Trend and Momentum
Background Color: The background color reflects the current trend direction:
Green/Lime: Uptrend, with lime indicating positive momentum.
Red/Maroon: Downtrend, with maroon indicating positive momentum within a downtrend.
Momentum Histogram: The histogram plot shows momentum, color-coded by the trend. A histogram above zero with green/lime indicates bullish momentum, while below zero with red/maroon indicates bearish momentum.
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Read RSI Bias:
The RSI bias line helps identify the current market state relative to overbought or oversold levels. The RSI value is plotted on the chart, with lines at 60 and 40 to mark these levels.
When the RSI crosses above 60, it suggests a bearish bias; crossing below 40 suggests a bullish bias.
Use Reversal Markers: The indicator places small circles on the chart at points where the short-term EMA crosses the long-term EMA, signaling potential trend reversals.
Bar Color Customization:
You can choose to color the bars based on either the trend or the RSI bias in the indicator settings. In the Images below I have changed the colors to fit my personal style , Blue for uptrend and Pink for downtrend:
Trend-Based: Bars will reflect the trend direction (green for uptrend or in this case blue, red for downtrend or in this case pink).
RSI-Based: Bars will reflect RSI conditions (yellow for overbought, maroon for oversold).
Image above: RSI is set to 4hr and Trend is set to daily, uses RSI bar color
Image above: RSI is set to 4hr and Trend is set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Image above: Both RSI and Trend are set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, without bar color
Image above: Both RSI and Trend are set to daily, how it looks on a clean chart
Example Use Case Swing Traders:
For instance, if you're trading a 4-hour chart of USDCHF:
Set the trend timeframe to Daily and the RSI timeframe to 4-Hour.
Watch for background color shifts and reversal markers to determine trend direction.
Use RSI bias to time your entries and exits, especially around overbought/oversold levels.
Enable bar coloring to quickly see when conditions favor either trend continuation or reversal.
This indicator is particularly effective for swing traders and those who want to align their trades with higher timeframe trends while using momentum and RSI for entry and exit signals.
For Day Traders
Timeframe Selection:
Trend Timeframe: Set to a higher intraday timeframe such as the 1 or 2 Hour chart.
RSI Timeframe: Set to a shorter timeframe like 15-10 Minutes or 5-Minutes to capture finer details of intraday momentum shifts.
Using the Indicator:
Trend Identification: Day traders can use the background color to quickly identify whether the market is in a bullish or bearish trend on the 1-Hour chart. A green background suggests looking for long opportunities, while a red background suggests short opportunities.
Momentum Analysis: The histogram can help day traders gauge the strength of the current trend. For example, if the histogram is green and above zero, the trader may consider buying pullbacks within the trend.
RSI Bias: Monitor RSI levels on the lower timeframe (e.g., 15-Minutes). If the RSI crosses below 40, it indicates an oversold condition, potentially signaling a buying opportunity, especially if it aligns with a bullish trend on the higher timeframe.
Trade Execution:
Look for entries when the RSI shows a reversal or pullback in the direction of the higher timeframe trend.
Use the trend reversal markers to confirm potential intraday reversals, adding extra confidence to trade setups.
For Scalpers
Timeframe Selection:
Trend Timeframe: Set to a short intraday timeframe like 15-Minutes or 5-Minutes.
RSI Timeframe: Use an even shorter timeframe, such as 1-Minute, to capture rapid price movements.
Final Notes:
The "Trend and RSI Bias Fusion" indicator is a powerful tool that combines trend analysis, momentum assessment, and RSI insights into one cohesive package. By integrating these different aspects, the indicator helps traders navigate complex market environments with greater clarity and confidence. Customize the settings to fit your specific trading style and market and use it to stay ahead of market trends and potential reversals.
My Scripts/Indicators/Ideas /Systems that I share are only for educational purposes!
Candle Wick Shadows [UkutaLabs]█ OVERVIEW
The Candle Wick Shadows Indicator identifies untested wicks in real time that occur when there is an imbalance in the number of buyers and sellers at a price-level. This imbalance occurs when a market exchange receives too many of one kind of order, and not enough of its counterpoint.
Candle Wick Shadows is a powerful trading indicator that will automatically identify and label strong ranges on traders’ charts that can be incorporated into a wide variety of different trading strategies.
█ USAGE
The script automatically identifies and measures real-time ranges of imbalance between buying and selling pressure in the market using real-time price-action information. These levels indicate potential Supply and Demand zones which serve to help the trader identify areas where price has changed direction in the past due to an imbalance of buyers and sellers.
The script also allows users to mirror higher time frame Candle Wick Shadows onto lower time frame charts to gain a stronger understanding of key levels on another scale.
█ SETTINGS
Configuration
- Show Labels: Determines whether or not identification labels are drawn on the chart.
- Max CWS Display: Determines the number of Candle Wick Shadows that will be drawn on the chart. This is for each higher timeframe option that is toggled, not the total.
Current Time Frame
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the current time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the current time frame.
- Bearish Color: Determines the color of bearish wick shadows from the current time frame.
5 Minute (Higher Timeframe)
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the 5 minute time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the 5 minute time frame.
- Bearish Color: Determines the color of bearish wick shadows from the 5 minute time frame.
15 Minute (Higher Timeframe)
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the 15 minute time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the 15 minute time frame.
- Bearish Color: Determines the color of bearish wick shadows from the 15 minute time frame.
30 Minute (Higher Timeframe)
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the 30 minute time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the 30 minute time frame.
- Bearish Color: Determines the color of bearish wick shadows from the 30 minute time frame.
60 Minute (Higher Timeframe)
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the 60 minute time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the 60 minute time frame.
- Bearish Color: Determines the color of bearish wick shadows from the 60 minute time frame.
240 Minute (Higher Timeframe)
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the 240 minute time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the 240 minute time frame.
- Bearish Color: Determines the color of bearish wick shadows from the 240 minute time frame.
Daily (Higher Timeframe)
-Wick Shadow (On/Off): Determines whether or not wick shadows are drawn from the daily time frame chart.
- Bullish Color: Determines the color of bullish wick shadows from the daily time frame.
- Bearish Color: Determines the color of bearish wick shadows from the daily time frame.
Candle Wick Retest [UkutaLabs]█ OVERVIEW
The Candle Wick Retest Indicator identifies untested wicks in real time that occur when there is an imbalance in the number of buyers and sellers at a price-level. This imbalance occurs when a market exchange receives too many of one kind of order, and not enough of its counterpoint.
Candle Wick Retest is a powerful trading indicator that will automatically identify and label strong ranges on traders’ charts that can be incorporated into a wide variety of different trading strategies.
█ USAGE
The script automatically identifies and measures real-time ranges of imbalance between buying and selling pressure in the market using real-time price-action information. These levels indicate potential Supply and Demand zones which serve to help the trader identify areas where price has changed direction in the past due to an imbalance of buyers and sellers.
The script also allows users to mirror higher time frame Candle Wick Retests onto lower time frame charts to gain a stronger understanding of key levels on another scale.
█ SETTINGS
Configuration
- Show Labels: Determines whether or not identification labels are drawn on the chart.
- Max CW Display: Determines the number of Candle Wick Retests that will be drawn on the chart. This is for each higher timeframe option that is toggled, not the total.
Current Time Frame
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the current time frame chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the current time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the current time frame.
5 Minute (Higher Timeframe)
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the 5 minute chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the 5 minute time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the 5 minute time frame.
15 Minute (Higher Timeframe)
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the 15 minute time frame chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the 15 minute time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the 15 minute time frame.
30 Minute (Higher Timeframe)
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the 30 minute time frame chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the 30 minute time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the 30 minute time frame.
60 Minute (Higher Timeframe)
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the 60 minute time frame chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the 60 minute time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the 60 minute time frame.
240 Minute (Higher Timeframe)
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the 240 minute time frame chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the 240 minute time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the 240 minute time frame.
Daily (Higher Timeframe)
- Wick Retest (On/Off): Determines whether wick retests will be drawn from the daily time frame chart.
- Wick Retest Bullish Color: Determines the color of bullish wick retests from the daily time frame.
- Wick Retest Bearish Color: Determines the color of bearish wick retests from the daily time frame.
Power Trends [UkutaLabs]█ OVERVIEW
The Power Trends Indicator is a versatile trading toolkit that offers unique insight into key price levels in the market. This script uses currently relevant price-action information to automatically detect pivot levels and use them to create powerful trendlines.
The aim of this script is to improve the trading experience of users by offering a versatile toolkit that can be used in a wide variety of trading strategies to help simplify the complexities of the market.
█ USAGE
The Power Trends Indicator will automatically identify pivot points in real-time using recent price-action information to ensure that all points being identified are relevant. Using these pivot points, the script then draws powerful trend lines that can be used as levels of resistance and support.
To ensure that only the most relevant information is being presented, only the most recent trend lines will be displayed on the user’s charts. As new trend lines are being drawn, older trend lines will become thinner so that traders can identify the most relevant lines at a glance.
The price of the most recent high and low pivot points will also be displayed on the chart and can be used as further levels of resistance and support.
When a recent pivot level is broken, it will be identified as a Break of Structure. This signifies that there may have been a change in market strength.
The Power Trends Indicator also supports multiple time frame mapping, allowing you to mirror the trend lines that would be drawn on higher time frame charts onto lower time frame charts. This feature allows traders to be aware of the market structure of multiple charts at a glance from a single chart.
When mirroring some higher time frame trend lines, lines may appear to not align properly with current time frame bars. This is done intentionally to ensure lines are being drawn accurately to their position on the higher time frame charts.
█ SETTINGS
Current Time Frame
• Display (On/Off): Determines whether or not trend lines are drawn from the current time frame.
• High Color: Determines the color of trend lines drawn on high pivots.
• Low Color: Determines the color of trend lines drawn on low pivots.
5 Minute (Higher Time Frame)
• Display (On/Off): Determines whether or not trend lines are drawn from the 5 minute higher time frame.
• High Color: Determines the color of trend lines drawn on high pivots from the 5 minute higher time frame.
• Low Color: Determines the color of trend lines drawn on low pivots from the 5 minute higher time frame.
15 Minute (Higher Time Frame)
• Display (On/Off): Determines whether or not trend lines are drawn from the 15 minute higher time frame.
• High Color: Determines the color of trend lines drawn on high pivots from the 15 minute higher time frame.
• Low Color: Determines the color of trend lines drawn on low pivots from the 15 minute higher time frame.
30 Minute (Higher Time Frame)
• Display (On/Off): Determines whether or not trend lines are drawn from the 30 minute higher time frame.
• High Color: Determines the color of trend lines drawn on high pivots from the 30 minute higher time frame.
• Low Color: Determines the color of trend lines drawn on low pivots from the 30 minute higher time frame.
60 Minute (Higher Time Frame)
• Display (On/Off): Determines whether or not trend lines are drawn from the 60 minute higher time frame.
• High Color: Determines the color of trend lines drawn on high pivots from the 60 minute higher time frame.
• Low Color: Determines the color of trend lines drawn on low pivots from the 60 minute higher time frame.
240 Minute (Higher Time Frame)
• Display (On/Off): Determines whether or not trend lines are drawn from the 240 minute higher time frame.
• High Color: Determines the color of trend lines drawn on high pivots from the 240 minute higher time frame.
• Low Color: Determines the color of trend lines drawn on low pivots from the 240 minute higher time frame.
Daily (Higher Time Frame)
• Display (On/Off): Determines whether or not trend lines are drawn from the daily time frame.
• High Color: Determines the color of trend lines drawn on high pivots from the daily higher time frame.
• Low Color: Determines the color of trend lines drawn on low pivots from the daily higher time frame.
Fair Value Gap [UkutaLabs]█ OVERVIEW
Fair Value Gaps are price jumps caused by the imbalance buying and selling pressures in trading and are most commonly used amongst price action traders. Fair Value Gaps are formed via a three-candle sequence in which a large candle’s neighbouring candles’ upper and lower wicks do not fully overlap the large candle.
The Fair Value Gaps Indicator also supports Multi Time Frame Plotting, allowing you to plot the Fair Value Gaps from higher time frames onto lower time frame charts.
The Fair Value Gaps Indicator is a powerful trading toolkit that provides users with more information than they would typically have available to them by allowing them to configure several charts worth of information onto one single chart.
█ USAGE
The script automatically identifies imbalances between buying and selling pressure in the market in real time, offering traders valuable insight into current market sentiment. These gaps are considered to be levels where the supply and demand of a commodity are imbalanced, and the price tends to return to fill these gaps (But are not guaranteed to).
The Fair Value Gaps Indicator also allows gaps from higher time frames to be drawn on lower time frame charts, providing traders with more information than they would typically have access to to further simplify the decision making process.
█ SETTINGS
Configuration
• Show Labels: Determines whether labels that identify which time frame a FVG is calculated from.
• Max FVG Display: Determines the limit to the number of FVGs that can be drawn from all time frames. Set this value to 0 to remove this limit.
Current Time Frame
• Display: Determines whether or not FVGs from the current time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the current time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the current time frame.
5 Minute (Higher Time Frame)
• Display: Determines whether or not FVGs from the 5 minute time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the 5 minute time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the 5 minute time frame.
15 Minute (Higher Time Frame)
• Display: Determines whether or not FVGs from the 15 minute time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the 15 minute time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the 15 minute time frame.
30 Minute (Higher Time Frame)
• Display: Determines whether or not FVGs from the 30 minute time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the 30 minute time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the 30 minute time frame.
60 Minute (Higher Time Frame)
• Display: Determines whether or not FVGs from the 60 minute time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the 60 minute time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the 60 minute time frame.
240 Minute (Higher Time Frame)
• Display: Determines whether or not FVGs from the 240 minute time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the 240 minute time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the 240 minute time frame.
Daily (Higher Time Frame)
• Display: Determines whether or not FVGs from the daily time frame will be drawn on the chart.
• Bullish Color: Determines the color of Bullish FVGs calculated from the daily time frame.
• Bearish Color: Determines the color of Bearish FVGs calculated from the daily time frame.
Breadth Indicators NYSE Percent Above Moving AverageBreadth Indicators NYSE - transmits the processed data from the Barchart provider
NYSE - Breadth Indicators
S&P 500 - Breadth Indicators
DOW - Breadth Indicators
RUSSEL 1000 - Breadth Indicators
RUSSEL 2000 - Breadth Indicators
RUSSEL 3000 - Breadth Indicators
Moving Average - 5, 20, 50, 100, 150, 200
The "Percentage above 50-day SMA" indicator measures the percentage of stocks in the index trading above their 50-day moving average. It is a useful tool for assessing the general state of the market and identifying overbought and oversold conditions.
One way to use the "Percentage above 50-day SMA" indicator in a trading strategy is to combine it with a long-term moving average to determine whether the trend is bullish or bearish. Another way to use it is to combine it with a short-term moving average to identify pullbacks and rebounds within the overall trend.
The purpose of using the "Percentage above 50-day SMA" indicator is to participate in a larger trend with a better risk-reward ratio. By using this indicator to identify pullbacks and bounces, you can reduce the risk of entering trades at the wrong time.
Bull Signal Recap:
150-day EMA of $SPXA50R crosses above 52.5 and remains above 47.50 to set the bullish tone.
5-day EMA of $SPXA50R moves below 40 to signal a pullback
5-day EMA of $SPXA50R moves above 50 to signal an upturn
Bear Signal Recap:
150-day EMA of $SPXA50R crosses below 47.50 and remains below 52.50 to set the bearish tone.
5-day EMA of $SPXA50R moves above 60 to signal a bounce
5-day EMA of $SPXA50R moves below 50 to signal a downturn
Tweaking
There are numerous ways to tweak a trading system, but chartists should avoid over-optimizing the indicator settings. In other words, don't attempt to find the perfect moving average period or crossover level. Perfection is unattainable when developing a system or trading the markets. It is important to keep the system logical and focus tweaks on other aspects, such as the actual price chart of the underlying security.
What do levels above and below 50% signify in the long-term moving average?
A move above 52.5% is deemed bullish, and below 47.5% is deemed bearish. These levels help to reduce whipsaws by using buffers for bullish and bearish thresholds.
How does the short-term moving average work to identify pullbacks or bounces?
When using a 5-day EMA, a move below 40 signals a pullback, and a move above 60 signals a bounce.
How is the reversal of pullback or bounce identified?
A move back above 50 after a pullback or below 50 after a bounce signals that the respective trend may be resuming.
How can you ensure that the uptrend has resumed?
It’s important to wait for the surge above 50 to ensure the uptrend has resumed, signaling improved breadth.
Can the system be tweaked to optimize indicator settings?
While there are various ways to tweak the system, seeking perfection through over-optimizing settings is advised against. It's crucial to keep the system logical and focus tweaks on the price chart of the underlying security.
RUSSIAN \ Русская версия.
Индикатор "Процент выше 50-дневной скользящей средней" измеряет процент акций, торгующихся в индексе выше их 50-дневной скользящей средней. Это полезный инструмент для оценки общего состояния рынка и выявления условий перекупленности и перепроданности.
Один из способов использования индикатора "Процент выше 50-дневной скользящей средней" в торговой стратегии - это объединить его с долгосрочной скользящей средней, чтобы определить, является ли тренд бычьим или медвежьим. Другой способ использовать его - объединить с краткосрочной скользящей средней, чтобы выявить откаты и отскоки в рамках общего тренда.
Цель использования индикатора "Процент выше 50-дневной скользящей средней" - участвовать в более широком тренде с лучшим соотношением риска и прибыли. Используя этот индикатор для выявления откатов и отскоков, вы можете снизить риск входа в сделки в неподходящее время.
Краткое описание бычьего сигнала:
150-дневная ЕМА на уровне $SPXA50R пересекает отметку 52,5 и остается выше 47,50, что задает бычий настрой.
5-дневная ЕМА на уровне $SPXA50R опускается ниже 40, сигнализируя об откате
5-дневная ЕМА на уровне $SPXA50R поднимается выше 50, сигнализируя о росте
Обзор медвежьих сигналов:
150-дневная ЕМА на уровне $SPXA50R пересекает уровень ниже 47,50 и остается ниже 52,50, что указывает на медвежий настрой.
5-дневная ЕМА на уровне $SPXA50R поднимается выше 60, сигнализируя о отскоке
5-дневная ЕМА на уровне $SPXA50 опускается ниже 50, что сигнализирует о спаде
Корректировка
Существует множество способов настроить торговую систему, но графологам следует избегать чрезмерной оптимизации настроек индикатора. Другими словами, не пытайтесь найти идеальный период скользящей средней или уровень пересечения. Совершенство недостижимо при разработке системы или торговле на рынках. Важно поддерживать логику системы и уделять особое внимание другим аспектам, таким как график фактической цены базовой ценной бумаги.
Что означают уровни выше и ниже 50% в долгосрочной скользящей средней?
Движение выше 52,5% считается бычьим, а ниже 47,5% - медвежьим. Эти уровни помогают снизить риски, используя буферы для бычьих и медвежьих порогов.
Как краткосрочная скользящая средняя помогает идентифицировать откаты или отскоки?
При использовании 5-дневной ЕМА движение ниже 40 указывает на откат, а движение выше 60 указывает на отскок.
Как определяется разворот отката или отскока?
Движение выше 50 после отката или ниже 50 после отскока сигнализирует о возможном возобновлении соответствующего тренда.
Как вы можете гарантировать, что восходящий тренд возобновился?
Важно дождаться скачка выше 50, чтобы убедиться в возобновлении восходящего тренда, сигнализирующего о расширении диапазона.
Можно ли настроить систему для оптимизации настроек индикатора?
Хотя существуют различные способы настройки системы, не рекомендуется стремиться к совершенству с помощью чрезмерной оптимизации настроек. Крайне важно сохранить логичность системы и сфокусировать изменения на ценовом графике базовой ценной бумаги.
RSI Multiple TimeFrame, Version 1.0RSI Multiple TimeFrame, Version 1.0
Overview
The RSI Multiple TimeFrame script is designed to enhance trading decisions by providing a comprehensive view of the Relative Strength Index (RSI) across multiple timeframes. This tool helps traders identify overbought and oversold conditions more accurately by analyzing RSI values on different intervals simultaneously. This is particularly useful for traders who employ multi-timeframe analysis to confirm signals and make more informed trading decisions.
Unique Feature of the new script (described in detail below)
Multi-Timeframe RSI Analysis
Customizable Timeframes
Visual Signal Indicators (dots)
Overbought and Oversold Layers with gradual Background Fill
Enhanced Trend Confirmation
Originality and Usefulness
This script combines the RSI indicator across three distinct timeframes into a single view, providing traders with a multi-dimensional perspective of market momentum. It also provides associated signals to better time dips and peaks. Unlike standard RSI indicators that focus on a single timeframe, this script allows users to observe RSI trends across short, medium, and long-term intervals, thereby improving the accuracy of entry and exit signals. This is particularly valuable for traders looking to align their short-term strategies with longer-term market trends.
Signal Description
The script also includes a unique signal feature that plots green and red dots on the chart to highlight potential buy and sell opportunities:
Green Dots : These appear when all three RSI values are under specific thresholds (RSI of the shortest timeframe < 30, the medium timeframe < 40, and the longest timeframe < 50) and the RSI of the shortest timeframe is showing an upward trend (current value is greater than the previous value, and the value two periods ago is greater than the previous value). This indicates a potential buying opportunity as the market may be shifting from an oversold condition.
Red Dots : These appear when all three RSI values are above specific thresholds (RSI of the shortest timeframe > 70, the medium timeframe > 60, and the longest timeframe > 50) and the RSI of the shortest timeframe is showing a downward trend (current value is less than the previous value, and the value two periods ago is less than the previous value). This indicates a potential selling opportunity as the market may be shifting from an overbought condition.
These signals help traders identify high-probability turning points in the market by ensuring that momentum is aligned across multiple timeframes.
Detailed Description
Input Variables
RSI Period (`len`) : The number of periods to calculate the RSI. Default is 14.
RSI Source (`src`) : The price source for RSI calculation, defaulting to the average of the high and low prices (`hl2`).
Timeframes (`tf1`, `tf2`, `tf3`) : The different timeframes for which the RSI is calculated, defaulting to 5 minutes, 1 hour, and 8 hours respectively.
Functionality
RSI Calculations : The script calculates the RSI for each of the three specified timeframes using the `request.security` function. This allows the RSI to be plotted for multiple intervals, providing a layered view of market momentum.
```pine
rsi_tf1 = request.security(syminfo.tickerid, tf1, ta.rsi(src, len))
rsi_tf2 = request.security(syminfo.tickerid, tf2, ta.rsi(src, len))
rsi_tf3 = request.security(syminfo.tickerid, tf3, ta.rsi(src, len))
```
Plotting : The RSI values for the three timeframes are plotted with different colors and line widths for clear visual distinction. This makes it easy to compare RSI values across different intervals.
```pine
p1 = plot(rsi_tf1, title="RSI 5m", color=color.rgb(200, 200, 255), linewidth=2)
p2 = plot(rsi_tf2, title="RSI 1h", color=color.rgb(125, 125, 255), linewidth=2)
p3 = plot(rsi_tf3, title="RSI 8h", color=color.rgb(0, 0, 255), linewidth=2)
```
Overbought and Oversold Levels : Horizontal lines are plotted at standard RSI levels (20, 30, 40, 50, 60, 70, 80) to visually identify overbought and oversold conditions. The areas between these levels are filled with varying shades of blue for better visualization.
```pine
h80 = hline(80, title="RSI threshold 80", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
h70 = hline(70, title="RSI threshold 70", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
...
fill(h70, h80, color=color.rgb(33, 150, 243, 95), title="Background")
```
Signal Plotting : The script adds green and red dots to indicate potential buy and sell signals, respectively. A green dot is plotted when all RSI values are under specific thresholds and the RSI of the shortest timeframe is rising. Conversely, a red dot is plotted when all RSI values are above specific thresholds and the RSI of the shortest timeframe is falling.
```pine
plotshape(series=(rsi_tf1 < 30 and rsi_tf2 < 40 and rsi_tf3 < 50 and (rsi_tf1 > rsi_tf1 ) and (rsi_tf1 > rsi_tf1 )) ? 1 : na, location=location.bottom, color=color.green, style=shape.circle, size=size.tiny)
plotshape(series=(rsi_tf1 > 70 and rsi_tf2 > 60 and rsi_tf3 > 50 and (rsi_tf1 < rsi_tf1 ) and (rsi_tf1 < rsi_tf1 )) ? 1 : na, location=location.top, color=color.red, style=shape.circle, size=size.tiny)
```
How to Use
Configuring Inputs : Adjust the RSI period and source as needed. Modify the timeframes to suit your trading strategy.
Interpreting the Indicator : Use the plotted RSI values to gauge momentum across different timeframes. Look for overbought conditions (RSI above 70, 60 and 50) and oversold conditions (RSI below 30, 40 and 50) across multiple intervals to confirm trade signals.
Signal Confirmation : Pay attention to the green and red dots that provide signals to better time dips and peaks. dots are printed when the lower timeframe (5mn by default) shows sign of reversal.
These signals are more reliable when confirmed across all three timeframes.
This script provides a nuanced view of RSI, helping traders make more informed decisions by considering multiple timeframes simultaneously. By combining short, medium, and long-term RSI values, traders can better align their strategies with overarching market trends, thus improving the precision of their trading actions.
ICT IPDAGuided by ICT tutoring, I create this versatile indicator "IPDA".
This indicator shows a different way of viewing the “IPDA” by calculating from START
(-20 / -40 / -60) to (+20 /+40 /+60) Days, showing the Highs and Lows of the IPDA of the Previous days and both of the subsequent ones, the levels of (-20 / -40 / -60) Days can be taken into consideration as objectives to be achieved in the range of days (+20 /+40 /+60)
The user has the possibility to:
- Choose whether to display IPDAs before and after START
- Choose to show High and Low levels
- Choose to show Prices
The indicator should be used as ICT shows in its concepts.
Example on how to evaluate a possible Start IPDA:
Example for Entry targeting IPDAs :
If something is not clear, comment below and I will reply as soon as possible.
Swing Action PriceEnglish:
**Description of "Swing Action Price" TradingView Script**
"Swing Action Price" is a custom technical indicator designed to identify swing highs and swing lows in a financial market. The script calculates and plots various lines on the chart to visualize these swing points. Swing highs are points where the price has made a local peak, while swing lows are points where the price has made a local trough.
The indicator displays the following lines on the chart:
1. Dotted lines representing each individual swing high and swing low identified on different timeframes (10, 30, 60, 100, 150, 200, 700, and 1000 bars).
2. Dotted lines representing the most recent swing high and swing low for the current bar.
How the indicator works:
1. The script uses historical price data to calculate swing highs and swing lows based on specific conditions.
2. For each of the mentioned timeframes, the indicator identifies the highest high and lowest low within a defined number of bars (10, 30, 60, etc.).
3. Once a new swing high or swing low is identified, the corresponding dotted lines are drawn on the chart, extending from the previous swing point to the current one.
The "Swing Action Price" indicator can be used by traders to visually identify key support and resistance levels in the market. It helps them recognize potential trend reversals or continuation points, which may be valuable for making trading decisions.
Please note that trading indicators should always be used in conjunction with other technical and fundamental analysis tools to make informed trading choices. The "Swing Action Price" indicator is offered under the Mozilla Public License 2.0, and the developer's username is "damianjorgeportillo."
Remember that past performance is not indicative of future results, and it's essential to exercise caution and apply risk management strategies when trading financial markets.
/******************************/
Spanish:
**Descripción del Script "Swing Action Price" en TradingView**
"Swing Action Price" es un indicador técnico personalizado diseñado para identificar máximos y mínimos en un mercado financiero. El script calcula y muestra diversas líneas en el gráfico para visualizar estos puntos de inflexión. Los máximos se producen cuando el precio alcanza un pico local, mientras que los mínimos ocurren cuando el precio alcanza un valle local.
El indicador muestra las siguientes líneas en el gráfico:
1. Líneas punteadas que representan cada máximo y mínimo individual identificado en diferentes marcos de tiempo (10, 30, 60, 100, 150, 200, 700 y 1000 barras).
2. Líneas punteadas que representan el máximo y mínimo más reciente para la barra actual.
Cómo funciona el indicador:
1. El script utiliza datos históricos de precios para calcular los máximos y mínimos en función de ciertas condiciones.
2. Para cada uno de los marcos de tiempo mencionados, el indicador identifica el máximo más alto y el mínimo más bajo dentro de un número específico de barras (10, 30, 60, etc.).
3. Una vez que se identifica un nuevo máximo o mínimo, se dibujan las líneas punteadas correspondientes en el gráfico, extendiéndose desde el punto de inflexión anterior hasta el actual.
El indicador "Swing Action Price" puede ser utilizado por traders para identificar visualmente niveles clave de soporte y resistencia en el mercado. Ayuda a reconocer posibles puntos de inversión o continuación de tendencia, lo que puede ser valioso para tomar decisiones comerciales.
Por favor, ten en cuenta que los indicadores de trading siempre deben utilizarse junto con otras herramientas de análisis técnico y fundamental para tomar decisiones comerciales informadas. El indicador "Swing Action Price" se ofrece bajo la Licencia Pública de Mozilla 2.0, y el nombre de usuario del desarrollador es "damianjorgeportillo".
Recuerda que el rendimiento pasado no garantiza resultados futuros, y es esencial ser cauteloso y aplicar estrategias de gestión de riesgos al operar en los mercados financieros.
Crypto Trend IndicatorThe Crypto Trend Indicator is a trend-following indicator specifically designed to identify bullish and bearish trends in the price of Bitcoin, and other cryptocurrencies. This indicator doesn't provide explicit instructions on when to buy or sell, but rather offers an understanding of whether the trend is bullish or bearish. It's important to note that this indicator is only useful for trend trading.
The band is a visual representation of the 30-day and 60-day Exponential Moving Average (EMA). When the 30-day EMA is above the 60-day EMA, the trend is bullish and the band is green. When the 30-day EMA is below the 60-day EMA, the trend is bearish and the band is red. When the 30-day EMA starts to converge with the 60-day EMA, the trend is neutral and the band is grey.
The line is a visual representation of the 20-week Simple Moving Average (SMA) in the daily timeframe. "Bull" and "Bear" signals are generated when the 20-day EMA is either above or below the 20-week SMA, in conjunction with a bullish or bearish trend. When the band is green and the 20-day EMA is above the 20-week SMA, a “Bull” signal emerges. When the band is red and the 20-day EMA is below the 20-week SMA, a “Bear” signal emerges. The 20-week SMA can potentially also function as a leading indicator, as substantial price deviations from the SMA typically indicate an overextended market.
While this indicator has traditionally identified bullish and bearish trends in various cryptocurrency assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, range-bound indicators can greatly improve the decision-making process when planning for entries and exits points.
Biddles OI Weighted Average PriceAhoy!
This script calculates Open Interested Weighted Average Price for the following lookback periods:
- 7, 30, 60
e.g. On the 1D chart, you will see OIWAP for the past 7, 30, and 60 days. It works on any timeframe though.
It works with any ticker that TV's OI indicator supports, and has ticker override if you are looking at an exchange that's unsupported, but for an asset that is.
e.g. If you're looking at Bybit's BTCUSDT.P which is unsupported- you can override to get OI data from Binance's BTCUSDT.P which is supported.
=====
Open-Sourced + Crowd Sourcing Goals
=====
I am open sourcing this in hopes we can work together to find interesting signal/observation, and make the script better.
The only way I could think of to calculate the OIWAP for the lookback periods was to manually factor in each period in the formula.
e.g. For the 60-period lookback, it's manually taking price and OI for each individual period.
I am also hoping other folks will make interesting observations.
With the few hours I've spent thus far, they seem to operate much like MA bands, with crossovers having similar implications.
But I feel like there are many other observations left unnoticed!
If you find any, hmu on twitter: @thalamu_
=====
Interesting Calculations in the Script, but not Plotted on the Chart
=====
There are calculations for up to 60 days of OIWAP taking change in OI rather than just OI.
There's one set for absolute value of change in OI, and one set for raw change in OI.
I didn't notice anything spectacularly interesting - but perhaps you will if you tinker with it!
=====
Find something cool? Have an improvement?
=====
Hmu on twitter: @thalamu_
RSI Candle Advanced V2RSI Advanced
As the period value is longer than 14, the RSI value sticks to the value of 50 and becomes useless.
Also, when the period value is less than 14, it moves excessively, so it is difficult for us to see the movement of the RSI .
So, using the period value and the RSI value as variables, I tried to make it easier to identify the RSI value through a new function expression.
This is how RSI Advanced was developed.
Period below 14 reduce the volatility of RSI , and period above 14 increase the volatility of RSI, allowing overbought and oversold zones to work properly and give you a better view of the trend.
By applying the custom algorithm so that the 'RSI Advanced' with period on a 5-minute timeframe has the same value as the 'original RSI' with period on a 60-minute timeframe.
As another example, an 'RSI Advanced' with a period in a 60-minute time frame has the same value as an 'original RSI' with a period in a 240-minute time frame.
Compare the difference in the RSI with a period value of 200 in the snapshot.
------------------------------------------------------------------------------------------
RSI Candlestick
RSI derives its value using only the closing price as a variable.
I solved the RSI equation in reverse and tried to include the high and low prices of candlesticks in the equation.
As a result, 'if the high or low was the closing price, the value of RSI would be like this' was implemented.
Just like when a candle comes down after setting a high price, an upper tail is formed when RSI Candle goes down after setting a high price!!
In divergence, we had to look only at the relationship between closing prices, but if we use RSI candles, we can find divergences in highs and highs, and lows and lows.
Existing indicators could not express "gap", but Version 2 made it possible to express "gap"!!!!!!
RSI can be displayed as candlesticks, bars and lines
Then enjoy my RSI!
----------------------------------------------------------------------------------------
RSI Advanced
기간값이 14보다 길어질수록 RSI값은 50값에 달라붙게 되어서 쓸모가 없어집니다.
또 기간값이 14보다 줄어들수록 과도하게 움직여서 우리는 RSI의 움직임을 보기가 힘듭니다.
그래서 기간 값과 RSI 값을 변수로 사용하여 새로운 함수 식을 통해 RSI 값을 식별하기 편하도록 해보았습니다.
이렇게 RSI Advanced가 개발되었습니다.
기간값이 14보다 낮으면 rsi의 변동폭이 줄어들고, 기간값이 14보다 크면 변동폭이 넓어져 과매수 및 과매도 영역이 제대로 작동하여 추세를 더 잘 볼 수 있습니다.
또한 저는 5분 타임프레임의 기간값이 168(=14*12)인 RSI가 주기 값이 14인 60분 타임프레임의 RSI와 동일한 값을 갖도록 적절한 함수 표현식을 적용하여 RSI를 변경했습니다.
다른 예로, 15분 시간 프레임에서 기간값이 56(=14*4)인 RSI는 60분 시간 프레임의 기간값이 14인 RSI와 동일한 값을 갖습니다.
기간값이 200인 RSI의 차이를 스냅샷에서 비교해보십시오.
-----------------------------
RSI Candlestick
RSI는 종가만을 변수로 사용하여 값을 도출해냅니다.
저는 RSI 식을 역으로 풀어내어서 캔들스틱의 고가와 저가, 시가를 식에 포함시켜보았습니다.
결과적으로, '만약 고가나 저가가 종가였다면 RSI의 값이 이럴것이다'를 구현해내었습니다.
캔들이 고가를 찍고 내려오면 윗꼬리가 생기듯 RSI Candle에서도 고가를 찍고 내려오면 윗꼬리가 생기는겁니다!!
다이버전스 또한 원래는 종가끼리의 관계만 봐야했지만 RSI 캔들을 이용한다면 고가와 고가, 저가와 저가에서도 다이버전스를 발견할 수 있습니다.
기존의 지표는 "갭"을 표현하지 못했지만 Version 2 에서는 "갭"을 표현할 수 있게 만들었습니다!!!!!!
그럼 잘 사용해주십시오!!!
PIVOT STRATEGY [INDIAN MARKET TIMING]
A Back-tested Profitable Strategy for Free!!
A PIVOT INTRADAY STRATEGY for 5 minute Time-Frame , that also explains the time condition for Indian Markets
The Timing can be changed to fit other markets, scroll down to "TIME CONDITION" to know more.
The commission is also included in the strategy .
The basic idea is when ,
1) Price crosses above ema1 ,indicated by pivot highest line in green color .
2) Price crosses below ema1 ,indicated by pivot lowest line in red color .
3) Candle high crosses above pivot highest , is the Long condition .
4) Candle low crosses below pivot lowest , is the Short condition .
5) Maximum Risk per trade for the intraday trade can be changed .
6) Default_qty_size is set to 60 contracts , which can be changed under settings → properties → order size .
7) ATR is used for trailing after entry, as mentioned in the inputs below.
// ═════════════════════════//
// ————————> INPUTS <————————— //
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Leftbars —————> Length of pivot highs and lows
Rightbars —————> Length of pivot highs and lows
Price Cross Ema —————> Added condition
ATR LONG —————> ATR stoploss trail for Long positions
ATR SHORT —————> ATR stoploss trail for Short positions
RISK —————> Maximum Risk per trade for the day
The strategy was back-tested on RELIANCE ,the input values and the results are mentioned under "BACKTEST RESULTS" below .
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// ————————> PROPERTIES<——————— //
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Default_qty_size ————> 60 contracts , which can be changed under settings
↓
properties
↓
order size
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// ————————> TIME CONDITION <————————— //
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The time can be changed in the script , Add it → click on ' { } ' → Pine editor→ making it a copy [right top corner} → Edit the line 25 .
The Indian Markets open at 9:15am and closes at 3:30pm .
The 'time_cond' specifies the time at which Entries should happen .
"Close All" function closes all the trades at 3pm, at the open of the next candle.
To change the time to close all trades , Go to Pine Editor → Edit the line 103 .
All open trades get closed at 3pm , because some brokers don't allow you to place fresh intraday orders after 3pm .
NSE:RELIANCE
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// ————————> BACKTEST RESULTS ( 128 CLOSED TRADES )<————————— //
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INPUTS can be changed for better back-test results.
The strategy applied to NIFTY ( 5 min Time-Frame and contract size 60 ) gives us 60% profitability y , as shown below
It was tested for a period a 6 months with a Profit Factor of 1.45 ,net Profit of 21,500Rs profit .
Sharpe Ratio : 0.311
Sortino Ratio : 0.727
The graph has a Linear Curve with consistent profits .
The INPUTS are as follows,
1) Leftbars ————————> 3
2) Rightbars ————————> 5
3) Price Cross Ema ——————> 150
4) ATR LONG ————————> 2.7
5) ATR SHORT ———————> 2.9
6) RISK —————————> 2500
7) Default qty size ——————> 60
NSE:RELIANCE
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