CYCLE BY RiotWolftradingDescription of the "CYCLE" Indicator
The "CYCLE" indicator is a custom Pine Script v5 script for TradingView that visualizes cyclic patterns in price action, dividing the trading day into specific sessions and 90-minute quarters (Q1-Q4). It is designed to identify and display market phases (Accumulation, Manipulation, Distribution, and Continuation/Reversal) along with key support and resistance levels within those sessions. Additionally, it allows customization of boxes, lines, labels, and colors to suit user preferences.
Main Features
Cycle Phases:
Accumulation (1900-0100): Represents the phase where large operators accumulate positions.
Manipulation (0100-0700): Identifies potential manipulative moves to mislead retail traders.
Distribution (0700-1300): The phase where large operators distribute their positions.
Continuation/Reversal (1300-1900): Indicates whether the price continues the trend or reverses.
90-Minute Quarters (Q1-Q4):
Divides each 6-hour cycle (360 minutes) into four 90-minute quarters (Q1: 00:00-01:30, Q2: 01:30-03:00, Q3: 03:00-04:30, Q4: 04:30-06:00 UTC).
Each quarter is displayed with a colored box (Q1: light purple, Q2: light blue, Q3: light gray, Q4: light pink) and labels (defaulted to black).
Support and Resistance Visualization:
Draws boxes or lines (based on settings) showing the high and low levels of each session.
Optionally displays accumulated volume at the highs and lows within the boxes.
Daily Lines and Last 3 Boxes:
How to Use the Indicator
Step 1: Add the Indicator to TradingView
Open TradingView and select the chart where you want to apply the indicator (e.g., UMG9OOR on a 5-minute timeframe, as shown in the screenshot).
Go to the Pine Editor (at the bottom of the TradingView interface).
Copy and paste the provided code.
Click Compile and then Add to Chart.
Step 2: Configure the Indicator
Click on the indicator name on the chart ("CYCLE") and select Settings (or double-click the name).
Adjust the options based on your needs:
Cycle Phases: Enable/disable phases (Accumulation, Manipulation, Distribution, Continuation/Reversal) and adjust their time slots if needed.
90-Minute Quarters: Enable/disable quarters (Q1-Q4).
Step 3: Interpret the Indicator
Identify Cycle Phases:
Observe the red boxes indicating the phases (Accumulation, Manipulation, etc.).
The high and low levels within each phase are potential support/resistance zones.
If volume is enabled, pay attention to the accumulated volume at highs and lows, as it may indicate the strength of those levels.
Use the 90-Minute Quarters (Q1-Q4):
The colored boxes (Q1-Q4) divide the day into 90-minute segments.
Each quarter shows the price range (high and low) during that period.
Use these boxes to identify price patterns within each quarter, such as breakouts or consolidations.
The labels (Q1, Q2, etc.) help you track time and anticipate potential moves in the next quarter.
Analyze Support and Resistance:
The high and low levels of each phase/quarter act as support and resistance.
Daily lines (if enabled) show key levels from the previous day, useful for planning entries/exits.
The "last 3 boxes below price" (if enabled) highlight potential support levels the price might target.
Avoid Manipulation:
During the Manipulation phase (0100-0700), be cautious of sharp moves or false breakouts.
Use the high/low levels of this phase to identify potential traps (as explained in your first question about manipulation candles).
Step 4: Trading Strategy
Entries and Exits:
Support/Resistance: Use the high/low levels of phases and quarters to set entry or exit points.
For example, if the price bounces off a Q1 support level, consider a buy.
Breakouts: If the price breaks a high/low of a quarter (e.g., Q2), wait for confirmation to enter in the direction of the breakout.
Volume: If accumulated volume is high near a key level, that level may be more significant.
Risk Management:
Place stop-loss orders below lows (for buys) or above highs (for sells) identified by the indicator.
Avoid trading during the Manipulation phase unless you have a specific strategy to handle false breakouts.
Time Context:
Use the quarters (Q1-Q4) to plan your trades based on time. For example, if Q3 is typically volatile in your market, prepare for larger moves between 03:00-04:30 UTC.
Step 5: Adjustments and Testing
Test on Different Timeframes: The indicator is set for a 5-minute timeframe (as in the screenshot), but you can test it on other timeframes (e.g., 1-minute, 15-minute) by adjusting the time slots if needed.
Adjust Colors and Styles: If the default colors are not visible on your chart, change them for better clarity.
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📌 1. **Accumulation: Strong Institutional Activity**
- During the **accumulation phase, we see **high volume: 82.773K, which suggests strong buying interest**, likely from institutional players.
- This sets the base for the following upward move in price.
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📌 2. **Manipulation: False Breakout with Lower Volume**
- Later, there's a manipulation phase where price breaks above previous highs, but the volume (71.814K) is **lower than during accumulation**.
- This implies that buyers are not as aggressive as before—no real demandbehind the breakout.
- It’s likely a bull trap, where smart money is selling into the breakout to exit their positions.
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### 📌 3. Distribution: Weakness and Lack of Demand
- The market enters a distribution phase, and volume drops even further (only 7.914K).
- Price struggles to go higher, and you start seeing rejections at the top.
- This shows that demand is drying up, and smart money is offloading positions**—not accumulating anymore.
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### 💡 Why Take the Short Here?
- Volume is not increasing with new highs—showing weak demand**.
- The manipulation volume is weaker than the accumulation volume, confirming the breakout was likely false.
- Structure starts to break down (Q levels falling), which confirms weakness.
- This creates a high-probability short setup:
- **Entry:** after confirmation of distribution and structural breakdown.
- **Stop loss:** above the manipulation high.
- **Target:** down toward previous lows or value zones.
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### ✅ Conclusion
Since the manipulation volume failed to exceed the accumulation volume, the breakout lacked real strength. Combined with decreasing volume in the distribution phase, this indicates fading demand and supply taking control—which justifies entering a short position.
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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.
Fibonacci Snap Tool [TradersPro]
OVERVIEW
The Fibonacci Snap tool automatically snaps to the swing high and swing low of the price data shown on the chart display. Fibonacci retracement levels can be used for entry, exit, or as a confirmation of trend continuation.
If the swing high on the chart comes before the swing low, the price is in a downtrend.If the swing high comes after the swing low, the price is in an uptrend.
We call the 23.60% Fibonacci level the momentum zone of the trend. Price in a solid trend, either up or down, will typically hold the 23.60% Fibonacci level as support (demand) in an uptrend or resistance (supply) in a downtrend.
Deeper Fibonacci levels of 38.20%, 50.00%, and 61.80% are corrective supply/demand zones. As price moves against the found trend, it can move into this range block we call the corrective zone.
Fibonacci retracement levels are used to identify potential supply/demand areas where price could reverse or consolidate. These levels are based on key ratios derived from the Fibonacci sequence, and we only use the core 23.60%, 38.20%, 50.00%, and 61.80% ratios.
CONCEPTS
Price action moves in trend cycles, these retracement levels help traders measure proportional relationships between the high/low swings in the price trend.
When a price trend is moving against the trend, traders can find opportunities to trade with the current trend at key Fibonacci levels. Fibonacci levels can be used to anticipate where price might find supply/demand imbalance and continue moving in the trend direction.
Traders apply the indicator by selecting a window of price they want to analyze in the chart display, and the Fibonacci Snap tool will snap to the high and low of the visible price display.
The Intent and Use of This Tool
The 23.60% level acts as a momentum or continuation of trend. The 38.20% to 61.80% range are corrective zones of the trend.
The 61.80% level, also known as the golden ratio (Google the term “Golden Ratio”; it's fun), can often represent the location of supply/demand imbalance.
In an uptrend, it can represent the area of no more selling supply, and the balance can shift to buying demand. In a downtrend, it can represent the area of no more buying demand and the balance can shift to selling supply.
When used with the Momentum Zones indicator, these two tools create a powerful combination for traders to find, implement, and manage trades.
Trading BehnamI've read around here various definitions for engulfs along the lines of "an engulf consumes all orders at a level to allow price to easily pass through it." . That doesn't make much sense to me, if the guys with billions of dollars want to break a level, they will break it and price will run off very often. We've seen it time and time again, they don't need to engulf levels to give us a nice opportunity to get into the trade with them, if they want to blast through a level, they will do so and price will run off. If they want an opportunity to accumulate more orders before price runs away, then it doesn't make sense to engulf the level, better to let price bounce from that level and then fill more orders, if the level breaks then they have to deliberately stop the market running away and move it back to the pre-engulf area as the market momentum would naturally make it run off after an engulf. Other ideas about it being a secret signal between the institutions don't make sense to me either. To be honest, I think any secret signals between competing institutions come in the form of them in a heavily encrypted chatroom telling each other what to do. This collusion has been reported on previously as traders align their activities at important moments.
So I think we can all agree something along the lines of:
Fakeout:
Fakeout is an engulf of an obvious swing high/low in order to stop out traders and induce breakout traders to trade in the wrong direction, thus generating liquidity for the move in the opposite direction.
What's not so clear is the definition of the engulf, I'd like to try to give some ideas on the purpose of the engulf and it's definition and see what others think.
Engulf:
An engulf is the consumption of orders at an important level, not necessarily a swing/high low but an area where we expect to see supply or demand. Taking out of the orders tells us that the supply or demand which was or should have been present is now not present and tells us the intent direction of the market. If price runs off as is often the case, this is not tradeable and is effectively just a "breakout", although breakouts are usually considered to be breaks of swing high and lows which are obvious to the average trader. For an engulf to be tradeable there must be a retrace following the engulf back in the original direction. This adds confusion as it initially resembles a fakeout. So the question is, why does price retrace after the engulf? If an engulf to the short side is a genuine engulf and not a fakeout to generate long liquidity, why does it not travel immediately south if market momentum is ultimately south.
A small pocket of demand beneath the engulfed level may make it retrace north as price moves between areas of liquidity, this pocket of demand may give price enough momentum to make it back up to the supply which broke the demand level if key market participants do not favour an immediate market drop.
Alternatively key market participants may step in and drive the market back upwards.
Price moving north back to supply after the engulf may occur or be favourable for various reasons:
1) We often talk about FO generating liquidity because of breakout trading, but an engulf can also generate liquidity from breakout traders. Short breakout traders would place their stop losses a small distance above the engulf (breakout). If key players absorb this selling or allow a demand level to push price back up, they can run price back up to supply taking out the stops of the breakout short traders and make quick profit and/or generate more liquidity for their own shorts.
2) To confuse traders, the ITs don't want the puzzle that is Forex to be easy to solve, if price never retraced after an engulf then engulfs of all levels would be FOs. Price would either break and immediately runoff or it would turn and runoff in the other direction. In order to keep people confused about whether price is faking out or breaking out, sometimes price should whipsaw by breaking out, briefly faking out and then continuing in the direction of the breakout. This whipsaw pattern is to us a tradeable engulf.
3) Market momentum may be mixed, key players are indecisive or inactive or the market is behaving erratically.
4) As previously mentioned there may be a small pocket of supply/demand just past the engulf which is causing a reaction. This could also be viewed as a FO on a different timeframe. If the market engulfs an H1 demand level, then retraces for 30 mins upwards to supply, this engulf would be a valid and very profitable FO for an M1 trader looking to get long.
Volatility Risk Premium GOLD & SILVER 1.0ENGLISH
This indicator (V-R-P) calculates the (one month) Volatility Risk Premium for GOLD and SILVER.
V-R-P is the premium hedgers pay for over Realized Volatility for GOLD and SILVER options.
The premium stems from hedgers paying to insure their portfolios, and manifests itself in the differential between the price at which options are sold (Implied Volatility) and the volatility GOLD and SILVER ultimately realize (Realized Volatility).
I am using 30-day Implied Volatility (IV) and 21-day Realized Volatility (HV) as the basis for my calculation, as one month of IV is based on 30 calendaristic days and one month of HV is based on 21 trading days.
At first, the indicator appears blank and a label instructs you to choose which index you want the V-R-P to plot on the chart. Use the indicator settings (the sprocket) to choose one of the precious metals (or both).
Together with the V-R-P line, the indicator will show its one year moving average within a range of +/- 15% (which you can change) for benchmarking purposes. We should consider this range the “normalized” V-R-P for the actual period.
The Zero Line is also marked on the indicator.
Interpretation
When V-R-P is within the “normalized” range, … well... volatility and uncertainty, as it’s seen by the option market, is “normal”. We have a “premium” of volatility which should be considered normal.
When V-R-P is above the “normalized” range, the volatility premium is high. This means that investors are willing to pay more for options because they see an increasing uncertainty in markets.
When V-R-P is below the “normalized” range but positive (above the Zero line), the premium investors are willing to pay for risk is low, meaning they see decreasing uncertainty and risks in the market, but not by much.
When V-R-P is negative (below the Zero line), we have COMPLACENCY. This means investors see upcoming risk as being lower than what happened in the market in the recent past (within the last 30 days).
CONCEPTS :
Volatility Risk Premium
The volatility risk premium (V-R-P) is the notion that implied volatility (IV) tends to be higher than realized volatility (HV) as market participants tend to overestimate the likelihood of a significant market crash.
This overestimation may account for an increase in demand for options as protection against an equity portfolio. Basically, this heightened perception of risk may lead to a higher willingness to pay for these options to hedge a portfolio.
In other words, investors are willing to pay a premium for options to have protection against significant market crashes even if statistically the probability of these crashes is lesser or even negligible.
Therefore, the tendency of implied volatility is to be higher than realized volatility, thus V-R-P being positive.
Realized/Historical Volatility
Historical Volatility (HV) is the statistical measure of the dispersion of returns for an index over a given period of time.
Historical volatility is a well-known concept in finance, but there is confusion in how exactly it is calculated. Different sources may use slightly different historical volatility formulas.
For calculating Historical Volatility I am using the most common approach: annualized standard deviation of logarithmic returns, based on daily closing prices.
Implied Volatility
Implied Volatility (IV) is the market's forecast of a likely movement in the price of the index and it is expressed annualized, using percentages and standard deviations over a specified time horizon (usually 30 days).
IV is used to price options contracts where high implied volatility results in options with higher premiums and vice versa. Also, options supply and demand and time value are major determining factors for calculating Implied Volatility.
Implied Volatility usually increases in bearish markets and decreases when the market is bullish.
For determining GOLD and SILVER implied volatility I used their volatility indices: GVZ and VXSLV (30-day IV) provided by CBOE.
Warning
Please be aware that because CBOE doesn’t provide real-time data in Tradingview, my V-R-P calculation is also delayed, so you shouldn’t use it in the first 15 minutes after the opening.
This indicator is calibrated for a daily time frame.
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ESPAŇOL
Este indicador (V-R-P) calcula la Prima de Riesgo de Volatilidad (de un mes) para GOLD y SILVER.
V-R-P es la prima que pagan los hedgers sobre la Volatilidad Realizada para las opciones de GOLD y SILVER.
La prima proviene de los hedgers que pagan para asegurar sus carteras y se manifiesta en el diferencial entre el precio al que se venden las opciones (Volatilidad Implícita) y la volatilidad que finalmente se realiza en el ORO y la PLATA (Volatilidad Realizada).
Estoy utilizando la Volatilidad Implícita (IV) de 30 días y la Volatilidad Realizada (HV) de 21 días como base para mi cálculo, ya que un mes de IV se basa en 30 días calendario y un mes de HV se basa en 21 días de negociación.
Al principio, el indicador aparece en blanco y una etiqueta le indica que elija qué índice desea que el V-R-P represente en el gráfico. Use la configuración del indicador (la rueda dentada) para elegir uno de los metales preciosos (o ambos).
Junto con la línea V-R-P, el indicador mostrará su promedio móvil de un año dentro de un rango de +/- 15% (que puede cambiar) con fines de evaluación comparativa. Deberíamos considerar este rango como el V-R-P "normalizado" para el período real.
La línea Cero también está marcada en el indicador.
Interpretación
Cuando el V-R-P está dentro del rango "normalizado",... bueno... la volatilidad y la incertidumbre, como las ve el mercado de opciones, es "normal". Tenemos una “prima” de volatilidad que debería considerarse normal.
Cuando V-R-P está por encima del rango "normalizado", la prima de volatilidad es alta. Esto significa que los inversores están dispuestos a pagar más por las opciones porque ven una creciente incertidumbre en los mercados.
Cuando el V-R-P está por debajo del rango "normalizado" pero es positivo (por encima de la línea Cero), la prima que los inversores están dispuestos a pagar por el riesgo es baja, lo que significa que ven una disminución, pero no pronunciada, de la incertidumbre y los riesgos en el mercado.
Cuando V-R-P es negativo (por debajo de la línea Cero), tenemos COMPLACENCIA. Esto significa que los inversores ven el riesgo próximo como menor que lo que sucedió en el mercado en el pasado reciente (en los últimos 30 días).
CONCEPTOS :
Prima de Riesgo de Volatilidad
La Prima de Riesgo de Volatilidad (V-R-P) es la noción de que la Volatilidad Implícita (IV) tiende a ser más alta que la Volatilidad Realizada (HV) ya que los participantes del mercado tienden a sobrestimar la probabilidad de una caída significativa del mercado.
Esta sobreestimación puede explicar un aumento en la demanda de opciones como protección contra una cartera de acciones. Básicamente, esta mayor percepción de riesgo puede conducir a una mayor disposición a pagar por estas opciones para cubrir una cartera.
En otras palabras, los inversores están dispuestos a pagar una prima por las opciones para tener protección contra caídas significativas del mercado, incluso si estadísticamente la probabilidad de estas caídas es menor o insignificante.
Por lo tanto, la tendencia de la Volatilidad Implícita es de ser mayor que la Volatilidad Realizada, por lo cual el V-R-P es positivo.
Volatilidad Realizada/Histórica
La Volatilidad Histórica (HV) es la medida estadística de la dispersión de los rendimientos de un índice durante un período de tiempo determinado.
La Volatilidad Histórica es un concepto bien conocido en finanzas, pero existe confusión sobre cómo se calcula exactamente. Varias fuentes pueden usar fórmulas de Volatilidad Histórica ligeramente diferentes.
Para calcular la Volatilidad Histórica, utilicé el enfoque más común: desviación estándar anualizada de rendimientos logarítmicos, basada en los precios de cierre diarios.
Volatilidad Implícita
La Volatilidad Implícita (IV) es la previsión del mercado de un posible movimiento en el precio del índice y se expresa anualizada, utilizando porcentajes y desviaciones estándar en un horizonte de tiempo específico (generalmente 30 días).
IV se utiliza para cotizar contratos de opciones donde la alta Volatilidad Implícita da como resultado opciones con primas más altas y viceversa. Además, la oferta y la demanda de opciones y el valor temporal son factores determinantes importantes para calcular la Volatilidad Implícita.
La Volatilidad Implícita generalmente aumenta en los mercados bajistas y disminuye cuando el mercado es alcista.
Para determinar la Volatilidad Implícita de GOLD y SILVER utilicé sus índices de volatilidad: GVZ y VXSLV (30 días IV) proporcionados por CBOE.
Precaución
Tenga en cuenta que debido a que CBOE no proporciona datos en tiempo real en Tradingview, mi cálculo de V-R-P también se retrasa, y por este motivo no se recomienda usar en los primeros 15 minutos desde la apertura.
Este indicador está calibrado para un marco de tiempo diario.
Volatility Risk Premium (VRP) 1.0ENGLISH
This indicator (V-R-P) calculates the (one month) Volatility Risk Premium for S&P500 and Nasdaq-100.
V-R-P is the premium hedgers pay for over Realized Volatility for S&P500 and Nasdaq-100 index options.
The premium stems from hedgers paying to insure their portfolios, and manifests itself in the differential between the price at which options are sold (Implied Volatility) and the volatility the S&P500 and Nasdaq-100 ultimately realize (Realized Volatility).
I am using 30-day Implied Volatility (IV) and 21-day Realized Volatility (HV) as the basis for my calculation, as one month of IV is based on 30 calendaristic days and one month of HV is based on 21 trading days.
At first, the indicator appears blank and a label instructs you to choose which index you want the V-R-P to plot on the chart. Use the indicator settings (the sprocket) to choose one of the indices (or both).
Together with the V-R-P line, the indicator will show its one year moving average within a range of +/- 15% (which you can change) for benchmarking purposes. We should consider this range the “normalized” V-R-P for the actual period.
The Zero Line is also marked on the indicator.
Interpretation
When V-R-P is within the “normalized” range, … well... volatility and uncertainty, as it’s seen by the option market, is “normal”. We have a “premium” of volatility which should be considered normal.
When V-R-P is above the “normalized” range, the volatility premium is high. This means that investors are willing to pay more for options because they see an increasing uncertainty in markets.
When V-R-P is below the “normalized” range but positive (above the Zero line), the premium investors are willing to pay for risk is low, meaning they see decreasing uncertainty and risks in the market, but not by much.
When V-R-P is negative (below the Zero line), we have COMPLACENCY. This means investors see upcoming risk as being lower than what happened in the market in the recent past (within the last 30 days).
CONCEPTS:
Volatility Risk Premium
The volatility risk premium (V-R-P) is the notion that implied volatility (IV) tends to be higher than realized volatility (HV) as market participants tend to overestimate the likelihood of a significant market crash.
This overestimation may account for an increase in demand for options as protection against an equity portfolio. Basically, this heightened perception of risk may lead to a higher willingness to pay for these options to hedge a portfolio.
In other words, investors are willing to pay a premium for options to have protection against significant market crashes even if statistically the probability of these crashes is lesser or even negligible.
Therefore, the tendency of implied volatility is to be higher than realized volatility, thus V-R-P being positive.
Realized/Historical Volatility
Historical Volatility (HV) is the statistical measure of the dispersion of returns for an index over a given period of time.
Historical volatility is a well-known concept in finance, but there is confusion in how exactly it is calculated. Different sources may use slightly different historical volatility formulas.
For calculating Historical Volatility I am using the most common approach: annualized standard deviation of logarithmic returns, based on daily closing prices.
Implied Volatility
Implied Volatility (IV) is the market's forecast of a likely movement in the price of the index and it is expressed annualized, using percentages and standard deviations over a specified time horizon (usually 30 days).
IV is used to price options contracts where high implied volatility results in options with higher premiums and vice versa. Also, options supply and demand and time value are major determining factors for calculating Implied Volatility.
Implied Volatility usually increases in bearish markets and decreases when the market is bullish.
For determining S&P500 and Nasdaq-100 implied volatility I used their volatility indices: VIX and VXN (30-day IV) provided by CBOE.
Warning
Please be aware that because CBOE doesn’t provide real-time data in Tradingview, my V-R-P calculation is also delayed, so you shouldn’t use it in the first 15 minutes after the opening.
This indicator is calibrated for a daily time frame.
ESPAŇOL
Este indicador (V-R-P) calcula la Prima de Riesgo de Volatilidad (de un mes) para S&P500 y Nasdaq-100.
V-R-P es la prima que pagan los hedgers sobre la Volatilidad Realizada para las opciones de los índices S&P500 y Nasdaq-100.
La prima proviene de los hedgers que pagan para asegurar sus carteras y se manifiesta en el diferencial entre el precio al que se venden las opciones (Volatilidad Implícita) y la volatilidad que finalmente se realiza en el S&P500 y el Nasdaq-100 (Volatilidad Realizada).
Estoy utilizando la Volatilidad Implícita (IV) de 30 días y la Volatilidad Realizada (HV) de 21 días como base para mi cálculo, ya que un mes de IV se basa en 30 días calendario y un mes de HV se basa en 21 días de negociación.
Al principio, el indicador aparece en blanco y una etiqueta le indica que elija qué índice desea que el V-R-P represente en el gráfico. Use la configuración del indicador (la rueda dentada) para elegir uno de los índices (o ambos).
Junto con la línea V-R-P, el indicador mostrará su promedio móvil de un año dentro de un rango de +/- 15% (que puede cambiar) con fines de evaluación comparativa. Deberíamos considerar este rango como el V-R-P "normalizado" para el período real.
La línea Cero también está marcada en el indicador.
Interpretación
Cuando el V-R-P está dentro del rango "normalizado",... bueno... la volatilidad y la incertidumbre, como las ve el mercado de opciones, es "normal". Tenemos una “prima” de volatilidad que debería considerarse normal.
Cuando V-R-P está por encima del rango "normalizado", la prima de volatilidad es alta. Esto significa que los inversores están dispuestos a pagar más por las opciones porque ven una creciente incertidumbre en los mercados.
Cuando el V-R-P está por debajo del rango "normalizado" pero es positivo (por encima de la línea Cero), la prima que los inversores están dispuestos a pagar por el riesgo es baja, lo que significa que ven una disminución, pero no pronunciada, de la incertidumbre y los riesgos en el mercado.
Cuando V-R-P es negativo (por debajo de la línea Cero), tenemos COMPLACENCIA. Esto significa que los inversores ven el riesgo próximo como menor que lo que sucedió en el mercado en el pasado reciente (en los últimos 30 días).
CONCEPTOS:
Prima de Riesgo de Volatilidad
La Prima de Riesgo de Volatilidad (V-R-P) es la noción de que la Volatilidad Implícita (IV) tiende a ser más alta que la Volatilidad Realizada (HV) ya que los participantes del mercado tienden a sobrestimar la probabilidad de una caída significativa del mercado.
Esta sobreestimación puede explicar un aumento en la demanda de opciones como protección contra una cartera de acciones. Básicamente, esta mayor percepción de riesgo puede conducir a una mayor disposición a pagar por estas opciones para cubrir una cartera.
En otras palabras, los inversores están dispuestos a pagar una prima por las opciones para tener protección contra caídas significativas del mercado, incluso si estadísticamente la probabilidad de estas caídas es menor o insignificante.
Por lo tanto, la tendencia de la Volatilidad Implícita es de ser mayor que la Volatilidad Realizada, por lo cual el V-R-P es positivo.
Volatilidad Realizada/Histórica
La Volatilidad Histórica (HV) es la medida estadística de la dispersión de los rendimientos de un índice durante un período de tiempo determinado.
La Volatilidad Histórica es un concepto bien conocido en finanzas, pero existe confusión sobre cómo se calcula exactamente. Varias fuentes pueden usar fórmulas de Volatilidad Histórica ligeramente diferentes.
Para calcular la Volatilidad Histórica, utilicé el enfoque más común: desviación estándar anualizada de rendimientos logarítmicos, basada en los precios de cierre diarios.
Volatilidad Implícita
La Volatilidad Implícita (IV) es la previsión del mercado de un posible movimiento en el precio del índice y se expresa anualizada, utilizando porcentajes y desviaciones estándar en un horizonte de tiempo específico (generalmente 30 días).
IV se utiliza para cotizar contratos de opciones donde la alta Volatilidad Implícita da como resultado opciones con primas más altas y viceversa. Además, la oferta y la demanda de opciones y el valor temporal son factores determinantes importantes para calcular la Volatilidad Implícita.
La Volatilidad Implícita generalmente aumenta en los mercados bajistas y disminuye cuando el mercado es alcista.
Para determinar la Volatilidad Implícita de S&P500 y Nasdaq-100 utilicé sus índices de volatilidad: VIX y VXN (30 días IV) proporcionados por CBOE.
Precaución
Tenga en cuenta que debido a que CBOE no proporciona datos en tiempo real en Tradingview, mi cálculo de V-R-P también se retrasa, y por este motivo no se recomienda usar en los primeros 15 minutos desde la apertura.
Este indicador está calibrado para un marco de tiempo diario.
Standard Error of the Estimate -Jon Andersen- V2Original implementation idea of bands by:
Traders issue: Stocks & Commodities V. 14:9 (375-379):
Standard Error Bands by Jon Andersen
Standard Error Bands are quite different than Bollinger's.
First, they are bands constructed around a linear regression curve.
Second, the bands are based on two standard errors above and below this regression line.
The error bands measure the standard error of the estimate around the linear regression line.
Therefore, as a price series follows the course of the regression line the bands will narrow , showing little error in the estimate. As the market gets noisy and random, the error will be greater resulting in wider bands .
Thanks to the work of @glaz & @XeL_arjona
In this version you can change the type of moving averages and the source of the bands.
Add a few studies of @dgtrd
1- ADX Colored Directional Movement Line
Directional Movement (DMI) (created by J. Welles Wilder ) consists of the Average Directional Index ( ADX ), to define whether or not there is a trend present, and Plus Directional Indicator (+D I) and Minus Directional Indicator (-D I) serve the purpose of determining trend direction
ADX Colored Directional Movement Line is custom interpretation of Directional Movement (DMI) with aim to present all 3 DMI indicator components with SINGLE line and ability to be added on top of the price chart (main chart)
How to interpret :
* triangle shapes:
▲- bullish : diplus >= diminus
▼- bearish : diplus < diminus
* colors:
green - bullish trend : adx >= strongTrend and di+ > di-
red - bearish trend : adx >= strongTrend and di+ < di-
gray - no trend : weekTrend < adx < strongTrend
yellow - week trend : adx < weekTrend
* color density:
darker : adx growing
lighter : adx falling
2- Volatility Colored Price/MA Line
Custom interpretation of the idea “Prices high above the moving average (MA) or low below it are likely to be remedied in the future by a reverse price movement”. Further details can be found under study “Price Distance to its MA by DGT”
How to interpret :
-▲ – Bullish , Price Action above Moving Average
-▼ – Bearish , Price Action below Moving Average
-Gray/Black - Low Volatility
-Green/Red – Price Action in Threshold Bands
-Dark Green/Red – Price Action Exceeds Threshold Bands
3- Volume Weighted Bar s
Volume Weighted Bars, a study of Kıvanç Özbilgiç, aims to present whether volume supports price movements. Volume Weighted Bars are calculated based on volume moving average.
How to interpret :
-Volume high above the volume moving average be displayed with red/green colors
-Average volume values will remain as they are and
-Volume low below the volume moving average will be indicated with darker colors
4- Fear & Greed index value, using technical anlysis approach calculated based on :
⮩1 - Price Momentum : Price Distance to its Moving Average
⮩2 - Strenght : Rate of Return, price movement over a period of time
⮩3 - Money Flow : Chaikin Money Flow, quantify changes in buying and selling pressure. CMF calculations is based on Accumulation/Distribution
⮩4 - Market Volatility : CBOE Volatility Index ( VIX ), the Volatility Index, or VIX , is a real-time market index that represents the market's expectation. It provides a measure of market risk and investors' sentiments
⮩5 -Safe Haven Demand: in this study GOLD demand is assumed
VN30 Effort-vs-Result Multi-Scanner — LinhVN30 Effort-vs-Result Multi-Scanner (Pine v5)
Cross-section scanner for Vietnam’s VN30 stocks that surfaces Effort vs Result footprints and related accumulation/distribution and volatility tells. It renders a ranked table (Top-N) with per-ticker signals and key metrics.
What it does
Scans up to 30 tickers (editable input.symbol slots) using one security() call per symbol → stays under Pine’s 40-call limit and runs reliably on any chart.
Scores each ticker by counting active signals, then ranks and lists the top names.
Optional metrics columns: zVol(60), zTR(60), ATR(20), HL/ATR(20).
Signals (toggleable)
Price/Volume – Effort vs Result
EVR Squeeze (stealth): z(Vol,60) > 4 & z(TR,60) < −0.5
5σ Vol, ≤1σ Ret: z(Vol,60) > 5 & |z(Return,60)| < 1
Wide Effort, Opposite Result: z(Vol,60) > 3 & close < open & z(CLV×Vol,60) > 1
Spread Compression, Heavy Tape: (H−L)/ATR(20) < 0.6 & z(Vol,60) > 3
No-Supply / No-Demand: close < close & range < 0.6×ATR(20) & vol < 0.5×SMA(20)
Momentum & Volatility
Vol-of-Vol Kink: z(ATR20,200) rising & z(ATR5,60) falling
BB Squeeze → Expansion: BBWidth(20) in low regime (z<−1.3) then close > upper band & z(Vol,60) > 2
RSI Non-Confirmation: Price LL/HH with RSI HL/LH & z(Vol,60) > 1
Accumulation/Distribution
OBV Divergence w/ Flat Price: OBV slope > 0 & |z(ret20,260)| < 0.3
Accumulation Days Cluster: ≥3/5 bars: up close, higher vol, close near high
Effort-Result Inversion (Down): big vol on down day then next day close > prior high
How to use
Set the timeframe (works best on 1D for EOD scans).
Edit the 30 symbol slots to your VN30 constituents.
Choose Top N, toggle Show metrics/Only matches and enable/disable scenarios.
Read the table: Rank, Ticker, (metrics), Score, and comma-separated Signals fired.
Method notes
Z-scores use a population-std estimate; CLV×Vol is used for effort/location.
Rolling counts avoid ta.sum; OBV is computed manually; all logic is Pine v5-safe.
Intraday-only ideas (true VWAP magnets, auction volume, flows, futures/options) are not included—Pine can’t cross-scan those datasets.
Disclaimer: Educational tool, not financial advice. Always confirm signals on the chart and with your process.
[blackcat] L1 Net Volume DifferenceOVERVIEW
The L1 Net Volume Difference indicator serves as an advanced analytical tool designed to provide traders with deep insights into market sentiment by examining the differential between buying and selling volumes over precise timeframes. By leveraging these volume dynamics, it helps identify trends and potential reversal points more accurately, thereby supporting well-informed decision-making processes. The key focus lies in dissecting intraday changes that reflect short-term market behavior, offering critical input for both swing and day traders alike. 📊
Key benefits encompass:
• Precise calculation of net volume differences grounded in real-time data.
• Interactive visualization elements enhancing interpretability effortlessly.
• Real-time generation of buy/sell signals driven by dynamic volume shifts.
TECHNICAL ANALYSIS COMPONENTS
📉 Volume Accumulation Mechanisms:
Monitors cumulative buy/sell volumes derived from comparative closing prices.
Periodically resets accumulation counters aligning with predefined intervals (e.g., 5-minute bars).
Facilitates identification of directional biases reflecting underlying market forces accurately.
🕵️♂️ Sentiment Detection Algorithms:
Employs proprietary logic distinguishing between bullish/bearish sentiments dynamically.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
Supports adaptive thresholds adjusting sensitivities based on changing market conditions flexibly.
🎯 Dynamic Signal Generation:
Detects transitions indicating dominance shifts between buyers/sellers promptly.
Triggers timely alerts enabling swift reactions to evolving market dynamics effectively.
Integrates conditional logic reinforcing signal validity minimizing erroneous activations.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Utilizes moving averages along with standardized deviation formulas generating precise net volume measurements.
Implements Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent alignment with established statistical principles preserving fidelity.
🖱️ User Interface Elements:
Dedicated plots displaying real-time net volume markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between net volume readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Reset Interval: Governs responsiveness versus stability balancing sensitivity/stability.
Price Source: Dictates primary data series driving volume calculations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
THANKS
Heartfelt acknowledgment extends to all developers contributing invaluable insights about volume-based trading methodologies! ✨
[blackcat] L2 Z-Score of PriceOVERVIEW
The L2 Z-Score of Price indicator offers traders an insightful perspective into how current prices diverge from their historical norms through advanced statistical measures. By leveraging Z-scores, it provides a robust framework for identifying potential reversals in financial markets. The Z-score quantifies the number of standard deviations that a data point lies away from the mean, thus serving as a critical metric for recognizing overbought or oversold conditions. 🎯
Key benefits encompass:
• Precise calculation of Z-scores reflecting true price deviations.
• Interactive plotting features enhancing visual clarity.
• Real-time generation of buy/sell signals based on crossover events.
STATISTICAL ANALYSIS COMPONENTS
📉 Mean Calculation:
Utilizes Simple Moving Averages (SMAs) to establish baseline price references.
Provides smooth representations filtering short-term noise preserving long-term trends.
Fundamental for deriving subsequent deviation metrics accurately.
📈 Standard Deviation Measurement:
Quantifies dispersion around established means revealing underlying variability.
Crucial for assessing potential volatility levels dynamically adapting strategies accordingly.
Facilitates precise Z-score derivations ensuring statistical rigor.
🕵️♂️ Z-SCORE DETECTION:
Measures standardized distances indicating relative positions within distributions.
Helps pinpoint extreme conditions signaling impending reversals proactively.
Enables early identification of trend exhaustion phases prompting timely actions.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Integrates SMAs along with standardized deviation formulas generating precise Z-scores.
Employs Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
🖱️ User Interface Elements:
Dedicated plots displaying real-time Z-score markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between Z-score readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Length: Governs responsiveness versus smoothing trade-offs balancing sensitivity/stability.
Price Source: Dictates primary data series driving Z-score computations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
[ AlgoChart ] - Compare MarketIndicator Description:
This indicator allows you to display a second asset, selectable from the input panel, in a separate window. Plotted on the same time scale as the first asset but with a distinct price scale, the indicator enables analysis of the relationships and relative movements of two financial instruments. It’s an ideal tool for understanding whether two assets move in a correlated or divergent manner.
Key Features:
Multi-Asset Comparison: Display two assets simultaneously to compare their trends.
Custom Scale: Each asset uses its own price scale, making comparative analysis easier.
Intuitive Interface: Easily select the second asset through the input panel.
Operational Applications:
Spread Trading: Identify optimal moments to execute spread trades when two highly correlated instruments move in opposite directions.
Supply & Demand: Pinpoint zones of interest on both assets, increasing the validity of support and resistance areas.
Exposure Reduction: Monitor instruments that move similarly to avoid exposing the portfolio in identical directions, thereby reducing the risk of double losses.
Additional Features:
Candle Color Change: When a directional divergence occurs between the two assets, the candles change color to highlight the event.
Customizable Notifications: Receive instant alerts when a divergence occurs, allowing you to act promptly.
CNN Fear and Greed Index JD modified from minusminusCNN Fear and Greed Index - www.cnn.com
Modified from minusminus -
See Documentation from CNN's website
CNN's Fear and Greed index is an attempt to quantitatively score the Fear and Greed in the SPX using 7 factors:
Market Momentum- S&P 500 (SPX) and its 125-day moving average
Stock Price Strength -Net new 52-week highs and lows on the NYSE
Stock Price Breadth - McClellan Volume Summation Index
Put and Call options - 5-day average put/call ratio
Market Volatility - VIX and its 50-day moving average
Safe Haven Demand - Difference in 20-day stock and bond returns
Junk Bond Demand - Yield spread: junk bonds vs. investment grade
Each Factor has a weight input for the final calculation initially set to a weight of 1. The final calculation of the index is a weighted average of each factor.
3 Factors have separate functions for calculation : See Code for Clarity
SPX Momentum : difference between the Daily CBOE:SPX index value and it's 125 Day Simple moving average.
Stock Price Strength : Net New 52-week highs and lows on the NYSE.
Function calculates a measure of Net New 52-week highs by:
NYSE 52-week highs (INDEX:MAHN) - all new NYSE Highs (INDEX:HIGH)
measure of Net New 52-week lows by:
NYSE 52-week lows (INDEX:MALN) - all new NYSE Lows (INDEX:LOWN)
Then calculate a ratio of Net New 52-week Highs and Lows over Total Highs and Lows then takes a 5-day moving average of that ratio-See Code
Stock Price Breadth is the McClellan Volume Summation Index :
First Calculate the McClellan Oscillator
Second Calculate the Summation Index
4 Factors are Straight data requests
5 Day Simple Moving Average of the Put-Call Ratio on SPY
50 Day Simple Moving Average of the SPX VIX
Difference between 20 Day Simple Moving Average of SPX Daily Close and 20 Day Simple Moving Average of 10Y Constant Maturity US Treasury Note
Yield Spread between ICE BofA US High Yield Index and ICE BofA US Investment Grade Corporate Yield Index
The Fear and Greed Index is a weighted average of these factors - which is then normalized to scale from 0 to 100 using the past 25 values - length parameter.
3 Zones are Shaded: Red for Extreme Fear, Grey for normal jitters, Green for Extreme Greed.
Disclaimer: This is not financial advice. These are just my ideas, and I am not an investment advisor or investment professional. This code is for informational purposes only and do your own analysis before making any investment decisions. This is an attempt to replicate in spirt an index CNN publishes on their website and in no way shape or form infringes on their content, calculations or proprietary information.
From CNN: www.cnn.com
FEAR & GREED INDEX FAQs
What is the CNN Business Fear & Greed Index?
The Fear & Greed Index is a way to gauge stock market movements and whether stocks are fairly priced. The theory is based on the logic that excessive fear tends to drive down share prices, and too much greed tends to have the opposite effect.
How is Fear & Greed Calculated?
The Fear & Greed Index is a compilation of seven different indicators that measure some aspect of stock market behavior. They are market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand. The index tracks how much these individual indicators deviate from their averages compared to how much they normally diverge. The index gives each indicator equal weighting in calculating a score from 0 to 100, with 100 representing maximum greediness and 0 signaling maximum fear.
How often is the Fear & Greed Index calculated?
Every component and the Index are calculated as soon as new data becomes available.
How to use Fear & Greed Index?
The Fear & Greed Index is used to gauge the mood of the market. Many investors are emotional and reactionary, and fear and greed sentiment indicators can alert investors to their own emotions and biases that can influence their decisions. When combined with fundamentals and other analytical tools, the Index can be a helpful way to assess market sentiment.
Order Block Drawing [TradingFinder]🔵 Introduction
Perhaps one of the most challenging tasks for Pine script developers (especially beginners) is properly drawing order blocks. While utilizing the latest technical analysis methods for "Price Action," beginners heavily rely on accurately plotting "Supply" and "Demand" zones, following concepts like "Smart Money Concept" and "ICT".
However, drawing "Order Blocks" may pose a challenge for developers. Therefore, to minimize bugs, increase accuracy, and speed up the process of coding order blocks, we have released the "Order Block Drawing" library.
Below, you can read more details about how to use this library.
Important :
This library has direct and indirect outputs. The indirect output includes the ranges of order blocks plotted on the chart. However, the direct output is a "Boolean" value that becomes "true" only when the price touches an order block, colloquially termed as "Mitigate." You can use this output for setting up alerts.
🔵 How to Use
First, you can add the library to your code as shown in the example below.
import TFlab/OrderBlockDrawing_TradingFinder/1
🟣Parameters
OBDrawing(OBType, TriggerCondition, DistalPrice, ProximalPrice, Index, OBValidDis, Show, ColorZone) =>
Parameters:
• OBType (string)
• TriggerCondition (bool)
• DistalPrice (float)
• ProximalPrice (float)
• Index (int)
• OBValidDis (int)
• Show (bool)
• ColorZone (color)
OBType : All order blocks are summarized into two types: "Supply" and "Demand." You should input your order block type in this parameter. Enter "Demand" for drawing demand zones and "Supply" for drawing supply zones.
TriggerCondition : Input the condition under which you want the order block to be drawn in this parameter.
DistalPrice : Generally, if each zone is formed by two lines, the farthest line from the price is termed "Distal." This input receives the price of the "Distal" line.
ProximalPrice : Generally, if each zone is formed by two lines, the nearest line to the price is termed "Proximal" line.
Index : This input receives the value of the "bar_index" at the beginning of the order block. You should store the "bar_index" value at the occurrence of the condition for the order block to be drawn and input it here.
OBValidDis : Order blocks continue to be drawn until a new order block is drawn or the order block is "Mitigate." You can specify how many candles after their initiation order blocks should continue. If you want no limitation, enter the number 4998.
Show : You may need to manage whether to display or hide order blocks. When this input is "On", order blocks are displayed, and when it's "Off", order blocks are not displayed.
ColorZone : You can input your preferred color for drawing order blocks.
🔵 Function Outputs
This function has only one output. This output is of type "Boolean" and becomes "true" only when the price touches an order block. Each order block can be touched only once and then loses its validity. You can use this output for alerts.
= Drawing.OBDrawing('Demand', Condition, Distal, Proximal, Index, 4998, true, Color)
[F][IND] - Time Range HighlighterDescription:
Introducing the Time Range Highlighter script for TradingView – a precision tool designed to enhance your chart analysis experience with a focus on simplicity and functionality. This script caters to traders who find value in isolating specific time intervals for a more detailed market study, akin to the concept of trading "macros".
Key Features:
1. Effortless Customization:
Define and highlight your preferred time ranges effortlessly. Tailor the script to align with your trading strategy by setting specific start and end times for enhanced precision.
2. Multi-Interval Support:
Seamlessly analyze multiple time ranges concurrently. Toggle between highlighted intervals with ease, allowing for a comprehensive examination of various market conditions without cluttering your chart.
3. Enable/Disable On-Demand:
Maintain control over the clutter on your chart. The enable/disable feature lets you activate or deactivate the highlighted time ranges at your discretion, ensuring a clean and unobstructed view when needed.
4. Focused Chart Analysis:
By visually emphasizing chosen time intervals, the script facilitates a focused analysis of critical market movements, enabling traders to identify patterns and trends with efficiency. This feature is particularly beneficial for those employing trading "macros" to filter out noise and concentrate on key periods.
Usage Instructions:
1. Apply the Time Range Highlighter script to your TradingView chart.
2. Customize the script settings to define specific time ranges tailored to your trading preferences.
3. Toggle between enabled and disabled states as needed to maintain clarity on your chart.
4. Leverage the script to streamline your chart analysis process and make more informed trading decisions, especially when employing trading "macros" to focus on specific market intervals.
Disclaimer:
This indicator is provided for educational purposes only. Trading involves risk, and users should consult with a financial professional before making any trading decisions.
Your Feedback Matters!
Please feel free to comment or reach out if you have any improvement suggestions or if you would like to request the development of a specific indicator. Your feedback is invaluable!
BTC bottom top MACRO indicator based on: Cost per transaction(w)Predicting tops and bottoms in any market is a challenging task, and the Bitcoin market is no exception. Many traders and analysts use a combination of various indicators and models to help them make educated guesses about where the market might be heading. One such metric that can provide valuable insights is the Bitcoin cost per transaction indicator.
Here's how it could potentially be superior to just using price action for predicting macro tops and bottoms:
Transaction Cost as an Indicator of Network Activity: The cost per transaction on the Bitcoin network can give an indication of how much activity is taking place. When transaction costs are high, it may signal increased network usage, which often coincides with periods of market enthusiasm or FOMO (Fear of Missing Out) that can precede market tops. Conversely, lower transaction costs might indicate reduced network activity, potentially signaling a lack of investor interest that might precede market bottoms.
Reflects Real-World Use and Demand: Unlike price action, which can be influenced by speculative trading and may not always reflect the underlying fundamentals, the cost per transaction is directly tied to the use of the Bitcoin network. It offers a more fundamental approach to understanding market dynamics.
Complements Price Action Analysis: While price action can give signals about potential tops and bottoms based on historical price patterns and technical analysis, the cost per transaction can add an additional layer of information by reflecting network activity. In this way, the two can be used together to give a more complete picture of the market.
May Precede Price Changes: Changes in transaction costs could potentially precede price changes, giving advanced warning of tops and bottoms. For instance, a sudden increase in transaction costs might indicate a surge in network activity and investor interest, potentially signaling a market top. On the other hand, a decrease in transaction costs might suggest declining network activity and investor interest, potentially signaling a market bottom.
However, it's important to note that while the cost per transaction can provide valuable insights, it's not a foolproof method for predicting market tops and bottoms. Like all indicators, it should be used in conjunction with other tools and analysis methods, and traders should also consider the broader market context. As always, past performance is not indicative of future results, and all trading and investment strategies carry the risk of loss.
HDT CloudsHDT Clouds combines custom clouds such as the 200EMA/MA cloud indicator to create high confluence bounce zones when combined with VWAP. The HDT indicator combines various clouds with the Volume Weighted Average Price indicator and Standard Deviations which allow users to identify areas on the chart where the stock may reverse.
On smaller time frames, like the 5/15/30minute, the 200ema/ma cloud and VWAP (when sitting in the same relative area) creates pockets of supply or demand.
In addition, the various moving average clouds, such as the 8/9ema cloud and the 34/50ema cloud, create areas of supply and demand depending on the overall trend. If the stock is trending very strongly to the upside, the 8/9ema can be used as a potential bounce area. Whereas, if the stock is trending, but not quite as strong, the stock may have demand at the 34-50ema where the stock could see a potential bounce to the upside. What sets this indicator apart from other moving average clouds is the incorporation of VWAP/Standard Deviation and the combining of a 200EMA/MA indicator which creates a strong pocket of demand even on lower time frames such as the 5 or 15 minute time frame.
Dump AlertsNYSE:BRK.B
By popular demand: An inverted version of my first indicator Pump Alerts in Pine Script with two alert conditions for trading bots and automated stock trading setups.
It's originally based on "Pump Catcher" by @joepegler
I modified some parts, hopefully improved the usability and enabled alerts, so you can use it to trigger bots like 3commas via webhooks or stock brokers partnering with TradingView.
Dump Alerts 📉 attempts to detect moments of abnormal and accelerating increase in volume concurrent with falling prices AKA "dumps". Small and big dumps.
I recommend trying different timeframes and tinkering with the lookback period as well as both threshold values.
Other than that it's pretty self-explanatory and beginner-friendly.
Free and Open Source. Let me know how you use it!
Support and Resistance levels from Options DataINTRODUCTION
This script is designed to visualize key support and resistance levels derived from options data on TradingView charts. It overlays lines, labels, and boxes to highlight levels such as Put Walls (gamma support), Call Walls (gamma resistance), Gamma Flip points, Vanna levels, and more.
These levels are intended to help traders identify potential areas of price magnetism, reversal, or breakout based on options market dynamics. All calculations and visualizations are based on user-provided data pasted into the input field, as Pine Script cannot directly fetch external options data due to platform limitations (explained below).
For convenience, my website allows users to interact with a bot that will generate the string for up to 30 tickers at once getting nearly real-time data on demand (data is cached for 15min). With the output string pasted into this indicator, it's a bliss to shuffle through your portfolio and see those levels for each ticker.
The script is open-source under TradingView's terms, allowing users to study, modify, and improve it. It draws inspiration from common options-derived metrics like gamma exposure and vanna, which are widely discussed in financial literature. No external code is copied without rights; all logic is original or based on standard mathematical formulas.
How the Options Levels Are Calculated
The levels displayed by this script are not computed within Pine Script itself—instead, they rely on pre-calculated values provided by the user (via a pasted data string). These values are derived from options chain data fetched from financial APIs (e.g., using libraries like yfinance in Python). Here's a step-by-step overview of how these levels are generally calculated externally before being input into the script:
Fetching Options Data:
Historical and current options chain data for a ticker (e.g., strikes, open interest, volume, implied volatility, expirations) is retrieved for near-term expirations (e.g., up to 90 days).
Current stock price is obtained from recent history.
Gamma Support (Put Wall) and Resistance (Call Wall):
Gamma Calculation: For each option, gamma (the rate of change of delta) is computed using the Black-Scholes formula:
gamma = N'(d1) / (S * sigma * sqrt(T))
where S is the stock price, K is the strike, T is time to expiration (in years), sigma is implied volatility, r is the risk-free rate (e.g., 0.0445), and N'(d1) is the normal probability density function.
Weighted gamma is multiplied by open interest and aggregated by strike.
The Put Wall is the strike below the current price with the highest weighted gamma from puts (acting as support).
The Call Wall is the strike above the current price with the highest weighted gamma from calls (acting as resistance).
Short-term versions focus on strikes closer to the money (e.g., within 10-15% of the price).
Gamma Flip Level:
Net dealer gamma exposure (GEX) is calculated across all strikes:
GEX = sum (gamma * OI * 100 * S^2 * sign * decay)
where sign is +1 for calls/-1 for puts, and decay is 1 / sqrt(T).
The flip point is the price where net GEX changes sign (from positive to negative or vice versa), interpolated between strikes.
Vanna Levels:
Vanna (sensitivity of delta to volatility) is calculated:
vanna = -N'(d1) * d2 / sigma
where d2 = d1 - sigma * sqrt(T).
Weighted by open interest, the highest positive and negative vanna strikes are identified.
Other Levels:
S1/R1: Significant strikes with high combined open interest and volume (80% OI + 20% volume), below/above price for support/resistance.
Implied Move: ATM implied volatility scaled by S * sigma * sqrt(d/365) (e.g., for 7 days).
Call/Put Ratio: Total call contracts divided by put contracts (OI + volume).
IV Percentage: Average ATM implied volatility.
Options Activity Level: Average contracts per unique strike, binned into levels (0-4).
Stop Loss: Dynamically set below the lowest support (e.g., Put Wall, Gamma Flip), adjusted by IV (tighter in low IV).
Fib Target: 1.618 extension from Put Wall to Call Wall range.
Previous day levels are stored for comparison (e.g., to detect Call Wall movement >2.5% for alerts).
Effect as Support and Resistance in Technical Trading
Options levels like gamma walls influence price action due to market maker hedging:
Put Wall (Gamma Support): High put gamma below price creates a "magnet" effect—market makers buy stock as price falls, providing support. Traders might look for bounces here as entry points for longs.
Call Wall (Gamma Resistance): High call gamma above price leads to selling pressure from hedging, acting as resistance. Rejections here could signal trims, sells or even shorts.
Gamma Flip: Where gamma exposure flips sign, often a volatility pivot—crossing it can accelerate moves (bullish above, bearish below).
Vanna Levels: Positive/negative vanna indicate volatility sensitivity; crosses may signal regime shifts.
Implied Move: Shows expected range; prices outside suggest overextension.
S1/R1 and Fib Target: Volume/OI clusters act as classic S/R; Fib extensions project upside targets post-breakout.
In trading, these are not guarantees—combine with TA (e.g., volume, trends). High activity levels imply stronger effects; low CP ratio suggests bearish sentiment. Alerts trigger on proximities/crosses for awareness, not advice.
Limitations of the TradingView Platform for Data Pulling
TradingView's Pine Script is sandboxed for security and performance:
No direct internet access or API calls (e.g., can't fetch yfinance data in-script).
Limited to chart data/symbol info; no real-time options chains.
Inputs are static per load; updates require manual pasting.
Caching isn't persistent across sessions.
This prevents dynamic data pulling, ensuring scripts remain lightweight but requiring external tools for fresh data.
Creative Solution for On-Demand Data Pulling
To overcome these limitations, users can use external tools or scripts (e.g., Python-based) to fetch and compute levels on demand. The tool processes tickers, generates a formatted string (e.g., "TICKER:level1,level2,...;TIMESTAMP:unix;"), and users paste it into the script's input. This keeps data fresh without violating platform rules, as computation happens off-platform. For example, run a local script to query APIs and output the string—adaptable for any ticker.
Script Functionality Breakdown
Inputs: Custom data string (parsed for levels/timestamp); toggles for short-term/previous/Vanna/stop loss; style options (colors, transparency).
Parsing: Extracts levels for the chart symbol; gets timestamp for "updated ago" display.
Drawing: Lines/labels for levels; boxes for gamma zones/implied move; clears old elements on updates.
Info Panel: Top-right summary with metrics (CP ratio, IV, distances, activity); emojis for quick status.
Alerts: Conditions for proximities, crosses, bounces (e.g., 0.5% bounce from Put Wall).
Performance: Uses vars for persistence; efficient for real-time.
This script is educational—test thoroughly. Not financial advice; past performance isn't indicative of future results. Feedback welcome via TradingView comments.
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
Cantom Chart - CL CTG vs BKDEnglish : This Pine Script indicator, named "Cantom Chart - CL CTG vs BKD," uniquely analyzes the immediate state of oil futures contracts to determine if they are in contango or backwardation. The script uses the price ratio between the nearest (CL1) and the next nearest (CL2) NYMEX crude oil futures contracts. It multiplies this ratio by 100 for clarity and scales fluctuations for enhanced visibility.
Key Features:
Dynamic Ratio Calculation: Computes the ratio (CL1/CL2 * 100) to determine the immediate market state.
Market State Interpretation: A ratio above 100 indicates backwardation, suggesting higher demand than supply, while a ratio below 100 indicates contango, suggesting higher supply than demand.
Volatility Adjustment: Amplifies market state changes by tripling the deviation from the baseline of 100, making it easier to observe subtle shifts.
Anomaly Detection: Caps the adjusted ratio at 125 for highs and 75 for lows, maintaining these limits until the ratio returns to normal levels.
Usage: This indicator is especially useful for traders analyzing supply-demand dynamics and inflationary pressures in the oil market. To apply it, simply add the script to your TradingView chart and adjust the 'Lower Threshold' and 'Upper Threshold' lines as needed based on your trading strategy.
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日本語 : この「Cantom Chart - CL CTG vs BKD」Pine Scriptインジケーターは、直近の原油先物契約がコンタンゴまたはバックワーデーションにあるかを特定するための独自の分析を提供します。最近の（CL1）と次の（CL2）NYMEX原油先物契約間の価格比を使用し、この比率に100を掛けて明確性を高め、変動の視認性を向上させます。
主要機能:
動的比率計算: 市場の即時状態を判断するために比率（CL1/CL2 * 100）を計算します。
市場状態の解釈: 比率が100を超える場合はバックワーデーション（需要が供給を上回る）、100未満の場合はコンタンゴ（供給が需要を上回る）を示します。
変動調整: 基準値100からの偏差を3倍にして、微妙な変化を容易に観察できるようにします。
異常値検出: 調整された比率を高値で125、低値で75に制限し、通常のレベルに戻るまでこれらの限界を維持します。
使用方法: このインジケーターは、原油市場における需給ダイナミクスとインフレ圧力を分析するトレーダーにとって特に有用です。使用するには、このスクリプトをTradingViewチャートに追加し、トレーディング戦略に基づいて「Lower Threshold」と「Upper Threshold」のラインを必要に応じて調整します。
Order Block Overlapping Drawing [TradingFinder]🔵 Introduction
Technical analysis is a fundamental tool in financial markets, helping traders identify key areas on price charts to make informed trading decisions. The ICT (Inner Circle Trader) style, developed by Michael Huddleston, is one of the most advanced methods in this field.
It enables traders to precisely identify and exploit critical zones such as Order Blocks, Breaker Blocks, Fair Value Gaps (FVGs), and Inversion Fair Value Gaps (IFVGs).
To streamline and simplify the use of these key areas, a library has been developed in Pine Script, the scripting language for the TradingView platform. This library allows you to automatically detect overlapping zones between Order Blocks and other similar areas, and visually display them on your chart.
This tool is particularly useful for creating indicators like Balanced Price Range (BPR) and ICT Unicorn Model.
🔵 How to Use
This section explains how to use the Pine Script library. This library assists you in easily identifying and analyzing overlapping areas between Order Blocks and other zones, such as Breaker Blocks and Fair Value Gaps.
To add "Order Block Overlapping Drawing", you must first add the following code to your script.
import TFlab/OrderBlockOverlappingDrawing/1
🟣 Inputs
The library includes the "OBOverlappingDrawing" function, which you can use to detect and display overlapping zones. This function identifies and draws overlapping zones based on the Order Block type, trigger conditions, previous and current prices, and other relevant parameters.
🟣 Parameters
OBOverlappingDrawing(OBType , TriggerConditionOrigin, distalPrice_Pre, proximalPrice_Pre , distalPrice_Curr, proximalPrice_Curr, Index_Curr , OBValidGlobal, OBValidDis, MitigationLvL, ShowAll, Show, ColorZone) =>
OBType (string)
TriggerConditionOrigin (bool)
distalPrice_Pre (float)
proximalPrice_Pre (float)
distalPrice_Curr (float)
proximalPrice_Curr (float)
Index_Curr (int)
OBValidGlobal (bool)
OBValidDis (int)
MitigationLvL (string)
ShowAll (bool)
Show (bool)
ColorZone (color)
In this example, various parameters are defined to detect overlapping zones and draw them on the chart. Based on these settings, the overlapping areas will be automatically drawn on the chart.
OBType : All order blocks are summarized into two types: "Supply" and "Demand." You should input your Current order block type in this parameter. Enter "Demand" for drawing demand zones and "Supply" for drawing supply zones.
TriggerConditionOrigin : Input the condition under which you want the Current order block to be drawn in this parameter.
distalPrice_Pre : Generally, if each zone is formed by two lines, the farthest line from the price is termed Pervious "Distal." This input receives the price of the "Distal" line.
proximalPrice_Pre : Generally, if each zone is formed by two lines, the nearest line to the price is termed Previous "Proximal" line.
distalPrice_Curr : Generally, if each zone is formed by two lines, the farthest line from the price is termed Current "Distal." This input receives the price of the "Distal" line.
proximalPrice_Curr : Generally, if each zone is formed by two lines, the nearest line to the price is termed Current "Proximal" line.
Index_Curr : This input receives the value of the "bar_index" at the beginning of the order block. You should store the "bar_index" value at the occurrence of the condition for the Current order block to be drawn and input it here.
OBValidGlobal : This parameter is a boolean in which you can enter the condition that you want to execute to stop drawing the block order. If you do not have a special condition, you should set it to True.
OBValidDis : Order blocks continue to be drawn until a new order block is drawn or the order block is "Mitigate." You can specify how many candles after their initiation order blocks should continue. If you want no limitation, enter the number 4998.
MitigationLvL : This parameter is a string. Its inputs are one of "Proximal", "Distal" or "50 % OB" modes, which you can enter according to your needs. The "50 % OB" line is the middle line between distal and proximal.
ShowAll : This is a boolean parameter, if it is "true" the entire order of blocks will be displayed, and if it is "false" only the last block order will be displayed.
Show : You may need to manage whether to display or hide order blocks. When this input is "On", order blocks are displayed, and when it's "Off", order blocks are not displayed.
ColorZone : You can input your preferred color for drawing order blocks.
🟣 Output
Mitigation Alerts : This library allows you to leverage Mitigation Alerts to detect specific conditions that could lead to trend reversals. These alerts help you react promptly in your trades, ensuring better management of market shifts.
🔵 Conclusion
The Pine Script library provided is a powerful tool for technical analysis, especially in the ICT style. It enables you to detect overlapping zones between Order Blocks and other significant areas like Breaker Blocks and Fair Value Gaps, improving your trading strategies. By utilizing this tool, you can perform more precise analysis and manage risks effectively in your trades.
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Overview
Equilibrium is a tool designed to measure the buying & selling pressure in the market. It is depicted as a “pressure gauge” that automatically adjusts as new candles are formed, providing a real-time indication of who's on top right now, buyers or sellers?
Background
Supply & demand is considered to be the main driving force of our modern economies, where the interaction between the two parties(sellers & buyers) leads to the determination of the fair price for a given product. Stock markets are no exception, they operate very much based around the idea of supply & demand.
In simple terms, supply refers to the availability of a product, and demand is the willingness of consumers to buy that product at a given price. It is obvious that different vendors may sell the same product at slightly different prices, and similarly, different customers may choose to buy the same product from different vendors at varying prices. The idea is that the price is allowed to fluctuate from time to time, but in a free & fair market, the price will eventually settle down to a value that makes both the parties happy. Such a state is known as the “Price-Equilibrium”, and this process is also referred to as the market mechanism.
This is the basic assumption around which this tool is based, the market is always trying to move towards a state of equilibrium.
Calculations
This tool takes a simplistic approach to estimate the degree of imbalance between buyers & sellers, here’s a brief summary of how the pressure is calculated:
- We compute the total lengths of red & green candles for a given period, i.e. price range multiplied by the volume for that candle.
- Then the distribution of each type of candle is calculated.
- Assuming more red candles denote more selling pressure, and green candles denote buying pressure, the gauge is populated cell by cell.
- As the pressure on one side increases, the intensity of the cell color also increases, signifying the extent to which one side is dominating.
How to use it
- The indicator is designed as a pressure gauge that moves up(vertical alignment) or to the right(horizontal alignment) as the buying pressure increases, and moves down or to the left as the selling pressure increases. How it is to be used & applied, that completely depends on your trading methodology. But, the general idea is that we expect the market to be in a state of equilibrium, and if that is not the case the tool will highlight that, and this is also where the opportunity lies to find suitable trades.
- Just by having an idea about who’s dominating the market currently, a trader can also pick sides wisely. Remember, the market is always striving to come back a state of equilibrium, and a slight imbalance can indicate the current trend, and more importantly, who’s more likely to make the next move.
User Settings
The tool offers some minimal configurations for the end user:
- You can choose to display the actual percentage value in the gauge(Show Text).
- You can adjust colors that denote buyers & sellers.
- You can change the layout of gauge, default is vertical(right side of the screen).
- Last, and most important, you can adjust the number of candles to traverse for calculating the pressure. Default is 50, can go upto 1000.
