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Options Max Pain Calculator [BackQuant]Options Max Pain Calculator A visualization tool that models option expiry dynamics by calculating "max pain" levels, displaying synthetic open interest curves, gamma exposure profiles, and pin-risk zones to help identify where market makers have the least payout exposure. What is Max Pain? Max Pain is the theoretical expiration price where the total dollar value of outstanding options would be minimized. At this price level, option holders collectively experience maximum losses while option writers (typically market makers) have minimal payout obligations. This creates a natural gravitational pull as expiration approaches. Core Features Visual Analysis Components: Max Pain Line: Horizontal line showing the calculated minimum pain level Strike Level Grid: Major support and resistance levels at key option strikes Pin Zone: Highlighted area around max pain where price may gravitate Pain Heatmap: Color-coded visualization showing pain distribution across prices Gamma Exposure Profile: Bar chart displaying net gamma at each strike level Real-time Dashboard: Summary statistics and risk metrics Synthetic Market Modeling** Since Pine Script cannot access live options data, the indicator creates realistic synthetic open interest distributions based on configurable market parameters including volume patterns, put/call ratios, and market maker positioning. How It Works Strike Generation: The tool creates a grid of option strikes centered around the current price. You can control the range, density, and whether strikes snap to realistic market increments. Open Interest Modeling: Using your inputs for average volume, put/call ratios, and market maker behavior, the indicator generates synthetic open interest that mirrors real market dynamics: Higher volume at-the-money with decay as strikes move further out Adjustable put/call bias to reflect current market sentiment Market maker inventory effects and typical short-gamma positioning Weekly options boost for near-term expirations Pain Calculation: For each potential expiry price, the tool calculates total option payouts: Call options contribute pain when finishing in-the-money Put options contribute pain when finishing in-the-money The strike with minimum total pain becomes the Max Pain level Gamma Analysis: Net gamma exposure is calculated at each strike using standard option pricing models, showing where hedging flows may be most intense. Positive gamma creates price support while negative gamma can amplify moves. Key Settings Basic Configuration: Number of Strikes: Controls grid density (recommended: 15-25) Days to Expiration: Time until option expiry Strike Range: Price range around current level (recommended: 8-15%) Strike Increment: Spacing between strikes Market Parameters: Average Daily Volume: Baseline for synthetic open interest Put/Call Volume Ratio: Market sentiment bias (>1.0 = bearish, <1.0 = bullish) It does not work if set to 1.0 Implied Volatility: Current option volatility estimate Market Maker Factors: Dealer positioning and hedging intensity Display Options: Model Complexity: Simple (line only), Standard (+ zones), Advanced (+ heatmap/gamma) Visual Elements: Toggle individual components on/off Theme: Dark/Light mode Update Frequency: Real-time or daily calculation Reading the Display Dashboard Table (Top Right): Current Price vs Max Pain Level Distance to Pain: Percentage gap (smaller = higher pin risk) Pin Risk Assessment: HIGH/MEDIUM/LOW based on proximity and time Days to Expiry and Strike Count Model complexity level Visual Elements: Red Line: Max Pain level where payout is minimized Colored Zone: Pin risk area around max pain Dotted Lines: Major strike levels (green = support, orange = resistance) Color Bar: Pain heatmap (blue = high pain, red = low pain/max pain zones) Horizontal Bars: Gamma exposure (green = positive, red = negative) Yellow Dotted Line: Gamma flip level where hedging behavior changes Trading Applications Expiration Pinning: When price is near max pain with limited time remaining, there's increased probability of gravitating toward that level as market makers hedge their positions. Support and Resistance: High open interest strikes often act as magnets, with max pain representing the strongest gravitational pull. Volatility Expectations: Above gamma flip: Expect dampened volatility (long gamma environment) Below gamma flip: Expect amplified moves (short gamma environment) Risk Assessment: The pin risk indicator helps gauge likelihood of price manipulation near expiry, with HIGH risk suggesting potential range-bound action. Best Practices Setup Recommendations Start with Model Complexity set to "Standard" Use realistic strike ranges (8-12% for most assets) Set put/call ratio based on current market sentiment Adjust implied volatility to match current levels Interpretation Guidelines: Small distance to pain + short time = high pin probability Large gamma bars indicate key hedging levels to monitor Heatmap intensity shows strength of pain concentration Multiple nearby strikes can create wider pin zones Update Strategy: Use "Daily" updates for cleaner visuals during trading hours Switch to "Every Bar" for real-time analysis near expiration Monitor changes in max pain level as new options activity emerges Important Disclaimers This is a modeling tool using synthetic data, not live market information. While the calculations are mathematically sound and the modeling realistic, actual market dynamics involve numerous factors not captured in any single indicator. Max pain represents theoretical minimum payout levels and suggests where natural market forces may create gravitational pull, but it does not guarantee price movement or predict exact expiration levels. Market gaps, news events, and changing volatility can override these dynamics. Use this tool as additional context for your analysis, not as a standalone trading signal. The synthetic nature of the data makes it most valuable for understanding market structure and potential zones of interest rather than precise price prediction. Technical Notes The indicator uses established option pricing principles with simplified implementations optimized for Pine Script performance. Gamma calculations use standard financial models while pain calculations follow the industry-standard definition of minimized option payouts. All visual elements use fixed positioning to prevent movement when scrolling charts, and the tool includes performance optimizations to handle real-time calculation without timeout errors.
Indicador Pine Script®
por BackQuant
8
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.
Indicador Pine Script®
por EdgeTools
DynamoSent DynamoSent Pro+ — Professional Listing (Preview) — Adaptive Macro Sentiment (v6) — Export, Adaptive Lookback, Confidence, Boxes, Heatmap + Dynamic OB/OS Preview / Experimental build. I’m actively refining this tool—your feedback is gold. If you spot edge cases, want new presets, or have market-specific ideas, please comment or DM me on TradingView. ⸻ What it is DynamoSent Pro+ is an adaptive, non-repainting macro sentiment engine that compresses VIX, DXY and a price-based activity proxy (e.g., SPX/sector ETF/your symbol) into a 0–100 sentiment line. It scales context by volatility (ATR%) and can self-calibrate with rolling quantile OB/OS. On top of that, it adds confidence scoring, a plain-English Context Coach, MTF agreement, exportable sentiment for other indicators, and a clean Light/Dark UI. Why it’s different • Adaptive lookback tracks regime changes: when volatility rises, we lengthen context; when it falls, we shorten—less whipsaw, more relevance. • Dynamic OB/OS (quantiles) self-calibrates to each instrument’s distribution—no arbitrary 30/70 lines. • MTF agreement + Confidence gate reduce false positives by highlighting alignment across timeframes. • Exportable output: hidden plot “DynamoSent Export” can be selected as input.source in your other Pine scripts. • Non-repainting rigor: all request.security() calls use lookahead_off + gaps_on; signals wait for bar close. Key visuals • Sentiment line (0–100), OB/OS zones (static or dynamic), optional TF1/TF2 overlays. • Regime boxes (Overbought / Oversold / Neutral) that update live without repaint. • Info Panel with confidence heat, regime, trend arrow, MTF readout, and Coach sentence. • Session heat (Asia/EU/US) to match intraday behavior. • Light/Dark theme switch in Inputs (auto-contrasted labels & headers). ⸻ How to use (examples & recipes) 1) EURUSD (swing / intraday blend) • Preset: EURUSD 1H Swing • Chart: 1H; TF1=1H, TF2=4H (default). • Proxies: Defaults work (VIX=D, DXY=60, Proxy=D). • Dynamic OB/OS: ON at 20/80; Confidence ≥ 55–60. • Playbook: • When sentiment crosses above 50 + margin with Δ ≥ signalK and MTF agreement ≥ 0.5, treat as trend breakout. • In Oversold with rising Coach & TF agreement, take fade longs back toward mid-range. • Alerts: Enable Breakout Long/Short and Fade; keep cooldown 8–12 bars. 2) SPY (daytrading) • Preset: SPY 15m Daytrade; Chart: 15m. • VIX (D) matters more; preset weights already favor it. • Start with static 30/70; later try dynamic 25/75 for adaptive thresholds. • Use Coach: in US session, when it says “Overbought + MTF agree → sell rallies / chase breakouts”, lean momentum-continuation after pullbacks. 3) BTCUSD (crypto, 24/7) • Preset: BTCUSD 1H; Chart: 1H. • DXY and BTC.D inform macro tone; keep Carry-forward ON to bridge sparse ticks. • Prefer Dynamic OB/OS (15/85) for wider swings. • Fade signals on weekend chop; Breakout when Confidence > 60 and MTF ≥ 1.0. 4) XAUUSD (gold, macro blend) • Preset: XAUUSD 4H; Chart: 4H. • Weights tilt to DXY and US10Y (handled by preset). • Coach + MTF helps separate trend legs from news pops. ⸻ Best practices • Theme: Switch Light/Dark in Inputs; the panel adapts contrast automatically. • Export: In another script → Source → DynamoSent Pro+ → DynamoSent Export. Build your own filters/strategies atop the same sentiment. • Dynamic vs Static OB/OS: • Static 30/70: fast, universal baseline. • Dynamic (quantiles): instrument-aware; use 20/80 (default) or 15/85 for choppy markets. • Confidence gate: Start at 50–60% to filter noise; raise when you want only A-grade setups. • Adaptive Lookback: Keep ON. For ultra-liquid indices, you can switch it OFF and set a fixed lookback. ⸻ Non-repainting & safety notes • All request.security() calls use lookahead=barmerge.lookahead_off and gaps=barmerge.gaps_on. • No forward references; signals & regime flips are confirmed on bar close. • History-dependent funcs (ta.change, ta.percentile_linear_interpolation, etc.) are computed each bar (not conditionally). • Adaptive lookback is clamped ≥ 1 to avoid lowest/highest errors. • Missing-data warning triggers only when all proxies are NA for a streak; carry-forward can bridge small gaps without repaint. ⸻ Known limits & tips • If a proxy symbol isn’t available on your plan/exchange, you’ll see the NA warning: choose a different symbol via Symbol Search, or keep Carry-forward ON (it defaults to neutral where needed). • Intraday VIX is sparse—using Daily is intentional. • Dynamic OB/OS needs enough history (see dynLenFloor). On short histories it gracefully falls back to static levels. Thanks for trying the preview. Your comments drive the roadmap—presets, new proxies, extra alerts, and integrations.
Indicador Pine Script®
por ACTradeFIN
KCandle Strategy 1.0# KCandle Strategy 1.0 - Trading Strategy Description ## Overview The **KCandle Strategy** is an advanced Pine Script trading system based on bullish and bearish engulfing candlestick patterns, enhanced with sophisticated risk management and position optimization features. ## Core Logic ### Entry Signal Generation - **Pattern Recognition**: Detects bullish and bearish engulfing candlestick formations - **EMA Filter**: Uses a customizable EMA (default 25) to filter trades in the direction of the trend - **Entry Levels**: - **Long entries** at 25% of the candlestick range from the low - **Short entries** at 75% of the candlestick range from the low - **Signal Validation**: Orange candlesticks indicate valid setup conditions ### Risk Management System #### 1. **Stop Loss & Take Profit** - Configurable stop loss in pips - Risk-reward ratio setting (default 2:1) - Visual representation with colored lines and labels #### 2. **Break-Even Management** - Automatically moves stop loss to break-even when specified R:R is reached - Customizable break-even offset for added protection - Prevents losing trades after reaching profitability #### 3. **Trailing Stop System** - **Activation Trigger**: Activates when position reaches specified R:R level - **Distance Control**: Maintains trailing stop at defined distance from entry - **Step Management**: Moves stop loss forward in incremental R steps - **Dynamic Protection**: Locks in profits while allowing for continued upside ### Advanced Features #### Position Management - **Pyramiding Support**: Optional multiple position entries with size reduction - **Order Expiration**: Pending orders automatically cancel after specified bars - **Position Sizing**: Percentage-based allocation with pyramid level adjustments #### Visual Interface - **Real-time Monitoring**: Comprehensive information panel with all strategy metrics - **Historical Tracking**: Visual representation of past trades and levels - **Color-coded Indicators**: Different colors for break-even, trailing, and standard stops - **Debug Options**: Optional labels for troubleshooting and optimization ## Key Parameters ### Basic Settings - **EMA Length**: Trend filter period - **Stop Loss**: Risk per trade in pips - **Risk/Reward**: Target profit ratio - **Order Validity**: Duration of pending orders ### Risk Management - **Break-Even R:R**: Profit level to trigger break-even - **Trailing Activation**: R:R level to start trailing - **Trailing Distance**: Stop distance from entry when trailing - **Trailing Step**: Increment for stop loss advancement ## Strategy Benefits 1. **Objective Entry Signals**: Based on proven candlestick patterns 2. **Trend Alignment**: EMA filter ensures trades align with market direction 3. **Robust Risk Control**: Multiple layers of protection (SL, BE, Trailing) 4. **Profit Optimization**: Trailing stops maximize winning trade potential 5. **Flexibility**: Extensive customization options for different market conditions 6. **Visual Clarity**: Complete visual feedback for trade management ## Ideal Use Cases - **Swing Trading**: Medium-term positions with trend-following approach - **Breakout Trading**: Capturing momentum from engulfing patterns - **Risk-Conscious Trading**: Suitable for traders prioritizing capital preservation - **Multi-Timeframe**: Adaptable to various timeframes and instruments --- *The KCandle Strategy combines traditional technical analysis with modern risk management techniques, providing traders with a comprehensive tool for systematic market participation.*
Estratégia Pine Script®
por jake_theboss
Z-Score For Loop | MisinkoMasterThe Z-Score For Loop (ZSFL) is a unique trend-following oscillator designed to detect potential reversals and momentum shifts earlier than traditional tools, providing traders with fast, adaptive, and reliable signals. Unlike common smoothing techniques (moving averages, medians, or modes), the ZSFL introduces a for-loop comparison method that balances speed and noise reduction, resulting in a powerful reversal-detection system. 🔎 Methodology The indicator is built in two main stages: Z-Score Calculation Formula: Z=(Source−Mean)/Standard Deviation Z= Standard Deviation (Source−Mean) ​ The user can select the averaging method for the mean: SMA, EMA, WMA, HMA, DEMA, or TEMA. Recommended: EMA, SMA, or WMA for balanced accuracy. The choice of biased (sample) or unbiased (population) standard deviation is also available. ➝ On its own, the raw Z-score is fast but noisy, requiring additional filtering. For Loop Logic (Noise Reduction) Instead of using traditional smoothing (which adds lag), the indicator applies a for loop comparison. The current Z-score is compared against previous values over a user-defined range (start → end). Each comparison adds or subtracts “points”: +1 point if the current Z-score is higher than a past Z-score. -1 point if it is lower. The final value is the cumulative score, reflecting whether the Z-score is generally stronger or weaker than its historical context. ➝ This approach keeps speed intact while removing much of the false noise that raw Z-scores generate. 📈 Trend Logic Bullish Signal (Cyan) → Triggered when the score crosses above the upper threshold (default +45). Bearish Signal (Magenta) → Triggered when the score crosses below the lower threshold (default -25). Neutral → When the score remains between the thresholds. Thresholds are adjustable, making the tool flexible for different assets and timeframes. 🎨 Visualization The ZSFL score is plotted as a main oscillator line. Upper and lower thresholds are plotted as static reference levels. The price chart can also be color-coded with trend signals (cyan for bullish, magenta for bearish) to provide immediate visual confirmation. ⚡ Features Adjustable Z-score length (len). Multiple average types for the mean (SMA, EMA, WMA, HMA, DEMA, TEMA). Toggle between biased vs. unbiased SD calculations. Adjustable For Loop range (start, end). Adjustable upper and lower thresholds for signal generation. Works as both an oscillator and a price overlay tool. ✅ Use Cases Reversal Detection → Spot early shifts before price confirms them. Trend Confirmation → Use thresholds to filter false reversals. System Filter → Combine with trend indicators to refine entries. Multi-Timeframe Setup → Works well across different timeframes for swing, day, or intraday trading. ⚠️ Limitations As with all oscillators, the ZSFL will generate false signals in sideways/choppy markets. Optimal parameters (length, loop size, thresholds) may differ across assets. It is not a standalone trading system — use alongside other forms of analysis (trend filters, volume, higher timeframe confluence).
Indicador Pine Script®
por MisinkoMaster
ORB Pro w/ Filters + Debug Overlay Update with Reason box fixThis indicator is designed to highlight high-probability reversal setups for intraday traders. It focuses on the cleanest, most reliable candlestick reversal patterns and combines them with trend, VWAP/EMA confluence, and a time-based filter to reduce noise. 🛠️ How It Works The script scans each bar for well-known reversal signals: Doji Reversal – small body, long wicks showing indecision. Hammer / Shooting Star – long wick ≥ 2× body, showing exhaustion. Engulfing Reversal – full body engulf of the prior candle. Additional filters include: ✅ VWAP/EMA Confluence (optional) – confirms reversals near key intraday levels. ✅ Time Window (default 9:30–10:30 NY) – avoids false signals later in the session. ✅ Trend Exhaustion Check – requires a short-term directional push before reversal. ✅ Signal Cooldown – limits to one clean signal per move. When conditions align, the script plots: 🟢 “Bull Rev” label below the bar for bullish reversals. 🔴 “Bear Rev” label above the bar for bearish reversals. ⚙️ Recommended Settings For the tightest, most reliable signals: Doji Body % → 25–30 Hammer Wick Multiple → 2.0 Confluence Tolerance % → 0.2–0.3 Time Filter → ON (9:30–10:30 NY) VWAP/EMA Filter → ON Cooldown Bars → 10–15 These settings minimize false positives and focus on the strongest reversals. 📈 Use Case This tool is best for: Intraday traders (stocks, ETFs, futures, crypto). Traders who use Opening Range Breakout (ORB) or similar systems but want a secondary tool for catching reversals. Anyone looking to filter out weak reversal patterns and focus on textbook setups. ⚠️ Disclaimer This script is for educational purposes only and should not be considered financial advice. Always test in simulation/paper trading before applying live 🚀 Catch textbook reversals with confidence. This indicator filters out noise and only plots high-probability reversal signals based on proven candlestick patterns + VWAP/EMA confluence. 🔥 Key Features: ✅ Detects Doji, Hammer/Shooting Star, and Engulfing Reversals ✅ VWAP & EMA confluence filter (optional) ✅ Time window filter (default 9:30–10:30 NY for max edge) ✅ Signal cooldown to avoid clutter ✅ Clean chart labels + alert conditions 🎯 Who’s It For? Day traders who want precision reversal entries ORB traders looking for secondary setups Intraday scalpers who value quality over quantity 👉 Designed for traders who want fewer, cleaner, higher-probability signals. ⚠️ Not financial advice. For educational use only _____ 🎯 ORB SET-UP DESCRIPTIONS: 🔧 Exact settings I’d recommend (to avoid that mess): requireClose = true requireRetest = true with retestPct = 0.2% minRangePct = 0.3%, maxRangePct = 1.5% volumeFilter = true, volumeLength = 20 trendFilter = true, emaLength = 20 cooldownBars = 6 (on 5m chart → 30 minutes) 🔑 ORB Range Settings Default sweet spot: 0.2% – 0.3% → This usually balances enough signals with reduced false breakouts. High volatility days (CPI, FOMC, big gaps): 0.3% – 0.5% → Prevents fake outs. Low volatility days (tight overnight range, slow open): 0.15% – 0.2% → Keeps you from sitting on hands all day. 📌 Filters you already added help you avoid noise EMA alignment Volume confirmation Optional stop/target logic This means you don’t have to shrink the box to 0.1% — the filters will keep you in higher-probability trades ✅ Why You Might NOT See a Signal Check box for reason signal to turn it off, updated coloring so that candles are more visable. ORB Box Too Wide If the opening range is large, price has to move much further to trigger a clean breakout. Wide box = fewer signals (but higher quality). No Clean Break + Hold Script waits for a candle to break above/below ORB and close strong enough. A wick poke doesn’t count. VWAP / EMA Filter Not Aligned If price breaks but VWAP/EMA trend filter disagrees → no signal. Keeps you out of fake moves against the trend. Confirmation Candle Missing (if enabled) Even if price breaks, the script may want the next bar to confirm direction before signaling. Cooldown / One-Signal-Per-Break Rule Some filters prevent back-to-back spam signals. Only the first clean setup is alerted.
Indicador Pine Script®
por TradeswithB
Volume Bubbles & Liquidity Heatmap [LuxAlgo]The Volume Bubbles & Liquidity Heatmap indicator highlights volume and liquidity clearly and precisely with its volume bubbles and liquidity heat map, allowing to identify key price areas. Customize the bubbles with different time frames and different display modes: total volume, buy and sell volume, or delta volume. 🔶 USAGE The primary objective of this tool is to offer traders a straightforward method for analyzing volume on any selected timeframe. By default, the tool displays buy and sell volume bubbles for the daily timeframe over the last 2,000 bars. Traders should be aware of the difference between the timeframe of the chart and that of the bubbles. The tool also displays a liquidity heat map to help traders identify price areas where liquidity accumulates or is lacking. 🔹 Volume Bubbles The bubbles have three possible display modes: Total Volume: Displays the total volume of trades per bubble. Buy & Sell Volume: Each bubble is divided into buy and sell volume. Delta Volume: Displays the difference between buy and sell volume. Each bubble represents the trading volume for a given period. By default, the timeframe for each bubble is set to daily, meaning each bubble represents the trading volume for each day. The size of each bubble is proportional to the volume traded; a larger bubble indicates greater volume, while a smaller bubble indicates lower volume. The color of each bubble indicates the dominant volume: green for buy volume and red for sell volume. One of the tool's main goals is to facilitate simple, clear, multi-timeframe volume analysis. The previous chart shows Delta Volume bubbles with various chart and bubble timeframe configurations. To correctly visualize the bubbles, traders must ensure there is a sufficient number of bars per bubble. This is achieved by using a lower chart timeframe and a higher bubble timeframe. As can be seen in the image above, the greater the difference between the chart and bubble timeframes, the better the visualization. 🔹 Liquidity Heatmap The other main element of the tool is the liquidity heatmap. By default, it divides the chart into 25 different price areas and displays the accumulated trading volume on each. The image above shows a 4-hour BTC chart displaying only the liquidity heatmap. Traders should be aware of these key price areas and observe how the price behaves in them, looking for possible opportunities to engage with the market. The main parameters for controlling the heatmap on the settings panel are Rows and Cell Minimum Size. Rows modifies the number of horizontal price areas displayed, while Cell Minimum Size modifies the minimum size of each liquidity cell in each row. As can be seen in the above BTC hourly chart, the cell size is 24 at the top and 168 at the bottom. The cells are smaller on top and bigger on the bottom. The color of each cell reflects the liquidity size with a gradient; this reflects the total volume traded within each cell. The default colors are: Red: larger liquidity Yellow: medium liquidity Blue: lower liquidity 🔹 Using Both Tools Together This indicator provides the means to identify directional bias and market timing. The main idea is that if buyers are strong, prices are likely to increase, and if sellers are strong, prices are likely to decrease. This gives us a directional bias for opening long or short positions. Then, we combine our directional bias with price rejection or acceptance of key liquidity levels to determine the timing of opening or closing our positions. Now, let's review some charts. This first chart is BTC 1H with Delta Weekly Bubbles. Delta Bubbles measure the difference between buy and sell volume, so we can easily see which group is dominant (buyers or sellers) and how strong they are in any given week. This, along with the key price areas displayed by the Liquidity Heatmap, can help us navigate the markets. We divided market behavior into seven groups, and each group has several bubbles, numbered from 1 to 17. Bubbles 1, 2, and 3: After strong buyers market consolidates with positive delta, prices move up next week. Bubbles 3, 4, and 5: Strength changes from buyers to sellers. Next week, prices go down. Bubbles 6 and 7: The market trades at higher prices, but with negative delta. Next week, prices go down. Bubbles 7, 8, and 9: Strength changes from sellers to buyers. Next weeks (9 and 10), prices go up. Bubbles 10, 11, and 12: After strong buyers prices trade higher with a negative delta. Next weeks (12 and 13) prices go down. Bubbles 12, 14, and 15: Strength changes from sellers to buyers; next week, prices increase. Bubbles 15 and 16: The market trades higher with a very small positive delta; next week, prices go down. Current bubble/week 17 is not yet finished. Right now, it is trading lower, but with a smaller negative delta than last week. This may signal that sellers are losing strength and that a potential reversal will follow, with prices trading higher. This is the same BTC 1H chart, but with price rejections from key liquidity areas acting as strong price barriers. When prices reach a key area with strong liquidity and are rejected, it signals a good time to take action. By observing price behavior at certain key price levels, we can improve our timing for entering or exiting the markets. 🔶 DETAILS 🔹 Bubbles Display From the settings panel, traders can configure the bubbles with four main parameters: Mode, Timeframe, Size%, and Shape. The image above shows five-minute BTC charts with execution over the last 3,500 bars, different display modes, a daily timeframe, 100% size, and shape one. The Size % parameter controls the overall size of the bubbles, while the Shape parameter controls their vertical growth. Since the chart has two scales, one for time and one for price, traders can use the Shape parameter to make the bubbles round. The chart above shows the same bubbles with different size and shape parameters. You can also customize data labels and timeframe separators from the settings panel. 🔶 SETTINGS Execute on last X bars: Number of bars for indicator execution 🔹 Bubbles Display Bubbles: Enable/Disable volume bubbles. Bubble Mode: Select from the following options: total volume, buy and sell volume, or the delta between buy and sell volume. Bubble Timeframe: Select the timeframe for which the bubbles will be displayed. Bubble Size %: Select the size of the bubbles as a percentage. Bubble Shape: Select the shape of the bubbles. The larger the number, the more vertical the bubbles will be stretched. 🔹 Labels Display Labels: Enable/Disable data labels, select size and location. 🔹 Separators Display Separators: Enable/Disable timeframe separators and select color. 🔹 Liquidity Heatmap Display Heatmap: Enable/Disable liquidity heatmap. Heatmap Rows: select number of rows to be displayed. Cell Minimum Size: Select the minimum size for each cell in each row. Colors. 🔹 Style Buy & Sell Volume Colors.
Indicador Pine Script®
por LuxAlgo
6
Smarter Money Concepts Dashboard [PhenLabs]📊Smarter Money Concepts Dashboard Version: PineScript™v6 📌Description The Smarter Money Concepts Dashboard is a comprehensive institutional trading analysis tool that combines six of our most powerful smarter money concepts indicators into one unified suite. This advanced system automatically detects and visualizes Fair Value Gaps, Inverted FVGs, Order Blocks, Wyckoff Springs/Upthrusts, Wick Rejection patterns, and ICT Market Structure analysis. Built for serious traders who need institutional-grade market analysis, this dashboard eliminates subjective interpretation by automatically identifying where smart money is likely positioned. The integrated real-time dashboard provides instant status updates on all active patterns, making it easy to monitor market conditions at a glance. 🚀Points of Innovation ● Multi-Module Integration: Six different SMC concepts unified in one comprehensive system ● Real-Time Dashboard Display: Live tracking of all active patterns with customizable positioning ● Advanced Volume Filtering: Institutional volume confirmation across all pattern types ● Automated Pattern Management: Smart memory system prevents chart clutter while maintaining relevant zones ● Probability-Based Wyckoff Detection: Mathematical probability calculations for spring/upthrust patterns ● Dual FVG System: Both standard and inverted Fair Value Gap detection with equilibrium analysis 🔧Core Components ● Fair Value Gap Engine: Detects standard FVGs with volume confirmation and equilibrium line analysis ● Inverted FVG Module: Advanced IFVG detection using RVI momentum filtering for inversion confirmation ● Order Block System: Institutional order block identification with customizable mitigation methods ● Wyckoff Pattern Recognition: Automated spring and upthrust detection with probability scoring ● Wick Rejection Analysis: High-probability reversal patterns based on wick-to-body ratios ● ICT Market Structure: Simplified institutional concepts with commitment tracking 🔥Key Features ● Comprehensive Pattern Detection: All major SMC concepts in one indicator with automatic identification ● Volume-Confirmed Signals: Multiple volume filters ensure only institutional-grade patterns are highlighted ● Interactive Dashboard: Real-time status display with active pattern counts and module status ● Smart Memory Management: Automatic cleanup of old patterns while preserving relevant market zones ● Full Alert System: Complete notification coverage for all pattern types and signal generations ● Customizable Display Options: Adjustable colors, transparency, and positioning for all visual elements 🎨Visualization ● Color-Coded Zones: Distinct color schemes for bullish/bearish patterns across all modules ● Dynamic Box Extensions: Automatically extending zones until mitigation or invalidation ● Equilibrium Lines: Fair Value Gap midpoint analysis with dotted line visualization ● Signal Markers: Clear spring/upthrust signals with directional arrows and probability indicators ● Dashboard Table: Professional-grade status panel with module activation and pattern counts ● Candle Coloring: Wick rejection highlighting with transparency-based visual emphasis 📖Usage Guidelines Fair Value Gap Settings ● Days to Analyze: Default 15, Range 1-100 - Controls historical FVG detection period ● Volume Filter: Enables institutional volume confirmation for gap validity ● Min Volume Ratio: Default 1.5 - Minimum volume spike required for gap recognition ● Show Equilibrium Lines: Displays FVG midpoint analysis for precise entry targeting Order Block Configuration ● Scan Range: Default 25 bars - Lookback period for structure break identification ● Volume Filter: Institutional volume confirmation for order block validation ● Mitigation Method: Wick or Close-based invalidation for different trading styles ● Min Volume Ratio: Default 1.5 - Volume threshold for significant order block formation Wyckoff Analysis Parameters ● S/R Lookback: Default 20 - Support/resistance calculation period for spring/upthrust detection ● Volume Spike Multiplier: Default 1.5 - Required volume increase for pattern confirmation ● Probability Threshold: Default 0.7 - Minimum probability score for signal generation ● ATR Recovery Period: Default 5 - Price recovery calculation for pattern strength assessment Market Structure Settings ● Auto-Detect Zones: Automatic identification of high-volume thin zones ● Proximity Threshold: Default 0.20% - Price proximity requirements for zone interaction ● Test Window: Default 20 bars - Time period for zone commitment calculation Display Customization ● Dashboard Position: Four corner options for optimal chart layout ● Text Size: Scalable from Tiny to Large for different screen configurations ● Pattern Colors: Full customization of all bullish and bearish zone colors ✅Best Use Cases ● Swing Trading: Identify major institutional zones for multi-day position entries ● Day Trading: Precise intraday entries at Fair Value Gaps and Order Block boundaries ● Trend Analysis: Market structure confirmation for directional bias establishment ● Risk Management: Clear invalidation levels provided by all pattern boundaries ● Multi-Timeframe Analysis: Works across all timeframes from 1-minute to monthly charts ⚠️Limitations ● Market Condition Dependency: Performance varies between trending and ranging market environments ● Volume Data Requirements: Requires accurate volume data for optimal pattern confirmation ● Lagging Nature: Some patterns confirmed after initial price movement has begun ● Pattern Density: High-volatility markets may generate excessive pattern signals ● Educational Tool: Requires understanding of smart money concepts for effective application 💡What Makes This Unique ● Complete SMC Integration: First indicator to combine all major smart money concepts comprehensively ● Real-Time Dashboard: Instant visual feedback on all active institutional patterns ● Advanced Volume Analysis: Multi-layered volume confirmation across all detection modules ● Probability-Based Signals: Mathematical approach to Wyckoff pattern recognition accuracy ● Professional Memory Management: Sophisticated pattern cleanup without losing market relevance 🔬How It Works 1. Pattern Detection Phase: ● Multi-timeframe scanning for institutional footprints across all enabled modules ● Volume analysis integration confirms patterns meet institutional trading criteria ● Real-time pattern validation ensures only high-probability setups are displayed 2. Signal Generation Process: ● Automated zone creation with precise boundary definitions for each pattern type ● Dynamic extension system maintains relevance until mitigation or invalidation occurs ● Alert system activation provides immediate notification of new pattern formations 3. Dashboard Update Cycle: ● Live status monitoring tracks all active patterns and module states continuously ● Pattern count updates provide instant feedback on current market condition density ● Commitment tracking for market structure analysis shows institutional engagement levels 💡Note: This indicator represents institutional trading concepts and should be used as part of a comprehensive trading strategy. Pattern recognition accuracy improves with understanding of smart money principles. Combine with proper risk management and multiple confirmation methods for optimal results.
Indicador Pine Script®
por PhenLabs
Liquidity Pro Map [ChartPrime]⯁ OVERVIEW Liquidity Pro Map is a market-structure tool that simulates liquidity distribution by splitting price history into buy-side and sell-side profiles. Using candle volume and the standard deviation of close, the indicator builds two mirrored volume maps on the right-hand side of the chart. It also extends liquidity levels backwards in time until they are crossed by price, allowing you to see which zones remain untouched and where liquidity is most likely resting. Cumulative skew lines and highlighted POC levels give additional clarity on imbalance between buyers and sellers. ⯁ KEY FEATURES Dual Liquidity Profiles: The chart is divided into buy-side (green) and sell-side (red) liquidity profiles, letting you instantly compare both sides of order flow. Level Extension Logic: Each liquidity level is extended back in time until price crosses it. If not crossed, it persists all the way to the indicator’s lookback period, marking zones that remain “untapped.” Dynamic Binning with Standard Deviation: The indicator distributes candle volumes into bins using close-price deviation, creating a more realistic liquidity map than static price levels. priceDeviation = ta.stdev(close, 25) * 2 priceReference = close > open ? low - priceDeviation : high + priceDeviation Cumulative Volume Skew Lines: Polylines on the right-hand side show the aggregated buy and sell volume profiles, making it easy to spot imbalance. POC Identification: Highest-volume levels on both sides are marked as POC (Point of Control) , providing key zones of interest. Clear Color Coding: Gradient shading intensifies with volume concentration—dark teal/green for buy zones, dark pink/red for sell zones. ⯁ HOW IT WORKS (UNDER THE HOOD) Volume Distribution: Each bar’s volume is assigned to a price bin based on its reference price (close ± standard deviation offset). Buy vs. Sell Splitting: If bins above last close price, volume is allocated to sell-side liquidity; otherwise, it’s allocated to buy-side liquidity. Level Extension: Boxes marking liquidity bins extend back until crossed by price. If uncrossed, they anchor all the way to the start of the lookback window. Cumulative Polylines: As bins are stacked, cumulative buy and sell values form skew polylines plotted at the right edge. POC Levels: The highest-volume bin on each side is highlighted with labels and arrows, marking where the heaviest liquidity is concentrated. ⯁ USAGE Use buy/sell profiles to see where liquidity is likely resting. Green shelves suggest potential support zones; red shelves suggest resistance or sell liquidity pools. Watch untouched extended levels —these often become magnets for price as liquidity is swept. Track POC levels as primary liquidity targets, where reactions or fakeouts are most common. Compare cumulative skew lines to judge which side dominates in volume. Heavy buy skew may indicate absorption of sell pressure, and vice versa. Adjust lookback period to switch between intraday liquidity maps and larger swing-based profiles. Use separator feature to hide bins borders for better visual clarity. Use as a confluence tool with OBs, support/resistance, and liquidity sweep setups. ⯁ CONCLUSION Liquidity Pro Map transforms candle volume into a structured simulation of where liquidity may rest across the chart. By dividing buy vs. sell profiles, extending untouched levels, and marking cumulative skew and POC, it equips traders with a clear visual map of potential liquidity pools. This allows for better anticipation of sweeps, reversals, and areas of high market activity.
Indicador Pine Script®
por ChartPrime
16
Info Panel (RSI, ADX, Volume,EMA, Delta)📊 Info Panel PRO — All-in-One Trader Dashboard Simplify market analysis at a glance. This powerful indicator displays key market metrics in a compact, customizable table directly overlaid on your chart — ideal for day trading, scalping, and swing trading strategies. 🔍 What’s Included: ✅ RSI (Relative Strength Index) — Measures overbought/oversold conditions. ✅ ADX (Average Directional Index) — Gauges trend strength (>25 = strong trend). ✅ Price vs 200 EMA on 4H timeframe — Strategic support/resistance level for multi-timeframe context. ✅ Current Bar Volume — Color-coded to reflect bullish/bearish sentiment. ✅ Volume Delta — Net buying/selling pressure on your chosen timeframe (default: 1 minute). ✅ CVD (Cumulative Volume Delta) — Daily running total of delta, resets each new trading day. ⚙️ Fully Customizable Settings: Adjustable lengths for RSI, ADX, and EMA. Select delta calculation timeframe — lower = more granular (e.g., “1” for 1-minute precision). Table position: top/bottom left/right corners. Color themes: Customize bullish, bearish, and neutral colors to match your style. 💡 Who Is This For? Scalpers & Day Traders needing real-time market context without clutter. Swing & Position Traders monitoring higher-timeframe structure and momentum. Order Flow & Volume Analysts tracking buyer/seller imbalance via delta and CVD. Beginners learning to read markets through consolidated, intuitive indicators. 🎯 Key Benefits: ✅ Clean, minimalist UI — stays out of your way while delivering critical data. ✅ Auto-formatting for large numbers (K, M, B) — easy readability. ✅ Visual cues (arrows, color coding) for instant decision-making. ✅ Works across all markets: Forex, Stocks, Crypto, Futures. 📌 How to Use: Add the indicator to your chart. Tweak settings to fit your trading style. Monitor real-time updates — all essential metrics visible in one place. Combine with other strategies (price action, S/R, VWAP) for signal confirmation. 📌 Pro Tip: For maximum edge, pair Info Panel PRO with liquidity zones, VWAP, or Market Profile tools. 📈 Trade smarter — let the market speak to you in clear, actionable terms. Author: Version: 1.0 Language: Pine Script v5 Overlay: Yes (draws directly on price chart) 😄 “If this indicator were a person, they’d be called ‘The One Who Knows Everything… But Never Gives Unsolicited Advice.’ …Unlike your ‘friend’ who yells ‘BUY!’ five minutes before the market crashes.” “A good trader isn’t the one who predicts the market. It’s the one who has everything on their chart — coffee optional. …Want the next indicator? Comment ‘YES’ below — and I’ll build you ‘Smart Alert PRO’ or ‘Volume Sniper’ next.” P.S. If this script saves even ONE trade — hit 👍. If it saves TWO — comment “THANK YOU” 🙏 If it saves THREE — expect “Volume Heatmap PRO” next week 😉🔥
Indicador Pine Script®
por Avefar1
RSI HIGHs and LOWs MarkerThis indicator marks significant RSI (14) pivot points directly on the price chart. Red markers above candles highlight confirmed RSI highs where the RSI value exceeded 75 (overbought zone). Green markers below candles highlight confirmed RSI lows where the RSI value dropped below 25 (oversold zone). These signals help traders quickly identify potential reversal zones and overextended market conditions without having to monitor the RSI window separately.
Indicador Pine Script®
por jurleitv
Supertrend0913This Pine Script (`@version=6`) combines **two Supertrend indicators** and a set of **moving averages (EMA & MA)** into one overlay chart tool for TradingView. **Key features:** * **Supertrend \ & \ :** * Each has independent ATR period, multiplier, and ATR calculation method. * Plots trend lines (green/red for \ , blue/yellow for \ ). * Generates **buy/sell signals** when trend direction changes. * Includes **alert conditions** for buy, sell, and trend reversals. * **Moving Averages:** * 6 EMAs (lengths 21, 55, 100, 200, 300, 400). * 5 SMAs (lengths 11, 23, 25, 39, 200). * Each plotted in different colors for trend visualization. 👉 In short: it’s a **combined trading tool** that overlays two configurable Supertrend systems with alerts plus multiple EMAs/SMAs to help identify trend direction, signals, and potential entry/exit points.
Indicador Pine Script®
por zxin0521
Hilly 3.0 Advanced Crypto Scalping Strategy - 1 & 5 Min ChartsHow to Use Copy the Code: Copy the script above. Paste in TradingView: Open TradingView, go to the Pine Editor (bottom of the chart), paste the code, and click “Add to Chart.” Check for Errors: Verify no errors appear in the Pine Editor console. The script uses Pine Script v5 (@version=5). Select Timeframe: 1-Minute Chart: Use defaults (emaFastLen=7, emaSlowLen=14, rsiLen=10, rsiOverbought=80, rsiOversold=20, slPerc=0.5, tpPerc=1.0, useCandlePatterns=false, patternLookback=10). 5-Minute Chart: Adjust to emaFastLen=9, emaSlowLen=21, rsiLen=14, rsiOverbought=75, rsiOversold=25, slPerc=0.8, tpPerc=1.5, useCandlePatterns=true, patternLookback=10. Apply to Chart: Use a liquid crypto pair (e.g., BTC/USDT, ETH/USDT on Binance or Coinbase). Verify Signals: Green “BUY” or “EMA BUY” labels and triangle-up arrows below candles for bullish signals (EMA crossovers, bullish engulfing, hammer, doji, morning star, three white soldiers, double bottom). Red “SELL” or “EMA SELL” labels and triangle-down arrows above candles for bearish signals (EMA crossovers, bearish engulfing, shooting star, doji, evening star, three black crows, double top). Green/red background highlights for signal candles. Backtest: Use TradingView’s Strategy Tester to evaluate performance over 1–3 months, checking Net Profit, Win Rate, and Drawdown. Demo Test: Run on a demo account to confirm signal visibility and performance before trading with real funds.
Estratégia Pine Script®
por Hilly1976
Z-Score Volume with CVD TrendZ-Score Volume & CVD Trend with Exhaustion Signals This powerful, all-in-one indicator combines statistical volume analysis, Cumulative Volume Delta (CVD), and a custom clustering algorithm to provide a clear and dynamic view of market sentiment. It is designed to help traders identify the prevailing trend and spot potential reversals or trend exhaustion before they happen. Important Note: This indicator is specifically designed and optimized for use during the Regular Trading Hours (RTH) New York session, which is typically characterized by high volume and volatility. Its signals may be less reliable in low-volume or overnight sessions. Core Concepts 1. Volume Z-Score The script first calculates a Z-score for volume, which measures how many standard deviations a bar's volume is from a moving average. This helps to identify statistically significant volume spikes that may signal institutional activity or a major shift in sentiment. 2. Cumulative Volume Delta (CVD) CVD plots the net difference between buying and selling volume over time. A rising CVD indicates a surplus of buying pressure, while a falling CVD shows a surplus of selling pressure. This provides a clear look at the direction of momentum. 3. Custom Clustering By combining the Volume Z-score and CVD delta, the script classifies each bar into one of six distinct "clusters." The purpose is to simplify complex data into actionable signals. High Conviction Bullish: High Z-score volume with strong CVD buying. High Conviction Bearish: High Z-score volume with strong CVD selling. Effort vs. Result: High Z-score volume with no clear CVD bias, indicating indecision or a struggle between buyers and sellers. Quiet Accumulation: Low volume with subtle CVD buying, suggesting passive accumulation. Quiet Distribution: Low volume with subtle CVD selling, suggesting passive distribution. Low Conviction/Noise: Low volume and low CVD, representing general market noise. Trend and Exhaustion Logic Trend Establishment: The indicator determines the overall trend (Bullish, Bearish, or Neutral) by analyzing the majority of recent clusters over a configurable lookback period. A Bullish Trend is confirmed when a majority of recent bars are either "High Conviction Bullish" or "Quiet Accumulation." A Bearish Trend is confirmed when a majority of recent bars are either "High Conviction Bearish" or "Quiet Distribution." Trend Exhaustion: This is a key feature for identifying potential reversals. The script looks for a divergence between price action and CVD within a confirmed trend. Bullish Exhaustion Signal: Occurs during a confirmed "Bullish Trend" when you see a bearish divergence (price makes a higher high, but CVD shows negative delta and a close lower than the open). This is a strong sign the uptrend may be running out of steam. Bearish Exhaustion Signal: Occurs during a confirmed "Bearish Trend" when you see a bullish divergence (price makes a lower low, but CVD shows positive delta and a close higher than the open). This indicates the downtrend may be exhausted. How to Interpret the Visuals Volume Bars: Colored to match the cluster they belong to. Background Color: Shows the overall trend (light green for bullish, light red for bearish). Circle Markers (bottom): Green circles indicate a bullish trend, and red circles indicate a bearish trend. Triangles and Circles (top): Represent the specific cluster of each bar. Trend Exhaustion Markers: Triangles above/below the bar signal potential trend exhaustion. Info Table: An optional table provides a real-time summary of all key metrics for the current bar. Settings Volume EMA Length: Adjusts the moving average used for the Volume Z-score calculation. Z-Score Look Back: Defines the number of bars to use for the volume and CVD percentile calculation. Lower/Upper Cluster Percentile: Use these to adjust the sensitivity of the clustering. Tighter ranges (e.g., 25/75) capture more data, while wider ranges (e.g., 10/90) will only signal truly extreme events. Trend Lookback Bars: Controls how many recent bars are considered when determining the trend. This script offers a comprehensive and easy-to-read way to integrate volume, momentum, and trend analysis into your trading. Happy Trading!
Indicador Pine Script®
por immediatePerso44773
Hilly 2.0 Advanced Crypto Scalping Strategy - 1 & 5 Min ChartsHow to Use Copy the Code: Copy the script above. Paste in TradingView: Open TradingView, go to the Pine Editor (bottom of the chart), paste the code, and click “Add to Chart.” Check for Errors: Verify no errors appear in the Pine Editor console. The script uses Pine Script v5 (@version=5). Select Timeframe: 1-Minute Chart: Use defaults (emaFastLen=7, emaSlowLen=14, rsiLen=10, rsiOverbought=80, rsiOversold=20, slPerc=0.5, tpPerc=1.0, useCandlePatterns=false). 5-Minute Chart: Adjust to emaFastLen=9, emaSlowLen=21, rsiLen=14, rsiOverbought=75, rsiOversold=25, slPerc=0.8, tpPerc=1.5, useCandlePatterns=true. Apply to Chart: Use a liquid crypto pair (e.g., BTC/USDT, ETH/USDT on Binance or Coinbase). Verify Signals: Green “BUY” or “EMA BUY” labels and triangle-up arrows below candles. Red “SELL” or “EMA SELL” labels and triangle-down arrows above candles. Green/red background highlights for signal candles. Arrows use size.normal for consistent visibility. Backtest: Use TradingView’s Strategy Tester to evaluate performance over 1–3 months, checking Net Profit, Win Rate, and Drawdown. Demo Test: Run on a demo account to confirm signal visibility and performance before trading with real funds.
Estratégia Pine Script®
por Hilly1976
Fisher Volume Transform | AlphaNattFisher Volume Transform | AlphaNatt A powerful oscillator that applies the Fisher Transform - converting price into a Gaussian normal distribution - while incorporating volume weighting to identify high-probability reversal points with institutional participation. "The Fisher Transform reveals what statistics professors have known for decades: when you transform market data into a normal distribution, turning points become crystal clear." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎲 THE MATHEMATICS Fisher Transform Formula: The Fisher Transform converts any bounded dataset into a Gaussian distribution: y = 0.5 × ln((1 + x) / (1 - x)) Where x is normalized price (-1 to 1 range) Why This Matters: Market extremes become statistically identifiable Turning points are amplified and clarified Removes the skew from price distributions Creates nearly instantaneous signals at reversals Volume Integration: Unlike standard Fisher Transform, this version weights price by relative volume: High volume moves get more weight Low volume moves get filtered out Identifies institutional participation Reduces false signals from retail chop ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 KEY ADVANTAGES Statistical Edge: Transforms price into normal distribution where extremes are mathematically defined Volume Confirmation: Only signals with volume support Early Reversal Detection: Fisher Transform amplifies turning points Clean Signals: Gaussian distribution reduces noise No Lag: Mathematical transformation, not averaging ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ SETTINGS OPTIMIZATION Fisher Period (5-30): 5-9: Very sensitive, many signals 10: Default - balanced sensitivity 15-20: Moderate smoothing 25-30: Major reversals only Volume Weight (0.1-1.0): 0.1-0.3: Minimal volume influence 0.5-0.7: Balanced price/volume 0.7: Default - strong volume weight 0.8-1.0: Volume dominant ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING SIGNALS Primary Signals: Zero Cross Up: Bullish momentum shift Zero Cross Down: Bearish momentum shift Signal Line Cross: Early reversal warning Extreme Readings (±75): Potential reversal zones Visual Interpretation: Cyan zones: Bullish momentum Magenta zones: Bearish momentum Gradient intensity: Strength of move Histogram: Raw momentum power ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 OPTIMAL USAGE Best Market Conditions: Range-bound markets (reversals clear) High volume periods Major support/resistance levels Divergence hunting Trading Strategies: 1. Extreme Reversal: Enter when oscillator exceeds ±75 and reverses 2. Zero Line Momentum: Trade crosses of zero line with volume confirmation 3. Signal Line Strategy: Early entry on signal line crosses 4. Divergence Trading: Price makes new high/low but Fisher doesn't ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Quantitative Trading Systems Version: 1.0 Classification: Statistical Transform Oscillator Not financial advice. Always DYOR.
Indicador Pine Script®
por AlphaNatt
Valid Monthly LevelsValid Monthly Levels (No Sweeps) + Smart Labels This tool automatically plots the highs and lows of each completed monthly candle and tracks their validity in real time. A level is considered valid until it has been swept (price trades strictly beyond that high or low). Once swept, the line and label can either be removed or dimmed depending on your settings. Key features: Monthly highs and lows: Each month’s range is marked with horizontal levels that extend forward. Valid vs. swept logic: Levels are only valid until breached; swept levels can be hidden or kept as dotted/grey lines. Smart labels: Each level is labeled with the month and year (e.g., Sep ’25 H/L). On higher timeframes, labels sit at the candle; on lower timeframes, labels automatically shift to the right edge so they don’t disappear off-screen. Customizable appearance: Choose colors for highs, lows, and swept levels; adjust line styles; and limit how many past months are shown. Clutter control: Cap the maximum number of labels, so your chart stays readable even on small intraday timeframes. This indicator is useful for traders who track monthly supply/demand extremes, liquidity sweeps, and higher-timeframe context when executing on lower timeframes.
Indicador Pine Script®
por JaydenUnplugged
Dow Theory Indicator## 🎯 Key Features of the Indicator ### 📈 Complete Implementation of Dow Theory - Three-tier trend structure: primary trend (50 periods), secondary trend (20 periods), and minor trend (10 periods). - Swing point analysis: automatically detects critical swing highs and lows. - Trend confirmation mechanism: strict confirmation logic based on consecutive higher highs/higher lows or lower highs/lower lows. - Volume confirmation: ensures price moves are supported by trading volume. ### 🕐 Flexible Timeframe Parameters All key parameters are adjustable, making it especially suitable for U.S. equities: Trend analysis parameters: - Primary trend period: 20–200 (default 50; recommended 50–100 for U.S. stocks). - Secondary trend period: 10–100 (default 20; recommended 15–30 for U.S. stocks). - Minor trend period: 5–50 (default 10; recommended 5–15 for U.S. stocks). Dow Theory parameters: - Swing high/low lookback: 5–50 (default 10). - Trend confirmation bar count: 1–10 (default 3). - Volume confirmation period: 10–100 (default 20). ### 🇺🇸 U.S. Market Optimizations - Session awareness: distinguishes Regular Trading Hours (9:30–16:00 EST) from pre-market and after-hours. - Pre/post-market weighting: adjustable weighting factor for signals during extended hours. - Earnings season filter: automatically adjusts sensitivity during earnings periods. - U.S.-optimized default parameters. ## 🎨 Visualization 1. Trend lines: three differently colored trend lines. 2. Background fill: green (uptrend) / red (downtrend) / gray (neutral). 3. Signal markers: arrows, labels, and warning icons. 4. Swing point markers: small triangles at key turning points. 5. Info panel: real-time display of eight key metrics. ## 🚨 Alert System - Trend turning to up/down. - Strong bullish/bearish signals (dual confirmation). - Volume divergence warning. - New swing high/low formed. ## 📋 How to Use 1. Open the Pine Editor in TradingView. 2. Copy the contents of dow_theory_indicator.pine. 3. Paste and click “Add to chart.” 4. Adjust parameters based on trading style: - Long-term investing: increase all period parameters. - Swing trading: use the default parameters. - Short-term trading: decrease all period parameters. ## 💡 Parameter Tips for U.S. Stocks - Large-cap blue chips (AAPL, MSFT): primary 60–80, secondary 25–30. - Mid-cap growth stocks: primary 40–60, secondary 18–25. - Small-cap high-volatility stocks: primary 30–50, secondary 15–20.
Indicador Pine Script®
por boyue_
Mikey’s Strategy (AutoEMA+) ⚙️ Core Idea: A long-only EMA crossover strategy that automatically selects the optimal EMA length (5–25) based on historical performance using a custom scoring system. 🎯 Entry Conditions: Price crosses above the selected EMA. (Optional) Price is above HTF EMA200 (higher timeframe trend). (Optional) ADX is above a minimum threshold. (Optional) Volume is above average × multiplier. A cooldown period has passed since the last exit. ❌ Exit Condition: Price closes fully below the EMA (current candle: high, low, and close all below), and the previous candle was above or touched the EMA. 🧠 Auto EMA Selection Logic: Scans EMAs in the range (e.g., 8–21) every N bars. Scores each EMA based on: Time price spent above the EMA Respect for EMA (touches) Trend consistency Distance from EMA (when above) Picks the EMA with the highest valid score, and updates only if it’s significantly better. 🛡️ Risk Management: No pyramiding (1 position max). Cooldown period after exit. No hard stop loss or take profit. 📊 Visuals & Alerts: Plots the selected EMA and optional HTF EMA200. Entry markers and info label on the chart. Alerts for valid entries and filtered signals.
Estratégia Pine Script®
por Mikey1411
Artharjan ADXArtharjan ADX (AADX) by Rrahul Desai @Artharjan 📌 Overview The Artharjan ADX (AADX) is an advanced implementation of the Average Directional Index (ADX) with customizable moving averages, momentum thresholds, and visually intuitive grading of bullish and bearish strength. Unlike the standard ADX indicator that only shows trend strength, AADX adds graded bullish/bearish conditions, alerts, smoothed DI signals, histogram visualizations, and background color fills to help traders quickly interpret market conditions. It is designed for traders who want early detection of trend strength, clean visual cues, and automated alert triggers for both bullish and bearish momentum setups. ⚙️ Key Features 🔹 Customizable Calculations DI Length (default 13) – controls sensitivity of directional indicators. +/- DI Smoothing – smooths DI signals with user-selected MA. Multiple Moving Average Types – SMA, EMA, WMA, RMA, VWMA, ALMA, Hull, SWMA, SMMA, TMA. ADX Smoothing – define how smooth/fast the ADX reacts. 🔹 Flexible Display Toggle between line plots or histogram view. Adjustable plot thickness. Option to plot averages of ADX, +DI, -DI for confirmation. Configurable background fills: ADX above/below momentum threshold. ADX rising/falling color shading. Trend-grade based color intensity. 🔹 Momentum & Thresholds Momentum Level (default 25) → defines “strong trend” zone. Crossover Threshold (default 15) → helps detect early DI crossovers. Color-coded histogram bars for +DI vs -DI difference: Above/below zero. Rising/falling momentum. 🔹 Bullish & Bearish Grading System The indicator assigns grades from 1 to 5 for both bullish and bearish setups, based on DI and ADX conditions: Bullish Grades Grade 1 → Very Weak Bullish Grade 2 → Weak Bullish Grade 3 → Moderate Bullish Grade 4 → Strong Bullish Grade 5 → Very Strong Bullish Bearish Grades Grade 1 → Very Weak Bearish Grade 2 → Weak Bearish Grade 3 → Moderate Bearish Grade 4 → Strong Bearish Grade 5 → Very Strong Bearish Labels are automatically plotted above bars to indicate the active grade. 🔹 Alerts Bullish Alert → when +DI crosses above its average below the threshold OR bullish conditions are met. Bearish Alert → when -DI crosses above its average below the threshold OR bearish conditions are met. These alerts make it possible to automate trading signals for scalping, intraday, and swing trading. 📊 Use Cases Trend Strength Measurement Spot when markets shift from range-bound to trending. Confirm the reliability of breakouts with strong ADX readings. Bullish vs Bearish Control Compare +DI vs -DI strength to gauge trend direction. Identify trend reversals early with DI slope changes. Momentum Confirmation Use ADX rising + DI grades to validate trade entries. Filter false breakouts with weak ADX. Trade Grading System Enter aggressively on Grade 4–5 signals. Stay cautious on Grade 1–2 signals. Automated Alerts & Screening Combine AADX alerts with strategy rules. Build scanners to highlight strong ADX setups across multiple stocks. 🎯 Trader’s Advantage More powerful than standard ADX → Adds slope, grading, alerts, and visualization. Adaptable to any style → Works for intraday scalping, swing trading, and positional analysis. Visual clarity → Color fills, histograms, and labels simplify decision-making. Customizable smoothing → Adjusts to fast or slow markets. ✅ Closing Note The Artharjan ADX (AADX) transforms the traditional ADX into a complete trend and momentum analyzer. It helps traders detect, confirm, and act on directional strength with clarity and confidence. With Thanks, Rrahul Desai @Artharjan
Indicador Pine Script®
por rsdesai005
Optimized ADX DI CCI Strategy### Key Features: - Combines ADX, DI+/-, CCI, and RSI for signal generation. - Supports customizable timeframes for indicators. - Offers multiple exit conditions (Moving Average cross, ADX change, performance-based stop-loss). - Tracks and displays trade statistics (e.g., win rate, capital growth, profit factor). - Visualizes trades with labels and optional background coloring. - Allows countertrading (opening an opposite trade after closing one). 1. **Indicator Calculation**: - **ADX and DI+/-**: Calculated using the `ta.dmi` function with user-defined lengths for DI and ADX smoothing. - **CCI**: Computed using the `ta.cci` function with a configurable source (default: `hlc3`) and length. - **RSI (optional)**: Calculated using the `ta.rsi` function to filter overbought/oversold conditions. - **Moving Averages**: Used for CCI signal smoothing and trade exits, with support for SMA, EMA, SMMA (RMA), WMA, and VWMA. 2. **Signal Generation**: - **Buy Signal**: Triggered when DI+ > DI- (or DI+ crosses over DI-), CCI > MA (or CCI crosses over MA), and optional ADX/RSI filters are satisfied. - **Sell Signal**: Triggered when DI+ < DI- (or DI- crosses over DI+), CCI < MA (or CCI crosses under MA), and optional ADX/RSI filters are satisfied. 3. **Trade Execution**: - **Entry**: Long or short trades are opened using `strategy.entry` when signals are detected, provided trading is allowed (`allow_long`/`allow_short`) and equity is positive. - **Exit**: Trades can be closed based on: - Opposite signal (if no other exit conditions are used). - MA cross (price crossing below/above the exit MA for long/short trades). - ADX percentage change exceeding a threshold. - Performance-based stop-loss (trade loss exceeding a percentage). - **Countertrading**: If enabled, closing a trade triggers an opposite trade (e.g., closing a long opens a short). 4. **Visualization**: - Labels are plotted at trade entries/exits (e.g., "BUY," "SELL," arrows). - Optional background coloring highlights open trades (green for long, red for short). - A statistics table displays real-time metrics (e.g., capital, win rates). 5. **Trade Tracking**: - Tracks the number of long/short trades, wins, and overall performance. - Monitors equity to prevent trading if it falls to zero. ### 2.3 Key Components - **Indicator Calculations**: Uses `request.security` to fetch indicator data for the specified timeframe. - **MA Function**: A custom `ma_func` handles different MA types for CCI and exit conditions. - **Signal Logic**: Combines crossover/under checks with recent bar windows for flexibility. - **Exit Conditions**: Multiple configurable exit strategies for risk management. - **Statistics Table**: Updates dynamically with trade and capital metrics. ## 3. Configuration Options The script provides extensive customization through input parameters, grouped for clarity in the TradingView settings panel. Below is a detailed breakdown of each setting and its impact. ### 3.1 Strategy Settings (Global) - **Initial Capital**: Default `10000`. Sets the starting capital for backtesting. - **Effect**: Determines the base equity for calculating position sizes and performance metrics. - **Default Quantity Type**: `strategy.percent_of_equity` (50% of equity). - **Effect**: Controls the size of each trade as a percentage of available equity. - **Pyramiding**: Default `2`. Allows up to 2 simultaneous trades in the same direction. - **Effect**: Enables multiple entries if conditions are met, increasing exposure. - **Commission**: 0.2% per trade. - **Effect**: Simulates trading fees, reducing net profit in backtesting. - **Margin**: 100% for long and short trades. - **Effect**: Assumes no leverage; adjust for margin trading simulations. - **Calc on Every Tick**: `true`. - **Effect**: Ensures real-time signal updates for precise execution. ### 3.2 Indicator Settings - **Indicator Timeframe** (`indicator_timeframe`): - **Options**: `""` (chart timeframe), `1`, `5`, `15`, `30`, `60`, `240`, `D`, `W`. - **Default**: `""` (uses chart timeframe). - **Effect**: Determines the timeframe for ADX, DI, CCI, and RSI calculations. A higher timeframe reduces noise but may delay signals. ### 3.3 ADX & DI Settings - **DI Length** (`adx_di_len`): - **Default**: `30`. - **Range**: Minimum `1`. - **Effect**: Sets the period for calculating DI+ and DI-. Longer periods smooth trends but reduce sensitivity. - **ADX Smoothing Length** (`adx_smooth_len`): - **Default**: `14`. - **Range**: Minimum `1`. - **Effect**: Smooths the ADX calculation. Longer periods produce smoother ADX values. - **Use ADX Filter** (`use_adx_filter`): - **Default**: `false`. - **Effect**: If `true`, requires ADX to exceed the threshold for signals to be valid, filtering out weak trends. - **ADX Threshold** (`adx_threshold`): - **Default**: `25`. - **Range**: Minimum `0`. - **Effect**: Sets the minimum ADX value for valid signals when the filter is enabled. Higher values restrict trades to stronger trends. ### 3.4 CCI Settings - **CCI Length** (`cci_length`): - **Default**: `20`. - **Range**: Minimum `1`. - **Effect**: Sets the period for CCI calculation. Longer periods reduce noise but may lag. - **CCI Source** (`cci_src`): - **Default**: `hlc3` (average of high, low, close). - **Effect**: Defines the price data for CCI. `hlc3` is standard, but users can choose other sources (e.g., `close`). - **CCI MA Type** (`ma_type`): - **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`. - **Default**: `SMA`. - **Effect**: Determines the moving average type for CCI signal smoothing. EMA is more responsive; VWMA weights by volume. - **CCI MA Length** (`ma_length`): - **Default**: `14`. - **Range**: Minimum `1`. - **Effect**: Sets the period for the CCI MA. Longer periods smooth the MA but may delay signals. ### 3.5 RSI Filter Settings - **Use RSI Filter** (`use_rsi_filter`): - **Default**: `false`. - **Effect**: If `true`, applies RSI-based overbought/oversold filters to signals. - **RSI Length** (`rsi_length`): - **Default**: `14`. - **Range**: Minimum `1`. - **Effect**: Sets the period for RSI calculation. Longer periods reduce sensitivity. - **RSI Lower Limit** (`rsi_lower_limit`): - **Default**: `30`. - **Range**: `0` to `100`. - **Effect**: Defines the oversold threshold for buy signals. Lower values allow trades in more extreme conditions. - **RSI Upper Limit** (`rsi_upper_limit`): - **Default**: `70`. - **Range**: `0` to `100`. - **Effect**: Defines the overbought threshold for sell signals. Higher values allow trades in more extreme conditions. ### 3.6 Signal Settings - **Cross Window** (`cross_window`): - **Default**: `0`. - **Range**: `0` to `5` bars. - **Effect**: Specifies the lookback period for detecting DI+/- or CCI crosses. `0` requires crosses on the current bar; higher values allow recent crosses, increasing signal frequency. - **Allow Long Trades** (`allow_long`): - **Default**: `true`. - **Effect**: Enables/disables new long trades. If `false`, only closing existing longs is allowed. - **Allow Short Trades** (`allow_short`): - **Default**: `true`. - **Effect**: Enables/disables new short trades. If `false`, only closing existing shorts is allowed. - **Require DI+/DI- Cross for Buy** (`buy_di_cross`): - **Default**: `true`. - **Effect**: If `true`, requires a DI+ crossover DI- for buy signals; if `false`, DI+ > DI- is sufficient. - **Require CCI Cross for Buy** (`buy_cci_cross`): - **Default**: `true`. - **Effect**: If `true`, requires a CCI crossover MA for buy signals; if `false`, CCI > MA is sufficient. - **Require DI+/DI- Cross for Sell** (`sell_di_cross`): - **Default**: `true`. - **Effect**: If `true`, requires a DI- crossover DI+ for sell signals; if `false`, DI+ < DI- is sufficient. - **Require CCI Cross for Sell** (`sell_cci_cross`): - **Default**: `true`. - **Effect**: If `true`, requires a CCI crossunder MA for sell signals; if `false`, CCI < MA is sufficient. - **Countertrade** (`countertrade`): - **Default**: `true`. - **Effect**: If `true`, closing a trade triggers an opposite trade (e.g., close long, open short) if allowed. - **Color Background for Open Trades** (`color_background`): - **Default**: `true`. - **Effect**: If `true`, colors the chart background green for long trades and red for short trades. ### 3.7 Exit Settings - **Use MA Cross for Exit** (`use_ma_exit`): - **Default**: `true`. - **Effect**: If `true`, closes trades when the price crosses the exit MA (below for long, above for short). - **MA Length for Exit** (`ma_exit_length`): - **Default**: `20`. - **Range**: Minimum `1`. - **Effect**: Sets the period for the exit MA. Longer periods delay exits. - **MA Type for Exit** (`ma_exit_type`): - **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`. - **Default**: `SMA`. - **Effect**: Determines the MA type for exit signals. EMA is more responsive; VWMA weights by volume. - **Use ADX Change Stop-Loss** (`use_adx_stop`): - **Default**: `false`. - **Effect**: If `true`, closes trades when the ADX changes by a specified percentage. - **ADX % Change for Stop-Loss** (`adx_change_percent`): - **Default**: `5.0`. - **Range**: Minimum `0.0`, step `0.1`. - **Effect**: Specifies the percentage change in ADX (vs. previous bar) that triggers a stop-loss. Higher values reduce premature exits. - **Use Performance Stop-Loss** (`use_perf_stop`): - **Default**: `false`. - **Effect**: If `true`, closes trades when the loss exceeds a percentage threshold. - **Performance Stop-Loss (%)** (`perf_stop_percent`): - **Default**: `-10.0`. - **Range**: `-100.0` to `0.0`, step `0.1`. - **Effect**: Specifies the loss percentage that triggers a stop-loss. More negative values allow larger losses before exiting. ## 4. Visual and Statistical Output - **Labels**: Displayed at trade entries/exits with arrows (↑ for buy, ↓ for sell) and text ("BUY," "SELL"). A "No Equity" label appears if equity is zero. - **Background Coloring**: Optionally colors the chart background (green for long, red for short) to indicate open trades. - **Statistics Table**: Displayed at the top center of the chart, updated on timeframe changes or trade events. Includes: - **Capital Metrics**: Initial capital, current capital, capital growth (%). - **Trade Metrics**: Total trades, long/short trades, win rate, long/short win rates, profit factor. - **Open Trade Status**: Indicates if a long, short, or no trade is open. ## 5. Alerts - **Buy Signal Alert**: Triggered when `buy_signal` is true ("Cross Buy Signal"). - **Sell Signal Alert**: Triggered when `sell_signal` is true ("Cross Sell Signal"). - **Usage**: Users can set up TradingView alerts to receive notifications for trade signals.
Estratégia Pine Script®
por osterhansi_
Fear & Greed [theUltimator5]This indicator attempts to replicate CNN's Fear & Greed Index methodology to measure market sentiment on a scale from 0-100. It combines seven key market components into a single sentiment score, where lower values indicate fear and higher values indicate greed. Note: It is impossible to perfectly replicate the true Fear & Greed indicator due to data limitations, so this indicator attempts to best replicate the output for each of the (7) components using available data. The uniqueness of this indicator comes from the calculation methods for the 7 components as well as the visual representation of the data, which includes a table and selectable plots for each of the 7 components which make up the overall sentiment. Existing variants of the Fear & Greed Index have substantial flaws in the calculations of several of the components which result in warped final sentiment numbers. This indicator attempts to better track all 7 components and provide a closer model to the actual Fear & Greed index. Here are the seven components and a brief description of how each are calculated: 1. Market Momentum Calculation: S&P 500 current price vs. 125-day moving average Measures how far the market has moved from its long-term trend Uses CNN-style Z-score normalization over 252 trading days Higher values indicate strong upward momentum (greed) Lower values suggest declining momentum (fear) 2. Stock Strength Calculation: S&P 500 RSI scaled to 252-day range Uses 14-period RSI of the S&P 500 index Normalizes RSI values based on their 252-day minimum and maximum Measures overbought/oversold conditions relative to recent history Higher values indicate overbought conditions (greed) Lower values suggest oversold conditions (fear) 3. Price Breadth Calculation: Modified McClellan Oscillator Primary: Uses NYSE advancing vs. declining issues with 7-day smoothing Fallback: Compares sector performance (QQQ, IWM vs. SPY) Measures how many stocks participate in market moves Broader participation indicates healthier trends Narrow breadth suggests selective or weak trends 4. Put/Call Ratio Calculation: Inverted CBOE Put/Call ratios Primary: CBOE Equity-only Put/Call ratio (more sensitive) Fallback: CBOE Total Put/Call ratio Uses 5-day average and applies CNN normalization Higher put/call ratios indicate fear (inverted to lower scores) Lower put/call ratios suggest complacency (higher scores) 5. Market Volatility Calculation: VIX relative to its 50-day average Compares current VIX level to its 50-day moving average Measures deviation from normal volatility expectations Higher VIX relative to average indicates fear (lower scores) Lower relative VIX suggests complacency (higher scores) 6. Safe Haven Demand Calculation: Stock returns vs. bond yield changes Compares 20-day smoothed S&P 500 returns to Treasury yield changes When stocks outperform bonds, indicates risk appetite (higher scores) When bonds outperform stocks, suggests risk aversion (lower scores) Uses Treasury 10-year yields as the safe haven benchmark 7. Junk Bond Demand Calculation: High-yield bond spread analysis Measures yield spread between junk bonds (JNK ETF) and Treasuries Compares current spread to its 5-day average Narrowing spreads indicate risk appetite (higher scores) Widening spreads suggest risk aversion (lower scores) The combined sentiment is plotted as a single line which changes color based on the current sentiment value. 0-25: Extreme Fear (Red) - Market panic, oversold conditions 26-45: Fear (Orange) - Cautious sentiment, bearish bias 46-55: Neutral (Yellow) - Balanced market sentiment 56-75: Greed (Light Green) - Optimistic sentiment, bullish bias 76-100: Extreme Greed (Green) - Market euphoria, potentially overbought There are dashed lines to represent the threshold values for each of the sentiments to better visualize transitions. The table displays each of the (7) components of the index and their respective values. The table can be toggled on/off and the position can be moved. An optional secondary line can be toggled on to display (1) of the (7) components as a unique color and the component name and value will highlight on the table. The secondary line can be used to dig into the main driving forces behind the overall index value.
Indicador Pine Script®
por TheUltimator5
RSI Crossover AlertRSI Crossover Alert Indicator - User Guide The RSI Crossover Alert Indicator is a comprehensive technical analysis tool that detects multiple types of RSI crossovers and generates real-time alerts. It combines traditional RSI analysis with signal lines, divergence detection, and multi-level crossing alerts. 1. Multiple Crossover Detection - RSI/Signal Line Cross: Signals a primary trend change. - RSI/Second Signal Cross: Confirmation signals for stronger trends. - Level Crossings: Crosses of Overbought 70, Oversold 30, and Midline 50. - Divergence Detection: Hidden and regular divergences for reversal signals. 2. Alert Types - Alert: RSI > Signal Description: Bullish momentum is building. Signal: Consider long positions. - Alert: RSI < Signal Description: Bearish momentum is building. Signal: Consider short positions. - Alert: RSI > 70 Description: Entering the overbought zone. Signal: Prepare for a potential reversal. - Alert: RSI < 30 Description: Entering the oversold zone. Signal: Watch for a bounce opportunity. - Alert: RSI crosses 50 Description: A shift in momentum. Signal: Trend confirmation. 3. Visual Components - Lines: RSI blue, Signal orange, Second Signal purple - Histogram: Visualizes momentum by showing the difference between RSI and the Signal line. - Background Zones: Red overbought, Green oversold - Markers: Up/down triangles to indicate crossovers. - Info Table: Real-time RSI values and status. Strategy 1: Classic Crossover - Entry Long: RSI crosses above the Signal Line AND RSI is below 50. - Entry Short: RSI crosses below the Signal Line AND RSI is above 50. - Take Profit: On the opposite signal. - Stop Loss: At the recent swing high/low. Strategy 2: Extreme Zone Reversal - Entry Long: RSI is below 30 and crosses above the Signal Line. - Entry Short: RSI is above 70 and crosses below the Signal Line. - Risk Management: Higher win rate but fewer signals. Use a minimum 2:1 risk-reward ratio. Strategy 3: Divergence Trading - Setup: Enable divergence alerts and look for price/RSI divergence. Wait for an RSI crossover for confirmation. - Entry: Enter on the crossover after the divergence appears. Place the stop loss beyond the starting point of the divergence. Strategy 4: Multi-Timeframe Confirmation 1. Check the higher timeframe e.g. Daily to identify the main trend. 2. Use the current timeframe e.g. 4H/1H for your entry. 3. Only enter in the direction of the main trend. 4. Use the RSI crossover as the entry trigger. Optimal Settings by Market - Forex Major Pairs RSI Length: 14, Signal Length: 9, Overbought/Oversold: 70/30 - Crypto High Volatility RSI Length: 10-12, Signal Length: 6-8, Overbought/Oversold: 75/25 - Stocks Trending RSI Length: 14-21, Signal Length: 9-12, Overbought/Oversold: 70/30 - Commodities RSI Length: 14, Signal Length: 9, Overbought/Oversold: 80/20 Risk Management Rules 1. Position Sizing: Never risk more than 1-2% on a single trade. Reduce size in ranging markets. 2. Stop Loss Placement: Place stops beyond the recent swing high/low for crossovers. Using an ATR-based stop is also effective. 3. Profit Taking: Take partial profits at a 1:1 risk-reward ratio. Switch to a trailing stop after reaching 2:1. 1. Filtering Signals - Combine with volume indicators. - Confirm the trend on a higher timeframe. - Wait for candlestick pattern confirmation. 2. Avoid Common Mistakes - Don't trade every single crossover. - Avoid taking signals against a strong trend. - Do not ignore risk management. 3. Market Conditions - Trending Market: Focus on midline 50 crosses. - Ranging Market: Look for reversals from overbought/oversold levels. - Volatile Market: Widen the overbought/oversold levels. - If you get too many false signals: Increase the signal line period, add other confirmation indicators, or use a higher timeframe. - If you are missing major moves: Decrease the RSI length, shorten the signal line period, or check your alert settings. Recommended Combinations 1. RSI + MACD: For dual momentum confirmation. 2. RSI + Bollinger Bands: For volatility-adjusted signals. 3. RSI + Volume: To confirm the strength of a signal. 4. RSI + Moving Averages: To use as a trend filter. This indicator provides a comprehensive RSI analysis. Success depends on proper configuration, risk management, and combining signals with the overall market context. Start with the default settings, then optimize based on your trading style and market conditions.
Indicador Pine Script®
por Hunk77