Optimised Volume-weighted Moving AverageAbout
This tool measures the profitability of every volume-weighted moving average length combination for the entire history of the instrument that it is applied,
and only displays the most profitable combination in real-time which means that this indicator is fully functional for trading.
The Optimised Volume-weighted Moving Average can be tested using a Volume-weighted Moving Average Strategy and the Strategy Tester panel on any instrument or time-stamp. It will always display the lengths of the most profitable exponential moving average lengths at the current moment in time.
This can be used on its own, or paired with the Intelligent Volume-weighted Moving Average (AI) for a better understanding of the indicators movements.
The Intelligent Volume-weighted Moving Average (AI) uses this tool as a predictive method for machine learning.
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Optimised Exponential Moving AverageAbout
This tool measures the profitability of every exponential moving average length combination for the entire history of the instrument that it is applied and only displays the most profitable combination in real-time meaning that this indicator is fully functional for trading.
The Optimised Exponential Moving Average can be tested using an Exponential Moving Average strategy and the Strategy Tester panel on any instrument or time-stamp. It will always display the lengths of the most profitable exponential moving average lengths at the current moment in time.
This can be used on its own, or paired with the Intelligent Exponential Moving Average (AI) for a better understanding of the indicators movements.
The Intelligent Exponential Moving Average (AI) uses this tool as a predictive method for machine learning.
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.
Pro AI Trading - Month Week OpenThis is a indicator that primarily marks monthly 1 hour initial balances, while highlighting every yearly half/quarter. Additionally has 9 different types of MA bands + D/W/M vertical separators. Marks custom % pivot points for easier zone marking. Possibility of generating signals based on mid line candle crosses.
Gabriel's Squeeze Momentum📊 Gabriel’s Squeeze Momentum — Deluxe Volatility + Momentum Suite
An advanced, all-in-one squeeze & momentum framework that times volatility compression/expansion and trend shifts, with optional CVD (cumulative volume delta) momentum, ATR zone context, Discontinued Signal Lines (DSL) scalps, Colored DMI trend label, Williams VIX Fix (WVF) low-volatility exhaustion pings, Buff’s VTTI/VPCI volume confirmation, and real-time divergence detection.
What it does:
Discover Squeezes. They occur when volatility contracts, often preceding significant price moves.
Measures momentum with a fast, ATR-normalized linear regression—optionally on Price or CVD—so you see direction and “how hard it’s pushing.”
🧭 Signal Legend ~ Colors the squeeze so you instantly know regime:
🟡 / 🟣 (Tight/Very Tight): Coiled spring; prepare a plan.
🔴 / ⚫ = (Regular/Wide): Watch for Divergences between Price and Momentum.
🟢 (Fired): Expansion started; trade with momentum cross and bias.
Adds context bands at ±1/±2/±3 ATR (“trend / expansion / OB-OS”) to filter late or weak signals.
DSL (Discontinued Signal Lines) give early scalp flips on momentum vs. adaptive bands.
DMI label & triangles communicate trend strength and whether +DI / −DI is in control.
Williams VIX Fix flags capitulation/exhaustion style spikes (with optional VIX proxy).
VTTI/VPCI modules confirm when volume aligns with price trend or contradicts it.
Divergences (regular & hidden) auto-draw with optional live (may repaint) or on-close.
🎢 Squeeze Momentum — How the Logic Works 🎢
The Squeeze Momentum model is built on the principle of volatility compression and expansion. In markets, periods of low volatility are often followed by explosive moves, while high volatility eventually contracts. The “squeeze” seeks to identify these compression phases and prepare traders for the likely expansion that follows.
This indicator achieves that by comparing Bollinger Bands (BB) to Keltner Channels (KC).
Bands: Bollinger vs. Keltner
Bollinger Bands (BB): Calculated using a Simple Moving Average (SMA) of price and standard deviations (σ) of the closing price. The bands expand and contract depending on volatility.
Keltner Channels (KC): Built from an SMA plus/minus multiples of the Average True Range (ATR). Unlike some simplified squeeze indicators that approximate ATR, this implementation uses a true ATR-based KC, ensuring accuracy across different assets and timeframes.
By comparing whether the Bollinger Bands are inside or outside the Keltner Channels, the indicator identifies different squeeze regimes, each representing a distinct volatility environment.
📦 Regime Colors
The squeeze states are color-coded for quick interpretation:
🔹Wide Squeeze (⚫): BB inside KC with a high ATR multiplier. Extremely low volatility, often before major expansion.
🔹Normal Squeeze (🔴): BB inside KC with a moderate ATR multiplier (about 25% more sensitive than Wide). Typical compression setting.
🔹Narrow Squeeze (🟡): BB inside KC with a lower ATR multiplier (about 50% more sensitive than Wide). Signals tighter compression.
🔹Very Narrow Squeeze (🟣): BB inside KC with the lowest ATR multiplier (100% more sensitive than Wide). Indicates extreme coiling.
🔹Fired Squeeze (🟢): BB break outside KC. Marks the release of volatility and potential trend acceleration.
This multi-layered system improves upon classical SQZPRO by using precisely calculated Keltner Channels and multiple sensitivity levels, giving traders more granular information about volatility states.
🔒 Multi-Timeframe Support
The indicator automatically adjusts squeeze thresholds for different timeframes — hourly, 4-hour, daily, weekly, and monthly charts. Each regime has been manually tuned for its timeframe, allowing traders to use the same tool whether scalping, swing trading, or holding longer-term positions.
🎯 Momentum Core
Detecting a squeeze is only half the equation — the indicator also includes a momentum engine to determine direction and strength.
Price momentum is measured as the distance of Close from its Highest High and Lowest Low range, smoothed with a Simple Moving Average, and refined with Linear Regression.
This value is then divided by ATR, normalizing momentum relative to volatility.
Optionally, CVD Mode (Cumulative Volume Delta ÷ Volume) can replace price momentum for assets where order-flow and volume dynamics dominate (e.g., crypto).
🦆 Signal Line
Momentum is paired with a Simple Moving Average signal line:
🔹Bullish: Momentum > Signal.
🔹Bearish: Momentum < Signal.
This crossover logic provides directional bias and filters for false squeezes.
🚀 When to Use Price vs. CVD
CVD Mode (Crypto, FX with tick volume): Best for assets with strong volume/order-flow signals.
Price Mode (Equities, Commodities, Higher TFs): Best for assets with irregular or thin volume data.
🛢️ATR Zones (context filter) 🛢️
Its design is straightforward yet effective: it measures the difference between the current price from its highest highs, lowest lows, and a moving average over a chosen period, then expresses that difference in terms of the Average True Range (ATR) over the same period. By normalizing price deviations against volatility, ATR provides a clear sense of how far and how fast price is moving relative to its “normal” range.
Interpreting the Zone
Positive Values: When it is above zero, price is trading above its HH, LL, and moving average, suggesting bullish momentum. The higher the value, the stronger the momentum relative to volatility.
Negative Values: When the Momentum is below zero, price is trading below its HH, LL, and moving average, signaling bearish momentum. The deeper the reading, the stronger the downside pressure.
Magnitude Matters: Because the Momentum is expressed in ATR units, traders can immediately gauge whether the move is small (less than 1 ATR), moderate (1–2 ATRs), or extreme (3+ ATRs). This makes it especially useful for assessing overbought or oversold conditions in a normalized way.
Strengths:
🔹Volatility-Normalized: Unlike simple squeeze momentum oscillators that have different OB/OS levels, this Momentum adjusts for volatility. This makes signals more consistent across assets with different volatility profiles.
🔹Simplicity:
±1 ATR: trending zone (bulls above +1, bears below −1)
±2 ATR: expansion (keep, add, or trail). Stretch/risk of mean reversion.
±3 ATR: potential exhaustion/mean-revert zone.
🔹Momentum Clarity: By framing momentum in ATR terms, it is easier to distinguish between a small deviation from trend and a genuinely significant move. Sometimes it is a good sign that it trend to ±3/2 ATR, looks for similar directional moves.
Color: The script shades +2/+3 (OB) and −2/−3 (OS) areas and provides swing alerts at ±1 ATR.
💚 What Are Discontinued Signal Lines (DSL)? 💚
In technical analysis, one of the most common tools for smoothing out noisy data is the signal line. This concept appears in many indicators, such as the MACD or stochastic oscillator, where the raw value of an indicator is compared to a smoothed version of itself. The signal line acts as a lagging filter, making it easier to identify shifts in momentum, crossovers, and directional changes.
While useful, the classic signal line approach has limitations. By design, a single smoothed line introduces lag, which means traders may receive signals later than ideal. Additionally, a one-size-fits-all smoothing process often struggles to adapt to different levels of volatility or rapidly changing market conditions.
This is where Discontinued Signal Lines (DSL) come in. DSL is an advanced extension of the traditional signal line concept. Instead of relying on just one smoothed comparison, DSL employs multiple adaptive lines that adjust dynamically to the current state of the indicator. These adaptive lines effectively “discontinue” the dependence on a single, fixed smoothing method, producing a more flexible and nuanced representation of market conditions.
How DSL Works?
Traditional Signal Line: Compares an the Momentum against its own moving average. Provides crossover signals when the raw indicator value moves above or below the smoothed line.
Strength: reduces noise. Weakness: delayed signals and limited adaptability.
DSL Extension: Uses multiple adaptive lines that respond differently to the indicator’s current behavior. Instead of one static moving average, the DSL approach creates faster and slower “reaction lines.” These lines adapt dynamically, capturing acceleration or deceleration in the indicator’s state.
Result: Traders see how momentum is evolving across multiple adaptive thresholds. This reduces false signals and improves responsiveness in volatile conditions.
Benefits of Discontinued Signal Lines
🔹Nuanced Trend Detection
DSL doesn’t just flag when momentum changes direction—it shows the quality of that shift, highlighting whether it is gaining strength, losing steam, or consolidating.
🔹Adaptability Across Markets
Because DSL adjusts to the Momentum’s own dynamics, it works well across different asset classes and timeframes, from equities and futures to forex and crypto.
🔹Earlier Signal Recognition
Multiple adaptive lines allow traders to spot developing trends earlier than with a single smoothed signal line, without being overwhelmed by raw indicator noise.
🔹Better Confirmation
DSL is particularly useful for confirmation. If both adaptive lines agree then a fill is applied in the direction, confidence in the trend is higher as the color turns bull/bear.
🔹Practical Uses
Momentum Trading: Spot acceleration or deceleration in trend strength.
Trend Confirmation: Verify whether a breakout has momentum behind it.
Noise Filtering: Smooth out erratic moves while retaining adaptability.
⚖️ Colored Directional Movement Index (CDMI) ⚖️
The Directional Movement Index (DMI), created by J. Welles Wilder, is one of the most respected trend-following indicators in technical analysis. It is actually a family of three separate indicators combined into one: the +DI (Positive Directional Indicator), the –DI (Negative Directional Indicator), and the ADX (Average Directional Index). Together, they measure not only whether the market is trending but also the strength of that trend. Traders have used the DMI for decades to identify trend direction, gauge momentum, and filter out periods of market noise.
However, despite its reliability, the traditional DMI can be challenging to interpret. Reading three separate lines at once and extracting meaningful signals requires both experience and careful observation. This complexity often discourages newer traders from fully utilizing its power.
The Colored Directional Movement Index (CDMI) is a modern reinterpretation of Wilder’s classic tool. It condenses the same information into a single visual line while using color, shape, and density to communicate what’s happening beneath the surface. The goal is simple: make the DMI’s insights faster to read, easier to act upon, and more intuitive to integrate into trading decisions.
Key Features of CDMI
🔹Color Scale for Trend Strength
The main triangle changes its base color depending on the strength of the DI reading. Dark Red or Green, colors correspond to stronger trends, while faded Gray or lighter yellow tones signal weaker or fading trends. This makes it visually clear when the market is consolidating versus trending strongly.
🔹Color Density for Momentum
Beyond strength, the CDMI uses color density to represent momentum in the trend’s strength. If the ADX is rising (trend gaining momentum), the triangles grows more darker. If the ADX is falling (trend losing momentum), the triangle becomes paler. This provides an instant sense of whether a trend is accelerating or decelerating.
🔹Directional Triangles for Trend Direction
To replace the separate +DI and –DI lines, the CDMI plots small triangle shapes along the bottom axis. An upward-facing triangle indicates that +DI is dominant, confirming bullish direction. A downward-facing triangle signals –DI dominance, confirming bearish direction. This way, both strength and direction are shown without the clutter of multiple overlapping lines.
🔹Label Display for Detailed Values
For traders who want precise data alongside the visuals, CDMI includes a label that shows:
Current trend strength (ADX value).
Current +DI and –DI values.
Momentum status of the ADX (rising or falling).
Historical values of DMI readings, so traders can track how the indicator has evolved over time.
Tooltips are also available to explain “How to read the colored DMI line”, making this version more beginner-friendly.
Why CDMI Matters
The CDMI retains the proven reliability of Wilder’s DMI while solving its biggest drawback—interpretation difficulty. Instead of juggling three separate plots, traders get a single, information-rich line supplemented with intuitive shapes and labels. This streamlined format makes trend verification, momentum analysis, and signal confirmation much faster.
For trading applications, the CDMI can help:
Confirm Entries by showing whether the market is trending strongly enough to justify a position.
Avoid False Signals by filtering out periods of low ADX (weak trend).
Enhance Timing by tracking momentum shifts in trend strength.
By simplifying the complexity of the original DMI into an elegant, color-coded tool, the CDMI makes one of technical analysis’ most advanced indicators practical for everyday use.
😅 The VIX, the Williams Vix Fix, and Market Bottoms 😎
The VIX, formally known as the CBOE Volatility Index, has long been considered one of the most reliable indicators for spotting major market bottoms. Often referred to as the “fear gauge,” it measures the market’s expectation of volatility in the S&P 500 over the next 30 days. When fear grips investors and volatility spikes, the VIX rises sharply. Historically, these moments of extreme fear often coincide with powerful buying opportunities, as markets have a tendency to rebound once panic selling exhausts itself.
Larry Williams, a well-known trader and author, developed the Williams Vix Fix as a way to replicate the insights of the VIX across any tradable asset. While the VIX itself is tied specifically to S&P 500 options, Williams wanted a tool that could capture similar panic-driven dynamics in stocks, futures, forex, and other markets where the VIX is not directly applicable. His “fix” uses price action and volatility formulas to approximate the same emotional extremes reflected in the official VIX, creating almost identical results in practice. This makes the Williams Vix Fix a powerful addition to the trader’s toolbox, allowing the same principle that works on U.S. equities to be applied universally.
One of the most important characteristics of both the VIX and the Williams Vix Fix is that they are far more reliable at signaling market bottoms than market tops. The reason is psychological as much as it is mathematical. At market bottoms, fear and panic are widespread. Retail investors often capitulate, selling in a frenzy as prices drop. This panic drives volatility higher, producing the spikes we see in the VIX. At the same time, professional traders and institutions—those with larger capital and more disciplined strategies—tend to step in when volatility is stretched. They buy when others are fearful, using the panic of retail investors as an opportunity to acquire assets at discounted prices. This confluence of retail panic and institutional buying power is what makes the VIX such a strong bottom-finding tool.
In contrast, at market tops, the dynamic is very different. Tops tend not to be marked by panic or fear. Instead, they form quietly as enthusiasm fades, liquidity dries up, and buying interest wanes. Investors are often complacent, assuming prices will continue to rise, while professional money begins distributing their positions. Because there is no surge in fear, volatility remains muted, and the VIX does not offer a clear warning. This is why traders who rely on the VIX or the Williams Vix Fix must understand its limitations: it is exceptional for detecting bottoms but less useful for anticipating tops.
For traders, the lesson is straightforward. When you see the VIX or Williams Vix Fix spiking to extreme levels, it often indicates a high-probability environment for a rebound. These tools should not be used in isolation, but when combined with support levels, sentiment indicators, and market breadth, they can provide some of the most reliable bottom-fishing signals available. While no indicator is perfect, few have stood the test of time as consistently as the VIX—and thanks to Williams’ adaptation, its power can now be applied to nearly every market.
Indicator Signals (Great in risk-off charts):
🔹Flags spike events (tops/bottoms) with both original and filtered (AE/FE) criteria.
🔹Great as a risk overlay: tighten stops into AE/FE, or require “no spike” to enter.
🤯 Volume Comfirmation: VTTI & VPCI (Buff Dormeier) 🤯
Volume Trend Technical Indicator (VTTI)
The Volume Trend Technical Indicator (VTTI) is a momentum-style tool that analyzes how volume trends interact with price movement. Unlike basic volume measures that simply report how many shares or contracts were traded, the VTTI evaluates whether volume is expanding or contracting in the same direction as the prevailing price trend. The underlying logic is that healthy trends are supported by rising volume, while weakening trends often occur on shrinking volume.
At its core, VTTI looks at the rate of change in volume compared to price movements. By smoothing and normalizing these relationships, the indicator helps traders determine whether momentum is accelerating, decelerating, or diverging.
Rising VTTI: Suggests that volume is confirming the current price trend, strengthening the case for continuation. Flips BG Green after crossing it's signal.
Falling VTTI: Indicates that the trend may be losing participation, often a sign of possible consolidation or reversal. Flips BG Red after crossing it's signal.
Traders often use VTTI to filter entries and exits. For example, if price breaks out but VTTI does not rise above zero, the breakout may lack conviction. On the other hand, when both price and VTTI are aligned, probability of continuation improves.
Volume Price Confirmation Indicator (VPCI)
The Volume Price Confirmation Indicator (VPCI), developed by Buff Dormeier, takes the relationship between price and volume a step further. While traditional indicators like On-Balance Volume (OBV) or Chaikin Money Flow look at cumulative patterns, VPCI breaks price and volume into trend and volatility components and then recombines them to measure how well they confirm each other.
In essence, VPCI asks: “Does volume confirm what price is signaling?”
The formula integrates:
Price Trend Component – whether the market is trending upward or downward.
Volume Trend Component – whether trading activity supports that price trend.
Volatility Adjustments – to account for irregular swings.
The resulting oscillator fluctuates around a zero line:
Positive VPCI: Indicates that price and volume trends are in agreement (bullish confirmation).
Negative VPCI: Suggests that price and volume are diverging (bearish warning or false move).
Crossovers of Zero: Can serve as potential buy or sell signals, depending on context.
A key strength of VPCI is its sensitivity to divergence. When prices continue rising but VPCI begins falling, it often foreshadows a weakening rally. Conversely, a rising VPCI during a flat or down market can highlight early accumulation.
VTTI (Entry Signal) vs. VPCI (Exit Signal)
While both indicators study price-volume dynamics, their focus differs:
VTTI is simpler, emphasizing the trend of volume relative to price for momentum confirmation.
VPCI is more advanced, decomposing both price and volume into multiple components to produce a nuanced oscillator.
Used together, they provide complementary insights. VTTI helps quickly spot whether volume is supporting a move, while VPCI offers deeper confirmation and highlights subtle divergences.
Note: The Up/Down Volume Alert works better on the 4 HR, for Daily scalps or 30 minute for HR scalps. Intraday it's 2/10 minute.
🦅 Divergence toolkit 🦅
Divergences in Technical Analysis
Divergence occurs when the price action of an asset moves in one direction while a technical indicator, such as RSI, MACD, or Momentum, moves in the opposite direction. This disagreement between price and indicator often signals a shift in underlying market dynamics. Traders use divergences to anticipate either potential reversals or continuations in trends.
There are two main types of divergences: regular divergences, which typically precede reversals, and hidden divergences, which suggest continuation of the current trend.
Regular Divergence (Reversal Signals)
A regular divergence occurs when price and indicator disagree during a trend extension. These divergences signal that momentum is no longer fully supporting the current trend and that a reversal may be imminent.
🔹Regular Bullish Divergence
Price Action: Forms a lower low.
Indicator: Forms a higher low.
Interpretation: Price is making new lows, but the indicator is gaining strength. This suggests that selling pressure is weakening, and a reversal to the upside may occur.
Example: RSI rising while price dips to fresh lows.
🔹Regular Bearish Divergence
Price Action: Forms a higher high.
Indicator: Forms a lower high.
Interpretation: Price is reaching new highs, but the indicator shows weakening momentum. This implies that buying pressure is fading, warning of a potential downside reversal.
Example: MACD histogram falling while price makes higher highs.
Regular divergences are often spotted near the end of trends and are most powerful when aligned with key support/resistance levels or overbought/oversold conditions.
Hidden Divergence (Continuation Signals)
A hidden divergence occurs during retracements within a trend. Unlike regular divergences, hidden divergences suggest that the prevailing trend still has strength and is likely to continue.
🔹Hidden Bullish Divergence
Price Action: Forms a higher low.
Indicator: Forms a lower low.
Interpretation: Price is retracing within an uptrend, but the indicator is overshooting downward. This shows that momentum remains intact, supporting continuation upward.
🔹Hidden Bearish Divergence
Price Action: Forms a lower high.
Indicator: Forms a higher high.
Interpretation: Price is retracing within a downtrend, while the indicator overshoots upward. This indicates that bearish momentum remains strong, supporting continuation downward.
Hidden divergences often appear during pullbacks, helping traders time entries in the direction of the prevailing trend.
Practical Use of Divergences
🔹Trend Reversal Alerts – Regular divergences are early warnings that a trend may be ending.
🔹Trend Continuation Signals – Hidden divergences help confirm that retracements are simply pauses, not full reversals.
🔹Confluence with Other Tools – Divergences are more reliable when combined with support/resistance, candlestick patterns, or volume analysis.
🔹Multi-Timeframe Analysis – Spotting divergences on higher timeframes often produces stronger signals.
🕭🔔🛎️ Alert 🛎️🔔🕭
🔹Squeeze
🟢 Fired Squeeze
⚫ Low (Wide) Squeeze / 🔴 Normal / 🟡 Tight / 🟣 Very Tight
🔹Momentum
🐂 Bullish Trend Reversal (Crossover of Momentum and Signal from sub −2)
🐻 Bearish Trend Reversal (Crossover of Momentum and Signal from above +2)
📈 Bullish Swing (cross above +1 ATR) / 📉 Bearish Swing (cross below −1 ATR)
🔹DSL
💚 Bullish DSL Scalp / 💔 Bearish DSL Scalp
🔹Volume
🎯 Strong Up Volume (VPCI > 0 and VTTI up)
⏳ Strong Down Volume (VPCI < 0 and VTTI down)
🔹Divergences
🦅 Bullish, 🦆 Bearish, 🦅 Bullish Hidden, 🦆 Bearish Hidden
Management: Search Vanguard ETFs in your browser, look up full list of VOO holdings. Download it, or copy paste all the ticker symbols. Place that with a AI, just ask it to place , in between each ticker. NVDA, TSLA, AVGO, etc. Create a new watchlist, in the + add all tickers separated by commas. Place a watchlist alert ⚠️ only available for premium + subscribers.
Practical playbook
1) Classic Squeeze Break
Setup: 🔴(D)/🟡(2D)/🟣(3D) squeeze → wait for 🟢(1HR) Fired.
Confirm: Momentum > Signal and above +1 ATR (or DMI strong & rising).
Manage: add on pullbacks that hold +1 ATR; scale near +2 ATR or WVF AE/FE.
2) DSL Scalp in Trend
Setup: Clear trend (DMI strong) + DSL bull/bear trigger in the direction of trend.
Filter: avoid tight/very tight yellow/purple unless you want micro-scalps.
Exit: opposite DSL or ATR midline loss.
3) Mean-Reversion Fade
Setup: Momentum extended to ±3 ATR, WVF spike, and a regular divergence.
Entry: Counter signal only when mom crosses back through ±3 ATR toward mid. Exit early if squeeze ⚫/🔴, Momentum may extend to ±3/2 ATR in the same direction.
Risk: reduce size; this is a fade, not trend following.
4) Volume-Confirmed Breakout
Setup: Squeeze → 🟢 Fired + VPCI > 0 and VTTI up → trend continuation.
Manage: trail behind +1 ATR (long) or −1 ATR (short). 9 SMA works good.
Inputs at a glance (key ones)
Mode: Price or CVD momentum; Squeeze Sensitivity (σ); Momentum Length; Signal Length; ATR Smoothing.
🧮 Colors:
SQZMOM: per squeeze regime, momentum, ATR fills.
DSL: On/Off, Fast/Slow, Length.
ATR Zones: Bullish/Bearish levels (±1), ±2/±3 zone lines & fills.
DMI: Lengths, key & weak thresholds, label on/off.
WVF/VIX: Lookbacks, bands, AE/FE toggles, VIX proxy symbol.
VTTI/VPCI: Fast/slow/signal (VTTI), Short/Long (VPCI), and volume source (Tick/CVD/NVI/PVI/OBV/PVT/AccDist/VWAP).
Divergences: Regular/Hidden toggles, Sensitivity %, Lifetime, Live vs On-Close, Lines/Labels.
🔎 Suggested defaults (feel free to tweak)
Calibration: Size Momentum, so that when it's above zero the asset is trending up. For the signal, it can be kept the same or lower.
Intraday (60–240m): σ = 2.0, 18~20, 3~5, DSL Fast, DMI key 23, weak 17.
Daily/Weekly: keep σ = 2.0, consider DSL Slow, DMI key 25, weak 20, widen ATR filters; lean on VPCI/VTTI (4-HR).
CVD mode: use where tick/volume quality is high (index futures, liquid equities, crypto majors).
🪟 Tips & caveats
Swing Screener: Favor liquid underlyings (index futures/ETFs, large caps). Large-Cap, 2 M Vol, Mid-Cap, 500K Vol. Squeeze: BB( 20) upper < KC (20) upper, and BB (20) lower > KC (20) lower. Optional: Price above 9 SMA, 21 SMA, and 50 SMA, they are my SMA of choice. 200 SMA too, unless you are willing to fish in a bear market. Vice-versa for shorts. Optional: ADX 4 HR > 17, or 23 depending on what you are looking for.
Scalp Screener: Same as above, change the D 9 SMA to 5, and the BB/KC from D to 1 HR. Scalps may last 2~3 days.
Position Screener: Change all daily setting to W, aside from Volume. Optional: PEG < 1.5, FCF > 0, ROA > 8% or ROE > 6%.
Good with Moving averages (9/21/50) and low-volume zones.
Position size by IV, ATR, and account risk. Consider stop/hedge rules around ±2/±3 ATR.
Let alerts stage your watchlist; act only on combined squeeze + momentum signals.
Divergences in live mode can repaint (Real-Time); for algo or alerts, use on-close.
Tight/Very tight squeezes are great for scalps but choppy; combine with DMI rising + VPCI>0.
±3 ATR is exhaustion context, not an auto-fade—look for WVF/Div/DSL confirmation.
For alerts, pair “Fired Squeeze + Bullish Swing” (or bearish) to avoid false starts.
🎯 How to Trade Entry ~ Recap:
Tight/very tight squeeze → fires → momentum crosses up (or DSL bull).
Exit/Flip: Momentum crosses down into/after expansion or hits +2/+3 ATR with fade signs. Filter: Avoid fresh longs at +3 ATR; avoid fresh shorts at −3 ATR unless fading with confirmation.
📐 Options Integrations
✅ Risk Reversal/Modified Risk Reversal (Bullish: Short Put + Long Call)
Use when: Squeeze fires up from 🟡/🟣 and momentum crosses above signal (or zero/DSL).
Playbook Entry: On or just after the bullish fire and momentum upcross. DMI or Volume supports trend as well.
Structure: Sell a put at/just below the −2 ATR reference (or recent swing support). Buy a call at/above the breakout zone (prior high/mid-range +1 to +2 ATR).
A classic risk reversal is a long call plus a short put. That’s a very bullish structure—you gain if the price rallies (via the call), and you collect a premium by selling a put. But it has a naked downside risk. The modified risk reversal fixes that by adding a long lower put (making the short put into a defined put credit spread).
Management: If momentum stays above signal, ride toward +2 → +3 ATR. Sell the put near the current price → receive big premium. Buy the lower put → spend part of that premium (risk cap). Buy the call above the current price → spend more, but the short put premium mostly pays for it.
Exits/Adjust: Momentum downcross or squeeze flips back on (new compression) → reduce. If price retests −1/−2 ATR and holds, you can roll the short put down/out.
Breakout = Big Success; No Breakout = you keep the initial credit. Reversal = Max loss is capped by the long lower put.
✅ Iron Condor (Neutral: Short OTM Put Spread + Short OTM Call Spread)
Use when: Squeeze is active (🟡/🟣), momentum is flat near zero, and there is no directional edge. 🟢 lasts for around 5~8 bars typically. I measure the historical duration of it, and wait for a range period to occur.
Playbook Entry: During compression, set wings outside ±2 ATR (or recent range extremes). I prefer identifying boxes where the rectangle pattern occurs on the chart.
Management: Time decay works while price remains trapped in the coil. High-winrate ~80%, but 1 loser can wipe most of the gains.
Exits/Adjust: If a squeeze fires and momentum breaks hard one way, close the losing side, consider converting to a vertical or rotating to a directional spread aligned with momentum.
4HR-Bullish, closing one wing:
Tip: Align daily/weekly context with your intraday entries. 9 > 50 on Weekly, similar on Daily. Sell premium into compression; switch to directional spreads on expansion and momentum confirmation.
✅ Naked Call/Puts (Directional: 10~30 Delta Calls)
Stick to naked calls and puts when the squeezes are fired from either 🔴 or ⚫.
Look for Strikes slightly out of the money with an OI and Volume spread less than <10%.
If Strike Date is >45, manage 21 Days before expiration. Scalp: Expiration Strikes of 1/4 of the Squeeze period. Leap: Expiration Strikes of 1.75x of the Squeeze period.
📐 Futures Integrations
Playbook Entry:
Verify if the squeeze on the hourly is red or green, and enter on the 2- or 5-minute during a similar squeeze state.
Trend-Following: Traditional 2 Renko Block above 21 SMA and Momentum is bullish, or vice versa. (2~ES, 5~NQ)
Structure: Go long at/just below the ATR reference (or recent swing support). Exit below the breakout zone (prior high/mid-range +1 to +2 ATR).
Management: If momentum stays above +1 ATR ride toward +2 → +3 ATR, etc. House-money, should be kept.
Exits/Adjust: Momentum downcross or squeeze flips back on (new compression) → exit. On Renko Charts, lower the sensitivity to 0.7~1. If price retests 0/−1/−2 ATR and holds, you can enter when the 9 SMA flips. The 50 SMA is better for Daily and up; I wouldn't trade against it then.
📌 FOMO Trading Playbook
Credits & License
Credits: @JF10R (Multi-Timeframe Squeeze), @BigBeluga (DSL), @OskarGallard (Colored DMI base), @ChrisMoody (WVF ideas), @PineCodersTASC (VTTI/VPCI), @EliCobra (Divergence toolkit).
License: Mozilla Public License 2.0 (MPL-2.0).
Author: © GabrielAmadeusLau
Edge Algo📈 Indicator Features:
• Provides accurate trades with up to 90% success rate
• Works on all currencies, stocks, crypto, and even futures
• Compatible with all timeframes: 1m / 5m / 15m / 30m / 1h / 1d
• Built on an AI system that detects stop-hunt zones to avoid stop-loss hits
• Gives you entry points, stop-loss (SL), and take-profit (TP) levels
Edge Algo📈 Indicator Features:
• Provides accurate trades with up to 90% success rate
• Works on all currencies, stocks, crypto, and even futures
• Compatible with all timeframes: 1m / 5m / 15m / 30m / 1h / 1d
• Built on an AI system that detects stop-hunt zones to avoid stop-loss hits
• Gives you entry points, stop-loss (SL), and take-profit (TP) levels
Edge Algo📈 Indicator Features:
• Provides accurate trades with up to 90% success rate
• Works on all currencies, stocks, crypto, and even futures
• Compatible with all timeframes: 1m / 5m / 15m / 30m / 1h / 1d
• Built on an AI system that detects stop-hunt zones to avoid stop-loss hits
• Gives you entry points, stop-loss (SL), and take-profit (TP) levels
eORB - Day EditionThe eORB – Day Edition (Enhanced Opening Range Breakout) is a powerful intraday trading indicator designed for Algo Trading, Scalpers, Day Traders, and ORB-based strategies. It combines classic ORB logic with advanced filters, multiple exit strategies, and smart risk management tools. The default setup is optimised for a 3-minute ETHUSD chart.
Key Features:-
# Opening Range Breakout (ORB)
- Defines intraday high/low for the first X minutes.
- Automatically updates breakout levels.
- Optional buffer (%) for precision entries.
# Day & Session Filters
- Enable/disable trading on specific weekdays.
- Flexible session time configuration.
# EMA Crossover
- Option to trade based on EMA crossover with ORB levels.
# Breakout Candle Logic
- Detects breakout candle high/low for secondary confirmation.
# RSI Filter
- Confirms signals using RSI thresholds (customisable).
# Exit Strategies
- ORB High/Low Exit
- Buffer Exit
- Trailing Stop Loss (TSL) with activation, lock, and increments
- Target & Stoploss (fixed points)
- Universal Exit (UTC time-based) with background highlight
# Trade Sync Logic
- Prevents consecutive Buy → Buy or Sell → Sell without the opposite signal in between.
# Alerts Ready
- Buy, Sell, and Exit conditions are available for alerts.
- Compatible with TradingView alert system (popup, email, SMS, webhook).
How to Use:-
1. Add indicator to the chart.
2. Set ORB Time & Session (e.g., 3 min ORB at market open).
3. Enable/disable filters (EMA, RSI, Breakout candle).
4. Configure exits (TSL, Target, Stoploss, Universal Exit).
5. Add alerts for automation or notifications.
- This indicator is ideal for Crypto, Nifty, BankNifty, Index Futures, and Stocks, but it can be applied to any asset.
- The default settings are optimised for ETHUSD.
How it Works – eORB Day Edition:-
Step 1 – Define the Range
- At market open, the indicator records the Opening Range High & Low for the first X minutes (configurable by the user).
- This creates a price boundary (box) that acts as support and resistance for the rest of the session.
- Optional buffers can be added to make signals more reliable.
Step 2 – Generate the Signal
- When price (or EMA, if enabled) crosses above the Opening Range High, a Buy signal is generated.
- When price (or EMA) crosses below the Opening Range Low, a Sell signal is generated.
- Extra filters like RSI and Breakout Candle confirmation can be turned on to reduce false breakouts.
- Built-in sync logic ensures signals alternate properly (no double Buy or double Sell without the opposite in between).
Step 3 – Manage the Exit
- Trades can exit using multiple methods:
- Target (fixed profit in points)
- Stoploss (fixed risk in points)
- Trailing Stop-loss (TSL) that locks profit and trails as price moves further in your favour
- ORB/Buffer exit when price re-enters the range
- Universal Exit at a fixed UTC time to close all positions for the day
- Exits are visualised on the chart with shapes, labels, and optional background highlights.
In simple terms:-
Step 1: DEFINE
- Opening Range (first X minutes) → Marks High & Low → Creates breakout zone
Step 2: SIGNAL
- Price / EMA crosses High (+ Buffer) → BUY
- Price / EMA crosses Low (- Buffer) → SELL
- + Optional filters: RSI, Breakout Candle
Step 3: EXIT
- Target | Stoploss | Trailing Stoploss | Universal Exit
Important Note on Alert Setup
- When using the RSI filter, signals may fluctuate in some edge cases where RSI hovers near the Buy or Sell level.
- To avoid this, it is recommended to use “Once Per Bar Close” as the alert trigger, since signals confirm only after the bar closes (especially helpful when Breakout Candle High/Low Crossover is enabled).
- If you choose not to use RSI, you can safely use “Once Per Bar” alerts, even when the Breakout Candle High/Low Crossover option is enabled.
Disclaimer:-
- This tool is for educational and research purposes only.
- It does not guarantee profits. Always backtest and use proper risk management before live trading. The author is not responsible for financial losses.
Developer: @ikunalsingh
Built using AI + the best of human logic.
Super AI SignalThis indicator helps cryptocurrency investors make informed decisions when placing bets such as long or short positions.
It incorporates various indicators to achieve a higher accuracy rate, and after extensive backtesting, it demonstrates a significant success rate.
I hope this indicator proves beneficial for your cryptocurrency investments!
Hidden Divergence with S/R & TP// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © Gemini
// @version=5
// This indicator combines Hidden RSI Divergence with Support & Resistance detection
// and provides dynamic take-profit targets based on ATR. It also includes alerts.
indicator("Hidden Divergence with S/R & TP", overlay=true)
// === INPUTS ===
rsiLengthInput = input.int(14, "RSI Length", minval=1)
rsiSMALengthInput = input.int(5, "RSI SMA Length", minval=1)
pivotLookbackLeft = input.int(5, "Pivot Left Bars", minval=1)
pivotLookbackRight = input.int(5, "Pivot Right Bars", minval=1)
atrPeriodInput = input.int(14, "ATR Period", minval=1)
atrMultiplierTP1 = input.float(1.5, "TP1 ATR Multiplier", minval=0.1)
atrMultiplierTP2 = input.float(3.0, "TP2 ATR Multiplier", minval=0.1)
atrMultiplierTP3 = input.float(5.0, "TP3 ATR Multiplier", minval=0.1)
// === CALCULATIONS ===
// Calculate RSI and its SMA
rsiValue = ta.rsi(close, rsiLengthInput)
rsiSMA = ta.sma(rsiValue, rsiSMALengthInput)
// Calculate Average True Range for Take Profits
atrValue = ta.atr(atrPeriodInput)
// Identify pivot points for Support and Resistance
pivotLow = ta.pivotlow(pivotLookbackLeft, pivotLookbackRight)
pivotHigh = ta.pivothigh(pivotLookbackLeft, pivotLookbackRight)
// Define variables to track divergence and TP levels
var bool bullishDivergence = false
var bool bearishDivergence = false
var float tp1Buy = na
var float tp2Buy = na
var float tp3Buy = na
var float tp1Sell = na
var float tp2Sell = na
var float tp3Sell = na
// Reset divergence flags at each new bar
bullishDivergence := false
bearishDivergence := false
// === HIDDEN DIVERGENCE LOGIC ===
// Hidden Bullish Divergence (Higher low in price, lower low in RSI)
// Price makes a higher low, while RSI makes a lower low, suggesting trend continuation.
for i = 1 to 50 // Look back up to 50 bars for a confirmed pivot low
if not na(pivotLow ) and close < close and rsiValue < rsiValue
// Check if price is making a higher low than the pivot low, and RSI is making a lower low
if low > low and rsiValue < rsiValue
bullishDivergence := true
break // Exit loop once divergence is found
// Hidden Bearish Divergence (Lower high in price, higher high in RSI)
// Price makes a lower high, while RSI makes a higher high, suggesting trend continuation.
for i = 1 to 50 // Look back up to 50 bars for a confirmed pivot high
if not na(pivotHigh ) and close > close and rsiValue > rsiValue
// Check if price is making a lower high than the pivot high, and RSI is making a higher high
if high < high and rsiValue > rsiValue
bearishDivergence := true
break // Exit loop once divergence is found
// === SETTING TP LEVELS AND ALERTS ===
if bullishDivergence
buySignalPrice = low - atrValue * 0.5 // Entry below the low
tp1Buy := buySignalPrice + atrValue * atrMultiplierTP1
tp2Buy := buySignalPrice + atrValue * atrMultiplierTP2
tp3Buy := buySignalPrice + atrValue * atrMultiplierTP3
// Alert for buying signal
alert("Hidden Bullish Divergence Detected on " + syminfo.ticker + " - Buy Signal", alert.freq_once_per_bar_close)
else
tp1Buy := na
tp2Buy := na
tp3Buy := na
if bearishDivergence
sellSignalPrice = high + atrValue * 0.5 // Entry above the high
tp1Sell := sellSignalPrice - atrValue * atrMultiplierTP1
tp2Sell := sellSignalPrice - atrValue * atrMultiplierTP2
tp3Sell := sellSignalPrice - atrValue * atrMultiplierTP3
// Alert for selling signal
alert("Hidden Bearish Divergence Detected on " + syminfo.ticker + " - Sell Signal", alert.freq_once_per_bar_close)
else
tp1Sell := na
tp2Sell := na
tp3Sell := na
// === PLOTTING SIGNALS AND TAKE PROFITS ===
// Plotting shapes for buy/sell signals
plotshape(bullishDivergence, title="Buy Signal", style=shape.triangleup, location=location.belowbar, color=color.new(color.green, 0), text="Buy", textcolor=color.black)
plotshape(bearishDivergence, title="Sell Signal", style=shape.triangledown, location=location.abovebar, color=color.new(color.red, 0), text="Sell", textcolor=color.black)
// Plotting take-profit lines
plot(tp1Buy, "TP1 Buy", color=color.new(color.lime, 0), style=plot.style_linebr)
plot(tp2Buy, "TP2 Buy", color=color.new(color.lime, 0), style=plot.style_linebr)
plot(tp3Buy, "TP3 Buy", color=color.new(color.lime, 0), style=plot.style_linebr)
plot(tp1Sell, "TP1 Sell", color=color.new(color.orange, 0), style=plot.style_linebr)
plot(tp2Sell, "TP2 Sell", color=color.new(color.orange, 0), style=plot.style_linebr)
plot(tp3Sell, "TP3 Sell", color=color.new(color.orange, 0), style=plot.style_linebr)
// Plotting the RSI and its SMA on a sub-pane
plot(rsiValue, "RSI", color.new(color.fuchsia, 0))
plot(rsiSMA, "RSI SMA", color.new(color.yellow, 0))
hline(50, "50 Midline", color=color.new(color.gray, 50))
// Plotting background for signals
bullishColor = color.new(color.green, 90)
bearishColor = color.new(color.red, 90)
bgcolor(bullishDivergence ? bullishColor : na, title="Bullish Divergence Zone")
bgcolor(bearishDivergence ? bearishColor : na, title="Bearish Divergence Zone")
// === EXPLANATION OF CONCEPTS ===
// Deep Knowledge of Market from AI:
// This indicator is based on a powerful, yet often misunderstood, concept: divergence.
// While standard divergence signals a potential trend reversal, hidden divergence signals a
// continuation of the prevailing trend. This is crucial for traders who want to capitalize
// on the momentum of a move rather than trying to catch tops and bottoms.
// Hidden Bullish Divergence: Occurs in an uptrend when price makes a higher low, but the
// RSI makes a lower low. This suggests that while there was a brief period of weakness, the
// underlying buying pressure is returning to push the trend higher. It’s a "re-energizing"
// of the bullish momentum.
// Hidden Bearish Divergence: Occurs in a downtrend when price makes a lower high, but the
// RSI makes a higher high. This indicates that while the sellers paused, the underlying
// selling pressure remains strong and is likely to continue pushing the price down. It's a
// subtle signal that the bears are regaining control.
// Combining Divergence with S/R: The true power of this indicator comes from its
// "confluence" principle. A divergence signal alone can be noisy. By requiring it to occur
// at a key support or resistance level (identified using pivot points), we are filtering
// out weaker signals and only focusing on high-probability setups where the market is
// likely to respect a previous area of interest. This tells us that not only is the trend
// likely to continue, but it is doing so from a strategic, well-defined point on the chart.
// Dynamic Take-Profit Targets: The take-profit targets are based on the Average True Range (ATR).
// ATR is a measure of market volatility. Using it to set targets ensures that your profit
// levels are dynamic and adapt to current market conditions. In a volatile market, your
// targets will be wider, while in a calm market, they will be tighter, helping you avoid
// unrealistic expectations and improving your risk management.
Kairos AR EdgeEN
Kairos AR Edge is a closed-source (invite-only) Forex indicator providing statistical analysis of Asian session box breakouts and relative currency strength across 28 major pairs. Unlike standard breakout or trend-following tools, it consolidates breakout behavior into a single overview, helping traders quickly identify directional bias and strong/weak currencies. This aggregation provides unique insight not easily obtained from separate pair analysis.
Important Clarification:
Reversal and Continuation percentages are calculated for the pair on which the indicator is applied , showing how often a breakout returns (Reversal) or continues (Continuation) within the selected session window.
The Currency Strength Table is independent of these percentages. It scores each currency from -7 to +7 based on participation in Asian box breakouts across all 28 pairs, providing a relative strength overview regardless of the active pair.
The -7/+7 scale is derived from historical breakout occurrences and provides a quick reference for currency strength ranking
Indicator operates on two levels:
Session Bias Statistics: Builds an Asian session box for the active pair and analyzes breakout behavior. Users can select:
Reversal Mode : Percentage of breakouts that return to the opposite side within the selected timeframe
Continuation Mode : Percentage of breakouts that continue in the same direction within the timeframe
Currency Strength Table: Aggregates breakout behavior across all 28 pairs to provide a relative currency strength score (-7 to +7)
Visual Tools: Optional pivot-based bullish/bearish triggers and automatic session box visualization provide additional informational support.
Main Features:
Customizable Asian session box (start/end times and timezone)
Reversal or Continuation statistical mode
Automatic update of high/low levels
Currency Strength Table (-7 to +7)
Statistical table with historical breakout percentages
Optional visual triggers (pivot-based patterns)
Light/Dark theme support
Originality and Value:
Consolidates 28 pairs into a single view for immediate identification of market bias
Provides statistical insight into breakout behavior, not just trend-following or generic breakout alerts
Offers a quick-reference Currency Strength Table to identify strong/weak currencies without tracking multiple pairs individually
Important Notes:
Statistics are based on historical data only – no guarantee of future results
Educational and informational purposes only; not financial or trading advice
Closed-source indicator with invite-only access. Access requests can be made by contacting the author or following the link in the Author’s Instructions field
IT
Kairos AR Edge è un indicatore closed-source (invite-only) che fornisce analisi statistica sulle rotture del box della sessione asiatica e forza relativa delle valute su 28 coppie principali. A differenza dei normali strumenti di breakout o trend-following, consolida il comportamento dei breakout in un’unica panoramica, aiutando i trader a identificare rapidamente bias direzionali e valute forti/deboli. Questa aggregazione offre insight unici non facilmente ottenibili analizzando coppie singole.
Chiarimento importante:
Le percentuali di Reversal e Continuation si riferiscono solo alla coppia su cui l’indicatore è applicato , calcolando quante volte una rottura ritorna (Reversal) o continua (Continuation) entro la finestra di sessione selezionata.
La Tabella di Forza Valute è indipendente da queste percentuali. Assegna a ciascuna valuta un punteggio da -7 a +7 in base alla partecipazione ai breakout del box asiatico su tutte le 28 coppie, fornendo un quadro della forza relativa indipendentemente dalla coppia attiva.
Il punteggio -7/+7 deriva dai breakout storici e fornisce un riferimento rapido per la forza delle valute.
Lo script opera su due livelli:
Statistiche Bias di Sessione: Costruisce il box della sessione asiatica per la coppia attiva e analizza i breakout. Modalità selezionabili:
Reversal : Percentuale di breakout che tornano verso il lato opposto entro la finestra temporale
Continuation : Percentuale di breakout che proseguono nella stessa direzione entro la finestra
Tabella di Forza Valute: Aggrega il comportamento dei breakout su tutte le 28 coppie, assegnando un punteggio da -7 a +7 per ciascuna valuta in base alla sua forza relativa
Strumenti Visivi: Box della sessione asiatica aggiornato automaticamente e trigger opzionali basati su pattern pivot, fornendo supporto informativo aggiuntivo.
Funzionalità principali:
Box della sessione asiatica personalizzabile (orari e timezone)
Modalità statistica: Reversal o Continuation
Aggiornamento automatico dei livelli high/low
Tabella di Forza Valute (-7 a +7)
Tabella statistica con percentuali di rottura storiche
Trigger visivi opzionali (pattern pivot)
Supporto tema chiaro/scuro
Originalità e Valore:
Consolida 28 coppie in un’unica panoramica per identificare immediatamente bias di mercato
Fornisce insight statistico sui breakout, non solo trend-following o alert generici
Tabella di Forza Valute rapida per identificare valute forti/deboli senza controllare molteplici coppie
Nota importante:
Le statistiche si basano solo su dati storici – nessuna garanzia di risultati futuri
Strumento educativo e informativo; non costituisce consiglio finanziario o di trading
Indicatore closed-source con accesso su invito. Le richieste di accesso possono essere fatte contattando l’autore o seguendo il link nelle istruzioni dell’autore
SPX Gamma Pin DetectorUnlock the power of gamma pinning in the S&P 500 (SPX) with this essential overlay indicator, designed for day traders and options enthusiasts. The SPX Gamma Pin Detector highlights key gamma strike levels where market makers and large positions create "sticky" price action, often leading to mean reversion and intraday pins. Based on advanced options flow insights (like those from SpotGamma transcripts), it plots critical support/resistance zones to help you anticipate reversals around high-gamma strikes—such as the 99th percentile levels that stabilize or propel SPX moves.
Key Features:
Visual Gamma Levels: Automatically plots the primary pin strike (e.g., 6475), upper gamma target (e.g., 6550), and lower risk-off support (e.g., 6400). These are customizable via inputs for real-time adaptation to market conditions.
Pin Alert Zone: A dynamic background highlight (yellow) activates when SPX is within 0.1% of the pin strike, signaling potential mean reversion opportunities—perfect for entering 0DTE call flies or put hedges pre-NFP or OPEX.
Buy Dip Alert: Generates TradingView alerts on crossovers above the lower tolerance (e.g., 0.5% below pin), with a message like "SPX near gamma pin - Enter fly!" to catch dip-buying flows from zero-DTE algos.
Vol Crush Filter (Beta): Includes a basic VIX threshold input (default <15) to boost signal strength during low-IV environments, where realized vol contracts and upside is cheap.
How It Works:
This Pine Script v5 indicator overlays horizontal lines and conditional backgrounds on your SPX (or ES1! futures) chart. It uses simple math tolerances to detect proximity to gamma hotspots, mimicking the "sticky gamma" dynamics from options positioning data. For example:
If SPX drifts toward the pin level post-data release (e.g., ADP/NFP), the alert fires to prompt bullish structures like the 6525/6550/6575 call fly (net debit ~$2.25 for $25 max profit).
Negative gamma voids below support warn of slippage risks, aligning with charm effects that support closes near 6465-6475.
Backtest it against historical pins (e.g., Tuesday's 6400 reversal with 5B delta buy) to see 70-80% hit rates in stable regimes. Ideal for our GrokPHDTrading day trading show—pair with transcript parses for edge in low-vol setups (VIX ~15, ATM IV 10-11%).
Usage Tips for Traders:
Setup: Add to a 1-min or 5-min SPX chart. Adjust strikes based on daily gamma maps (e.g., from SpotGamma or our tools).
Entry Signals: Alert triggers? Scale into mean-reversion plays—buy the dip if holds support, target pin for 3-5x ROI.
Risk Management: Stop below risk-off level; hedge with OTM put flies (~$0.30 debit) for tail risks like VIX spikes to 19+.
Customization: Tweak tolerances for ES or SPY equivalents (e.g., SPY 645 for SPX 6465). Add VIX plot for vol confirmation.
Training Integration: Use in our Phase 2: Setup Execution modules—simulates gamma edges for 80% win-rate drills.
Disclaimer: This indicator is for educational and informational purposes only. It draws from public options analysis but does not provide financial advice. Always backtest, use proper risk management, and consult a professional. Past performance isn't indicative of future results. Not affiliated with SpotGamma—purely inspired by their methodologies for our AI-driven trading tools at GrokPHDTrading.com.
Invite to Community: Love gamma trading? Subscribe to our show for live NFP breakdowns and affiliate links to premium flow tools. Questions? Drop in the comments or join our Discord for Pine tweaks!
Snehal Desai's Nifty Predictor This script will let you know all major indicator's current position and using AI predict what is going to happen nxt. for any quetions you can mail me at snehaldesai37@gmail.com. for benifit of all.
Racktor Analysis Assistant
Racktor Analysis Assistant — Feature Overview
The Racktor Analysis Assistant is a multi-module market-structure toolkit that plots pivots, BoS/ChoCh levels, session breakouts, inside bars, and higher-timeframe BTS/STB trap signals — with complete styling controls and alerting.
Smart Pivot Engine (ZigZag Core)
- Adaptive pivot period switching based on timeframe threshold.
- ZigZag stream tracks pivot types (H/L, HH/HL/LH/LL) with Major & Minor streams.
- Clean visuals: optional ZigZag line & pivot labels with customizable style, width, and color.
Major & Minor Structure Signals
- Detects BoS and ChoCh for both Major and Minor swings.
- Updates External Trend on Major events and Internal Trend on Minor events.
- One-time triggers per level via locking.
- Per-category styling for Major/Minor Bullish & Bearish BoS and ChoCh.
- Alerts with symbol, pivot, timeframe, and time, limited to specific timeframes if desired.
Inside Bar Module
- Toggleable Inside Bar detection.
- Custom colors for bullish and bearish inside bars.
- Optional alerts on detection.
Session Breakout Suite
- Custom session window with shaded box.
- On session close, plots High/Mid/Low breakout lines extendable for N hours.
- Optional previous day & week high/low lines.
- Breakout vs Liquidity Sweep modes (close-based or wick-based confirmation).
- Display styles: Fixed (triangles) or Moving (vertical dotted lines).
- Alerts for “first event” or “every event.”
BTS/STB Trap (Higher-Timeframe ID1/ID2 Logic)
- BTS/STB toggle with selectable check timeframe (default: 4H).
- STB (bullish, Sell→Buy): strict ID1/ID2 relationships, both candles bullish; green circle below HTF ID1 low.
- BTS (bearish, Buy→Sell): strict ID1/ID2 relationships, both candles bearish; red circle above HTF ID1 high.
- Non-repainting; dots appear only at HTF candle close.
- Timeframe-aware rendering (dots show only on selected timeframe).
- Alerts for STB/BTS at HTF close.
Styling & Limits
- Per-feature color/style/width customization.
- Generous limits for boxes, labels, and lines.
- Session tools limited to ≤ 120-minute charts for accuracy.
Anti-Repaint
- HTF signals use lookahead_off and HTF-close gating to avoid repainting.
- BoS/ChoCh and Session logic track prior values and use locks to prevent duplicates.
Quick Start
Set the Timeframe Threshold and pivot periods for lower/higher TFs.
Enable desired Major/Minor BoS/ChoCh lines and customize styles.
Activate Inside Bar Module if required.
Configure Session Breakout window, mode, and alert settings.
Enable BTS/STB detection, keeping 4H default or selecting a custom TF.
Add alerts for chosen signals and let the assistant annotate structure, sessions, and HTF traps.
Best Use with Racktor's Core Trading Strategy
For traders who want structure clarity without clutter, this Analysis-Assistant is built to keep your chart actionable and adaptive.
Custom Price Labels (10 liquidity key levels)A simple indicator for liquidity key level trader:
Add your key level price and key note.
You can adjust the color and font.
How to find key level:
Daily high and Low for key event
eg: NVDA earning, Jackson Hole Day Pump, AI bubble report day dump, Aug Labor Data Revision day dump. If market is consolidating, these key event price level are trend target and reversal level.
MultiPrem+Detailed Description of MultiPrem+
MultiPrem+ is a versatile TradingView Pine Script indicator designed to enhance the analysis of multi-leg option strategies by calculating and visualizing the combined premium of various predefined option setups. It allows users to select from a comprehensive list of popular option strategies, such as Short Straddle, Iron Condor, Butterfly Call, and more, and dynamically computes the net premium, Greeks (Delta and Theta), volume, and other key metrics for the selected strategy. The indicator overlays these calculations on the chart, providing real-time insights into the potential profitability and risk of the strategy based on the underlying asset's price movement.
The core functionality revolves around fetching data for up to four option legs (e.g., calls and puts at different strikes) using TradingView's `request.security` function. It supports indices like NIFTY, BANKNIFTY, and SENSEX, with customizable ATM strike levels, strike width multipliers, and expiry dates. The script calculates the combined premium by summing the premiums of each leg, adjusted for position direction (long or short), and displays the results in a compact table on the chart. It also includes technical indicator overlays (e.g., SMA, RSI, MACD) to contextualize the strategy within market trends, and generates alerts with strategy metrics for automated notifications.
The indicator is particularly suited for option traders who want to monitor strategy performance without manual calculations, offering a blend of quantitative metrics and visual feedback. It operates on any timeframe but is optimized for intraday or short-term trading, where option premiums fluctuate rapidly.
### Unique Features
MultiPrem+ stands out from standard option analysis tools on TradingView due to several innovative features:
1. **Dynamic Multi-Leg Strategy Support**: Unlike basic option chain indicators that focus on single legs or simple spreads, MultiPrem+ supports a wide range of advanced multi-leg strategies (e.g., Iron Condor Wide, Reverse Iron Condor, Butterfly Call/Put). It automatically configures strike prices, directions, and approximate Greeks based on the selected strategy, saving time and reducing errors in setup.
2. **Combined Premium Visualization**: The indicator plots the net premium as a line on the chart, colored based on whether it's a credit or debit strategy and its relation to a selected technical indicator (e.g., green if below SMA for potential buys). This unique visualization helps traders see how the strategy's value evolves over time, providing an at-a-glance view of profitability.
3. **Integrated Greeks Calculation**: It computes net Delta (directional risk) and net Theta (time decay) for the entire strategy, factoring in leg directions. For credit strategies like Short Straddle, Theta is positive to reflect time decay benefits, a nuance not commonly found in free indicators.
4. **Strategy Suggestion Engine**: Based on RSI and net Theta, it suggests alternative strategies (e.g., "Bear Put Spread" if RSI is overbought). This AI-like recommendation system is unique, helping novices or busy traders pivot quickly to more suitable setups.
5. **Customizable Alerts**: The script generates JSON-formatted alerts with key metrics (premium, net Delta, net Theta, etc.), which can be integrated with TradingView's alert system or external tools for automated trading signals.
6. **Compact Table Display**: A dynamic table shows leg-specific details (Type, Strike, Position, Premium, Qty, Delta, Theta, Volume) without cluttering the chart. It's positionable and sized for usability.
### How a User Can Gain Valuable Analysis from It
MultiPrem+ empowers users to conduct sophisticated option strategy analysis, offering insights that can improve decision-making and risk management. Here's how users can leverage it for valuable outcomes:
1. **Strategy Evaluation and Selection**: Traders can quickly test different strategies by changing the selection and ATM strike. For instance, in a sideways market, selecting "Short Straddle" shows the net credit and positive Theta, highlighting potential profits from time decay. The suggestion engine further aids by recommending alternatives if current conditions (e.g., high RSI) suggest a mismatch, helping users optimize for market volatility or direction.
2. **Risk Assessment with Greeks**: Net Delta indicates directional bias (e.g., near zero for delta-neutral strategies like Iron Condor), allowing users to hedge against price moves. Net Theta quantifies daily time decay, crucial for theta-positive strategies—users can analyze how much profit they might gain per day if the underlying asset stays range-bound. This is especially valuable for income-focused traders.
3. **Premium and Volume Monitoring**: By plotting combined premium, users can track strategy value in real-time, identifying entry/exit points when premium crosses a moving average (e.g., buy when below EMA). Volume data per leg helps gauge liquidity, avoiding low-volume options that could lead to poor fills.
4. **Integration with Technical Indicators**: Overlaying strategies on RSI or MACD enables hybrid analysis. For example, a user might enter a Bull Call Spread when RSI is oversold, using the indicator's plot to visualize potential premium gains alongside RSI crossovers.
5. **Alert-Driven Trading**: Custom alerts notify users of premium changes or suggested strategy shifts, enabling hands-off monitoring. This is useful for busy traders, who can set notifications for when net Theta exceeds a threshold, signaling favorable decay conditions.
6. **Educational and Backtesting Tool**: Beginners can experiment with strategies to understand how strikes and widths affect outcomes. Advanced users can backtest by replaying historical data, analyzing how strategies performed in past markets.
Overall, MultiPrem+ transforms option trading from manual spreadsheet work into an interactive, visual experience, helping users spot opportunities, manage risks, and optimize returns with data-driven insights. For best results, combine it with external option pricing tools for precise Greeks, as the script uses approximate values.
Updated timestamp for alerts: `2025-09-06 13:50:00` (1:50 PM IST, September 06, 2025).
MTF Options Signals (message-free)script made to help with options profitability. made using ai to increase portfolio profitability
Stock Fundamentals Health Map
I came up with this script because, like a lot of us, I was always bugging AI about every ticker under the sun—asking for breakdowns, forecasts, you name it. But then it hit me: wouldn't it be way faster if I could just glance at the stock chart and get a quick snapshot of the company's financial guts right there?. Also, i didnt bother looking up another indicator script because i want it that way.
This "Stock Fundamentals Health Map" is basically your jumping-off point before you go full detective mode on the fundamentals. It's not meant to be the end-all-be-all, just a smart way to spot red flags or green lights without wasting hours.
Here's the deal: TradingView has this treasure of financial stats for stocks—stuff like margins, ratios, growth numbers, and more—pulled from their database after earnings drops. The script grabs 40 of those for your chosen period (Fiscal Year, Quarter, Half, or Trailing Twelve Months—you pick in the settings, and 40 because your broke boy doesnt have a premium TV sub).
But raw numbers? Meh, they're just digits. So, we grade 'em. Think of it like a report card for the company: Excellent (or "Great" in some spots), Good, Fair, Poor, or Weak (I called it "Pathetic" in my head at first, but toned it down).
How do we grade? Based on thresholds for each metric. For instance, a Gross Margin over 60%? Excellent, baby—that's premium efficiency. 40-60%? Solid Good. Down to under 10%? Weak, might wanna think twice. Same logic for everything else: Altman Z-Score (bankruptcy risk—higher is safer), Beneish M-Score (earnings manipulation detector—lower is cleaner), ROE, EV/EBITDA, you get the idea. But hey, maybe you disagree with my defaults. No sweat—the settings let you tweak every single threshold. Want to be stricter on Debt-to-Equity? Crank it up. Think Dividend Yield needs a higher bar for "Excellent"? Go for it. It's your world; I'm just scripting in it.
Dont know what all those metrics mean? Use the tool tip. Still dont understand? Keep the defaults.
Once graded, we don't stop there. Each metric gets a weight (default is 1, for equal love), but if you're obsessed with Free Cash Flow Margin over, say, Asset Turnover, bump its weight to 2, 5, or even 100. FFT FAFO. The script multiplies grades by weights, adds 'em up, and spits out an overall score and grade for the stock. Excellent if it's crushing it (90%+), down to Weak if it's wheezing. Plus, it categorizes the stock type—Growth, Value, Quality, Dividend, Momentum—based on how it scores in those buckets. Handy for knowing if it's a high-flyer or a dead divi.
And because not all stocks are created equal, it throws in sector-specific smarts. REITs get FFO and AFFO grades (funds from operations—key for real estate trusts). Tech and Healthcare? R&D Intensity to check if they're innovating or slacking. Energy folks get Capex-to-Sales (lower is better for efficiency in that capital-hungry world). Utilities? Debt Service Coverage to see if they can handle the bills. If your ticker doesn't fit those, it skips 'em—no junk data. You dont see all that because TV might have that data with N/A entered in it.
The output? A clean table slapped on your chart (top-right by default and cant move it around, because being at the top and being right is all you need). Columns for metrics, values + grades, all color-coded: green for Excellent, lime for Good, yellow Fair, orange Poor, red Weak. Headers in blue, text customizable—pick your colors, transparency, sizes. It's overlay=true, so it vibes with your price action without cluttering.
Sure, these numbers are just what TradingView's crack team inputs post-earnings—could be off, or laggy, or whatever. They don't predict the future; markets are wild. But it's a lot better than panic-buying on a hunch. Gives you that quick financial health map to ponder before you leap into a trade that could change your life... or your portfolio's. ;)
If you need the source code, ask Grok AI. I got it from there. Too lazy to do that? Follow me on X and i'll dm you after you prove that you are not a bot.
Machine Learning : Neural Network Prediction -EasyNeuro-Machine Learning: Neural Network Prediction
— An indicator that learns and predicts price movements using a neural network —
Overview
The indicator “Machine Learning: Neural Network Prediction” uses price data from the chart and applies a three-layer Feedforward Neural Network (FNN) to estimate future price movements.
Key Features
Normally, training and inference with neural networks require advanced programming languages that support machine learning frameworks (such as TensorFlow or PyTorch) as well as high-performance hardware with GPUs. However, this indicator independently implements the neural network mechanism within TradingView’s Pine Script environment, enabling real-time training and prediction directly on the chart.
Since Pine Script does not support matrix operations, the backpropagation algorithm—necessary for neural network training—has been implemented entirely through scalar operations. This unique approach makes the creation of such a groundbreaking indicator possible.
Significance of Neural Networks
Neural networks are a core machine learning method, forming the foundation of today’s widely used generative AI systems, such as OpenAI’s GPT and Google’s Gemini. The feedforward neural network adopted in this indicator is the most classical architecture among neural networks. One key advantage of neural networks is their ability to perform nonlinear predictions.
All conventional indicators—such as moving averages and oscillators like RSI—are essentially linear predictors. Linear prediction inherently lags behind past price fluctuations. In contrast, nonlinear prediction makes it theoretically possible to dynamically anticipate future price movements based on past patterns. This offers a significant benefit for using neural networks as prediction tools among the multitude of available indicators.
Moreover, neural networks excel at pattern recognition. Since technical analysis is largely based on recognizing market patterns, this makes neural networks a highly compatible approach.
Structure of the Indicator
This indicator is based on a three-layer feedforward neural network (FNN). Every time a new candlestick forms, the model samples random past data and performs online learning using stochastic gradient descent (SGD).
SGD is known as a more versatile learning method compared to standard gradient descent, particularly effective for uncertain datasets like financial market price data. Considering Pine Script’s computational constraints, SGD is a practical choice since it can learn effectively from small amounts of data. Because online learning is performed with each new candlestick, the indicator becomes a little “smarter” over time.
Adjustable Parameters
Learning Rate
Specifies how much the network’s parameters are updated per training step. Values between 0.0001 and 0.001 are recommended. Too high causes divergence and unstable predictions, while too low prevents sufficient learning.
Iterations per Online Learning Step
Specifies how many training iterations occur with each new candlestick. More iterations improve accuracy but may cause timeouts if excessive.
Seed
Random seed for initializing parameters. Changing the seed may alter performance.
Architecture Settings
Number of nodes in input and hidden layers:
Increasing input layer nodes allows predictions based on longer historical periods. Increasing hidden layer nodes increases the network’s interpretive capacity, enabling more flexible nonlinear predictions. However, more nodes increase computational cost exponentially, risking timeouts and overfitting.
Hidden layer activation function (ReLU / Sigmoid / Tanh):
Sigmoid:
Classical function, outputs between 0–1, approximates a normal distribution.
Tanh:
Similar to Sigmoid but outputs between -1 and 1, centered around 0, often more accurate.
ReLU:
Simple function (outputs input if ≥ 0, else 0), efficient and widely effective.
Input Features (selectable and combinable)
RoC (Rate of Change):
Measures relative price change over a period. Useful for predicting movement direction.
RSI (Relative Strength Index):
Oscillator showing how much price has risen/fallen within a period. Widely used to anticipate direction and momentum.
Stdev (Standard Deviation, volatility):
Measures price variability. Useful for volatility prediction, though not directional.
Optionally, input data can be smoothed to stabilize predictions.
Other Parameters
Data Sampling Window:
Period from which random samples are drawn for SGD.
Prediction Smoothing Period:
Smooths predictions to reduce spikes, especially when RoC is used.
Prediction MA Period:
Moving average applied to smoothed predictions.
Visualization Features
The internal state of the neural network is displayed in a table at the upper-right of the chart:
Network architecture:
Displays the structure of input, hidden, and output layers.
Node activations:
Shows how input, hidden, and output node values dynamically change with market conditions.
This design allows traders to intuitively understand the inner workings of the neural network, which is often treated as a black box.
Glossary of Terms
Feature:
Input variables fed to the model (RoC/RSI/Stdev).
Node/Unit:
Smallest computational element in a layer.
Activation Function:
Nonlinear function applied to node outputs (ReLU/Sigmoid/Tanh).
MSE (Mean Squared Error):
Loss function using average squared errors.
Gradient Descent (GD/SGD):
Optimization method that gradually adjusts weights in the direction that reduces loss.
Online Learning:
Training method where the model updates sequentially with each new data point.
EMA Cross Alert V666 [noFuck]EMA Cross Alert — What it does
EMA Cross Alert watches three EMAs (Short, Mid, Long), detects their crossovers, and reports exactly one signal per bar by priority: EARLY > Short/Mid > Mid/Long > Short/Long. Optional EARLY mode pings when Short crosses Long while Mid is still between them—your polite early heads-up.
Why you might like it
Three crossover types: s/m, m/l, s/l
EARLY detection: earlier hints, not hype
One signal per bar: less noise, more focus
Clear visuals: tags, big cross at signal price, EARLY triangles
Alert-ready: dynamic alert text on bar close + static alertconditions for UI
Inputs (plain English)
Short/Mid/Long EMA length — how fast each EMA reacts
Extra EMA length (visual only) — context EMA; does not affect signals
Price source — e.g., Close
Show cross tags / EARLY triangles / large cross — visual toggles
Enable EARLY signals (Short/Long before Mid) — turn early pings on/off
Count Mid EMA as "between" even when equal (inclusive) — ON: Mid counts even if exactly equal to Short or Long; OFF (default): Mid must be strictly between them
Enable dynamic alerts (one per bar close) — master alert switch
Alert on Short/Mid, Mid/Long, Short/Long, EARLY — per-signal alert toggles
Quick tips
Start with defaults; if you want more EARLY on smooth/low-TF markets, turn “inclusive” ON
Bigger lengths = calmer trend-following; smaller = faster but choppier
Combine with volume/structure/risk rules—the indicator is the drummer, not the whole band
Disclaimer
Alerts, labels, and triangles are not trade ideas or financial advice. They are informational signals only. You are responsible for entries, exits, risk, and position sizing. Past performance is yesterday; the future is fashionably late.
Credits
Built with the enthusiastic help of Code Copilot (AI)—massively involved, shamelessly proud, and surprisingly good at breakfasting on exponential moving averages.
Crypto Perp Calc v1Advanced Perpetual Position Calculator for TradingView
Description
A comprehensive position sizing and risk management tool designed specifically for perpetual futures trading. This indicator eliminates the confusion of calculating leveraged positions by providing real-time position metrics directly on your chart.
Key Features:
Interactive Price Selection: Click directly on chart to set entry, stop loss, and take profit levels
Accurate Lot Size Calculation: Instantly calculates the exact position size needed for your margin and leverage
Multiple Entry Support: DCA into positions with up to 3 entry points with customizable allocation
Multiple Take Profit Levels: Scale out of positions with up to 3 TP targets
Comprehensive Risk Metrics: Shows dollar P&L, account risk percentage, and liquidation price
Visual Risk/Reward: Color-coded boxes and lines display your trade setup clearly
Real-time Info Table: All critical position data in one organized panel
Perfect for traders using perpetual futures who need precise position sizing with leverage.
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How to Use
Quick Start (3 Clicks)
1. Add the indicator to your chart
2. Click three times when prompted:
First click: Set your entry price
Second click: Set your stop loss
Third click: Set your take profit
3. Read the TOTAL LOTS value from the info table (highlighted in yellow)
4. Use this lot size in your exchange when placing the trade
Detailed Setup
Step 1: Configure Your Account
Enter your account balance (total USDT in account)
Set your margin amount (how much USDT to risk on this trade)
Choose your leverage (1x to 125x)
Select Long or Short position
Step 2: Set Price Levels
Main levels use interactive clicking (Entry, SL, TP)
For multiple entries or TPs, use the settings panel to manually input prices and percentages
Step 3: Read the Results
The info table shows:
TOTAL LOTS - The position size to enter on your exchange
Margin Used - Your actual capital at risk
Notional - Total position value (margin × leverage)
Max Risk - Dollar amount you'll lose at stop loss
Total Profit - Dollar amount you'll gain at take profit
R:R Ratio - Risk to reward ratio
Account Risk - Percentage of account at risk
Liquidation - Price where position gets liquidated
Step 4: Advanced Features (Optional)
Multiple Entries (DCA):
Enable "Use Multiple Entries"
Set up to 3 entry prices
Allocate percentage for each (must total 100%)
See individual lot sizes for each entry
Multiple Take Profits:
Enable "Use Multiple TPs"
Set up to 3 TP levels
Allocate percentage to close at each level (must total 100%)
View profit at each target
Visual Elements
Blue lines/labels: Entry points
Red lines/labels: Stop loss
Green lines/labels: Take profit targets
Colored boxes: Visual risk (red) and reward (green) zones
Info table: Can be positioned anywhere on screen
Alerts
Set price alerts for:
Entry zones reached
Stop loss approached
Take profit levels hit
Works with TradingView's alert system
Tips for Best Results
Always verify the lot size matches your intended risk
Check the liquidation price stays far from your stop loss
Monitor the account risk percentage (recommended: keep under 2-3%)
Use the warning indicators if risk exceeds margin
For quick trades, use single entry/TP; for complex strategies, use multiple levels
Example Workflow
Find your trade setup using your analysis
Add this indicator and click to set levels
Check risk metrics in the table
Copy the TOTAL LOTS value
Enter this exact position size on your exchange
Set alerts for key levels if desired
This tool bridges the gap between TradingView charting and exchange execution, ensuring your position sizing is always accurate when trading with leverage.
Disclaimer, this was coded with help of AI, double check calculations if they are off.
