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.
Educational
SAI Powerful Trading V1📌 Strategy Description for Publication
SAI Powerful Trading V1 (Basic Version)
This strategy is designed to provide simple yet effective buy/sell signals by combining multiple technical tools into a single system. It is suitable for beginners and experienced traders who want clear entry and exit points.
📊 Plotted Elements
EMA (Blue) = Fast trend filter.
EMA (Red) = Slow trend filter.
SMI (Blue) with EMA-SMI (Orange) = Momentum oscillator.
Buy Signal (Green Label "Call") below candles.
Sell Signal (Red Label "Put") above candles.
⚠️ Notes
This is the basic version of the strategy (no stop-loss or advanced risk management).
Use on your preferred timeframe and asset.
For educational purposes only – not financial advice.
Entry Signals (Long/Short)The indicator visualizes precise entry signals for long and short setups directly on the price chart. Long is marked with a green triangle-up, short with a red triangle-down. To contextualize trend structure, the Fast EMA (5) is plotted in black and the Slow EMA (20) in blue (line width 1). Signals print only at bar close for reproducible execution. Applicable across all timeframes—ideal for top-down analysis from the 195-minute chart through daily to weekly.
MSFusion- MultiScoreFusionThis Pine Script strategy, MSFusion - MultiScoreFusion, combines Ichimoku components and Hull Moving Average (HMA) signals to generate a composite score for each bar.
It evaluates several conditions—such as price crossing above HMA55, Tenkan and Kijun lines, and price position relative to the Ichimoku cloud—and assigns scores to each.
The script displays a label with the total score and a tooltip listing the contributing conditions when a strong bullish signal is detected. This approach helps traders quickly assess market momentum and trend strength using multiple technical criteria.
Momentum Moving Averages | MisinkoMasterThe Momentum Moving Averages (MMA) indicator blends multiple moving averages into a single momentum-scoring framework, helping traders identify whether market conditions are favoring upside momentum or downside momentum.
By comparing faster, more adaptive moving averages (DEMA, TEMA, ALMA, HMA) against a baseline EMA, the MMA produces a cumulative score that reflects the prevailing strength and direction of the trend.
🔎 Methodology
Moving Averages Used
EMA (Exponential Moving Average) → Baseline reference.
DEMA (Double Exponential Moving Average) → Reacts faster than EMA.
TEMA (Triple Exponential Moving Average) → Even faster, reduces lag further.
ALMA (Arnaud Legoux Moving Average) → Smooth but adaptive, with adjustable σ and offset.
HMA (Hull Moving Average) → Very responsive, reduces lag, ideal for momentum shifts.
Scoring System
Each comparison is made against the EMA baseline:
If another MA is above EMA → +1 point.
If another MA is below EMA → -1 point.
The total score reflects overall momentum:
Positive score → Bullish bias.
Negative score → Bearish bias.
Trend Logic
Bullish Signal → When the score crosses above 0.1.
Bearish Signal → When the score crosses below -0.1.
Neutral or sideways trends are identified when the score remains between thresholds.
📈 Visualization
All five moving averages are plotted on the chart.
Colors adapt to the current score:
Cyan (Bullish bias) → Positive momentum.
Magenta (Bearish bias) → Negative momentum.
Overlapping fills between MAs highlight zones of convergence/divergence, making momentum shifts visually clear.
⚡ Features
Adjustable length parameter for all MAs.
Adjustable ALMA parameters (sigma and offset).
Cumulative momentum score system to filter false signals.
Works across all markets (crypto, forex, stocks, indices).
Overlay design for direct chart integration.
✅ Use Cases
Trend Confirmation → Ensure alignment with market momentum.
Momentum Shifts → Spot when faster MAs consistently outperform the baseline EMA.
Entry & Exit Filter → Avoid trades when the score is neutral or indecisive.
Divergence Visualizer → Filled zones make it easier to see when MAs begin separating or converging.
Low History Required → Unlike most For Loops, this script does not require that much history, making it less lagging and more responsive
⚠️ Limitations
Works best in trending conditions; performance decreases in sideways/choppy ranges.
Sensitivity of signals depends on chosen length and ALMA settings.
Should not be used as a standalone buy/sell system—combine with volume, structure, or higher timeframe analysis.
Trend Magic EMA RMI Trend Sniper📌 Indicator Name:
Trend Magic + EMA + MA Smoothing + RMI Trend Sniper
📝 Description:
This is a multi-functional trend and momentum indicator that combines four powerful tools into a single overlay:
Trend Magic – Plots a dynamic support/resistance line based on CCI and ATR.
Helps identify trend direction (green = bullish, red = bearish).
Acts as a trailing stop or dynamic level for trade entries/exits.
Exponential Moving Average (EMA) – Smooths price data to highlight the underlying trend.
Customizable length, source, and offset.
Serves as a trend filter or moving support/resistance.
MA Smoothing + Bollinger Bands (Optional) – Adds a secondary smoothing filter based on your choice of SMA, EMA, WMA, VWMA, or SMMA.
Optional Bollinger Bands visualize volatility expansion/contraction.
Great for spotting consolidations and breakout opportunities.
RMI Trend Sniper – A momentum-based system combining RSI and MFI.
Highlights bullish (green) or bearish (red) conditions.
Plots a Range-Weighted Moving Average (RWMA) channel to gauge price positioning.
Provides visual BUY/SELL labels and optional bar coloring for fast decision-making.
📊 Uses & Trading Applications:
✅ Trend Identification: Spot the dominant market direction quickly with Trend Magic & EMA.
✅ Momentum Confirmation: RMI Sniper helps confirm whether the market has strong bullish or bearish pressure.
✅ Dynamic Support/Resistance: Trend Magic & EMA act as adaptive levels for stop-loss or trailing positions.
✅ Volatility Analysis: Optional Bollinger Bands show squeezes and potential breakout setups.
✅ Entry/Exit Signals: BUY/SELL alerts and color-coded candles make spotting trade opportunities simple.
💡 Best Use Cases:
Swing Trading: Follow Trend Magic + EMA alignment for higher probability trades.
Scalping/Intraday: Use RMI signals with bar coloring for quick momentum entries.
Trend Following Strategies: Ride trends until Trend Magic flips direction.
Breakout Trading: Watch for price closing outside the Bollinger Bands with RMI confirmation.
Double Median SD Bands | MisinkoMasterThe Double Median SD Bands (DMSDB) is a trend-following tool designed to capture market direction in a way that balances responsiveness and smoothness, filtering out excessive noise without introducing heavy lag.
Think of it like a house:
A jail (too restrictive) makes you miss opportunities.
No house at all (too unsafe) leaves you exposed to false signals.
DMSDB acts like a comfortable house with windows—protecting you from the noise while still letting you see what’s happening in the market.
🔎 Methodology
The script works in the following steps:
Standard Deviation (SD) Calculation
Computes the standard deviation of the selected price source (ohlc4 by default).
The user can choose whether to use biased (sample) or unbiased (population) standard deviation.
Raw Bands Construction
Upper Band = source + (SD × multiplier)
Lower Band = source - (SD × multiplier)
The multiplier can be adjusted for tighter or looser bands.
First Median Smoothing
Applies a median filter over half of the length (len/2) to both bands.
This reduces noise without creating excessive lag.
Second Median Smoothing
Applies another median filter over √len to the already smoothed bands.
This produces a balance:
Cutting the length → maintains responsiveness.
Median smoothing → reduces whipsaws.
The combination creates a fast yet clean band system ideal for trend detection.
📈 Trend Logic
The trend is detected based on price crossing the smoothed bands:
Long / Bullish (Purple) → when price crosses above the upper band.
Short / Bearish (Gold) → when price crosses below the lower band.
Neutral → when price remains between the bands.
🎨 Visualization
Upper and lower bands are plotted as colored lines.
The area between the bands is filled with a transparent zone that reflects the current bias:
Purple shading = Bullish zone.
Golden shading = Bearish zone.
This creates a visual tunnel for trend confirmation, helping traders quickly identify whether price action is trending or consolidating.
⚡ Features
Adjustable Length parameter (len) for dynamic control.
Adjustable Band Multiplier for volatility adaptation.
Choice between biased vs. unbiased standard deviation.
Double median smoothing for clarity + responsiveness.
Works well on cryptocurrencies (e.g., BTCUSD) but is flexible enough for stocks, forex, and indices.
✅ Use Cases
Trend Following → Ride trends by staying on the correct side of the bands.
Entry Timing → Use crossovers above/below bands for entry triggers.
Filter for Other Strategies → Can serve as a directional filter to avoid trading against the trend.
⚠️ Limitations & Notes
This is a trend-following tool, so it will perform best in trending conditions.
In sideways or choppy markets, whipsaws may still occur (although smoothing reduces them significantly).
The indicator is not a standalone buy/sell system. For best results, combine with volume, momentum, or higher-timeframe confluence.
All of this makes for a really unique & original tool, as it removes noise but keeps good responsitivity, using methods from many different principles which make for a smooth a very useful tool
Supertrend DashboardOverview
This dashboard is a multi-timeframe technical indicator dashboard based on Supertrend. It combines:
Trend detection via Supertrend
Momentum via RSI and OBV (volume)
Volatility via a basic candle-based metric (bs)
Trend strength via ADX
Multi-timeframe analysis to see whether the trend is bullish across different timeframes
It then displays this info in a table on the chart with colors for quick visual interpretation.
2️⃣ Inputs
Dashboard settings:
enableDashboard: Toggle the dashboard on/off
locationDashboard: Where the table appears (Top right, Bottom left, etc.)
sizeDashboard: Text size in the table
strategyName: Custom name for the strategy
Indicator settings:
factor (Supertrend factor): Controls how far the Supertrend lines are from price
atrLength: ATR period for Supertrend calculation
rsiLength: Period for RSI calculation
Visual settings:
colorBackground, colorFrame, colorBorder: Control dashboard style
3️⃣ Core Calculations
a) Supertrend
Supertrend is a trend-following indicator that generates bullish or bearish signals.
Logic:
Compute ATR (atr = ta.atr(atrLength))
Compute preliminary bands:
upperBand = src + factor * atr
lowerBand = src - factor * atr
Smooth bands to avoid false flips:
lowerBand := lowerBand > prevLower or close < prevLower ? lowerBand : prevLower
upperBand := upperBand < prevUpper or close > prevUpper ? upperBand : prevUpper
Determine direction (bullish / bearish):
dir = 1 → bullish
dir = -1 → bearish
Supertrend line = lowerBand if bullish, upperBand if bearish
Output:
st → line to plot
bull → boolean (true = bullish)
b) Buy / Sell Trigger
Logic:
bull = ta.crossover(close, supertrend) → close crosses above Supertrend → buy signal
bear = ta.crossunder(close, supertrend) → close crosses below Supertrend → sell signal
trigger → checks which signal was most recent:
trigger = ta.barssince(bull) < ta.barssince(bear) ? 1 : 0
1 → Buy
0 → Sell
c) RSI (Momentum)
rsi = ta.rsi(close, rsiLength)
Logic:
RSI > 50 → bullish
RSI < 50 → bearish
d) OBV / Volume Trend (vosc)
OBV tracks whether volume is pushing price up or down.
Manual calculation (safe for all Pine versions):
obv = ta.cum( math.sign( nz(ta.change(close), 0) ) * volume )
vosc = obv - ta.ema(obv, 20)
Logic:
vosc > 0 → bullish
vosc < 0 → bearish
e) Volatility (bs)
Measures how “volatile” the current candle is:
bs = ta.ema(math.abs((open - close) / math.max(high - low, syminfo.mintick) * 100), 3)
Higher % → stronger candle moves
Displayed on dashboard as a number
f) ADX (Trend Strength)
= ta.dmi(14, 14)
Logic:
adx > 20 → Trending
adx < 20 → Ranging
g) Multi-Timeframe Supertrend
Timeframes: 1m, 3m, 5m, 10m, 15m, 30m, 1H, 2H, 4H, 12H, 1D
Logic:
for tf in timeframes
= request.security(syminfo.tickerid, tf, f_supertrend(ohlc4, factor, atrLength))
array.push(tf_bulls, bull_tf ? 1.0 : 0.0)
bull_tf ? 1.0 : 0.0 → converts boolean to number
Then we calculate user rating:
userRating = (sum of bullish timeframes / total timeframes) * 10
0 → Strong Sell, 10 → Strong Buy
4️⃣ Dashboard Table Layout
Row Column 0 (Label) Column 1 (Value)
0 Strategy strategyName
1 Technical Rating textFromRating(userRating) (color-coded)
2 Current Signal Buy / Sell (based on last Supertrend crossover)
3 Current Trend Bullish / Bearish (based on Supertrend)
4 Trend Strength bs %
5 Volume vosc → Bullish/Bearish
6 Volatility adx → Trending/Ranging
7 Momentum RSI → Bullish/Bearish
8 Timeframe Trends 📶 Merged cell
9-19 1m → Daily Bullish/Bearish for each timeframe (green/red)
5️⃣ Color Logic
Green shades → bullish / trending / buy
Red / orange → bearish / weak / sell
Yellow → neutral / ranging
Example:
dashboard_cell_bg(1, 1, colorFromRating(userRating))
dashboard_cell_bg(1, 2, trigger ? color.green : color.red)
dashboard_cell_bg(1, 3, superBull ? color.green : color.red)
Makes the dashboard visually intuitive
6️⃣ Key Logic Flow
Calculate Supertrend on current timeframe
Detect buy/sell triggers based on crossover
Calculate RSI, OBV, Volatility, ADX
Request Supertrend on multiple timeframes → convert to 1/0
Compute user rating (percentage of bullish timeframes)
Populate dashboard table with colors and values
✅ The result: You get a compact, fast, multi-timeframe trend dashboard that shows:
Current signal (Buy/Sell)
Current trend (Bullish/Bearish)
Momentum, volatility, and volume cues
Trend across multiple timeframes
Overall technical rating
It’s essentially a full trend-strength scanner directly on your chart.
Squeeze Momentum Indicator [Carlos Mauricio Vizcarra]El squezee Momentum para la comunidad de Rafael Cepeda trader
DynamoSent DynamoSent Pro+ — Professional Listing (Preview)
— Adaptive Macro Sentiment (v6)
— Export, Adaptive Lookback, Confidence, Boxes, Heatmap + Dynamic OB/OS
Preview / Experimental build. I’m actively refining this tool—your feedback is gold.
If you spot edge cases, want new presets, or have market-specific ideas, please comment or DM me on TradingView.
⸻
What it is
DynamoSent Pro+ is an adaptive, non-repainting macro sentiment engine that compresses VIX, DXY and a price-based activity proxy (e.g., SPX/sector ETF/your symbol) into a 0–100 sentiment line. It scales context by volatility (ATR%) and can self-calibrate with rolling quantile OB/OS. On top of that, it adds confidence scoring, a plain-English Context Coach, MTF agreement, exportable sentiment for other indicators, and a clean Light/Dark UI.
Why it’s different
• Adaptive lookback tracks regime changes: when volatility rises, we lengthen context; when it falls, we shorten—less whipsaw, more relevance.
• Dynamic OB/OS (quantiles) self-calibrates to each instrument’s distribution—no arbitrary 30/70 lines.
• MTF agreement + Confidence gate reduce false positives by highlighting alignment across timeframes.
• Exportable output: hidden plot “DynamoSent Export” can be selected as input.source in your other Pine scripts.
• Non-repainting rigor: all request.security() calls use lookahead_off + gaps_on; signals wait for bar close.
Key visuals
• Sentiment line (0–100), OB/OS zones (static or dynamic), optional TF1/TF2 overlays.
• Regime boxes (Overbought / Oversold / Neutral) that update live without repaint.
• Info Panel with confidence heat, regime, trend arrow, MTF readout, and Coach sentence.
• Session heat (Asia/EU/US) to match intraday behavior.
• Light/Dark theme switch in Inputs (auto-contrasted labels & headers).
⸻
How to use (examples & recipes)
1) EURUSD (swing / intraday blend)
• Preset: EURUSD 1H Swing
• Chart: 1H; TF1=1H, TF2=4H (default).
• Proxies: Defaults work (VIX=D, DXY=60, Proxy=D).
• Dynamic OB/OS: ON at 20/80; Confidence ≥ 55–60.
• Playbook:
• When sentiment crosses above 50 + margin with Δ ≥ signalK and MTF agreement ≥ 0.5, treat as trend breakout.
• In Oversold with rising Coach & TF agreement, take fade longs back toward mid-range.
• Alerts: Enable Breakout Long/Short and Fade; keep cooldown 8–12 bars.
2) SPY (daytrading)
• Preset: SPY 15m Daytrade; Chart: 15m.
• VIX (D) matters more; preset weights already favor it.
• Start with static 30/70; later try dynamic 25/75 for adaptive thresholds.
• Use Coach: in US session, when it says “Overbought + MTF agree → sell rallies / chase breakouts”, lean momentum-continuation after pullbacks.
3) BTCUSD (crypto, 24/7)
• Preset: BTCUSD 1H; Chart: 1H.
• DXY and BTC.D inform macro tone; keep Carry-forward ON to bridge sparse ticks.
• Prefer Dynamic OB/OS (15/85) for wider swings.
• Fade signals on weekend chop; Breakout when Confidence > 60 and MTF ≥ 1.0.
4) XAUUSD (gold, macro blend)
• Preset: XAUUSD 4H; Chart: 4H.
• Weights tilt to DXY and US10Y (handled by preset).
• Coach + MTF helps separate trend legs from news pops.
⸻
Best practices
• Theme: Switch Light/Dark in Inputs; the panel adapts contrast automatically.
• Export: In another script → Source → DynamoSent Pro+ → DynamoSent Export. Build your own filters/strategies atop the same sentiment.
• Dynamic vs Static OB/OS:
• Static 30/70: fast, universal baseline.
• Dynamic (quantiles): instrument-aware; use 20/80 (default) or 15/85 for choppy markets.
• Confidence gate: Start at 50–60% to filter noise; raise when you want only A-grade setups.
• Adaptive Lookback: Keep ON. For ultra-liquid indices, you can switch it OFF and set a fixed lookback.
⸻
Non-repainting & safety notes
• All request.security() calls use lookahead=barmerge.lookahead_off and gaps=barmerge.gaps_on.
• No forward references; signals & regime flips are confirmed on bar close.
• History-dependent funcs (ta.change, ta.percentile_linear_interpolation, etc.) are computed each bar (not conditionally).
• Adaptive lookback is clamped ≥ 1 to avoid lowest/highest errors.
• Missing-data warning triggers only when all proxies are NA for a streak; carry-forward can bridge small gaps without repaint.
⸻
Known limits & tips
• If a proxy symbol isn’t available on your plan/exchange, you’ll see the NA warning: choose a different symbol via Symbol Search, or keep Carry-forward ON (it defaults to neutral where needed).
• Intraday VIX is sparse—using Daily is intentional.
• Dynamic OB/OS needs enough history (see dynLenFloor). On short histories it gracefully falls back to static levels.
Thanks for trying the preview. Your comments drive the roadmap—presets, new proxies, extra alerts, and integrations.
Ichimoku + MTF Dashboard (Confidence + Row Shading)Name: Ichimoku + Multi-Timeframe (MTF) Dashboard
Purpose
This indicator is designed to give a complete trend, momentum, and alignment picture of a stock across multiple timeframes (hourly, daily, weekly) using the Ichimoku Kinko Hyo system. It combines:
Classic Ichimoku signals: Tenkan/Kijun crossovers, cloud position (Kumo), Chikou span, and cloud twists.
MTF Dashboard: Aggregates hourly, daily, and weekly Ichimoku conditions into a clean visual table.
Dynamic coloring: Each signal is represented with green/red fills, and rows are shaded for full alignment. Aggregate column highlights mixed signals in yellow.
Z-Score For Loop | MisinkoMasterThe Z-Score For Loop (ZSFL) is a unique trend-following oscillator designed to detect potential reversals and momentum shifts earlier than traditional tools, providing traders with fast, adaptive, and reliable signals.
Unlike common smoothing techniques (moving averages, medians, or modes), the ZSFL introduces a for-loop comparison method that balances speed and noise reduction, resulting in a powerful reversal-detection system.
🔎 Methodology
The indicator is built in two main stages:
Z-Score Calculation
Formula:
Z=(Source−Mean)/Standard Deviation
Z=
Standard Deviation
(Source−Mean)
The user can select the averaging method for the mean: SMA, EMA, WMA, HMA, DEMA, or TEMA.
Recommended: EMA, SMA, or WMA for balanced accuracy.
The choice of biased (sample) or unbiased (population) standard deviation is also available.
➝ On its own, the raw Z-score is fast but noisy, requiring additional filtering.
For Loop Logic (Noise Reduction)
Instead of using traditional smoothing (which adds lag), the indicator applies a for loop comparison.
The current Z-score is compared against previous values over a user-defined range (start → end).
Each comparison adds or subtracts “points”:
+1 point if the current Z-score is higher than a past Z-score.
-1 point if it is lower.
The final value is the cumulative score, reflecting whether the Z-score is generally stronger or weaker than its historical context.
➝ This approach keeps speed intact while removing much of the false noise that raw Z-scores generate.
📈 Trend Logic
Bullish Signal (Cyan) → Triggered when the score crosses above the upper threshold (default +45).
Bearish Signal (Magenta) → Triggered when the score crosses below the lower threshold (default -25).
Neutral → When the score remains between the thresholds.
Thresholds are adjustable, making the tool flexible for different assets and timeframes.
🎨 Visualization
The ZSFL score is plotted as a main oscillator line.
Upper and lower thresholds are plotted as static reference levels.
The price chart can also be color-coded with trend signals (cyan for bullish, magenta for bearish) to provide immediate visual confirmation.
⚡ Features
Adjustable Z-score length (len).
Multiple average types for the mean (SMA, EMA, WMA, HMA, DEMA, TEMA).
Toggle between biased vs. unbiased SD calculations.
Adjustable For Loop range (start, end).
Adjustable upper and lower thresholds for signal generation.
Works as both an oscillator and a price overlay tool.
✅ Use Cases
Reversal Detection → Spot early shifts before price confirms them.
Trend Confirmation → Use thresholds to filter false reversals.
System Filter → Combine with trend indicators to refine entries.
Multi-Timeframe Setup → Works well across different timeframes for swing, day, or intraday trading.
⚠️ Limitations
As with all oscillators, the ZSFL will generate false signals in sideways/choppy markets.
Optimal parameters (length, loop size, thresholds) may differ across assets.
It is not a standalone trading system — use alongside other forms of analysis (trend filters, volume, higher timeframe confluence).
Turtle Trading by the Nato FinancialsFor the Turtle-style breakout system (with ATR stop, 20-day breakout, 10-day exit, and filters like ADX/volume), I will share the win rates once testing is done.
Trend Compass (Manual)## Trend Compass (Manual) - A Discretionary Trader's Dashboard
### Summary
Trend Compass is a simple yet powerful dashboard designed for discretionary traders who want a constant, visual reminder of their market analysis directly on their chart. Instead of relying on automated indicators, this tool gives you **full manual control** to define the market state across different timeframes or conditions.
It helps you stay aligned with your higher-level analysis (e.g., HTF bias, current market structure) and avoid making impulsive decisions that go against your plan.
### Key Features
- **Fully Manual Control:** You decide the trend. No lagging indicators, no confusing signals. Just your own analysis, displayed clearly.
- **Multiple Market States:** Define each row as an `Uptrend`, `Downtrend`, `Pullback`, or `Neutral` market.
- **Customizable Rows:** Display up to 8 rows. You can label each one however you like (e.g., "D1", "H4", "Market Structure", "Liquidity Bias").
- **Flexible Panel:** Change all colors, text sizes, and place the panel in any of the 9 positions on your chart.
- **Clean & Minimalist:** Designed to provide essential information at a glance without cluttering your chart.
### How to Use
1. **Add to Chart:** Add the indicator to your chart.
2. **Open Settings:** Go into the indicator settings.
3. **Configure Rows:**
- In the "Rows (Manual Control)" section, set the "Number of rows" you want to display.
- For each row, give it a custom **Label** (e.g., "m15").
- Select its current state from the dropdown menu (`Uptrend`, `Downtrend`, etc.).
- To remove a row, simply set its state to `Hidden`.
4. **Customize Style:**
- In the "Panel & Visual Style" section, adjust colors, text sizes, and the panel's position to match your chart's theme.
This tool is perfect for price action traders, ICT/SMC traders, or anyone who values a clean chart and a disciplined approach to their analysis.
Price Persistence ScreenerPrice Persistence Screener
Pine Script v6 | Inspired by @pradeepbonde on X
This indicator, inspired by the insights of @pradeepbonde , is designed to identify stocks with high price persistence—stocks that consistently close higher than the previous day's close over various lookback periods. As described by Pradeep Bonde, stocks with high persistence are strong candidates for trading pullbacks or consolidations, as they often resume their upward trend due to aggressive buying and low selling pressure. This tool helps traders screen for such stocks and visualize their persistence across multiple timeframes.
Features:
Measures price persistence by counting bars where the closing price exceeds the previous bar’s close for fixed periods: 499, 252, 126, 60, 40, 20, 15, 10, and 5 bars.
Includes a customizable lookback period (1 to 499 bars) for flexible analysis.
Allows users to set a custom persistence threshold (0% to 100%) to highlight strong bullish trends.
How It Works:
For each lookback period, the indicator calculates how many times the closing price is higher than the previous bar’s close.
A higher count indicates stronger bullish persistence, signaling stocks with sustained upward momentum.
Usage:
This screener is aimed to be used on pine screener to see data in columns. Add this indicator to you favorites and in pine screener scan on your watchlist of up to 1000 stocks
Adjust the custom lookback period and threshold via input settings.
Sort columns to compare persistence across timeframes and identify stocks with high persistence for swing trading or long-term holding.
Settings:
Custom Lookback Period (Bars): Set the number of bars for the custom persistence calculation (default: 100).
Custom Persistence Threshold (%): Define the percentage threshold for highlighting high persistence in the custom period (default: 70%).
Credits:
This indicator is based on the price persistence concept shared by @pradeepbonde
in his YouTube video (www.youtube.com). He explains that stocks with high persistence—those consistently closing higher day after day—are strong candidates for trading pullbacks, as they tend to resume their upward trend. This screener automates and visualizes that concept, making it easier for traders to identify such stocks.
Note:
Ensure sufficient historical data is available for accurate calculations, especially for longer periods like 499 bars. if stock is less than 499 bars.
High persistence stocks may eventually lose momentum, signaling potential reversals or shorting opportunities, as noted by @pradeepbonde
.
Use this indicator as part of a broader trading strategy to screen strong trends with custom lookback scan, combining it with other technical or fundamental analysis.
CQ_Fibonacci IntraDay Range [UL]THIS INDICATOR IS MEMBER OF A SET OF 3 INDICATORS:
1. CQ_Fibonacci intraday Range (This one)
2. CQ_Fibonacci intraweek Range
3. CQ_Fibonacci Intramonth Range
If you are using my CQ_MTF Target Price Lines indicator, this indicator is automatically loaded along with it.
The Fibonacci Period Range Indicator is a powerful trading tool designed to draw levels of support and resistance based on key Fibonacci levels. This script identifies the high and low of a user-specified range and then draws several levels of support and resistance within this range. Additionally, it can draw extension levels outside the specified range, which are also based on Fibonacci levels. These extension levels can be turned off in the indicator settings. Each level is labeled to help traders understand what each line represents, and these labels can also be turned off in the settings.
The purpose of this script is to simplify the trading experience by allowing users to customize the time period that is identified and then draw levels of support and resistance based on the price action during this time.
Usage
In the indicator settings, users have access to a setting called Session Range, which allows them to control the range that will be used. The script will then identify the high and low of the specified range and draw several levels of support and resistance based on Fibonacci levels within this range. Users can also choose to display extension levels that show more levels outside the range. These lines will extend until the end of the current trading day at 5:00 pm EST.
Settings
Configuration
• Display Mode: Determines the number of days that will be displayed by the script.
• Show Labels: Determines whether or not identifying labels will be displayed on each line.
• Font Size: Determines the text size of labels.
• Label Position: Determines the justification of labels.
• Extension Levels: Determines whether or not extension levels will be drawn outside the high and low of the specified range.
Session
• Session Range: Determines the time period that will be used for calculations.
• Offset (+/-): Determines how many hours the session should be offset by.
Squeeze Momentum Indicator [Comunidad Rafael Cepeda]Este indicador fue adaptado para el uso de la comunidad de Rafael cepeda Trader
Intraday Bar CounterThis indicator plots a counter on the chart that tracks the number of bars since the beginning of the current day.
The counter resets to zero on the first bar of each new calendar day (midnight). This functionality is provided only on intraday and tick charts.
The indicator is designed to operate on a wide range of symbols without requiring manual adjustments for specific trading sessions.
CQ_Fibonacci IntraMonth Range [UL]THIS INDICATOR IS MEMBER OF A SET OF 3 INDICATORS:
1. CQ_Fibonacci intraday Range
2. CQ_Fibonacci intraweek Range
3. CQ_Fibonacci Intramonth Range (This One)
If you are using my CQ_MTF Target Price Lines indicator, this indicator is automatically loaded along with it.
The Fibonacci Period Range Indicator is a powerful trading tool designed to draw levels of support and resistance based on key Fibonacci levels. This script identifies the high and low of a user-specified range and then draws several levels of support and resistance within this range. Additionally, it can draw extension levels outside the specified range, which are also based on Fibonacci levels. These extension levels can be turned off in the indicator settings. Each level is labeled to help traders understand what each line represents, and these labels can also be turned off in the settings.
The purpose of this script is to simplify the trading experience by allowing users to customize the time period that is identified and then draw levels of support and resistance based on the price action during this time.
Usage
In the indicator settings, users have access to a setting called Session Range, which allows them to control the range that will be used. The script will then identify the high and low of the specified range and draw several levels of support and resistance based on Fibonacci levels within this range. Users can also choose to display extension levels that show more levels outside the range. These lines will extend until the end of the current trading day at 5:00 pm EST.
Settings
Configuration
• Display Mode: Determines the number of days that will be displayed by the script.
• Show Labels: Determines whether or not identifying labels will be displayed on each line.
• Font Size: Determines the text size of labels.
• Label Position: Determines the justification of labels.
• Extension Levels: Determines whether or not extension levels will be drawn outside the high and low of the specified range.
Session
• Session Range: Determines the time period that will be used for calculations.
• Offset (+/-): Determines how many hours the session should be offset by.
CQ_Fibonacci IntraWeek Range [UL]THIS INDICATOR IS MEMBER OF A SET OF 3 INDICATORS:
1. CQ_Fibonacci intraday Range
2. CQ_Fibonacci intraweek Range (This One)
3. CQ_Fibonacci Intramonth Range
If you are using my CQ_MTF Target Price Lines indicator, this indicator is automatically loaded along with it.
The Fibonacci Period Range Indicator is a powerful trading tool designed to draw levels of support and resistance based on key Fibonacci levels. This script identifies the high and low of a user-specified range and then draws several levels of support and resistance within this range. Additionally, it can draw extension levels outside the specified range, which are also based on Fibonacci levels. These extension levels can be turned off in the indicator settings. Each level is labeled to help traders understand what each line represents, and these labels can also be turned off in the settings.
The purpose of this script is to simplify the trading experience by allowing users to customize the time period that is identified and then draw levels of support and resistance based on the price action during this time.
Usage
In the indicator settings, users have access to a setting called Session Range, which allows them to control the range that will be used. The script will then identify the high and low of the specified range and draw several levels of support and resistance based on Fibonacci levels within this range. Users can also choose to display extension levels that show more levels outside the range. These lines will extend until the end of the current trading day at 5:00 pm EST.
Settings
Configuration
• Display Mode: Determines the number of days that will be displayed by the script.
• Show Labels: Determines whether or not identifying labels will be displayed on each line.
• Font Size: Determines the text size of labels.
• Label Position: Determines the justification of labels.
• Extension Levels: Determines whether or not extension levels will be drawn outside the high and low of the specified range.
Session
• Session Range: Determines the time period that will be used for calculations.
• Offset (+/-): Determines how many hours the session should be offset by.
Scenario Screener — Consolidation → Bullish SetupThe script combines multiple indicators to filter out false signals and only highlight strong conditions:
Consolidation Check
Uses ATR % of price → filters out stocks in tight ranges.
Uses Choppiness Index → confirms sideways/non-trending behavior.
Momentum Shift (Bullish Bias)
MACD Histogram > 0 → bullish momentum starting.
RSI between 55–70 → strength without being overbought.
Stochastic %K & %D > 70 → confirms strong momentum.
Volume & Accumulation
Chaikin Money Flow (CMF > 0) → buying pressure.
Chaikin Oscillator > 0 (debug only) → accumulation phase.
Trend Direction
+DI > -DI (from DMI) → buyers stronger than sellers.
ADX between 18–40 → healthy trend strength (not too weak, not overheated).
Breakout Filter (Optional)
If enabled, requires price to cross above 20 SMA before signal confirmation.
📈 Outputs
✅ Green label (“MATCH”) below the bar when all bullish conditions align.
✅ Background highlight (light green) when signal appears.
✅ Info Table (top-right) summarizing key values:
Signal = True/False
MACD, CMF, Chaikin values
Parametric Multiplier Backtester🧪 An experimental educational tool for visual market analysis and idea testing through the multiplication and interaction of core technical parameters. It allows you to observe in real time how the combination of indicators affects the resulting curve and the potential efficiency of trading strategies.
📖 Detailed Description
1. Philosophy & Purpose of the Tool
This backtester is not created to search for the “Holy Grail,” but for deep learning and analysis. It is intended for:
👶 Beginner traders – to visually understand how basic indicators work and interact with each other.
🧠 Experienced analysts – to search for new ideas and non-obvious relationships between different aspects of the market (trend, volatility, momentum, volume).
The core idea is combining parameters through multiplication.
👉 Why multiplication? Unlike simple addition, multiplication strengthens signals only when several factors align in the same direction. If at least one parameter shows weakness (close to zero in normalized form), it suppresses the overall result, serving as a filter for false signals.
2. How does it work?
Step 1: Parameter Selection
The tool gathers data from 9 popular indicators: 📈 Price, RSI, ADX, Momentum, ROC, ATR, Volume, Acceleration, Slope.
Step 2: Normalization
Since these indicators differ in nature and scale (e.g., RSI from 0–100 vs ATR in points), they are brought to a unified range. Each parameter is normalized within a given period (Normalization Period). This is the key step for proper functioning.
Step 3: Multiplication
The parameters enabled by the user are multiplied, creating a new derived value — Product Line. This line is an aggregated reflection of the selected market model.
Step 4: Smoothing
The resulting line can be noisy. The Smooth Product Line function (via SMA) reduces noise and highlights the main trend.
Step 5: Interpretation
The smoothed Product Line is compared with its own moving average (Mean Line). Crossovers generate trading signals.
3. What conclusions can be drawn from multiplying parameters?
⚡ RSI × Momentum × Volume – Strength of momentum confirmed by volume. High values may indicate strong, volume-backed moves.
📊 ADX × ATR – Strength of trend and its volatility. High values may signal the beginning of a strong trending move with high volatility.
🚀 Price × Slope × Acceleration – Combined speed and acceleration of the trend. Shows not only where price is going, but with what acceleration.
❌ Disabling parameters – By turning parameters on/off (e.g., Volume), you can instantly see how important each factor is for the current market situation.
4. Real-Time Mode & Instant Feedback
The main educational value of this tool is interactivity:
🔄 Turn indicators on/off in real time.
⏱ Change their periods and instantly observe how the Product Line shape and behavior changes.
📉 Immediately see how these changes affect historical trading signals (blue/red arrows) and strategy performance metrics (Profit Factor, Net Profit, etc.).
This process develops “market intuition” and helps understand which settings work better under different conditions (trend vs range).
5. Default Settings & Recommendations
⚙️ Default settings are optimized for demonstration on the 4H timeframe of the SOLUSDT crypto pair.
Parameter Settings: Switch group (Use RSI, Use ADX, etc.).
Normalization Period (20): Lower = more sensitive, Higher = smoother.
Smooth Product Line (true): Enabled by default for clarity.
Smoothing Period (200): Main sensitivity setting.
Trend Filter: Optional 200-SMA filter. Strategy trades only in the main trend direction.
⚠️ Important Warning: This is an experimental & educational tool. The signals it generates are the result of a mathematical model and are not a ready-to-use trading strategy. Always backtest ideas and apply risk management before risking real money.
