Position Size CalculatorDESCRIPTION:
This indicator is essentially a calculator that prompts the user to enter 3 variables upon activation: Entry Price, Stop Loss Price, and Risk Amount ($). From those variables, the calculator will then output what the ideal amount of shares that should be purchased to meet your risk amount limit.
SAMPLE USE CASES:
1) Trading Futures: Upon calculating the amount of shares to purchase to enter a position, you can multiply that amount by the current share price, this will give you an idea on whether or not you require some leverage to get into your position.
2) Spot Trading / Simple Stock Trading: Upon entering the required information, you will know how many shares to purchase to meet your risk amount limit.
Analisis Fundamental
Alerta 10 Velas Consecutivas (Bull/Bear)despues de 10 velas alcista o bajitas probabilidad que haga reversion
Reversión 3 velas grandes lejos de EMA3reversión después de 3 velas grandes alcistas o bajistas en media movil de 3 periodos
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.
ES/NQ, Pre-Market High & Low (04:00 AM - 09:30 AM)This indicator marks the Pre market high and Pre market low from 04:00am to 09:30am for any us Index
Fed Funds Rate-of-ChangeFed Funds Rate-of-Change
What it does:
This indicator pulls the Effective Federal Funds Rate (FRED:FEDFUNDS, monthly) and measures how quickly it’s changing over a user-defined lookback. It offers stabilized change metrics that avoid the “near-zero blow-up” you see with naive % ROC. The plot turns red only when the signal is below the lower threshold and heading down (i.e., value < –threshold and slope < 0).
This indicator is meant to be useful in monitoring fast cuts on the part of the FED - a signal that has preceded recession or market pullbacks in times prior.
Change modes: Percentage, log and delta.
Percent ROC (ε floor): 100 * (now - prev) / max(prev, ε)
Log change (ε): 100 * (ln(now + ε) - ln(prev + ε))
Delta (bps): (now - prev) * 100 (basis points; avoids percentage math)
Tip: For “least drama,” use Delta (bps). For relative change without explosions near zero, use Log change (ε).
Key inputs:
Lookback (months): ROC window in calendar months (because source is monthly).
Change Metric: one of the three options above.
ε (percentage points): small constant (e.g., 0.25 pp) used by Percent ROC (ε) and Log change (ε) to stabilize near-zero values.
EMA Smoothing length: light smoothing of the computed series.
Clip |value| at: optional hard cap to tame outliers (0 = off).
Threshold % / Threshold bps: lower/upper threshold band; unit adapts to the selected metric.
Plot as histogram: optional histogram view.
Coloring / signal logic
Red: value is below the lower threshold (–threshold) and the series is falling on the current bar.
How to use:
Add to any chart (timeframe doesn’t matter; data is monthly under the hood).
Pick a Change Metric and set Lookback (e.g., 3–6 months).
Choose a reasonable threshold:
Percent/Log: try 10–20%
Delta (bps): try 50–100 bps
Optionally smooth (EMA 3–6) and/or clip extreme spikes.
Interpretation
Sustained red often marks periods of accelerating downside in the Fed Funds change metric (e.g., policy easing momentum when using bps).
Neutral (gray) provides context without implying direction bias.
Notes & limitations
Source is monthly FRED series; values update on monthly closes and are stable (no intrabar repainting of the monthly series).
Threshold units switch automatically with the metric (%, %, or bps).
Smoothing/clip are convenience tools; adjust conservatively to avoid masking important shifts.
Kraliyet Trend Dashboard (Sabit + R:R + BE Alarm)v6 Sertaç AKMANRoyal Trend Dashboard combines EMA8–EMA200, SuperTrend, MACD, RSI, and ATR in one panel to automatically spot trend direction, pullbacks, and breakout entries while showing live risk-reward. It marks EMA8 touches and next-bar confirmations, triggers an ENTRY alert on the latest pivot break, calculates SL via SuperTrend±ATR or ATR×multiplier, and sets TP1/TP2 by R multiples. While in a trade it tracks live R:R and, when TP1 hits, prompts SL = BE to lock profits. Markers are pinned to price and the dashboard docks to any corner; on lower timeframes (e.g., 15m) you can align with a 1H higher-timeframe filter. In short: the Trend → Pullback → Breakout playbook with disciplined risk management and ready-made alerts—fast and practical.
Mongoose Global Conflict Risk Index v1Overview
The Mongoose Global Conflict Risk Index v1 is a multi-asset composite indicator designed to track the early pricing of geopolitical stress and potential conflict risk across global markets. By combining signals from safe havens, volatility indices, energy markets, and emerging market equities, the index provides a normalized 0–10 score with clear bias classifications (Neutral, Caution, Elevated, High, Shock).
This tool is not predictive of headlines but captures when markets are clustering around conflict-sensitive assets before events are widely recognized.
Methodology
The indicator calculates rolling rate-of-change z-scores for eight conflict-sensitive assets:
Gold (XAUUSD) – classic safe haven
US Dollar Index (DXY) – global reserve currency flows
VIX (Equity Volatility) – S&P 500 implied volatility
OVX (Crude Oil Volatility Index) – energy stress gauge
Crude Oil (CL1!) – WTI front contract
Natural Gas (NG1!) – energy security proxy, especially Europe
EEM (Emerging Markets ETF) – global risk capital flight
FXI (China ETF) – Asia/China proxy risk
Rules:
Safe havens and vol indices trigger when z-score > threshold.
Energy triggers when z-score > threshold.
Risk assets trigger when z-score < –threshold.
Each trigger is assigned a weight, summed, normalized, and scaled 0–10.
Bias classification:
0–2: Neutral
2–4: Caution
4–6: Elevated
6–8: High
8–10: Conflict Risk-On
How to Use
Timeframes:
Daily (1D) for strategic signals and early warnings.
4H for event shocks (missiles, sanctions, sudden escalations).
Weekly (1W) for sustained trends and macro build-ups.
What to Look For:
A single trigger (for example, Gold ON) may be noise.
A cluster of 2–3 triggers across Gold, USD, VIX, and Energy often marks early stress pricing.
Elevated readings (>4) = caution; High (>6) = rotation into havens; Shock (>8) = market conviction of conflict risk.
Practical Application:
Monitor as a heatmap of global stress.
Combine with fundamental or headline tracking.
Use alert conditions at ≥4, ≥6, ≥8 for systematic monitoring.
Notes
This indicator is for informational and educational purposes only.
It is not financial advice and should be used in conjunction with other analysis methods.
US Net Liquidity + M2 / US Debt (FRED)US Net Liquidity + M2 / US Debt
🧩 What this chart shows
This indicator plots the ratio of US Net Liquidity + M2 Money Supply divided by Total Public Debt.
US Net Liquidity is defined here as the Federal Reserve Balance Sheet (WALCL) minus the Treasury General Account (TGA) and the Overnight Reverse Repo facility (ON RRP).
M2 Money Supply represents the broad pool of liquid money circulating in the economy.
US Debt uses the Federal Government’s total outstanding debt.
By combining net liquidity with M2, then dividing by total debt, this chart provides a structural view of how much monetary “fuel” is in the system relative to the size of the federal debt load.
🧮 Formula
Ratio
=
(
Fed Balance Sheet
−
(
TGA
+
ON RRP
)
)
+
M2
Total Public Debt
Ratio=
Total Public Debt
(Fed Balance Sheet−(TGA+ON RRP))+M2
An optional normalization feature scales the ratio to start at 100 on the first valid bar, making long-term trends easier to compare.
🔎 Why it matters
Liquidity vs. Debt Growth: The numerator (Net Liquidity + M2) captures the monetary resources available to markets, while the denominator (Debt) reflects the expanding obligation of the federal government.
Market Signal: Historically, shifts in net liquidity and money supply relative to debt have coincided with major turning points in risk assets like equities and Bitcoin.
Context: A rising ratio may suggest that liquidity conditions are improving relative to debt expansion, which can be supportive for risk assets. Conversely, a falling ratio may highlight tightening conditions or debt outpacing liquidity growth.
⚙️ How to use it
Overlay this chart against S&P 500, Bitcoin, or gold to analyze correlations with asset performance.
Watch for trend inflections—does the ratio bottom before equities rally, or peak before risk-off periods?
Use normalization for long historical comparisons, or raw values to see the absolute ratio.
📊 Data sources
This indicator pulls from FRED (Federal Reserve Economic Data) tickers available in TradingView:
WALCL: Fed balance sheet
RRPONTSYD: Overnight Reverse Repo
WTREGEN: Treasury General Account
M2SL: M2 money stock
GFDEBTN: Total federal public debt
⚠️ Notes
Some FRED series are updated weekly, others monthly—set your chart timeframe accordingly.
If any ticker is unavailable in your plan, replace it with the equivalent FRED symbol provided in TradingView.
This indicator is intended for macro analysis, not short-term trading signals.
CAGR Indicator (Flexible Holding Period)CAGR Indicator (Flexible Holding Period)
The CAGR Indicator (Flexible Holding Period) is designed to convert any cumulative investment outcome into a standardized, annualized growth rate that can be compared across assets, strategies, and time horizons. Its core metric is the compound annual growth rate, which represents the constant yearly rate that, if compounded smoothly, transforms an initial value into a final value over a specified horizon. By annualizing returns, the indicator removes distortions caused by unequal test lengths and allows direct comparison with benchmarks such as index returns or risk-free rates.
Conceptually, the indicator proceeds in two stages: measuring growth and normalizing time. Growth is summarized by the growth multiple, which is the ratio of ending value to starting value when concrete values are provided, or equivalently 1 plus total percentage return divided by 100 when only a cumulative percent is known. Time is normalized by converting the user’s holding period into a year-equivalent, so that a 45-day, 30-week, 18-month, or multi-year interval can all be mapped onto a common annual scale. The conversions use widely accepted approximations: days divided by 365.25, weeks divided by approximately 52.1429, and months divided by 12, while years are used as entered.
Once growth and time are expressed in compatible units, the indicator applies the standard compounding identity: CAGR = (Growth Multiple)^(1/T) − 1, where T is the year-equivalent holding period. This transformation inverts the compounding process and yields the geometric mean rate of return per year. Because the geometric mean is path-independent, the CAGR summarizes start-to-finish performance without reference to the sequence of gains and losses. The output therefore reflects the constant annual rate that would have produced the observed terminal value from the initial value if returns had been smooth.
The indicator admits two data entry modes to accommodate common reporting practices. In Start/End Values mode, the user supplies initial and final portfolio values; the indicator computes the growth multiple as end divided by start and also displays absolute profit or loss in currency terms to aid practical interpretation. In Total PnL (%) mode, the user supplies a cumulative return percentage; the indicator converts this to a growth multiple and estimates a corresponding ending value for display, while the CAGR computation itself relies only on the multiple and the time horizon.
Validity checks ensure that reported numbers are meaningful. The growth multiple must be strictly positive; cumulative losses at or below minus one hundred percent make the multiple nonpositive and render the CAGR undefined. The holding period must be positive and convertible to a year-equivalent. In Start/End mode, the starting value must exceed zero to avoid division by zero and degenerate ratios. When these conditions are not met, the indicator withholds a numeric result and signals that the quantity is not well defined.
Interpreting the output requires recognizing both its strengths and its limits. The CAGR is a concise, comparable measure of long-run performance that abstracts from timing and volatility. It is particularly useful for benchmarking strategies of different durations, setting policy targets for funds, communicating results to stakeholders, and aligning outcomes with hurdle rates. However, because it is path-independent, the CAGR does not reflect interim drawdowns, variance, or tail risk. It also presumes a lump-sum investment with no intermediate cash flows; when deposits or withdrawals occur, internal rate of return methods such as IRR or XIRR are more appropriate.
Typical applications include comparing backtests with unequal sample lengths, reporting consolidated results from discrete projects on a common annual basis, and translating short-horizon event outcomes (for example, a multi-week campaign) into an annualized figure for decision-making. The indicator’s auxiliary displays, such as total profit or loss in currency and the explicit statement of the original holding period alongside its year-equivalent, improve transparency and auditability of the transformation.
Users should remain mindful of several caveats. Time conversions rely on conventional averages and may differ from calendar-exact counts by small amounts, which is usually immaterial but worth noting for edge cases. Selection bias can inflate reported CAGRs if intervals are cherry-picked; robust practice involves rolling windows, out-of-sample tests, and sensitivity analysis. Most importantly, the CAGR should be paired with risk and stability measures—such as maximum drawdown, Sharpe or Sortino ratios, downside deviation, or ulcer index—to form a complete assessment of a strategy’s quality.
In sum, the indicator operationalizes a simple but powerful idea: separate the measurement of growth from the normalization of time, then apply the compounding identity to express outcomes as a consistent per-year rate. By combining flexible period inputs with a rigorous geometric transformation, it enables fair, intelligible comparisons while encouraging the complementary use of risk diagnostics to avoid over-reliance on a single summary statistic.
Stocker++Stocker++ Trading Indicator: Complete User Guide
This comprehensive trading indicator combines technical analysis, fundamental analysis, risk management, and value investing principles into an integrated decision-making system. Here's how to use it effectively for investment decisions.
Core Functionality Overview
The indicator provides six customizable data tables that display on your chart, each serving a specific analytical purpose. You can enable/disable individual tables and adjust their positions, colors, and text sizes to suit your preferences.
Table 1: Risk Management and Volume Analysis
Risk Management Section
This table calculates your optimal position size based on your account size and risk tolerance. Key components include:
Account Size and Risk Parameters: Enter your total trading capital and the percentage you're willing to risk per trade (typically 1-2%). The indicator automatically calculates the dollar amount at risk.
Stop Loss Calculation: Choose between two methods - ATR-based (Average True Range) or Low of Day. The ATR method provides a volatility-adjusted stop loss, while LoD uses the day's low as support.
Position Sizing: The indicator calculates exactly how many shares to buy based on your risk parameters and stop loss distance. It also shows your total position size as both a dollar amount and percentage of your account.
Liquidity Analysis: Critical safety features include:
Maximum allowed position based on daily volume (prevents you from taking positions too large for the stock's liquidity)
Minimum required daily volume for your position size
Liquidity ratio showing if there's sufficient volume for your trades
Float analysis indicating what percentage of shares are publicly tradeable
Position impact assessment showing how your trade might affect the stock price
Volume Analysis Section
Provides real-time liquidity metrics:
Average daily dollar volume (20-day average)
Average daily share volume
Relative volume (current vs average)
Volume buzz (unusual activity indicator)
Table 2: Company Information and Analyst Ratings
Company Metrics
Displays essential market data:
Daily price change in dollars
ATR (14-day volatility measure)
Average Daily Range percentage
Low of Day price and distance from current price
Market capitalization
Total shares outstanding
Float shares and percentage
Free cash flow and yield
Employee count and shareholder numbers
Sector and industry classification
Gap analysis (today's low vs yesterday's high)
Analyst Recommendations
Shows consensus analyst opinions:
Number of buy, strong buy, sell, strong sell, and hold ratings
Total analyst coverage
Date of most recent recommendations
Table 3: Earnings History
Displays quarterly earnings performance across multiple periods:
Standardized EPS (adjusted for one-time items)
Reported EPS
Analyst estimates
Earnings surprise (beat/miss) with percentages
Revenue actuals vs estimates
Revenue surprise percentages
Color coding: Green for beats, red for misses
Table 4: Comprehensive Financial Analysis
Income Statement Metrics
Quarterly revenue with gross profit margins
Operating income and margins
Net income and profit margins
Earnings per share
Balance Sheet Analysis
Total assets, liabilities, and equity
Cash and equivalents
Total debt
Debt-to-equity ratio (risk indicator)
Valuation Metrics
Market cap and enterprise value
EV/Revenue ratio
Price-to-book ratio
Book value per share
Return on Equity (ROE)
Return on Assets (ROA)
Key Multipliers
P/E ratio (Price to Earnings)
P/S ratio (Price to Sales)
PEG ratio (P/E to Growth)
EV/EBITDA
Advanced Valuation Analysis
The indicator calculates fair value using multiple methodologies:
Graham Number for profitable companies
DCF (Discounted Cash Flow) model
Revenue-based valuation for unprofitable companies
Asset-based valuation for pre-revenue companies
It provides:
Fair value estimate with methodology used
Current price vs fair value percentage
Investment rating (0-10 scale)
Long-term outlook assessment
Warren Buffett Criteria Section
Evaluates stocks against Buffett's investment principles:
ROE Quality (must exceed 15%)
Debt Payoff Time (should be under 3 years)
Economic Moat score (competitive advantages)
Owner Earnings (Buffett's preferred cash flow metric)
Margin of Safety (discount to intrinsic value)
Overall Buffett Score (0-5 scale)
Table 5: Investment Summary Dashboard
This synthesizes all analysis into actionable insights:
Investment Grade: Letter grade (A-F) based on weighted scoring of liquidity, cash flow, valuation, and Buffett criteria
Decision Output: Clear BUY, HOLD, or AVOID recommendation
Risk Assessment: Categorizes overall risk as minimal, low, moderate, or high
Key Summary Metrics:
Valuation status with margin of safety percentage
Buffett score and verdict
Liquidity quality and float percentage
Cash flow quality and FCF yield
Risk alerts for critical issues
Investment Strategy Framework
Entry Criteria
For a BUY signal, the indicator requires:
Investment score ≥7 out of 10
Margin of safety >25% (stock trading below fair value)
Float percentage >20% (configurable)
FCF margin >5% or cash runway >2 years
Buffett score ≥3 out of 5
Position Sizing Strategy
Set your account size and risk percentage (1-2% recommended)
The indicator calculates optimal share count based on stop loss distance
Verify the position doesn't exceed liquidity constraints
Check position impact - should be <0.1% of float for minimal market impact
Risk Management Rules
Use the calculated stop loss level (ATR or LoD based)
Ensure position size doesn't exceed 30% of account (or the calculated maximum)
Verify average daily volume is at least 200x your position size
Monitor the liquidity ratio - should be >2x for safe entry/exit
Fundamental Quality Checks
Before investing, ensure:
Positive or improving margins (gross, operating, net)
Debt-to-equity ratio <2 (preferably <1)
Positive free cash flow or adequate cash runway
ROE >15% for established companies
Revenue growth and earnings consistency
Exit Considerations
Consider selling when:
Stock reaches fair value (margin of safety approaches 0%)
Fundamental metrics deteriorate significantly
Debt levels become concerning (D/E >2)
Free cash flow turns negative without clear path to profitability
Technical indicators (moving averages) show breakdown
Moving Averages Component
The indicator includes six customizable moving averages (SMA or EMA) with individual:
Period lengths (default: 10, 20, 50, 100, 150, 200)
Timeframes (can use higher timeframes on lower charts)
Colors for visual distinction
Use these for trend identification and support/resistance levels.
Practical Usage Tips
For Growth Investors: Focus on revenue growth, improving margins, and moderate valuation with emphasis on long-term outlook
For Value Investors: Prioritize margin of safety >25%, Buffett score ≥4, and fundamental strength
For Traders: Use volume analysis, technical levels, and strict position sizing with stop losses
For Risk-Averse Investors: Only consider stocks with investment grade A or B, minimal risk assessment, and strong cash positions
Warning Indicators
The system highlights critical risks:
Low float (<20%) - high volatility risk
Cash burn with <2 years runway
Overvaluation >150% of fair value
High debt (D/E >2)
Insufficient liquidity for position size
Stock Valuation Models - Professional Investment Analysis Tool📊 Overview
Stock Valuation Models is a comprehensive financial analysis indicator that combines multiple valuation methodologies to calculate intrinsic stock value. This professional-grade tool implements 7 different valuation methods , risk assessment framework, and financial health metrics to provide data-driven investment decisions.
🎯 Key Features
📈 Multiple Valuation Methods
Graham's Valuation - Conservative asset-based approach by Benjamin Graham
Multiples Valuation - Market-based P/E and P/B ratios from sector peers
Discounted Cash Flow (DCF) - Future cash flow projections with present value calculation
Dividend Discount Model - Gordon Growth Model for dividend-paying stocks
FCFF Model - Enterprise-level Free Cash Flow to Firm analysis
EVA Model - Economic Value Added measurement above cost of capital
Advanced Multiples - Enterprise Value ratios (EV/EBITDA, EV/Sales)
🏥 Financial Health Metrics
Altman Z-Score - Bankruptcy prediction and financial distress assessment
Piotroski F-Score - 9-point fundamental strength evaluation
Beneish M-Score - Earnings manipulation detection system
Magic Formula - Joel Greenblatt's combined quality and value scoring
⚖️ Risk Assessment Framework
Multi-Factor Risk Scoring - Fundamental, market, quality, and data quality risks
Risk-Adjusted Margin of Safety - Dynamic safety thresholds based on risk level
Position Sizing Guidance - Risk-appropriate investment allocation recommendations
🔍 Data Quality System
Real-Time Quality Tracking - Visual warnings for insufficient data
Fallback Methodology - Alternative calculations when primary data unavailable
Confidence Scoring - Method agreement and data quality assessment
⚙️ Settings & Parameters
Main Settings
Margin of Safety (%) - Minimum discount required before buying (Default: 15%)
Table Font Size - Choose between "Small" and "Normal" text size
Valuation Methods
Graham's Valuation - Best for mature, stable companies with strong fundamentals
Multiples Valuation - Compares to industry peers using dynamic sector ratios
Discounted Cash Flow - Ideal for growth companies with predictable cash flows
Dividend Discount Model - For consistent dividend-paying stocks (disabled by default)
FCFF Model - Enterprise approach for leveraged companies and M&A analysis
EVA Model - Measures value creation above cost of capital
Advanced Multiples - Wall Street standard EV ratios for professional analysis
Additional Metrics
Magic Formula - Combined quality and value scoring system
Altman Z-Score - Bankruptcy risk assessment (Safe >2.99, Distress <1.81)
Piotroski F-Score - Fundamental quality score (Excellent ≥8, Poor <4)
Beneish M-Score - Manipulation detector (High Risk >-2.22, Low Risk ≤-2.22)
🔧 How It Works
Dynamic Calculations
Sector-Based Ratios - Automatically detects company sector and applies appropriate valuation multiples
Economic Integration - Uses real-time risk-free rates, VIX volatility, and GDP growth data
Quality Weighting - Adjusts method weights based on company type (growth/mature/distressed) and market conditions
Negative Value Handling - Shows actual calculated values but excludes negative results from weighted average
Risk-Adjusted Analysis
VIX Integration - Higher market volatility increases required margin of safety
Sector Risk Premiums - Energy and Financial sectors get higher risk multipliers
Quality Adjustments - High Piotroski F-Score companies get lower risk ratings
Data Quality Impact - Insufficient data increases risk score and safety requirements
Visual Display
Horizontal Table Layout - Organized by method groups (Valuation → Results → Risk → Health)
Color-Coded Results - Green/Yellow/Red indicators for risk levels and recommendations
Warning Symbols - ⚠️ for data quality issues, ❌ for excluded negative values
Dollar Amounts - Both percentage and dollar-based margin of safety calculations
📈 Interpretation Guide
💎 Intrinsic Value Results
Weighted Average - Combines all enabled methods based on intelligent weighting
Confidence Level - High/Medium/Low based on method agreement and data quality
Method Count - Number of successful valuation calculations
🎯 Margin of Safety
Percentage - Current discount/premium to calculated intrinsic value
Dollar Amount - Absolute dollar difference per share
Buy Price - Risk-adjusted target purchase price
⚖️ Risk Assessment
Low Risk (Green) - Normal position sizing (3-5%)
Medium Risk (Yellow) - Reduced position sizing (1-3%)
High Risk (Red) - Minimal position sizing (<1%)
📊 Recommendations
STRONG BUY - Low risk + adequate margin + high confidence
BUY - Meets risk-adjusted margin requirements
HOLD - Positive margin but higher risk
SELL - Insufficient margin for risk level
🎓 Educational Tooltips
Every parameter includes detailed explanations accessible by hovering over the setting. Learn about:
When to use each valuation method
How different metrics are calculated
Interpretation thresholds and ratings
Risk factors and quality indicators
💡 Best Practices
🚀 For Growth Stocks
Enable DCF and Advanced Multiples
Focus on Piotroski F-Score for quality assessment
Use higher margin of safety due to volatility
💰 For Value Stocks
Enable Graham's and Multiples Valuation
Check Altman Z-Score for financial stability
Consider Magic Formula rating
📈 For Dividend Stocks
Enable Dividend Discount Model
Focus on sustainable dividend coverage
Check for consistent dividend history
⚠️ For Distressed Situations
Prioritize Graham's asset-based approach
Monitor Altman Z-Score closely
Use higher risk-adjusted margins
⚠️ Important Notes & Data Limitations
📅 Data Timing Considerations
Fundamental Data Lag - Company financial data (earnings, cash flows, balance sheet items) may be 1-3 months behind current market conditions
Quarterly Reporting Delays - Most recent available data reflects the company's situation as of the last filed quarterly/annual report
Market vs. Fundamentals Gap - Stock prices react instantly to news, while fundamental data updates occur periodically
Accuracy Impact - Recent business changes, market events, or company developments may not be reflected in current calculations
🔧 Technical Limitations
Data Dependencies - Requires fundamental data availability from TradingView
Quality Warnings - Pay attention to ⚠️ symbols indicating insufficient data
Risk Context - Always consider risk score in investment decisions
Market Conditions - Tool automatically adjusts for market volatility (VIX)
Sector Specificity - Ratios automatically adjust based on company's sector
💡 Best Practice Recommendations
Supplement with Current Analysis - Always combine with recent news, earnings calls, and management guidance
Monitor Data Quality - Check when the underlying financial data was last updated
Consider Market Context - Factor in recent market events that may affect company performance
Use as Starting Point - Treat calculations as baseline analysis requiring additional research
🔗 Methodology
Based on established academic research and professional practices:
Benjamin Graham - Security Analysis principles
Joel Greenblatt - Magic Formula methodology
Edward Altman - Z-Score bankruptcy prediction
Joseph Piotroski - Fundamental analysis scoring
Messod Beneish - Earnings manipulation detection
Modern Portfolio Theory - Risk-adjusted decision making
This indicator is designed for educational and analytical purposes. Always conduct additional research and consider consulting with financial professionals before making investment decisions.
Navigator Range Pro+Title Navigator Range Pro+
What it is Navigator Range Pro+ is a confluence-first indicator that blends multi-timeframe (MTF) trend bias with a Dealing Range (DR) framework. It helps you quickly see when higher timeframes align and pairs that bias with clean breakout triggers from a current range. Designed to reduce noise and keep charts readable.
What you’ll see
Dealing Range: Auto-detected range top/bottom with a midline. Choose Stuck (pivot-based, fixed) or Dynamic (rolling highest/lowest) modes.
MTF Bias: Higher timeframe trend bias derived from a selectable moving average (SMA/EMA).
Compact Info Panel (table): A configurable on-chart panel that summarizes each higher timeframe’s bias, optional lower-timeframe analog labels, and a confluence tally. You can position it, resize text, and set columns/rows to fit your layout.
Clean Charting: Flip labels are optional and default to off, so alerts can fire without covering price action.
How it works
Bias engine: Computes bullish/bearish bias for each selected higher timeframe using your chosen MA length/type, then aggregates them into a confluence count.
DR engine: Finds or follows the current trading range and calculates a midline reference for signals or context.
Signals: You can use pure confluence, pure DR breakouts, or a combined “Bias + DR” confirmation for higher-quality entries.
Inputs to know
HTF Ranges (comma separated): Higher timeframes to assess (e.g., W,D,240,60,15).
MA Length/Type: Controls the bias engine’s sensitivity.
DR Mode: Stuck (pivot-based, fixed until a new pivot confirms) or Dynamic (rolling high/low by lookback).
Swing Length / Dynamic Lookback / Extend Right: Shape how the range is found and displayed.
Panel Position / Text Size / Panel Columns / Panel Rows: Customize the on-chart table.
Alerts: Min HTFs to align and Strict alignment (no opposite) to refine confluence.
Show Flip Labels on Chart: Optional visual flip labels; alerts are unaffected if kept off.
Alert conditions
Multi-TF Confluence Bullish: Minimum number of HTFs are bullish (optionally strict).
Multi-TF Confluence Bearish: Minimum number of HTFs are bearish (optionally strict).
DR Breakout Up: Close crosses above DR top.
DR Breakout Down: Close crosses below DR bottom.
Bias + DR Combo Bullish: Bullish confluence and price above your DR threshold (Midline or Top/Bottom).
Bias + DR Combo Bearish: Bearish confluence and price below your DR threshold (Midline or Top/Bottom).
Tips
For live trading, “Once per bar close” alerts are the safest and most consistent.
Increase the Min HTFs to align to reduce noise; switch Combo Threshold to Top/Bottom for fewer, stronger momentum entries.
Keep flip labels off to maintain a clean chart (alerts still fire).
Disclaimer This script is for educational and informational purposes only and does not constitute financial, investment, or trading advice. Trading involves risk, including the risk of loss. You are solely responsible for your own trading decisions. Past performance does not guarantee future results. Always test on a demo and consult a licensed professional where appropriate.
Mark Every Fair Value Gap (FVG) [Short Boxes + Dashed-on-Fill]marks out every fair valuer gap on everytime fraame
VIX Price BoxVIX Price Box (Customizable Colors)
This indicator displays the current VIX (CBOE Volatility Index) value in a fixed box on the top-right corner of the chart. It’s designed to give traders a quick, at-a-glance view of market volatility without needing to switch tickers.
Features
Pulls the live VIX price and updates automatically on every bar.
Displays the value inside a table box that stays fixed in the top-right corner.
Threshold-based coloring: the text color changes depending on whether the VIX is below, between, or above your chosen threshold levels.
5 built-in color modes:
Custom mode – choose your own colors for low, medium, and high volatility zones.
Adjustable threshold levels, background color, and frame color.
Use Cases
Monitor overall market risk sentiment while trading other instruments.
Identify periods of low vs. high volatility at a glance.
Pair with strategies that rely on volatility (options trading, hedging, breakout setups, etc.).
Financial Table by QuarterFinancial Table by Quarter
Summary
This indicator was created to help fundamental traders analyze historical financial data directly on the chart, eliminating the need to switch between screens. The table displays key metrics for each calendar year, broken down by quarter (Q1-Q4) with an annual total, providing a clear overview of a company's growth at a glance. It is also highly customizable to fit your trading style and chart theme.
Key Features
Financial Data Table: Organizes data by calendar year, showing details for each quarter (Q1, Q2, Q3, Q4) and a "Total" column for the annual summary.
Selectable Metrics (3 Options): Easily switch between three crucial financial metrics via a dropdown menu:
Total Revenue
Net Income
EPS (Earnings Per Share)
Highly Customizable:
Table Position: Choose from 9 standard positions on your chart.
Lookback Years: Adjust the number of historical years to display (from 1 to 20).
Number Format: Select how large numbers are displayed (Automatic K/M/B/T, Millions only, Billions only, or the full number).
Decimal Places: Control the precision of the numbers from 0 to 4 decimal places.
Negative Number Style: Display negative values in three standard formats: with a minus sign, with a red minus sign, or in red parentheses.
Full Color Customization: You can change the colors of the title, headers, individual data columns, text, and borders to perfectly match your chart's theme.
How to Use & Interpretation
Analyze Growth Trends: Use the table to look for consistent growth in Total Revenue and Net Income, both quarter-over-quarter and year-over-year. A healthy company should show steady, reliable growth.
Spot Anomalies: The table makes it easy to see if a specific quarter had unusually high or low performance, which may warrant further investigation into the company's reports for that period.
Compare Metrics: Switch between "Total Revenue" and "Net Income" to see how well revenue growth translates into actual profit. Growing revenue with declining income could be a red flag regarding cost control or shrinking margins.
Preliminary Valuation: Switch to "EPS" to track profitability on a per-share basis. This is a key factor for assessing the P/E ratio trend and understanding if the company is creating more value for its shareholders over time.
Limitations
Stocks Only: This indicator uses request.financial, which is only available for stocks. It will not work on Forex, Crypto, or Futures.
Data Depth: TradingView typically provides about 8-10 years of historical financial data. Even if you set the lookback to 20, the indicator will only display the maximum amount of data available for that specific stock.
Support & ResistanceEnglish:
This indicator identifies support and resistance zones based on pivot points and high-volume areas.
It dynamically draws boxes to highlight key price levels where buying or selling pressure is concentrated.
Green zones = support (positive volume)
Red zones = resistance (negative volume)
Dashed boxes = breakout/failed support or resistance
Solid boxes = holding support or resistance
It also marks:
Resistance turning into support (R→S)
Support turning into resistance (S→R)
Breakout labels for quick recognition
This tool helps traders visually track volume-backed supply and demand zones to anticipate future price reactions.
中文 (Chinese):
本指标基于枢轴点与高成交量区域识别支撑与阻力。
它会动态绘制矩形框，标记价格在买卖力量集中的关键水平。
绿色区域 = 支撑（正成交量）
红色区域 = 阻力（负成交量）
虚线框 = 突破或失效的支撑/阻力
实线框 = 有效保持的支撑/阻力
同时标注：
阻力转为支撑 (R→S)
支撑转为阻力 (S→R)
突破标签，便于快速识别
该工具帮助交易者直观追踪成交量验证的供需区域，以便预判未来价格反应。
OG OHLC MarkerDraws, OHLC for Previous day and Today with options to add alerts when any PD Array is swept
Higher High Lower Low Higher High Lower Low 🦉{Phanchai} — TradingView Description
Structure detector with dynamic Support/Resistance, customizable labels, and ready-made alerts (Pine v6).
This script marks market structure turning points — HH (Higher High), HL (Higher Low), LH (Lower High), LL (Lower Low) — and builds segmented Support/Resistance lines from those turns. Labels and colors are fully customizable and the script ships with multiple alert conditions.
What it does
Detects swing pivots using left/right bar windows, then classifies each confirmed swing as HH/HL/LH/LL.
Plots compact labels at the confirmed pivot bars with tooltips (English).
Derives dynamic Support / Resistance : every time structure flips, the previous level is closed and a new segment starts, extending to the right .
Provides alert conditions for any label and for specific first-occurrence shifts (e.g., first HH after a bearish label).
How it works (in short)
A pivot high/low confirms only after Right Bars candles have closed; labels and S/R appear at that confirmation bar.
An internal backbone (zigzag-like) is built from confirmed pivots, with light consistency checks to avoid contradictory sequences.
Structure rules compare the recent five pivots (A…E) to decide HH/HL/LH/LL.
S/R is updated from structure: e.g., in an up leg, new HLs refresh Support; in a down leg, new LHs refresh Resistance.
Alerts included
Any structure label (HH/HL/LH/LL) — Fires on any new label.
First LL after HL/HH — First bearish break after a bullish label.
First HH after LL/LH — First bullish break after a bearish label.
LL or HL formed — Any low-side label.
LH or HH formed — Any high-side label.
HL formed
HH formed
LL formed
LH formed
How to use (quick start)
Add the indicator to your chart.
Choose Left/Right Bars for your timeframe (e.g., 5–10 for intraday; larger for higher timeframes).
Pick your label colors/sizes and S/R style.
Right-click the chart → Add alert… → Condition: this indicator → select the desired alert.
Notes & tips
Because pivots require Right Bars to confirm, labels and S/R appear with a natural delay of that many bars. This avoids repainting.
Raising Left/Right Bars reduces noise and increases the average distance between pivots; lowering them increases sensitivity.
Structure is strict: sometimes you may see two HL (or two LH) in a row if the intermediate opposite swing didn’t qualify as HH/LH (or LL/HL).
S/R segments are drawn with line objects ; they are controlled via Inputs (style/width/color), not the Style tab.
This tool highlights structure; it’s not a standalone entry/exit system. Combine with volume, trend, or risk management rules.
Built with Pine v6. Clean, compact labels; segmented S/R that updates only on confirmed changes; comprehensive alerts ready for automation.
Earnings line & P/E Tracker# Earnings line & P/E Tracker
**A comprehensive fundamental analysis indicator that overlays earnings data and P/E ratios directly on your price charts.**
## 📊 Key Features
### Automatic Data Retrieval
- **Real-time financial data** pulled directly from TradingView's financial database
- **Multiple data sources**: Earnings Per Share (Basic/Diluted), Total Revenue, Net Income
- **Flexible periods**: TTM (Trailing Twelve Months), FQ (Quarterly), FY (Annual)
- **Live P/E ratio calculation** based on current price and TTM earnings
### Visual Display Options
- **Earnings progression line** overlaid on price chart for easy comparison
- **P/E ratio plot** with distinctive circle markers
- **Comprehensive data table** showing all key metrics in real-time
- **Dark mode optimized** with high-contrast colors for excellent readability
### Optional Event Tracking
- **Custom earnings dates** input for upcoming releases
- **Visual markers** on earnings announcement dates
- **Background highlighting** during earnings weeks
- **Smart alerts** for significant P/E changes and data updates
## 🎯 Perfect For
- **Fundamental analysts** comparing earnings growth vs stock price movement
- **Value investors** tracking P/E ratios and earnings trends
- **Earnings season trading** with visual release date markers
- **Long-term investors** monitoring fundamental health alongside technical analysis
## ⚙️ Customization Options
### Data Selection
- Choose between EPS Basic, EPS Diluted, Total Revenue, or Net Income
- Select TTM, quarterly, or annual reporting periods
- Toggle individual display elements on/off
### Visual Styling
- Customizable colors for earnings line, P/E ratio, and event markers
- Adjustable line width and styling options
- Moveable data table with size and position controls
### Event Management
- Input custom earnings release dates
- Enable/disable earnings event markers
- Background highlighting for earnings periods
- Configurable alert thresholds
## 📈 How It Works
1. **Automatic Detection**: The indicator automatically detects available fundamental data for your selected symbol
2. **Real-time Updates**: Financial metrics update as new data becomes available
3. **Visual Integration**: Earnings data is scaled and overlaid directly on your price chart
4. **Status Monitoring**: Clear indicators show data availability and freshness
## 🔧 Setup Instructions
1. Add the indicator to your chart
2. Select your preferred data source (EPS recommended for P/E tracking)
3. Choose time period (TTM recommended for most analyses)
4. Customize colors and display options to your preference
5. Optionally add upcoming earnings dates for event tracking
## 💡 Pro Tips
- **Use TTM EPS** for the most accurate P/E ratio calculations
- **Compare earnings line slope** with price movement to spot divergences
- **Enable earnings events** to prepare for volatility around announcements
- **Works best on daily/weekly timeframes** for fundamental analysis
## ⚠️ Data Availability
- Requires stocks with available fundamental data in TradingView's database
- Most major US stocks, ETFs, and international equities supported
- Limited data may be available for small-cap or recently listed companies
- Clear "No Data" indicator when fundamental data is unavailable
## 🎨 Display Features
- **High contrast colors** optimized for both light and dark chart themes
- **Clean, professional table** displaying all key metrics
- **Intuitive visual markers** for earnings events and data points
- **Responsive design** that adapts to different chart sizes
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**Perfect for traders and investors who want to combine fundamental analysis with technical charting in a single, comprehensive view.**
## ⚠️ Important Disclaimer
**This indicator is provided for educational and informational purposes only. The author (raptor2030) is not responsible for:**
- **Data accuracy or completeness** - Financial data is sourced from TradingView's database and may contain errors, delays, or omissions
- **Trading decisions** - This tool should not be used as the sole basis for investment decisions
- **Financial losses** - Past performance does not guarantee future results
- **Data reliability** - Third-party data sources may experience outages or provide incorrect information
- **Market timing** - Earnings dates and projections may be inaccurate or outdated
**Always verify critical information from official company sources and consult with qualified financial professionals before making investment decisions.**
**Use this indicator at your own risk. The author disclaims all liability for any direct, indirect, or consequential damages arising from the use of this script.**
Fundamentals PanelFundamentals Panel Description
The Fundamentals Panel is a versatile Pine Script indicator that displays key financial metrics—Market Cap, P/E Ratio, P/S Ratio, and PEG Ratio—in a clean, customizable table on your TradingView chart. Designed for investors and traders, this tool brings essential company fundamentals directly to your chart, saving time and enhancing decision-making.
Quick Insights: View critical valuation metrics (Market Cap, P/E, P/S, PEG) at a glance without leaving your chart, ideal for fundamental analysis or screening stocks.
Customizable Display: Toggle each metric on or off via input settings to focus on what matters most to your strategy.
Adjustable Font Size: Choose from Small, Normal, or Large text sizes to ensure readability suits your chart setup and screen preferences.
Reliable Data: Pulls data directly from TradingView’s financial database, using diluted shares and trailing metrics for accuracy across most stocks.
Debugging Support: Includes hidden plots (visible in the Data Window) to verify raw data like shares outstanding, revenue, and PEG, helping you trust the numbers.
How It Works
The indicator fetches:
Market Cap: Calculated using diluted shares outstanding and current price.
P/E Ratio: Price divided by trailing twelve-month (TTM) diluted EPS.
P/S Ratio: Market cap divided by TTM total revenue.
PEG Ratio: Trailing PEG from TradingView’s data, with an additional calculated PEG for cross-checking.
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FOMC Fund Rate 2022–2025(0.1)This indicator visualizes the Federal Open Market Committee (FOMC) meetings from 2022 through 2025.
It plots vertical lines on the announcement dates and attaches labels showing:
The decision (rate hike ⭡, cut ⭣, or hold ⭤).
The size of the rate change in percentage points.
The cumulative Federal Funds Rate path in parentheses.
Features:
Accurate timestamps for each FOMC meeting (UTC+1).
Customizable line style, width, and color.
Label color and text color options.
Placeholder labels for future meetings to maintain the timeline.
Use this script to keep track of historical Fed policy decisions and visualize the rate path over time directly on your chart.
