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CAGR 5 & 10 Years, Auto-Detect Timeframe# CAGR 5 & 10 Years, Auto-Detect Timeframe ## Overview This indicator automatically calculates the **Compound Annual Growth Rate (CAGR)** for 5-year and 10-year periods, adapting intelligently to different asset types and timeframes. ## Key Features ### 🤖 **Smart Market Detection** - **Automatically detects** if the asset operates 24/7 (crypto, crypto futures) or traditional hours (stocks, forex) ### ⏰ **Multi-Timeframe Support** **Compatible timeframes**: 1H, 2H, 3H, 4H, 6H, 8H, 12H, 1D, 3D, 1W, 1M, 3M, 6M, 12M ### 📊 **Visual Display** - **Green line**: 5-year CAGR percentage - **Blue line**: 10-year CAGR percentage - **Zero reference line** for easy interpretation ## Use Cases - **Long-term performance analysis** across different timeframes - **Cross-asset comparison** with automatic market type adjustment - **Investment planning** with standardized annual growth rates - **Historical perspective** on asset performance Perfect for investors and analysts who need consistent, comparable growth metrics across different assets and market types.
Pine Script® 인디케이터
Jordibes의
EPS, Revenue & NPM Growth Marker v2This indicator visually tracks fundamental growth by plotting smart labels on earnings dates, showing three key metrics: EPS (Earnings Per Share) Revenue (Sales) Net Profit Margin (NPM) It overlays compact, readable labels on the chart allowing you to evaluate a company's financial performance — both sequentially (QoQ) and annually (YoY). EPS, Revenue & NPM Growth Marker v2 uses both official earnings data and fallback logic (EPS/Revenue/NPM changes) to ensure consistent detection of earnings events. ⭐ Key Features Three Metrics: Displays EPS, Revenue, and Net Profit Margin Growth Breakdown: Shows Absolute, Quarter-over-Quarter %, and Year-over-Year % change Smart Arrows: Trend direction shown with emoji arrows (🡩🡫) Earnings Detection: Aligns labels to official earnings events or fallback data Highly Customizable: Full control over visible metrics, growth types, label style, placement, and max label count Clean Display: Keeps the chart clean with a limit on max labels ⚙️ Customization Options You can personalize the indicator to match your charting style: Select metrics to display: EPS, Revenue, NPM Toggle row types: Absolute, QoQ %, YoY % Choose label position: Above Bar, Below Bar, Top, or Bottom Customize line style, colors, and label text color Set the maximum number of labels shown on chart 🧠How It Works Absolute Value: Latest reported figure for each metric (e.g., EPS = ₹3.20) QoQ % Change: (Current - Previous Quarter) / |Previous| * 100 YoY % Change: (Current - Same Quarter Last Year) / |Previous Year| * 100 The script uses TradingView’s request.earnings() data when available. If official earnings data is missing, it intelligently detects earnings events based on changes in EPS, Revenue, or NPM figures. This is a major update to EPS & Sales/Revenue Growth Marker script, with expanded metrics, enhanced logic, and greater customization. Developed & Published by: @learningvitals
Pine Script® 인디케이터
learningvitals의
1
MSTY-WNTR Rebalancing SignalMSTY-WNTR Rebalancing Signal ## Overview The **MSTY-WNTR Rebalancing Signal** is a custom TradingView indicator designed to help investors dynamically allocate between two YieldMax ETFs: **MSTY** (YieldMax MSTR Option Income Strategy ETF) and **WNTR** (YieldMax Short MSTR Option Income Strategy ETF). These ETFs are tied to MicroStrategy (MSTR) stock, which is heavily influenced by Bitcoin's price due to MSTR's significant Bitcoin holdings. MSTY benefits from upward movements in MSTR (and thus Bitcoin) through a covered call strategy that generates income but caps upside potential. WNTR, on the other hand, provides inverse exposure, profiting from MSTR declines but losing in rallies. This indicator uses Bitcoin's momentum and MSTR's relative strength to signal when to hold MSTY (bullish phases), WNTR (bearish phases), or stay neutral, aiming to optimize returns by switching allocations at key turning points. Inspired by strategies discussed in crypto communities (e.g., X posts analyzing MSTR-linked ETFs), this indicator promotes an active rebalancing approach over a "set and forget" buy-and-hold strategy. In simulated backtests over the past 12 months (as of August 4, 2025), the optimized version has shown potential to outperform holding 100% MSTY or 100% WNTR alone, with an illustrative APY of ~125% vs. ~6% for MSTY and ~-15% for WNTR in one scenario. **Important Disclaimer**: This is not financial advice. Past performance does not guarantee future results. Always consult a financial advisor. Trading involves risk, and you could lose money. The indicator is for educational and informational purposes only. ## Key Features - **Momentum-Based Signals**: Uses a Simple Moving Average (SMA) on Bitcoin's price to detect bullish (price > SMA) or bearish (price < SMA) trends. - **RSI Confirmation**: Incorporates MSTR's Relative Strength Index (RSI) to filter signals, avoiding overbought conditions for MSTY and oversold for WNTR. - **Visual Cues**: - Green upward triangle for "Hold MSTY". - Red downward triangle for "Hold WNTR". - Yellow cross for "Switch" signals. - Background color: Green for MSTY, red for WNTR. - **Information Panel**: A table in the top-right corner displays real-time data: BTC Price, SMA value, MSTR RSI, and current Allocation (MSTY, WNTR, or Neutral). - **Alerts**: Configurable alerts for holding MSTY, holding WNTR, or switching. - **Optimized Parameters**: Defaults are tuned (SMA: 10 days, RSI: 15 periods, Overbought: 80, Oversold: 20) based on simulations to reduce whipsaws and capture trends effectively. ## How It Works The indicator's logic is straightforward yet effective for volatile assets like Bitcoin and MSTR: 1. **Primary Trigger (Bitcoin Momentum)**: - Calculate the SMA of Bitcoin's closing price (default: 10-day). - Bullish: Current BTC price > SMA → Potential MSTY hold. - Bearish: Current BTC price < SMA → Potential WNTR hold. 2. **Secondary Filter (MSTR RSI Confirmation)**: - Compute RSI on MSTR stock (default: 15-period). - For bullish signals: If RSI > Overbought (80), signal Neutral (avoid overextended rallies). - For bearish signals: If RSI < Oversold (20), signal Neutral (avoid capitulation bottoms). 3. **Allocation Rules**: - Hold 100% MSTY if bullish and not overbought. - Hold 100% WNTR if bearish and not oversold. - Neutral otherwise (e.g., during choppy or extreme markets) – consider holding cash or avoiding trades. 4. **Rebalancing**: - Switch signals trigger when the hold changes (e.g., from MSTY to WNTR). - Recommended frequency: Weekly reviews or on 5% BTC moves to minimize trading costs (aim for 4-6 trades/year). This approach leverages Bitcoin's influence on MSTR while mitigating the risks of MSTY's covered call drag during downtrends and WNTR's losses in uptrends. ## Setup and Usage 1. **Chart Requirements**: - Apply this indicator to a Bitcoin chart (e.g., BTCUSD on Binance or Coinbase, daily timeframe recommended). - Ensure MSTR stock data is accessible (TradingView supports it natively). 2. **Adding to TradingView**: - Open the Pine Editor. - Paste the script code. - Save and add to your chart. - Customize inputs if needed (e.g., adjust SMA/RSI lengths for different timeframes). 3. **Interpretation**: - **Green Background/Triangle**: Allocate 100% to MSTY – Bitcoin is in an uptrend, MSTR not overbought. - **Red Background/Triangle**: Allocate 100% to WNTR – Bitcoin in downtrend, MSTR not oversold. - **Yellow Switch Cross**: Rebalance your portfolio immediately. - **Neutral (No Signal)**: Panel shows "Neutral" – Hold cash or previous position; reassess weekly. - Monitor the panel for key metrics to validate signals manually. 4. **Backtesting and Strategy Integration**: - Convert to a strategy script by changing `indicator()` to `strategy()` and adding entry/exit logic for automated testing. - In simulations (e.g., using Python or TradingView's backtester), it has outperformed buy-and-hold in volatile markets by ~100-200% relative APY, but results vary. - Factor in fees: ETF expense ratios (~0.99%), trading commissions (~$0.40/trade), and slippage. 5. **Risk Management**: - Use with a diversified portfolio; never allocate more than you can afford to lose. - Add stop-losses (e.g., 10% trailing) to protect against extreme moves. - Rebalance sparingly to avoid over-trading in sideways markets. - Dividends: Reinvest MSTY/WNTR payouts into the current hold for compounding. ## Performance Insights (Simulated as of August 4, 2025) Based on synthetic backtests modeling the last 12 months: - **Optimized Strategy APY**: ~125% (by timing switches effectively). - **Hold 100% MSTY APY**: ~6% (gains from BTC rallies offset by downtrends). - **Hold 100% WNTR APY**: ~-15% (losses in bull phases outweigh bear gains). In one scenario with stronger volatility, the strategy achieved ~4533% APY vs. 10% for MSTY and -34% for WNTR, highlighting its potential in dynamic markets. However, these are illustrative; real results depend on actual BTC/MSTR movements. Test thoroughly on historical data. ## Limitations and Considerations - **Data Dependency**: Relies on accurate BTC and MSTR data; delays or gaps can affect signals. - **Market Risks**: Bitcoin's volatility can lead to false signals (whipsaws); the RSI filter helps but isn't perfect. - **No Guarantees**: This indicator doesn't predict the future. MSTR's correlation to BTC may change (e.g., due to regulatory events). - **Not for All Users**: Best for intermediate/advanced traders familiar with ETFs and crypto. Beginners should paper trade first. - **Updates**: As of August 4, 2025, this is version 1.0. Future updates may include volume filters or EMA options. If you find this indicator useful, consider leaving a like or comment on TradingView. Feedback welcome for improvements!
Pine Script® 인디케이터
Smartmate의
NightWatch 24/5 [theUltimator5]NightWatch 24/5 is a comprehensive indicator designed to seamlessly display both regular and overnight trading (BOATS exchange) into a single chart. Current TV limitations don't allow both overnight trading and regular exchanges to appear on the same chart due to timeframe visibility settings. We can either select between RTH (Regular Trading Hours) or ETH (Extended Trading Hours). There is no option to show 24 hour charts when looking at a stock. This indicator attempts to solve this issue. Please read the entire description thoroughly because this indicator takes a little bit of setup to work properly! ---IMPORTANT-- - This indicator MUST be used over a liquid cryptocurrency chart, like Bitcoin. It requires access to something that trades 24/7 and has volume data for all periods. Bitcoin on Coinbase is the best option. Please select Bitcoin as your main ticker before adding this indicator to the chart. ------------------- This indicator combines the price of both the regular trading hours and the overnight trading to create a single price line and volume candles. You can select view settings to either overlay the price on the chart, or have it below the chart. Volume can be toggled on or off as well. Default settings: Ticker = GME Overlay Candles on Main Chart = true Display Data = Both Price and Volume Show Status Table = true Here is an explanation for each of these settings: Ticker - Type in the ticker you want to track overnight and intraday data for Overlay Candles on Main chart - This will push the price candles onto the main chart area instead of below it. Volume candles will remain in their own separate pane below. This is useful if you want to track both price and volume without adding the indicator twice. Display Data - This determines what data to show. Volume, price, or both volume and price. Show Status Table - This toggles on or off the table that shows the ticker name, current session, and the price (change) of the ticker since the most recent daily close. If you overlay the price onto the chart, the price of the stock you are looking at will likely be a VERY different price than the crypto it is overlaying against. There are a couple workarounds. You can either zoom into the chart around the price of the stock you are looking at (time consuming), or you can go into your object tree and drag the indicator up into the main chart area. This will overlay the price onto the crypto while maintaining it's own unique y-axis. After you move the indicator up, you can add the indicator back a second time, then change the settings to only show the volume candles. You can then toggle off the table on one of the two so you don't see duplicate tables. This is the setting I am showing in my chart above. The indicator is added twice with the price being pulled up into the same window as Bitcoin, then a second instance below showing just volume. --LIMITATIONS-- Since the indicator requires the use of a 24 hour market ticker like Bitcoin, it DOES NOT display extended hours data. The price and volume data STOPS at 16:00 EST then resumes back up at 20:00 EST when BOATS opens. At 04:00, the price and volume then stops until 09:30, when the regular trading hours begin. This causes a flat line in the price during those periods. Unfortunately, there is no current workaround to this issue. If Bitcoin becomes illiquid (or whatever crypto you choose), it will only populate data for the ticker you want if there is data available for that crypto at the same time period. A gap in Bitcoin volume will show a gap in trade activity for your ticker.
Pine Script® 인디케이터
TheUltimator5의
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation. Introduction and Motivation Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment. The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data. Theoretical Framework and Methodology Composite Index Construction The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures. The mathematical formulation of the composite index follows: USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t) Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011). Weighting Methodology The weighting scheme incorporates findings from economic research: Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points. Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019). Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004). Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology). Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000). Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology. Data Normalisation and Scaling Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows: Normalized_i,t = (R_i,t / 252) × 100 Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution. Implementation and Technical Architecture The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation. User Interface Design and Customization Framework The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments. Visual Theme System The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions. Glow Effect Implementation The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability. Table Display Architecture The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability. The table displays twenty economic indicators with the following information architecture: - Category classification for cognitive grouping - Indicator names with standard economic nomenclature - Current values with intelligent number formatting - Percentage change calculations with directional indicators - Cross-asset market implications using standardised notation - Risk assessment using three-tier classification (HIGH/MED/LOW) - Data update timestamps for temporal reference Index Customisation Parameters The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices. Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification. Cross-Asset Market Implications The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012). The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation: Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions. Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications. Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators. Aggregated Market Impact Analysis The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes. Alert System Architecture The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes: Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period. Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation. Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability. Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration. Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods. Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions. Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points. Performance Optimization and Technical Implementation The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications. Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration. Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016). Risk Assessment Framework Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns. Empirical Validation and Performance Business Cycle Correspondence Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods. The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications. Cross-Asset Predictive Ability The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements. These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation. Practical Applications and Use Cases Institutional Asset Allocation The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction. The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies. Risk Management Applications The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment. The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions. Economic Research Applications The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions. The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations. Enhanced User Experience and Operational Features The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments. Multi-Screen and Multi-User Adaptability The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings. Cognitive Load Management The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006). Real-Time Decision Support The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods. Professional Alert Management The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels. Data Quality and Reliability Features The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity. Customisation for Institutional Workflows The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances. Limitations and Constraints Data Dependency The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications. Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases. Structural Relationship Stability The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights. The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies. Frequency Limitations Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies. The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources. Future Research Directions Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach. The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors. Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications. Conclusion The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications. The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation. The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools. References Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427. Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press. Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408. Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180. Cochrane, J. H. (2005). Asset pricing. Princeton University Press. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press. Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205. Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119. Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120. Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media. Fisher, I. (1930). The theory of interest. Macmillan. Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley. Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin. Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press. Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14. Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson. Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50. Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson. Nielsen, J. (1993). Usability engineering. Academic Press. Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books. OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall. Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution. Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill. Sommerville, I. (2016). Software engineering. Pearson. Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351-394. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press. Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
Pine Script® 인디케이터
EdgeTools의
6
Gold Z-Score Dashboard - 100-Bar Label Cleanup📌 Indicator Name: Gold Z-Score Dashboard — 100-Bar Label Cleanup 🧾 Description: This indicator leverages a statistical approach to detect overbought and oversold conditions using the Z-Score, a measure of price deviation from its moving average. It intelligently combines trend, volume, and volatility filters to reduce false signals and improve trading precision. ✅ Key Features: Z-Score Logic: Highlights extreme price moves by comparing current price to its recent average, normalized by standard deviation. Trend Filter (Optional): Uses a higher-timeframe EMA to confirm signals only in the trend direction. Volume Filter (Optional): Confirms signals only when current volume exceeds its average, avoiding low-activity noise. ATR Filter (Optional): Ensures signals occur during sufficient market volatility. Label Cleanup: Each signal label is automatically removed after 100 bars to keep your chart organized. Built-In Alerts: Get notified instantly when the market enters overbought or oversold zones. 🧠 How It Works: The Z-Score is calculated as: (Price−EMA)/StandardDeviation When the Z-Score crosses below -threshold, an oversold (long) signal is generated. When it crosses above +threshold, an overbought (short) signal is triggered. Signals are filtered based on user settings: ✅ Trend must be aligned with higher timeframe EMA ✅ Volume must be above its moving average ✅ ATR must indicate adequate market movement 📈 Best Used For: Spotting mean reversion opportunities Avoiding false reversals with smart filters Cleaner signal visualization via automatic label expiry
Pine Script® 인디케이터
carlosfilipemt의
BTCUSD Multi TP Trade Signal📘 Indicator Description: BTCUSD Multi TP Trade Signal This indicator is designed to generate high-quality Buy/Sell signals on BTCUSD, using a simple yet effective EMA crossover strategy. It visually plots all associated Take-Profit (TP) and Stop-Loss (SL) levels, allowing traders to plan and manage their trades with precision. 🔑 Key Features ✅ Trade Direction Control: Select to trade Long, Short, or Both directions ✅ Signal Generation: Uses EMA 20/50 crossover logic for trend confirmation ✅ Visual Trade Levels: Plots 4 customizable TP levels and a fixed SL on the chart ✅ Trend Filter Option: Align signals with higher timeframe (HTF) market direction ✅ User-Controlled Settings: Adjustable profit/stop targets and filtering logic ✅ Non-executing tool: Ideal for manual, visual, or alert-based trading ⚙️ Input Settings Parameter Function Strategy Direction Filters signals by direction (all, long, short) Length of Filter Period for trend filter (SMA) on HTF Candle Time Resolution for time-based conditions Length of ATR ATR period for potential future enhancements HTF Higher Time Frame (e.g., Weekly) for trend alignment Use Filter Toggle the HTF filter ON/OFF Stop Loss Fixed SL in USD Take Profit 1–4 TP levels in USD from entry price 📊 How It Works A Buy signal is plotted when EMA 20 crosses above EMA 50 and other conditions are met. A Sell signal is plotted when EMA 20 crosses below EMA 50. Each trade signal includes clearly marked TP1, TP2, TP3, TP4, and SL levels. Optional HTF trend filter ensures signals align with the broader market trend. 🧠 Best Use Cases Works best on 15-minute to 1-hour BTCUSD charts Ideal for trend-following intraday or swing trading Use with confluence (volume, price action, or key levels) for best results
Pine Script® 인디케이터
javedaliksa022의
CGPT Golden Cross / Death Cross AlertThis custom indicator identifies Golden Cross (Gx) and Death Cross (Dx) events using either EMA or SMA moving averages. A Golden Cross occurs when a short-term MA (e.g., 50) crosses above a long-term MA (e.g., 200), signaling potential bullish momentum. A Death Cross signals potential bearish momentum, with the short-term MA crossing below the long-term MA. It includes: 📈 Customizable MA types (EMA or SMA) ⚙️ Adjustable fast & slow MA lengths 🟢🔴 Chart labels for Gx (green) and Dx (red) 🎯 Background highlights for visual trend shifts 🔔 Built-in alert conditions for real-time notifications Ideal for crypto, stocks, or forex swing and trend trading
Pine Script® 인디케이터
Shazch24의
Distrodisco_v1.4What it does: Defines a “distribution session” (customizable time window) and tracks that session’s high/low to compute its distribution width as a percentage. Compares the current session’s distribution to historical same-day-of-week distributions to detect when it crosses above the median (i.e., a meaningful breakout in context). Tags the breakout direction (long or short) based on wick extremes and prior-session pivots. After a tagged break, tracks the pullback/retrace: how far price reverses back toward the tag (used for SL tuning). Simultaneously measures how far price extends beyond the break before the retrace begins—this “extension before retrace” can be used to calibrate realistic take-profit targets. Maintains historical accumulators for both retrace sizes and extensions so you can see distributions over time. Key metrics shown in the table: Total Days / Median Hits : Coverage of historical samples and how often distribution crosses its median. Pullback Rate: Percentage of median breaks that produced a pullback (including live/active ones if the session ends mid-retrace). Current / Historical Distribution Stats: Current session’s width vs. historical median/average for that weekday. Reversion Ret (revAbs): The largest pullback after a break (live for the session), used as a de-facto stop-loss gauge. Hist Median Ret: Median of completed historical retraces (and active ones at session end if not closed). 90%ile Ret: Upper-bound reference for retrace size—what the larger retraces look like. >= X% PBs: User-defined threshold (e.g., enter 0.05 for 0.05%) showing the percentage of historical retraces that met or exceeded that magnitude. Extension Median / 90%ile / Last Ext: How far price typically runs past the break before reversing—used for take-profit calibration. (If not yet enabled, these are forthcoming additions.) Inputs: Distribution Session / Timezone: Define the intra-day window to consider for distribution measurement. Max Distribution % to Include: Caps abnormally wide distributions from polluting historical buckets. Filter Out Abnormally Large Days: Toggle to exclude outliers. Min Pullback to Count (%): Threshold to count “meaningful” retraces in the historical percentage bucket. Enter e.g. 0.05 to represent 0.05%. Table styling: Color and positioning for easy visibility.
Pine Script® 인디케이터
Nejjie73-Pack_Trader의
2
EPS+Sales+Net Profit+MCap+Sector & Industry📄 Full Description This script displays a comprehensive financial data panel directly on your TradingView chart, helping long-term investors and swing traders make informed decisions based on fundamental trends. It consolidates key financial metrics and business classification data into a single, visually clear table. 🔍 Key Features: 🧾 Financial Metrics (Auto-Fetched via request.financial): EPS (Earnings Per Share) – Displayed with trend direction (QoQ or YoY). Sales / Revenue – In ₹ Crores (for Indian stocks), trend change also included. Net Profit – Also in ₹ Crores, along with percentage change. Market Cap – Automatically calculated using outstanding shares × price, shown in ₹ Cr. Free Float Market Cap – Based on float shares × price, also in ₹ Cr. 🏷️ Sector & Industry Info: Automatically identifies and displays the Sector and Industry of the stock using syminfo.sector and syminfo.industry. Displayed inline with metrics, making it easy to know what business the stock belongs to. 📊 Table View: Compact and responsive table shown on your chart. Columns: Date | EPS | QoQ | Sales | QoQ | Net Profit | QoQ | Metrics Metrics column dynamically shows: Market Cap Free Float Sector (Row 4) Industry (Row 5) 🌗 Appearance: Supports Dark Mode and Mini Mode toggle. You can also customize: Number of data points (last 4+ quarters or years) Table position and size 🎯 Use Case: This script is ideal for: Fundamental-focused traders who use EPS/Sales trends to identify momentum. Swing traders who combine price action with fundamental tailwinds. Portfolio builders who want to see sector/industry alignment quickly. It works best with fundamentally sound stocks where earnings and profitability are a major factor in price movements. ✅ Important Notes: Script uses request.financial which only works with supported symbols (mostly stocks). Market Cap and Free Float are calculated in ₹ Crores. All financial values are rounded and formatted for readability (e.g., 1,234 Cr). 🙏 Credits: Developed and published by Sameer Thorappa Built with a clean, minimalist approach for high readability and functionality.
Pine Script® 인디케이터
sameer_thorappa의
2
Recession Warning Model [BackQuant]Recession Warning Model Overview The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments. Purpose The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly. Core Features - Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress. - Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries. - Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix). - Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads. - Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability). - Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals. - Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals. - Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R. Model Complexity Levels Users can select from four tiers to balance simplicity and depth: 1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring. 2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule. 3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth. 4. Research : Unlocks all indicators plus experimental settings for advanced users. Key Indicators Below is a summary of the 13 indicators, their data sources, and economic significance: - Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress. - Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs. - Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions. - Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator. - Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending. - PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction. - VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress. - S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption. - M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction. - NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions. - Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress. - JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns. - Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating. Data Processing - Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period). - Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback). - Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise. - Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations. Probability Calculation 1. Each indicator’s binary signal is weighted according to user settings or dynamic performance. 2. Weights are normalized to sum to 100% across active indicators. 3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix. 4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes. 5. Optional smoothing ensures stable outputs. Display and Visualization - Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands. - Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards. - Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence. - Ensemble Mode : Averages traditional and split probabilities for a balanced view. - Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis). - Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses. Practical Applications - Asset Allocation : Adjust equity or bond exposures based on sustained probability increases. - Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis. - Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%. - Trading Filters : Disable short-term strategies during high-risk regimes. - Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated. Configuration Guidelines - Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred. - Use dynamic weighting with at least one economic cycle of data for optimal performance. - Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals. - Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization. - Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading. Limitations - Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading. - Historical data availability may constrain dynamic weighting on shorter timeframes. - Model accuracy depends on the quality and timeliness of economic data feeds. Final Note The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
Pine Script® 인디케이터
BackQuant의
3