Ralph Indicator - ZaraTrust Smart MoneyThe Ralph Indicator – ZaraTrust Smart Money is a powerful yet simple Smart Money Concepts (SMC) based tool designed for traders who want to trade like institutions. It auto-detects high-probability Buy/Sell zones, Support/Resistance levels, and Demand/Supply areas on the chart — giving you clear, visual, and actionable signals without the clutter.
⸻
🔍 Key Features:
✅ Smart Money Structure
• Uses pivot-based logic to identify potential structure points
• Helps you understand market flow (e.g., BOS, CHoCH simplified logic)
✅ Automatic Support & Resistance
• Plots major levels based on significant highs and lows
• Helps catch key reversal or breakout zones
✅ Demand & Supply Zones
• Visually shows areas where price may react strongly
• Based on smart pivot detection from recent swings
✅ Buy/Sell Trade Signals
• Highlights buy when price breaks resistance (possible bullish shift)
• Highlights sell when price breaks support (possible bearish shift)
✅ Clean & Easy UI
• Toggle features on/off from settings panel
• Labels and shapes are plotted clearly on the chart for instant reading
⸻
🛠️ Recommended Use:
• Use on 15min to 4H timeframe for intraday or swing trading
• Combine with price action (e.g., confirmation candles, liquidity grab)
• Works best when paired with institutional logic (OBs, FVG, liquidity)
⸻
⚠️ Disclaimer:
This indicator is a tool, not a signal service.
It does not guarantee 98% accuracy, but it’s designed to highlight smart money zones and high-probability areas. Always do your own risk management and backtest before using on a live account.
基本面分析
10 EMA, 20 EMA & 50 SMAThis script plots three key moving averages on the price chart to help identify trends and potential trade opportunities:
10 EMA (Exponential Moving Average):
A fast-reacting average that captures short-term price momentum. Useful for spotting quick trend changes.
20 EMA (Exponential Moving Average):
A medium-term average that smooths out more noise while still being responsive to price changes.
50 SMA (Simple Moving Average):
A widely-used long-term trend indicator. It smooths price data over a longer period and is often used to define overall market direction.
VampFX Kill Zone🦇 VampFX Kill Zone Indicator
Built for Smart Money Traders by Vamp FX
This custom Kill Zone tool highlights the optimal institutional trading window — when volume, liquidity, and precision align.
🔹 What It Does:
• Shades the VampFX Kill Zone (default: 8:00 AM to 12:30 PM UTC-4 / New York)
• Designed for New York session scalping/sniping
• Helps isolate high-probability Smart Money setups (liquidity sweeps, FVGs, BOS entries)
🔧 Default Settings:
• Timezone: UTC -4 (New York)
• Session Start: 08:00
• Session End: 12:30
• Adjustable to fit your strategy or local session bias
⸻
📈 Why Use It:
The VampFX Kill Zone reflects when algos run, liquidity gets manipulated, and clean entries occur.
Avoid noise — trade when the market actually moves.
“We don’t chase the market. We wait inside the zone… then strike with precision.”
— 🦇 VampFX Code
Advanced Risk Appetite Index ProThe Advanced Risk Appetite Index (RAI) represents a sophisticated institutional-grade measurement system for quantifying market risk sentiment through proprietary multi-factor fundamental analysis. This indicator synthesizes behavioral finance theory, market microstructure research, and macroeconomic indicators to provide real-time assessment of market participants' risk tolerance and investment appetite.
## Theoretical Foundation
### Academic Framework
The Risk Appetite Index is grounded in established financial theory, particularly the behavioral finance paradigm introduced by Kahneman and Tversky (1979) in their seminal work on prospect theory¹. The indicator incorporates insights from market microstructure theory (O'Hara, 1995)² and extends the risk-on/risk-off framework developed by Kumar and Lee (2006)³ through advanced statistical modeling techniques.
The theoretical foundation draws from multiple academic disciplines:
**Behavioral Finance**: The indicator recognizes that market participants exhibit systematic biases in risk perception, as documented by Shefrin and Statman (1985)⁴. These cognitive biases create measurable patterns in asset pricing and cross-asset relationships.
**Market Microstructure**: Following the work of Hasbrouck (1991)⁵, the model incorporates liquidity dynamics and market structure effects that influence risk sentiment transmission.
**Macroeconomic Theory**: The indicator integrates insights from monetary economics (Taylor, 1993)⁶ and international finance (Dornbusch, 1976)⁷ to capture policy impact on market sentiment.
### Methodological Approach
The Advanced Risk Appetite Index employs a proprietary multi-factor modeling approach that combines elements of:
1. **Advanced Factor Analysis**: Following established methodologies from Fama and French (1993)⁸, the system identifies fundamental factors that explain risk appetite variations.
2. **Regime-Adaptive Modeling**: Incorporating insights from Hamilton (1989)⁹ on regime-switching models to adapt to changing market conditions.
3. **Robust Statistical Framework**: Implementation of robust estimation methods (Huber, 1981)¹⁰ to ensure signal reliability and minimize noise impact.
## Technical Architecture
### Proprietary Multi-Factor Framework
The indicator processes information from multiple fundamental market dimensions through a sophisticated weighting and normalization system. The specific factor selection and weighting methodology represents proprietary intellectual property developed through extensive empirical research and optimization.
**Statistical Processing**: All inputs undergo robust statistical transformation using advanced normalization techniques based on Rousseeuw and Croux (1993)²⁰ to ensure consistent signal generation across different market environments.
**Dynamic Adaptation**: The system incorporates dynamic weighting adjustments based on market regime detection, drawing from the dynamic factor model literature (Stock and Watson, 2002)²¹.
**Quality Assurance**: Multi-layered quality assessment ensures signal reliability through proprietary filtering mechanisms that evaluate:
- Factor consensus requirements
- Signal persistence validation
- Data quality thresholds
- Regime-dependent adjustments
## Implementation and Usage
### Professional Visualization
The indicator provides institutional-grade visualization through:
**Multi-Theme Color Schemes**: Eight professional color themes optimized for different trading environments, following data visualization best practices (Tufte, 2001)²².
**Dynamic Background System**: Real-time visual feedback system that provides immediate market risk appetite assessment.
**Signal Quality Indicators**: Professional-grade visual representations of signal strength and reliability metrics.
### Analytics Dashboard
The comprehensive dashboard provides key institutional metrics including:
- Strategy position status and signal tracking
- Risk level assessment and market sentiment indicators
- Uncertainty measurements and volatility forecasting
- Trading signal quality and regime identification
- Performance analytics and model diagnostics
### Professional Alert System
Comprehensive alert framework covering:
- Entry and exit signal notifications
- Threshold breach warnings
- Market regime change alerts
- Signal quality degradation warnings
## Trading Applications
### Signal Generation Framework
The indicator generates professionally validated signals through proprietary algorithms:
**Long Entry Signals**: Generated when risk appetite conditions satisfy multiple proprietary criteria, indicating favorable risk asset exposure conditions.
**Position Management Signals**: Generated when risk appetite deteriorates below critical thresholds, suggesting defensive positioning requirements.
### Risk Management Integration
The indicator seamlessly integrates with institutional risk management frameworks through:
- Real-time regime identification and classification
- Advanced volatility forecasting capabilities
- Crisis detection and early warning systems
- Comprehensive uncertainty quantification
### Multi-Timeframe Applications
While optimized for daily analysis, the indicator supports various analytical timeframes for:
- Strategic asset allocation decisions
- Tactical portfolio rebalancing
- Risk management applications
## Empirical Validation
### Performance Characteristics
The indicator has undergone extensive empirical validation across multiple market environments, demonstrating:
- Consistent performance across different market regimes
- Robust signal generation during crisis periods
- Effective risk-adjusted return enhancement capabilities
### Statistical Validation
All model components and signal generation rules have been validated using:
- Comprehensive out-of-sample testing protocols
- Monte Carlo simulation analysis
- Cross-regime performance evaluation
- Statistical significance testing
## Model Specifications
### Market Applications and Target Instruments
**Primary Target Market**: The Advanced Risk Appetite Index is specifically optimized for S&P 500 Index (SPX) analysis, where it demonstrates peak performance characteristics. The model's proprietary factor weighting and signal generation algorithms have been calibrated primarily against SPX historical data, ensuring optimal sensitivity to US large-cap equity market dynamics.
**Secondary Market Applications**: While designed for SPX, the indicator demonstrates robust performance across other major equity indices, including:
- NASDAQ-100 (NDX) and related instruments
- Dow Jones Industrial Average (DJIA)
- Russell 2000 (RUT) for small-cap exposure
- International indices with sufficient liquidity and data availability
**Cross-Market Validation**: The model's fundamental approach to risk appetite measurement provides meaningful signals across different equity markets, though performance characteristics may vary based on market structure, liquidity, and regional economic factors.
### Data Requirements
The indicator requires access to institutional-grade market data across multiple asset classes and economic indicators. Specific data requirements and processing methodologies are proprietary.
### Computational Framework
The system utilizes advanced computational techniques including:
- Robust statistical estimation methods
- Dynamic factor modeling approaches
- Regime-switching algorithms
- Real-time signal processing capabilities
## Limitations and Risk Disclosure
### Model Limitations
**Data Dependency**: The indicator requires comprehensive market data and may experience performance variations during periods of limited data availability.
**Regime Sensitivity**: Performance characteristics may vary across different market regimes and structural breaks.
### Risk Warnings
**Past Performance Disclaimer**: Historical results do not guarantee future performance. All trading involves substantial risk of loss.
**Model Risk**: Quantitative models are subject to model risk and may fail to predict future market movements accurately.
**Market Risk**: The indicator does not eliminate market risk and must be used within comprehensive risk management frameworks.
## Professional Applications
### Target Users
The Advanced Risk Appetite Index is designed for:
- Institutional portfolio managers and investment professionals
- Risk management teams and quantitative analysts
- Professional traders and hedge fund managers
- Academic researchers and financial consultants
### Integration Capabilities
The indicator supports integration with:
- Portfolio optimization and management systems
- Risk management and monitoring platforms
- Automated trading and execution systems
- Research and analytics databases
## References
1. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
2. O'Hara, M. (1995). Market Microstructure Theory. Cambridge, MA: Blackwell Publishers.
3. Kumar, A., & Lee, C. M. (2006). Retail Investor Sentiment and Return Comovements. Journal of Finance, 61(5), 2451-2486.
4. Shefrin, H., & Statman, M. (1985). The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence. Journal of Finance, 40(3), 777-790.
5. Hasbrouck, J. (1991). Measuring the Information Content of Stock Trades. Journal of Finance, 46(1), 179-207.
6. Taylor, J. B. (1993). Discretion versus Policy Rules in Practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
7. Dornbusch, R. (1976). Expectations and Exchange Rate Dynamics. Journal of Political Economy, 84(6), 1161-1176.
8. Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.
9. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
10. Huber, P. J. (1981). Robust Statistics. New York: John Wiley & Sons.
11. Breeden, D. T. (1979). An Intertemporal Asset Pricing Model with Stochastic Consumption and Investment Opportunities. Journal of Financial Economics, 7(3), 265-296.
12. Mishkin, F. S. (1990). What Does the Term Structure Tell Us About Future Inflation? Journal of Monetary Economics, 25(1), 77-95.
13. Estrella, A., & Hardouvelis, G. A. (1991). The Term Structure as a Predictor of Real Economic Activity. Journal of Finance, 46(2), 555-576.
14. Collin-Dufresne, P., Goldstein, R. S., & Martin, J. S. (2001). The Determinants of Credit Spread Changes. Journal of Finance, 56(6), 2177-2207.
15. Carr, P., & Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
16. Engel, C. (1996). The Forward Discount Anomaly and the Risk Premium: A Survey of Recent Evidence. Journal of Empirical Finance, 3(2), 123-192.
17. Ranaldo, A., & Söderlind, P. (2010). Safe Haven Currencies. Review of Finance, 14(3), 385-407.
18. Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
19. Pástor, L., & Stambaugh, R. F. (2003). Liquidity Risk and Expected Stock Returns. Journal of Political Economy, 111(3), 642-685.
20. Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.
21. Stock, J. H., & Watson, M. W. (2002). Dynamic Factor Models. Oxford Handbook of Econometrics, 1, 35-59.
22. Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.
Real 10Y Yield (DGS10 - T10YIE)The Real 10Y Yield (DGS10 – T10YIE) indicator computes the inflation-adjusted U.S. 10-year Treasury yield by subtracting the 10-year breakeven inflation rate (T10YIE) from the nominal 10-year Treasury yield (DGS10), both sourced directly from FRED. By filtering out inflation expectations, this script reveals the true, real borrowing cost over a 10-year horizon—one of the most reliable gauges of overall risk sentiment and capital–market health.
How It Works
Data Inputs
• DGS10 (Nominal 10-Year Treasury Yield)
• T10YIE (10-Year Breakeven Inflation Rate)
Both series are fetched on a daily timeframe via request.security from FRED.
Real Yield Calculation
pine
Copy
Edit
real10y = DGS10 – T10YIE
A positive value indicates that nominal yields exceed inflation expectations (real yields are positive), while a negative value signals deep-negative real rates.
Thresholds & Coloring
• Bullish Zone: Real yield < –0.1 %
• Bearish Zone: Real yield > +0.1 %
The background turns green when real yields drop below –0.1 %, reflecting an ultra-accommodative environment that historically aligns with risk-on rallies. It turns red when real yields exceed +0.1 %, indicating expensive real borrowing costs and a potential shift toward risk-off.
Alerts
• Deep-Negative Real Yields (Bullish): Triggers when real yield < –0.1 %
• High Real Yields (Bearish): Triggers when real yield > +0.1 %
Why It’s Powerful
Forward-Looking Sentiment Gauge
Real yields incorporate both market-implied inflation and nominal rates, making them a leading indicator for risk appetite, equity flows, and crypto demand.
Clear, Actionable Zones
The –0.1 % / +0.1 % thresholds cleanly delineate structurally bullish vs. bearish regimes, removing noise and false signals common in nominal-only yield studies.
Macro & Cross-Asset Confluence
Combine with equity indices, dollar strength (DXY), or credit spreads for a fully contextual macro view. When real yields break deeper negative alongside weakening dollar, it often precedes stretch in risk assets.
Automatic Alerts
Never miss regime shifts—alerts notify you the moment real yields breach key zones, so you can align your strategy with prevailing macro momentum.
How to Use
Add to a separate pane for unobstructed visibility.
Monitor breaks beneath –0.1 % for early “risk-on” signals in stocks, commodities, and crypto.
Watch for climbs above +0.1 % to hedge or rotate into defensive assets.
Combine with your existing trend-following or mean-reversion strategies to improve timing around major market turning points.
–––
Feel free to adjust the threshold lines to your preferred sensitivity (e.g., tighten to ±0.05 %), or overlay with moving averages to smooth out whipsaws. This script is ideal for macro traders, portfolio managers, and quantitative quants who demand a distilled, inflation-adjusted view of real rates.
National Financial Conditions Index (NFCI)This is one of the most important macro indicators in my trading arsenal due to its reliability across different market regimes. I'm excited to share this with the TradingView community because this Federal Reserve data is not only completely free but extraordinarily useful for portfolio management and risk assessment.
**Important Disclaimers**: Be aware that some NFCI components are updated only monthly but carry significant weighting in the composite index. Additionally, the Fed occasionally revises historical NFCI data, so historical backtests should be interpreted with some caution. Nevertheless, this remains a crucial leading indicator for financial stress conditions.
---
## What is the National Financial Conditions Index?
The National Financial Conditions Index (NFCI) is a comprehensive measure of financial stress and liquidity conditions developed by the Federal Reserve Bank of Chicago. This indicator synthesizes over 100 financial market variables into a single, interpretable metric that captures the overall state of financial conditions in the United States (Brave & Butters, 2011).
**Key Principle**: When the NFCI is positive, financial conditions are tighter than average; when negative, conditions are looser than average. Values above +1.0 historically coincide with financial crises, while values below -1.0 often signal bubble-like conditions.
## Scientific Foundation & Research
The NFCI methodology is grounded in extensive academic research:
### Core Research Foundation
- **Brave, S., & Butters, R. A. (2011)**. "Monitoring financial stability: A financial conditions index approach." *Economic Perspectives*, 35(1), 22-43.
- **Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010)**. "Financial conditions indexes: A fresh look after the financial crisis." *US Monetary Policy Forum Report*, No. 23.
- **Kliesen, K. L., Owyang, M. T., & Vermann, E. K. (2012)**. "Disentangling diverse measures: A survey of financial stress indexes." *Federal Reserve Bank of St. Louis Review*, 94(5), 369-397.
### Methodological Validation
The NFCI employs Principal Component Analysis (PCA) to extract common factors from financial market data, following the methodology established by **English, W. B., Tsatsaronis, K., & Zoli, E. (2005)** in "Assessing the predictive power of measures of financial conditions for macroeconomic variables." The index has been validated through extensive academic research (Koop & Korobilis, 2014).
## NFCI Components Explained
This indicator provides access to all five official NFCI variants:
### 1. **Main NFCI**
The primary composite index incorporating all financial market sectors. This serves as the main signal for portfolio allocation decisions.
### 2. **Adjusted NFCI (ANFCI)**
Removes the influence of credit market disruptions to focus on non-credit financial stress. Particularly useful during banking crises when credit markets may be impaired but other financial conditions remain stable.
### 3. **Credit Sub-Index**
Isolates credit market conditions including corporate bond spreads, commercial paper rates, and bank lending standards. Important for assessing corporate financing stress.
### 4. **Leverage Sub-Index**
Measures systemic leverage through margin requirements, dealer financing, and institutional leverage metrics. Useful for identifying leverage-driven market stress.
### 5. **Risk Sub-Index**
Captures market-based risk measures including volatility, correlation, and tail risk indicators. Provides indication of risk appetite shifts.
## Practical Trading Applications
### Portfolio Allocation Framework
Based on the academic research, the NFCI can be used for portfolio positioning:
**Risk-On Positioning (NFCI declining):**
- Consider increasing equity exposure
- Reduce defensive positions
- Evaluate growth-oriented sectors
**Risk-Off Positioning (NFCI rising):**
- Consider reducing equity exposure
- Increase defensive positioning
- Favor large-cap, dividend-paying stocks
### Academic Validation
According to **Oet, M. V., Eiben, R., Bianco, T., Gramlich, D., & Ong, S. J. (2011)** in "The financial stress index: Identification of systemic risk conditions," financial conditions indices like the NFCI provide early warning capabilities for systemic risk conditions.
**Illing, M., & Liu, Y. (2006)** demonstrated in "Measuring financial stress in a developed country: An application to Canada" that composite financial stress measures can be useful for predicting economic downturns.
## Advanced Features of This Implementation
### Dynamic Background Coloring
- **Green backgrounds**: Risk-On conditions - potentially favorable for equity investment
- **Red backgrounds**: Risk-Off conditions - time for defensive positioning
- **Intensity varies**: Based on deviation from trend for nuanced risk assessment
### Professional Dashboard
Real-time analytics table showing:
- Current NFCI level and interpretation (TIGHT/LOOSE/NEUTRAL)
- Individual sub-index readings
- Change analysis
- Portfolio guidance (Risk On/Risk Off)
### Alert System
Professional-grade alerts for:
- Risk regime changes
- Extreme stress conditions (NFCI > 1.0)
- Bubble risk warnings (NFCI < -1.0)
- Major trend reversals
## Optimal Usage Guidelines
### Best Timeframes
- **Daily charts**: Recommended for intermediate-term positioning
- **Weekly charts**: Suitable for longer-term portfolio allocation
- **Intraday**: Less effective due to weekly update frequency
### Complementary Indicators
For enhanced analysis, combine NFCI signals with:
- **VIX levels**: Confirm stress readings
- **Credit spreads**: Validate credit sub-index signals
- **Moving averages**: Determine overall market trend context
- **Economic surprise indices**: Gauge fundamental backdrop
### Position Sizing Considerations
- **Extreme readings** (|NFCI| > 1.0): Consider higher conviction positioning
- **Moderate readings** (|NFCI| 0.3-1.0): Standard position sizing
- **Neutral readings** (|NFCI| < 0.3): Consider reduced conviction
## Important Limitations & Considerations
### Data Frequency Issues
**Critical Warning**: While the main NFCI updates weekly (typically Wednesdays), some underlying components update monthly. Corporate bond indices and commercial paper rates, which carry significant weight, may cause delayed reactions to current market conditions.
**Component Update Schedule:**
- **Weekly Updates**: Main NFCI composite, most equity volatility measures
- **Monthly Updates**: Corporate bond spreads, commercial paper rates
- **Quarterly Updates**: Banking sector surveys
- **Impact**: Significant portion of index weight may lag current conditions
### Historical Revisions
The Federal Reserve occasionally revises NFCI historical data as new information becomes available or methodologies are refined. This means backtesting results should be interpreted cautiously, and the indicator works best for forward-looking analysis rather than precise historical replication.
### Market Regime Dependency
The NFCI effectiveness may vary across different market regimes. During extended sideways markets or regime transitions, signals may be less reliable. Consider combining with trend-following indicators for optimal results.
**Bottom Line**: Use NFCI for medium-term portfolio positioning guidance. Trust the directional signals while remaining aware of data revision risks and update frequency limitations. This indicator is particularly valuable during periods of financial stress when reliable guidance is most needed.
---
**Data Source**: Federal Reserve Bank of Chicago
**Update Frequency**: Weekly (typically Wednesdays)
**Historical Coverage**: 1973-present
**Cost**: Free (public Fed data)
*This indicator is for educational and analytical purposes. Always conduct your own research and risk assessment before making investment decisions.*
## References
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. *Economic Perspectives*, 35(1), 22-43.
English, W. B., Tsatsaronis, K., & Zoli, E. (2005). Assessing the predictive power of measures of financial conditions for macroeconomic variables. *BIS Papers*, 22, 228-252.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. *US Monetary Policy Forum Report*, No. 23.
Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Bank of Canada Working Paper*, 2006-02.
Kliesen, K. L., Owyang, M. T., & Vermann, E. K. (2012). Disentangling diverse measures: A survey of financial stress indexes. *Federal Reserve Bank of St. Louis Review*, 94(5), 369-397.
Koop, G., & Korobilis, D. (2014). A new index of financial conditions. *European Economic Review*, 71, 101-116.
Oet, M. V., Eiben, R., Bianco, T., Gramlich, D., & Ong, S. J. (2011). The financial stress index: Identification of systemic risk conditions. *Federal Reserve Bank of Cleveland Working Paper*, 11-30.
QBCore Algø Pro EditionQBCore Algo Pro Edition is a smart-money-based indicator designed for precision trading .
This tool includes real-time CHoCH/BOS detection, internal & swing structure mapping, fair value gaps, premium/discount zones, and dynamic order block logic.
Crowding model ║ BullVision🔬 Overview
The Crypto Crowding Model Pro is a sophisticated analytical tool designed to visualize and quantify market conditions across multiple cryptocurrencies. By leveraging Relative Strength Index (RSI) and Z-score calculations, this indicator provides traders with an intuitive and detailed snapshot of current crypto market dynamics, highlighting areas of extreme momentum, crowded trades, and potential reversal points.
⚙️ Key Concepts
📊 RSI and Z-Score Analysis
RSI (Relative Strength Index) evaluates the momentum and strength of each cryptocurrency, identifying overbought or oversold conditions.
Z-Score Normalization measures each asset's current price deviation relative to its historical average, identifying statistically significant extremes.
🎯 Crowding Analytics
An integrated analytics panel provides real-time crowding metrics, quantifying market sentiment into four distinct categories:
🔥 FOMO (Fear of Missing Out): High momentum, potential exhaustion.
❄️ Fear: Low momentum, potential reversal or consolidation.
📈 Recovery: Moderate upward momentum after a downward trend.
💪 Strength: Stable bullish conditions with sustained momentum.
🖥️ Visual Scatter Plot
Assets are plotted on a dynamic scatter plot, positioning each cryptocurrency according to its RSI and Z-score.
Color coding, symbol shapes, and sizes help quickly identify main market segments (BTC, ETH, TOTAL, OTHERS) and individual asset conditions.
🧩 Quadrant Classification
Assets are categorized into four quadrants based on their momentum and deviation:
Overbought Extended: High RSI and positive Z-score.
Recovery Phase: Low RSI but positive Z-score.
Oversold Compressed: Low RSI and negative Z-score.
Strong Consolidation: High RSI but negative Z-score.
🔧 User Customization
🎨 Visual Settings
Bar Scale: Adjust the scatter plot visual scale.
Asset Visibility: Optionally display key market benchmarks (TOTAL, BTC, ETH, OTHERS).
Gradient Background: Enhances visual interpretation of asset clusters.
Crowding Analytics Panel: Toggle the analytics panel on/off.
📊 Indicator Parameters
RSI Length: Defines the calculation period for RSI.
Z-score Lookback: Historical lookback period for normalization.
Crowding Alert Threshold: Sets alert sensitivity for crowded market conditions.
🎯 Zone Settings
Quadrant Labels: Displays descriptive labels for each quadrant.
Danger Zones: Highlights extreme RSI levels indicative of heightened market risk.
📈 Visual Output
Dynamic Scatter Plot: Visualizes asset positioning clearly and intuitively.
Gradient and Grid: Professional gridlines and subtle gradient backgrounds assist visual assessment.
Danger Zone Highlights: Visually indicates RSI extremes to warn of potential market turning points.
Crowding Analytics Panel: Real-time summary of market sentiment and asset distribution.
🔍 Use Cases
This indicator is particularly beneficial for traders and analysts looking to:
Identify crowded trades and potential reversal points.
Quickly assess overall market sentiment and individual asset strength.
Integrate a robust momentum analysis into broader technical or fundamental strategies.
Enhance market timing and improve risk management decisions.
⚠️ Important Notes
This indicator does not provide explicit buy or sell signals.
It is intended solely for informational, analytical, and educational purposes.
Past performance and signals are not indicative of future market results.
Always combine with additional tools and analysis as part of comprehensive decision-making.
Buy sell Trend VolumeThis indicator analyzes the flow of volume and price changes to identify potential trends.
Understanding Volume Indicator: A Comprehensive Guide
Introduction. The volume indicator is a vital tool investors and traders use to understand the liquidity and market activity in trading.
Auto LevelsSimple auto level tracker that automatically detects and plots the high/low for the current week, day, and month, as well as the previous week/day/month.
Includes a built-in dashboard that shows how close or far price is from each level, along with directional guidance (above/below). The closest level to current price is automatically highlighted for quick awareness.
Everything is fully toggleable to only show the levels and info that is needed.
Ultra Supply & DemandUltra Supply and Demand fixed.
Order Block Detection: Identifies potential order blocks (demand/supply zones)
Custom Time LinesMarks out London and Asia Session open times and close times to help when trading New York Session
Asset Premium/Discount Monitor📊 Overview
The Asset Premium/Discount Monitor is a tool for analyzing the relative value between two correlated assets. It measures when one asset is trading at a premium or discount compared to its historical relationship with another asset, helping traders identify potential mean reversion opportunities, or pairs trading opportunities.
🎯 Use Cases
Perfect for analyzing:
NASDAQ:MSTR vs CRYPTO:BTCUSD - MicroStrategy's premium/discount to Bitcoin
NASDAQ:COIN vs BITSTAMP:BTCUSD - Coinbase's relative value to Bitcoin
NASDAQ:TSLA vs NASDAQ:QQQ - Tesla's premium to tech sector
Regional banks AMEX:KRE vs AMEX:XLF - Individual bank stocks vs financial sector
Any two correlated assets where relative value matters
Example of a trade: MSTR vs BTC - When indicator shows MSTR at 95% percentile (extreme premium): Short MSTR, Buy BTC. Then exit when the spread reverts to the mean, say 40-60% percentile.
🔧 How It Works
Core Calculation
Ratio Analysis: Calculates the price ratio between your asset and the correlated asset
Historical Baseline: Establishes the "normal" relationship using a 252-day moving average. You can change this.
Premium Measurement: Measures current deviation from historical average as a percentage
Statistical Context: Provides percentile rankings and standard deviation bands
The Math
Premium % = (Current Ratio / Historical Average Ratio - 1) × 100
🎨 Customization Options
Correlated Asset: Choose any symbol for comparison
Lookback Period: Adjust historical baseline (50-1000 days)
Smoothing: Reduce noise with moving average (1-50 days)
Visual Toggles: Show/hide bands and percentile lines
Color Themes: Customize premium/discount colors
📊 Interpretation Guide
Premium/Discount Reading
Positive %: Asset trading above historical relationship (premium)
Negative %: Asset trading below historical relationship (discount)
Near 0%: Asset at fair value relative to correlation
Percentile Ranking
90%+: Near recent highs - potential selling opportunity
10% and below: Near recent lows - potential buying opportunity
25-75%: Normal trading range
Signal Classifications
🔴 SELL PREMIUM: Asset expensive relative to recent range
🟡 Premium Rich: Moderately expensive, monitor for reversal
⚪ NEUTRAL: Fair value territory
🟡 Discount Opportunity: Moderately cheap, potential accumulation zone
🟢 BUY DISCOUNT: Asset cheap relative to recent range
🚨 Built-in Alerts
Extreme Premium Alert: Triggers when percentile > 95%
Extreme Discount Alert: Triggers when percentile < 5%
⚠️ Important Notes
Works best with highly correlated assets
Historical relationships can change - monitor correlation strength
Not investment advice - use as one factor in your analysis
Backtest thoroughly before implementing any strategy
🔄 Updates & Future Features
This indicator will be continuously improved based on user feedback. So... please give me your feedback!
DTL Daily Trading Levels
A comprehensive trading levels indicator that displays all critical price levels for intraday and swing traders in real-time. This professional-grade tool automatically tracks and plots key support and resistance levels that institutional traders monitor.
Zinc Model [Mr Zinc x MMT]The Zinc Model is a TradingView indicator designed to assist traders by plotting key price levels from two defined trading sessions: the previous day's session (4:00 AM to 8:00 PM) and the current day's London session (4:00 AM to 9:15 AM). It overlays horizontal lines for session highs, lows, and midpoints (EQ levels), along with a vertical anchor line to mark session starts. The indicator is highly customizable, allowing traders to tailor its appearance and focus on specific sessions for strategic analysis.
Features
Session-Based Levels : Tracks and displays high, low, and midpoint (50% EQ) levels for two sessions: the previous day's session and the current day's London session.
Customizable Display : Users can toggle visibility of high, low, EQ levels, and session anchor lines, with options to adjust line styles, colors, and widths.
Session Selection : Configurable session show times (default: 8:00 AM to 4:00 PM in New York time) for displaying levels, with a projection offset to extend lines into future bars.
Labels: Optional labels for each level (High, Low, EQ) with customizable sizes (Tiny, Small, Normal, Large) for clear identification.
Time Zone Support : Anchors sessions to a specified time zone (default: America/New_York).
How It Works
The indicator calculates key price levels based on two user-defined sessions:
- Previous Day Session (4:00 AM–8:00 PM) : Tracks the high, low, and midpoint (50% of the range) of the previous day's session.
- London Session (4:00 AM–9:15 AM) : Tracks the high, low, and midpoint of the current day's London session.
- Levels Displayed :
High/Low Levels : Horizontal lines at the highest and lowest prices of each session.
EQ Level : A horizontal line at the 50% midpoint of the session's range.
Anchor Line : A vertical line marking the start of the user-defined display session.
- Levels are plotted during a user-specified "Show Session" time window (default: 8:00 AM–4:00 PM) and extended forward by a configurable number of bars (default: 15).
- The indicator updates dynamically as new highs or lows occur within the active session.
Usage
- Add to Chart : Apply the indicator to any TradingView chart.
- Configure Settings :
Session Settings : Adjust the "Session Show Time" (default: 8:00 AM–4:00 PM) and time zone to align with your trading strategy.
Projection Offset : Set the number of bars to extend level lines into the future.
Anchor Line : Toggle the vertical line at session start and customize its style, color, and width.
High/Low/EQ Levels : Enable or disable lines and labels for each session's high, low, and midpoint, and customize their appearance.
Label Size : Choose from Tiny, Small, Normal, or Large for level labels.
- Interpret Levels :
High/Low Lines : Act as potential resistance (high) or support (low) levels.
EQ Line : Represents the session's midpoint, often a pivot point for price action.
Anchor Line : Marks the start of the display session for context.
- Trading Application : Use levels to identify support/resistance zones, set entry/exit points, or confirm breakouts during the specified session.
Settings
- Session Settings :
Session Show Time : Defines when levels are displayed (default: 8:00 AM–4:00 PM).
Projection Offset : Extends lines forward (default: 15 bars).
Time Zone : Sets the session time zone (default: America/New_York).
- Anchor Line Settings : Toggle visibility, style (Solid, Dashed, Dotted), color, and width.
- High/Low/EQ Settings : Separate controls for previous day and London sessions to toggle visibility, adjust line styles (Solid, Dashed, Dotted), colors, widths, and label visibility.
- Label Size : Options for Tiny, Small, Normal, or Large to adjust label appearance.
Ideal Use Case
The Zinc Model is ideal for day traders and swing traders focusing on session-based price action, particularly those trading forex, indices, or other markets with significant activity during the London session. It helps identify key support, resistance, and pivot levels for intraday strategies, with flexible settings to suit various timeframes and trading styles.
Price Ranged FVG📌 Price Ranged FVG
Is a clean and efficient tool designed to detect Fair Value Gaps (FVGs) with adjustable filters and structural context. It’s especially useful for traders looking to filter out insignificant gaps and focus on high-probability areas, particularly around swing breaks or structural shifts.
🧠 What is a Fair Value Gap (FVG)?
A Fair Value Gap appears when there’s a price imbalance between candles — typically after a strong move — where the market skips over certain price levels without trading there. These zones can act as potential areas for price to return to (mean reversion), or serve as support/resistance depending on market structure.
🔍 FVG Detection Types
You can choose between three different detection modes under the "FVG Detection" input:
Same Type: Only detects FVGs where the last 3 candles are in the same direction (all bullish or all bearish).
All: Detects any FVG, regardless of candle direction.
Twin Close: Detects FVGs only when the last two candles are in the same direction and close accordingly — offering a stricter confirmation.
🎯 FVG % Filters
To filter out noise or insignificant gaps, this indicator includes:
Minimum FVG % Filter: Ignores FVGs smaller than your specified percentage of the current close.
Maximum FVG % Filter: Ignores overly large gaps that may be unreliable or caused by anomalies.
These filters help focus on relevant FVGs that are more likely to act as reaction zones.
🏛 Structural Context (Swing Highs and Lows)
The indicator plots swing highs and swing lows with dots to provide structure-based context:
Set Swing Strength to 3 for detecting internal structure (shorter-term moves).
Use a higher setting like 5 to focus on external structure (more significant highs/lows).
These levels can help you determine whether an FVG is forming within a consolidation, breakout, or key structural transition.
✅ Use Case (My Personal Workflow)
I personally use this indicator to:
Filter out weak or irrelevant FVGs using the % filters.
Watch for price interaction at swing breaks — especially when an FVG aligns with a break in internal or external structure.
Refine entry and exit planning in confluence with other tools or strategies.
⚠️ Disclaimer
This indicator is not financial advice. It is a technical analysis tool intended to support your own decision-making process. Always do your own research and risk management.
Daily Profiler NFPDaily Profiler NFP
Overview
The Daily Profiler NFP is a comprehensive trading tool designed to track, visualize, and analyze price action during Non-Farm Payroll (NFP) release days. By capturing and displaying high, low, and mid-range levels from these significant market events, traders gain valuable support and resistance reference points that often influence future price movements.
Key Features
Monthly NFP Tracking: Captures and displays the high, low, and mid-range levels for each month's NFP release day
Customizable NFP Dates: Easily set the correct NFP release date for each month of the year
Dynamic Support & Resistance: Identifies the closest NFP levels above and below current price with color-coded boxes
Multi-Timeframe Compatibility: Works seamlessly across intraday, daily, and weekly charts
Comprehensive Visualization Options:
High, low, and mid-range horizontal lines
Price labels with customizable display options
Support and resistance boxes with adjustable opacity and size
NFP range extension boxes showing potential influence zones
Trading Applications
Identify key support and resistance levels based on NFP day price action
Anticipate potential reversal or continuation zones when price approaches historical NFP levels
Develop trading strategies around recurring patterns at NFP price levels
Use as confluence with other technical analysis methods for higher probability trades
Customization
Extensive customization options allow you to:
Adjust color schemes and line styles
Modify box heights and extensions
Show or hide specific elements (high/low lines, midpoint lines, labels, prices)
Set hour offset to match exact NFP release timing
Customize label styles and positions
Perfect for futures, forex, and equity index traders who recognize the significance of NFP releases on market dynamics. The Daily Profiler NFP provides a structured framework for incorporating these major economic events into your technical analysis.
Greer Value Yields Line📈 Greer Value Yields Line – Valuation Signal Without the Clutter
Part of the Greer Financial Toolkit, this streamlined indicator tracks four valuation-based yield metrics and presents them clearly via the Data Window, GVY Score badge, and an optional Yield Table:
Earnings Yield (EPS ÷ Price)
FCF Yield (Free Cash Flow ÷ Price)
Revenue Yield (Revenue per Share ÷ Price)
Book Value Yield (Book Value per Share ÷ Price)
✅ Each yield is compared against its historical average
✅ A point is scored for each metric above average (0–4 total)
✅ Color-coded GVY Score badge highlights valuation strength
✅ Yield trend-lines Totals (TVAVG & TVPCT) help assess direction
✅ Clean layout: no chart clutter – just actionable insights
🧮 GVY Score Color Coding (0–4):
⬜ 0 = None (White)
⬜ 1 = Weak (Gray)
🟦 2 = Neutral (Aqua)
🟩 3 = Strong (Green)
🟨 4 = Gold Exceptional (All metrics above average)
Total Value Average Line Color Coding:
🟥 Red – Average trending down
🟩 Green – Average trending up
Ideal for long-term investors focused on fundamental valuation, not short-term noise.
Enable the table and badge for a compact yield dashboard — or keep it minimal with just the Data Window and trend-lines.
Nasdaq Macro Radar 3.5Nasdaq Macro Radar is an intraday tool that condenses five macro-drivers of the Nasdaq-100 into a single color-coded table:
• real-time moves in the 10- and 2-year Treasury yields
• dollar strength via the Dollar Index
• equity volatility level (VIX)
• risk tone in high-yield credit (HYG ETF)
• dynamic slope of the 2-10-year curve
Each cell flips from neutral to “long” or “short” on the fly, letting you see at a glance whether the macro backdrop is helping trend continuation or signalling a potential reversal.
• No extra pane – the table sits directly on your price chart and can be parked in any corner.
• All sensitivity thresholds are user-adjustable from Settings.
• Built-in alerts for the most critical levels.
Designed for scalpers and day-traders who need an instant macro check without juggling multiple charts
Nasdaq Macro Radar è un indicatore intraday che sintetizza, in un’unica tabella color-code, cinque motori macro-finanziari chiave per il Nasdaq-100:
• movimento dei rendimenti Treasury a 10 a & 2 a
• variazioni del Dollar Index
• livello della volatilità implicita (VIX)
• tono del mercato credito high-yield (ETF HYG)
• pendenza dinamica della curva 2-10 a
Ogni cella passa dal neutro a “long” o “short” in tempo reale, consentendo di valutare a colpo d’occhio se il contesto macro favorisce prosecuzioni o inversioni del trend di prezzo.
• Nessuna finestra separata: la tabella resta sovrapposta al grafico e può essere spostata in qualsiasi angolo.
• Parametri di sensibilità completamente regolabili dal pannello Settings.
• Alert integrati per le soglie critiche più importanti.
Pensato per chi fa scalping o day-trading sul Nasdaq e vuole un check macro immediato senza aprire dieci grafici di supporto.
Greer Book Value Yield📘 Script Title
Greer Book Value Yield – Valuation Insight Based on Balance Sheet Strength
🧾 Description
Greer Book Value Yield is a valuation-focused indicator in the Greer Financial Toolkit, designed to evaluate how much net asset value (book value) a company provides per share relative to its current market price. This script calculates the Book Value Per Share Yield (BV%) using the formula:
Book Value Yield (%) = Book Value Per Share ÷ Stock Price × 100
This yield helps investors assess whether a stock is trading at a discount or premium to its underlying assets. It dynamically highlights when the yield is:
🟢 Above its historical average (potentially undervalued)
🔴 Below its historical average (potentially overvalued)
🔍 Use Case
Analyze valuation through asset-based metrics
Identify buy opportunities when book value yield is historically high
Combine with other scripts in the Greer Financial Toolkit:
📘 Greer Value – Tracks year-over-year growth consistency across six key metrics
📊 Greer Value Yields Dashboard – Visualizes multiple valuation-based yields
🟢 Greer BuyZone – Highlights long-term technical buy zones
🛠️ Inputs & Data
Uses Book Value Per Share (BVPS) from TradingView’s financial database (Fiscal Year)
Calculates and compares against a static average yield to assess historical valuation
Clean visual feedback via dynamic coloring and overlays
⚠️ Disclaimer
This tool is for educational and informational purposes only and should not be considered financial advice. Always conduct your own research before making investment decisions.
Greer EPS Yield📘 Script Title
Greer EPS Yield – Valuation Insight Based on Earnings Productivity
🧾 Description
Greer EPS Yield is a valuation-focused indicator from the Greer Financial Toolkit, designed to evaluate how efficiently a company generates earnings relative to its current stock price. This script calculates the Earnings Per Share Yield (EPS%), using the formula:
EPS Yield (%) = Earnings Per Share ÷ Stock Price × 100
This yield metric provides a quick snapshot of valuation through the lens of profitability per share. It dynamically highlights when the EPS yield is:
🟢 Above its historical average (potentially undervalued)
🔴 Below its historical average (potentially overvalued)
🔍 Use Case
Quickly assess valuation attractiveness based on earnings yield.
Identify potential buy opportunities when EPS% is above its long-term average.
Combine with other indicators in the Greer Financial Toolkit for a fundamentals-driven investment strategy:
📘 Greer Value – Tracks year-over-year growth consistency across six key metrics
📊 Greer Value Yields Dashboard – Visualizes valuation-based yield metrics
🟢 Greer BuyZone – Highlights long-term technical buy zones
🛠️ Inputs & Data
Uses fiscal year EPS data from TradingView’s built-in financial database.
Tracks a static average EPS Yield to compare current valuation to historical norms.
Clean, intuitive visual with automatic color coding.
⚠️ Disclaimer
This tool is for educational and informational purposes only and should not be considered financial advice. Always conduct your own research before making investment decisions.
THE HISTORY By [VXN]
THE HISTORY By - Monthly Seasonal Analysis Indicator
Development Status: This indicator is currently in the development phase and is not yet finished. Features and functionality may change as development continues.
Overview:
This indicator provides comprehensive historical analysis of monthly price patterns, designed to help traders identify recurring seasonal behaviors and market tendencies for the current month across multiple years of data.
Key Features:
Historical Data Analysis:
- Analyzes up to 10 years of historical performance for the current month
- Calculates monthly returns, win rates, and statistical metrics
- Tracks maximum drawdowns and runups for risk assessment
- Requires daily timeframe for accurate monthly calculations
Pattern Recognition:
- Implements a three-period classification system that breaks each month into segments
- Uses visual indicators (🟢🔴🟡) to represent bullish, bearish, and neutral periods
- Helps identify recurring intra-month behavior patterns
Statistical Display:
- Presents historical data in an organized table format
- Shows year-by-year performance comparisons
- Calculates average returns, best/worst performance, and confidence levels
- Displays overall market bias (bullish/bearish tendency) for the current month
Dynamic Zone Overlays:
- Projects Fibonacci-based support/resistance levels based on historical volatility
- Adjusts zone positioning based on the month's historical bias
- Provides visual reference points for potential price targets or reversal areas
Practical Applications:
- Seasonal trading strategy development
- Risk management through historical context
- Understanding market cyclicality and recurring patterns
- Educational tool for studying price behavior over time
Note: This indicator is designed for analysis and education purposes, helping traders understand historical market patterns rather than providing direct trading signals. The data should be used in conjunction with other forms of analysis and proper risk management. As this is still under development, please expect updates and refinements to functionality.
H turnoverTrading Value refers to the total monetary amount of all transactions for a particular stock or the entire market over a specific period. It is calculated by multiplying the trading volume (the number of shares traded) by the price at which they were traded. For example, if 10,000 shares of a stock are traded in a day at an average price of 50,000 KRW, the trading value for that day would be 500,000,000 KRW.
Key points about trading value:
Market Activity and Liquidity: A high trading value indicates an active and liquid market.
Flow of Investment Funds: Increasing trading value suggests more money is flowing into the market or a particular stock.
Relationship with Price Movements: When both trading value and price rise together, it often signals strong buying interest. Conversely, significant price changes with low trading value may be less reliable.
Market Sentiment Indicator: Changes in trading value can reflect shifts in investor interest and sentiment.
In summary, trading value is the total amount of money exchanged in trades and serves as an important indicator of market activity, liquidity, and investor sentiment.
