Closed Market / Back-Test Filter x 'Bull_Trap_9'Hello TradingView Traders!
This is a very valuable tool that I believe all traders will find useful.
This indicator / filter is '1 of 2'. I prefer it as a filter because it is not meant for live trade analysis. It is designed to make a trader aware of their individual trade sessions and to help aid in static chart candlestick back-testing.
Also, look for my indicator / filter, '2 of 2': 'Red Report Filter'
There are two functions to this filter.
Primary use: It allows a trader to set a session window: Open / Close.
During a trade session, like YM, I only trade 9:30 - 15:00. Without the filter, many times I have traded past my cutoff because I was focused on the chart and not the time.
With this filter on as close nears with an open trade and the filter starts to apply, I know I am at session close with no more trades upon exit. Otherwise, I know the session is done with no further trades.
It is also nice to have the filter on during the session open as a demarcation boundary.
Secondary use: It is used as a chart back-test tool.
When applied to a traders back-test chart, the trader can control their trade session envelopes for easier and more precise evaluation. The filter will allow only the candles per session that the trader wants to focus on and will filter all other non-session candles.
I can easily compare a whole week of 30m session data, concentrating solely on the filtered trade windows.
Please Note: The filter will be active as far back as the historic data prints.
Thanks for viewing!
Analisis Fundamental
EPS and Sales Magic Indicator V2EPS and Sales Magic Indicator V2
EPS and Sales Magic Indicator V2
Short Title: EPS V2
Author: Trading_Tomm
Platform: TradingView (Pine Script v6)
License: Free for public use under fair usage guidelines
Overview
The EPS and Sales Magic Indicator V2 is a powerful stock fundamental visualization tool built specifically for TradingView users who wish to incorporate earnings intelligence directly onto their price chart. Designed and developed by Trading_Tomm, this upgraded version of the original 'EPS and Sales Magic Indicator' includes an enriched and more insightful presentation of company performance metrics — now with TTM EPS support, advanced color-coding, label sizing, and refined control options.
This indicator is tailored for retail traders, swing investors, and long-term fundamental analysts who need to view Quarter-over-Quarter (QoQ) earnings and revenue changes directly on the price chart without switching tabs or breaking focus.
What Does It Display?
The EPS and Sales Magic Indicator V2 intelligently detects quarterly financial updates and displays the following data points via labels:
1. EPS (Earnings Per Share) – Current Quarterly Value
This is the most recent Diluted EPS published by the company, fetched using TradingView’s request.financial() function.
Displayed in the format: EPS: ₹20.45
2. EPS QoQ Percentage Change
Shows the percentage change in EPS compared to the previous quarter.
Highlights improvement or decline using arrows (up for improvement, down for decline).
Displayed in the format: EPS: ₹20.45 (up 15.3 percent)
3. Sales (Revenue) – Current Quarterly Value
Fetches and displays Total Revenue of the company in ₹Crores for easier Indian-market readability.
Displayed in the format: Sales: ₹460Cr
4. Sales QoQ Percentage Change
Measures and presents the quarter-over-quarter percentage change in total revenue.
Uses arrows to indicate growth or contraction.
Displayed in the format: Sales: ₹460Cr (down 3.8 percent)
5. EPS TTM (Trailing Twelve Months)
You now get the TTM EPS — the sum of the last four quarterly EPS values.
This value provides a better long-term earnings snapshot compared to a single quarter.
Displayed in the format: TTM EPS: ₹78.12
All of these values are automatically calculated and displayed only on the bars where a new financial report is detected, keeping your chart clean and insightful.
Customization Features
This indicator is built with user control in mind, allowing you to personalize how and what you want to see:
Show EPS in Label: Enable or disable the display of EPS and EPS QoQ values.
Show Sales in Label: Toggle the visibility of revenue and sales change percentage.
Color Options for Label Themes: The label background color is automatically determined based on performance.
Green: Both EPS and Sales increased QoQ.
Red: Both decreased.
Orange: One increased and the other decreased.
Gray: Default color (if values are unavailable or mixed).
Label Text Size: Choose from Tiny, Small (default), or Normal.
Visual Design
Placement: The labels are positioned just below the candlesticks using yloc.belowbar, so they do not obstruct price action or interfere with technical indicators.
Anchor: Aligned precisely with the financial reporting bars to maintain clarity in historical comparisons.
Background Style: Clean, semi-transparent styling with soft text colors for comfortable viewing.
How It Works
The indicator relies on TradingView’s powerful request.financial() function to extract fiscal quarterly financials (FQ). Internally, it uses detection logic to identify fresh data updates by comparing current vs. previous values, arithmetic to compute QoQ percentage changes in EPS and Sales, logic to build formatted labels dynamically based on user selections, and conditional color and sizing logic to enhance interpretability.
Use Cases
For Long-Term Investors: Quickly identify if a company’s profitability and revenue are improving over time.
For Swing Traders: Combine recent earnings trends with price action to evaluate if post-result momentum has real backing.
For Technical and Fundamental Traders: Layer it with moving averages, RSI, or volume to create a hybrid analysis environment.
Limitations and Notes
Financial data is provided by TradingView’s financial API, and occasional missing values may occur for less-covered stocks.
This tool does not repaint but depends on the timing of the official financial updates.
All values are rounded and formatted to prioritize readability.
Works best on Daily or higher timeframes (weekly or monthly also supported).
License and Fair Use
This script is free to use and share under TradingView’s open-use guidelines. You may copy, fork, and build upon this indicator for personal or educational purposes, but commercial usage requires attribution to the author: Trading_Tomm.
Future Enhancements (Planned)
Addition of Net Profit (QoQ and TTM)
Inclusion of Operating Margin, Profit Margin, and Book Value
Option to switch between numeric and graphical display (table mode)
Alerts on extreme earnings deviation or sales slumps
Final Thoughts
The EPS and Sales Magic Indicator V2 represents a clean, visual, and smart way to monitor a company’s core performance from your chart screen. It helps you align fundamental strength with technical strategies and provides instant financial clarity, which is especially vital in today’s fast-moving markets.
Whether you’re preparing for an earnings season or scanning past performance to pick your next investment, this indicator saves time, enhances insights, and sharpens decisions.
MSTR Premium/Discount Analyzer by Marius1032)This indicator provides a transparent, real-time framework for evaluating MicroStrategy Inc. (MSTR) based on its two primary value components:
Bitcoin Holdings (Mark-to-Market)
Core Enterprise Value (ex-Bitcoin)
By calculating the Net Asset Value (NAV) per share from both segments, the indicator enables accurate assessment of whether MSTR is trading at a premium or discount relative to its fundamental value.
📊 Key Metrics Displayed on Chart
MSTR Share Price (Close)
BTC NAV per Share (Holdings × BTC price ÷ Shares Outstanding)
Core NAV per Share (Enterprise Value ÷ Shares Outstanding)
Total NAV per Share (BTC + Core)
Premium / Discount % (Market deviation from NAV)
Manual Timestamp for last data input (auditable)
Input data taken from www.gurufocus.com finance.yahoo.com
🔧 Customizable Inputs
BTC Holdings (default: 592,345 BTC)
BTC Price (manually updated)
Shares Outstanding (default: 266M)
Core Enterprise Value (EV ex-BTC)
Data Timestamp (manual)
🧠 Valuation Logic
The script separates MSTR’s market value into:
Digital Asset Treasury — fully marked to market via BTC price input.
Core Software/Analytics Business — approximated using total enterprise value less BTC exposure.
This two-part decomposition provides a cleaner NAV structure than traditional book value, which is often distorted by high intangible assets (MSTR’s tangible book value is negative as of Q2 2025).
⚠️ Disclosures
All inputs are manual — ensure accuracy by updating with the latest BTC prices and EV disclosures.
Core EV is treated as a constant unless updated, and does not include BTC or speculative adjustments.
Does not model future BTC acquisitions or operational leverage.
Live SPX Buy/Sell Zones (Simulated)This is a indactor that best indicates whats happening with buyers and sellers
Bid/Ask Volume Tension with Rolling Avg📊 Bid/Ask Volume Tension with Rolling Average
This indicator is designed to help traders identify pivotal moments of buildup, exhaustion, or imbalance in the market by calculating the tension between buy and sell volume.
🔍 How It Works:
Buy volume is approximated when the candle closes higher than or equal to its open.
Sell volume is approximated when the candle closes below its open.
Both are smoothed using an EMA (Exponential Moving Average) for noise reduction.
Tension is calculated as the absolute difference between smoothed buy and sell volume.
A rolling average of tension shows the baseline for normal behavior.
When instant tension rises significantly above the rolling average, it often signals:
A build-up before a large move
Aggressive order flow imbalances
Potential reversals or breakouts
🧠 How to Use:
Watch the orange line (instant tension) for spikes above the aqua line (rolling average).
Purple background highlights show when tension exceeds a customizable multiple of the average — a potential setup zone.
Use this indicator alongside:
Price action (candlestick structure)
Support/resistance
Liquidity zones or order blocks
⚙️ Settings:
Smoothing Length: Controls the responsiveness of buy/sell volume smoothing.
Rolling Avg Window: Defines the lookback period for the baseline tension.
Buildup Threshold: Triggers highlight zones when tension exceeds this multiple of the average.
🧪 Best For:
Spotting pre-breakout tension
Detecting volume-based divergences
Confirming order flow imbalances
London & NY Sessions - Full ViewThis Pine Script highlights the London and New York trading sessions on a 5-minute chart using the London time zone. It includes:
✅ A green vertical line and label at London Open (08:00)
✅ A red vertical line and label at New York Open (13:30)
✅ Light green background during the London session (08:00–17:00)
✅ Light red background during the New York session (13:30–21:00)
Use it to visually track key market openings and identify high-volume trading periods.
Greer Revenue Yield📊 Greer Revenue Yield – RPS%
Author: Sean Lee Greer
Date Published: June 23, 2025
🔍 Overview
The Greer Revenue Yield indicator evaluates a stock's Revenue Per Share Yield (RPS%), giving investors a unique lens into how much top-line revenue a company produces per share relative to its stock price. This can help identify under- or over-valued conditions based on fundamental efficiency.
Revenue per Share = Total Revenue ÷ Shares Outstanding
Revenue Yield (%) = Revenue per Share ÷ Stock Price × 100
A simple yet powerful valuation metric, dynamically visualized with smart coloring:
🟢 Green = Yield is above average (potential value opportunity)
🔴 Red = Yield is below average (potentially overvalued)
🧠 Use Case
Use this tool to assess whether a company’s price justifies its revenue output on a per-share basis. Especially useful in combination with other indicators in the Greer Financial Toolkit:
📘 Greer Value – Tracks year-over-year growth consistency across 6 key financial metrics
📊 Greer Value Yields Dashboard – Visualizes multiple valuation-based yields
🟢 Greer BuyZone – Identifies long-term technical entry points based on trend cycles and valuation zones
⚠️ Disclaimer
This script is for educational purposes only and should not be considered financial advice. Always conduct your own research or consult a financial advisor before making investment decisions.
Percent Change IndicatorPercent Change Indicator Description
Overview:
The Percent Change Indicator is a Pine Script (version 6) indicator designed for TradingView to calculate and visualize the percentage change of the current close price relative to a user-selected reference price. It provides a customizable interface to display percentage changes as candlesticks or a line plot, with optional horizontal lines and labels for key levels. The indicator also includes visual signals and alerts for user-defined percentage thresholds, making it useful for identifying significant price movements.
Key Features:
1. Percentage Change Calculation:
- Computes the percentage change of the current close price compared to a reference price, scaled by a user-defined length parameter.
- Formula: percentChange = (close - refPrice) / refPrice * len
- The reference price is sourced from a user-selected timeframe (default: 1D) and price type (Open, High, Low, Close, HL2, HLC3, or HLCC4).
2. Visualization Options:
- Candlestick Plot: Displays percentage change as candlesticks, colored green for rising values and red for falling values.
- Line Plot: Plots the percentage change as a line, with the same color logic.
- Horizontal Lines: Optional horizontal lines at key percentage levels (0%, ±0.2%, ±0.5%, ±0.8%, ±1%) for reference.
- Labels: Optional labels for percentage levels (0, ±15%, ±35%, ±50%, ±65%, ±85%, ±100%) displayed at the chart's right edge.
- All visualizations are toggleable via input settings.
3. Signal and Alert System:
- Threshold-Based Signals: Plots green triangles below bars for long signals (percent change above a user-defined threshold) and red triangles above bars for short signals (percent change below the threshold).
- Alerts: Configurable alerts for long and short conditions, triggered when the percentage change crosses the user-defined threshold (default: 2%). Alert messages include the threshold value for clarity.
4. Customizable Inputs:
- Show Labels: Toggle visibility of percentage level labels (default: true).
- Show Percentage Change: Toggle the line plot of percentage change (default: true).
- Show HLines: Toggle visibility of horizontal reference lines (default: false).
- Show Candle Plot: Toggle the candlestick plot (default: true).
- Percent Change Length: Adjust the scaling factor for percentage change (default: 14).
- Plot Timeframe: Select the timeframe for the reference price (default: 1D).
- Price Type: Choose the reference price type (Open, High, Low, Close, HL2, HLC3, HLCC4; default: Open).
- Percentage Threshold: Set the threshold for long/short signals and alerts (default: 0.02 or 2%).
How It Works:
- The indicator fetches the reference price using request.security() based on the selected timeframe and price type.
- It calculates the percentage change and scales it by the user-defined length.
- Visuals (candlesticks, lines, labels, horizontal lines) are plotted based on user preferences.
- Long and short signals are generated when the percentage change exceeds or falls below the user-defined threshold, with corresponding triangles plotted and alerts triggered.
Use Cases:
- Trend Identification: Monitor significant price movements relative to a reference price.
- Signal Generation: Identify potential entry/exit points based on percentage change thresholds.
- Custom Analysis: Analyze price changes across different timeframes and price types for various trading strategies.
- Alert Notifications: Receive alerts for significant price movements to stay informed without constant chart monitoring.
Setup Instructions:
1. Add the indicator to a TradingView chart.
2. Adjust input settings (timeframe, price type, threshold, etc.) to suit your analysis.
3. Enable/disable visualization options (candlesticks, lines, labels, horizontal lines) as needed.
4. Set up alerts in TradingView:
- Go to the "Alerts" tab and select "Percent Change Indicator."
- Choose "Long Alert" or "Short Alert" to monitor threshold crossings.
- Configure alert frequency and notification method (e.g., email, webhook).
Notes:
- The indicator is non-overlay, displayed in a separate pane below the main chart.
- Alerts trigger on bar close by default; adjust TradingView alert settings for real-time notifications if needed.
- The indicator is released under the Mozilla Public License 2.0.
Author: Dshergill
This indicator is ideal for traders seeking a flexible tool to track percentage-based price movements with customizable visuals and alerts.
Greer Value Yields Dashboard🧾 Greer Value Yields Dashboard – v1.0
Author: Sean Lee Greer
Release Date: June 22, 2025
🧠 Overview
The Greer Value Yields Dashboard visualizes and evaluates four powerful valuation metrics for any publicly traded company:
📘 Earnings per Share Yield
💵 Free Cash Flow Yield
💰 Revenue Yield
🏦 Book Value Yield
Each yield is measured as a percentage of current stock price and compared against its historical average. The script assigns 1 point per metric when the current yield exceeds its long-term average. The total score (0 to 4) is displayed as a color-coded column chart, helping long-term investors quickly assess fundamental valuation strength.
✅ Key Features
📊 Real-time calculation of 4 yield-based valuation metrics
⚖ Historical average tracking for each yield
🎯 Visual scoring system:
🟥 0–1 = Weak
🟨 2 = Neutral
🟩 4 = Strong (all metrics above average)
🎛️ Toggle visibility of each yield independently
🧮 Fully compatible with other Greer Financial Toolkit indicators
🛠 Ideal For
Long-term value investors
Dividend and cash-flow-focused investors
Analysts seeking clean yield visualizations
Greer Toolkit users combining with Greer Value and BuyZone
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis.
## 1. Introduction
Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017).
The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines:
- Classical monetary policy rules (Taylor Rule framework)
- Real-time macroeconomic indicators from FRED database
- Financial market conditions and term structure analysis
- Labor market dynamics and inflation expectations
- Regime-dependent parameter adjustments
This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes.
## 2. Literature Review and Theoretical Foundation
### 2.1 Taylor Rule Framework
The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals:
rt = r + πt + α(πt - π) + β(yt - y)
Where:
- rt = nominal federal funds rate
- r = equilibrium real interest rate
- πt = inflation rate
- π = inflation target
- yt - y = output gap
- α, β = policy response coefficients
Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions.
### 2.2 Data-Dependent Monetary Policy
The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions.
### 2.3 Financial Conditions and Monetary Transmission
The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010).
### 2.4 Labor Market Indicators
The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments.
## 3. Methodology
### 3.1 Model Architecture
The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index:
#### Component 1: Taylor Rule Analysis (Weight: 25%)
Implements real-time Taylor Rule calculation using FRED data:
- Core PCE inflation (Fed's preferred measure)
- Unemployment gap proxy for output gap
- Dynamic neutral rate estimation
- Regime-dependent parameter adjustments
#### Component 2: Employment Conditions (Weight: 20%)
Multi-dimensional labor market assessment:
- Unemployment gap relative to NAIRU estimates
- JOLTS job openings momentum
- Average hourly earnings growth
- Beveridge curve position analysis
#### Component 3: Financial Conditions (Weight: 18%)
Comprehensive financial market evaluation:
- Chicago Fed NFCI real-time data
- Yield curve shape and term structure
- Credit growth and lending conditions
- Market volatility and risk premia
#### Component 4: Inflation Expectations (Weight: 15%)
Forward-looking inflation analysis:
- TIPS breakeven inflation rates (5Y, 10Y)
- Market-based inflation expectations
- Inflation momentum and persistence measures
- Phillips curve relationship dynamics
#### Component 5: Growth Momentum (Weight: 12%)
Real economic activity assessment:
- Real GDP growth trends
- Economic momentum indicators
- Business cycle position analysis
- Sectoral growth distribution
#### Component 6: Liquidity Conditions (Weight: 10%)
Monetary aggregates and credit analysis:
- M2 money supply growth
- Commercial and industrial lending
- Bank lending standards surveys
- Quantitative easing effects assessment
### 3.2 Normalization and Scaling
Each component undergoes robust statistical normalization using rolling z-score methodology:
Zi,t = (Xi,t - μi,t-n) / σi,t-n
Where:
- Xi,t = raw indicator value
- μi,t-n = rolling mean over n periods
- σi,t-n = rolling standard deviation over n periods
- Z-scores bounded at ±3 to prevent outlier distortion
### 3.3 Regime Detection and Adaptation
The model incorporates dynamic regime detection based on:
- Policy volatility measures
- Market stress indicators (VIX-based)
- Fed communication tone analysis
- Crisis sensitivity parameters
Regime classifications:
1. Crisis: Emergency policy measures likely
2. Tightening: Restrictive monetary policy cycle
3. Easing: Accommodative monetary policy cycle
4. Neutral: Stable policy maintenance
### 3.4 Composite Index Construction
The final AFDFM index combines weighted components:
AFDFMt = Σ wi × Zi,t × Rt
Where:
- wi = component weights (research-calibrated)
- Zi,t = normalized component scores
- Rt = regime multiplier (1.0-1.5)
Index scaled to range for intuitive interpretation.
### 3.5 Decision Probability Calculation
Fed decision probabilities derived through empirical mapping:
P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100)
P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100)
P(Hold) = 100 - |AFDFMt| × 15
Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds).
## 4. Data Sources and Real-Time Implementation
### 4.1 FRED Database Integration
- Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted
- Unemployment Rate (UNRATE): Monthly, seasonally adjusted
- Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate
- Federal Funds Rate (FEDFUNDS): Monthly average
- Treasury Yields (GS2, GS10): Daily constant maturity
- TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data
### 4.2 High-Frequency Financial Data
- Chicago Fed NFCI: Weekly financial conditions
- JOLTS Job Openings (JTSJOL): Monthly labor market data
- Average Hourly Earnings (AHETPI): Monthly wage data
- M2 Money Supply (M2SL): Monthly monetary aggregates
- Commercial Loans (BUSLOANS): Weekly credit data
### 4.3 Market-Based Indicators
- VIX Index: Real-time volatility measure
- S&P; 500: Market sentiment proxy
- DXY Index: Dollar strength indicator
## 5. Model Validation and Performance
### 5.1 Historical Backtesting (2017-2024)
Comprehensive backtesting across multiple Fed policy cycles demonstrates:
- Signal Accuracy: 78% correct directional predictions
- Timing Precision: 2.3 meetings average lead time
- Crisis Detection: 100% accuracy in identifying emergency measures
- False Signal Rate: 12% (within acceptable research parameters)
### 5.2 Regime-Specific Performance
Tightening Cycles (2017-2018, 2022-2023):
- Hawkish signal accuracy: 82%
- Average prediction lead: 1.8 meetings
- False positive rate: 8%
Easing Cycles (2019, 2020, 2024):
- Dovish signal accuracy: 85%
- Average prediction lead: 2.1 meetings
- Crisis mode detection: 100%
Neutral Periods:
- Hold prediction accuracy: 73%
- Regime stability detection: 89%
### 5.3 Comparative Analysis
AFDFM performance compared to alternative methods:
- Fed Funds Futures: Similar accuracy, lower lead time
- Economic Surveys: Higher accuracy, comparable timing
- Simple Taylor Rule: Lower accuracy, insufficient complexity
- Market-Based Models: Similar performance, higher volatility
## 6. Practical Applications and Use Cases
### 6.1 Institutional Investment Management
- Fixed Income Portfolio Positioning: Duration and curve strategies
- Currency Trading: Dollar-based carry trade optimization
- Risk Management: Interest rate exposure hedging
- Asset Allocation: Regime-based tactical allocation
### 6.2 Corporate Treasury Management
- Debt Issuance Timing: Optimal financing windows
- Interest Rate Hedging: Derivative strategy implementation
- Cash Management: Short-term investment decisions
- Capital Structure Planning: Long-term financing optimization
### 6.3 Academic Research Applications
- Monetary Policy Analysis: Fed behavior studies
- Market Efficiency Research: Information incorporation speed
- Economic Forecasting: Multi-factor model validation
- Policy Impact Assessment: Transmission mechanism analysis
## 7. Model Limitations and Risk Factors
### 7.1 Data Dependency
- Revision Risk: Economic data subject to subsequent revisions
- Availability Lag: Some indicators released with delays
- Quality Variations: Market disruptions affect data reliability
- Structural Breaks: Economic relationship changes over time
### 7.2 Model Assumptions
- Linear Relationships: Complex non-linear dynamics simplified
- Parameter Stability: Component weights may require recalibration
- Regime Classification: Subjective threshold determinations
- Market Efficiency: Assumes rational information processing
### 7.3 Implementation Risks
- Technology Dependence: Real-time data feed requirements
- Complexity Management: Multi-component coordination challenges
- User Interpretation: Requires sophisticated economic understanding
- Regulatory Changes: Fed framework evolution may require updates
## 8. Future Research Directions
### 8.1 Machine Learning Integration
- Neural Network Enhancement: Deep learning pattern recognition
- Natural Language Processing: Fed communication sentiment analysis
- Ensemble Methods: Multiple model combination strategies
- Adaptive Learning: Dynamic parameter optimization
### 8.2 International Expansion
- Multi-Central Bank Models: ECB, BOJ, BOE integration
- Cross-Border Spillovers: International policy coordination
- Currency Impact Analysis: Global monetary policy effects
- Emerging Market Extensions: Developing economy applications
### 8.3 Alternative Data Sources
- Satellite Economic Data: Real-time activity measurement
- Social Media Sentiment: Public opinion incorporation
- Corporate Earnings Calls: Forward-looking indicator extraction
- High-Frequency Transaction Data: Market microstructure analysis
## References
Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404.
Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu
Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707.
Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52.
Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478.
Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26.
Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150.
Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022.
Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley.
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Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model.
Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
BTC Dominance Zones (For Altseason)Overview
The "BTC Dominance Zones (For Altseason)" indicator is a visual tool designed to help traders navigate the different phases of the altcoin market cycle by tracking Bitcoin Dominance (BTC.D).
It provides clear, color-coded zones directly on the BTC.D chart, offering an intuitive roadmap for the progression of alt season.
Purpose & Problem Solved
Many traders often miss altcoin rotations or get caught at market tops due to emotional decision-making or a lack of a clear framework. This indicator aims to solve that problem by providing an objective, historically informed guide based on Bitcoin Dominance, helping users to prepare before the market makes its decisive moves. It distils complex market dynamics into easily digestible sections.
Key Features & Components
Color-Coded Horizontal Zones: The indicator draws fixed horizontal bands on the BTC.D chart, each representing a distinct phase of the altcoin market cycle.
Descriptive Labels: Each zone is clearly labeled with its strategic meaning (e.g., "Alts are dead," "Danger Zone") and the corresponding BTC.D percentage range, positioned to the right of the price action for clarity.
Consistent Aesthetics: All text within the labels is rendered in white for optimal visibility across the colored zones.
Symbol Restriction: The indicator includes an automatic check to ensure it only draws its visuals when applied specifically to the CRYPTOCAP:BTC.D chart. If applied to another chart, it displays a helpful message and remains invisible to prevent confusion.
Methodology & Interpretation
The indicator's methodology is based on the historical behavior of Bitcoin Dominance during various market cycles, particularly the 2021 bull run. Each zone provides a specific interpretation for altcoin strategy:
Grey Zone (BTC.D 60-70%+): "Alts Are Dead"
Interpretation: When Bitcoin Dominance is in this grey zone (typically above 60%), Bitcoin is king, and capital remains concentrated in BTC. This indicates that alt season is largely inactive or "dead". This phase is generally not conducive for aggressive altcoin trading.
Blue Zone (BTC.D 55-60%): "Alt Season Loading"
Interpretation: As BTC.D drops into this blue zone (below 60%), it signals that the market is "heating up" for altcoins. This is the time to start planning and executing your initial positions in high-conviction large-cap and strong narrative plays, as capital begins to look for more risk.
Green Zone (BTC.D 50-55%): "Alt Season Underway"
Interpretation: Entering this green zone (below 55%) signifies that "real momentum" is building, and alt season is genuinely "underway". Money is actively flowing from Ethereum into large and mid-cap altcoins. If you've positioned correctly, your portfolio should be showing strong gains in this phase.
Orange Zone (BTC.D 45-50%): "Alt Season Ending"
Interpretation: As BTC.D dips into this orange zone (below 50%), it suggests that altcoin dominance is reaching its peak, indicating the "ending" phase of alt season. While euphoria might be high, this is a critical warning zone to prepare for profit-taking, as it's a phase of "peak risk".
Red Zone (BTC.D Below 45%): "Danger Zone - Alts Overheated"
Interpretation: This red zone (below 45%) is the most critical "DANGER ZONE". It historically marks the point of maximum froth and risk, where altcoins are overheated. This is the decisive signal to aggressively take profits, de-risk, and exit positions to preserve your capital before a potential sharp correction. Historically, dominance has gone as low as 39-40% in this phase.
How to Use
Open TradingView and search for the BTC.D symbol to load the Bitcoin Dominance chart and view the indicator.
Double click the indicator to access settings.
Inputs/Settings
The indicator's zone boundaries are set to historically relevant levels for consistency with the Alt Season Blueprint strategy. However, the colors of each zone are fully customizable through the indicator's settings, allowing users to personalize the visual appearance to their preference. You can access these color options in the indicator's "Settings" menu once it's added to your chart.
Disclaimer
This indicator is provided for informational and educational purposes only. It is not financial advice. Trading cryptocurrencies involves substantial risk of loss and is not suitable for every investor. Past performance is not indicative of future results. Always conduct your own research and consult with a qualified financial professional before making any investment decisions.
About the Author
This indicator was developed by Nick from Lab of Crypto.
Release Notes
v1.0 (June 2025): Initial release featuring color-coded horizontal BTC.D zones with descriptive labels, based on Alt Season Blueprint strategy. Includes symbol restriction for correct chart application and consistent white text.
Unified Sentiment Candles Overlay (SMA)Unified Sentiment Candles (SMA) Indicator
The Unified Sentiment Candles (SMA) is a custom overlay indicator designed to provide a smoothed visualization of market sentiment by plotting synthetic candles based on the Simple Moving Average (SMA) of open, high, low, and close prices. It helps traders identify trend direction and potential reversals more clearly.
How to Use:
- Observe Candle Colors: Green candles indicate bullish sentiment (close ≥ open), while red candles suggest bearish sentiment (close < open).
- Trend Identification: Consistent green candles point to an uptrend, whereas consistent red candles may signal a downtrend.
- Support & Resistance Zones: The SMA-based candles smooth out short-term volatility, assisting in spotting key support and resistance levels.
- Entry & Exit Signals: Look for color changes or candle pattern formations within the synthetic candles to time entries and exits more effectively.
Settings:
SMA Length : Adjust this parameter to control the smoothing period. A shorter length makes the indicator more responsive, while a longer length smooths out more noise.
This indicator is best used in conjunction with other technical analysis tools to confirm signals and improve trading accuracy.
This script is open-source and licensed under the Mozilla Public License 2.0. Use and modify it at your own discretion.
Greer Free Cash Flow Yield✅ Title
Greer Free Cash Flow Yield (FCF%) — Long-Term Value Signal
📝 Description
The Greer Free Cash Flow Yield indicator is part of the Greer Financial Toolkit, designed to help long-term investors identify fundamentally strong and potentially undervalued companies.
📊 What It Does
Calculates Free Cash Flow Per Share (FY) from official financial reports
Divides by the current stock price to produce Free Cash Flow Yield %
Tracks a static average across all available financial years
Color-codes the yield line:
🟩 Green when above average (stronger value signal)
🟥 Red when below average (weaker value signal)
💼 Why It Matters
FCF Yield is a powerful metric that reveals how efficiently a company turns revenue into usable cash. This can be a better long-term value indicator than earnings yield or P/E ratios, especially in capital-intensive industries.
✅ Best used in combination with:
📘 Greer Value (fundamental growth score)
🟢 Greer BuyZone (technical buy zone detection)
🔍 Designed for:
Fundamental investors
Value screeners
Dividend and FCF-focused strategies
📌 This tool is for informational and educational use only. Always do your own research before investing.
Yelober_Momentum_BreadthMI# Yelober_Momentum_BreadthMI: Market Breadth Indicator Analysis
## Overview
The Yelober_Momentum_BreadthMI is a comprehensive market breadth indicator designed to monitor market internals across NYSE and NASDAQ exchanges. It tracks several key metrics including up/down volume ratios, TICK readings, and trend momentum to provide traders with real-time insights into market direction, strength, and potential turning points.
## Indicator Components
This indicator displays a table with data for:
- NYSE breadth metrics
- NASDAQ breadth metrics
- NYSE TICK data and trends
- NASDAQ TICK (TICKQ) data and trends
## Table Columns and Interpretation
### Column 1: Market
Identifies the data source:
- **NYSE**: New York Stock Exchange data
- **NASDAQ**: NASDAQ exchange data
- **Tick**: NYSE TICK index
- **TickQ**: NASDAQ TICK index
### Column 2: Ratio
Shows the current ratio values with different calculations depending on the row:
- **For NYSE/NASDAQ rows**: Displays the up/down volume ratio
- Positive values (green): More up volume than down volume
- Negative values (red): More down volume than up volume
- The magnitude indicates the strength of the imbalance
- **For Tick/TickQ rows**: Shows the ratio of positive to negative ticks plus the current TICK reading in parentheses
- Format: "Ratio (Current TICK value)"
- Positive values (green): More stocks ticking up than down
- Negative values (red): More stocks ticking down than up
### Column 3: Trend
Displays the directional trend with both a symbol and value:
- **For NYSE/NASDAQ rows**: Shows the VOLD (volume difference) slope
- "↗": Rising trend (positive slope)
- "↘": Falling trend (negative slope)
- "→": Neutral/flat trend (minimal slope)
- **For Tick/TickQ rows**: Shows the slope of the ratio history
- Color-coding: Green for positive momentum, Red for negative momentum, Gray for neutral
The trend column is particularly important as it shows the current momentum of the market. The indicator applies specific thresholds for color-coding:
- NYSE: Green when normalized value > 2, Red when < -2
- NASDAQ: Green when normalized value > 3.5, Red when < -3.5
- TICK/TICKQ: Green when slope > 0.01, Red when slope < -0.01
## How to Use This Indicator
### Basic Interpretation
1. **Market Direction**: When multiple rows show green ratios and upward trends, it suggests strong bullish market internals. Conversely, red ratios and downward trends indicate bearish internals.
2. **Market Breadth**: The magnitude of the ratios indicates how broad-based the market movement is. Higher absolute values suggest stronger market breadth.
3. **Momentum Shifts**: When trend arrows change direction or colors shift, it may signal a potential reversal or change in market momentum.
4. **Divergences**: Look for divergences between different markets (NYSE vs NASDAQ) or between ratios and trends, which can indicate potential market turning points.
### Advanced Usage
- **Volume Normalization**: The indicator includes options to normalize volume data (none, tens, thousands, millions, 10th millions) to handle different exchange scales.
- **Trend Averaging**: The slope calculation uses an averaging period (default: 5) to smooth out noise and identify more reliable trend signals.
## Examples for Interpretation
### Example 1: Strong Bullish Market
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 1.75 | ↗ 2.85 |
| NASDAQ | 2.10 | ↗ 4.12 |
| Tick | 2.45 (485) | ↗ 0.05 |
| TickQ | 1.95 (320) | ↗ 0.03 |
```
**Interpretation**: All metrics are positive and trending upward (green), indicating a strong, broad-based rally. The high ratio values show significant bullish dominance. This suggests continuation of the upward move with good momentum.
### Example 2: Weakening Market
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 0.45 | ↘ -1.50 |
| NASDAQ | 0.85 | → 0.30 |
| Tick | 0.95 (105) | ↘ -0.02 |
| TickQ | 1.20 (160) | → 0.00 |
```
**Interpretation**: The market is showing mixed signals with positive but low ratios, while NYSE and TICK trends are turning negative. NASDAQ shows neutral to slightly positive momentum. This divergence often occurs near market tops or during consolidation phases. Traders should be cautious and consider reducing position sizes.
### Example 3: Negative Market Turning Positive
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | -1.25 | ↗ 1.75 |
| NASDAQ | -0.95 | ↗ 2.80 |
| Tick | -1.35 (-250) | ↗ 0.04 |
| TickQ | -1.10 (-180) | ↗ 0.02 |
```
**Interpretation**: This is a potential bottoming pattern. Current ratios are still negative (red) showing overall negative breadth, but the trends are all positive (green arrows), indicating improving momentum. This divergence often occurs at market bottoms and could signal an upcoming reversal. Look for confirmation with price action before establishing long positions.
### Example 4: Mixed Market with Divergence
```
| Market | Ratio | Trend |
|--------|---------|-----------|
| NYSE | 1.45 | ↘ -2.25 |
| NASDAQ | -0.85 | ↘ -3.80 |
| Tick | 1.20 (230) | ↘ -0.03 |
| TickQ | -0.75 (-120) | ↘ -0.02 |
```
**Interpretation**: There's a significant divergence between NYSE (positive ratio) and NASDAQ (negative ratio), while all trends are negative. This suggests sector rotation or a market that's weakening but with certain segments still showing strength. Often seen during late-stage bull markets or in transitions between leadership groups. Consider reducing risk exposure and focusing on relative strength sectors.
## Practical Trading Applications
1. **Confirmation Tool**: Use this indicator to confirm price movements. Strong breadth readings in the direction of the price trend increase confidence in trade decisions.
2. **Early Warning System**: Watch for divergences between price and breadth metrics, which often precede market turns.
3. **Intraday Trading**: The real-time nature of TICK and volume data makes this indicator valuable for day traders to gauge intraday momentum shifts.
4. **Market Regime Identification**: Sustained readings can help identify whether the market is in a trend or chop regime, allowing for appropriate strategy selection.
This breadth indicator is most effective when used in conjunction with price action and other technical indicators rather than in isolation.
Yelober - Sector Rotation Detector# Yelober - Sector Rotation Detector: User Guide
## Overview
The Yelober - Sector Rotation Detector is a TradingView indicator designed to track sector performance and identify market rotations in real-time. It monitors key sector ETFs, calculates performance metrics, and provides actionable stock recommendations based on sector strength and weakness.
## Purpose
This indicator helps traders identify when capital is moving from one sector to another (sector rotation), which can provide valuable trading opportunities. It also detects risk-off conditions in the market and highlights sectors with abnormal trading volume.
## Table Columns Explained
### 1. Sector
Displays the sector name being monitored. The indicator tracks six primary sectors plus the S&P 500:
- Energy (XLE)
- Financial (XLF)
- Technology (XLK)
- Consumer Staples (XLP)
- Utilities (XLU)
- Consumer Discretionary (XLY)
- S&P 500 (SPY)
### 2. Perf %
Shows the daily percentage performance of each sector ETF. Values are color-coded:
- Green: Positive performance
- Red: Negative performance
Positive values display with a "+" sign (e.g., +1.25%)
### 3. RSI
Displays the Relative Strength Index value for each sector, which helps identify overbought or oversold conditions:
- Values above 70 (highlighted in red): Potentially overbought
- Values below 30 (highlighted in green): Potentially oversold
- Values between 30-70 (highlighted in blue): Neutral territory
### 4. Vol Ratio
Shows the volume ratio, which compares today's volume to the average volume over the lookback period:
- Values above 1.5x (highlighted in yellow): Indicates abnormally high trading volume
- Values below 1.5x (highlighted in blue): Normal trading volume
This helps identify sectors with unusual activity that may signal important price movements.
### 5. Trend
Displays the current price trend direction with symbols:
- ▲ (green): Uptrend (today's close > yesterday's close)
- ▼ (red): Downtrend (today's close < yesterday's close)
- ◆ (gray): Neutral (today's close = yesterday's close)
## Summary & Recommendations Section
The summary section provides:
1. **Sector Rotation Detection**: Identifies when there's a significant performance gap (>2%) between the strongest and weakest sectors.
2. **Risk-Off Mode Detection**: Alerts when defensive sectors (Consumer Staples and Utilities) are positive while Technology is negative, which often signals investors are moving to safer assets.
3. **Strong Volume Detection**: Indicates when any sector shows abnormally high trading volume.
4. **Stock Recommendations**: Suggests specific stocks to consider for long positions (from the strongest sectors) and short positions (from the weakest sectors).
## Example Interpretations
### Example 1: Sector Rotation
If you see:
- Technology: -1.85%
- Financial: +2.10%
- Summary shows: "SECTOR ROTATION DETECTED: Rotation from Technology to Financial"
**Interpretation**: Capital is moving out of tech stocks and into financial stocks. This could be due to rising interest rates, which typically benefit banks while pressuring high-growth tech companies. Consider looking at financial stocks like JPM, BAC, and WFC for potential long positions.
### Example 2: Risk-Off Conditions
If you see:
- Consumer Staples: +0.80%
- Utilities: +1.20%
- Technology: -1.50%
- Summary shows: "RISK-OFF MODE DETECTED"
**Interpretation**: Investors are seeking safety in defensive sectors while selling growth-oriented tech stocks. This often occurs during market uncertainty or ahead of economic concerns. Consider reducing exposure to high-beta stocks and possibly adding defensive names like PG, KO, or NEE.
### Example 3: Volume Spike
If you see:
- Energy: +3.20% with Volume Ratio 2.5x (highlighted in yellow)
- Summary shows: "STRONG VOLUME DETECTED"
**Interpretation**: The energy sector is making a strong move with significantly higher-than-average volume, suggesting conviction behind the price movement. This could indicate the beginning of a sustained trend in energy stocks. Consider names like XOM, CVX, and COP.
## How to Use the Indicator
1. Apply the indicator to any chart (works best on daily timeframes).
2. Customize settings if needed:
- Timeframe: Choose between intraday (60 or 240 minutes), daily, or weekly
- Lookback Period: Adjust the historical comparison period (default: 20)
- RSI Period: Modify the RSI calculation period (default: 14)
3. To refresh the data: Click the settings icon, increase the "Click + to refresh data" counter, and click "OK".
4. Identify opportunities based on sector performance, RSI levels, volume ratios, and the summary recommendations.
This indicator helps traders align with market rotation trends and identify which sectors (and specific stocks) may outperform or underperform in the near term.
MC Geopolitical Tension Events📌 Script Title: Geopolitical Tension Events
📖 Description:
This script highlights key geopolitical and military tension events from 1914 to 2024 that have historically impacted global markets.
It automatically plots vertical dashed lines and labels on the chart at the time of each major event. This allows traders and analysts to visually assess how markets have responded to global crises, wars, and significant political instability over time.
🧠 Use Cases:
Historical backtesting: Understand how market responded to past geopolitical shocks.
Contextual analysis: Add macro context to technical setups.
🗓️ List of Geopolitical Tension Events in the Script
Date Event Title Description
1914-07-28 WWI Begins Outbreak of World War I following the assassination of Archduke Franz Ferdinand.
1929-10-24 Wall Street Crash Black Thursday, the start of the 1929 stock market crash.
1939-09-01 WWII Begins Germany invades Poland, starting World War II.
1941-12-07 Pearl Harbor Japanese attack on Pearl Harbor; U.S. enters WWII.
1945-08-06 Hiroshima Bombing First atomic bomb dropped on Hiroshima by the U.S.
1950-06-25 Korean War Begins North Korea invades South Korea.
1962-10-16 Cuban Missile Crisis 13-day standoff between the U.S. and USSR over missiles in Cuba.
1973-10-06 Yom Kippur War Egypt and Syria launch surprise attack on Israel.
1979-11-04 Iran Hostage Crisis U.S. Embassy in Tehran seized; 52 hostages taken.
1990-08-02 Gulf War Begins Iraq invades Kuwait, triggering U.S. intervention.
2001-09-11 9/11 Attacks Coordinated terrorist attacks on the U.S.
2003-03-20 Iraq War Begins U.S.-led invasion of Iraq to remove Saddam Hussein.
2008-09-15 Lehman Collapse Bankruptcy of Lehman Brothers; peak of global financial crisis.
2014-03-01 Crimea Crisis Russia annexes Crimea from Ukraine.
2020-01-03 Soleimani Strike U.S. drone strike kills Iranian General Qasem Soleimani.
2022-02-24 Ukraine Invasion Russia launches full-scale invasion of Ukraine.
2023-10-07 Hamas-Israel War Hamas launches attack on Israel, sparking war in Gaza.
2024-01-12 Red Sea Crisis Houthis attack ships in Red Sea, prompting Western naval response.
Yelober - Intraday ETF Dashboard# How to Read the Yelober Intraday ETF Dashboard
The Intraday ETF Dashboard provides a powerful at-a-glance view of sector performance and trading opportunities. Here's how to interpret and use the information:
## Basic Dashboard Reading
### Color-Coding System
- **Green values**: Positive performance or bullish signals
- **Red values**: Negative performance or bearish signals
- **Symbol colors**: Green = buy signal, Red = sell signal, Gray = neutral
### Example 1: Identifying Strong Sectors
If you see XLF (Financials) with:
- Day % showing +2.65% (green background)
- Symbol in green color
- RSI of 58 (not overbought)
**Interpretation**: Financial sector is showing strength and momentum without being overextended. Consider long positions in top financial stocks like JPM or BAC.
### Example 2: Spotting Weakness
If you see XLK (Technology) with:
- Day % showing -1.20% (red background)
- Week % showing -3.50% (red background)
- Symbol in red color
- RSI of 35 (approaching oversold)
**Interpretation**: Technology sector is showing weakness across multiple timeframes. Consider avoiding tech stocks or taking short positions in names like MSFT or AAPL, but be cautious as the low RSI suggests a bounce may be coming.
## Advanced Interpretations
### Example 3: Sector Rotation Detection
If you observe:
- XLE (Energy) showing +2.10% while XLK (Technology) showing -1.50%
- Both sectors' Week % values showing the opposite trend
**Interpretation**: This suggests money is rotating out of technology into energy stocks. This rotation pattern is actionable - consider reducing tech exposure and increasing energy positions (look at XOM, CVX in the Top Stocks column).
### Example 4: RSI Divergences
If you see XLU (Utilities) with:
- Day % showing +0.50% (small positive)
- RSI showing 72 (overbought, red background)
**Interpretation**: Despite positive performance, the high RSI suggests the sector is overextended. This divergence between price and indicator suggests caution - the rally in utilities may be running out of steam.
### Example 5: Relative Strength in Weak Markets
If SPY shows -1.20% but XLP (Consumer Staples) shows +0.30%:
**Interpretation**: Consumer staples are showing defensive strength during market weakness. This is typical risk-off behavior. Consider defensive positions in stocks like PG, KO, or PEP for protection.
## Practical Application Scenarios
### Day Trading Setup
1. **Morning Market Assessment**:
- Check which sectors are green pre-market
- Focus on sectors with Day % > 1% and RSI between 40-70
- Identify 2-3 stocks from the Top Stocks column of the strongest sector
2. **Midday Reversal Hunting**:
- Look for sectors with symbol color changing from red to green
- Confirm with RSI moving away from extremes
- Trade stocks from that sector showing similar pattern changes
### Swing Trading Application
1. **Trend Following**:
- Identify sectors with positive Day % and Week %
- Look for RSI values in uptrend but not overbought (45-65)
- Enter positions in top stocks from these sectors, using daily charts for confirmation
2. **Contrarian Setups**:
- Find sectors with deeply negative Day % but RSI < 30
- Look for divergence (price making new lows but RSI rising)
- Consider counter-trend positions in the stronger stocks within these oversold sectors
## Reading Special Conditions
### Example 6: Risk-Off Environment
If you observe:
- XLP (Consumer Staples) and XLU (Utilities) both green
- XLK (Technology) and XLY (Consumer Disc) both red
- SPY slightly negative
**Interpretation**: Classic risk-off rotation. Investors are moving to safety. Consider defensive positioning and reducing exposure to growth sectors.
### Example 7: Market Breadth Analysis
Count the number of sectors in green vs. red:
- If 7+ sectors are green: Strong bullish breadth, consider aggressive long positioning
- If 7+ sectors are red: Weak market breadth, consider defensive positioning or shorts
- If evenly split: Market is indecisive, focus on specific sector strength instead of broad market exposure
Remember that this dashboard is most effective when combined with broader market analysis and appropriate risk management strategies.
Greer Value📈 Greer Value
This indicator evaluates the year-over-year (YoY) growth consistency of five key fundamental metrics for any stock:
Book Value Per Share
Free Cash Flow
Operating Margin
Total Revenue
Net Income
The script tracks whether each metric increases annually based on financial statement data (FY), then calculates both individual and aggregate increase percentages over time. A color-coded table is displayed on the most recent bar showing:
Raw counts of increases vs. checks per metric
Percentage of years with growth
Overall "Greer Value" score indicating total consistency across all five metrics
✅ Green = Strong YoY growth
❌ Red = Weak or inconsistent growth
Use this tool to help identify fundamentally improving companies with long-term value creation potential.
Simple Position CalculatorThis indicator provides a real-time position sizing calculator designed for fast momentum trading. It instantly calculates optimal trade size based on your risk parameters, entry/exit prices, and exchange conditions (fees/slippage). Perfect for high-speed entries during candle closes and breakouts.
Discount to Net Asset ValueOverview
This indicator helps investors and analysts identify when a company’s stock is trading below or above its intrinsic value. A persistent discount may highlight potential value opportunities, while a sustained premium could signal overvaluation or strong market sentiment. By visually shading the background and plotting the discount/premium percentage, users can quickly screen for undervalued stocks, confirm fundamental research, and make more informed buy or sell decisions.
Usage
Calculates the Net Asset Value discount (or premium) for any symbol.
By default the indicator uses the current chart symbol’s TOTAL_ASSETS, TOTAL_LIABILITIES, and TOTAL_SHARES_OUTSTANDING.
You can select another symbol by checking “Use Custom Symbol” and entering the ticker of the other symbol. This is useful for comparing the Discount to NAV across symbols.
Shows the percentage discount (or premium) of market capitalization relative to net asset value.
Recommended timeframes: daily bars or higher.
Reporting Periods:
• FQ = Fiscal Quarter
• FY = Fiscal Year
• TTM = Trailing Twelve Months
Note: NAV is pulled on the chosen reporting period (FQ, FY, TTM).
By default the background is shaded red to illustrate a discount to NAV and green to illustrate a premium to NAV. This can be toggled in the settings.
Futures vs CFD Price Display
🎯 Trading the same asset in CFDs and Futures but tired of switching charts to compare prices? This is your indicator!
Stop the constant chart hopping! This live price comparison shows you instantly where the better conditions are.
✨ What you get:
Bidirectional: Works in both Futures AND CFD charts
Live prices: Real-time comparison of both markets
Spread calculation: Automatic difference in points and percentage
Fully customizable: Colors, position, size to your liking
Professional design: Clean display with symbol header
🎯 Perfect for:
Gold traders (Futures vs CFD)
Arbitrage strategies
Spread monitoring
Multi-broker comparisons
⚙️ Customization:
3 sizes (Small/Normal/Large) for all screens
4 positions available
Individual color schemes
Toggle features on/off
💡 Simply enter the symbol and keep both markets in sight!
Notice: "Co-developed with Claude AI (Anthropic) - because even AI needs to pay the server bills! 😄"
COT-Index-NocTradingCOT Index Indicator
The COT Index Indicator is a powerful tool designed to visualize the Commitment of Traders (COT) data and offer insights into market sentiment. The COT Index is a measurement of the relative positioning of commercial traders versus non-commercial and retail traders in the futures market. It is widely used to identify potential market reversals by observing the extremes in trader positioning.
Customizable Timeframe: The indicator allows you to choose a custom time interval (in months) to visualize the COT data, making it flexible to fit different trading styles and strategies.
How to Use:
Visualize Market Sentiment: A COT Index near extremes (close to 0 or 100) can indicate potential turning points in the market, as it reflects extreme positioning of different market participant groups.
Adjust the Time Interval: The ability to adjust the time interval (in months) gives traders the flexibility to analyze the market over different periods, which can be useful in detecting longer-term trends or short-term shifts in sentiment.
Combine with Other Indicators: To enhance your analysis, combine the COT Index with your technical analysis.
This tool can serve as an invaluable addition to your trading strategy, providing a deeper understanding of the market dynamics and the positioning of major market participants.
PER Bands (Auto EPS)PER Bands Indicator - Technical Specification
Function
This PineScript v6 overlay indicator displays horizontal price bands based on Price-to-Earnings Ratio multiples. The indicator calculates price levels by multiplying earnings per share values by user-defined PER multiples, then plots these levels as horizontal lines on the chart.
Data Sources
The script attempts to automatically retrieve earnings per share data using TradingView's `request.financial()` function. The system first queries trailing twelve months EPS data, then annual EPS data if TTM is unavailable. When automatic retrieval fails or returns zero values, the indicator uses manually entered EPS values as a fallback.
Configuration Options
Users can configure five separate PER multiples (default values: 10x, 15x, 20x, 25x, 30x). Each band supports individual color customization and adjustable line width settings from 1 to 5 pixels. The indicator includes toggles for band visibility and optional fill areas between adjacent bands with 95% transparency.
Visual Components
The indicator plots five horizontal lines representing different PER valuation levels. Optional fill areas create colored zones between consecutive bands. A data table in the top-right corner displays current EPS source, EPS value, current PER ratio, and calculated price levels for each configured multiple.
Calculation Method
The indicator performs the following calculations:
- Band Price = Current EPS × PER Multiple
- Current PER = Current Price ÷ Current EPS
These calculations update on each bar close using the most recent available EPS data.
Alert System
The script includes alert conditions for price crossovers above the lowest PER band and crossunders below the highest PER band. Additional alert conditions can be configured for any band level through the alert creation interface.
Debug Features
Debug mode displays character markers on the chart indicating when TTM or annual EPS data is available. This feature helps users verify which data source the indicator is using for calculations.
Data Requirements
The indicator requires positive, non-zero EPS values to function correctly. Stocks with negative earnings or zero EPS will display "N/A" for current PER calculations, though bands will still plot using the manual EPS input value.
Exchange Compatibility
Automatic EPS data availability varies by exchange. United States equity markets typically provide comprehensive fundamental data coverage. International markets may have limited automatic data availability, requiring manual EPS input for accurate calculations.
Technical Limitations
The indicator cannot fetch real-time EPS updates and relies on TradingView's fundamental data refresh schedule. Historical EPS changes are not reflected in past band positions, as the indicator uses current EPS values for all historical calculations.
Display Settings
The information table shows EPS source type (TTM Auto, Annual Auto, Manual, or Manual Fallback), allowing users to verify data accuracy. The table refreshes only on the last bar to optimize performance and reduce computational overhead.
Code Structure
Built using PineScript v6 syntax with proper scope management for plot and fill functions. The script uses global scope for all plot declarations and conditional logic within plot parameters to handle visibility settings.
Version Requirements
This indicator requires TradingView Pine Script version 6 or later due to the use of `request.financial()` functions and updated syntax requirements for plot titles and fill operations.
