Macro-Sentiment Index Model (MSIM)Macro-Sentiment Index Model (MSIM) is a comprehensive trading strategy developed to analyze and interpret the broader macroeconomic and market sentiment. The strategy integrates various quantitative signals, including market volatility, trading volume, market breadth, and economic indicators, to assess the prevailing mood in the financial markets. This sentiment analysis is then used to guide trading decisions, helping identify optimal entry and exit points based on underlying market conditions. The model is specifically designed to capture the shifts in investor sentiment, which have been shown to significantly influence market behavior (Fleming et al., 2001).
The MSIM utilizes a multi-faceted approach to measure sentiment. Drawing from the theory that macroeconomic variables can influence financial markets (Stock & Watson, 2002), the strategy incorporates market volatility (VIX), volume measures, and long-term market trends. These indicators help form a robust view of the market’s risk appetite and potential for price movement. For instance, high volatility often signals increased market uncertainty (Bollerslev, 1986), while volume-based indicators provide insights into investor conviction (Chen, 1991).
Additionally, the model incorporates macroeconomic proxies like GDP growth, interest rates, and unemployment data, leveraging the findings of macroeconomic studies that indicate a direct correlation between these factors and market performance (Hamilton, 1994). By normalizing these economic indicators, the model provides a standardized sentiment score that reflects the aggregated impact of these factors on the market’s outlook.
The MSIM aims to exploit market inefficiencies by responding to shifts in sentiment before they manifest in price movements. Studies have shown that sentiment indicators, such as the Advance-Decline Line and the Stock-Bond Ratio, can be predictive of future price movements (Neely, 2010). The model integrates these indicators into a single composite sentiment score, which is then filtered through momentum signals to refine entry points. This approach is grounded in behavioral finance theory, which suggests that investor sentiment plays a crucial role in driving asset prices, sometimes beyond the reach of fundamental data alone (Shiller, 2000).
The strategy is designed to identify long opportunities when sentiment is particularly favorable, with a focus on minimizing risk during adverse conditions. By analyzing market trends alongside macroeconomic signals, the MSIM helps traders stay aligned with the prevailing market forces.
References:
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Chen, S. S. (1991). The determinants of stock market liquidity. Journal of Financial and Quantitative Analysis, 26(3), 283-305.
• Fleming, M. J., Kirby, C. W., & Ostdiek, B. (2001). The economic value of volatility timing. Journal of Financial and Quantitative Analysis, 36(1), 113-134.
• Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
• Neely, C. J. (2010). The behavior of exchange rates: A survey of recent empirical literature. International Finance Discussion Papers, 981.
• Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
• Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
Analisi fondamentale
QoQ Economic & Financial Indicator ChangesA straightforward indicator for analyzing quarter-over-quarter (QoQ) percentage changes in economic and financial data series. Perfect for visualizing dynamic changes in:
Economic Indicators (GDP, House Price Indices, Employment Figures)
Company Financial Metrics (Revenue, EPS, Operating Margins)
Balance Sheet Items (Assets, Liabilities, Equity)
Cash Flow Statement Components
Other Quarterly Economic & Financial Data
Features:
Automatically calculates QoQ percentage changes
Color-coded visualization (green for positive, red for negative changes)
Displays exact percentage values
Includes adjustable scale factor for different data series
Zero line reference for easy trend identification
Earnings Expansion ProjectionThis indicator has no counterpart in the platform and is a professional-grade earnings visualization tool that plots EPS expansion directly on your charts, inspired by institutional-level technical analysis platforms.
The indicator creates a distinctive earnings expansion projection curve that can be a leading indicator of price direction moves.
Key features:
Clean, institutional-style, EPS-expansion projection line overlaid on price action
Visual earnings surprise indicators with beat/miss multipliers
Dashboard for rapid fundamental assessment including the stocks win rate on beatings / missing earnings historically and other fundamental information not readily available on Tradingview
What is it doing?
It collects all earnings results available and will interpolate the numbers so that we see earnings expansion as a curve.
The video below describes usage
Note: Valid on the weekly time-frame only.
Global M2 Index Percentage### **Global M2 Index Percentage**
**Description:**
The **Global M2 Index Percentage** is a custom indicator designed to track and visualize the global money supply (M2) in a normalized percentage format. It aggregates M2 data from major economies (e.g., the US, EU, China, Japan, and the UK) and adjusts for exchange rates to provide a comprehensive view of global liquidity. This indicator helps traders and investors understand the broader macroeconomic environment, identify trends in money supply, and make informed decisions based on global liquidity conditions.
---
### **How It Works:**
1. **Data Aggregation**:
- The indicator collects M2 data from key economies and adjusts it using exchange rates to calculate a global M2 value.
- The formula for global M2 is:
\
2. **Normalization**:
- The global M2 value is normalized into a percentage (0% to 100%) based on its range over a user-defined period (default: 13 weeks).
- The formula for normalization is:
\
3. **Visualization**:
- The indicator plots the M2 Index as a line chart.
- Key reference levels are highlighted:
- **10% (Red Line)**: Oversold level (low liquidity).
- **50% (Black Line)**: Neutral level.
- **80% (Green Line)**: Overbought level (high liquidity).
---
### **How to Use the Indicator:**
#### **1. Understanding the M2 Index:**
- **Below 10%**: Indicates extremely low liquidity, which may signal economic contraction or tight monetary policy.
- **Above 80%**: Indicates high liquidity, which may signal loose monetary policy or potential inflationary pressures.
- **Between 10% and 80%**: Represents a neutral to moderate liquidity environment.
#### **2. Trading Strategies:**
- **Long-Term Investing**:
- Use the M2 Index to assess global liquidity trends.
- **High M2 Index (e.g., >80%)**: Consider investing in risk assets (stocks, commodities) as liquidity supports growth.
- **Low M2 Index (e.g., <10%)**: Shift to defensive assets (bonds, gold) as liquidity tightens.
- **Short-Term Trading**:
- Combine the M2 Index with technical indicators (e.g., RSI, MACD) for timing entries and exits.
- **M2 Index Rising + RSI Oversold**: Potential buying opportunity.
- **M2 Index Falling + RSI Overbought**: Potential selling opportunity.
#### **3. Macroeconomic Analysis**:
- Use the M2 Index to monitor the impact of central bank policies (e.g., quantitative easing, rate hikes).
- Correlate the M2 Index with inflation data (CPI, PPI) to anticipate inflationary or deflationary trends.
---
### **Key Features:**
- **Customizable Timeframe**: Adjust the lookback period (e.g., 13 weeks, 26 weeks) to suit your trading style.
- **Multi-Economy Data**: Aggregates M2 data from the US, EU, China, Japan, and the UK for a global perspective.
- **Normalized Output**: Converts raw M2 data into an easy-to-interpret percentage format.
- **Reference Levels**: Includes key levels (10%, 50%, 80%) for quick analysis.
---
### **Example Use Case:**
- **Scenario**: The M2 Index rises from 49% to 62% over two weeks.
- **Interpretation**: Global liquidity is increasing, potentially due to central bank stimulus.
- **Action**:
- **Long-Term**: Increase exposure to equities and commodities.
- **Short-Term**: Look for buying opportunities in oversold assets (e.g., RSI < 30).
---
### **Why Use the Global M2 Index Percentage?**
- **Macro Insights**: Understand the broader economic environment and its impact on financial markets.
- **Risk Management**: Identify periods of high or low liquidity to adjust your portfolio accordingly.
- **Enhanced Timing**: Combine with technical analysis for better entry and exit points.
---
### **Conclusion:**
The **Global M2 Index Percentage** is a powerful tool for traders and investors seeking to incorporate macroeconomic data into their strategies. By tracking global liquidity trends, this indicator helps you make informed decisions, whether you're trading short-term or planning long-term investments. Add it to your TradingView charts today and gain a deeper understanding of the global money supply!
---
**Disclaimer**: This indicator is for informational purposes only and should not be considered financial advice. Always conduct your own research and consult with a professional before making investment decisions.
Auto Last Earnings AVWAP
This script provides an automated approach to tracking critical post-earnings price levels. You can add it to a chart and then flip through your watchlist to see the anchored AVWAPs without the need to do it manually one by one.
Core Features:
Automatically detects earnings dates and anchors VWAP calculations without manual input
Calculates volume-weighted average price specifically from the last earnings release
Identifies and visualizes significant earnings gaps between reporting periods
Volume-Based Signal Detection:
Monitors VWAP crosses with volume confirmation (requires 1.5x normal volume)
Labels high-volume breakouts with clear directional signals
Uses a 6-bar adaptive volume baseline to filter out noise
Practical Applications:
AVWAP anchored at earnings offers a great price support level that should be considered when deciding to buy/sell the stock. This script eliminates manual VWAP anchoring and reduces chart management time
Key Differentiators:
First note: coding VWAP anchoring in pine is more challenging that one would think. The source code is open to help other users and hopefully inspire different applications.
No need to manually anchor the VWAP
Draws earnings gap from earnings to earnings (if auto mode)
Detects breakouts through the AVWAP line
O'Neil Earnings StabilityO'Neil Earnings Stability Indicator
This indicator implements William O'Neil's earnings stability analysis, a key factor in identifying high-quality growth stocks. It measures both earnings stability (1-99 scale) and growth rate.
Scale Interpretation:
• 1-25: Highly stable earnings (ideal)
• 26-30: Moderately stable
• >30: More cyclical/less dependable
The stability score is calculated by measuring deviations from the earnings trend line, with lower scores indicating more consistent growth. Combined with the annual growth rate (target ≥25%), this helps identify stocks with both steady and strong earnings growth.
Optimal Criteria:
✓ Stability Score < 25
✓ Annual Growth > 25%
This tool helps filter out stocks with erratic earnings patterns and identify those with proven, sustainable growth records. Green label indicates both criteria are met; red indicates one or both criteria failed."
Would you like me to modify any part of this description or add more details about specific aspects of the calculation?
The key concepts in these calculations:
Stability Score (1-99 scale):
Lower score = more stable
Takes average deviation from mean earnings
Uses logarithmic scaling to emphasize smaller deviations
Multiplies by 20 to get into 1-99 range
Score ≤ 25 meets O'Neil's criteria
Growth Rate:
Year-over-year comparison (current quarter vs same quarter last year)
Calculated as percentage change
Growth ≥ 25% meets O'Neil's criteria
O'Neil's Combined Criteria:
Stability Score should be ≤ 25 (indicating stable earnings)
Growth Rate should be ≥ 25% (indicating strong growth)
Both must be met for ideal conditions
Short Int. Tracker W/% inc/decShort Interest Tracker with % Difference - Indicator Explanation
This Short Interest Tracker is designed to monitor short interest data and highlight potential trends based on the relationship between short interest and the stock price. It calculates the percentage difference between the current short interest and the 2-day simple moving average (SMA), helping identify whether short interest is increasing or decreasing over time.
Key Functions:
Short Interest Tracking:
The indicator tracks daily short interest data using FINRA's short volume data for the current stock (e.g., FINRA:SYMBOL_SHORT_VOLUME).
It compares current short interest with a 2-day SMA to assess if short interest is rising or falling. This helps identify whether more traders are betting against the stock or covering their positions.
Visual Display:
The data is displayed in a table on the chart, divided into sections for:
1 Day Short Interest
Last 3 Days Short Interest
Weekly Short Interest
Each section shows the short interest volume, the corresponding SMA, and the percentage difference. It also displays a signal indicating if short interest is increasing, decreasing, or stable.
Signal Interpretation:
A "Stable" signal means that short interest is neither increasing nor decreasing.
An "Increasing" signal indicates that more traders are opening short positions, betting against the stock. OR- averaging their short position. Keep an eye for a price increase and a short int. increase, this could be the perfect storm.
A "Decreasing" signal shows that traders are closing their short positions, indicating reduced bearish sentiment.
Color Customization:
You can customize the colors for each section of the table, including the background and text colors, to make the display more visually appealing and suited to your preference.
Understanding Short Interest & Price Movements:
Short interest can be a powerful indicator of market sentiment. Here's what the data can tell you:
Increasing Short Interest: Typically signals that traders are expecting the stock price to fall. More shares are being borrowed and sold by short sellers, betting against the stock.
Price Rising Despite Increasing Short Interest: If short interest increases but the stock price is rising, it suggests that short sellers are attempting to push the price down but are failing as the market is moving in the opposite direction. This could be due to buying pressure from long investors or positive news.
Short Sellers Getting Trapped: In some cases, short sellers become trapped when the price rises despite their short positions. They may be forced to buy back shares to cover their positions, which further drives the price up. This dynamic can lead to a short squeeze, where the price rises rapidly as more shorts cover.
Potential for a Short Squeeze: When the price rises against increasing short interest, a short squeeze may occur, causing even more shorts to cover their positions, which can send the stock price soaring.
Summary:
This indicator provides insight into short interest trends, helping you identify whether more traders are betting against the stock or if short sellers are being forced to cover their positions. It also provides a visual signal on the chart, so you can quickly spot periods where short interest is increasing or decreasing, and where price movements might signal a potential short squeeze.
POA Volume TrackerCustomizable Volume Tracker Indicator
This custom TradingView indicator tracks and displays volume data across three key market sessions: Pre-Market, Open Market, and After-Hours. It is designed to give traders insights into the volume activity during these distinct trading periods, helping them make more informed decisions.
Key Features:
Pre-Market Volume (PMV): Shows the total volume of trades during the pre-market session.
Open Market Volume (OMV): Displays the total volume during regular market hours.
After-Hours Volume (AHV): Shows the total volume in the after-hours session.
All three values are displayed in an easy-to-read format, and the script includes the following customization options:
Adjustable Label Position: Choose between placing the label Above the current candle or to the Right side of the candle.
Customizable Offsets: Adjust the vertical and horizontal positioning of the label to suit your preferences.
Volume Formatting: The script automatically formats the volume values for easier readability, displaying large volumes with suffixes like "k" for thousands and "M" for millions.
Customization Options:
Text Size: Choose the size of the text used in the label.
Text and Background Colors: Adjust the color of the label text and background to match your chart style.
Positioning: Move the label up or down, and adjust how far to the right (or left) it appears next to the current candle.
Volume Formatting: Automatically formats volume values to be more readable (e.g., 1,250,000 becomes 1.25M).
This indicator is perfect for traders who want to keep track of volume data for different market sessions and adjust how that data is displayed on their charts in a clear and customizable manner.
How to Use:
Add this script to your chart, and choose your desired market session timeframes.
Use the input options to adjust the label’s position and appearance.
View the real-time volume data for Pre-Market, Open Market, and After-Hours, all in one place.
You wanna catch some gainz? pair this with the Market Cap indicator for some solid TA and catch some $ with the big boys.
SPY/TLT Strategy█ STRATEGY OVERVIEW
The "SPY/TLT Strategy" is a trend-following crossover strategy designed to trade the relationship between TLT and its Simple Moving Average (SMA). The default configuration uses TLT (iShares 20+ Year Treasury Bond ETF) with a 20-period SMA, entering long positions on bullish crossovers and exiting on bearish crossunders. **This strategy is NOT optimized and performs best in trending markets.**
█ KEY FEATURES
SMA Crossover System: Uses price/SMA relationship for signal generation (Default: 20-period)
Dynamic Time Window: Configurable backtesting period (Default: 2014-2099)
Equity-Based Position Sizing: Default 100% equity allocation per trade
Real-Time Visual Feedback: Price/SMA plot with trend-state background coloring
Event-Driven Execution: Processes orders at bar close for accurate backtesting
█ SIGNAL GENERATION
1. LONG ENTRY CONDITION
TLT closing price crosses ABOVE SMA
Occurs within specified time window
Generates market order at next bar open
2. EXIT CONDITION
TLT closing price crosses BELOW SMA
Closes all open positions immediately
█ ADDITIONAL SETTINGS
SMA Period: Simple Moving Average length (Default: 20)
Start Time and End Time: The time window for trade execution (Default: 1 Jan 2014 - 1 Jan 2099)
Security Symbol: Ticker for analysis (Default: TLT)
█ PERFORMANCE OVERVIEW
Ideal Market Conditions: Strong trending environments
Potential Drawbacks: Whipsaws in range-bound markets
Backtesting results should be analyzed to optimize the MA Period and EMA Filter settings for specific instruments
Combined RSI with SMA, ADX/DI, and Stochastic IndicatorHow to Use and Apply the Indicator
This indicator combines the **RSI with SMA**, **ADX/DI**, and **Stochastic Oscillator**, providing multiple perspectives for technical analysis. Here's how you can use and apply it effectively:
1. **RSI with SMA (Relative Strength Index with Simple Moving Average)**
- **Purpose**: Measures momentum and identifies overbought or oversold levels.
- **Features**:
- **RSI (14-period)**: Tracks momentum.
- **SMA (20-period)**: Smooths the RSI for trend clarity.
- **Visual Enhancements**:
- Dashed lines at user-defined levels (default: 55 and 45).
- Highlighted zones: Above 55 (green), below 45 (red).
- Fill between RSI and SMA to indicate convergence/divergence.
- **Usage**:
- **Overbought/Oversold**: Look for RSI crossing above 70 (overbought) or below 30 (oversold).
- **Trend Strength**: If RSI stays consistently above 50 (bullish) or below 50 (bearish).
- **Divergence**: When RSI and SMA move apart, consider it a potential signal of trend change.
2. **ADX and DI (Average Directional Index with Directional Indicators)**
- **Purpose**: Measures trend strength and direction.
- **Features**:
- **ADX (14-period)**: Shows trend strength (higher values indicate stronger trends).
- **+DI and -DI**: Represent bullish and bearish directional movements.
- **Color Coding**:
- **Green**: Bullish trend.
- **Red**: Bearish trend.
- **Orange**: Weak/no trend (when ADX is below the "range" level).
- **Dynamic Fill**: Highlights areas based on whether +DI > -DI or vice versa.
- **Usage**:
- **Strong Trend**: When ADX > 30, trend is strong.
- **Bullish/Bearish Bias**: Compare +DI and -DI:
- **+DI > -DI**: Bullish bias.
- **-DI > +DI**: Bearish bias.
- **Caution Zone**: If ADX < 15, avoid trading as the market lacks direction.
### 3. **Stochastic Oscillator**
- **Purpose**: Identifies overbought and oversold conditions.
- **Features**:
- Tracks %K (fast line) and %D (signal line) for crossovers.
- Highlights overbought (>80) and oversold (<20) regions.
- Fills between %K and %D for easy visualization of crossovers.
- **Usage**:
- **Overbought/Oversold**: Look for price reversals when %K crosses %D in these regions.
- **Entry Signals**:
- Buy: %K crosses above %D in the oversold region (<20).
- Sell: %K crosses below %D in the overbought region (>80).
- **Confirm Trends**: Combine with ADX or RSI to validate signals.
General Application:
1. **Setup**: Add this script to your chart in TradingView.
2. **Interpretation**:
- Use **RSI with SMA** to identify momentum and potential trend reversals.
- Confirm trend strength and direction with **ADX/DI**.
- Refine entries/exits with **Stochastic Oscillator**.
3. **Alerts**:
- Enable alerts for buy/sell signals in ADX/DI to avoid missing key moves.
4. **Risk Management**:
- Avoid trading during low ADX periods (<15) as the market lacks direction.
- Combine signals with support/resistance levels or price patterns for better accuracy.
By integrating these indicators, this script allows for a comprehensive market analysis to enhance your decision-making.
TTM FCFF Yield %An indicator that shows the Free Cash Flow yield daily for the underlying ticker. Useful for when you need to screen for ideas, or the news just broke out and you want to make a calculated purchase - rather than buying at whatever price it is at the moment.
Green line tracks daily Free Cash Flow yield to Enterprise Value.
Where Free Cash Flow is defined as = Cashflow from Operations + Depreciation and Amortization (from the income statement) - Capital Expenditure (fixed assets) - Change in Working Capital
And where Enterprise Value is defined as = Market Capitalization + Net Debt
Red line tracks Free Cash Flow of financial year and what FCFF yield does that equate to if the stock current trades at the price right now.
Reminder: When working with international equities. Be mindful of whether they report FQ or FH. For example, France only reports FH, so it's better to use TTM FHFree Cash Flow results. If you didn't toggle FH in the indicator settings, it will automatically set as FQ and it will not show anything.
GLHF
Net Unrealized Profit Loss | JeffreyTimmermansNet Unrealized Profit Loss (NUPL)
The "Net Unrealized Profit Loss" (NUPL) indicator is a highly regarded tool for assessing Bitcoin investor sentiment by analyzing the relationship between Market Value and Realized Value. This Pine Script implementation, developed by Jeffrey Timmermans, includes additional features such as dynamic labels, alerts, and thresholds with color-coded bands, enhancing its usability for traders and analysts.
Core Concepts Behind NUPL
Market Value (MV):
Defined as the current Bitcoin price multiplied by the number of coins in circulation.
Equivalent to market capitalization in traditional finance.
Realized Value (RV):
Calculated by considering the price at which each Bitcoin last moved (e.g., transferred between wallets).
The average price of all these transactions is multiplied by the total coins in circulation.
Net Unrealized Profit Loss (NUPL):
Formula: NUPL = (Market Value − Realized Value) : Market Value × 100
Measures the proportion of paper profits or losses held by investors relative to the market cap.
Significance of NUPL:
Tracks investor sentiment over time.
A high NUPL value indicates that most investors are in profit, often signaling potential market overheating.
A low or negative NUPL suggests pessimism and undervaluation, which may precede market recovery.
How to View the Chart
The NUPL chart uses distinct percentage bands to delineate various market phases. These bands provide context for understanding investor sentiment and market stages:
Extreme Low Values (< 0%): Indicates widespread losses; the market may be near capitulation.
Neutral Value (0%): A balance between profit and loss; often signifies a transition phase.
Slightly High to High Values (> 0% to 50%): Increasing profits suggest growing optimism; early stages of bullish trends.
Extreme High Values (> 75%): Signals overheating; often corresponds to excessive greed, which may precede corrections.
The colored bands visually represent these stages, enabling traders to identify key turning points.
Features of the Script
Querying Data
The indicator uses data from two key sources:
Bitcoin Market Cap (MC1): GLASSNODE:BTC_MARKETCAP
Bitcoin Realized Cap (MCR): COINMETRICS:BTC_MARKETCAPREAL
These values are fetched using the request.security function to ensure daily accuracy, regardless of the chart's timeframe.
Threshold Calculation
The script computes NUPL values dynamically and compares them against historical lows:
Calculated using the ta.lowest function over a 1,000-bar lookback period.
The average of the historical low and the current NUPL value, providing a dynamic baseline.
Value Classification
NUPL is categorized into sentiment levels with corresponding weights:
< Low Threshold: 1 (Extreme Bearish)
Low to 0: 0.75 (Moderate Bearish)
0 to 25: 0.25 (Neutral to Slightly Bullish)
25 to 50: -0.25 (Moderate Bullish)
50 to 75 : -0.75 (Strong Bullish)
> 75: -1 (Extreme Bullish)
Visual Elements
NUPL Line Plot:
The NUPL line is plotted in orange for clear visibility.
Threshold Bands:
Horizontal thresholds ranging from -160 to 160 and are plotted, representing key sentiment levels. Bands are categorized as:
Extreme High/Low Values
Significant High/Low Values
Neutral Values
Fill Colors:
Red Shades (Bearish Sentiment): Above neutral levels.
Green Shades (Bullish Sentiment): Below neutral levels.
The opacity of fills decreases as sentiment moves from extreme to neutral values.
Dynamic Label:
A real-time label displays the current NUPL value and sentiment classification.
Positioned directly on the NUPL line for immediate insight.
Alerts:
The indicator includes two alerts for crossing key thresholds:
NUPL Above 0% Alert: Triggers when NUPL crosses above the neutral value, signaling a shift to positive sentiment.
NUPL Below 0% Alert: Triggers when NUPL crosses below the neutral value, indicating a shift to negative sentiment.
Alerts are configured with alert.freq_once_per_bar to avoid redundancy during intra-bar fluctuations.
Use Cases
Identifying Market Extremes:
Use NUPL levels to pinpoint moments of extreme greed or fear, which often precede market reversals.
Long-Term Strategy:
NUPL trends can assist strategic investors in deciding when to accumulate during pessimistic phases or take profits during euphoria.
Market Sentiment Analysis:
Provides a macro perspective on the prevailing investor sentiment, offering valuable context for trading decisions.
Conclusion
The Net Unrealized Profit Loss (NUPL) indicator combines advanced data processing with intuitive visualization to deliver actionable insights into Bitcoin market sentiment. With its real-time alerts, dynamic labels, and comprehensive banding system, this tool is indispensable for traders and investors seeking to understand and anticipate market movements based on sentiment analysis.
-Jeffrey
Thin Liquidity Zones [PhenLabs]Thin Liquidity Zones with Volume Delta
Our advanced volume analysis tool identifies and visualizes significant liquidity zones using real-time volume delta analysis. This indicator helps traders pinpoint and monitor critical price levels where substantial trading activity occurs, providing precise volume flow measurement through lower timeframe analysis.
The tool works by leveraging the fact that hedge funds, institutions, and other large market participants strategically fill their orders in areas of thin liquidity to minimize slippage and market impact. By detecting these zones, traders gain valuable insights into potential areas of accumulation, distribution, and liquidity traps, allowing for more informed trading decisions.
🔍 Key Features
Real-time volume delta calculation using lower timeframe data
Dynamic zone creation based on volume spikes
Automatic timeframe optimization
Size-filtered zones to avoid noise
Custom delta timeframe scanning
Flexible analysis period selection
📊 Visual Demonstration
💡 How It Works
The indicator continuously scans for high-volume areas where trading activity exceeds the specified threshold (default 6.0x average volume). When detected, it creates zones that display the net volume delta, showing whether buying or selling pressure dominated that price level.
Key zone characteristics:
Size filtering prevents noise from large price swings
Volume delta shows actual buying/selling pressure
Zones automatically expire based on lookback period
Real-time updates as new volume data arrives
⚙️ Settings
Time Settings
Analysis Timeframe: 15M to 1W options
Custom Period: User-defined bar count
Delta Timeframe: Automatic or manual selection
Volume Analysis
Volume Threshold: Minimum spike multiple
Volume MA Length: Averaging period
Maximum Zone Size: Size filter percentage
Display Options
Zone Color: Customizable with transparency
Delta Display: On/Off toggle
Text Position: Left/Center/Right alignment
📌 Tips for Best Results
Adjust volume threshold based on instrument volatility
Monitor zone clusters for potential support/resistance
Consider reducing max zone size in volatile markets
Use in conjunction with price action and other indicators
⚠️ Important Notes
Requires volume data from your data provider
Lower timeframe scanning may impact performance
Maximum 500 zones maintained for optimization
Zone creation is filtered by both volume and size
🔧 Volume Delta Calculation
The indicator uses TradingView’s advanced volume delta calculation, which:
Scans lower timeframe data for precision
Measures actual buying vs selling pressure
Updates in real-time with new data
Provides clear positive/negative flow indication
This tool is ideal for traders focusing on volume analysis and order flow. It helps identify key levels where significant trading activity has occurred and provides insight into the nature of that activity through volume delta analysis.
Note: Performance may vary based on your chart’s timeframe. Adjust settings according to your trading style and the instrument’s characteristics. Past performance is not indicative of future results, DYOR.
Dividend Payout RatioShows the dividend payout ratio for the specific stock.
The dividend payout ratio indicates if the dividend payout is sustainable in the long run. Therefore,
1. A dividend payout under 50% is considered sustainable. The line will be green
2. A dividend payout over 50% and under 100%, the line will be yellow, is considered a stock to investigate since the dividend payout may have to be cut in the future which will cause the stock price to crash.
3. A dividend payout over 100%, the line will be red, is considered unsustainable and the dividend payout will most likely be cut in the near future. It can take up to a few years for the dividend cut to happen if the financials do not improve.
If the dividend payout ratio is over 100%, this means that the company is paying a dividend amount over $1 for every $1 earned. Therefore, the company must borrow money or find another source to pay the dividend amount that the company cannot afford to pay with the current earnings.
Calculating the dividend payout ratio:
Divide dividend paid by earnings after tax and multiplying the result by 100%.
SPDR Relativ Sector MomentumThe SPDR Relativ Sector Momentum Indicator is designed to evaluate the momentum of key U.S. market sectors relative to the broader market, represented by the S&P 500 Index (SPY). This indicator uses momentum-based techniques to assess sector performance and highlight relative strength or weakness over a given period. It leverages rate of change (ROC) as the primary momentum measure and incorporates smoothing via a simple moving average (SMA).
Methodology
This measure is smoothed over a configurable length (default: 20 periods) to filter noise and highlight trends. Sector momentum is computed for 11 key SPDR ETFs:
• XLE: Energy
• XLB: Materials
• XLI: Industrials
• XLY: Consumer Discretionary
• XLP: Consumer Staples
• XLV: Healthcare
• XLF: Financials
• XLK: Technology
• XLC: Communication Services
• XLU: Utilities
• XLRE: Real Estate
Momentum for the SPY is calculated similarly and serves as a benchmark.
Visualization
The indicator displays relative momentum values in a structured table, with high-contrast colors for better readability. The table dynamically updates sector performance, allowing users to easily track which sectors are outperforming or underperforming SPY. Additionally, the relative momentum values are plotted as individual lines around a zero baseline, providing visual confirmation of trends.
Applications
1. Portfolio Allocation: By identifying leading and lagging sectors, investors can allocate resources to sectors with strong momentum and reduce exposure to weaker sectors.
2. Trend Identification: The zero baseline helps users distinguish between sectors with positive and negative relative momentum.
3. Momentum Trading: The indicator aids in trading strategies that capitalize on sector rotations by highlighting momentum shifts.
Theoretical Background
Momentum strategies are grounded in behavioral finance theory and empirical research. They exploit the tendency of securities with strong past performance to continue outperforming in the short term. Jegadeesh and Titman (1993) demonstrated that momentum strategies yield significant returns over intermediate horizons (3-12 months). Applying this framework to sectors enhances the granularity of momentum analysis.
Limitations
While momentum strategies have shown historical efficacy, they are prone to mean reversion during periods of market instability (Barroso & Santa-Clara, 2015). Moreover, sector ETFs may exhibit varying levels of liquidity and sensitivity to macroeconomic factors, affecting signal reliability.
References
1. Jegadeesh, N., & Titman, S. (1993). “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” The Journal of Finance.
2. Barroso, P., & Santa-Clara, P. (2015). “Momentum Has Its Moments.” Journal of Financial Economics.
3. Moskowitz, T. J., & Grinblatt, M. (1999). “Do Industries Explain Momentum?” The Journal of Finance.
This indicator provides a practical tool for evaluating sector-specific momentum dynamics, grounded in robust financial theory. Its modular design allows customization, making it a versatile instrument for momentum-based sector analysis.
Accumulated Funding RateAccumulated Funding Rate
for future contract -ve/+ve funding fees that indicate long and short opening so that price differance between Spot and Future is balance buy exchange funding between long and short holder
-ve rate means Short is high so short holder has to pay fees to Long to correction in Price and vise versa
so over the periode of time accumulated rate its indicates the Bubble which can be explode any time to Liquidation of inbalance Long/Short Ratio some time its take longer period but its indicated bubbles direction
maximum -ve rates indicate Short opened from long period of time so when its liquidate/exit
then price will be correct to its original price that was struck due Short holder over the time and then now market will liquidate/exit those unstable Short like 50X/25X leverage and correct the price
Year-over-Year % Change for PCEPILFEHello, traders!
This indicator is specifically for FRED:PCEPILFE , which is a 'Personal Consumption Expenditures (PCE) Index excluding food and energy.'
What this indicator does is compare the monthly data to that of the same month last year to see how it has changed over the year. This comparison method is widely known as YoY(Year-over-Year).
While I made this indicator to use for FRED:PCEPILFE , you may use it for different charts as long as they show monthly data.
FRED:PCEPILFE is one of the main measures of inflation the Federal Reserve uses.
You can see the YoY % change of the PCE Index excluding food and energy in the official website for the Bureau of Labor Statistics, but unfortunately, I couldn't find one in TradingView.
So instead, I decided to make my own indicator showing the changes using FRED:PCEPILFE .
The code is very simple: it compares the data to the data 12 points ago because 12 points would mean 12 months in this chart. We then multiply the result by 100 for percentage.
Doing so, we compare the current month to the same month of the previous year.
Because I am only interested in the YoY % Change of the index, I pulled the indicator all the way up, covering the original chart data entirely. (Or you could achieve the same by simply moving your indicator to the pane above. But this way, the original chart data is also visible.)
I hope this indicator helps you with your analysis. Feel free to ask questions if have any!
God bless!
US Treasury Yields ROC1. Motivation and Context
The yield curve, which represents the relationship between bond yields and their maturities, plays a pivotal role in macroeconomic analysis and market forecasting. Changes in the slope or curvature of the yield curve are often indicative of investor expectations about economic growth, inflation, and monetary policy. For example:
• Steepening curves may indicate economic optimism and rising inflation expectations.
• Flattening curves are often associated with slower growth or impending recessions.
Analyzing these dynamics with quantitative tools such as the rate of change (ROC) enables traders and analysts to identify actionable patterns in the market. As highlighted by Gürkaynak, Sack, and Wright (2007), the term structure of interest rates embeds significant economic information, and understanding its movements is crucial for both policy makers and market participants.
2. Methodology
2.1 Input Parameters
The script takes the following key input:
• ROC Period (roc_length): Determines the number of bars over which the rate of change is calculated. This is an adjustable parameter (14 by default), allowing users to adapt the analysis to different timeframes.
2.2 Data Sources
The yields of the US Treasury securities for different maturities are fetched from TradingView using the request.security() function:
• 2-Year Yield (TVC:US02Y)
• 5-Year Yield (TVC:US05Y)
• 10-Year Yield (TVC:US10Y)
• 30-Year Yield (TVC:US30Y)
These yields are central to identifying trends in short-term versus long-term rates.
2.3 Visualization
Plots: The ROC values for each maturity are plotted in distinct colors for clarity:
• 2Y: Blue
• 5Y: Yellow
• 10Y: Green
• 30Y: Red
Background Highlight: The script uses color-coded backgrounds to visualize the identified curve regimes:
• Bull Steepener: Neon Green
• Bear Steepener: Bright Red
• Bull Flattener: Blue
• Bear Flattener: Orange
2.4 Zero Line
A horizontal zero line is included as a reference point, allowing users to easily identify transitions from negative to positive ROC values, which may signal shifts in the yield curve dynamics.
3. Implications for Financial Analysis
By automating the identification of yield curve dynamics, this script aids in:
• Macroeconomic Forecasting:
Steepeners and flatteners are associated with growth expectations and monetary policy changes. For instance, Bernanke and Blinder (1992) emphasize the predictive power of the yield curve for future economic activity.
• Trading Strategies:
Yield curve steepening or flattening can inform bond market strategies, such as long/short duration trades or curve positioning.
4. References
1. Bernanke, B. S., & Blinder, A. S. (1992). “The Federal Funds Rate and the Channels of Monetary Transmission.” American Economic Review, 82(4), 901–921.
2. Gürkaynak, R. S., Sack, B., & Wright, J. H. (2007). “The U.S. Treasury Yield Curve: 1961 to the Present.” Journal of Monetary Economics, 54(8), 2291–2304.
3. TradingView Documentation. “request.security Function.” Retrieved from TradingView.
Thrax - Pullback based short side scalping⯁ This indicator is built for short trades only.
⤞ Pullback based scalping is a strategy where a trader anticipates a pullback and makes a quick scalp in this pullback. This strategy usually works in a ranging market as probability of pullbacks occurrence in ranging market is quite high.
⤞ The strategy is built by first determining a possible candidate price levels having high chance of pullbacks. This is determined by finding out multiple rejection point and creating a zone around this price. A rejection is considered to be valid only if it comes to this zone after going down by a minimum pullback percentage. Once the price has gone down by this minimum pullback percentage multiple times and reaches the zone again chances of pullback goes high and an indication on chart for the same is given.
⯁ Inputs
⤞ Zone-Top : This input parameter determines the upper range for the price zone.
⤞ Zone bottom : This input parameter determines the lower range for price zone.
⤞ Minimum Pullback : This input parameter determines the minimum pullback percentage required for valid rejection. Below is the recommended settings
⤞ Lookback : lookback period before resetting all the variables
⬦Below is the recommended settings across timeframes
⤞ 15-min : lookback – 24, Pullback – 2, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ 5-min : lookback – 50, pullback – 1% - 1.5%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ 1-min : lookback – 100, pullback – 1%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
⤞ Anything > 30-min : lookback – 11, pullback – 3%, Zone Top Size %– 0.4, Zone Bottom Size % – 0.2
✵ This indicator gives early pullback detection which can be used in below ways
1. To take short trades in the pullback.
2. To use this to exit an existing position in the next few candles as pullback may be incoming.
📌 Kindly note, it’s not necessary that pullback will happen at the exact point given on the chart. Instead, the indictor gives you early signals for the pullback
⯁ Trade Steup
1. Wait for pullback signal to occur on the chart.
2. Once the pullback warning has been displayed on the chart, you can either straight away enter the short position or wait for next 2-4 candles for initial sign of actual pullback to occurrence.
3. Once you have initiated short trade, since this is pullback-based strategy, a quick scalp should be made and closed as price may resume it’s original direction. If you have risk appetite you can stay in the trade longer and trial the stops if price keeps pulling back.
4. You can zone top as your stop, usually zone top + some% should be used as stop where ‘some %’ is based on your risk appetite.
5. It’s important to note that this indicator gives early sings of pullback so you may actually wait for 2-3 candles post ‘Pullback warning’ occurs on the chart before entering short trade.
BTC Mercenary ModelBitcoin Market Cycle Evaluation Using Subjective Z-Scores
Introduction:
I've crafted a unique indicator for Bitcoin that synthesizes multiple market indicators into a single, actionable Z-score, aiming to offer insights into the current market cycle phase. Here's the methodology:
Methodology:
Alpha Validation: Each component indicator has been tested for its predictive power (alpha) against Bitcoin's market cycle peaks and troughs from at least the last two cycles. This ensures each indicator contributes meaningfully to our model.
Z-Score Synthesis: By converting each indicator's value into a Z-score, we normalize their contributions. The average of these Z-scores provides a refined signal, indicating whether Bitcoin is in an overbought or oversold state relative to historical norms.
Features:
Individual Indicator Customization: Users can tweak inputs to optimize each indicator's alpha, enhancing the model's predictive accuracy.
Historical Averages: The script provides visibility into how both technical and fundamental indicators have scored in the past, offering a benchmark for current conditions.
ROC Flexibility: Adjust the Rate of Change (ROC) period to suit your analysis timeframe, allowing for more personalized market cycle interpretation.
Indicators Integrated:
Fundamental:
MVRV (Market Value to Realized Value) - Measures market sentiment vs. actual value.
Bitcoin Thermocap - Relates Bitcoin's market cap to its transaction volume.
NUPL (Net Unrealized Profit/Loss) - Indicates holder's profit or loss status.
CVDD (Coin Days Destroyed) - Shows the movement of long-held coins.
SOPR (Spent Output Profit Ratio) - Highlights whether coins are being spent at a profit or loss.
Technical:
RSI (Relative Strength Index) - Identifies overbought/oversold conditions.
CCI (Commodity Channel Index) - Detects cyclical turns in Bitcoin's price.
Multiple Moving Averages - For trend analysis over various time frames.
Sharpe Ratio - Evaluates risk-adjusted return.
Pi Cycle Indicator - Predicts cycle tops based on moving average crossovers.
Hodrick-Prescott Filter - Separates trend from cycle in price data.
VWAP (Volume Weighted Average Price) - Provides a trading benchmark.
How It Works Together:
This model uses a weighted average of Z-scores from these indicators to give a comprehensive view of Bitcoin's market cycle. The Z-scores are not just summed but considered in context; for example, when fundamental indicators like MVRV suggest an overvaluation while technical ones like RSI indicate a near-term correction, the model's output reflects this nuanced interaction.
Future Developments:
The next step is to include sentiment analysis, potentially from social media or news sentiment, to further refine our cycle predictions.
Chart Example:
Symbol/Timeframe: BTCUSD on a daily chart.
Script Name: Bitcoin Cycle Z-Score Evaluator
Feedback Encouraged:
I'm eager to receive feedback on how this model could be further tailored or expanded for better market insights.
-CM
Spent Output Profit Ratio | JeffreyTimmermansSOPR
The "Spent Output Profit Ratio" , aka SOPR indicator is a valuable tool designed to analyze the profitability of spent Bitcoin outputs. SOPR is derived by dividing the selling price of Bitcoin by its purchase price, offering insights into market participants' profit-taking or loss-cutting behavior.
This script features two selectable SOPR metrics:
SOPR 30D: A 30-day Exponential Moving Average (EMA) for short-term trend analysis.
SOPR 365D: A 365-day EMA for assessing long-term profitability trends.
How It Works
Key Levels: The horizontal reference line at 1.0 acts as a critical threshold:
Above 1.0: Market participants are generally in profit, indicating bullish sentiment.
Below 1.0: Market participants are selling at a loss, often signaling bearish sentiment.
Background Colors
Green: Indicates bullish conditions when the selected SOPR value is above 1.
Red: Highlights bearish conditions when the value is below 1.
Dynamic Selection
Easily switch between SOPR 30D and SOPR 365D in the settings for tailored analysis.
Features
Customizable SOPR Selection: Toggle between 30-day and 365-day SOPR views based on your trading preferences.
Dynamic Label: A floating label displays the current SOPR value in real-time, along with the selected SOPR metric for easy monitoring.
Background Highlights: Visual cues for bullish and bearish conditions simplify chart interpretation.
Real-Time Alerts
Bullish Alerts: Triggered when the selected SOPR crosses above 1.
Bearish Alerts: Triggered when the selected SOPR crosses below 1.
Clean Visualization
The indicator includes a horizontal reference line and clear color schemes for easy trend identification.
The SOPR Indicator is an essential tool for traders and analysts seeking to understand Bitcoin market sentiment and profitability trends. Whether used for short-term trades or long-term market analysis, this script provides actionable insights to refine your decision-making process.
-Jeffrey
[EAK]Median Growth% Hist. Vs Estimate=Indicator " Growth rate (Hist.VS Est.)" ซึ่งใช้แสดงอัตราการเติบโตของของบริษัท โดยเปรียบเทียบระหว่างข้อมูลในอดีต (Historical) กับการประมาณการ (Estimate) การตั้งค่าต่างๆ มีดังนี้
Inputs (ข้อมูลนำเข้า) : ส่วนนี้ใช้กำหนดรายละเอียดของข้อมูลที่นำมาคำนวณ
-Period (ช่วงเวลา): เลือกช่วงเวลาที่ต้องการวิเคราะห์ เช่น TTM (Trailing Twelve Months) คือ 12 เดือนล่าสุด หรือ FY ที่ใช้ข้อมูลของปีล่าสุด
-Show EPS Growth% (แสดง % การเติบโตทบต้นของ EPS): เลือกให้แสดงเปอร์เซ็นต์การเติบโตเฉลี่ยทบต้นของ EPS
-Show Median Growth% (แสดง % การเติบโตมัธยฐาน): เลือกให้แสดงมัธยฐานของการเติบโตเฉลี่ยทบต้น
-Show EPS Estimate by TradingView (แสดงการประมาณการ EPS โดย TradingView): แสดงข้อมูลการประมาณการ EPS จาก TradingView(เป็นการเรียกข้อมูลล่าสุดที่มีในเทรดดิ้งวิว ดังนั้นโปรดตรวจสอบ ว่าเป็นข้อมูลของปีไหน)
-Position (ตำแหน่ง): กำหนดตำแหน่งการแสดงผลของ Indicator บนหน้าจอ เช่น middle_rigth... (กลาง_ขวา)
-Text Size (ขนาดตัวอักษร): กำหนดขนาดตัวอักษรของข้อมูลที่แสดง เช่น normal (ปกติ)
โดยสรุป Indicator นี้ช่วยให้เห็นภาพรวมของการเติบโตของกำไรบริษัท โดยเปรียบเทียบอดีตกับการคาดการณ์ ทำให้วิเคราะห์แนวโน้มและตัดสินใจลงทุนได้ง่ายขึ้น และการตั้งค่าต่างๆ ช่วยให้ปรับแต่งการแสดงผลให้เหมาะสมกับการใช้งานของผู้ใช้
หมายเหตุ
EPSm=EPS CAGR(Median)
REVm=Revenue CAGR(Median)
OPm=Operating income CAGR(Median)
DIVm=Dividend CAGR(Median)
EQm=Total Equity CAGR(Median)
ASm=Total Asset CAGR(Median)
***Indicatorนี้ ออกแบบมาใช้กับ กราฟDay1 เท่านั้น โดยใช้การเรียกข้อมูลด้วยการนับBarย้อนหลัง อาจมีการผิดพลาดได้บ้าง ดังนั้นหากต้องการความแม่นยำที่สูงโปรดตรวจสอบข้อมูลกับแหล่งข้อมูลที่น่าเชื่อถืออีกครั้ง***
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Indicator " Growth rate (Hist.VS Est.)" is designed to display a company's growth rate by comparing historical data with estimates. The configuration options are as follows:
Inputs:
Period: Select the analysis period, such as TTM (Trailing Twelve Months), representing the last 12 months, or FY, which uses last full year data.
Show EPS Growth%: Enables the display of the compound annual growth rate (CAGR) of EPS.
Show Median Growth%: Enables the display of the median of the compound annual growth rate.
Show EPS Estimate by TradingView: Displays EPS estimate data from TradingView. (This retrieves the latest data available in TradingView; therefore, please verify the corresponding fiscal year.)
Position: Sets the indicator's display position on the chart, such as middle_right.
Text Size: Sets the text size of the displayed information, such as normal.
In summary, this indicator provides an overview of a company's earnings growth by comparing historical performance with forecasts, facilitating trend analysis and investment decisions. The various settings allow users to customize the display for their specific needs.
Note:
EPSm = EPS CAGR (Median)
REVm = Revenue CAGR (Median)
OPm = Operating income CAGR (Median)
DIVm = Dividend CAGR (Median)
EQm = Total Equity CAGR (Median)
ASm = Total Asset CAGR (Median)
***This indicator is designed for use on daily (D1) charts only. As it retrieves data by counting back bars, some inaccuracies may occur. For accuracy, please cross-reference the information with reliable sources.
Correlation Coefficient Master TableThe Correlation Coefficient Master Table is a comprehensive tool designed to calculate and visualize the correlation coefficient between a selected base asset and multiple other assets over various time periods. It provides traders and analysts with a clear understanding of the relationships between assets, enabling them to analyze trends, diversification opportunities, and market dynamics. You can define key parameters such as the base asset’s data source (e.g., close price), the assets to compare against (up to six symbols), and multiple lookback periods for granular analysis.
The indicator calculates the covariance and normalizes it by the product of the standard deviations. The correlation coefficient ranges from -1 to +1, with +1 indicating a perfect positive relationship, -1 a perfect negative relationship, and 0 no relationship.
You can specify the lookback periods (e.g., 15, 30, 90, or 120 bars) to tailor the calculation to their analysis needs. The results are visualized as both a line plot and a table. The line plot shows the correlation over the primary lookback period (the Chart Length), which can be used to inspect a certain length close up, or could be used in conjunction with the table to provide you with five lookback periods at once for the same base asset. The dynamically created table provides a detailed breakdown of correlation values for up to six target assets across the four user-defined lengths. The table’s cells are formatted with rounded values and color-coded for easy interpretation.
This indicator is ideal for traders, portfolio managers, and market researchers who need an in-depth understanding of asset interdependencies. By providing both the numerical correlation coefficients and their visual representation, users can easily identify patterns, assess diversification strategies, and monitor correlations across multiple timeframes, making it a valuable tool for decision-making.
