GEX Options Flow Pro 100% free
INTRODUCTION
This script is designed to visualize advanced options-derived metrics and levels on TradingView charts, including Gamma Exposure (GEX) walls, gamma flip points, vanna levels, delta-neutral prices (DEX), max pain, implied moves, and more. It overlays dynamic lines, labels, boxes, and an info table to highlight potential support, resistance, volatility regimes, and flow dynamics based on options data.
These visualizations aim to help users understand how options market structure might influence price action, such as areas of potential stability (positive GEX) or volatility (negative GEX). All data is user-provided via pasted strings, as Pine Script cannot fetch external options data directly due to platform limitations (detailed below).
The script is open-source under TradingView's terms, allowing study, modification, and improvement. It draws inspiration from standard options Greeks and exposure metrics (e.g., gamma, vanna, charm) discussed in financial literature like Black-Scholes models and dealer positioning analyses. No external code is copied; all logic is original or based on mathematical formulas.
Disclaimer: This is an educational tool only. It does not provide investment advice, trading signals, or guarantees of performance. Past data is not indicative of future results. Use at your own risk, and combine with your own analysis. Not intended for qualified investors only.
How the Options Levels Are Calculated
Levels are not computed in Pine Script—they rely on pre-calculated values from external tools (e.g., Python scripts using libraries like yfinance for options chains). Here's how they're typically derived externally before pasting into the script:
Fetching Options Data: Retrieve options chain for a ticker: strikes, open interest (OI), volume, implied volatility (IV), expirations (e.g., shortest: 0-7 DTE, short: 7-14 DTE, medium: ~30 DTE, long: ~90 DTE). Get current price and 5-day history for context.
Gamma Walls (Put/Call Walls): Compute gamma for each option using Black-Scholes: gamma = N'(d1) / (S * σ * √T) where S = spot price, K = strike, T = time to expiration (years), σ = IV, N'(d1) = normal PDF. Aggregate GEX at strikes: GEX = sign * gamma * OI * 100 * S^2 * 0.01 (per 1% move, with sign based on dealer positioning: typically short calls/puts = negative GEX). Put Wall: Highest absolute GEX put strike below S (support via dealer buying on dips). Call Wall: Highest absolute GEX call strike above S (resistance via dealer selling on rallies). Secondary/Tertiary: Next highest levels. Historical walls track tier-1 levels over 5 days.
Gamma Flip: Net GEX profile across prices: Sum GEX for all options at hypothetical spots. Flip point: Interpolated price where net GEX changes sign (stable above, volatile below).
Vanna Levels: Vanna = -N'(d1) * d2 / σ. Weighted by OI; highest positive/negative strikes.
DEX (Delta-Neutral Price): Net dealer delta: Sum (delta * OI * 100 * sign), with delta from Black-Scholes. DEX: Price where net delta = 0 (interpolated).
Max Pain: Strike minimizing total intrinsic value for all options holders.
Skew: 25-delta skew: IV difference between 25-delta put and call (interpolated).
Net GEX/Delta: Total signed GEX/delta at current S.
Implied Move: ATM IV * √(DTE/365) for 1σ range.
C/P Ratio: (Call OI + volume) / (Put OI + volume).
Smart Stop Loss: Below lowest support (e.g., Put Wall, gamma flip), buffered by IV * √(DTE/30).
Other Metrics: IV: ATM average. 5-day metrics: Avg volume, high/low.
External tools handle dealer assumptions (e.g., short calls/puts) and scaling (per % move).
Effect as Support and Resistance in Technical Trading
Options levels reflect dealer hedging dynamics:
Put Wall (Gamma Support): High put GEX creates buying pressure on dips (dealers hedge short puts by buying stock). Use for long entries, bounces, or stops below.
Call Wall (Gamma Resistance): High call GEX leads to selling on rallies. Good for trims, shorts, or reversals.
Gamma Flip: Pivot for volatility—above: dampened moves (positive GEX, mean reversion); below: amplified trends (negative GEX, momentum).
Vanna Levels: Sensitivity to IV changes; crosses may signal vol shifts.
DEX: Dealer delta neutral—bullish if price below with positive delta.
Max Pain: Price magnet minimizing option payouts.
Implied Move/Confidence Bands: Expected ranges (1σ/2σ/3σ); breakouts suggest extremes.
Liquidity Zones: Wall ranges as price magnets.
Smart Stop Loss: Protective level below supports, IV-adjusted.
C/P Ratio & Skew: Sentiment (high C/P = bullish; high skew = put demand).
Net GEX: Positive = low vol strategies (e.g., condors); negative = momentum trades.
Combine with TA (e.g., volume, trends). High activity strengthens effects; alerts on crosses/proximities for awareness.
Limitations of the TradingView Platform for Data Pulling
Pine Script is sandboxed:
No API calls or internet access (can't fetch options data directly).
Limited to chart/symbol data; no real-time chains.
Inputs static per load; manual updates needed.
Caching not persistent across sessions.
This ensures lightweight scripts but requires external data sourcing.
Creative Solution for On-Demand Data Pulling
Users can use external tools (e.g., Python scripts with yfinance) to fetch/compute data on demand. Generate a formatted string (ticker,timestamp|term1_data|term2_data|...), paste into inputs. Tools can process multiple tickers, cache for ~15-30 min, and output strings for quick portfolio scanning. Run locally or via custom setups for near-real-time updates without platform violations.
For convenience, a free bot is available on my website that accepts commands like !gex to generate both current data strings (for all expiration terms) and historical walls data on demand. This allows users to easily obtain fresh or cached data (refreshed every ~30 min) for pasting into the indicator—ideal for scanning portfolios without manual coding.
Script Functionality Breakdown
Inputs: Data strings (current/historical); term selector (Shortest/Short/Medium/Long); toggles (historical walls, GEX profile, secondaries, vanna, table, max pain, DEX, stop loss, implied move, liquidity, bands); colors/styles.
Parsing: Extracts term-specific data; validates ticker match; gets timestamp for freshness.
Drawing: Dynamic lines/labels (width/color by GEX strength); boxes (moves, zones, bands); clears on updates.
Info Table: Dashboard with status (freshness emoji), Greeks (GEX/delta with emojis), vol (IV/skew), levels (distances), flow (C/P, vol vs 5D).
Historical Walls: Displays past tier-1 walls on daily+ timeframes.
Alerts: 20+ conditions (e.g., near/cross walls, GEX sign change, high IV).
Performance: Efficient for real-time; smart label positioning.
Release Notes
Initial release: Full features including multi-term support, enhanced table with emojis/sentiment, dynamic visuals, smart stop loss.
Data String Format: TICKER,TIMESTAMP|TERM1_DATA|TERM2_DATA|TERM3_DATA|TERM4_DATA Where each TERM_DATA = val0,val1,...,val30 (31 floats: current_price, prev_close, call_wall_1, call_wall_1_gex, ..., low_5d). Historical: TICKER|TERM1_HIST|... where TERM_HIST = date:cw,pw;date:cw,pw;...
Feedback welcome in comments. Educational only—not advice.
스크립트에서 "implied"에 대해 찾기
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
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Options Greeks AnalyzerOptions Greeks Analyzer (Training & Learning Guide)
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1. Introduction
Options trading is advanced compared to regular stock trading, and one of the most important aspects is Options Greeks. Greeks are mathematical values that measure how the price of an option will react to changes in various factors such as the underlying asset’s price, volatility, interest rates, and time to expiry.
This Options Greeks Analyzer tool is built using TradingView Pine Script v5. It serves as a real time training and analysis dashboard that helps learners visualize how options greeks behave, how option prices change, and how traders can make informed decisions.
📌 Educational Disclaimer:
This tool is only for training and learning purposes. It is not a financial advice tool nor to be used for live trading decisions. The data shown is theoretical Black Scholes model calculations, which may differ from actual option market prices.
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2. How the Tool Works
The Options Greeks Analyzer is divided into different modules. Below is a step by step walkthrough:
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Step 1: User Inputs
• Implied Volatility (IV%) — You can manually enter volatility, which is the most important factor in option pricing. Higher IV = higher option premium.
• Expiry Selection — Choose from preset durations like 7D, 14D, 30D etc. Days to expiry directly affect time decay (Theta).
• Strike Price Mode — You can select either:
o ATM (At-the-Money = Current price of stock/index)
o Custom strike (Enter your own strike price)
• Risk-Free Rate (%) — A small interest rate factor (like government bond yield) used for theoretical valuation.
• Table Customization — Choose table size, position, and whether to show price lines for easy visibility.
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Step 2: Market Data & Volatility
• The tool takes the current market price (Spot Price) as input.
• It calculates realized volatility from historical price fluctuations (using past 30 bars/log returns).
• Implied Volatility (manual input) is then compared to realized vol:
o If IV > Historical Volatility → Market pricing is “expensive” (HIGH IV RANK).
o If IV < Historical Volatility → Market is “cheap” (LOW IV RANK).
o Otherwise, it’s MEDIUM.
📌 Why it matters?
Traders can decide whether buying or selling options is favorable. Beginners learn that timing entry with volatility is more critical than just looking at market direction.
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Step 3: Black-Scholes Formula
The core engine uses the Black-Scholes model, a mathematical formula widely used to compute option fair prices.
It uses the following inputs:
• Current price (Spot)
• Strike Price
• Time to Expiry (T)
• Risk Free Rate (r)
• Implied Volatility (σ)
This produces:
• Call Option Price
• Put Option Price
📌 This teaches learners how premiums are derived theoretically and why the same strike can have different values depending on IV and time.
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Step 4: Option Greeks Calculation
The tool computes the first order Greeks:
• Delta → Measures how much the option price changes when the underlying stock moves by 1 point.
(Call Delta ranges 0–1, Put Delta ranges -1 to 0).
• Gamma → Sensitivity of Delta to price change. A measure of volatility risk.
• Theta → Time decay. Shows how much value option loses as each day passes. Calls and Puts have negative Theta (decay).
• Vega → Measures how sensitive option price is to volatility changes.
• Rho → Interest rate sensitivity. Mostly minor in equity options but important for training.
📌 New traders learn how each factor impacts profits/losses. Instead of random guessing, they see mathematical impact in numbers.
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Step 5: Dashboard & Visualization
The tool builds a professional dashboard table on the chart.
It shows categories such as:
1. Asset Info — Spot, Strike, DTE (days to expiry), IV%, IV Rank, 1-Day Trend, Moneyness (ATM/OTM/ITM).
2. Option Prices — Call, Put, Break-even levels, Time Value, Expected Move (%), Realized vs Implied Vol.
3. Greeks with Visual Progress Bars — Easily shows Delta, Gamma, Vega, Theta, Rho in intuitive graphical representations.
4. Status Bar — Suggests theoretical bias like:
o HIGH IV → Favor Option Selling
o LOW IV → Favor Option Buying
o MEDIUM → Neutral observation
5. Recommendation Line — Offers training-based suggestions like “Buy Straddles”, “Sell Call Spreads”, etc. These are not signals, but scenarios to learn strategies.
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3. How It Helps Beginners
1. Learn Greeks in Action:
Beginners often memorize formulas but never see real-time changes. This dashboard updates every bar to show how Greeks change dynamically.
2. Compare Volatilities:
Traders understand difference between historical vs implied volatility and why option premiums behave differently.
3. Understand Risk Levels:
The tool highlights when Gamma risk is high (danger for sellers) or when Theta is most favorable.
4. Training Mode for Strategies:
Helps beginners experiment by changing IV, strike, expiry and seeing how straddles, spreads, naked options would behave theoretically.
5. Prepares Before Live Trading:
Safe environment to practice option analysis without risking capital.
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4. Educational Use Cases
• Scenario 1: Change expiry from 7D to 30D — see how Theta becomes slower for longer expiries.
• Scenario 2: Increase IV from 25% to 80% — watch how option premiums inflate, and recommendation changes from “Buy” to “Sell”.
• Scenario 3: Select OTM vs ITM strikes — check how delta moves from near 0 to near 1.
By running these scenarios, learners understand why professional traders hedge Greeks instead of directional gambling.
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5. Disclaimer
This Options Greeks Analyzer is built strictly for educational and training purposes.
• It uses theoretical formulas (Black-Scholes) that may not match actual option market prices.
• The recommendations are for learning strategy logic only, not real-world execution signals.
• Trading in options carries significant risks and may result in capital loss.
📌 Always consult with a financial advisor before applying real strategies.
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✅ Summary
This Options Greeks Analyzer:
• Teaches how Greeks, IV, and premiums work.
• Provides a real-time interactive dashboard for training.
• Helps beginners practice option scenarios safely.
• Is meant strictly for learning and not live trading execution.
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Disclaimer from aiTrendview
This script and its trading signals are provided for training and educational purposes only. They do not constitute financial advice or a guaranteed trading system. Trading involves substantial risk, and there is the potential to lose all invested capital. Users should perform their own analysis and consult with qualified financial professionals before making any trading decisions. aiTrendview disclaims any liability for losses incurred from using this code or trading based on its signals. Use this tool responsibly, and trade only with risk capital.
Fear Volatility Gate [by Oberlunar]The Fear Volatility Gate by Oberlunar is a filter designed to enhance operational prudence by leveraging volatility-based risk indices. Its architecture is grounded in the empirical observation that sudden shifts in implied volatility often precede instability across financial markets. By dynamically interpreting signals from globally recognized "fear indices", such as the VIX, the indicator aims to identify periods of elevated systemic uncertainty and, accordingly, restrict or flag potential trade entries.
The rationale behind the Fear Volatility Gate is rooted in the understanding that implied volatility represents a forward-looking estimate of market risk. When volatility indices rise sharply, it reflects increased demand for options and a broader perception of uncertainty. In such contexts, price movements can become less predictable, more erratic, and often decoupled from technical structures. Rather than relying on price alone, this filter provides an external perspective—derived from derivative markets—on whether current conditions justify caution.
The indicator operates in two primary modes: single-source and composite . In the single-source configuration, a user-defined volatility index is monitored individually. In composite mode, the filter can synthesize input from multiple indices simultaneously, offering a more comprehensive macro-risk assessment. The filtering logic is adaptable, allowing signals to be combined using inclusive (ANY), strict (ALL), or majority consensus logic. This allows the trader to tailor sensitivity based on the operational context or asset class.
The indices available for selection cover a broad spectrum of market sectors. In the equity domain, the filter supports the CBOE Volatility Index ( CBOE:VIX VIX) for the S&P 500, the Nasdaq-100 Volatility Index ( CBOE:VXN VXN), the Russell 2000 Volatility Index ( CBOEFTSE:RVX RVX), and the Dow Jones Volatility Index ( CBOE:VXD VXD). For commodities, it integrates the Crude Oil Volatility Index ( CBOE:OVX ), the Gold Volatility Index ( CBOE:GVZ ), and the Silver Volatility Index ( CBOE:VXSLV ). From the fixed income perspective, it includes the ICE Bank of America MOVE Index ( OKX:MOVEUSD ), the Volatility Index for the TLT ETF ( CBOE:VXTLT VXTLT), and the 5-Year Treasury Yield Index ( CBOE:FVX.P FVX). Within the cryptocurrency space, it incorporates the Bitcoin Volmex Implied Volatility Index ( VOLMEX:BVIV BVIV), the Ethereum Volmex Implied Volatility Index ( VOLMEX:EVIV EVIV), the Deribit Bitcoin Volatility Index ( DERIBIT:DVOL DVOL), and the Deribit Ethereum Volatility Index ( DERIBIT:ETHDVOL ETHDVOL). Additionally, the user may define a custom instrument for specialized tracking.
To determine whether market conditions are considered high-risk, the indicator supports three modes of evaluation.
The moving average cross mode compares a fast Hull Moving Average to a slower one, triggering a signal when short-term volatility exceeds long-term expectations.
The Z-score mode standardizes current volatility relative to historical mean and standard deviation, identifying significant deviations that may indicate abnormal market stress.
The percentile mode ranks the current value against a historical distribution, providing a relative perspective particularly useful when dealing with non-normal or skewed distributions.
When at least one selected index meets the condition defined by the chosen mode, and if the filtering logic confirms it, the indicator can mark the trading environment as “blocked”. This status is visually highlighted through background color changes and symbolic markers on the chart. An optional tabular interface provides detailed diagnostics, including raw values, fast-slow MA comparison, Z-scores, percentile levels, and binary risk status for each active index.
The Fear Volatility Gate is not a predictive tool in itself but rather a dynamic constraint layer that reinforces discipline under conditions of macro instability. It is particularly valuable when trading systems are exposed to highly leveraged or short-duration strategies, where market noise and sentiment can temporarily override structural price behavior. By synchronizing trading signals with volatility regimes, the filter promotes a more cautious, informed approach to decision-making.
This approach does not assume that all volatility spikes are harmful or that market corrections are imminent. Rather, it acknowledges that periods of elevated implied volatility statistically coincide with increased execution risk, slippage, and spread widening, all of which may erode the profitability of even the most technically accurate setups.
Therefore, the Fear Volatility Gate acts as a protective mechanism.
Oberlunar 👁️⭐
H2-25 cuts (bp)This custom TradingView indicator tracks and visualizes the implied pricing of Federal Reserve rate cuts in the market, specifically for the second half of 2025. It does so by comparing the price differences between two specific Fed funds futures contracts: one for June 2025 and one for December 2025. These contracts are traded on the Chicago Board of Trade (CBOT) and are a widely-used market gauge of the expected path of U.S. interest rates.
The indicator calculates the difference between the implied rates for June and December 2025, and then multiplies the result by 100 to express it in basis points (bps). Each 0.01 change in the spread corresponds to a 1-basis point change in expectations for future rate cuts. A positive value indicates that the market is pricing in a higher likelihood of one or more rate cuts in 2025, while a negative value suggests that the market expects the Fed to hold rates steady or even raise them.
The plot represents the difference in implied rate cuts (in basis points) between the two contracts:
June 2025 (ZQM2025): A contract representing the implied Fed funds rate for June 2025.
December 2025 (ZQZ2025): A contract representing the implied Fed funds rate for December 2025.
VIX AnalyticsThis script is designed to serve traders, analysts, and investors who want a real-time, comprehensive view of market volatility, risk sentiment, and implied movements. It combines multiple institutional-grade volatility indices into one clear dashboard and interprets them with actionable insights — directly on your chart.
🔍 Features Included
🟦VIX (CBOE Volatility Index)
Measures market expectation of 30-day S&P 500 volatility.
Color-coded interpretation ranges:
Under 13: Extreme Complacency
15–20: Stable Market
20–30: Moderate Risk
30–40: High Volatility
Over 40: Panic
🟪 VVIX (Volatility of Volatility Index)
Tracks the volatility of VIX itself.
Interpreted as a risk gauge of how aggressively traders are hedging volatility exposure.
Under 80: Market Complacency
80–100: Normal Environment
100–120: Caution — Rising Volatility of Volatility
Over 120: High Stress — Elevated Hedging Activity
🟨 SKEW Index
Measures the perceived tail risk of the S&P 500 — i.e., the probability of a black swan event.
Below 110: Potential Complacency
120–140: Moderate Tail Risk
Above 140: High Tail Risk
🧮 VIX/VVIX Ratio
Gauges relative fear levels between expected volatility and the volatility of volatility.
Under 0.5: Low Ratio — VVIX Overextended
Over 0.9: High Ratio — VIX Leading
📈 VIX Percentile (1-Year Range)
Shows where the current VIX sits relative to its 1-year high/low.
Under 20%: Volatility is Cheap
Over 70%: Fear is Elevated — Reversal Possible
📉 SPX Implied Point Moves
Projects expected moves in SPX using VIX-derived volatility:
Daily
Weekly
Monthly
Helps size positions or define expected price ranges based on volatility regime.
📊 ATR Values (5, 13, 21 periods)
Traditional volatility using historical prices.
Provided alongside implied data for comparison.
🧠 Unique Logic & Interpretation Layer
This script doesn’t just show raw data — it interprets it. It reads the relationship between VIX, VVIX, and SKEW to highlight:
When market volatility may be underpriced
When hidden tail risks are forming
When to be cautious of volatility expansions
How current implied movement compares to past realized volatility
✅ Use Cases
Day traders: Know when volatility is low or expanding before scalping or swinging.
Options traders: Identify whether implied volatility is cheap or expensive.
Portfolio managers: Gauge when hedging is in demand and adjust exposure.
Risk managers: Crosscheck if current volatility aligns with macro risk events.
⚙️ Settings
Customizable table placement: Move the dashboard to any corner of your chart.
No repainting or lag: Data updates in real-time using official CBOE and SPX feeds.
Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures).
Key Components of the DVDM:
1. Implied Volatility (IV):
The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000).
2. Historical Volatility (HV):
The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012).
3. Volatility Spread:
The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior.
4. Dynamic Thresholds:
Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004).
Trading Logic:
1. Long Entry:
A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums.
2. Short Entry:
A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty.
3. Exit Conditions:
Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence.
Advantages of the DVDM:
1. Adaptability:
Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments.
2. Quantitative Precision:
The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making.
3. Market Versatility:
The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance.
Scientific Relevance:
The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982).
References:
1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397.
2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education.
4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539.
5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
VolbandsThe Volbands indicator dynamically plots upper and lower volatility bands based on implied daily moves derived from volatility indices. This tool provides a visual forecast of the next trading day's price range, helping traders anticipate potential price movement boundaries.
Key Features:
1. Auto-Detect Volatility Index: Volbands automatically detects the appropriate volatility index based on the current symbol. For example, it uses the VIX for S&P 500, VXN for Nasdaq 100, and custom indexes like VXAPL for Apple. Users can also manually select a specific volatility index if preferred.
2. Projected Bands:
- The indicator plots the projected upper and lower bands for the next trading day using the implied move from the volatility index.
- Displays today’s projected bands as a reference and overlays next day’s bands with a slight offset, visually indicating the anticipated range.
3. Dynamic Updates: The indicator updates automatically as new bars are added, ensuring that users have up-to-date projections based on the latest volatility data.
4. Highlighting Extreme Price Action: Candles that close outside of the projected bands are colored in yellow, highlighting moments of higher-than-expected volatility.
5. Informative Table: A customizable table displays relevant information, including:
- The selected or auto-detected volatility index
- Implied daily move percentage
- Projected upper and lower levels
Potential Applications:
- Risk Management: The Volbands indicator can help traders set more informed stop-loss and take-profit levels based on volatility-driven price projections.
- Identifying Overbought/Oversold Conditions: Price movement outside the projected bands may indicate overbought or oversold conditions, potentially signaling trade opportunities.
-Enhancing Entry and Exit Points: The projected bands act as soft support and resistance levels, assisting traders in timing entries and exits in anticipation of volatility-driven price reactions.
Future Enhancements:
Potential improvements to expand functionality could include:
- Additional Volatility Indices: Expanding coverage to include more assets and volatility indices.
- Alerts: Setting alerts for when prices breach the projected bands, enabling traders to react quickly to unexpected price movements.
- Customization of Bands: Adding options for users to adjust the implied move percentage, creating customized bands that reflect individual trading strategies.
This indicator combines implied volatility with price action, offering valuable insights to traders on expected price ranges and volatility.
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
Black-Scholes Model CalculatorOverview
The Black-Scholes Model Calculator TradingView Indicator is an advanced tool designed for options traders to calculate key Greek values, including Theta, Gamma, Delta, Rho, and Vega. By integrating this indicator into your TradingView charts, you can perform sophisticated options analysis, enhance your understanding of options pricing, and make more informed trading decisions.
Key Features
1. Comprehensive Greeks Calculation:
Theta : Measure the sensitivity of the option's price to the passage of time, helping you understand time decay.
Gamma : Determine the rate of change of Delta, providing insights into how Delta will change as the underlying asset price moves.
Delta : Calculate the sensitivity of the option's price to changes in the price of the underlying asset.
Rho : Evaluate the sensitivity of the option's price to changes in interest rates.
Vega : Assess the sensitivity of the option's price to changes in implied volatility.
2. User Input Parameters :
Strike Price : Enter the strike price of the option to tailor the calculations to your specific option.
Days Remaining : Input the number of days remaining until the option's expiration, providing accurate time-based calculations.
Implied Volatility (IV) : Specify the implied volatility for both call and put options to reflect market expectations.
3. Visual and Analytical Insights :
Display the calculated Greek values directly on your TradingView chart for quick reference and analysis.
Clear and intuitive presentation of the data, making it easy to interpret and apply to your trading strategy.
How to Use
1. Insert Strike Price : Start by entering the strike price of the option you are analyzing. This is essential for calculating the Greeks accurately.
2. Days Remaining : Input the number of days left until the option's expiration. This factor is crucial for determining Theta and other time-sensitive Greeks.
3. Implied Volatility (IV) : Provide the implied volatility values for both call and put options. This input is vital for calculating Vega and assessing how changes in volatility affect option prices.
Benefits
Enhanced Options Trading : Gain a deeper understanding of how different factors affect option pricing by using the calculated Greeks.
Strategic Planning : Utilize the Greek values to formulate and adjust your options trading strategies based on time decay, price movements, interest rate changes, and volatility shifts.
Risk Management : Improve your risk management by understanding the potential changes in option prices and adjusting your positions accordingly.
Practical Application
1. Theta Management : Monitor Theta to understand how time decay is impacting your option positions, especially for short-term trades.
2. Gamma and Delta Adjustments : Use Gamma and Delta to hedge your positions and manage the risk associated with price movements in the underlying asset.
3. Rho Considerations : Evaluate Rho to factor in interest rate changes, which can be particularly useful in long-term options trading.
4. Vega Analysis : Analyze Vega to assess the impact of volatility changes and adjust your strategies in volatile market conditions.
Conclusion
The Black-Scholes Model Calculator TradingView Indicator is an indispensable tool for any serious options trader. By providing precise calculations of Theta, Gamma, Delta, Rho, and Vega, it empowers you to make more informed trading decisions, manage risks effectively, and optimize your options trading strategies. Integrate this indicator into your TradingView setup to take your options trading to the next level.
CE - 42MACRO Equity Factor Table This is Part 1 of 2 from the 42MACRO Recreation Series
The CE - 42MACRO Equity Factor Table is a whole toolbox packaged in a single indicator.
It aims to provide a probabilistic insight into the market realized GRID Macro Regime, use a multiplex of important Assets and Indices to form a high probability Implied Correlation expectation and allows to derive extra market insights by showing the most important aggregates and their performance over multiple timeframes... and what that might mean for the whole market direction, as well as the underlying asset.
WARNING
By the nature of the macro regimes, the outcomes are more accurate over longer Chart Timeframes (Week to Months).
However, it is also a valuable tool to form a proper,
market realized, short to medium term bias.
NOTE
This Indicator is intended to be used alongside the 2nd part "CE - 42MACRO Yield and Macro"
for a more wholistic approach and higher accuracy.
Due to coding limitations they can not be merged into one Indicator.
Methodology:
The Equity Factor Table tracks specifically chosen Assets to identify their performance and add the combined performances together to visualize 42MACRO's GRID Equity Model.
For this it uses the below Assets, with more to come:
Dividend Compounders ( AMEX:SPHD )
Mid Caps ( AMEX:VO )
Emerging Markets ( AMEX:EEM )
Small Caps ( AMEX:IWM )
Mega Cap Growth ( NASDAQ:QQQ )
Brazil ( AMEX:EWZ )
United Kingdom ( AMEX:EWU )
Growth ( AMEX:IWF )
United States ( AMEX:SPY )
Japan ( AMEX:DXJ )
Momentum ( AMEX:MTUM )
China ( AMEX:FXI )
Low Beta ( AMEX:SPLV )
International ex-US ( NASDAQ:ACWX )
India ( AMEX:INDA )
Eurozone ( AMEX:EZU )
Quality ( AMEX:QUAL )
Size ( AMEX:OEF )
Functionalities:
1. Correlations
Takes a measure of Cross Market Correlations
2. Implied Trend
Calculates the trend for each Asset and uses the Correlation to obtain the Implied Trend for the underlying Asset
There are multiple functionalities to enhance Signal Speed and precision...
Reading a signal only over a certain threshold, otherwise being colored in gray to signal noise or unclear market behavior
Normalization of Signal
Double Normalization of Signal for more Speed... ideal for the Crypto Market
Using an additional Hull Moving Average to enhance Signal Speed
Additional simple Background coloring to get a Signal from the HMA
Barcoloring based on the Implied Correlation
3. Equity Factor Table
Shows market realized Asset performance
Provides the approximate realized GRID market regimes
Informs about "Risk ON" and "Risk OFF" market states
Now into the juicy stuff...
Visuals:
There is a variety of options to change visual settings of what is plotted and where
+ additional considerations.
Everything that is relevant in the underlying logic which can improve comprehension can be visualized with these options.
More to come
Market Correlation:
The Market Correlation Table takes the Correlation of all the Assets to the Asset on the Chart,
it furthermore uses the Normalized KAMA Oscillator by IkkeOmar to analyse the current trend of every single Asset.
(To enhance the Signal you can apply the mentioned Indicator on the relevant Assets to find your target Asset movements that you intend to capture...
and then change the length of the Indicator in here)
It then Implies a Correlation based on the Trend and the Correlation to give a probabilistically adjusted expectation for the future Chart Asset Movement.
This is strengthened by taking the average of all Implied Trends.
Thus the Correlation Table provides valuable insights about probabilistically likely Movement of the Asset over the defined time duration,
providing alpha for Traders and Investors alike.
Equity Factors:
The table provides valuable information about the current market environment (whether it's risk on or risk off),
the rough GRID models from 42MACRO and the actual market performance.
This allows you to obtain a deeper understanding of how the market works and makes it simple to identify the actual market direction,
makes it possible to derive overall market Health and shows market strength or weakness.
Utility:
The Equity Factor Table is divided in 4 Sections which are the GRID regimes:
Economic Growth:
Goldilocks
Reflation
Economic Contraction:
Inflation
Deflation
Top 5 Equity Factors:
Are the values green for a specific Column?
If so then the market reflects the corresponding GRID behavior.
Bottom 5 Equity Factors:
Are the values red for a specific Column?
If so then the market reflects the corresponding GRID behavior.
So if we have Goldilocks as current regime we would see green values in the Top 5 Goldilocks Cells and red values in the Bottom 5 Goldilocks Cells.
You will find that Reflation will look similar, as it is also a sign of Economic Growth.
Same is the case for the two Contraction regimes.
This whole Indicator, as well as the second part, is based to a majority on 42MACRO's models.
I only brought them into TV and added things on top of it.
If you have questions or need a more in-depth guide DM me.
Will make a guide to all functionalities if necessity becomes apparent.
GM
Intellxis Premium InsightUnderstanding the Intellxis - Premium Insight Indicator
This guide provides a way to understand the output of the Premium Insight plugin for TradingView. Its core feature is the "Premium Status" column, which analyzes how an option's premium behaves relative to the underlying asset's price. Use the below guide to decode every status message and leverage this powerful plugin in your trading.
Call Option Statuses
Strong (Spot 🡅): The Call premium is increasing as the underlying asset price rises. This confirms a bullish trend and indicates the option is behaving as expected.
Down (Spot 🡇): The Call premium is decreasing as the underlying asset price falls. This is the normal, expected behavior for a call option in a downtrend.
Down (Spot ⟷): The Call premium is decreasing while the underlying asset price is flat. This erosion of value is due to the passage of time and is an expected behavior.
Weak (Spot 🡅): The Call premium is decreasing slightly even though the underlying asset price is rising. This is an anomaly and suggests weakness in the bullish move.
Flat (Spot 🡅): The Call premium is not changing despite a rise in the underlying asset price. This indicates the premium is not responding to a favorable move, which is a sign of weakness.
Strong (Spot 🡇): The Call premium is increasing even though the underlying asset price is falling. This is a highly counter-intuitive signal and could point to a sharp increase in implied volatility.
MELTDOWN (Spot 🡅): The Call premium is collapsing significantly while the underlying asset price is RISING. This contradicts normal option behavior and may signal an imminent reversal or volatility crush.
MELTDOWN (Spot ⟷): The Call premium is collapsing significantly while the underlying is flat. This suggests a massive drop in implied volatility or other strong selling pressure not related to price direction.
Down Significantly (Spot 🡇): The Call premium is dropping significantly as the underlying spot price is moving down.
Up (Spot ⟷): The Call premium is increasing while the underlying spot price is flat. This is likely due to a sudden increase in volatility.
Flat (Spot ⟷): Normal: The Call premium is flat and the underlying spot price is also flat.
Put Option Statuses
Strong (Spot 🡇): The Put premium is increasing as the underlying asset price falls. This confirms a bearish trend and indicates the option is behaving as expected.
Down (Spot 🡅): The Put premium is decreasing as the underlying asset price rises. This is the normal, expected behavior for a put option in an uptrend.
Down (Spot ⟷): The Put premium is decreasing while the underlying asset price is flat. This erosion of value is due to the passage of time and is an expected behavior.
Weak (Spot 🡇): The Put premium is dropping slightly even though the underlying asset price is falling. This is an anomaly and suggests weakness in the bearish move.
Flat (Spot 🡇): The Put premium is not changing despite a fall in the underlying asset price. This indicates the premium is not responding to a favorable move, which is a sign of weakness.
Strong (Spot 🡅): The Put premium is increasing even though the underlying asset price is rising. This is a highly counter-intuitive signal and could point to a sharp increase in implied volatility.
MELTDOWN (Spot 🡇): The Put premium is collapsing significantly while the underlying asset price is FALLING. This contradicts normal option behavior and may signal an imminent reversal or volatility crush.
MELTDOWN (Spot ⟷): The Put premium is collapsing significantly while the underlying is flat. This suggests a massive drop in implied volatility or other strong selling pressure not related to price direction.
Down Significantly (Spot 🡅): The Put premium is dropping significantly as the underlying spot price is moving up.
Up (Spot ⟷): The Put premium is increasing while the underlying spot price is flat. This is likely due to a sudden increase in volatility.
Flat (Spot ⟷): The Put premium is flat and the underlying spot price is also flat.
Exchange and Symbol by BULL┃NETThe B | N EXSY (Exchange and Symbol by BULL | NET)
indicator provides traders using CFD brokers with the most significant price and time events from the stock exchange of the underlying original index or security. For example traders are able to easily identify the price at the Daily Open and Close time of up to three additional stock exchanges. Traders can choose from a huge list of options including the values from the current and previous Day, Week, Month and Year. In addition traders can enable the display of the Expected Move by either implied or historical volatility. The indicator can show Open Gaps (gap between close and open of two trading sessions) also which traders would usually see only on the original chart of an index or security.
The B | N EXSY indicator can help traders to make better entry decisions based on the real market sessions.
█ ⚠️ DISCLAIMER – READ BEFORE YOU USE ⚠️
█ CONCEPTS
CFD Brokers allow you to trade many indices, securities and assets up to 24 hours per day and 7 days per week (24/7). Other than Crypto Assets indices and securities get the highest transaction volume during the session of a stock market. Most importantly while its “Home Stock Market” is open.
For example the NASDAQ or S&P500 will see the highest volume during the business hours of the New York Stock Exchange (NYSE) between 9:30am and 4:00pm (America New York Time). Most CFD Providers however will open their Trading session approximately 9.5 hours before the NYSE opens and even 2 hours before Japan and Australia open the markets.
The German DAX on the other hand is listed on the Deutsche Börse Xetra which is open from 9:00 to 17:00 (Europe Berlin Time). CFD Brokers will open the DAX for trading differently between 9 and 5.5 hours before the XETRA opens.
Therefore most available indicators for visualizing the day open will show different results. Traders at Broker A will tell a totally different story than traders at Broker B who opened 3 hours later.
Furthermore people trading the NASDAQ often keep an eye on the London Stock Exchange (LSE) as well and those trading the NIKKEI often watch the NYSE besides its home at the Japan Exchange Group (JPX).
Advanced traders know about the importance of those information and I have seen thousands of charts where people draw horizontal lines to mark the open and closing prices as well as the session highs and lows. They do it every day and often for different indices and securities. A time consuming job.
Here is where B | N EXSY steps in to give traders objective information for Intraday trading (Daily timeframe and below). More or less automatically. Choose your primary stock exchange (e.g. the NYSE if you trade the NASDAQ) and optionally a second and third stock exchange you are interested in. Individually select the price events you like to see or keep the defaults. Make your own cosmetic decision on how you want the data to be displayed. Save your chart and you will never have to draw a horizontal line again to see the High of the current session, the Low of last week, the monthly Open or yesterdays Close. Sharing ideas with other traders in the chat groups will be easy because everyone is relying on the same information. Even across different CFD Brokers (with slightly different prices of course). Your Technical Analysis can become much more efficient.
█ FEATURES
B | N EXSY is highly customizable. The default settings are optimized for the NASDAQ during the NYSE session. Following you get an overview of all options in the settings menu.
— LOWER TIMEFRAME
The “Lower Timeframe in Minutes” defaults to 30 minutes and should work with most CFD Brokers and stock exchanges. If not you will get a huge warning on the chart suggesting different settings. If e.g. a CFD Broker opens the Dax session at 3:15 but the XETRA opens at 9:00 you have to change the setting to 15.
— STOCK EXCHANGE
Primary is mandatory and defaults to NYSE (New York Stock Exchange) which is the home of the NASDAQ, the S&P 500, the Dow Jones and many others. Usually you select the home stock exchange of the instrument you trade. E.g. XETRA for the DAX, JPX for the NIKKEI or HKEX for the HANG SENG.
The Second and Third stock exchange is optional and defaults to NONE. If e.g. you trade Nvidia with NYSE as the primary stock exchange and you are interested in the High and Low of the European Session select LSE (London Stock Exchange) or XETRA (Deutsche Börse Xetra) as the second stock exchange. By default the indicator will show only information about the current day and week for the second and third stock exchange but you can change that later.
— VISUALIZE SESSIONS
Beginners and less advanced traders sometimes want to see the time span of a session. By default this feature is disabled because it adds more noise to the chart. You can select each of the three stock exchanges individually and select your preferred color.
— CUSTOM STOCK EXCHANGE
Whether your preferred Stock Exchange is missing in the dropdowns or you have a special purpose (see the HOW TO USE section) you can add your own ”Stock Exchange” to the chart.
Name and Country are optional and get displayed in tooltips only. Opening, Closing and Timezone are important. Enter the Open and Close time as HOUR:MINUTE in 24 hour notation (22:00 instead of 10:00pm). The timezone can be provided as time offset in GMT or UTC notation (e.g. GMT+2 or UTC-5) or as a time zone name listed in the IANA Time Zone Database ( e.g. "America/New_York" or “Europe/Berlin”). If you do it wrong the indicator will give wrong results or don’t work at all.
— EXPECTED MOVE IMPLIED VOLATILITY
With this setting you can enable the calculation and display of the Expected Move (EM). Option and Future traders should be familiar with this feature. Those who never heard about should read about it on the internet. Your favorite search engine will provide you with lots of information about it.
After enabling the feature you have to select a source to calculate the EM. The drop down menu contains popular sources and are named after the indices they are based on. It is crucial that the setting match the index, symbol or asset you are trading. If e.g. you are trading a CFD for the NASDAQ you have to select Nasdaq as source. Wrong settings will lead to wrong calculations.
If the source you need is missing you select manually and enter the implied Volatility in the field “Value for manual calculation”. If e.g. you trade the Nikkei you have to enter the current value of the JNIV manually because it is not listed at TradingView so I can’t add it.
The other settings control the Line Color and Style, the Label Color and Size as well as the Text Color.
The indicator will display the EM+ and EM- as well as the 2 and 3 Sigma EM +/-. On the Daily Chart it will display the Weekly Expected Moves. On any timeframe below you will get the Daily Expected Moves.
— EXPECTED MOVE HISTORICAL VOLATILITY
Other than the feature above, this one calculates the EM based on historical volatility.
After enabling the feature you have to enter the amount of days to look back to calculate volatility. Like you would do for a SMA, EMA or RSI. The default is 10 days. Depending on what asset you trade you might play a little with this setting.
The other settings control the Line Color and Style, the Label Color and Size as well as the Text Color.
Like with the Expected Move Implied Volatility this setting will show weekly data on the daily timeframe and daily information on intraday timeframes.
— LABEL AND LINE COSMETICS
The settings in this section control how lines and labels get positioned on the chart and which information the labels show.
● Bar Offset
The bar offset controls the horizontal distance to the last bar on the chart where lines end. By default it is “2” bars to the right. If you use other indicators which show information on the right side you can increase this value to avoid overlapping.
● Bar Anchor
The bar anchor controls where lines start. Default is “lastbar”.
Lastbar sets the start of lines to the last bar of the chart. This provides a very clean chart without lines crossing bars to the left.
Moving sets lines to start at the bar at which the price event occurred. The line for the daily open (DO) price will stay at the opening bar of the stock market and it will do so when it becomes the previous day open (PDO) the next day. The line that marks the session High (DH) will be anchored to the highest bar while the stock market is open. Therefore it might be moving with the advancing chart. The same counts for the session Low (DL). The next day these lines become the previous day high or low (PDH / PDL) and stay at the highest/lowest bar from the day before. This logic is forwarded to all other lines (weekly, monthly, yearly). This gives traders a quick orientation on which bar a price event occurred but a less clean chart.
If you choose Day as bar anchor all lines will start at the beginning of the Brokers trading session in which the price event took place. This is also true for the roll over event when e.g. the Week Open (WO) will become the Previous Week Open (PWO) next Week. Unlike the “moving” setting the new WO and PWO will be anchored to the beginning of the Week. Traders will have a box like view into the past.
● Label Distance Divisor
This setting is used to calculate the minimum vertical distance of labels in means of price points. The internal formular takes the day close price and divides it by the number entered in this field. If e.g. the daily closing price was 5000 the minimum vertical distance would become 1 price point if you enter 5000 for this setting. If the price difference of two events would then be less than 1 the labels would be positioned higher and lower to prevent overlapping. The default value is fine for the Nasdaq (~ 19000 / 5000 = 3.8 at the time of writing). For other indices, securities and assets you should change the divider to your likings or as needed to set the trigger for repositioning labels.
● Distance Modifier
This setting is used to control the vertical shift of the label. The default of Zero disables the setting and activates an internal function which makes a decision based on the used timeframe on the chart (0.1 less than m30, 0.5 from m30 to h4, 0.75 above h4 and 1 for daily). The logic takes the minimum vertical distance and multiplies it by the distance modifier.
In the example above for the label distance divider a label would shift by 1.9 price points on a 30 minute chart if two lines trigger the minimum vertical distance. On the upper line the label moves up and on the lower line it moves down. If three lines are too close to each other the label in the middle does not get moved. If more lines break the minimum distance some labels will overlap until the price is advancing. Those events happen most likely during the opening of a stock exchange.
Price events with equal price, e.g. Day and Week Open at the start of a new week or Day, Week, Month, Year High in the event of a new ATH will get lined up (stacked) horizontally.
While this cosmetic corrections have limits overlapping can be reduced to a minimum.
● Show Price
● Show Exchange
Labels can show up to three information. The price, the stock exchange and the event. The event however can’t be disabled. If you select both options you will see something like
5347.84 for the Day Close of the S&P 500 on the New York Stock Exchange
With this two settings you can disable the display of price and/or stock exchange.
If you have chosen to use more than one stock exchange the setting for “Show Exchange” will be ignored. Otherwise you would not know which Day Close (DC) or Day High (DH) belongs to which stock exchange
● Enable Tooltip
If you decide to hide the price and/or exchange on the label it can be useful to get this information in a tooltip while hovering with the mouse over the label. On the contrary it might become annoying with labels popping up if you have a nervous mouse finger. The feature is disabled by default.
● Equalize Label Size
The size of labels is one of the most discussed issues. Some say it is too small other say it is too big. Label size matters on different devices. “Normal” labels can be too large on a smartphone and too small on a 4k display. And the size is crucial for the automatic horizontal stacking of labels. You simply can’t line up a small, normal and large label in Pine Script (the programming language at TradingView). The stacking is done by prepending labels with spaces to shift them to the right.
This setting overloads all individual size settings for the price events below and activates the automatic horizontal stacking of labels with equal price. It is a convenient way to change the size of all labels with one click in case you have different layouts for different devices.
If you disable this feature you can set the label size individually but you lose the horizontal stacking. This can be useful for traders who display only a few price events or for educational purpose where you want to point out a special event.
— CURRENT DAY
This setting controls which price events of the current day (current session) get displayed and how they appear.
Primary O/C
Enable the Day Open (DO) and Close (DC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Day High (DH) and Low (DL) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Day Open (DO) and Close (DC) for the second and third stock exchange. Enabled by default.
Other H/L
Enable the Day High (DH) and Low (DL) for the second and third stock exchange. Enabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— PREVIOS DAY
This setting controls which price events of the previous day get displayed and how they appear.
Primary O/C
Enable the Previous Day Open (PDO) and Close (PDC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Previous Day High (PDH) and Low (PDL) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Previous Day Open (PDO) and Close (PDC) for the second and third stock exchange. Disabled by default.
Other H/L
Enable the Previous Day High (PDH) and Low (PDL) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— OPENING HOUR
This setting controls whether and how to display the famous opening hour (High and Low within the first 60 minutes after stock market opens)
Primary Cur
Display the Current Day Opening Hour High (OH) and Low (OL) for the primary stock exchange. Enabled by default.
Primary Pre
Display the Previous Day Opening Hour High (POH) and Low (POL) for the primary stock exchange. Enabled by default.
Other Cur
Display the Current Day Opening Hour High (OH) and Low (OL) for the second and third stock exchange. Disabled by default.
Other Pre
Display the Previous Day Opening Hour High (POH) and Low (POL) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— CURRENT WEEK
This setting controls which price events of the current week get displayed and how they appear.
Primary O/C
Enable the Week Open (WO) and Close (WC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Week High (WH) and Low (WL) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Week Open (WO) and Close (WC) for the second and third stock exchange. Enabled by default.
Other H/L
Enable the Week High (WH) and Low (WL) for the second and third stock exchange. Enabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— PREVIOUS WEEK
This setting controls which price events of the previous week get displayed and how they appear.
Primary O/C
Enable the Previous Week Open (PWO) and Close (PWC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Previous Week High (PWH) and Low (PWL) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Previous Week Open (PWO) and Close (PWC) for the second and third stock exchange. Disabled by default.
Other H/L
Enable the Previous Week High (PWH) and Low (PWL) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— CURRENT MONTH
This setting controls which price events of the current month get displayed and how they appear.
Primary O/C
Enable the Month Open (MO) and Close (MC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Month High (MH) and Low (ML) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Month Open (MO) and Close (MC) for the second and third stock exchange. Disabled by default.
Other H/L
Enable the Month High (MH) and Low (ML) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— PREVIOUS MONTH
This setting controls which price events of the previous month get displayed and how they appear.
Primary O/C
Enable the Previous Month Open (PMO) and Close (PMC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Previous Month High (PMH) and Low (PML) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Previous Month Open (PMO) and Close (PMC) for the second and third stock exchange. Disabled by default.
Other H/L
Enable the Previous Month High (PMH) and Low (PML) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— CURRENT YEAR
This setting controls which price events of the current year get displayed and how they appear.
Primary O/C
Enable the Year Open (YO) and Close (YC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Year High (YH) and Low (YL) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Year Open (YO) and Close (YC) for the second and third stock exchange. Disabled by default.
Other H/L
Enable the Year High (YH) and Low (YL) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— PREVIOUS YEAR
This setting controls which price events of the previous year get displayed and how they appear.
Primary O/C
Enable the Previous Year Open (PYO) and Close (PYC) for the primary stock exchange. Enabled by default.
Primary H/L
Enable the Previous Year High (PYH) and Low (PYL) for the primary stock exchange. Enabled by default.
Other O/C
Enable the Previous Year Open (PYO) and Close (PYC) for the second and third stock exchange. Disabled by default.
Other H/L
Enable the Previous Year High (PYH) and Low (PYL) for the second and third stock exchange. Disabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— ALL TIME HIGH
This setting controls whether the All Time High gets displayed on the daily chart and how it appears. See the limitations section (Amount of data) for details why the ATH will be displayed in the daily timeframe only.
Primary ATH
Enable the All Time High (ATH) for the primary stock exchange. Enabled by default.
OTHER ATH
Enable the All Time High (ATH) for the second and third stock exchange. Enabled by default.
The settings below control the Line Color and Style, the Label Color and Size as well as the Text Color.
— GAPFINDER
If you look at the original charts of an index (not the CFD Broker chart) you will see mostly every day a price difference between the closing price of the last session and the opening price of the current session. There are many names for those gaps. I call them Open Gaps or Kassa Gaps. Advanced traders know the market tends to close those gaps more or less quickly. Which is one more reason to know where the real previous day close was.
There are market conditions where those gaps are not closed within the new session. Those gap leftovers will usually be closed in the future. Some earlier, some later. If those gaps get more and more you quickly lose track and if the time comes to close one of the gaps you might not remember or recognize the price has reached an old gap. The charts of CFDs don’t even show such gaps due to the fact they trade nearly 24 hours per day.
The Gapfinder will display such leftovers after the end of the next session. If e.g. the previous day close was at 18000 and the market opens the next session at 18200 we have an Open Gap of 200 price points. If the Low of this session is 18100 after the session closes there would be rest gap of 100 price points. The Gapfinder then would mark it with a rectangle colored according to the direction of the Gap.
Bullish gaps result from an opening price (DO) and the current Day Low (DL) being higher than the previous day close (PDC).
Bearish gaps arise from an opening price (DO) and the current Day High (DH) being lower than the previous day closing price (PDC).
If you like you can change the color for the gaps and the text color.
— MISCELLANEOUS
To streamline the appearance of prices they are set to display two decimals only. Numbers get rounded! However, trading currency pairs or crypto assets might need to display the full amount of decimals. In that case simply disable the setting “2 Decimals”.
By default the indicator will display a small table in the lower right corner of the chart. It contains information about the current symbol, the selected primary stock exchange and the volatility. If you don’t like or need it you can disable it.
The “Unreliable Data” checkbox usually should not affect you. But if it does it can be really helpful. The B | N EXSY indicator uses Lower Timeframe Data to match CFD Broker and Stock Exchange opening times. If e.g. a CFD Broker opens at 0:00 and the stock exchange at 9:30 the script uses data from the 30 Minutes timeframe if you view the chart at any timeframe higher than 30 Minutes. Why? Because if you chose a four hours timeframe there is simply no bar that starts at 9:30 in this case. The CFD brokers h4 bars will start at 0:00, 4:00, 8:00, 12:00 and so on.
Sometimes the data stream of the Broker and TradingView get out of sync and a 4 hour bar eventually returns just 6x 30 Minutes instead of 8. During development of the indicator I came across of at least two brokers with such an issue. Only in one time frame and a specific period of time. If this happens the price information might be wrong. A Day High might be to low, a Day Close missing or the Day Open not be found. In such cases your trade might fail. To prevent such situations the indicator performs a daily consistency check at 12:00 during the session for an exchange in its time zone if this option is enabled.
In case the data are found unreliable you will see a label above the bar with further information in the tooltip of the label. You should than compare the information from this timeframe with the lower timeframe selected in the field below. Anway, it is a rare issue and if you, like me, work on multiple timeframes in parallel this bug probably won’t affect you.
— HOLIDAYS
● Holidays
If there is a holiday on a stock market the original chart of an index will simply show no bars for that day. CFD Broker charts will only show no bars if it is an international holiday or the broker itself is affected by the holiday. Take for example Memorial Day in the U.S. Although the NYSE is closed you can trade e.g. the NASDAQ until around 17:30 European Time which is the closing time of the LSE and XETRA. Unfortunately the closing time in Europe is after the opening time in the U.S. If the price goes up in the overlapping time you eventually see a new Weekly High (WH) if you rely on the chart of the CFD Broker. To avoid such misleading information the B | N EXSY indicator allows you to enter holidays for each stock market individually. If the indicator finds a holiday it will not store or add data for this day.
By default there are already the market holidays entered for the NYSE, XETRA, FSX and LSE in 2024. If you want to add your own holidays you have to follow some simple rules:
1. The entry must start in a new line below existing entries (carriage return)
2. The entry starts with the shortcut of the stock exchange exactly as you see them in the dropdown menu.
3. The stock exchange gets separated from the holidays with a colon (:)
4. Each holiday is entered as YYYY-MM-DD
5. Holidays get separated with a single whitespace
The entry for the Japan Exchange Group (JPX) in 2025 would start with:
JPX: 2025-01-01, 2025-01-02, 2025-01-03, 2025-01-08
Completed by the rest of the holiday.
If you make your own entries please keep a copy of the line you added because it will be replaced by the defaults if the indicator gets an update. Best practices would be to provide your holiday string in the comment section and I add it as a default.
● Early Close
Some stock exchanges close the market early before some holidays. In that case the indicator won’t be able to fetch the closing price for that day and the daily roll over won’t work for the day after the holiday. To prevent chaos you can enter the days with early close in this field.
By default the early closing days of the NYSE are already entered. If you want to add your own early closing days you have to follow some simple rules:
1. The entry must start in a new line below existing entries (carriage return)
2. The entry starts with the shortcut of the stock exchange exactly as you see them in the dropdown menu.
3. The stock exchange gets separated from the days with a colon (:)
4. Each early closing day is entered as YYYY-MM-DD-HH-MM where HH-MM is the closing time of this day entered in 24 hours format in the timezone of the stock exchange
5. Days get separated with a single whitespace
The entry for the day before Thanksgiving at the NYSE in 2025 would be:
NYSE:2025-11-25-13-00
This is because the market will close early at 1:00 PM on November 25, 2025, the day before Thanksgiving. The time is provided in 24-hour format as 13:00.
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Disclaimer BullNet: The information provided in this document is for educational and informational purposes only and does not constitute financial, investment, or trading advice. Any use of the content is at your own risk. No liability is assumed for any losses or damages resulting from reliance on this information. Trading financial instruments involves significant risks, including the potential loss of all invested capital. There is no guarantee of profits or specific outcomes. Please conduct your own research and consult a professional financial advisor if needed.
Disclaimer TradingView: According to the www.tradingview.com
Copyright: 2025-BULLNET - All rights reserved.
Roadmap:
Version 1.0 03.03.2025
Weekly Covered Calls Strategy with IV & Delta LogicWhat Does the Indicator Do?
this is interactive you must use it with your options chain to input data based on the contract you want to trade.
Visualize three strike price levels for covered calls based on:
Aggressive (closest to price, riskier).
Moderate (mid-range, balanced).
Low Delta (farthest, safer).
Incorporate Implied Volatility (IV) from the options chain to make strike predictions more realistic and aligned with market sentiment. Adjust the risk tolerance by modifying Delta inputs and IV values. Risk is defined for example .30 delta means 30% chance of your shares being assigned. If you want to generate steady income with your shares you might want to lower the risk of them being assigned to .05 or 5% etc.
How to Use the Indicator with the Options Chain
Start with the Options Chain:
Look for the following data points from your options chain:
Implied Volatility (IV Mid): Average IV for a particular strike price.
Delta:
~0.30 Delta: Closest strike (Aggressive).
~0.15–0.20 Delta: Mid-range strike (Moderate).
~0.05–0.10 Delta: Far OTM, safer (Low Delta).
Strike Price: Identify strike prices for the desired Deltas.
Open Interest: Check liquidity; higher OI ensures tighter spreads.
Input IV into the Indicator:
Enter the IV Mid value (e.g., 0.70 for 70%) from the options chain into the Implied Volatility field of the indicator.
Adjust Delta Inputs Based on Risk Tolerance:
Aggressive Delta: Increase if you want strikes closer to the current price (riskier, higher premium).
Default: 0.2 (20% chance of shares being assigned).
Moderate Delta: Balanced risk/reward.
Default: 0.12 (12%)
Low Delta: Decrease for safer, farther OTM strikes.
Default: 0.05 (5%)
Visualize the Chart:
Once inputs are updated:
Red Line: Aggressive Strike (closest, riskiest, higher premium).
Blue Line: Moderate Strike (mid-range).
Green Line: Low Delta Strike (farthest, safer).
Step-by-Step Workflow Example
Open the options chain and note:
Implied Volatility (IV Mid): Example 71.5% → input as 0.715.
Delta for desired strikes:
Aggressive: 0.30 Delta → Closest strike ~ $455.
Moderate: 0.15 Delta → Mid-range strike ~ $470.
Low Delta: 0.05 Delta → Farther strike ~ $505.
Open the indicator and adjust:
IV Mid: Enter 0.715.
Aggressive Delta: Leave at 0.12 (or adjust to bring strikes closer).
Moderate Delta: Leave at 0.18.
Low Delta: Adjust to 0.25 for safer, farther strikes.
View the chart:
Compare the indicator's strikes (red, blue, green) with actual options chain strikes.
Use the visualization to: Validate the risk/reward for each strike.
Align strikes with technical trends, support/resistance.
Adjusting Inputs Based on Risk Tolerance
Higher Risk: Increase Aggressive Delta (e.g., 0.15) for closer strikes.
Use higher IV values for volatile stocks.
Moderate Risk: Use default values (0.12–0.18 Delta).
Balance premiums and probability.
Lower Risk: Increase Low Delta (e.g., 0.30) for farther, safer strikes.
Focus on higher IV stocks with good open interest.
Key Benefits
Simplifies Strike Selection: Visualizes the three risk levels directly on the chart.
Aligns with Market Sentiment: Incorporates IV for realistic forecasts.
Customizable for Risk: Adjust inputs to match personal risk tolerance.
By combining the options chain (IV, Delta, and liquidity) with the technical chart, you get a powerful, visually intuitive tool for covered call strategies.
IBIT Premium to CoinbaseThe BTC ETF premium indicator for TradingView is a specialized tool designed to measure and visualize the premium or discount of the iShares Bitcoin Trust (IBIT), an investment vehicle that holds Bitcoin, relative to the actual price of Bitcoin on the Coinbase exchange. This indicator can be particularly insightful for traders interested in the BTC securities market and those analyzing the demand for Bitcoin as reflected by institutional investment products.
#### Description:
The BTC ETF premium indicator in TradingView leverages an advanced Pine Script algorithm to calculate the premium (or discount) percentage of IBIT compared to the spot price of Bitcoin (BTC/USD) on Coinbase. The premium is a critical insight that reflects market sentiment and potentially arbitrage opportunities between the trust's share price and the underlying cryptocurrency asset.
Here's how the indicator works:
1. **Calculation Methodology:**
- **Implied Bitcoin Price of IBIT:** We determine the implied price of Bitcoin within IBIT by dividing the IBIT closing price by the known ratio of Bitcoin per share.
- **IBIT Premium to Coinbase:** The percentage premium is then calculated as:
$$\text{IBIT Premium} = \frac{(\text{Implied Bitcoin Price of IBIT } - \text{Actual Bitcoin Price on Coinbase})}{\text{Actual Bitcoin Price on Coinbase}} \times 100$$
- This calculation is performed using the closing prices on a per-minute basis to ensure timely and accurate analysis.
2. **Visualization:** The indicator plots the premium as a step line chart, making it easy to visualize changes over time. A dynamic label accompanies the plot, displaying the implied Bitcoin price, the actual percentage premium or discount, and whether the premium is trending up or down compared to the previous day's value.
3. **Usage Scenario:** Traders can use this indicator to monitor the live premium 24/7 and analyze how it behaves during different market conditions, including when the equity market, where IBIT is traded, is closed.
#### Additional Features:
- **Color-Coding:** The premium is color-coded in green when positive (premium) and in red when negative (discount), aiding quick visual assessment.
- **Zero-Line Reference:** A horizontal line is drawn at zero to easily identify when IBIT is trading at par with the spot price of Bitcoin.
- **Real-Time Label Updates:** The label updates in real time with the latest premium/discount information and includes an arrow to signify the trend direction.
#### Access and Usage:
The indicator can be favorited or added to your TradingView charts. You are also welcome to use the source code as a foundation for further customization to suit your trading strategies.
#### Notes:
Please consider that the IBIT has specific trading hours, and the indicator can show live changes even when its market is closed, which might lead to discrepancies from official static data. For best performance, use this indicator alongside the IBIT candlestick chart on TradingView.
GBTC Premium to CoinbaseThe BTC ETF premium indicator for TradingView is a specialized tool designed to measure and visualize the premium or discount of the Grayscale Bitcoin Trust (GBTC), an investment vehicle that holds Bitcoin, relative to the actual price of Bitcoin on the Coinbase exchange. This indicator can be particularly insightful for traders interested in the BTC securities market and those analyzing the demand for Bitcoin as reflected by institutional investment products.
#### Description:
The BTC ETF premium indicator in TradingView leverages an advanced Pine Script algorithm to calculate the premium (or discount) percentage of GBTC compared to the spot price of Bitcoin (BTC/USD) on Coinbase. The premium is a critical insight that reflects market sentiment and potentially arbitrage opportunities between the trust's share price and the underlying cryptocurrency asset.
Here's how the indicator works:
1. **Calculation Methodology:**
- **Implied Bitcoin Price of GBTC:** We determine the implied price of Bitcoin within GBTC by dividing the GBTC closing price by the known ratio of Bitcoin per share.
- **GBTC Premium to Coinbase:** The percentage premium is then calculated as:
$$\text{GBTC Premium} = \frac{(\text{Implied Bitcoin Price of GBTC} - \text{Actual Bitcoin Price on Coinbase})}{\text{Actual Bitcoin Price on Coinbase}} \times 100$$
- This calculation is performed using the closing prices on a per-minute basis to ensure timely and accurate analysis.
2. **Visualization:** The indicator plots the premium as a step line chart, making it easy to visualize changes over time. A dynamic label accompanies the plot, displaying the implied Bitcoin price, the actual percentage premium or discount, and whether the premium is trending up or down compared to the previous day's value.
3. **Usage Scenario:** Traders can use this indicator to monitor the live premium 24/7 and analyze how it behaves during different market conditions, including when the equity market, where GBTC is traded, is closed.
#### Additional Features:
- **Color-Coding:** The premium is color-coded in green when positive (premium) and in red when negative (discount), aiding quick visual assessment.
- **Zero-Line Reference:** A horizontal line is drawn at zero to easily identify when GBTC is trading at par with the spot price of Bitcoin.
- **Real-Time Label Updates:** The label updates in real time with the latest premium/discount information and includes an arrow to signify the trend direction.
#### Access and Usage:
The indicator can be favorited or added to your TradingView charts. You are also welcome to use the source code as a foundation for further customization to suit your trading strategies.
#### Notes:
Please consider that the GBTC has specific trading hours, and the indicator can show live changes even when its market is closed, which might lead to discrepancies from official static data. For best performance, use this indicator alongside the GBTC candlestick chart on TradingView.
VOLQ Sigma TableThis indicator replaces the implied volatility of VOLQ with the daily volatility and reflects that value into the price on the NDX chart to create the VOLQ standard deviation table.
It will only be useful for stocks related to the Nasdaq Index.
For example, NDX, QQQ or so.
And we want to predict the range of weekly fluctuations by plotting those values as a line in the future.
It is expressed as High 2σ by adding the standard deviation 2 sigma value of the VOLQ value from last week's closing price.
It is expressed as High 1σ by adding the standard deviation 1 sigma value of the VOLQ value from last week's closing price.
It is expressed as Low 1σ by subtracting the standard deviation 1 sigma value of the VOLQ value from the closing price of the previous week.
It is expressed as Low 2σ by subtracting the standard deviation 2 sigma value of the VOLQ value from last week's closing price.
1day predicts daily fluctuations.
2day predicts 2-day fluctuations.
3day predicts 3-day fluctuations.
4day predicts 4-day fluctuations.
5day predicts 5-day fluctuations.
In the settings you can select the start date to display the VOLQ line via input.
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What motivated me to create this indicator?
From my point of view, the reason for classifying vix volq historical volatility (realized volatility) is that the most important point is that VIXX and VolQ are calculated from implied volatility. It can be standardized as one-month volatility. There are many strike prices, but exchanges use the implied volatility of options traded on their own exchanges.
Because historical volatility depends on how the period is set, to compare with VIXX, we compare it with a month, that is, 20 business days. One-month implied volatility means (actually different depending on the strike price), because option traders expect that the one-month volatility will be this much, and it is the volatility created by volatility trading.
So we see it as the volatility expected by derivatives traders, especially volatility traders.
I'm trying to infer what the market thinks will fluctuate this much from the numbers generated there.
Cox-Ross-Rubinstein Binomial Tree Options Pricing Model [Loxx]Cox-Ross-Rubinstein Binomial Tree Options Pricing Model is an options pricing panel calculated using an N-iteration (limited to 300 in Pine Script due to matrices size limits) "discrete-time" (lattice based) method to approximate the closed-form Black–Scholes formula. Joshi (2008) outlined varying binomial options pricing model furnishes a numerical approach for the valuation of options. Significantly, the American analogue can be estimated using the binomial tree. This indicator is the complex calculation for Binomial option pricing. Most folks take a shortcut and only calculate 2 iterations. I've coded this to allow for up to 300 iterations. This can be used to price American Puts/Calls and European Puts/Calls. I'll be updating this indicator will be updated with additional features over time. If you would like to learn more about options, I suggest you check out the book textbook Options, Futures and other Derivative by John C Hull.
***This indicator only works on the daily timeframe!***
A quick graphic of what this all means:
In the graphic, "n" are the steps, in this case we can do up to 300, in production we'd need to do 5-15K. That's a lot of steps! You can see here how the binomial tree fans out. As I said previously, most folks only calculate 2 steps, here we are calculating up to 300.
Want to learn more about Simple Introduction to Cox, Ross Rubinstein (1979) ?
Watch this short series "Introduction to Basic Cox, Ross and Rubinstein (1979) model."
Limitations of Black Scholes options pricing model
This is a widely used and well-known options pricing model, factors in current stock price, options strike price, time until expiration (denoted as a percent of a year), and risk-free interest rates. The Black-Scholes Model is quick in calculating any number of option prices. But the model cannot accurately calculate American options, since it only considers the price at an option's expiration date. American options are those that the owner may exercise at any time up to and including the expiration day.
What are Binomial Trees in options pricing?
A useful and very popular technique for pricing an option involves constructing a binomial tree. This is a diagram representing different possible paths that might be followed by the stock price over the life of an option. The underlying assumption is that the stock price follows a random walk. In each time step, it has a certain probability of moving up by a certain percentage amount and a certain probability of moving down by a certain percentage amount. In the limit, as the time step becomes smaller, this model is the same as the Black–Scholes–Merton model.
What is the Binomial options pricing model ?
This model uses a tree diagram with volatility factored in at each level to show all possible paths an option's price can take, then works backward to determine one price. The benefit of the Binomial Model is that you can revisit it at any point for the possibility of early exercise. Early exercise is executing the contract's actions at its strike price before the contract's expiration. Early exercise only happens in American-style options. However, the calculations involved in this model take a long time to determine, so this model isn't the best in rushed situations.
What is the Cox-Ross-Rubinstein Model?
The Cox-Ross-Rubinstein binomial model can be used to price European and American options on stocks without dividends, stocks and stock indexes paying a continuous dividend yield, futures, and currency options. Option pricing is done by working backwards, starting at the terminal date. Here we know all the possible values of the underlying price. For each of these, we calculate the payoffs from the derivative, and find what the set of possible derivative prices is one period before. Given these, we can find the option one period before this again, and so on. Working ones way down to the root of the tree, the option price is found as the derivative price in the first node.
Inputs
Spot price: select from 33 different types of price inputs
Calculation Steps: how many iterations to be used in the Binomial model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 300
Strike Price: the strike price of the option you're wishing to model
% Implied Volatility: here you can manually enter implied volatility
Historical Volatility Period: the input period for historical volatility; historical volatility isn't used in the CRRBT process, this is to serve as a sort of benchmark for the implied volatility,
Historical Volatility Type: choose from various types of implied volatility, search my indicators for details on each of these
Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country
% Manual Risk-free Rate: here you can manually enter the risk-free rate
Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate
% Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield
Adjust for Dividends?: choose if you even want to use use dividends
Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation
Time Now Type: choose how you want to calculate time right now, see the tool tip
Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc
Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours
Expiry date settings: here you can specify the exact time the option expires
Take notes:
Futures don't risk free yields. If you are pricing options of futures, then the risk-free rate is zero.
Dividend yields are calculated using TradingView's internal dividend values
This indicator only works on the daily timeframe
Included
Option pricing panel
Loxx's Expanded Source Types
IV Rank (tasty-style) — VIXFix / HV ProxyIV Rank (tasty-style) — VIXFix / HV Proxy
Overview
This indicator replicates tastytrade’s IV Rank calculation—but built entirely inside TradingView.
Because TradingView does not expose live option-chain implied volatility, the script lets you choose between two widely used price-based IV proxies:
VIXFix (Williams VIX Fix): a fast-reacting volatility estimate derived from price extremes.
HV(30): 30-day annualized historical volatility of daily log returns.
The goal is to approximate the “rich vs. cheap” option volatility environment that traders use to decide whether to sell or buy premium.
Formula
IV Rank answers the question: Where is current implied volatility relative to its own 1-year range?
𝐼
𝑉
𝑅
=
𝐼
𝑉
𝑐
𝑢
𝑟
𝑟
𝑒
𝑛
𝑡
−
𝐼
𝑉
1
𝑦
𝐿
𝑜
𝑤
𝐼
𝑉
1
𝑦
𝐻
𝑖
𝑔
ℎ
−
𝐼
𝑉
1
𝑦
𝐿
𝑜
𝑤
×
100
IVR=
IV
1yHigh
−IV
1yLow
IV
current
−IV
1yLow
×100
IVcurrent: Current value of the chosen IV proxy.
IV1yHigh/Low: Highest and lowest proxy values over the user-defined lookback (default 252 trading days ≈ 1 year).
IVR = 0 → Current IV equals its 1-year low
IVR = 100 → Current IV equals its 1-year high
IVR ≈ 50 → Current IV sits mid-range
How to Use
High IV Rank (≥50–60%)
Options are relatively expensive → short-premium strategies (credit spreads, iron condors, straddles) may be more attractive.
Low IV Rank (≤20%)
Options are relatively cheap → long-premium strategies (debit spreads, calendars, diagonals) may offer better risk/reward.
Combine with your own analysis, liquidity checks, and risk management.
Inputs & Customization
IV Source: Choose “VIXFix” or “HV(30)” as the volatility proxy.
IVR Lookback: Rolling window for 1-year high/low (default 252 trading days).
VIXFix Parameters: Length and stdev multiplier to fine-tune sensitivity.
Info Label: Optional on-chart label displays current IV proxy, 1-year high/low, and IV Rank.
Alerts: Optional alerts when IVR crosses 50, falls below 20, or rises above 80.
Notes & Limitations
This indicator does not pull real option-chain IV.
It provides a close structural analogue to tastytrade’s IV Rank using price-derived proxies for markets where options data is not directly available.
For live option IV, use broker platforms or third-party data feeds alongside this script.
Tags: IV Rank, Implied Volatility, Tastytrade, VIXFix, Historical Volatility, Options, Premium Selling, Debit Spreads, Market Volatility
ATR > VXN Alert (5m)ATR > VXN Volatility Divergence Indicator
This custom TradingView indicator monitors real-time volatility divergence between realized volatility (via Average True Range, ATR) and implied volatility (via the CBOE NASDAQ Volatility Index, VXN). It is inspired by the GJR-GARCH (Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroskedasticity) model, which captures asymmetric volatility dynamics—particularly how markets respond more sharply to negative shocks than to positive ones.
Core Logic:
Chart on NQ (5 minute timeframe)
ATR (5-min) reflects realized intraday volatility of the Nasdaq 100 futures (NQ).
VXN (5-min, delayed) represents forward-looking implied volatility.
The indicator highlights regime shifts in volatility:
ATR < VXN: Volatility compression → potential energy building up (market coiling).
ATR > VXN: Volatility expansion → real movement exceeds expectations → potential breakout zone.
Visuals & Alerts:
Background turns green when ATR crosses above VXN, signaling a bullish expansion regime.
Background turns red when ATR drops below VXN, signaling compression or risk-off environment.
Custom alerts trigger on volatility regime shifts for breakout traders.
Application (Manual GJR-GARCH Strategy):
Similar to how the GJR-GARCH model captures volatility clustering and asymmetry, this indicator identifies when actual price volatility (ATR) begins to spike beyond implied forecasts (VXN), often after periods of contraction—mirroring a conditional variance shock in the GARCH framework.
Traders can align with directional bias using technical confluence (order flow, structure breaks, liquidity zones) once expansion is confirmed.
4 diffs (CB & IBIT Premium)📊 Script Name: 4 diffs (CB & IBIT Premium)
Version: Pine Script® v6
Overlay: Yes (table displayed on chart)
🧠 What it Does:
This script tracks four important Bitcoin price differentials to monitor spot/perpetual/futures price inefficiencies and ETF premium/discounts, and displays them in a live table on the chart. It helps traders identify arbitrage opportunities or institutional pricing signals.
📈 Displayed Metrics:
Coinbase Premium
→ Difference between Coinbase spot and Binance spot prices.
→ Use case: US vs. offshore spot market divergence.
Coinbase Spot vs Binance Perpetual
→ Difference between Coinbase spot and Binance perpetual price.
→ Use case: Spot-perp basis, often used for funding rate insights or market stress.
Bybit vs Binance Perpetual
→ Difference between Bybit perp and Binance perp price.
→ Use case: Compare derivative pricing across major offshore exchanges.
IBIT Premium (CME vs ETF-implied)
→ Compares CME futures price vs. IBIT’s implied spot BTC value
→ IBIT implied BTC = IBIT ETF price ÷ (BTC held / shares outstanding)
→ Use case: Gauge institutional premium/discount and ETF arbitrage clues.
🛠️ Customization:
Text color of the table is adjustable via the input setting.
📌 Visual Output:
A fixed 2×4 table appears in the top-right corner of the chart.
Each row shows a label and the live price difference in USD.
Dynamic Portfolio TrackerDynamic Portfolio Tracker
The Dynamic Portfolio Tracker is a visual tool for actively managing and monitoring a multi-asset portfolio directly on TradingView. It allows users to input up to 15 custom assets (with a default setup for 5), define how much of each asset they hold, and assign a target allocation percentage to each. The script then calculates live market prices, total portfolio value, current vs. target weightings, and provides clear, color-coded instructions on whether to buy, sell, or hold each asset. It displays all this data in an on-chart table, showing both the dollar amount and the quantity to adjust for each asset, helping users keep their portfolio aligned with their strategy in real time.
How to Use the Inputs (What Each Field Means)
1. Portfolio Assets (Tickers)
Fields: Asset 1 Ticker, Asset 2 Ticker, …, Asset 15 Ticker
What it does: Lets you select which assets (crypto, stocks, etc.) you want to track. These are live symbols pulled from TradingView.
2. Asset Quantities
Fields: Asset 1 Amount, Asset 2 Amount, …, Asset 15 Amount
What it means: How much of each asset you currently hold. For example:
• 0.03 BTC
• 2.1 ETH
Why it’s needed: The script multiplies this by the live price to calculate the current dollar value of each asset in your portfolio.
3. Target %
Fields: Asset 1 Implied %, Asset 2 Implied %, …, Asset 15 Implied %
What it means: Your desired allocation for each asset. For example:
• 40% BTC
• 20% ETH
• 10% SOL, etc.
Important: These must total 100% or less across all assets. The script checks this and shows an error if the total exceeds 100%.
The Dynamic Portfolio Tracker displays two powerful on-chart tables:
1. Main Table — Per Asset Breakdown
This table shows detailed, real-time information for each asset in your portfolio. Each row represents a different asset, and each column has a specific meaning:
Column What It Means
Asset = The symbol of the asset (e.g., BTCUSD, ETHUSD), auto-stripped from the exchange name.
Price = The current market price of the asset, pulled live from TradingView.
Quantity = How much of that asset you currently hold, entered manually in the inputs.
Target % = The percentage of your total portfolio you want this asset to represent.
Actual % = What percentage of your portfolio it currently makes up (based on price × quantity).
Target Value = How much (in $) this asset should be worth in your portfolio.
Actual Value = How much (in $) this asset is currently worth.
Instruction = Whether to Buy, Sell, or Hold to match your target allocation.
Value Change = The dollar amount you’d need to buy/sell to rebalance this asset.
Units to Trade = The number of asset units to buy/sell to reach the target value.
2. Portfolio Summary Table — Portfolio Totals
This smaller table appears in the top-right corner and summarizes your entire portfolio at a glance:
Target % = Total of all your assigned target allocations (should equal 100%).
Actual % = Actual portfolio composition (always 100% unless your capital is zero).
Target Value = Total value your portfolio should be based on your target percentages.
Actual Value = Current live total value of your portfolio.
If there’s a discrepancy between Target Value and Actual Value, the difference is shown in each row of the main table, so you can adjust individual assets accordingly.
Privacy First: Hide Sensitive Financial Data
A unique feature of this tool is the ability to hide sensitive financial data, such as:
• Target Value
• Actual Value
• Total Portfolio Value
You can turn these off using toggle settings, and they’ll be replaced with a crossed-out eye icon (👁️🗨️) — just like on modern crypto exchanges. This feature makes the script safe for streaming, screenshots, or sharing publicly while protecting your privacy.
But more importantly:
Feelings are the enemy of good investing.
Seeing the value of your portfolio fluctuate can trigger fear or greed. By hiding your dollar values, you’re not just securing your data — you’re reducing the temptation to react emotionally.
It’s just numbers. Systems over Feelings.
Table Automatically Adapts to Your Asset Count
The Dynamic Portfolio Tracker is designed to scale with your portfolio. Simply choose how many assets you want to track (up to 15), and the table will automatically resize to fit exactly that number — no wasted space or empty rows.
• Select 1 to 15 assets using the “Number of Assets” input
• The table expands or contracts dynamically to show only those rows
• All calculations, summaries, and layout elements adjust accordingly in real time
This keeps the interface clean, focused, and perfectly tailored to your setup — whether you’re tracking 3 coins or managing a full portfolio of 12+ tokens.
Customize Your Table to Match Your Style
The Dynamic Portfolio Tracker offers a full suite of visual customization options, allowing you to tailor the table to your charting style or stream layout. You can:
• Choose text colors for labels, values, and headers
• Set background colors for the full table and header row — or turn them off completely for a clean, transparent look
• Control border and frame settings, including color, thickness, or disabling them entirely
• Pick custom colors for Buy and Sell signals in the rebalance column
• Adjust table font size from tiny to large to match your resolution or preferences
Special Thanks
This tool wouldn’t exist without the knowledge and inspiration gained through The Real World. A sincere thank you to the Investing Master, the Guides, and Professor Adam — your frameworks and lessons brought clarity, discipline, and structure to this build.
And of course, glory to L4 — where real men are made.
GEX Profile [PRO] Real Auto-Updated Gamma Exposure Levels𝗥𝗲𝗮𝗹 𝗚𝗘𝗫 𝗟𝗲𝘃𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗔𝘂𝘁𝗼-𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟲𝟱+ 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗼𝘀𝘁 𝗟𝗶𝗾𝘂𝗶𝗱 𝗨.𝗦. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗦𝘆𝗺𝗯𝗼𝗹𝘀 (including 𝟬𝗗𝗧𝗘 𝗳𝗼𝗿 𝗦𝗣𝗫, SPY, QQQ, TLT, IWM, etc...)
🔃 Dynamic Updates : Receive precise GEX levels with auto-updating metrics up to 5 times a day throughout the trading session—no manual refresh needed!
🍒 Strategically Developed : Built by experienced options traders to meet the needs of serious options market participants.
🕒 0DTE? No Problem! : Designed with 0DTE traders in mind, our indicator keeps you updated with GEX levels and seamless auto-refresh to capture every crucial market shift.
📈 Optimized for Option Traders : See accurate GEX and NETGEX profiles for multiple expirations to maximize strategic potential.
🔶 Comprehensive GEX Levels
This indicator provides unparalleled insight into market dynamics with levels like Call/Put Support, Resistance, HVL (High Volatility Level), and Call/Put Walls. These levels are auto-updated based on live market movements and reflect gamma shifts and volatility signals essential for options traders.
🔶 Ideal for 0DTE and Multi-Leg Strategies
Track essential GEX levels across expirations with our unique Cumulative (⅀) and Selected Alone (⊙) calculation models. Customize your view to reveal high-impact levels across multiple expirations or focus on a specific expiration for a targeted strategy.
🔶 Coverage of 165+ Highly Liquid U.S. Symbols
Compatible with over 165 U.S. market symbols, including SP:SPX , AMEX:SPY , NASDAQ:QQQ , NASDAQ:TLT , AMEX:GLD , NASDAQ:NVDA , and more. The watchlist is expanding continuously to meet the needs of active traders. List of Compatible Symbols Available Here: www.tradingview.com
🔶How does the indicator work and why is it unique?
This is not just another GEX indicator. It incorporates 15min delayed option chain data from ORATS as data provider, processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the key GEX levels using specific formulas (see detailed below). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
Unlike other providers that only set GEX levels at market open, this indicator adjusts dynamically throughout the day, providing updated insights across the trading day and capturing gamma shifts as the market moves.
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🌑 𝗗 𝗢 𝗖 𝗨 𝗠 𝗘 𝗡 𝗧 𝗔 𝗧 𝗜 𝗢 𝗡 🌑
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🔶 Understanding GEX (Gamma Exposure) and Gamma Profiling
Gamma Exposure (GEX) is a crucial concept in options trading because it reveals how options market positions can influence the dynamics of asset prices. In essence, GEX measures the collective gamma exposure of options market participants, impacting overall market stability and price movements.
🔹 What is GEX?
At its core, GEX captures the aggregate impact of gamma, a key options Greek, which tells us how an option's delta changes in response to price movements in the underlying asset. Positive or negative GEX levels can reflect the collective bullish or bearish stance of the market:
Positive GEX (far above HVL) : Indicates a net bullish positioning by options holders. When GEX is strongly positive, it suggests that as the asset price increases, market participants might need to buy more of the asset to maintain their hedges. This behavior can fuel further upward momentum.
Negative GEX (far below HVL) : Implies a net bearish positioning. In a strongly negative GEX environment, declines in the asset's price might prompt participants to sell, potentially exacerbating the downward movement.
🔹 The Influence of GEX on Strike Prices and Expiration
A unique feature of GEX is its impact near expiration dates. As options approach expiration, GEX levels can “pin” the price to specific strike levels, where options positions are concentrated. This pinning effect arises as market makers adjust their hedging strategies, often causing the asset price to gravitate towards certain strike prices, where a large volume of options contracts sits.
🟨 Overview of our GEX Calculation Models for Options Traders 🟨
Our GEX indicator models were developed with serious options traders in mind, providing flexibility beyond typical GEX providers. We know that using GEX levels for multi-leg strategies, where the underlying doesn't need a strong trend to be profitable , calls for a nuanced approach that aligns with different trading horizons. Here’s a detailed breakdown of our GEX calculation models and how they support strategic trading across varying timeframes.
Thus, the HVL an orher CALL/PUT WALLS depends on the indicator's selected calculation mode and expiration. The NETGEX profile of the chosen expiration appears on the HVL line , which automatically updates five times during trading hours , except for 0DTE, which reflects the value set at market open.
🔶 Cumulative Expiration (⅀) Calculation Method
This method aggregates GEX data for all expirations up to the selected date , giving you a more comprehensive view of market dynamics. We recommend using this method, as it allows you to see how combined expirations impact GEX levels, which can be critical when setting up trades with a longer time horizon.
🔶 Selected Alone (⊙) Calculation Method
This option displays the GEX profile specific to only the chosen expiration , providing a unique, time-bound view. This approach is ideal for those seeking precise insight into how an individual expiration is performing without the broader context of other expirations.
🔶 Example of using calculation methods:
With options trading, especially for multi-leg strategies, choosing the right expiration and calculation model is crucial. Let’s break down an example:
Suppose you’re considering a Friday (4DTE) front-leg diagonal on the SPX at the start of the week. In this case, the focus isn’t strictly on any single expiration (like 0DTE or 4DTE individually), but rather on what might happen cumulatively by Friday across all expirations . Here, the Cumulative Expiration (⅀) model comes into play, as it shows you an aggregated view of the GEX profile, factoring in all strikes and legs for all expirations leading up to the selected date.
For most use cases, we recommend setting your indicator to the Cumulative (⅀) model , which provides a broad and insightful look at GEX levels across multiple expirations. However, you can always switch to Selected Alone (⊙) for targeted analysis of an individual expiration. Remember, 0DTE defaults to “Selected Alone”, and Every Expiry always shows a cumulative value by default.
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🟦 HVL (High Volatility Level) 🟦
Also known as the Gamma FLIP level or Zero Gamma , it represents the price level at which the gamma environment transitions from positive to negative or vice versa. The High Volatility Level (HVL) is a critical point for understanding gamma shifts and anticipating volatility. This shift influences how market makers hedge their positions, potentially increasing or dampening market volatility.
🔷 Understanding the Gamma Flip and HVL
At its core, the gamma flip represents the point where market makers may transition from a net positive to a net negative gamma position, or the reverse. When prices move above HVL, gamma is positive, often leading to lower volatility due to the stabilizing effects of market makers’ hedging. Conversely, when prices drop below HVL, gamma flips negative, and hedging by market makers can amplify volatility as they trade with the direction of price movements.
The HVL (High Volatility Level) is particularly important as it signals a shift in the impact of price movements on the GEX profile. Using the cumulative calculation mode, GEX values are aggregated across all strikes and expirations up to the selected expiration, helping to pinpoint the point where the GEX curve's slope changes from negative to positive.
🔷 Implications for Traders and Market Makers
For market makers, crossing below HVL into a negative gamma zone means that they hedge in the same direction as price movements, potentially amplifying volatility. For traders, understanding HVL's role is essential to choosing strategies that align with the prevailing volatility regime:
Positive GEX 🟢:
Above HVL, where GEX is positive, market makers hedge by buying stocks as prices fall and selling as prices rise. This has a stabilizing effect, creating a lower-volatility environment.
Negative GEX 🔴:
Below HVL, where GEX is negative, market makers' hedging aligns with price movements, increasing volatility. Here, they buy as prices rise and sell as they fall, reinforcing price direction.
🔷 HVL as a Momentum and Volatility Indicator
The HVL offers traders insight into potential shifts in market momentum. For example, above HVL, if the price increases, Net GEX also rises, which stabilizes prices as market makers hedge in opposition to price direction. Below HVL, however, a price rise decreases Net GEX, creating conditions where market makers’ hedging amplifies price movements, resulting in a more volatile environment.
HVL also acts as a significant support level, often preceding put supports. If the price falls below this level, traders may expect heightened volatility and increased bearish sentiment.
Knowing the location of HVL is vital for positioning yourself on the right side of volatility. By monitoring the HVL, traders can better anticipate shifts in sentiment and align strategies with prevailing market dynamics.
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🟩 Call Resistance and Call Wall Levels 🟩
In options trading, understanding GEX levels like Call Resistance and Call Wall levels is crucial for navigating potential price inflection points. Our indicator provides these levels directly on your chart, allowing you to customize and optimize your trading approach. Here’s a detailed guide to help you understand and use Call Resistance and additional Call Wall levels effectively.
🟢 Call Resistance Level
The Call Resistance Level is a key point where our model indicates heightened Call GEX concentration. This level serves as a potential resistance area where price movement may face a barrier, slowing or even reversing before a breakout. Here’s how the Call Resistance Level can influence market behavior:
Resistance and Price Reversal ⬇️ : Similar to the Put Support level, the Call Resistance acts as a "sticky" price level, where upward movement encounters resistance. When the price approaches this level, it’s common for market makers to begin shorting to maintain delta neutrality. This shorting activity, combined with the potential monetization of calls, introduces a technical bearish force in the short term, often causing the price to bounce downward.
Upside Acceleration Point ⬆️ : If investors reposition calls to higher strikes as the price reaches Call Resistance, this level can roll up, allowing the price to push upward and potentially accelerating the rally. This effect can drive the market to higher levels as market makers adjust their positions accordingly.
🟢 Additional Call Wall Levels
Our model identifies the second and third-highest Call GEX levels, known as additional Call Walls. These levels are often secondary resistance points but hold significance as they add layers of possible resistance or breakout points. They offer similar potential as the primary Call Resistance level, acting as either:
Resistance Zones: Slowing the price momentum as it approaches these levels.
Inflection Points for Upside Momentum: Allowing for a possible continuation of upward movement if prices break through.
🟢 How to Trade the Call Resistance Level
To use the Call Resistance level effectively, look for possible price rejections or consolidations as the price approaches this zone. Here are the main scenarios:
Bounce to Downside: As the price nears the Call Resistance level, market makers’ delta-hedging activity (through shorting) can turn this level into a short-term bearish force, leading to price pullbacks.
Rolling the Position: For bulls, a key objective at the Call Resistance level is to see investors roll their call positions higher, effectively moving the resistance up. This repositioning may lead to incremental price gains as the Call Resistance level rises with each roll.
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🟥 Put Support and Put Wall Levels 🟥
In options trading, understanding GEX levels like Put Support and secondary Put Wall levels is essential for managing potential price support points and gauging downside risk. Our indicator places these levels directly on your chart, allowing for customization to enhance your trading strategy. Here’s a detailed guide to help you leverage the Put Support and additional Put Wall levels effectively.
🔴 Put Support Level
The Put Support Level is a key zone where our model shows the highest concentration of negative GEX, representing an area with substantial put option interest. This level functions as a potential support zone, where price may stabilize or bounce upward, or as an inflection point, signaling increased downside momentum. Here’s how the Put Support Level can affect market behavior:
Support and Price Reversal🔺 : Similar to how Call Resistance operates on the upside, the Put Support Level often acts as a "sticky" level on the downside, where price finds support. As the asset price moves closer to this level, market makers begin adjusting their positions, frequently buying to maintain delta neutrality. This activity can create a temporary short squeeze, pushing prices back up.
Downside Acceleration Point 🔻 : If the asset continues moving lower, triggering more hedging activity, this level can become a tipping point for accelerated downside momentum.
🔴 Additional Put Wall Levels
Our model also identifies the second and third-highest negative GEX levels, known as secondary Put Walls. These levels are often seen as secondary support points and hold significance by adding layers of support or potential downside inflection points. Like the primary Put Support Level, they can act in two ways:
Support Zones: Helping slow price declines as they approach these levels.
Downside Inflection Points: Allowing further price decline if the support fails.
🔴 How Investors Hedge with Put Options
Investors commonly use put options to hedge long positions and protect portfolios, especially during times of market stress when implied volatility rises. This demand for puts increases the Put Skew, as market makers short to remain delta hedged.
As prices approach the Put Support Level, the hedging activity often intensifies because more puts become At the Money (ATM) or In the Money (ITM). To realize the value of their hedges, investors typically monetize these puts at this level, triggering the closing of short positions by market makers and resulting in a price bounce.
🔴 The Role of Implied Volatility
Implied Volatility (IV) is also a critical factor since it directly influences market flows. If IV driving put flows decreases, market makers may buy back shorts, which contributes to the bounce at the Put Support Level. Additionally, another Greek, Vanna—representing changes in delta due to IV shifts—plays a vital role here. As IV changes, Vanna affects delta-hedging adjustments, adding a layer of complexity to understanding market makers' actions around these support levels.
🔴 Possible Price Scenarios at the Put Support Level
When the price reaches the Put Support Level, there are generally two scenarios:
Bounce to Upside🔺 : The Put Support Level is where substantial put hedging activity happens. As prices approach, market makers adjust their delta by buying, which can push prices back up.
Roll Positions🔻 : After monetizing puts, investors have two options: roll hedges to higher strikes if they expect a bullish move, or open new out-of-the-money puts at lower strikes. If new hedges are set at lower levels, the Put Support level may also shift lower, creating a new bearish force as market makers begin hedging these new positions.
🟨 Customizing Put Support/Call Resistance and Put/Call Wall Levels on Your Chart
Our indicator settings provide extensive customization options for displaying Put Support, Call Resistance, and Put/Call Wall levels.
You can:
adjust the depth to highlight the highest positive or negative NETGEX levels
choose to display relative data, show only the colored strike line
adjust the offset for enhanced visibility.
This flexibility helps you focus on the critical details that best align with your trading strategy, ensuring a clearer and more tailored view of the GEX levels on your chart.
Currently, we examine the top three levels with the highest positive and negative NETGEX values, allowing you to view seven key GEX levels on your chart (3 Call + 1 HVL + 3 Put). However, in the near future, we plan to expand this to seven levels per side, resulting in a total of up to 15 significant GEX levels on the chart instead of the current 7. This enhancement will cater to all needs, especially benefiting 0DTE traders.
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🔶 ADDITIONAL IMPORTANT COMMENTS
🔹- Why is there a slight difference between the displayed data and other GEX provider's data like MenthorQ, GammaEdge, SpotGamma, GEXBot, etc?
There are two reasons for this, and one is beyond our control:
🔹 (1) Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before U.S. market close.
You don’t need to refresh your window; our latest refreshed data pack is always automatically applied to your indicator. You can see the time elapsed since the last update by hovering over the HVL.
🔹 (2) GEX Levels with Intraday Updates Based on Price Movements
The TanukiTrade Options GEX Indicator for TradingView provides open interest data with a 15-minute delay after the market opens. Using this data, we calculate and update the relevant levels throughout the trading day, reflecting almost real-time price changes and gamma values. Unlike other GEX providers, who set their GEX levels solely at market open without further updates, we dynamically adjust our levels intraday to capture significant price shifts.
🔹 Automatic & Seamless Intraday Updates and Special Cases
For our indicator, the HVL (High Volatility Level) reflects the selected calculation mode and expiration. We update these NETGEX profiles five times throughout the trading day, with one exception: 0DTE data, which is set at market open and does not update intraday due to the rapid narrowing of gamma levels . Note that similar to other GEX providers, our 0DTE remains fixed at open, while cumulative values update during the day based on almost real-time market movements.
🔹Consistent SPX 0DTE GEX Levels with Morning Open Interest Updates Only
For SPX, the 0DTE (Zero Days to Expiration) options and GEX levels are calculated based on openinterest data provided by the clearinghouse at market open. Due to the exponential narrowing of gamma levels throughout the day, we do not update these levels intraday, unlike other expirations. Therefore, if you select the expiring contract on that day, you’ll see the exact morning level, as it was calculated at market open. This status is also published the previous evening, based on the data available then, so you can already view the levels for the following day’s 1DTE (next day’s 0DTE) before market close. After market open, around 15 minutes later, this level is updated with the latest open interest data and remains unchanged for the rest of the day. Other providers take a similar approach. We do not support intraday volume-based GEX calculations, as our benchmarks show this can produce misleading results.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived GEX metrics are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with paid delayed data and we are not a data provider; therefore, we do not bear any financial or other liability.
