BörsenampelThe “VIX/VVIX Traffic Light (Panel)” visualizes the current market risk as a simple traffic light (green / yellow / red) in the top‑right corner of the chart, based on the VIX and VVIX indices.
How it works
The script loads the VIX and VVIX indices via request.security and evaluates them using user‑defined threshold levels.
Green: VIX and VVIX are below their “green” thresholds, indicating a calm market environment and more risk‑on conditions.
Red: VIX and VVIX are above their “red” thresholds, signalling stress or panic phases with elevated risk.
Yellow: Transitional zone between the two extremes.
Chart display
A small panel with the title “Traffic Light” is shown in the upper‑right corner of the chart.
The central box displays the current status (“GREEN”, “YELLOW”, “RED”) with a matching background color.
Optionally, the current VIX and VVIX values are shown below the status.
Inputs and usage
Symbols for VIX and VVIX can be freely chosen (default: CBOE:VIX and CBOE:VVIX).
The green/red thresholds can be adjusted to fit personal volatility rules or different markets.
Indicateurs et stratégies
Volatility Risk PremiumTHE INSURANCE PREMIUM OF THE STOCK MARKET
Every day, millions of investors face a fundamental question that has puzzled economists for decades: how much should protection against market crashes cost? The answer lies in a phenomenon called the Volatility Risk Premium, and understanding it may fundamentally change how you interpret market conditions.
Think of the stock market like a neighborhood where homeowners buy insurance against fire. The insurance company charges premiums based on their estimates of fire risk. But here is the interesting part: insurance companies systematically charge more than the actual expected losses. This difference between what people pay and what actually happens is the insurance premium. The same principle operates in financial markets, but instead of fire insurance, investors buy protection against market volatility through options contracts.
The Volatility Risk Premium, or VRP, measures exactly this difference. It represents the gap between what the market expects volatility to be (implied volatility, as reflected in options prices) and what volatility actually turns out to be (realized volatility, calculated from actual price movements). This indicator quantifies that gap and transforms it into actionable intelligence.
THE FOUNDATION
The academic study of volatility risk premiums began gaining serious traction in the early 2000s, though the phenomenon itself had been observed by practitioners for much longer. Three research papers form the backbone of this indicator's methodology.
Peter Carr and Liuren Wu published their seminal work "Variance Risk Premiums" in the Review of Financial Studies in 2009. Their research established that variance risk premiums exist across virtually all asset classes and persist over time. They documented that on average, implied volatility exceeds realized volatility by approximately three to four percentage points annualized. This is not a small number. It means that sellers of volatility insurance have historically collected a substantial premium for bearing this risk.
Tim Bollerslev, George Tauchen, and Hao Zhou extended this research in their 2009 paper "Expected Stock Returns and Variance Risk Premia," also published in the Review of Financial Studies. Their critical contribution was demonstrating that the VRP is a statistically significant predictor of future equity returns. When the VRP is high, meaning investors are paying substantial premiums for protection, future stock returns tend to be positive. When the VRP collapses or turns negative, it often signals that realized volatility has spiked above expectations, typically during market stress periods.
Gurdip Bakshi and Nikunj Kapadia provided additional theoretical grounding in their 2003 paper "Delta-Hedged Gains and the Negative Market Volatility Risk Premium." They demonstrated through careful empirical analysis why volatility sellers are compensated: the risk is not diversifiable and tends to materialize precisely when investors can least afford losses.
HOW THE INDICATOR CALCULATES VOLATILITY
The calculation begins with two separate measurements that must be compared: implied volatility and realized volatility.
For implied volatility, the indicator uses the CBOE Volatility Index, commonly known as the VIX. The VIX represents the market's expectation of 30-day forward volatility on the S&P 500, calculated from a weighted average of out-of-the-money put and call options. It is often called the "fear gauge" because it rises when investors rush to buy protective options.
Realized volatility requires more careful consideration. The indicator offers three distinct calculation methods, each with specific advantages rooted in academic literature.
The Close-to-Close method is the most straightforward approach. It calculates the standard deviation of logarithmic daily returns over a specified lookback period, then annualizes this figure by multiplying by the square root of 252, the approximate number of trading days in a year. This method is intuitive and widely used, but it only captures information from closing prices and ignores intraday price movements.
The Parkinson estimator, developed by Michael Parkinson in 1980, improves efficiency by incorporating high and low prices. The mathematical formula calculates variance as the sum of squared log ratios of daily highs to lows, divided by four times the natural logarithm of two, times the number of observations. This estimator is theoretically about five times more efficient than the close-to-close method because high and low prices contain additional information about the volatility process.
The Garman-Klass estimator, published by Mark Garman and Michael Klass in 1980, goes further by incorporating opening, high, low, and closing prices. The formula combines half the squared log ratio of high to low prices minus a factor involving the log ratio of close to open. This method achieves the minimum variance among estimators using only these four price points, making it particularly valuable for markets where intraday information is meaningful.
THE CORE VRP CALCULATION
Once both volatility measures are obtained, the VRP calculation is straightforward: subtract realized volatility from implied volatility. A positive result means the market is paying a premium for volatility insurance. A negative result means realized volatility has exceeded expectations, typically indicating market stress.
The raw VRP signal receives slight smoothing through an exponential moving average to reduce noise while preserving responsiveness. The default smoothing period of five days balances signal clarity against lag.
INTERPRETING THE REGIMES
The indicator classifies market conditions into five distinct regimes based on VRP levels.
The EXTREME regime occurs when VRP exceeds ten percentage points. This represents an unusual situation where the gap between implied and realized volatility is historically wide. Markets are pricing in significantly more fear than is materializing. Research suggests this often precedes positive equity returns as the premium normalizes.
The HIGH regime, between five and ten percentage points, indicates elevated risk aversion. Investors are paying above-average premiums for protection. This often occurs after market corrections when fear remains elevated but realized volatility has begun subsiding.
The NORMAL regime covers VRP between zero and five percentage points. This represents the long-term average state of markets where implied volatility modestly exceeds realized volatility. The insurance premium is being collected at typical rates.
The LOW regime, between negative two and zero percentage points, suggests either unusual complacency or that realized volatility is catching up to implied volatility. The premium is shrinking, which can precede either calm continuation or increased stress.
The NEGATIVE regime occurs when realized volatility exceeds implied volatility. This is relatively rare and typically indicates active market stress. Options were priced for less volatility than actually occurred, meaning volatility sellers are experiencing losses. Historically, deeply negative VRP readings have often coincided with market bottoms, though timing the reversal remains challenging.
TERM STRUCTURE ANALYSIS
Beyond the basic VRP calculation, sophisticated market participants analyze how volatility behaves across different time horizons. The indicator calculates VRP using both short-term (default ten days) and long-term (default sixty days) realized volatility windows.
Under normal market conditions, short-term realized volatility tends to be lower than long-term realized volatility. This produces what traders call contango in the term structure, analogous to futures markets where later delivery dates trade at premiums. The RV Slope metric quantifies this relationship.
When markets enter stress periods, the term structure often inverts. Short-term realized volatility spikes above long-term realized volatility as markets experience immediate turmoil. This backwardation condition serves as an early warning signal that current volatility is elevated relative to historical norms.
The academic foundation for term structure analysis comes from Scott Mixon's 2007 paper "The Implied Volatility Term Structure" in the Journal of Derivatives, which documented the predictive power of term structure dynamics.
MEAN REVERSION CHARACTERISTICS
One of the most practically useful properties of the VRP is its tendency to mean-revert. Extreme readings, whether high or low, tend to normalize over time. This creates opportunities for systematic trading strategies.
The indicator tracks VRP in statistical terms by calculating its Z-score relative to the trailing one-year distribution. A Z-score above two indicates that current VRP is more than two standard deviations above its mean, a statistically unusual condition. Similarly, a Z-score below negative two indicates VRP is unusually low.
Mean reversion signals trigger when VRP reaches extreme Z-score levels and then shows initial signs of reversal. A buy signal occurs when VRP recovers from oversold conditions (Z-score below negative two and rising), suggesting that the period of elevated realized volatility may be ending. A sell signal occurs when VRP contracts from overbought conditions (Z-score above two and falling), suggesting the fear premium may be excessive and due for normalization.
These signals should not be interpreted as standalone trading recommendations. They indicate probabilistic conditions based on historical patterns. Market context and other factors always matter.
MOMENTUM ANALYSIS
The rate of change in VRP carries its own information content. Rapidly rising VRP suggests fear is building faster than volatility is materializing, often seen in the early stages of corrections before realized volatility catches up. Rapidly falling VRP indicates either calming conditions or rising realized volatility eating into the premium.
The indicator tracks VRP momentum as the difference between current VRP and VRP from a specified number of bars ago. Positive momentum with positive acceleration suggests strengthening risk aversion. Negative momentum with negative acceleration suggests intensifying stress or rapid normalization from elevated levels.
PRACTICAL APPLICATION
For equity investors, the VRP provides context for risk management decisions. High VRP environments historically favor equity exposure because the market is pricing in more pessimism than typically materializes. Low or negative VRP environments suggest either reducing exposure or hedging, as markets may be underpricing risk.
For options traders, understanding VRP is fundamental to strategy selection. Strategies that sell volatility, such as covered calls, cash-secured puts, or iron condors, tend to profit when VRP is elevated and compress toward its mean. Strategies that buy volatility tend to profit when VRP is low and risk materializes.
For systematic traders, VRP provides a regime filter for other strategies. Momentum strategies may benefit from different parameters in high versus low VRP environments. Mean reversion strategies in VRP itself can form the basis of a complete trading system.
LIMITATIONS AND CONSIDERATIONS
No indicator provides perfect foresight, and the VRP is no exception. Several limitations deserve attention.
The VRP measures a relationship between two estimates, each subject to measurement error. The VIX represents expectations that may prove incorrect. Realized volatility calculations depend on the chosen method and lookback period.
Mean reversion tendencies hold over longer time horizons but provide limited guidance for short-term timing. VRP can remain extreme for extended periods, and mean reversion signals can generate losses if the extremity persists or intensifies.
The indicator is calibrated for equity markets, specifically the S&P 500. Application to other asset classes requires recalibration of thresholds and potentially different data sources.
Historical relationships between VRP and subsequent returns, while statistically robust, do not guarantee future performance. Structural changes in markets, options pricing, or investor behavior could alter these dynamics.
STATISTICAL OUTPUTS
The indicator presents comprehensive statistics including current VRP level, implied volatility from VIX, realized volatility from the selected method, current regime classification, number of bars in the current regime, percentile ranking over the lookback period, Z-score relative to recent history, mean VRP over the lookback period, realized volatility term structure slope, VRP momentum, mean reversion signal status, and overall market bias interpretation.
Color coding throughout the indicator provides immediate visual interpretation. Green tones indicate elevated VRP associated with fear and potential opportunity. Red tones indicate compressed or negative VRP associated with complacency or active stress. Neutral tones indicate normal market conditions.
ALERT CONDITIONS
The indicator provides alerts for regime transitions, extreme statistical readings, term structure inversions, mean reversion signals, and momentum shifts. These can be configured through the TradingView alert system for real-time monitoring across multiple timeframes.
REFERENCES
Bakshi, G., and Kapadia, N. (2003). Delta-Hedged Gains and the Negative Market Volatility Risk Premium. Review of Financial Studies, 16(2), 527-566.
Bollerslev, T., Tauchen, G., and Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia. Review of Financial Studies, 22(11), 4463-4492.
Carr, P., and Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
Garman, M. B., and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53(1), 67-78.
Mixon, S. (2007). The Implied Volatility Term Structure of Stock Index Options. Journal of Empirical Finance, 14(3), 333-354.
Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65.
HTF Candles & Levels Visualizer - SRHTF Candles & Levels Visualizer is a clean higher‑timeframe visualization tool designed to complement any trading strategy by giving clear context of larger‑TF structure directly on your current chart. It plots the previous high and low for up to three user‑selectable timeframes, and draws them as extended levels with optional labels, making it easy to see where current price sits relative to key higher‑timeframe zones.
The script also renders compact proxy candles for each selected timeframe to the right of current price, so you can visually track HTF candle development without switching charts. Each HTF slot has independent settings: timeframe, color, number of displayed candles, and visibility toggles, along with global controls for line style, label size, candle spacing, and colors.
This tool does not generate trading signals; it focuses purely on multi‑timeframe context and market structure visualization to support your own entries, exits, and risk management.
AlphaTrend | APEX [Singularity]This is a customized Trend Tracer style system designed to capture high-quality moves while filtering out noise. It combines three core "Engines":
1. Kinetic Trend Engine (The "Ribbon")
Logic: Uses a Dual-ALMA Ribbon (Arnaud Legoux Moving Average).
Fast Line (Leader): Responsive, hugs price.
Slow Line (Laggard): Smooth, validates structure.
Signals: "BUY" and "SELL" labels trigger exactly when the ribbon twists (Crossover/Crossunder).
Filters:
Entropy & Hurst: Measures market chaos. The ribbon turns Gray/Faded during choppy conditions to warn against trading.
2. Flow Engine (Whale Validation)
Whale Volume: Checks for relative volume spikes (> 1.2x average) and Money Flow intensity.
Confirmation: Signals are stronger when accompanied by the Whale Icon (🐋), indicating institutional participation.
3. Liquidity Magnets (Targets)
Logic: Automatically detects recent Swing Highs and Lows.
Visuals: Dashed lines extend forward to act as dynamic Support/Resistance levels or Take Profit targets.
Behavior: Lines disappear when price tests (breaks) them, indicating "Liquidity Taken".
Visuals
Cloud: Dynamic Green/Red fill between the ribbon lines.
HUD: Heads-Up Display showing current Trend, Market State (Clean/Chop), Flow Status, and Active Magnets.
Labels: Clean "Tag" style labels for entry signa
Kurtosis with Skew Crossover Focused OscillatorDescription:
This indicator highlights Skewness/Kurtosis crossovers for short-term trading:
Green upward arrows: Skew crosses above Kurtosis → potential long signal.
Red downward arrows: Skew crosses below Kurtosis → potential short signal.
Yellow upward arrows: Extreme negative skew (skew ≤ -1.7) → potential oversold/reversal opportunity.
Oscillator Pane:
Orange = Skewness (smoothed)
Blue = Kurtosis (adjusted, smoothed)
Zero line = visual reference
Usage:
Primarily for 2–5 minute charts, highlighting statistical anomalies and potential short-term reversals that can be used in conjunction with OBV and/or CVD
Arrows signal potential entries based on skew/kurt dynamics.
Potential ideas???????
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Add Supporting Market Context
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Currently, signals are purely based on skew/kurt crossovers. Adding supporting indicators could improve reliability:
Volume / CVD: Identify when crossovers occur with real buying/selling pressure.
Wick Imbalance: Detect forced moves in price structure.
Volatility Regime (Parkinson / ATR): Filter signals during high volatility spikes or compressions.
Experimentation: Try weighting these supporting signals to dynamically confirm or filter skew/kurt crossovers and see if false signals decrease on 2–5 minute charts.
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Dynamic Thresholds & Scaling
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Right now, the extreme skew signal is triggered at a fixed level (skew ≤ -1.7). Future improvements could include:
Adaptive thresholds: Scale extreme skew levels based on recent standard deviation or intraday volatility.
Kurtosis thresholds: Introduce a cutoff for kurtosis to identify “fat-tail” events.
Experimentation: Backtest different adaptive thresholds for both skew and kurt, and see how it affects the precision vs. frequency of signals.
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Multi-Timeframe or Combined Oscillator
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Skew/kurt signals could be combined across multiple intraday timeframes (e.g., 1-min, 3-min, 5-min) to improve confirmation.
Create a composite oscillator that blends short-term and slightly longer-term skew/kurt values to reduce noise.
Experimentation: Compare a single timeframe approach vs multi-timeframe composite, and measure signal reliability and lag.
I'm leaving this open so anyone can experiment with it as this project may be on the backburner, but these are my thoughts so far
The Rumer's Box Theory“The Rumer's Box Theory” is a visual trading indicator that allows traders to quickly identify the previous daily candle’s high and low across any timeframe. It displays a purple box spanning the previous day’s high to low, with a blue horizontal line marking the 50% midpoint for quick reference. The settings also provide options to extend the box and midpoint line to the left, giving traders flexibility in how the indicator appears on the chart.
BLACK SWAN SWEEP (DANIELPEREZ)Crt de velas especificas después del sweep buscar la confirmación del order block para tomar una operacio .
Check specific candlesticks after the sweep to find order block confirmation before taking a trade.
Dynamic 15-Ticker Multi-Symbol Table 2025 EditionTitle:
Dynamic 15-Ticker Multi-Symbol Table 2025 Edition
Description:
This script provides a multi-ticker table for TradingView charts. It is fully open-source and free to use. The table displays up to 15 tickers, including SPY as the baseline symbol. The script updates in real-time on any timeframe.
Features:
SPY baseline: The first row always shows SPY for reference.
Custom tickers: Add up to 14 additional tickers via the input settings. Rows without tickers remain hidden.
Price and direction: Each ticker row displays the current price and an indicator of direction based on recent price movement.
RSI (14) indicator: Shows the current relative strength index value with a simple directional marker.
Volume formatting: Displays volume values in thousands, millions, or billions automatically. Volume change is indicated with directional markers.
Stable layout: The table uses alternating row colors for readability and maintains consistent row count without collapsing or disappearing rows.
Real-time updates: All displayed values refresh automatically on any chart timeframe.
How to use:
Add the script to your chart.
Enter your chosen tickers in the input settings. SPY will remain as the first ticker automatically.
Tickers not entered will remain hidden. When a ticker is removed, the row will be removed-dynamically.
Observe live prices, RSI values, and volume changes directly on your chart without switching symbols.
Additional notes:
The script is fully open-source; users are encouraged to modify or improve it.
No external links or references are required to understand its function.
This script does not repaint and does not require additional requests to update values.
The Rumer's Box Theory“The Rumer's Box Theory” is a visual trading indicator designed to help traders quickly identify the previous daily candle’s high and low ranges across all timeframes. The indicator draws a purple box spanning the previous day’s high to low, with a blue horizontal line at the 50% midpoint for easy reference.
DAILY AND WEEKLY MID LINESDAILY AND WEEKLY MID LINES INDICATOR
Description:
This indicator calculates and visualizes the dynamic midpoint (mid) of the current day and week in real-time. It provides traders with key reference levels based on developing price action.
Features:
Daily Mid Line:
Color: Orange
Thickness: 3 pixels
Style: Solid line
Updates: Automatically recalculates with each new candle
Calculation: Average of the day's highest high and lowest low from market open
Weekly Mid Line:
Color: Blue
Thickness: 3 pixels
Style: Dashed line
Updates: Continuously recalculates throughout the week
Calculation: Average of the week's highest high and lowest low from week start
How It Works:
At the start of each new trading day (00:00), the daily mid line resets and begins calculating from the first candle
At the start of each new trading week (typically Monday), the weekly mid line resets and begins fresh calculations
Both lines extend automatically to the right as new candles form
The lines are dynamic - they adjust as new highs/lows are made during the day/week
Trading Applications:
Support/Resistance Levels:
The mid lines act as natural equilibrium points where price may find temporary support or resistance
Daily mid can serve as intraday pivot, weekly mid as broader market balance point
Trend Analysis:
Price consistently above mid lines suggests bullish momentum
Price consistently below mid lines suggests bearish momentum
Relationship between daily and weekly mid lines shows multi-timeframe alignment
Entry/Exit Signals:
Price crossing above daily mid may indicate short-term bullish momentum
Price crossing below daily mid may indicate short-term bearish momentum
Weekly mid breaks can signal more significant trend changes
Market Context:
Distance between price and mid lines indicates market extremity
Steeper mid line slopes suggest stronger directional momentum
Flat mid lines suggest range-bound or consolidating markets
Confluence Trading:
Combine with other indicators (RSI, MACD, moving averages) for confirmation
Use as dynamic levels for stop-loss placement or take-profit targets
Best Practices:
More effective on higher timeframes (1H, 4H, Daily) for clearer signals
Works well in trending markets where mid lines act as moving support/resistance
Monitor for price rejection or acceptance at mid levels for trading decisions
Use in conjunction with volume analysis for confirmation
Psychological Significance:
Mid points often represent fair value areas where buyers and sellers find temporary equilibrium, making them natural decision points for market participants.
This indicator is particularly useful for day traders, swing traders, and position traders looking for dynamic, real-time reference points that adapt to current market conditions rather than relying on static historical levels.
ATR% Multiple from MA (with QQQ Reference)ATR% Multiple from MA (with QQQ Reference)
This indicator measures how extended a stock's price is from its moving average, normalized by volatility (ATR). It's useful for identifying overbought/oversold conditions and timing profit-taking.
How it works:
ATR% = ATR / Current Price (volatility as % of price)
% Gain From MA = How far price is from the moving average
ATR% Multiple From MA = % Gain From MA ÷ ATR%
Features:
Displays ATR% Multiple for the current symbol
Adds QQQ ATR% Multiple as a market benchmark reference
Shows % Gain From MA and ATR % for additional context
Customizable MA type (SMA, EMA, WMA, VWMA) and lengths
Usage:
Values of 7-10+ suggest taking partial profits (price is extended)
Negative values suggest oversold conditions
Compare your stock's extension to QQQ to gauge relative strength
Inspired by jfsrev's original ATR% Multiple from 50-MA concept, with added QQQ market reference:
Indian Scalper 2025 – PSAR + SMA50 + RSI≤50 + High Volume (75%)Best 1-min / 2-min scalping strategy for NIFTY, BANKNIFTY, FINNIFTY & liquid stocks in 2025
✓ PSAR flip + SMA-50 trend filter
✓ RSI ≤50 (avoids chasing)
✓ Only high-volume candles (bright colour)
✓ Loud mobile alerts with price & SL
✓ 1:2+ RR with PSAR trailing
Works like magic 9:15–11:30 AM and 2–3:20 PM
Made with love for the Indian trading community ♥
ATR% Multiple from MA (with QQQ Reference)ATR% Multiple from MA (with QQQ Reference)
This indicator measures how extended a stock's price is from its moving average, normalized by volatility (ATR). It's useful for identifying overbought/oversold conditions and timing profit-taking.
How it works:
ATR% = ATR / Current Price (volatility as % of price)
% Gain From MA = How far price is from the moving average
ATR% Multiple From MA = % Gain From MA ÷ ATR%
Features:
Displays ATR% Multiple for the current symbol
Adds QQQ ATR% Multiple as a market benchmark reference
Shows % Gain From MA and ATR % for additional context
Customizable MA type (SMA, EMA, WMA, VWMA) and lengths
Usage:
Values of 7-10+ suggest taking partial profits (price is extended)
Negative values suggest oversold conditions
Compare your stock's extension to QQQ to gauge relative strength
Inspired by jfsrev's original ATR% Multiple from 50-MA concept, with added QQQ market reference:
IV Walls (Open Source Code)Russell Capital Group
Code is completely open source. You are encouraged to make a copy as it is necessary for applying the indicator to multiple symbols. Each day's derived data must be plotted by code. Data is derived from the Fractal X software.
Message @ryd3rama on discord for more information or help.
Rating for each momentMoment Score Labels is a Pine v5 overlay indicator that shows momentum “ratings” (0–100) directly on the chart. It prints a vertical score label on every candle (rolling window to avoid label limits) and adds vertical SETUP/ENTRY/EXIT markers for both long and short signals. Signals are based on a weighted mix of trend (MA alignment + slope), momentum (RSI + MACD histogram), breakout (Donchian high/low), and volatility contraction, with an optional Daily regime filter and optional volume/breakout confirmations.
🟡 GOLD 4H HUD v8.9 — Loose ICT OB + Strong/Weak + FVG/HVN/LVNGOLD 4H HUD v8.9 is a clean, structured Smart Money Concepts (SMC)–based analysis tool designed exclusively for XAUUSD on the 4-hour timeframe.
It focuses on the three most important elements for institutional orderflow analysis:
✔ Loose ICT Order Blocks (Demand/Supply)
✔ Fair Value Gaps (FVG)
✔ Volume Profile Zones (HVN/LVN/POC)
The script builds a professional-style HUD that displays the key institutional regions and structural levels that matter most for gold traders.
📌 Key Features
1 — Market Structure Engine (HH/HL & BOS)
The indicator detects:
Minor swing Highs and Lows
Last confirmed HH / HL levels
Break of Structure (BOS) for directional bias
EMA-200 trend filter (UP / DOWN / NEUTRAL)
This gives traders a clean structural read without clutter or noise.
2 — Loose FVG Engine (Tolerance-Based ICT Gaps)
A soft-threshold FVG engine detects “loose” Fair Value Gaps using a 0.1% price tolerance.
This method ensures:
Fewer missed imbalances
Cleaner OB/FVG alignment
Higher accuracy on 4H gold displacement legs
FVGs automatically shift to the right side of the chart for clean visualization.
3 — Order Block Engine (Demand/Supply + Strong/Weak Classification)
A simplified ICT-style OB engine scans the past few candles whenever BOS is detected.
It identifies:
Demand OB during bullish BOS
Supply OB during bearish BOS
Strong OB if fully nested inside an active FVG
Weak OB otherwise
OB boxes include:
Clear color coding (strong vs. weak)
Price range labels inside each box
Automatic right-shift for visual clarity
4 — Volume Profile Engine (POC / HVN / LVN / VAH / VAL)
Based on a rolling window (default 120 bars), the script builds a lightweight volume distribution.
It displays:
POC (Point of Control)
HVN (High Volume Node)
LVN (Low Volume Node)
Value Area High / Low
HVN/LVN zones are shown as right-shifted colored boxes with price labels.
These zones help identify:
Institutional accumulation
Low-liquidity rejection points
Areas where price tends to react strongly
5 — Support / Resistance Mapping
The script automatically generates:
OB-based support/resistance
Swing-high/swing-low levels
HVN/LVN structural levels
These are displayed in the HUD for fast reference.
6 — Professional HUD Panel
A compact, easy-to-read HUD summarizes:
Trend direction
Latest HH/HL
OB ranges (Strong/Weak)
HVN/LVN price zones
POC
Multi-layer support & resistance
This turns the script into a fully functional analysis dashboard.
📌 What This Indicator Is NOT
To avoid misunderstanding:
It does not take entries or generate buy/sell signals
It does not auto-detect CHOCH, MSS, SMT, or sweeps
It is not a trading bot
This tool is designed as an institutional-style map and analysis HUD, not a strategy.
📌 Best Use Case
This indicator is ideal for traders who want to:
Read institutional structure on XAUUSD
Identify clean Demand/Supply zones
Visualize FVG/OB/HVN interactions
Track high-value liquidity levels
Build directional bias on 4H before dropping to execution timeframes
⚠ Important Note
This tool is designed exclusively for the 4H timeframe.
Using it on lower timeframes will display a warning.
