XAUUSD ULTIMATE+BB 🥇 [GOLD OPTIMIZED]🥇 XAUUSD ULTIMATE 100% - Best Gold Indicator
The most complete trading system for GOLD (XAUUSD) - 20+ indicators in ONE tool!
🔥 WHAT YOU GET:
✅ COMPLETE TRADING SYSTEM
- Buy/Sell signals with 0-100% confidence score
- Automatic SL/TP levels (optimized for gold)
- Real-time profit tracking in $ and %
- Clean visual interface with live dashboard
✅ POWERFUL FEATURES
- 📊 Bollinger Bands - Full visualization
- 📈 SuperTrend - Dynamic trend line
- 🎯 Divergence Detection - Early reversals
- 🕯️ Candlestick Patterns - Hammer, Engulfing, etc
- 💎 Order Blocks - Smart Money levels
- 🕐 Session Lines - London/NY high volatility periods
✅ SMART SIGNAL SYSTEM
- Multi-indicator confirmation (EMAs, RSI, MACD, Stochastic, ADX)
- Fast Entry Mode - Catches early moves
- Aggressive Mode - More signals
- Volume confirmation included
- Psychological levels ($50 increments)
✅ EASY TO USE
1. Add to XAUUSD chart
2. Adjust sensitivity (1-10)
3. Wait for BUY/SELL arrows
4. Follow displayed SL/TP levels
✅ ALERTS INCLUDED
- Buy/Sell signals
- Divergence alerts
- Profit targets (0.15%, 0.30%)
- Bollinger Band extremes
🎯 BEST FOR:
- Gold scalping (M5-M15)
- Day trading (M15-H1)
- All experience levels
⚙️ FULLY CUSTOMIZABLE
- Adjustable sensitivity
- Show/hide any feature
- Custom SL/TP multipliers
- Choose your trading style
💡 WHY IT'S THE BEST:
- Gold-specific optimization
- 20+ indicators working together
- Professional-grade accuracy
- Clean, easy-to-read interface
- Works in all market conditions
Wskaźniki i strategie
Nova Trades | Opening Range IndicatorNova Trades | Opening Range With Confluences
Overview
The Nova Trades ORB Simple indicator is a clean, educational implementation of Opening Range Breakout (ORB) methodology combined with Exponential Moving Average (EMA) trend filtering. This script is designed to help traders visualize market structure during the critical opening session and identify high-probability breakout opportunities.
What Makes This Implementation Unique
1. Real-Time Dynamic ORB Tracking
Unlike static ORB indicators that plot fixed levels, this script:
Updates ORB high/low levels in real-time during the opening range period
Dynamically adjusts line positions as new highs/lows form within the ORB window
Uses line.set_y1() and line.set_y2() to provide smooth, live updates without cluttering the chart
Automatically extends ORB levels into the future for easy visual reference
2. Integrated Status Dashboard
The script includes a comprehensive real-time status table that shows:
Current ORB period status (ACTIVE vs COMPLETE)
Calculated ORB range size (useful for volatility assessment)
Current price position relative to ORB levels (ABOVE/BELOW/INSIDE)
Price position relative to EMA (trend context)
First breakout direction detection (BULLISH/BEARISH/PENDING)
This dashboard eliminates the need to manually assess market conditions and provides instant decision-making information.
3. Breakout Detection Logic
The script employs a first-breakout-only tracking system that:
Waits for the ORB period to complete before flagging breakouts
Records only the first directional break after ORB completion
Prevents false signals from intraday price whipsaws
Maintains breakout status throughout the trading session for consistency
4. EMA Confluence Filter
While many ORB scripts exist and EMA is a standard indicator, this script's value lies in how they work together:
Trading Edge: The combination provides a two-factor confirmation system:
ORB Breakout = Short-term momentum shift (microstructure)
EMA Position = Intermediate trend alignment (macrostructure)
Why This Matters:
ORB breakouts above ORB high + price above EMA = Aligned bullish momentum (highest probability long setups)
ORB breakouts below ORB low + price below EMA = Aligned bearish momentum (highest probability short setups)
Conflicting signals (e.g., ORB breakout up but price below EMA) = Lower probability, potential reversal zones
5. Customizable Time Periods
Supports multiple ORB timeframes (5m, 15m, 30m, 45m, 60m) because:
Different securities have different volatility profiles
Intraday traders may prefer shorter ORB periods (5-15m)
Position traders may prefer longer ORB periods (45-60m)
Allows optimization for specific trading styles and instruments
6. Clean Visual Design
Market open line clearly marks session start
Color-coded ORB levels (customizable) for instant visual recognition
Minimal chart clutter with toggle options for each component
Data window plots for programmatic strategy access
How It Works
Opening Range Breakout (ORB) Calculation
Initialization: At 9:30 AM NY time (market open), the script begins tracking
Range Formation: During the selected timeframe (default 30 minutes):
Continuously updates the highest high → ORB High
Continuously updates the lowest low → ORB Low
Range Completion: After the ORB period ends, levels are locked
Breakout Detection: Price breaking above ORB High (bullish) or below ORB Low (bearish) triggers the breakout flag
EMA Trend Filter
Calculates exponential moving average (default 50-period, customizable 1-500)
Provides trend context: Price > EMA = uptrend, Price < EMA = downtrend
Acts as dynamic support/resistance level
Combined Strategy Logic
Why Open Source?
This script is published as open source to:
Provide educational value to the trading community
Demonstrate clean coding practices for ORB implementations
Allow traders to customize and adapt to their specific needs
Serve as a foundation for more complex strategy development
The code uses standard Pine Script functions (ta.ema(), line.new(), table.new()) intentionally to maintain transparency and educational value.
Disclaimer
This indicator is for educational and informational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always practice proper risk management.
SMC & ICTSMC & ICT Concepts
Key Features:
• Real-time Market Structure: MSS (Market Structure Shift), BOS, CHOCH with labels
• Order Blocks (Bullish & Bearish) – auto-mitigation & breaker detection
• Fair Value Gaps (FVG), Implied FVG, Balance Price Range (BPR)
• Liquidity Grabs (Buyside/Sellside pools from equal highs/lows)
• Volume Imbalance (VI) detection
• Displacement candles
• Killzones: New York, London Open/Close, Asian session background highlight
• NWOG (New Week Opening Gap) & NDOG (New Day Opening Gap)
• Automatic Fibonacci Retracement & Extension between latest FVG, OB, Liquidity, or VI
• Two display modes:
→ Present Mode: Shows only recent & relevant structures (clean chart – recommended for live trading)
→ Historical Mode: Shows full structure history
Perfect confluence tool for scalping, day trading, and swing trading.
Hierarchical Hidden Markov ModelHierarchical Hidden Markov Models (HHMMs) are an advanced version of standard Hidden Markov Models (HMMs). While HMMs model systems with a single layer of hidden states, each transitioning to other states based on fixed probabilities, HHMMs introduce multiple layers of hidden states. This hierarchical structure allows for more complex and nuanced modeling of systems, making HHMMs particularly useful in representing systems with nested states or regimes. In HHMMs, the hidden states are organized into levels, where each state at a higher level is defined by a set of states at a lower level. This nesting of states enables the model to capture longer-term dependencies in the time series, as each state at a higher level can represent a broader regime, and the states within it can represent finer sub-regimes. For example, in financial markets, a high-level state might represent a general market condition like high volatility, while the nested lower-level states could represent more specific conditions such as trending or oscillating within the high volatility regime.
The hierarchical nature of HHMMs is facilitated through the concept of termination probabilities. A termination probability is the probability that a given state will stop emitting observations and transition control back to its parent state. This mechanism allows the model to dynamically switch between different levels of the hierarchy, thereby modeling the nested structure effectively. Beside the transition, emission and initial probabilities that generally define a HMM, termination probabilities distinguish HHMMs from HMMs because they define when the process in a sub-state concludes, allowing the model to transition back to the higher-level state and potentially move to a different branch of the hierarchy.
In financial markets, HHMMs can be applied similiarly to HMMs to model latent market regimes such as high volatility, low volatility, or neutral, along with their respective sub-regimes. By identifying the most likely market regime and sub-regime, traders and analysts can make informed decisions based on a more granular probabilistic assessment of market conditions. For instance, during a high volatility regime, the model might detect sub-regimes that indicate different types of price movements, helping traders to adapt their strategies accordingly.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. These posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequence. Out-of-sample predictions, on the other hand, offer a forward-looking evaluation to test the model's predictive capability.
MODEL TESTING:
When the "Test Out of Sample" option is enabled, the indicator plots the selected display settings based on models' out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of data points not included in the training process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probabilities for a particular state suggest that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas lower complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is useful to assess the stability of the model complexity as well as understand where changes come from when a shift happens. A model with irregular complexity values can be strong sign of overfitting, as it suggests that the process that the model is capturing changes siginficantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
Hidden Markov ModelHidden Markov Models (HMMs) are a class of statistical models used to represent systems that follow a Markov process with hidden states. In such models, the system being modeled transitions between a finite number of states, with the probability of each transition dependent only on the current state. The hidden states are not directly observable; instead, we observe a sequence of emissions or outputs generated by these states. HMMs are widely used in various fields, including speech recognition, bioinformatics, and financial market analysis. In the context of financial markets, HMMs can be utilized to model the latent market regimes (e.g., bullish, bearish, or neutral) that influence the observed market data such as asset prices or returns. By estimating the posterior probabilities of these hidden states, traders and analysts can identify the most likely market regime and make informed decisions based on the probabilistic assessment of market conditions.
The Hidden Markov Model (HMM) comprises several states that work together to model the hidden market dynamics. The states represent the unobservable market regimes such as bullish, bearish or neutral. The states are 'hidden' in nature because we need to infer them from the data and cannot directly observe them.
Model components:
Initial Probabilities: These denote the likelihood of starting in each hidden state. They can be related to long-run probabilities, reflecting the overall likelihood of each state across extended periods. In equilibrium, these initial probabilities may converge to the stationary distribution of the Markov chain.
Transition Probabilities: These capture the likelihood of moving between states, including the probability of remaining in the current state. They model how market regimes evolve over time, allowing the HMM to adapt to changing conditions.
Emission Probabilities: Also known as observation likelihoods, these represent the probability of observing specific market data (like returns) given each hidden state. Emission probabilities can be often represented by continuous probability distributions. In our case we are using a laplace distribution with its location parameter reflecting the central tendency of returns in each state and the scale reflecting the dispersion or the magnitude of the returns.
The power of HMMs in financial modeling lies in their ability to capture complex market dynamics probabilistically. By analyzing patterns in market, the model can estimate the likelihood of being in each state at any given time. This can reveal insights into market behavior and dynamics that might not be apparent from data alone.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. It is crucial to understand that these posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequeence. Out-of-sample predictions on the other hand offer a forward-looking evaluation to test the model's predictive capability.
MODEL TEST:
When the "Test Out of Sample” option is enabled, the indicator plots the selected display settings based on models out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of datapoints that were not included in the traning process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is rigorously tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probability for a particular state indicate a higher likelihood that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas too low complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is also useful to assess the stability of the model complexity. A model with irregular complexity values can be sign of overfitting, as it suggests that the process that the model is capturing changes significantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
元宝均线趋势指标Yuanbao Moving Average Trend Indicator (元宝均线趋势指标)
A powerful, trend-following indicator designed to simplify market dynamics while capturing reliable trend signals—named for its "gold ingot" (Yuanbao) inspiration, symbolizing stability, precision, and wealth accumulation in trading. Built on optimized moving average (MA) logic, this tool filters noise, identifies trend direction, and highlights potential entry/exit zones, making it suitable for forex, stocks, cryptocurrencies, and commodities across all timeframes (from 1-minute scalping to daily swing trading).
Core Logic & Features
1. Multi-Layered MA Architecture
Combines short-term, medium-term, and long-term moving averages (customizable lengths) to balance responsiveness and reliability:
Short MA (e.g., 20-period): Tracks recent price momentum for timely signals.
Medium MA (e.g., 50-period): Confirms trend strength and filters false breakouts.
Long MA (e.g., 200-period): Acts as a dynamic support/resistance level and identifies major trend direction.
All MA types (SMA, EMA, WMA) are selectable—tailor to your trading style (EMA for faster reactions, SMA for smoother trends).
2. Trend Direction Visualization
Intuitive color-coding and line styling eliminate guesswork:
Bullish Trend: Short MA above Medium MA, and Medium MA above Long MA—lines turn green (customizable) to signal upward momentum.
Bearish Trend: Short MA below Medium MA, and Medium MA below Long MA—lines turn red (customizable) to indicate downward pressure.
Sideways/Consolidation: MAs cluster closely (with a built-in "range filter" to reduce noise)—lines turn blue (customizable) to alert neutral market conditions.
3. Dynamic Support/Resistance Zones
The indicator automatically highlights key levels based on MA crossovers and price interactions:
When price pulls back to the Medium/Long MA in a bullish trend: The MA line thickens to mark a potential "support zone" for long entries.
When price rallies to the Medium/Long MA in a bearish trend: The MA line thickens to mark a potential "resistance zone" for short entries.
Breaks above/below clustered MAs trigger "trend reversal alerts" (optional pop-up/alert conditions).
4. Customization for All Traders
Flexible parameters to adapt to any asset or strategy:
Adjust MA periods (short/medium/long) for different volatility levels (e.g., shorter periods for crypto, longer for blue-chip stocks).
Toggle MA type (SMA/EMA/WMA) to match your analysis style.
Customize color schemes, line thickness, and alert conditions (crossovers, trend shifts, price touches).
Enable/disable "noise reduction mode" (smoothes price data to filter choppy markets).
How to Use
Entry Signals
Long Entry:
Bullish trend confirmed (green MA stack: Short > Medium > Long).
Price pulls back to Medium MA (or Long MA for stronger trends) and bounces.
Optional: Confirm with volume or a candlestick pattern (e.g., hammer, bullish engulfing).
Short Entry:
Bearish trend confirmed (red MA stack: Short < Medium < Long).
Price rallies to Medium MA (or Long MA for stronger trends) and rejects.
Optional: Confirm with volume or a candlestick pattern (e.g., shooting star, bearish engulfing).
Exit Signals
Take Profit: Target next resistance/support level, or trail stop using the Short MA (exit if price crosses below Short MA in a bullish trend).
Stop Loss: Place below the Long MA (bullish trades) or above the Long MA (bearish trades) to limit downside.
Trend Reversal: Exit if the MA stack flips color (e.g., green → red for long trades).
Why Choose Yuanbao MA Trend Indicator?
Simplicity: No complex calculations—clear visual cues for trend direction and key levels.
Versatility: Works on all assets (forex, BTC, stocks, oil) and timeframes (1min, 15min, 4h, daily).
Reliability: Multi-MA confirmation reduces false signals, ideal for both beginners and experienced traders.
Customization: Adapt to your trading style, whether you’re a scalper, day trader, or swing trader.
Tips for Optimal Performance
For high-volatility assets (e.g., crypto), use shorter MA periods (e.g., 15/30/100) to stay responsive.
For low-volatility assets (e.g., bonds, blue-chip stocks), use longer MA periods (e.g., 50/100/200) for smoother trends.
Combine with oscillators (e.g., RSI, MACD) to avoid trading against overbought/oversold conditions.
Always test parameters on historical data before live trading—adjust based on asset-specific volatility.
BHA BUY SELLit will generate BUY SELL Signals and Support and Resistance levels and works on all instruments and all timeframes
John NQ levels. v2NQ levels lines critical points
so you can take longs or shorts from levels that fail or become resistance.
enjoy
GOD MODE HUNT v2.0 — SCREENER ULTIME 2025test screener pour détecter les crypto basée sur des règles strict
Gold Multi-Timeframe Strength Analyzer (XAU Basket Table)Gold Multi-Timeframe Strength Analyzer (XAU Basket Table)
This indicator is designed to measure real Gold strength across multiple XAU pairs and timeframes, all displayed inside a clean, structured performance table.
It reads the percentage change from open → close for each timeframe:
Weekly (W)
Daily (D)
4H (H4)
1H (H1)
By collecting these percent-change values from all XAU-based currency pairs, this indicator gives a full multi-dimensional view of Gold’s real momentum in the global market.
SWhat This Indicator Does
1. Multi-Timeframe Gold Strength
The indicator calculates and compares percent change for every selected XAU pair:
XAUUSD, XAUJPY, XAUEUR, XAUGBP, XAUAUD, XAUCHF, XAUCAD, XAUNZD
(Users can add or edit pairs freely.)
Positive % = bullish movement
Negative % = bearish movement
This creates a real strength map of Gold across the world.
2. Buy & Sell Simulation (True Market Pressure)
All positive percentages are combined into Buy Simulation Strength,
and all negative percentages combined into Sell Simulation Strength.
This reveals:
Which direction has more pressure
How strong buyers vs sellers truly are
How consistent Gold movement is across multiple pairs
This is NOT a technical signal — this is raw data-driven pressure analysis.
3. Automated Trend Bias
The indicator determines Buy Bias or Sell Bias based on the total aggregated strength.
No repainting.
No lag.
No crossover logic.
Just pure open-to-close price movement data.
4. Safe vs Unsafe Trade Detection
The indicator measures the difference between total buy pressure and total sell pressure.
Large difference → Safe Trade
Small difference → Unsafe Trade (choppy or transition zone)
This helps avoid false entries during sideways markets.
Why This Indicator Is Powerful
Uses real percent-change data from multiple Gold pairs
Reads global XAU movement, not just one chart
Confirms bias with objective mathematical logic
Works for trend, swing, and intraday Gold traders
Reduces noise and clarifies direction
This is essentially a Global Gold Strength Dashboard.
How To Use It
Check the Trend Bias (Buy or Sell).
Confirm Safe Trade status.
Use your main chart for entry timing.
Treat this table as your “Gold market confidence layer.”
My Multiple MA-BandsRelease Notes
MY-BAND – Adaptive Moving Average Channel Indicator
MY-BAND is a customizable Moving Average Band / Channel indicator designed to help traders clearly visualize trend direction, dynamic support & resistance, and market structure on any timeframe.
This indicator builds adaptive price bands around Moving Averages, making it easier to identify:
Trend continuation
Trend reversal
Volatility expansion and contraction
Key breakout and pullback zones
It works perfectly for crypto, forex, and stock markets.
🔧 Key Features
Multi-Timeframe MA Bands
HiLo & LLMA Moving Average Types
Dynamic Channel Width
ZigZag Structure Detection
Average Center Line
Trend Bending Option
Support & Resistance Layer
Fully Adjustable Inputs
Works on All Timeframes
📊 How to Use
Trend Trading
Price above upper band → Strong bullish trend
Price below lower band → Strong bearish trend
Pullback Entries
Enter on pullback to middle MA in trend direction
Breakout Trading
Strong breakout outside the band signals continuation
Market Structure
ZigZag feature helps identify swing highs & lows
⚙️ Inputs Explanation
MA Timeframe (MA TF) – Select the timeframe for MA calculation
Length 1 & Length 2 – Fine-tune band sensitivity
MA Type – Choose between HiLo or LLMA
Width – Controls band distance
AVG Line – Show central average line
Zigzag – Display market structure swings
Extend – Extend channel into the future
Bending – Smooth adaptive band behavior
✅ Best For
Trend Followers
Scalpers
Swing Traders
Crypto Futures Traders
Breakout & Pullback Strategies
⚠️ Disclaimer
This indicator is for educational and analytical purposes only. It does not provide financial advice. Always use proper risk management and confirm signals with other indicators.
Smoothed Heiken Ashi - Thrust Body HighlightSmoothed Heiken Ashi – Thrust Body Highlight is a price–action visualization tool designed to make strong directional “thrust” candles stand out and filter out noisy, wick-heavy bars.
Instead of using raw OHLC data, this script first applies an EMA smoothing (user-defined length) to open, high, low, and close, then builds a smoothed Heiken Ashi candle from those values. It then measures the total range of each HA candle and compares the wick size to that range. When the lower wick is small and the candle closes above its open, the bar is highlighted as a bull thrust (green). When the upper wick is small and the candle closes below its open, the bar is highlighted as a bear thrust (red). All other candles are shown as neutral (gray), helping you visually focus only on strong, decisive moves.
Use this indicator to:
Quickly spot momentum thrusts in the current trend
Filter out choppy, indecisive price action
Refine entries/exits when combined with your existing strategy (structure, EMAs, volume, etc.)
Inputs
Smoothing Length: EMA length used to smooth price before building Heiken Ashi candles.
Max Lower Wick % for Bull Thrust: Maximum lower wick as a percentage of total range for a candle to qualify as a bullish thrust.
Max Upper Wick % for Bear Thrust: Maximum upper wick as a percentage of total range for a candle to qualify as a bearish thrust.
This tool is intended as an aid to visual analysis, not as a standalone buy/sell signal.
SwiftEdge – Professional Levels Dashboard 2025SwiftEdge – Professional Levels Dashboard 2025
by SwiftEdge
A clean, powerful, all-in-one indicator combining the most important daily, weekly, and intraday reference levels used by serious traders.
Features:
• Yesterday’s Close (Anchor), High & Low
• Previous Week High & Low
• Live Today’s High & Low (updates in real time)
• Adaptive Volatility Forecast – projects today’s expected range based on recent true range and regime detection
• Yesterday’s POC + simulated Value Area (VAH/VAL)
• Built-in Risk Calculator – shows position size based on account % risk and today’s forecast range
• Fully customizable colors, line thickness, and visibility
No repainting · Works on all timeframes · Perfect for GER40, NQ, ES, BTC, and major indices
Clean chart. Clear levels. Better decisions.
Created with precision by SwiftEdge.
EMAs Cloud by LuigiTradezWhat you get now:
Beautiful EMA cloud with dynamic coloring
Regular Bullish/Bearish Divergence (big green/red triangles + "DIV")
Hidden Bullish/Bearish Divergence (smaller aqua/orange triangles + "H")
Fully customizable RSI length and lookback
Built-in alert conditions (you can create alerts in TradingView)
SmartFlow Trend Engine SmartFlow Trend Engine (STE) is a premium trend-strength model designed for intraday & positional traders who want a cleaner, faster way to identify market direction without relying on lagging indicators.
Instead of using moving averages, oscillators, or traditional momentum tools, STE uses a proprietary flow-based algorithm that tracks how efficiently price is moving through the session.
The result is a real-time trend score that instantly tells you whether the market is dominated by buyers or sellers.
What SmartFlow helps you do
✔ Identify trend continuation early
✔ Spot weak or fading trends before price reverses
✔ Stay aligned with market direction (great for option sellers)
✔ Avoid chop zones by confirming whether the day has real strength
✔ Gain confidence during volatile intraday movement
How to use (Simple Rules)
✔ Green background → Strong positive flow (bullish pressure)
✔ Red background → Negative flow (bearish pressure)
✔ TrendScore line gives additional clarity on momentum strength
✔ Works beautifully on index options, futures, and stocks
Best for
✔ BankNifty / Nifty option sellers
✔ Positional traders
✔ Intraday scalpers
✔ Index futures traders
✔ Anyone who needs a simple, reliable trend confirmation tool
Protected Algorithm (Invite-Only Script)
SmartFlow Trend Engine uses a protected calculation model designed exclusively for invite-only users.
The underlying logic is not based on common indicators, making it extremely difficult to reverse-engineer and ensuring premium value for subscribers.
BTC Spot vs Perpetual CVD Divergence + Delta Confirm + Band FillThis indicator detects real market turning points by comparing Spot vs Perpetual CVD flows to identify forced positioning changes, leverage clean-ups, and true spot absorption.
It tracks normalized CVD for both Spot and Perps, calculates the divergence between them, and applies a dynamic volatility-based threshold to filter noise. Signals only trigger at confirmed pivot points, ensuring accuracy over early false reversals. An optional Delta confirmation layer further validates setups by requiring aggressive market flow in the direction of the pivot reversal.
This tool is not designed for blind entries — it highlights high-probability reversal zones. Best used in combination with VWAP, HTF structure, OI, and funding rate analysis to time optimal entries via pullbacks and momentum confirmation.
✅ Ideal for:
• Identifying local tops & bottoms
• Tracking spot vs leverage dominance
• Trading mean reversion and squeeze setups
• Flow-based scalping
❌ Not intended for:
• Chasing breakouts
• Standalone entry signals without price structure
Multi-TF Harmonic + UT Bot + RSI Scanner [Final Fixed]Overview This indicator is an all-in-one dashboard designed to monitor 4 key timeframes (5m, 15m, 1H, 4H) simultaneously on a single chart. It seeks Confluence by combining the Counter-trend strategy of Harmonic Patterns with the Trend-following strategy of the UT Bot, backed by RSI momentum analysis.
Core Logic
Harmonic Patterns: Detects Gartley, Bat, Butterfly, Crab, Deep Crab, and Cypher patterns. It highlights when the price enters the Potential Reversal Zone (PRZ).
UT Bot: Identifies the current trend direction (Buy/Sell) using ATR Trailing Stop logic with Heikin Ashi smoothing.
RSI: Monitors Overbought (>70) and Oversold (<30) levels.
Signal Conditions
LONG ENTRY: Bullish Pattern + Price in PRZ + UT Bot Buy Trend.
SHORT ENTRY: Bearish Pattern + Price in PRZ + UT Bot Sell Trend.
WATCH: Price is in PRZ, but the trend has not yet aligned with the pattern direction.
How to Use Simply apply this indicator to any chart. The dashboard (default: Bottom Left) will display the status for 5m, 15m, 1H, and 4H timeframes without needing to switch charts.
YBL_LUXE — Squeeze Momentum (Overlay + Divergences + Liquidity YBL_LUXE — Squeeze Momentum (Overlay + Div + Liquidity + EMA Reversals)
This is an overlay version of a Squeeze Momentum framework that combines 4 powerful concepts on top of price:
• BB vs KC “squeeze” condition
• Momentum-based bar coloring
• Price/Momentum divergences
• Liquidity grabs (sweeps of HH/LL)
• EMA-based reversal logic (engulfing + momentum flips)
All in a single YBL_LUXE tool that sits directly on your candles (overlay).
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🎛 1. Squeeze Logic (BB vs KC)
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The classic squeeze engine:
• Bollinger Bands:
– BB Length
– BB Mult
• Keltner Channels:
– KC Length
– KC Mult
The script computes:
• Squeeze ON → BB is inside KC (compression).
• Squeeze OFF → BB is outside KC (expansion).
Optionally:
• You can display squeeze ON/OFF dots below candles:
– Gray dot = squeeze ON.
– Yellow dot = squeeze OFF.
This lets you see where volatility is compressed and where it’s expanding.
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📈 2. Momentum & Bar Coloring
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Momentum is built as:
1. momBase = price – SMA(price, BB length)
2. mom = linear regression of momBase over 20 bars.
Momentum conditions:
• momUp = mom ≥ 0
• momRising = mom > mom
Bar color logic:
• Strong bullish / rising momentum → bright lime/green tone.
• Bearish / falling momentum → red/orange tone.
If “Paint bars” is enabled:
• Candles are recolored according to momentum bias.
• Later, special events (divs, liquidity, reversals) override this base color with higher priority.
Priority of colors:
1. Reversal
2. Liquidity sweep
3. Divergence
4. Basic momentum
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🔍 3. Divergences (Price vs Momentum)
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The script detects classic divergences using pivots:
• Price pivots:
– PH (pivot high)
– PL (pivot low)
• Momentum pivots:
– PMH (pivot high on momentum)
– PML (pivot low on momentum)
You can control:
• Pivot sensitivity (left/right bars).
• Synchronization tolerance between price and momentum pivots (bars apart).
Bearish divergence:
• Price makes a higher high (current PH > previous PH)
• Momentum makes a lower high (current PMH < previous PMH).
Bullish divergence:
• Price makes a lower low
• Momentum makes a higher low.
Visuals:
• Bar colors for divergence (aqua / purple).
• Optional labels:
– “Div+” for bullish divergence.
– “Div-” for bearish divergence.
If enabled:
• Lines from previous pivot to current pivot (PH→PH, PL→PL) are drawn:
– Style (solid/dotted/dashed) and width are configurable.
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💧 4. Liquidity Grabs (Sweeps of HH/LL)
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The script scans a lookback window for:
• Previous highest high (prevHH).
• Previous lowest low (prevLL).
Then checks:
• Sweep of highs:
– Current high > prevHH.
– And optional “rejection” condition:
– If “Wick only” is ON → close back below prevHH.
– Else → close below the high.
• Sweep of lows:
– Current low < prevLL.
– And optional “wick only” logic mirrored.
Visuals:
• Special bar colors for sweeps (yellow for highs, blue for lows).
• Optional labels:
– “Liq+” = liquidity taken below (bullish context).
– “Liq-” = liquidity taken above (bearish context).
Alerts:
• “Liquidity Grab (High)”
• “Liquidity Grab (Low)”
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🔁 5. EMA-Based Reversals (Engulfing + Momentum Flip)
────────────────────────
Reversal logic combines:
1. Engulfing pattern:
• Bullish engulf:
– Strong bull candle engulfing prior bear candle body.
• Bearish engulf:
– Strong bear candle engulfing prior bull candle body.
2. Momentum flip:
• Bull flip → momentum crosses above 0.
• Bear flip → momentum crosses below 0.
3. Liquidity sweep gate (optional):
• You can require a recent sweep:
– For bullish reversal → recent sweep of lows.
– For bearish reversal → recent sweep of highs.
• Controlled by:
– “Need sweep” toggle.
– “Sweep lookback” window.
4. EMA confirmation:
• EMA confirmation (enable/disable).
• EMA length.
• Optional EMA slope filter:
– For bulls: EMA rising above a minimum slope.
– For bears: EMA falling below a negative minimum slope.
Final reversal conditions:
• Bullish Rev:
– (Engulfing or momentum flip)
– AND sweep gate (if enabled)
– AND EMA gate (if enabled).
• Bearish Rev:
– Mirror logic for the opposite side.
Visuals:
• Distinct bar colors for bullish vs bearish reversals.
• Optional labels:
– “Rev+” for bullish reversals.
– “Rev-” for bearish reversals.
Alerts:
• “Reversal Alcista (EMA)”
• “Reversal Bajista (EMA)”
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⚙️ Style & UX Options
────────────────────────
• Paint bars by momentum (ON/OFF).
• Show arrows/labels on candles (Div/Liq/Rev).
• Only last label per type (keeps chart clean).
• Show squeeze dots ON/OFF.
• Custom colors for each event type:
– Divergences (bull/bear).
– Liquidity sweeps (high/low).
– Reversals (bull/bear).
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📍 Suggested Usage
────────────────────────
This script is best used as:
• An overlay decision-support tool, not an auto-entry system.
• A way to cluster multiple confluences:
– Squeeze → context of volatility regime.
– Momentum color → directional bias.
– Divergences → exhaustion/counter-move potential.
– Liquidity sweeps → stop-hunt zones.
– EMA-based reversals → structured re-entry confirmation.
Works well on:
• Index futures, forex majors, crypto pairs.
• 5m / 15m / 1H charts depending on your style (scalping or intraday swing).
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⚠️ Disclaimer
────────────────────────
This script is for educational and research purposes only and does not constitute financial advice. Trading involves substantial risk and is not suitable for every investor. Always test your ideas and use proper risk management before trading live.
────────────────────────
© YBL / Yuriel — YBL_LUXE Squeeze Momentum Overlay
────────────────────────
Steamroom Levels V3 - Dynamic IVOptions flow visualization tool displaying Gamma Exposure (GEX) levels and IV-derived pivot levels with intelligent auto-timeframe selection.
Overview
Steamroom Levels V3 visualizes two components of options market structure on your chart: Gamma Exposure (GEX) levels and Steamroom Pivots. These levels are derived from derivatives market data and help traders identify potential support, resistance, and expected price ranges based on options positioning and implied volatility.
Core Components
Gamma Exposure (GEX) Levels
Gamma Exposure represents aggregate options positioning at various strike prices. When market makers sell options, they hedge their exposure by buying or selling the underlying asset. This hedging activity can create predictable price behavior around key strike levels.
Four GEX level types are displayed:
Put Wall (Major) : The strike with the highest concentration of put gamma. As price approaches, dealer hedging may create buying pressure, often acting as support.
Put Wall Minor : Secondary put gamma concentrations providing interim support zones.
Call Wall (Major) : The strike with the highest concentration of call gamma. Dealer hedging may create selling pressure as price rises toward this level, often acting as resistance.
Call Wall Minor : Secondary call gamma concentrations providing interim resistance zones.
Steamroom Pivots
Steamroom Pivots are support and resistance levels calculated using implied volatility data from the options market. The calculation method works as follows:
Methodology:
The indicator takes the selected IV timeframe value (1-day, 5-day, 30-day, or 90-day implied volatility expressed as a decimal)
Three proprietary multipliers are applied to this IV value to create bands above and below the anchor price
The previous daily close serves as the anchor point
This produces three resistance levels (R1, R2, R3) above the anchor and three support levels (S1, S2, S3) below
The Six Pivot Levels:
R1 / S1 – Nearest pivot levels; represent the first reaction zones
R2 / S2 – Extended pivot levels; secondary targets
R3 / S3 – Outer pivot levels; represent significant price extensions
The specific multipliers used are calibrated based on observed market behavior and are not disclosed, but the general approach uses implied volatility as a measure of expected price movement scaled to create meaningful intraday and swing trading levels.
Auto IV Timeframe Selection
The indicator automatically selects the appropriate implied volatility timeframe based on your chart's timeframe. This ensures pivot levels remain relevant to your trading horizon.
Default Auto Behavior:
Chart Timeframe IV Selected
Up to 30 minutes 1-day IV
31 minutes to 4 hours 5-day IV
4 hours to 1 week 30-day IV
Above 1 week 90-day IV
Customizable Thresholds:
You can adjust these cutoffs in the settings:
"Auto: 1-day IV up to (min)" – Default: 30
"Auto: 5-day IV up to (min)" – Default: 240 (4 hours)
"Auto: 30-day IV up to (min)" – Default: 10080 (1 week)
Manual Override:
Select 1-day, 5-day, 30-day, or 90-day directly to lock in a specific IV timeframe regardless of chart timeframe.
Info Table
An on-chart table displays the currently active IV timeframe. When using Auto mode, it shows which IV was selected (e.g., "IV: 1-day IV (Auto)").
Table Settings:
Show/Hide toggle
Position: Top Left (default), Top Right, Bottom Left, Bottom Right, Top Center, or Bottom Center
Text size: Tiny, Small, Normal, Large, Huge
Text and background color customization
Data Input
This indicator requires external data input. Paste your formatted data string into the "Paste V3 Data" field in settings. The indicator automatically matches data to the current chart symbol.
The data format supports multiple symbols simultaneously. Only levels matching the active chart are displayed.
How To Use
GEX Levels
Put Wall levels may act as support; Call Wall levels may act as resistance
Minor walls provide interim reaction zones
Breaks through major walls may indicate momentum shifts
Steamroom Pivots
R1/S1 are the nearest pivot levels – common intraday reaction points
R2/S2 serve as extended targets
R3/S3 mark outer boundaries for significant moves
Confluence between GEX levels and pivots strengthens a price zone's significance
Customization Options
GEX Settings
Toggle visibility for levels and labels
Show/hide prices in labels
Line extension direction
Label size and offset
Pivot Settings
Toggle visibility for levels and labels
Show/hide prices in labels
IV timeframe selection (Auto or manual)
Auto threshold customization
Line extension direction
Label size and offset
Styling
Independent colors for Put Wall, Put Minor, Call Wall, Call Minor
Line styles: Solid, Dotted, Dashed
Line width: 1-4 pixels
Pivot color with independent styles per level pair (R1/S1, R2/S2, R3/S3)
Technical Notes
Multi-symbol data supported; only matching symbol levels are displayed
Pivots anchor to the confirmed daily close
Auto IV selection uses native TradingView timeframe detection
Visual elements are efficiently managed and cleaned up on each update
Disclaimer
This indicator is for informational and educational purposes only. Displayed levels are based on options market data and do not guarantee future price behavior. Past performance is not indicative of future results. Always conduct your own analysis and manage risk appropriately. Trading involves substantial risk of loss.
YBL – Smart Money Volume++ (Neón)YBL – Smart Money Volume++ (Neón)
Smart Money Volume++ (Neón) is a class-based volume engine that separates Smart Money vs Retail activity, detects explosive volume events on a lower timeframe, and projects them as persistent levels and neon “bubbles” on your main chart.
It’s designed for intraday futures, FX and crypto traders who want to:
• See where big players (Smart Money) are really active.
• Distinguish between Retail and Smart flows at each price.
• Track how much volume is currently “in profit” or “in loss” for each class.
• Get a live hit-ratio style P/L table (Retail vs Smart).
• Use a clean “Mini Mode” for iPad / light layouts.
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🔍 Core Logic
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1. Lower Timeframe Volume
• The script pulls volume from a configurable lower timeframe:
– Auto mode chooses 1s/3s/5s style LTF depending on your chart timeframe.
– Or you can lock a fixed LTF (1/3/5/15).
• All LTF volume samples are stored inside a rolling window (zLen × LTF resolution).
2. Robust Volume Outlier Detection (Z / MAD-Z)
• For the stored LTF volumes, the script computes:
– Mean and standard deviation (classic Z-score).
– Median and MAD (Median Absolute Deviation) for robust Z.
• If “Use MAD-Z” is enabled:
– Outliers are detected using a robust MAD-based z-score.
• Threshold:
– |Z| > ThEff, where ThEff is an adaptive threshold scaled by recent dispersion (thAbs × (1 + k × dispersion)).
3. Event Classification: Retail vs Smart Money
For each flagged LTF spike:
• Candle direction:
– Bullish spike → bull event.
– Bearish spike → bear event.
• Price location vs the current HTF candle:
– If “Filter in body” is enabled:
• If the spike lands right at the close of the bar → Retail.
• If the spike lands strictly inside the body (between open and close) → Smart Money.
• Otherwise defaults to Retail.
– If disabled:
• In-body → Smart.
• Outside → Retail.
4. ATR Proximity Filter (optional)
• You can require spikes to occur within an ATR-based band around the current price:
– | spikePrice – close | <= ATR(len) × ATR_mult.
• This helps remove out-of-context anomalies far from current trading activity.
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📊 Levels, Glow & Persistence
────────────────────────
Each accepted event (Retail or Smart) creates:
• One core horizontal level at the spike price.
• An optional glow line around it to enhance visibility (Neon style).
The script maintains arrays of:
• Prices, types (bull/bear), class (Retail/Smart), creation bar, and volume associated with each level.
Behaviour:
• Levels extend forward with each new bar.
• Optionally:
– They auto-delete if the candle body fully crosses the level (“clean on body”).
– They expire after N bars (Decay Bars), unless set to 0 (no decay).
• A maximum number of stored levels is enforced to keep performance stable, with a reduced limit in Mini Mode.
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💥 Neon Bubbles (Per-Bar Highlight)
────────────────────────
On each bar:
• The strongest spike (by absolute Z) is chosen to draw a single “bubble” at the event price.
• Bubble size encodes intensity (higher |Z| → larger bubble).
• Color encodes:
– Direction (bull vs bear).
– Class: Retail palette vs Smart Money palette (YBL Neón color set).
• Optional “anti-overlap” offset slightly shifts bubbles when they are large, to avoid covering key price details.
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📈 P/L Hit-Ratio Table (Retail vs Smart)
────────────────────────
The script tracks, for all active levels:
• For each level, its associated volume and whether it is currently “in profit” or “in loss” based on direction:
– Bull level in profit if price >= level.
– Bear level in profit if price <= level.
It aggregates:
• Retail Profit Volume vs Retail Loss Volume.
• Smart Profit Volume vs Smart Loss Volume.
From this it computes:
• Retail Hit% = (Retail Profit Volume / (Retail Profit + Loss Volume)) × 100
• Smart Hit% = (Smart Profit Volume / (Smart Profit + Loss Volume)) × 100
The table shows:
• Rows: Retail, Smart.
• Columns: Class, Profit, Loss, Hit%.
• Background intensity for Profit/Loss cells scales with relative volume (stronger color for larger volumes).
You can configure:
• Table size (Tiny / Small / Medium / Large).
• Table position (top/bottom/middle, left/center/right).
• “Mini Mode” automatically forces Tiny size for smaller screens (e.g. iPad).
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🎛 Inputs Overview
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• Modo Rápido / Mini Mode
– Mini Mode: reduces history, disables glow expansion and sets the table to Tiny size for lighter layouts.
• Cálculo
– Z/MAD window length.
– LTF mode: Auto or fixed (1 / 3 / 5 / 15).
– Use MAD-Z (robust instead of classical Z).
– Base threshold |Z| and adaptive scaling factor k.
• Filtros
– Filter in body (Smart vs Retail classification).
– ATR filter on/off, ATR length and multiplier.
• Visual
– Show levels on/off.
– Decay bars (0 = persist).
– Clean on body (delete levels when fully crossed by candle body).
– Show bubbles and anti-overlap behaviour.
– Extra glow width.
• Filtro de Clase
– Show only Smart Money, only Retail, or Both.
• Tabla P/L
– Show/hide the table, table size and position.
• Colores (YBL Neón)
– Smart Bull / Bear neon colors.
– Retail Bull / Bear colors.
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📍 Suggested Usage
────────────────────────
• Use as a Smart vs Retail context overlay on your main trading system:
– Look for Smart Money clusters (multiple Smart levels aligning).
– Watch when Smart Money levels hold vs when Retail gets trapped.
– Monitor the P/L hit-ratio:
• Rising Smart Hit% vs falling Retail Hit% can validate institutional bias.
• Combine with:
– Session filters (NY, London) via separate tools.
– Trend filters (EMA, structure).
– Orderflow / CVD tools for confirmation.
As always, no single indicator should be used as a standalone decision engine. Treat this as a volume-intelligence layer on top of price action and risk management.
────────────────────────
⚠️ Disclaimer
────────────────────────
This script is for educational and research purposes only and does not constitute financial advice. Trading involves substantial risk and may not be suitable for every investor. Always test thoroughly and use proper risk management before trading live.
────────────────────────
© YBL / Yuriel — Smart Money Volume++ (Neón)
────────────────────────
John NQ indicator v1NQ traders
puts lines on critical levels
so you have a good view of where to long or short, where price struggles
you can use it in multi timeframe change colors and thicknes of lines in settings.
enjoy ... version 2 will be out soon
YBL – Double Axis Volume (Profile + Vertical)YBL – Double Axis Volume (Profile + Vertical)
This tool builds a clean horizontal volume profile for a custom lookback window and shows which side (buyers vs sellers) is winning at each price level using an inner “bubble” style bar.
It is designed for intraday futures, crypto and FX traders who want to see:
• Where volume is truly traded (POC & high-volume nodes)
• Which side is dominating each level (aggressive buyers vs sellers proxy)
• A clear Value Area (VAH/VAL) zone that acts as dynamic S/R
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🔍 How it works
────────────────────────
• The script scans a user-defined number of bars (lookback).
• Price range (high–low) is split into bins (price levels).
• For each bin it sums:
– Bullish volume (green candles)
– Bearish volume (red candles)
Scalping 4D+ Engine (Advanced Entry Modes {SMC})Scalping 4D+ Engine (Advanced Entry Modes {SMC}) is a next-generation quantitative trading model engineered for traders who want fewer but higher-probability signals.
This system combines Smart Money Concepts (SMC), quantitative volume analysis, volatility regime modeling, and momentum confirmation into a unified scoring engine that filters out noise and highlights only the strongest directional opportunities.
Unlike conventional indicators that rely on a single trigger (EMA crosses, RSI oversold, MACD flips), the SMC 4D+ engine evaluates multiple market dimensions simultaneously, allowing it to track the true underlying state of the market before issuing a BUY or SELL signal.
