Adaptive RSIAdaptive RSI
Adaptive RSI is an enhanced version of the classic Relative Strength Index designed to automatically adjust its behavior to changing market conditions. The indicator can operate both as a mean-reversion oscillator and as a trend-following momentum tool, allowing traders to detect high/low value zones while also capturing directional moves.
Unlike the traditional RSI, which uses a fixed smoothing method, Adaptive RSI dynamically changes its calculation speed depending on market activity. This helps reduce false signals in slow or choppy markets while allowing faster responses during strong moves.
🔍 Concept & Idea
The goal behind Adaptive RSI is to make RSI responsive when opportunities appear and more conservative during uncertain or low-activity environments.
By automatically adjusting its internal smoothing and reaction speed, the indicator attempts to balance:
• Early entries during strong market moves
• Reduced noise during consolidation
• Mean-reversion opportunities in ranging markets
• Momentum confirmation in trending markets
This adaptive behavior makes the oscillator more versatile across multiple market conditions.
⚙️ How It Works
The indicator evaluates market activity using three drivers:
• True Range (volatility)
• Volume activity
• Rate of price change
Users can define which of these factors has priority. The script then checks up to three conditions; the more conditions that are satisfied, the faster and more responsive the RSI calculation becomes.
This creates multiple internal speed tiers ranging from smooth and conservative to highly responsive.
After the adaptive RSI is calculated, an additional adaptive smoothing layer is applied using the same logic, improving signal clarity while preserving responsiveness.
An optional feature allows the RSI to use a special Rate-of-Change weighted price source. This feature is more advanced and mainly intended for users who understand how weighted price construction affects oscillators.
A divergence measure between the base RSI and the smoothed Adaptive RSI is also plotted to help visualize shifts in momentum strength.
⚙️ Key Features
• Adaptive RSI calculation speed
• Works for both trend-following and mean-reversion approaches
• Adjustable long and short signal thresholds
• Overbought and oversold zone highlighting
• Divergence histogram between RSI and adaptive smoothing
• Trend-based coloring and visual signal markers
• Optional ROC-weighted source for advanced users
🧩 Inputs Overview
• RSI calculation length and smoothing length
• Price source selection or optional special weighted source
• Speed tier selection (slow, medium, fast behavior)
• Activity priority order (volatility, volume, momentum)
• Long/short and overbought/oversold thresholds
📌 Usage Notes
• Can be used both for trend continuation and mean-reversion strategies.
• Adaptive logic helps reduce noise during sideways markets.
• Strong moves may cause faster RSI transitions due to adaptive speed selection.
• Signals may update intrabar on lower timeframes.
• Works best when combined with risk management and confirmation tools.
• No indicator is perfect; always test before live use.
This script is intended for analytical purposes only and does not provide financial advice.
지표 및 전략
Length Adaptive MA SuperTrendLength Adaptive MA SuperTrend
Length Adaptive MA SuperTrend is a third-generation evolution of the SuperTrend concept, designed to improve signal accuracy while maintaining high responsiveness across different market conditions. The indicator dynamically adjusts its moving-average length to better match current market activity, allowing it to react quickly in fast markets while remaining stable during slower phases.
This adaptive behavior helps traders and investors visualize trend direction more clearly while reducing unnecessary noise, making the tool suitable for both beginners and advanced users seeking a responsive trend overlay.
🔍 How It Works
The indicator uses a moving average as the foundation for a SuperTrend-style structure, but instead of keeping the moving-average length fixed, it continuously adapts to changing market environments.
The script compares average activity levels across three horizons:
• Long-term period
• Medium-term period (half length)
• Short-term period (square-root length)
Activity is measured using one of three selectable drivers:
• ATR (volatility)
• Volume
• Standard deviation
Whichever period shows the strongest average activity becomes the active length used for calculating the moving-average base. This allows the indicator to automatically shift between faster and slower behavior depending on market conditions.
After selecting the active length, the result is slightly smoothed using the chosen moving-average type to produce a cleaner and more stable trend structure.
ATR-based bands are then applied around the adaptive base, and trend direction changes when price crosses these bands.
⚙️ Key Features
• Adaptive moving-average length selection
• Automatic adjustment between short, medium, and long market conditions
• Multiple smoothing types (SMA, EMA, WMA, HMA, VWMA, DEMA, TEMA, EWMA)
• ATR-based SuperTrend structure
• Trend transition markers
• Optional candle coloring based on active trend
🧩 Inputs Overview
• Moving-average smoothing type
• Base length and price source
• ATR length and multiplier
• Adaptive driver selection (ATR, Volume, or Standard Deviation)
📌 Usage Notes
• Helps visualize prevailing market trends across changing environments.
• Automatically adapts speed for trending and consolidating markets.
• Signals may change intrabar on lower timeframes.
• Best used with confirmation tools and proper risk management.
• Intended as an analytical tool, not financial advice.
Multiple Factor Adaptive MA SuperTrendMultiple Factor Adaptive MA SuperTrend
Multiple Factor Adaptive MA SuperTrend is an enhanced trend-following overlay that builds on the classical SuperTrend concept by introducing an adaptive moving-average base. The indicator dynamically adjusts to changing market conditions to produce smoother and faster trend signals, helping traders better track directional moves while reducing unnecessary noise.
Instead of relying on a fixed moving-average base, the indicator updates its baseline only when market conditions justify it. This creates a stabilizing effect during consolidation while allowing quicker reactions when volatility, momentum, or activity increases.
🔍 How It Works
The indicator combines:
• A user-selectable Moving Average as the core trend base
• ATR-based volatility bands to detect trend transitions
• An adaptive filter that determines when the base should update
The adaptive mechanism evaluates market conditions using one of several selectable drivers:
• ATR expansion (volatility increase)
• Rate-of-change acceleration
• Rising trading volume
• Increasing divergence between price and the moving average
If the chosen condition signals increased activity or market change, the moving-average base updates normally. Otherwise, the previous base value is retained, effectively smoothing the trend structure and filtering minor fluctuations.
Volatility bands are then calculated around this adaptive base using ATR multiplied by a configurable factor. Trend changes occur when price crosses these bands.
When price breaks above the upper band, a bullish trend is activated and the lower band becomes the trailing support. When price breaks below the lower band, a bearish trend is activated and the upper band acts as trailing resistance.
⚙️ Key Features
• Adaptive moving-average baseline
• Multiple MA types including SMA, EMA, WMA, HMA, VWMA, DEMA, TEMA, and EWMA
• ATR-based volatility bands
• Multiple adaptation modes (volatility, momentum, volume, divergence)
• Reduced noise during consolidation phases
• Smooth trend visualization and transition markers
🧩 Inputs Overview
• Moving-average type and length
• Price source selection
• ATR length and multiplier
• Adaptive filter method selection
📌 Usage Notes
• Useful for identifying prevailing market direction and trend shifts.
• Adaptive filtering can help reduce false signals during sideways markets.
• Signals may update intrabar on lower timeframes.
• Best results are achieved when combined with confirmation tools or risk management rules.
• This script is intended for analytical purposes and does not provide financial advice.
Adaptive EMA (Momentum Entry & Crash Protection)This script is the result of extensive backtesting to find the perfect balance between capturing high-volume momentum and protecting capital during market crashes.
It is not just a standard EMA crossover; it is a fine-tuned trend-following system designed for maximum profit margins.
🚀 KEY OPTIMIZATIONS:
1. Adaptive Logic (Auto-Switching):
The script automatically detects your timeframe and applies the most effective parameters:
• Intraday (≤ 4H): Uses EMA 9 & 21. This classic setup is perfect for filtering noise in short-term trading.
• Swing/Long-Term (> 4H): Uses EMA 7 & 14. *CRITICAL UPDATE:* After testing, the 7/14 combination proved to offer higher profit margins than the traditional 7/21. It reacts faster to major trend reversals, allowing you to lock in profits sooner before a dump.
2. Professional Visuals:
• Fast Line (Gold - 1px): Represents the immediate momentum.
• Slow Line (Deep Blue - 2px): Represents the baseline trend.
• Glow Effect: A subtle white border ensures the lines remain visible even on dark charts.
• Clean Chart Policy: Gradient background signals are included but *disabled by default* to keep your workspace clutter-free. You can enable them in the settings if you prefer visual zones.
💎 HOW TO TRADE:
• Entry (Pump): When the Gold line crosses ABOVE the Blue line. This indicates a surge in volume and upward momentum.
• Exit (Protection): When the Gold line crosses BELOW the Blue line. This is your signal to exit and protect your gains before the price collapses.
No manual configuration is needed. Just add it to your chart, and it adapts instantly.
Daily SR - Locked VersionRiverSide Indicator - User Guide📊 What is RiverSide?RiverSide is a dynamic channel indicator that creates Upper and Lower bands around a Moving Average (MA). The bands automatically change color based on their position relative to the EMA 200, helping you identify market trends.🎯 Key Features1. Dynamic Bands
Upper Band = MA × (1 + Deviation %)
Lower Band = MA × (1 - Deviation %)
Bands expand and contract based on the MA value
2. Color-Coded Trend
🔵 Blue Lines = Bullish trend (MA above EMA 200)
🔴 Red Lines = Bearish trend (MA below EMA 200)
3. Customizable Settings
MA Period: Default 50 (adjustable)
MA Type: EMA, SMA, WMA, or RMA
Deviation: Default 0.14% (adjustable from 0.1% to 100%)
Applied Price: Close, Open, High, Low, HL2, HLC3, OHLC4
Adaptive MA SuperTrendAdaptive MA SuperTrend
Adaptive MA SuperTrend is a trend-following overlay indicator designed to deliver smoother and more responsive signals than the classical SuperTrend by dynamically combining two moving averages with volatility-based band calculations.
Instead of relying on a single average, the script calculates a selectable pair of moving averages and continuously assigns them as the upper or lower base depending on which value is greater at each bar. This adaptive swapping allows the structure to respond better to changing market conditions while preserving overall trend stability.
A volatility component is then added to the bases using either:
• Average True Range (ATR)
• Standard Deviation (SD)
The selected volatility measure is multiplied by a configurable factor to create adaptive bands around the moving-average bases. Price crossing these bands determines trend direction changes.
When price crosses above the upper band, the trend switches bullish and the lower band becomes the trailing support line. When price crosses below the lower band, the trend switches bearish and the upper band becomes the trailing resistance line. Only the active trend side is plotted to reduce visual noise and improve chart clarity.
Multiple moving-average pair options are provided, allowing users to choose combinations that match their preferred balance between smoothness and responsiveness, including SMA, EMA, WMA, HMA, VWMA, DEMA, TEMA, and ALMA-based combinations. Additional parameters are available when ALMA is selected.
⚙️ Key Features
• Adaptive swapping between two moving averages
• Choice of MA pairs with different responsiveness profiles
• ATR or Standard Deviation volatility bands
• Configurable volatility length and multiplier
• Optional ALMA tuning parameters
• Trend visualization with color-coded support/resistance lines
• Signal markers displayed on trend transitions
🧩 Inputs Overview
• Moving average pair selection
• Moving average length and price source
• Volatility method, length, and multiplier
• Optional ALMA offset and sigma parameters
📌 Usage Notes
• Designed to help visualize prevailing trend direction and potential trend shifts.
• Can be combined with confirmation tools or risk management rules within broader strategies.
• Signals are generated when price crosses volatility-adjusted moving-average bands; signals may update intrabar, especially on lower timeframes.
• This script is intended for analytical purposes and does not constitute financial advice. Users should test and validate performance within their own workflow before applying it to live trading.
Threshold AO VisualisationThe channel is a set of classic indicators with the ability to be customized, allowing for comprehensive market analysis and the ability to find entry points.
Trigonum ChannelAn awesome oscillator that allows you to identify market waves with a mean deviation limit to filter out noise.
Luminous Trend Wave [Pineify]```
Luminous Trend Wave - Hull MA Based Normalized Momentum Oscillator
The Luminous Trend Wave (Pineify) is a momentum oscillator designed to provide clear, responsive trend signals while minimizing the lag commonly associated with traditional momentum indicators. By combining Hull Moving Average (HMA) calculations with ATR-based normalization and hyperbolic tangent transformation, LTW delivers a bounded oscillator that works consistently across different assets and timeframes.
Key Features
Hull Moving Average foundation for reduced lag trend detection
ATR normalization for universal applicability across all markets
Bounded output range (-100 to +100) using mathematical tanh transformation
Dynamic gradient coloring that reflects momentum intensity
Built-in signal line for momentum confirmation
Automatic alerts for trend reversals and momentum shifts
How It Works
The indicator operates through a four-stage calculation process:
Trend Basis Calculation: The indicator first calculates a Hull Moving Average (HMA) of the closing price. HMA was chosen specifically because it provides significantly less lag compared to Simple or Exponential Moving Averages while maintaining smoothness. This allows the oscillator to respond quickly to genuine price movements.
Distance Measurement: The raw distance between the current close price and the HMA trend line is calculated. This distance represents how far price has deviated from its smoothed trend.
ATR Normalization: The distance is then divided by the Average True Range (ATR) over the same lookback period. This normalization step is crucial - it makes the oscillator readings comparable across different assets regardless of their price levels or typical volatility. A stock trading at $500 and one at $5 will produce equivalent readings when their relative movements are similar.
Tanh Transformation: Finally, the normalized value is passed through a hyperbolic tangent function scaled by a sensitivity multiplier. The mathematical formula (e^2x - 1) / (e^2x + 1) naturally bounds the output between -100 and +100, preventing extreme spikes while preserving the directional information.
Trading Ideas and Insights
Zero Line Crossovers: When the oscillator crosses above zero, it indicates a shift from bearish to bullish momentum. Conversely, crossing below zero signals bearish momentum. These crossovers can be used as entry triggers when confirmed by other analysis.
Overbought/Oversold Levels: Readings above +80 suggest overbought conditions where price has extended significantly above its trend. Readings below -80 indicate oversold conditions. These extremes often precede mean reversion moves.
Signal Line Divergence: When the main oscillator (histogram) is above the signal line, momentum is increasing. When below, momentum is decreasing. This relationship helps identify the strength of the current move.
Momentum Fading: The indicator automatically fades the color intensity when the oscillator value is closer to the signal line than to the extremes, visually indicating weakening momentum before potential reversals.
How Multiple Indicators Work Together
LTW integrates three distinct technical concepts into a cohesive system:
Hull MA + ATR Integration: The Hull Moving Average provides the trend direction while ATR provides the volatility context. Together, they answer not just "where is the trend?" but "how significant is the current deviation relative to normal market movement?"
Mathematical Bounding + Visual Mapping: The tanh transformation ensures readings stay within predictable bounds, while the gradient coloring maps these bounded values to intuitive visual feedback. Strong bullish readings appear in bright green, strong bearish in bright red, with smooth transitions between.
Oscillator + Signal Line System: Similar to MACD's relationship between the MACD line and signal line, LTW uses a WMA-smoothed signal line to filter noise and confirm momentum direction. The interplay between the faster oscillator and slower signal creates actionable crossover signals.
Unique Aspects
Universal Normalization: Unlike many oscillators that produce different reading ranges on different assets, LTW's ATR normalization ensures consistent interpretation whether trading forex, crypto, stocks, or commodities.
Sensitivity Control: The sensitivity parameter allows traders to adjust how aggressively the oscillator responds to price changes. Higher values make it more responsive (useful for scalping), while lower values smooth out noise (better for swing trading).
Visual Momentum Feedback: The gradient coloring and transparency adjustments provide immediate visual feedback about trend strength without requiring traders to interpret numerical values.
How to Use
Add the indicator to your chart - it displays in a separate pane below price.
Watch for zero line crossovers as primary trend signals. Bullish when crossing above, bearish when crossing below.
Use the ±80 levels as caution zones where reversals become more likely.
Monitor the relationship between the histogram and signal line - histogram above signal indicates strengthening momentum.
Pay attention to color intensity - faded colors indicate weakening momentum and potential reversal zones.
Set alerts for automated notifications on trend changes and momentum shifts.
Customization
Trend Lookback (default: 21): Controls the HMA period. Lower values increase responsiveness but may generate more false signals. Higher values provide smoother trends but with more lag.
Signal Smoothing (default: 5): Adjusts the WMA period for the signal line. Higher values create a slower signal line with fewer crossovers.
Sensitivity (default: 1.5): Multiplier for the tanh transformation. Increase for more reactive signals, decrease for smoother readings.
Colors: Fully customizable bullish and bearish colors to match your chart theme.
Gradients: Toggle gradient coloring on/off based on preference.
Conclusion
The Luminous Trend Wave indicator offers traders a mathematically sound approach to momentum analysis. By combining the low-lag properties of Hull Moving Average with ATR-based normalization and bounded output transformation, LTW provides consistent, interpretable signals across any market. The visual feedback system makes trend strength immediately apparent, while the signal line crossovers offer clear entry and exit timing. Whether used as a standalone tool or combined with price action analysis, LTW helps traders identify trend direction, momentum strength, and potential reversal zones with clarity.
```
XAUUSD 1M SCALP BY ELIRAN"The 1% Sniper" Strategy: Fast Forex Trading (1-Minute Chart) This is a strategy for disciplined traders looking for short, sharp market moves. The goal is to achieve a daily/weekly target of a single 1%, which will accumulate to the $1,000.1 pullback target. Technical SetupTimeframe: 1 minute chart ($1M$).Recommended assets: Major forex pairs with low spreads (like $EUR/USD$ or $GBP/USD$).Supporting indicators: Moving average ( NYSE:EMA \ 20/50$) to identify a short-term trend, and supply and demand areas ($Supply\ &\ Demand$).2. ExecutionEntry: Identify strong momentum on the minute chart. Enter only when there is a built-in confirmation (e.g.: a "hammer" candle on a support level or a breakout of a market structure).Risk management: NGM:RISK \ Per\ Trade$ is fixed. Since the target is 1% per portfolio, we are looking for a risk-reward ratio ($R:R$) of at least $1:2$.The Goal: Once the portfolio has reached a 1% profit that day – close the screen. This discipline is what will get you to $1,000 faster without "Putting" money back into the market. 3. The financial roadmap In this strategy, we are not looking for a single "hit", but consistency: Base capital: $2,250. Daily target: 1% ($\approx $22.5). The path to withdrawal: After about 45 successful trading days (or less, if you increase the lot carefully), you reach the $1,000 withdrawal target. Why does it work for you? Short screen time: A 1-minute chart allows you to find opportunities quickly, take your percentage and go about your business. Clear goal: Instead of dreaming of millions, you are focused on the next 1%. This makes the path to the next portfolio much more tangible. Protection of the capital: Working on a few percentages protects your $2,250 from too sharp fluctuations. Important to remember: On a 1-minute chart, the "noise" in the market is high. Make sure you work with a broker who has low commissions so that they They won't eat your 1% profit.
Seasonality (Prev Month Close Expected)Seasonality Indicator
This indicator shows how an asset has historically behaved during each calendar month. It highlights the typical price direction and strength for the current month based on long-term seasonal patterns.
The projected zone on the chart represents the average historical outcome for the ongoing month, allowing traders to quickly see whether current price action is developing in line with, above, or below its usual seasonal behavior. A heatmap summarizes monthly performance across years, making recurring strong and weak periods easy to identify.
Vladimir Popdimitrov
Break asian range break alerts
- stratégie break ou réintégration possible avec alertes intégrées .
asian range break
Cross-Market Regime Scanner [BOSWaves]Cross-Market Regime Scanner - Multi-Asset ADX Positioning with Correlation Network Visualization
Overview
Cross-Market Regime Scanner is a multi-asset regime monitoring system that maps directional strength and trend intensity across correlated instruments through ADX-based coordinate positioning, where asset locations dynamically reflect their current trending versus ranging state and bullish versus bearish bias.
Instead of relying on isolated single-asset trend analysis or static correlation matrices, regime classification, spatial positioning, and intermarket relationship strength are determined through ADX directional movement calculation, percentile-normalized coordinate mapping, and rolling correlation network construction.
This creates dynamic regime boundaries that reflect actual cross-market momentum patterns rather than arbitrary single-instrument levels - visualizing trending assets in right quadrants when ADX strength exceeds thresholds, positioning ranging assets in left quadrants during consolidation, and incorporating correlation web topology to reveal which instruments move together or diverge during regime transitions.
Assets are therefore evaluated relative to ADX-derived regime coordinates and correlation network position rather than conventional isolated technical indicators.
Conceptual Framework
Cross-Market Regime Scanner is founded on the principle that meaningful market insights emerge from simultaneous multi-asset regime awareness rather than sequential single-instrument analysis.
Traditional trend analysis examines assets individually using separate chart windows, which often obscures the broader cross-market regime structure and correlation patterns that drive coordinated moves. This framework replaces isolated-instrument logic with unified spatial positioning informed by actual ADX directional measurements and correlation relationships.
Three core principles guide the design:
Asset positioning should be determined by ADX-based regime coordinates that reflect trending versus ranging state and directional bias simultaneously.
Spatial mapping must normalize ADX values to place assets within consistent quadrant boundaries regardless of instrument volatility characteristics.
Correlation network visualization reveals which assets exhibit coordinated behavior versus divergent regime patterns during market transitions.
This shifts regime analysis from isolated single-chart monitoring into unified multi-asset spatial awareness with correlation context.
Theoretical Foundation
The indicator combines ADX directional movement calculation, coordinate normalization methodology, quadrant-based regime classification, and rolling correlation network construction.
A Wilder's smoothing implementation calculates ADX, +DI, and -DI for each monitored asset using True Range and directional movement components. The ADX value relative to a configurable threshold determines X-axis positioning (ranging versus trending), while the difference between +DI and -DI determines Y-axis positioning (bearish versus bullish). Coordinate normalization caps values within fixed boundaries for consistent quadrant placement. Pairwise correlation calculations over rolling windows populate a network graph where line thickness and opacity reflect correlation strength.
Five internal systems operate in tandem:
Multi-Asset ADX Engine : Computes smoothed ADX, +DI, and -DI values for up to 8 configurable instruments using Wilder's directional movement methodology.
Coordinate Transformation System : Converts ADX strength and directional movement into normalized X/Y coordinates with threshold-relative scaling and boundary capping.
Quadrant Classification Logic : Maps coordinate positions to four distinct regime states—Trending Bullish, Trending Bearish, Ranging Bullish, Ranging Bearish—with color-coded zones.
Historical Trail Rendering : Maintains rolling position history for each asset, drawing gradient-faded trails that visualize recent regime trajectory and velocity.
Correlation Network Calculator : Computes pairwise return correlations across all enabled assets, rendering weighted connection lines in circular web topology with strength-based styling.
This design allows simultaneous cross-market regime awareness rather than reacting sequentially to individual instrument signals.
How It Works
Cross-Market Regime Scanner evaluates markets through a sequence of multi-asset spatial processes:
Data Request Processing : Security function retrieves high, low, and close values for up to 8 configurable symbols with lookahead offset to ensure confirmed bar data.
ADX Calculation Per Asset : True Range computed from high-low-close relationships, directional movement derived from up-moves versus down-moves, smoothed via Wilder's method over configurable period.
Directional Index Derivation : +DI and -DI calculated as smoothed directional movement divided by smoothed True Range, scaled to percentage values.
Coordinate Transformation : X-axis position equals (ADX - threshold) * 2, capped between -50 and +50; Y-axis position equals (+DI - -DI), capped between -50 and +50.
Quadrant Assignment : Positive X indicates trending (ADX > threshold), negative X indicates ranging; positive Y indicates bullish (+DI > -DI), negative Y indicates bearish.
Trail History Management : Configurable-length position history maintains recent coordinates for each asset, rendering gradient-faded lines connecting sequential positions.
Velocity Vector Calculation : 7-bar coordinate change converted to directional arrow overlays showing regime momentum and trajectory.
Return Correlation Processing : Bar-over-bar returns calculated for each asset, pairwise correlations computed over rolling window.
Network Graph Construction : Assets positioned in circular topology, correlation lines drawn between pairs exceeding threshold with thickness/opacity scaled by correlation strength, positive correlations solid green, negative correlations dashed red.
Risk Regime Scoring : Composite score aggregates bullish risk-on assets (equities, crypto, commodities) minus bullish risk-off assets (gold, dollar, VIX), generating overall market risk sentiment with colored candle overlay.
Together, these elements form a continuously updating spatial regime framework anchored in multi-asset momentum reality and correlation structure.
Interpretation
Cross-Market Regime Scanner should be interpreted as unified spatial regime boundaries with correlation context:
Top-Right Quadrant (TREND ▲) : Assets positioned here exhibit ADX above threshold with +DI exceeding -DI - confirmed bullish trending conditions with directional conviction.
Bottom-Right Quadrant (TREND ▼) : Assets positioned here exhibit ADX above threshold with -DI exceeding +DI - confirmed bearish trending conditions with directional conviction.
Top-Left Quadrant (RANGE ▲) : Assets positioned here exhibit ADX below threshold with +DI exceeding -DI - ranging consolidation with bullish bias but insufficient trend strength.
Bottom-Left Quadrant (RANGE ▼) : Assets positioned here exhibit ADX below threshold with -DI exceeding +DI - ranging consolidation with bearish bias but insufficient trend strength.
Position Trails : Gradient-faded lines connecting recent coordinate history reveal regime trajectory - curved paths indicate regime rotation, straight paths indicate sustained directional conviction.
Velocity Arrows : Directional vectors overlaid on current positions show 7-bar regime momentum - arrow length indicates speed of regime change, angle indicates trajectory direction.
Correlation Web : Circular network graph positioned left of main quadrant map displays pairwise asset relationships - solid green lines indicate positive correlation (moving together), dashed red lines indicate negative correlation (diverging moves), line thickness reflects correlation strength magnitude.
Asset Dots : Multi-layer glow effects with color-coded markers identify each asset on both quadrant map and correlation web-symbol labels positioned adjacent to current location.
Regime Summary Bar : Vertical boxes on right edge display condensed regime state for each enabled asset - box background color reflects quadrant classification, border color matches asset identifier.
Risk Regime Candles : Overlay candles on price chart colored by composite risk score - green indicates risk-on dominance (bullish equities/crypto exceeding bullish safe-havens), red indicates risk-off dominance (bullish gold/dollar/VIX exceeding bullish risk assets), gray indicates neutral balance.
Quadrant positioning, trail trajectory, correlation network topology, and velocity vectors outweigh isolated single-asset readings.
Signal Logic & Visual Cues
Cross-Market Regime Scanner presents spatial positioning insights rather than discrete entry signals:
Regime Clustering : Multiple assets congregating in same quadrant suggests broad market regime consensus - all assets in TREND ▲ indicates coordinated bullish momentum across instruments.
Regime Divergence : Assets splitting across opposing quadrants reveals intermarket disagreement - equities in TREND ▲ while safe-havens in TREND ▼ suggests healthy risk-on environment.
Quadrant Transitions : Assets crossing quadrant boundaries mark regime shifts - movement from left (ranging) to right (trending) indicates breakout from consolidation into directional phase.
Trail Curvature Patterns : Sharp curves in position trails signal rapid regime rotation, straight trails indicate sustained directional conviction, loops indicate regime uncertainty with back-and-forth oscillation.
Velocity Acceleration : Long arrows indicate rapid regime change momentum, short arrows indicate stable regime persistence, arrow direction reveals whether asset moving toward trending or ranging state.
Correlation Breakdown Events : Previously strong correlation lines (thick, opaque) suddenly thinning or disappearing indicates relationship decoupling - often precedes major regime transitions.
Correlation Inversion Signals : Assets shifting from positive correlation (solid green) to negative correlation (dashed red) marks structural market regime change - historically correlated assets beginning to diverge.
Risk Score Extremes : Composite score reaching maximum positive (all risk-on bullish, all risk-off bearish) or maximum negative (all risk-on bearish, all risk-off bullish) marks regime conviction extremes.
The primary value lies in simultaneous multi-asset regime awareness and correlation pattern recognition rather than isolated timing signals.
Strategy Integration
Cross-Market Regime Scanner fits within macro-aware and intermarket analysis approaches:
Regime-Filtered Entries : Use quadrant positioning as directional filter for primary trading instrument - favor long setups when asset in TREND ▲ quadrant, short setups in TREND ▼ quadrant.
Correlation Confluence Trading : Enter positions when target asset and correlated instruments occupy same quadrant - multiple assets in TREND ▲ provides conviction for long exposure.
Divergence-Based Reversal Anticipation : Monitor for regime divergence between correlated assets - if historically aligned instruments split to opposite quadrants, anticipate mean-reversion or regime rotation.
Breakout Confirmation via Cross-Asset Validation : Confirm primary instrument breakouts by verifying correlated assets simultaneously transitioning from ranging to trending quadrants.
Risk-On/Risk-Off Positioning : Use composite risk score and safe-haven positioning to determine overall market environment - scale risk exposure based on risk regime dominance.
Velocity-Based Timing : Enter during periods of high regime velocity (long arrows) when momentum carries assets decisively into new quadrants, avoid entries during low velocity regime uncertainty.
Multi-Timeframe Regime Alignment : Apply higher-timeframe regime scanner to establish macro context, use lower-timeframe price action for entry timing within aligned regime structure.
Correlation Web Pattern Recognition : Identify regime transitions early by monitoring correlation network topology changes - previously disconnected assets forming strong correlations suggests regime coalescence.
Technical Implementation Details
Core Engine : Wilder's smoothing-based ADX calculation with separate True Range and directional movement tracking per asset
Coordinate Model : Threshold-relative X-axis scaling (trending versus ranging) with directional movement differential Y-axis (bullish versus bearish)
Normalization System : Boundary capping at ±50 for consistent spatial positioning regardless of instrument volatility
Trail Rendering : Rolling array-based position history with gradient alpha decay and width tapering
Correlation Engine : Return-based pairwise correlation calculation over rolling window with configurable lookback
Network Visualization : Circular topology with trigonometric positioning, weighted line rendering based on correlation magnitude
Risk Scoring : Composite calculation aggregating directional states across classified risk-on and risk-off asset categories
Performance Profile : Optimized for 8 simultaneous security requests with efficient array management and conditional rendering
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-regime monitoring for intraday correlation shifts and short-term regime rotations
15 - 60 min : Intraday regime structure with meaningful ADX development and correlation stability
4H - Daily : Swing and position-level macro regime identification with sustained trend classification
Weekly - Monthly : Long-term regime cycle tracking with structural correlation pattern evolution
Suggested Baseline Configuration:
ADX Period : 14
ADX Smoothing : 14
Trend Threshold : 25.0
Trail Length : 15
Correlation Period : 50
Min |Correlation| to Show Line : 0.3
Web Radius : 30
Show Quadrant Colors : Enabled
Show Regime Summary Bar : Enabled
Show Velocity Arrows : Enabled
Show Correlation Web : Enabled
These suggested parameters should be used as a baseline; their effectiveness depends on the selected assets' volatility profiles, correlation characteristics, and preferred spatial sensitivity, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Assets clustering too tightly : Decrease Trend Threshold (e.g., 20) to spread ranging/trending separation, or increase ADX Period for smoother ADX calculation reducing noise.
Assets spreading too widely : Increase Trend Threshold (e.g., 30-35) to demand stronger ADX confirmation before classifying as trending, tightening quadrant boundaries.
Trail too short to show trajectory : Increase Trail Length (20-25) to visualize longer regime history, revealing sustained directional patterns.
Trail too cluttered : Decrease Trail Length (8-12) for cleaner visualization focusing on recent regime state, reducing visual complexity.
Unstable ADX readings : Increase ADX Period and ADX Smoothing (18-21) for heavier smoothing reducing bar-to-bar regime oscillation.
Sluggish regime detection : Decrease ADX Period (10-12) for faster response to directional changes, accepting increased sensitivity to noise.
Too many correlation lines : Increase Min |Correlation| threshold (0.4-0.6) to display only strongest relationships, decluttering network visualization.
Missing significant correlations : Decrease Min |Correlation| threshold (0.2-0.25) to reveal weaker but potentially meaningful relationships.
Correlation too volatile : Increase Correlation Period (75-100) for more stable correlation measurements, reducing network line flickering.
Correlation too stale : Decrease Correlation Period (30-40) to emphasize recent correlation patterns, capturing regime-dependent relationship changes.
Velocity arrows too sensitive : Modify 7-bar lookback in code to longer period (10-14) for smoother velocity representation, or increase magnitude threshold for arrow display.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Macro-aware trading approaches requiring cross-market regime context for directional bias
Intermarket analysis strategies monitoring correlation breakdowns and regime divergences
Portfolio construction decisions requiring simultaneous multi-asset regime classification
Risk management frameworks using safe-haven positioning and risk-on/risk-off scoring
Trend-following systems benefiting from cross-asset regime confirmation before entry
Mean-reversion strategies identifying regime extremes via clustering patterns and correlation stress
Reduced Effectiveness:
Single-asset focused strategies not incorporating cross-market context in decision logic
High-frequency trading approaches where multi-security request latency impacts execution
Markets with consistently weak correlations where network topology provides limited insight
Extremely low volatility environments where ADX remains persistently below threshold for all assets
Instruments with erratic or unreliable ADX characteristics producing unstable coordinate positioning
Integration Guidelines
Confluence : Combine with BOSWaves structure, volume analysis, or primary instrument technical indicators for entry timing within aligned regime
Quadrant Respect : Trust signals occurring when primary trading asset occupies appropriate quadrant for intended trade direction
Correlation Context : Prioritize setups where target asset exhibits strong correlation with instruments in same regime quadrant
Divergence Awareness : Monitor for safe-haven assets moving opposite to risk assets - regime divergence validates directional conviction
Velocity Confirmation : Favor entries during periods of strong regime velocity indicating decisive momentum rather than regime oscillation
Risk Score Alignment : Scale position sizing and exposure based on composite risk score - larger positions during clear risk-on/risk-off environments
Trail Pattern Recognition : Use trail curvature to identify regime stability (straight) versus rotation (curved) versus uncertainty (looped)
Multi-Timeframe Structure : Apply higher-timeframe regime scanner for macro filter, lower-timeframe for tactical positioning within established regime
Disclaimer
Cross-Market Regime Scanner is a professional-grade multi-asset regime visualization and correlation analysis tool. It uses ADX-based coordinate positioning and rolling correlation calculation but does not predict future regime transitions or guarantee relationship persistence. Results depend on selected assets' characteristics, parameter configuration, correlation stability, and disciplined interpretation. Security request timing may introduce minor latency in real-time data retrieval. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, fundamental macro awareness, and comprehensive risk management.
Gold Decisions [DayFunded]Gold Decisions 🎯
A multi-timeframe decision system designed specifically for XAUUSD (Gold) traders who want clarity, not noise.
🔍 What It Does
This indicator helps you identify high-probability trade setups by checking 5 key conditions:
1️⃣ Direction — Weekly + Daily must agree (no fighting the trend!)
2️⃣ Breakout — Daily closes beyond a key H4 zone
3️⃣ Pullback — Price returns to the cleared level (no chasing!)
4️⃣ Structure — 15-minute confirms with a break of structure
5️⃣ Entry — Clean directional close = signal
When all gates pass, you get a simple BUY or SELL label with confidence level (H/M/L).
📊 Features
✅ Clean, minimal chart labels (no spam!)
✅ Smart panel showing exactly what to watch for
✅ Win/Loss tracking to see historical performance
✅ H4 Supply/Demand zones auto-detected
✅ Asia session levels (Gold reacts to these!)
✅ Weekly/Daily high-low reference points
✅ Pullback target line for easy visual
⚠️ Important Notes
This is an indicator, not an EA — it does NOT place trades
Signals fire on confirmed bar close — no repainting
Works best on 15m to 4H timeframes
Designed for XAUUSD but may work on other pairs
🎁 Free to Use
This script is completely free. If you find it helpful, a follow or comment is always appreciated!
📖 How to Use
Add to your Gold chart (15m-4H recommended)
Watch the panel for "WATCH FOR" guidance
Wait for BUY/SELL signal
Check confidence level (H = High, M = Medium, L = Low)
Manage your own risk
Not financial advice. Trade responsibly. ✌️
JAMS Intraday Forex EMA Trend Strategy (MTF + Sessions + DD)Strategy focused on following current trend with triple confirmation based on EMAs and VWAPs
ma_libraryTitle: Library: Advanced Moving Average Collection
Description:
This library provides a comprehensive set of Moving Average algorithms, ranging from standard filters (SMA, EMA) to adaptive trendlines (KAMA, FRAMA) and experimental smoothers (ALMA, JMA).
It has been fully optimized for Pine Script v6, ensuring efficient execution and strict robustness against na (missing) values. Unlike standard implementations that propagate na values, these functions dynamically recalculate weights to maintain continuity in disjointed datasets.
🧩 Library Features
Robustness: Non-recursive filters ignore na values within the lookback window. Recursive filters maintain state to prevent calculation breaks.
Optimization: Logic updated to v6 standards, utilizing efficient loops and var persistence.
Standardization: All functions utilize a consistent f_ prefix and standardized parameters for easy integration.
Scope: Contains over 35 different smoothing algorithms.
📊 Input Requirements
Source (src): The data series to smooth (usually close, hl2, etc.).
Length (length): The lookback period (must be a simple int).
Specifics: Some adaptive MAs (like f_evwma) require volume data, while others (like f_alma) require offset/sigma settings.
🛠️ Integration Example
You can import the library and call functions directly, or use the built-in f_selector to create dynamic inputs for your users.
code
Pine
download
content_copy
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//@version=6
indicator("MA Library Demo", overlay=true)
// Import the library
import YourUsername/ma_/1 as ma
// --- Example 1: Direct Function Call ---
// calculating Jurik Moving Average (JMA)
float jma_val = ma.f_jma(close, 14)
plot(jma_val, "JMA", color=color.yellow, linewidth=2)
// --- Example 2: User Selector ---
// Allowing the user to choose the MA type via settings
string selected_type = input.string("ALMA", "MA Type", options= )
int length = input.int(20, "Length")
// Using the generic selector function
float dynamic_ma = ma.f_selector(close, length, selected_type)
plot(dynamic_ma, "Dynamic MA", color=color.aqua)
📋 Included Algorithms
The following methods are available (prefixed with f_):
Standard: SMA, EMA, WMA, VWMA, RMA
Adaptive: KAMA (Kaufman), FRAMA (Fractal), VIDYA (Chande/VARMA), VAMA (Vol. Adjusted)
Low Lag: ZLEMA (Zero Lag), HMA (Hull), JMA (Jurik), DEMA, TEMA
Statistical/Math: LSMA (Least Squares), GMMA (Geometric Mean), FLSMA (Fisher Least Squares)
Advanced/Exotic:
ALMA (Arnaud Legoux)
EIT (Ehlers Instantaneous Trend)
ESD (Ehlers Simple Decycler)
AHMA (Ahrens)
BMF (Blackman Filter)
CMA (Corrective)
DSWF (Damped Sine Wave)
EVWMA (Elastic Vol. Weighted)
HCF (Hybrid Convolution)
LMA (Leo)
MD (McGinley Dynamic)
MF (Modular Filter)
MM (Moving Median)
QMA (Quick)
RPMA (Repulsion)
RSRMA (Right Sided Ricker)
SMMA (Smoothed)
SSMA (Shapeshifting)
SWMA (Sine Weighted)
TMA (Triangular)
TSF (True Strength Force)
VBMA (Variable Band)
ATR Volatility ChannelATR Volatility Channel
This indicator plots adaptive upper and lower volatility bands using EMA-smoothed highs and lows, expanded by ATR. Unlike Bollinger Bands, it uses true range instead of standard deviation, so the bands expand smoothly and predictably with actual price volatility.
It highlights dynamic support, resistance, and fair value, and can be used for ATR level bounces and trend structure analysis.
Settings:
EMA Length: Smooths the highs and lows to calculate the channel (default: 10)
ATR Length: Period used for the Average True Range (default: 14)
ATR Multiplier: Scales the channel width (default: 2)
Show Upper / Lower / Median
Swing IA Cockpit [v2]//@version=5
indicator("Swing IA Cockpit ", overlay=true, max_bars_back=500)
// === INPUTS ===
mode = input.string("Pullback", title="Entry Mode", options= )
corrLen = input.int(60, "Correlation Window Length")
scoreWeightBias = input.float(0.6, title="Weight: Bias", minval=0, maxval=1)
scoreWeightTiming = 1.0 - scoreWeightBias
// === INDICATEURS H1 ===
ema200_H1 = ta.ema(close, 200)
ema50_H1 = ta.ema(close, 50)
rsi_H1 = ta.rsi(close, 14)
donchianHigh = ta.highest(high, 20)
donchianLow = ta.lowest(low, 20)
atr_H1 = ta.atr(14)
avgATR_H1 = ta.sma(atr_H1, 50)
body = math.abs(close - open)
avgBody = ta.sma(body, 20)
// === H4 / D1 ===
close_H4 = request.security(syminfo.tickerid, "240", close)
ema200_H4 = request.security(syminfo.tickerid, "240", ta.ema(close, 200))
rsi_H4 = request.security(syminfo.tickerid, "240", ta.rsi(close, 14))
atr_H4 = request.security(syminfo.tickerid, "240", ta.atr(14))
avgATR_H4 = request.security(syminfo.tickerid, "240", ta.sma(ta.atr(14), 50))
close_D1 = request.security(syminfo.tickerid, "D", close)
ema200_D1 = request.security(syminfo.tickerid, "D", ta.ema(close, 200))
// === CORRÉLATIONS ===
dxy = request.security("TVC:DXY", "60", close)
spx = request.security("SP:SPX", "60", close)
gold = request.security("OANDA:XAUUSD", "60", close)
corrDXY = ta.correlation(close, dxy, corrLen)
corrSPX = ta.correlation(close, spx, corrLen)
corrGold = ta.correlation(close, gold, corrLen)
// === LOGIQUE BIAIS ===
biasLong = close_D1 > ema200_D1 and close_H4 > ema200_H4 and rsi_H4 >= 55
biasShort = close_D1 < ema200_D1 and close_H4 < ema200_H4 and rsi_H4 <= 45
bias = biasLong ? "LONG" : biasShort ? "SHORT" : "NEUTRAL"
// === LOGIQUE TIMING ===
isBreakoutLong = mode == "Breakout" and high > donchianHigh and close > ema200_H1 and rsi_H1 > 50
isBreakoutShort = mode == "Breakout" and low < donchianLow and close < ema200_H1 and rsi_H1 < 50
var float breakoutPrice = na
var int breakoutBar = na
if isBreakoutLong or isBreakoutShort
breakoutPrice := close
breakoutBar := bar_index
validPullbackLong = mode == "Pullback" and not na(breakoutBar) and bar_index <= breakoutBar + 3 and close > ema50_H1 and low <= ema50_H1
validPullbackShort = mode == "Pullback" and not na(breakoutBar) and bar_index <= breakoutBar + 3 and close < ema50_H1 and high >= ema50_H1
timingLong = isBreakoutLong or validPullbackLong
timingShort = isBreakoutShort or validPullbackShort
// === SCORES ===
scoreTrend = (close_D1 > ema200_D1 ? 20 : 0) + (close_H4 > ema200_H4 ? 20 : 0)
scoreMomentumBias = (rsi_H4 >= 55 or rsi_H4 <= 45) ? 20 : 10
scoreCorr = 0
scoreCorr += biasLong and corrDXY < 0 ? 10 : 0
scoreCorr += biasLong and corrSPX > 0 ? 10 : 0
scoreCorr += biasLong and corrGold >= 0 ? 10 : 0
scoreCorr += biasShort and corrDXY > 0 ? 10 : 0
scoreCorr += biasShort and corrSPX < 0 ? 10 : 0
scoreCorr += biasShort and corrGold <= 0 ? 10 : 0
scoreCorr := math.min(scoreCorr, 30)
scoreVolBias = atr_H4 > avgATR_H4 ? 10 : 0
scoreBias = scoreTrend + scoreMomentumBias + scoreCorr + scoreVolBias
scoreStruct = (timingLong or timingShort) ? 40 : 0
scoreMomentumTiming = rsi_H1 > 50 or rsi_H1 < 50 ? 25 : 10
scoreTrendH1 = (close > ema50_H1 and ema50_H1 > ema200_H1) or (close < ema50_H1 and ema50_H1 < ema200_H1) ? 20 : 10
scoreVolTiming = atr_H1 > avgATR_H1 ? 15 : 5
scoreTiming = scoreStruct + scoreMomentumTiming + scoreTrendH1 + scoreVolTiming
scoreTotal = scoreBias * scoreWeightBias + scoreTiming * scoreWeightTiming
scoreLong = biasLong ? scoreTotal : 0
scoreShort = biasShort ? scoreTotal : 0
delta = scoreLong - scoreShort
scoreExtMomentum = (rsi_H4 > 55 ? 10 : 0)
scoreExtVol = atr_H4 > avgATR_H4 ? 10 : 0
scoreExtStructure = body > avgBody ? 10 : 5
scoreExtCorr = (scoreCorr > 15 ? 10 : 5)
scoreExtension = scoreExtMomentum + scoreExtVol + scoreExtStructure + scoreExtCorr
// === VERDICT FINAL ===
verdict = "NO TRADE"
verdict := bias == "NEUTRAL" or math.abs(delta) < 10 or scoreTotal < 70 ? "NO TRADE" :
scoreTotal < 80 ? "WAIT" :
scoreTotal >= 85 and math.abs(delta) >= 20 and scoreExtension >= 60 ? "TRADE A+" :
"TRADE"
// === TABLE COCKPIT ===
var table cockpit = table.new(position.top_right, 2, 9, border_width=1)
if bar_index % 5 == 0
table.cell(cockpit, 0, 0, "Bias", bgcolor=color.gray)
table.cell(cockpit, 1, 0, bias)
table.cell(cockpit, 0, 1, "ScoreBias", bgcolor=color.gray)
table.cell(cockpit, 1, 1, str.tostring(scoreBias))
table.cell(cockpit, 0, 2, "ScoreTiming", bgcolor=color.gray)
table.cell(cockpit, 1, 2, str.tostring(scoreTiming))
table.cell(cockpit, 0, 3, "ScoreTotal", bgcolor=color.gray)
table.cell(cockpit, 1, 3, str.tostring(scoreTotal))
table.cell(cockpit, 0, 4, "ScoreLong", bgcolor=color.gray)
table.cell(cockpit, 1, 4, str.tostring(scoreLong))
table.cell(cockpit, 0, 5, "ScoreShort", bgcolor=color.gray)
table.cell(cockpit, 1, 5, str.tostring(scoreShort))
table.cell(cockpit, 0, 6, "Delta", bgcolor=color.gray)
table.cell(cockpit, 1, 6, str.tostring(delta))
table.cell(cockpit, 0, 7, "Extension", bgcolor=color.gray)
table.cell(cockpit, 1, 7, str.tostring(scoreExtension))
table.cell(cockpit, 0, 8, "Verdict", bgcolor=color.gray)
table.cell(cockpit, 1, 8, verdict, bgcolor=verdict == "TRADE A+" ? color.green : verdict == "TRADE" ? color.lime : verdict == "WAIT" ? color.orange : color.red)
// === ALERTS ===
alertcondition(verdict == "TRADE A+" and bias == "LONG", title="TRADE A+ LONG", message="TRADE A+ signal long")
alertcondition(verdict == "TRADE A+" and bias == "SHORT", title="TRADE A+ SHORT", message="TRADE A+ signal short")
alertcondition(verdict == "NO TRADE", title="NO TRADE / RANGE", message="Marché confus ou neutre — pas de trade")
Volume Profile Skew [BackQuant]Volume Profile Skew
Overview
Volume Profile Skew is a market-structure indicator that answers a specific question most volume profiles do not:
“Is volume concentrating toward lower prices (accumulation) or higher prices (distribution) inside the current profile range?”
A standard volume profile shows where volume traded, but it does not quantify the shape of that distribution in a single number. This script builds a volume profile over a rolling lookback window, extracts the key profile levels (POC, VAH, VAL, and a volume-weighted mean), then computes the skewness of the volume distribution across price bins. That skewness becomes an oscillator, smoothed into a regime signal and paired with visual profile plotting, key level lines, and historical POC tracking.
This gives you two layers at once:
A full profile and its important levels (where volume is).
A skew metric (how volume is leaning within that range).
What this indicator is based on
The foundation comes from classical “volume at price” concepts used in Market Profile and Volume Profile analysis:
POC (Point of Control): the price level with the highest traded volume.
Value Area (VAH/VAL): the zone containing the bulk of activity, commonly 70% of total volume.
Volume-weighted mean (VWMP in this script): the average price weighted by volume, a “center of mass” for traded activity.
Where this indicator extends the idea is by treating the volume profile as a statistical distribution across price. Once you treat “volume by price bin” as a probability distribution (weights sum to 1), you can compute distribution moments:
Mean: where the mass is centered.
Standard deviation: how spread-out it is.
Skewness: whether the distribution has a heavier tail toward higher or lower prices.
This is not a gimmick. Skewness is a standard statistic in probability theory. Here it is applied to “volume concentration across price”, not to returns.
Core concept: what “skew” means in a volume profile
Imagine a profile range from Low to High, split into bins. Each bin has some volume. You can get these shapes:
Balanced profile: volume is fairly symmetric around the mean, skew near 0.
Bottom-heavy profile: more volume at lower prices, with a tail toward higher prices, skew tends to be positive.
Top-heavy profile: more volume at higher prices, with a tail toward lower prices, skew tends to be negative.
In this script:
Positive skew is labeled as ACCUMULATION.
Negative skew is labeled as DISTRIBUTION.
Near-zero skew is NEUTRAL.
Important: accumulation here does not mean “buying will immediately pump price.” It means the profile shape suggests more participation at lower prices inside the current lookback range. Distribution means participation is heavier at higher prices.
How the volume profile is built
1) Define the analysis window
The profile is computed on a rolling window:
Lookback Period: number of bars included (capped by available history).
Profile Resolution (bins): number of price bins used to discretize the high-low range.
The script finds the highest high and lowest low in the lookback window to define the price range:
rangeHigh = highest high in window
rangeLow = lowest low in window
binSize = (rangeHigh - rangeLow) / bins
2) Create bin midpoints
Each bin gets a midpoint “price” used for calculations:
price = rangeLow + binSize * (b + 0.5)
These midpoints are what the mean, variance, and skewness are computed on.
3) Distribute each candle’s volume into bins
This is a key implementation detail. Real volume profiles require tick-level data, but Pine does not provide that. So the script approximates volume-at-price using candle ranges:
For each bar in the lookback:
Determine which bins its low-to-high range touches.
Split that candle’s total volume evenly across the touched bins.
So if a candle spans 6 bins, each bin gets volume/6 from that bar. This is a practical, consistent approximation for “where trading could have occurred” inside the bar.
This approach has tradeoffs:
It does not know where within the candle the volume truly traded.
It assumes uniform distribution across the candle range.
It becomes more meaningful with larger samples (bigger lookback) and/or higher timeframes.
But it is still useful because the purpose here is the shape of the distribution across the whole window, not exact microstructure.
Key profile levels: POC, VAH, VAL, VWMP
POC (Point of Control)
POC is found by scanning bins and selecting the bin with maximum volume. The script stores:
pocIndex: which bin has max volume
poc price: midpoint price of that bin
Value Area (VAH/VAL) using 70% volume
The script builds the value area around the POC outward until it captures 70% of total volume:
Start with the POC bin.
Expand one bin at a time to the side with more volume.
Stop when accumulated volume >= 70% of total profile volume.
Then:
VAL = rangeLow + binSize * lowerIdx
VAH = rangeLow + binSize * (upperIdx + 1)
This produces a classic “where most business happened” zone.
VWMP (Volume-Weighted Mean Price)
This is essentially the center of mass of the profile:
VWMP = sum(price * volume ) / totalVolume
It is similar in spirit to VWAP, but it is computed over the profile bins, not from bar-by-bar typical price.
Skewness calculation: turning the profile into an oscillator
This is the main feature.
1) Treat volumes as weights
For each bin:
weight = volume / totalVolume
Now weights sum to 1.
2) Compute weighted mean
Mean price:
mean = sum(weight * price )
3) Compute weighted variance and std deviation
Variance:
variance = sum(weight * (price - mean)^2)
stdDev = sqrt(variance)
4) Compute weighted third central moment
Third moment:
m3 = sum(weight * (price - mean)^3)
5) Standardize to skewness
Skewness:
rawSkew = m3 / (stdDev^3)
This standardization matters. Without it, the value would explode or shrink based on profile scale. Standardized skewness is dimensionless and comparable.
Smoothing and regime rules
Raw skewness can be jumpy because:
profile bins change as rangeHigh/rangeLow shift,
one high-volume candle can reshape the distribution,
volume regimes change quickly in crypto.
So the indicator applies EMA smoothing:
smoothedSkew = EMA(rawSkew, smooth)
Then it classifies regime using fixed thresholds:
Bullish (ACCUMULATION): smoothedSkew > +0.25
Bearish (DISTRIBUTION): smoothedSkew < -0.25
Neutral: between those values
Signals are generated on threshold cross events:
Bull signal when smoothedSkew crosses above +0.25
Bear signal when smoothedSkew crosses below -0.25
This makes the skew act like a regime oscillator rather than a constantly flipping color.
Volume Profile plotting modes
The script draws the profile on the last bar, using boxes for each bin, anchored to the right with a configurable offset. The width of each profile bar is normalized by max bin volume:
volRatio = binVol / maxVol
barWidth = volRatio * width
Three style modes exist:
1) Gradient
Uses a “jet-like” gradient based on volRatio (blue → red). Higher-volume bins stand out naturally. Transparency increases as volume decreases, so low-volume bins fade.
2) Solid
Uses the current regime color (bull/bear/neutral) for all bins, with transparency. This makes the profile read as “structure + regime.”
3) Skew Highlight
Highlights bins that match the skew bias:
If skew bullish, emphasize lower portion of profile.
If skew bearish, emphasize higher portion of profile.
Else, keep most bins neutral.
This is a visual “where the skew is coming from” mode.
Historical POC tracking and Naked POCs
This script also treats POCs as meaningful levels over time, similar to how traders track old VA levels.
What is a “naked POC”?
A “naked POC” is a previously formed POC that has not been revisited (retested) by price since it was recorded. Many traders watch these as potential reaction zones because they represent prior “maximum traded interest” that the market has not re-engaged with.
How this script records POCs
It stores a new historical POC when:
At least updatebars have passed since the last stored POC, and
The POC has changed by at least pochangethres (%) from the last stored value.
New stored POCs are flagged as naked by default.
How naked becomes tested
On each update, the script checks whether price has entered a small zone around a naked POC:
zoneSize = POC * 0.002 (about 0.2%)
If bar range overlaps that zone, mark it as tested (not naked).
Display controls:
Highlight Naked POCs: draws and labels untested POCs.
Show Tested POCs: optionally draw tested ones in a muted color.
To avoid clutter, the script limits stored POCs to the most recent 20 and avoids drawing ones too close to the current POC.
On-chart key levels and what they mean
When enabled, the script draws the current lookback profile levels on the price chart:
POC (solid): the “most traded” price.
VAH/VAL (dashed): boundaries of the 70% value area.
VWMP (dotted): volume-weighted mean of the profile distribution.
Interpretation framework (practical, not mystical):
POC often behaves like a magnet in balanced conditions.
VAH/VAL define the “accepted” area, breaks can signal auction continuation.
VWMP is a fair-value reference, useful as a mean anchor when skew is neutralizing.
Oscillator panel and histogram
The skew oscillator is plotted in a separate pane:
Line: smoothedSkew, colored by regime.
Histogram: smoothedSkew as bars, colored by sign.
Fill: subtle shading above/below 0 to reinforce bias.
This makes it easy to read:
Direction of bias (positive vs negative).
Strength (distance from 0 and from thresholds).
Transitions (crosses of ±0.25).
Info table: what it summarizes
On the last bar, a table prints key diagnostics:
Current skew value (smoothed).
Regime label (ACCUMULATION / DISTRIBUTION / NEUTRAL).
Current POC, VAH, VAL, VWMP.
Count of naked POCs still active.
A simple “volume location” hint (lower/higher/balanced).
This is designed for quick scanning without reading the entire profile.
Alerts
The indicator includes alerts for:
Skew regime shifts (cross above +0.25, cross below -0.25).
Price crossing above/below current POC.
Approaching a naked POC (within 1% of any active naked POC).
The “approaching naked POC” alert is useful as a heads-up that price is entering a historically important volume magnet/reaction zone.
How to use it properly
1) Regime filter
Use skew regime to decide what type of trades you should prioritize:
ACCUMULATION (positive skew): market activity is heavier at lower prices, pullbacks into value or below VWMP often matter more.
DISTRIBUTION (negative skew): activity is heavier at higher prices, rallies into value or above VWMP often matter more.
NEUTRAL: mean-reversion and POC magnet behavior tends to dominate.
This is not “buy when green.” It is context for what the auction is doing.
2) Level-based execution
Combine skew with VA/POC levels:
In neutral regimes, expect rotations around POC and inside VA.
In strong skew regimes, watch for acceptance away from POC and reactions at VA edges.
3) Naked POCs as targets and reaction zones
Naked POCs can act like unfinished business. Common workflows:
As targets in rotations.
As areas to reduce risk when price is approaching.
As “if it breaks cleanly, trend continuation” markers when price returns with force.
Parameter tuning guidance
Lookback
Controls how “local” the profile is.
Shorter: reacts faster, more sensitive to recent moves.
Longer: more stable, better for swing context.
Bins
Controls resolution of the profile.
Higher bins: more detail, more computation, more sensitive profile shape.
Lower bins: smoother, less detail, more stable skew.
Smoothing
Controls how noisy the skew oscillator is.
Higher smoothing: fewer regime flips, slower response.
Lower smoothing: more responsive, more false transitions.
POC tracking settings
Update interval and threshold decide how many historical POCs you store and how different they must be. If you set them too loose, you will spam levels. If too strict, you will miss meaningful shifts.
Limitations and what not to assume
This indicator uses candle-range volume distribution because Pine cannot see tick-level volume-at-price. That means:
The profile is an approximation of where volume could have traded, not exact tape data.
Skew is best treated as a structural bias, not a precise signal generator.
Extreme single-bar events can distort the distribution briefly, smoothing helps but cannot remove reality.
Summary
Volume Profile Skew takes standard volume profile structure (POC, Value Area, volume-weighted mean) and adds a statistically grounded measure of profile shape using skewness. The result is a regime oscillator that quantifies whether volume concentration is leaning toward lower prices (accumulation) or higher prices (distribution), while also plotting the full profile, key levels, and historical naked POCs for actionable context.
XAUUSD ELIRANTo build a professional and accurate description of your strategy, I have distilled the information you shared into a neat "Trading Plan". This strategy combines strict financial discipline with a desire for consistent growth in the Forex market.
Here is a suggestion for describing your strategy:
The "Safe Profit" Strategy: Capital Management and Growth in the Forex Market
The strategy focuses on preserving equity while creating cash flow for withdrawal and leveraging profits to purchase additional trading portfolios. The goal is to reduce personal risk and increase purchasing power in the market.
1. Capital and Withdrawal Goals
Starting Capital/Base: $2,250.
Periodic Profit Target: $1,000.
Withdrawal Policy: Upon reaching the profit target, the $1,000 is immediately withdrawn for "cash out" and reinvestment in additional trading portfolios.
2. Operational Logic (The Workflow)
The strategy operates in cycles of accumulation -> withdrawal -> expansion:
Accumulation phase: Focus on trading Forex assets with the aim of achieving a return of approximately 44% on the base portfolio.
Withdrawal phase: Defining the first $1,000 as net profit that leaves the market to ensure "money in your pocket".
Expansion phase: Using part of the profit that is withdrawn to purchase an additional trading portfolio, which allows for increased trading volume without increasing the risk on the original portfolio.
3. Advantages of the strategy
Psychological risk management: Knowing that you are withdrawing money "home" reduces mental stress and allows for cleaner decision-making.
Smart leverage: Purchasing additional portfolios creates diversification of risks between different accounts.
Self-discipline: Pre-defined profit and withdrawal targets prevent the "greed trap" that exists in Forex.
