Session Breakout, Retest, Reversal + Large Move Alert## **Session Breakout, Retest, Reversal + Large Move Alert**
### Overview
A powerful multi-functional indicator designed for day traders and futures traders to identify session-based breakout opportunities, retest confirmations, and significant price movements across all futures contracts (Gold, E-mini S&P 500, Nasdaq, Crude Oil, and more).
### Key Features
**๐ Pre-Market Session Tracking**
- Automatically calculates pre-market/overnight session highs and lows
- Displays session ranges with customizable colors and styling
- Extends lines through the entire trading session for easy reference
- Supports overnight sessions (e.g., 4 PM โ 7:30 AM for Gold futures)
**๐ Breakout Detection**
- Identifies breakouts above/below pre-market highs and lows
- Uses close-price confirmation to filter false signals from wicks
- Displays "BO โ" and "BO โ" labels at breakout points
- Generates instant alerts when breakouts occur
**โป๏ธ Retest Failed Tracking**
- Monitors price retests after breakouts
- Detects when retests fail to reach previous support/resistance
- Labels "RF" (Retest Failed) for high-probability trade setups
- Helps identify reversal opportunities
**๐ First 5-Minute Analysis**
- Captures first 5 minutes of market open (customizable timeframe)
- Tracks first 5-minute highs and lows separately
- Essential for mean-reversion and breakout confirmation strategies
- Blue lines extend through the trading session for easy tracking
**โก Large Move Alerts**
- Detects significant price movements based on point thresholds
- Individual thresholds for 5+ different symbols:
- GC (Gold): 15 points
- ES (E-mini S&P 500): 15 points
- NQ (E-mini Nasdaq): 50 points
- CL (Crude Oil): 1.5 points
- Custom: Fully adjustable
- Auto-detects symbol from chart ticker
- Labels show exact point movement and candle direction
### Customization Options
**Symbol Configuration**
- **Auto-Mode**: Automatically detects trading symbol from chart ticker
- **Manual-Mode**: Select specific symbol (GC, ES, NQ, CL, or Custom)
**Session Settings**
- Fully customizable pre-market session time (24-hour format)
- Adjustable market open time for first 5-minute window
- Market close hour and minute configuration
- Support for any timezone
**Point Move Thresholds by Symbol**
- Set independent thresholds for each of your trading symbols
- Quickly adjust settings when switching between different futures
- Includes helpful tooltips for recommended values
**Display & Styling**
- Toggle all visual elements on/off individually
- Customizable colors for all lines and labels:
- Pre-market high/low colors
- Breakout labels (up/down)
- Retest failed labels
- First 5-minute session lines
- Large move indicators
- Text size options: tiny, small, normal, large, huge
### How It Works
1. **Session Tracking**: The indicator identifies your pre-market session and marks the high and low with labeled lines (PH/PL)
2. **Breakout Signal**: Once the market opens, it monitors for close prices above/below the pre-market levels and alerts you with "BO โ" or "BO โ"
3. **Retest Confirmation**: After a breakout, it tracks retests and labels "RF" when the retest fails to reach the opposite extreme, confirming trade direction
4. **Large Move Detection**: Simultaneously monitors for significant point moves that exceed your symbol-specific thresholds
5. **Alert Triggers**: Get real-time alerts for:
- Breakout Up/Down
- Any Breakout
- Large Move events
### Alert Conditions
The indicator includes four alert conditions:
- **Breakout Up Alert**: Price closes above pre-market high
- **Breakout Down Alert**: Price closes below pre-market low
- **Any Breakout Alert**: Either breakout condition triggers
- **Large Move Alert**: Point movement exceeds threshold for current symbol
### Ideal For
- โ
Day traders (breakout/retest strategies)
- โ
Futures traders (Gold, Oil, Stock Index Contracts)
- โ
Intraday scalpers (first 5-minute analysis)
- โ
Swing traders (session-based levels)
- โ
Multi-symbol traders (independent thresholds per symbol)
### Disclaimer
This indicator is designed for educational and informational purposes. Past performance does not guarantee future results. Always use proper risk management and position sizing. Test thoroughly on historical data before trading live.
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๐ Trading Checklist โ Precision Entry SystemTake your trading discipline to the next level with this Precision Trading Checklist for TradingView. Designed for intraday traders following liquidity, structure, and Smart Money Concepts (SMC) AKA ICT Concepts, this overlay ensures you never miss a key confirmation before entering a trade.
Features:
โ
Pre-Market Preparation: Track previous session highs/lows, AM/PM sessions, and key liquidity zones.
โ
Bias & Narrative Check: Quickly confirm daily trend, price position relative to daily open, and higher timeframe confluence.
โ
Session-Specific Rules: Focused sessions like Silver Bullet (10:00โ11:30), Afternoon (13:30โ15:00), and Final Hour (15:00โ16:00).
โ
Structure & Setup Validation: Confirm liquidity sweeps, market structure shifts, expansion candles, fair value gaps, and order blocks.
โ
Risk Management Reminders: Stop-loss, target points, risk percentage, breakeven management, and pyramiding rules.
โ
Post-Trade Journaling: Document entries, session, setup type, trade outcome, and grading for continuous improvement.
โ
Golden Rules: Visual reminders to enforce discipline, avoid emotional trades, and respect session limits.
Why Use It:
This checklist is perfect for traders who want to stay consistent, minimise mistakes, and follow a disciplined routine. Displayed as an overlay on your chart, it provides all essential checks in one glance, keeping you focused on the setup rather than scrolling through notes or separate trackers.
How to use:
Add the indicator to your chart
Click the settings/gear icon
Check off items as you complete them
The checklist on your chart updates in real-time with green checkmarks!
The checkboxes will persist as long as the indicator is on your chart,
making it perfect for tracking your pre-trade and post-trade routines!
Follow the checklist items step by step before entering trades.
Use the session-specific guidelines to filter setups.
Journal your trades post-execution for growth and analysis.
Smart Money Flow Index (SMFI) - Advanced SMC [PhenLabs]๐Smart Money Flow Index (SMFI)
Version: PineScriptโขv6
๐Description
The Smart Money Flow Index (SMFI) is an advanced Smart Money Concepts implementation that tracks institutional trading behavior through multi-dimensional analysis. This comprehensive indicator combines volume-validated Order Block detection, Fair Value Gap identification with auto-mitigation tracking, dynamic Liquidity Zone mapping, and Break of Structure/Change of Character detection into a unified system.
Unlike basic SMC indicators, SMFI employs a proprietary scoring algorithm that weighs five critical factors: Order Block strength (validated by volume), Fair Value Gap size and recency, proximity to Liquidity Zones, market structure alignment (BOS/CHoCH), and multi-timeframe confluence. This produces a Smart Money Score (0-100) where readings above 70 represent optimal institutional setup conditions.
๐Points of Innovation
Volume-Validated Order Block Detection โ Only displays Order Blocks when formation candle exceeds customizable volume multiplier (default 1.5x average), filtering weak zones and highlighting true institutional accumulation/distribution
Auto-Mitigation Tracking System โ Fair Value Gaps and Order Blocks automatically update status when price mitigates them, with visual distinction between active and filled zones preventing trades on dead levels
Proprietary Smart Money Score Algorithm โ Combines weighted factors (OB strength 25%, FVG proximity 20%, Liquidity 20%, Structure 20%, MTF 15%) into single 0-100 confidence rating updating in real-time
ATR-Based Adaptive Calculations โ All distance measurements use 14-period Average True Range ensuring consistent function across any instrument, timeframe, or volatility regime without manual recalibration
Dynamic Age Filtering โ Automatically removes liquidity levels and FVGs older than configurable thresholds preventing chart clutter while maintaining relevant levels
Multi-Timeframe Confluence Integration โ Analyzes higher timeframe bias with customizable multipliers (2-10x) and incorporates HTF trend direction into Smart Money Score for institutional alignment
๐งCore Components
Order Block Engine โ Detects institutional supply/demand zones using characteristic patterns (down-move-then-strong-up for bullish, up-move-then-strong-down for bearish) with minimum volume threshold validation, tracks mitigation when price closes through zones
Fair Value Gap Scanner โ Identifies price imbalances where current candle's low/high leaves gap with two-candle-prior high/low, filters by minimum size percentage, monitors 50% fill for mitigation status
Liquidity Zone Mapper โ Uses pivot high/low detection with configurable lookback to mark swing points where stop losses cluster, extends horizontal lines to visualize sweep targets, manages lifecycle through age-based removal
Market Structure Analyzer โ Tracks pivot progression to identify trend through higher-highs/higher-lows (bullish) or lower-highs/lower-lows (bearish), detects Break of Structure and Change of Character for trend/reversal confirmation
Scoring Calculation Engine โ Evaluates proximity to nearest Order Blocks using ATR-normalized distance, assesses FVG recency and distance, calculates liquidity proximity with age weighting, combines structure bias and MTF trend into smoothed final score
๐ฅKey Features
Customizable Display Limits โ Control maximum Order Blocks (1-10), Liquidity Zones (1-10), and FVG age (10-200 bars) to maintain clean charts focused on most relevant institutional levels
Gradient Strength Visualization โ All zones render with transparency-adjustable coloring where stronger/newer zones appear more solid and weaker/older zones fade progressively providing instant visual hierarchy
Educational Label System โ Optional labels identify each zone type (Bullish OB, Bearish OB, Bullish FVG, Bearish FVG, BOS) with color-coded text helping traders learn SMC concepts through practical application
Real-Time Smart Money Score Dashboard โ Top-right table displays current score (0-100) with color coding (green >70, yellow 30-70, red <30) plus trend arrow for at-a-glance confidence assessment
Comprehensive Alert Suite โ Configurable notifications for Order Block formation, Fair Value Gap detection, Break of Structure events, Change of Character signals, and high Smart Money Score readings (>70)
Buy/Sell Signal Integration โ Automatically plots triangle markers when Smart Money Score exceeds 70 with aligned market structure and fresh Order Block detection providing clear entry signals
๐จVisualization
Order Block Boxes โ Shaded rectangles extend from formation bar spanning high-to-low of institutional candle, bullish zones in green, bearish in red, with customizable transparency (80-98%)
Fair Value Gap Zones โ Rectangular areas marking imbalances, active FVGs display in bright colors with adjustable transparency, mitigated FVGs switch to gray preventing trades on filled zones
Liquidity Level Lines โ Dashed horizontal lines extend from pivot creation points, swing highs in bearish color (short targets above), swing lows in bullish color (long targets below), opacity decreases with age
Structure Labels โ "BOS" labels appear above/below price when Break of Structure confirmed, colored by direction (green bullish, red bearish), positioned at 1% beyond highs/lows for visibility
Educational Info Panel โ Bottom-right table explains key terminology (OB, FVG, BOS, CHoCH) and score interpretation (>70 high probability) with semi-transparent background for readability
๐Usage Guidelines
General Settings
Show Order Blocks โ Default: On, toggles visibility of institutional supply/demand zones, disable when focusing solely on FVGs or Liquidity
Show Fair Value Gaps โ Default: On, controls FVG zone display including active and mitigated imbalances
Show Liquidity Zones โ Default: On, manages liquidity line visibility, disable on lower timeframes to reduce clutter
Show Market Structure โ Default: On, toggles BOS/CHoCH label display
Show Smart Money Score โ Default: On, controls score dashboard visibility
Order Block Settings
OB Lookback Period โ Default: 20, Range: 5-100, controls bars scanned for Order Block patterns, lower values detect recent activity, higher values find older blocks
Min Volume Multiplier โ Default: 1.5, Range: 1.0-5.0, sets minimum volume threshold as multiple of 20-period average, higher values (2.0+) filter for strongest institutional candles
Max Order Blocks to Display โ Default: 3, Range: 1-10, limits simultaneous Order Blocks shown, lower settings (1-3) maintain focus on most recent zones
Fair Value Gap Settings
Min FVG Size (%) โ Default: 0.3, Range: 0.1-2.0, defines minimum gap size as percentage of close price, lower values detect micro-imbalances, higher values focus on significant gaps
Max FVG Age (bars) โ Default: 50, Range: 10-200, removes FVGs older than specified bars, lower settings (10-30) for scalping, higher (100-200) for swing trading
Show FVG Mitigation โ Default: On, displays filled FVGs in gray providing visual history, disable to show only active untouched imbalances
Liquidity Zone Settings
Liquidity Lookback โ Default: 50, Range: 20-200, sets pivot detection period for swing highs/lows, lower values (20-50) mark shorter-term liquidity, higher (100-200) identify major swings
Max Liquidity Age (bars) โ Default: 100, Range: 20-500, removes liquidity lines older than specified bars, adjust based on timeframe
Liquidity Sensitivity โ Default: 0.5, Range: 0.1-1.0, controls pivot detection sensitivity, lower values mark only major swings, higher values identify minor swings
Max Liquidity Zones to Display โ Default: 3, Range: 1-10, limits total liquidity levels shown maintaining chart clarity
Market Structure Settings
Pivot Length โ Default: 5, Range: 3-15, defines bars to left/right for pivot validation, lower values (3-5) create sensitive structure breaks, higher (10-15) filter for major shifts
Min Structure Move (%) โ Default: 1.0, Range: 0.1-5.0, sets minimum percentage move required between pivots to confirm structure change
Multi-Timeframe Settings
Enable MTF Analysis โ Default: On, activates higher timeframe trend analysis incorporation into Smart Money Score
Higher Timeframe Multiplier โ Default: 4, Range: 2-10, multiplies current timeframe to determine analysis timeframe (4x on 15min = 1hour)
Visual Settings
Bullish Color โ Default: Green (#089981), sets color for bullish Order Blocks, FVGs, and structure elements
Bearish Color โ Default: Red (#f23645), defines color for bearish elements
Neutral Color โ Default: Gray (#787b86), controls color of mitigated zones and neutral elements
Show Educational Labels โ Default: On, displays text labels on zones identifying type (OB, FVG, BOS), disable once familiar with patterns
Order Block Transparency โ Default: 92, Range: 80-98, controls Order Block box transparency
FVG Transparency โ Default: 92, Range: 80-98, sets Fair Value Gap zone transparency independently from Order Blocks
Alert Settings
Alert on Order Block Formation โ Default: On, triggers notification when new volume-validated Order Block detected
Alert on FVG Formation โ Default: On, sends alert when Fair Value Gap appears enabling quick response to imbalances
Alert on Break of Structure โ Default: On, notifies when BOS or CHoCH confirmed
Alert on High Smart Money Score โ Default: On, alerts when Smart Money Score crosses above 70 threshold indicating high-probability setup
โ
Best Use Cases
Order Block Retest Entries โ After Break of Structure, wait for price retrace into fresh bullish Order Block with Smart Money Score >70, enter long on zone reaction targeting next liquidity level
Fair Value Gap Retracement Trading โ When price creates FVG during strong move then retraces, enter as price approaches unfilled gap expecting institutional orders to continue trend
Liquidity Sweep Reversals โ Monitor price approaching swing high/low liquidity zones against prevailing Smart Money Score trend, after stop hunt sweep watch for rejection into premium Order Block/FVG
Multi-Timeframe Confluence Setups โ Identify alignment when current timeframe Order Block coincides with higher timeframe FVG plus MTF analysis showing matching trend bias
Break of Structure Continuations โ After BOS confirms trend direction, trade pullbacks to nearest Order Block or FVG in direction of structure break using Smart Money Score >70 as entry filter
Change of Character Reversal Plays โ When CHoCH detected indicating potential reversal, look for Smart Money Score pivot with opposing Order Block formation then enter on structure confirmation
โ ๏ธLimitations
Lagging Pivot Calculations โ Pivot-based features (Liquidity Zones, Market Structure) require bars to right of pivot for confirmation, meaning these elements identify levels retrospectively with delay equal to lookback period
Whipsaw in Ranging Markets โ During choppy conditions, Order Blocks fail frequently and structure breaks produce false signals as Smart Money Score fluctuates without clear institutional bias, best used in trending markets
Volume Data Dependency โ Order Block volume validation requires accurate volume data which may be incomplete on Forex pairs or limited in crypto exchange feeds
Subjectivity in Scoring Weights โ Proprietary 25-20-20-20-15 weighting reflects general institutional behavior but may not optimize for specific instruments or market regimes, user cannot adjust factor weights
Visual Complexity on Lower Timeframes โ Sub-hour timeframes generate excessive zones creating cluttered charts, requires aggressive display limit reduction and higher minimum thresholds
No Fundamental Integration โ Indicator analyzes purely technical price action and volume without incorporating economic events, news catalysts, or fundamental shifts that override technical levels
๐กWhat Makes This Unique
Unified SMC Ecosystem โ Unlike indicators displaying Order Blocks OR FVGs OR Liquidity separately, SMFI combines all three institutional concepts plus market structure into single cohesive system
Proprietary Confidence Scoring โ Rather than manual setup assessment, automated Smart Money Score quantifies probability by weighting five institutional dimensions into actionable 0-100 rating
Volume-Filtered Quality โ Eliminates weak Order Blocks forming without institutional volume confirmation, ensuring displayed zones represent genuine accumulation/distribution
Adaptive Lifecycle Management โ Automatically updates mitigation status and removes aged zones preventing trades on dead levels through continuous validity and age monitoring
Educational Integration โ Built-in tooltips, labeled zones, and reference panel make indicator functional for both learning Smart Money Concepts and executing strategies
๐ฌHow It Works
Order Block Detection โ Scans for patterns where strong directional move follows counter-move creating last down-candle before rally (bullish OB) or last up-candle before sell-off (bearish OB), validates formations only when candle exhibits volume exceeding configurable multiple (default 1.5x) of 20-bar average volume
Fair Value Gap Identification โ Compares current candleโs high/low against two-candles-prior low/high to detect price imbalances, calculates gap size as percentage of close and filters micro-gaps below minimum threshold (default 0.3%), monitors whether subsequent price fills 50% triggering mitigation status
Liquidity Zone Mapping โ Employs pivot detection using configurable lookback (default 50 bars) to identify swing highs/lows where retail stops cluster, extends horizontal reference lines from pivot creation and applies age-based filtering to remove stale zones
Market Structure Analysis โ Tracks pivot progression using structure-specific lookback (default 5 bars) to determine trend, confirms uptrend when new pivot high exceeds previous by minimum move percentage, detects Break of Structure when price breaks recent pivot level, flags Change of Character for potential reversals
Multi-Timeframe Confluence โ When enabled, requests security data from higher timeframe (current TF ร HTF multiplier, default 4x), compares HTF close against HTF 20-period MA to determine bias, contributes ยฑ50 points to score ensuring alignment with institutional positioning on superior timeframe
Smart Money Score Calculation โ Evaluates Order Block component via ATR-normalized distance producing max 100-point contribution weighted at 25%, assesses FVG factor through age penalty and distance at 20% weight, calculates Liquidity proximity at 20%, incorporates structure bias (ยฑ50-100 points) at 20%, adds MTF component at 15%, applies 3-period smoothing to reduce volatility
Visual Rendering and Lifecycle โ Draws Order Block boxes, Fair Value Gap rectangles with color coding (green/red active, gray mitigated), extends liquidity dashed lines with fade-by-age opacity, plots BOS labels, displays Smart Money Score dashboard, continuously updates checking mitigation conditions and removing elements exceeding age/display limits
๐กNote:
The Smart Money Flow Index combines multiple Smart Money Concepts into unified institutional order flow analysis. For optimal results, use the Smart Money Score as confluence filter rather than standalone entry signal โ scores above 70 indicate high-probability setups but should be combined with risk management, higher timeframe bias, and market regime understanding.
Quantum Flux Universal Strategy Summary in one paragraph
Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version.
Scope and intent
โข Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto
โข Timeframes. One minute to daily
โข Default demo used in the publication. QQQ on one hour
โข Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns
โข Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
โข Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps.
โข What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise.
โข Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state.
โข Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols.
Method overview in plain language
The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction.
Base measures
โข Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale.
โข Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop.
โข Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves.
Components
โข Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner.
โข Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends.
โข Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response.
โข Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance.
โข Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot.
Fusion rule
โข Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent.
โข The blend power input scales the driver mix. Zero means fixed spans. One means full driver control.
โข Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes.
Signal rule
โข Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one.
โข Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one.
โข When polarity flips from plus to minus, the strategy closes any long and enters a short.
โข When flux crosses above the guide, the strategy closes any short.
What you will see on the chart
โข White polarity plot around the zero line
โข A dotted reference line at zero named Zen
โข Green background tint for positive polarity and red background tint for negative polarity
โข Strategy long and short markers placed by the TradingView engine at entry and at close conditions
โข No table in this version to keep the visual clean and portable
Inputs with guidance
Setup
โข Price source. Default ohlc4. Stable for noisy symbols.
โข Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive.
โข Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price.
Logic
โข Guide span. Typical range 4 to 12. A small guide smooths without eating turns.
โข Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing.
โข Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work.
โข Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise.
โข Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve.
โข Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak.
โข Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong.
โข Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline.
โข Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols.
โข Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs.
Filters
โข Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends.
โข Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping.
โข Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version.
Alerts
โข This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows.
Strategy has been currently adapted for the QQQ asset with 30/60min timeframe.
For other assets may require new optimization
Properties visible in this publication
โข Initial capital 25000
โข Base currency Default
โข Default order size method percent of equity with value 5
โข Pyramiding 1
โข Commission 0.05 percent
โข Slippage 10 ticks
โข Process orders on close ON
โข Bar magnifier ON
โข Recalculate after order is filled OFF
โข Calc on every tick OFF
Honest limitations and failure modes
โข Past results do not guarantee future outcomes
โข Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency
โข Gap heavy symbols may benefit from the MAD Z normalization
โข Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context
โข Session time is the exchange time of the chart
โข If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference
Open source reuse and credits
โข None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank
โข Method and fusion are original in construction and disclosure
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
Strategy add on block
Strategy notice
Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used.
Entries and exits
โข Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero
โข Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short
โข Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed
โข Tie handling. Not applicable in this version because there are no fixed stops or targets
Position sizing
โข Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice
Properties used on the published chart
โข Initial capital 25000
โข Base currency Default
โข Default order size percent of equity with value 5
โข Pyramiding 1
โข Commission 0.05 percent
โข Slippage 10 ticks
โข Process orders on close ON
โข Bar magnifier ON
โข Recalculate after order is filled OFF
โข Calc on every tick OFF
Dataset and sample size
โข Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour
โข Trade count in sample 324 on the example chart
Release notes template for future updates
Version 1.1.
โข Add alertcondition lines for long, short, and exit short
โข Add optional table with component readouts
โข Add optional stop model with a distance unit expressed as ATR or a percent of price
Notes. Backward compatibility Yes. Inputs migrated Yes.
MK_OSFT-Momentum Confluence DetectorMOMENTUM CONFLUENCE DETECTOR - Trading Indicator Overview
What This Indicator Does
The Momentum Confluence Detector is a comprehensive Pine Script indicator designed to identify high-probability trading opportunities by detecting momentum bars that align with multiple confluence factors. It combines traditional technical analysis with advanced Smart Money Concepts to filter out noise and highlight the most significant price movements.
CORE FUNCTIONALITY
๐ Momentum Bar Detection Identifies unusual volume and bar size expansion using customizable multipliers
Detects bullish, bearish, and neutral momentum bars based on OHLC relationships
Uses moving averages to establish baseline volume and bar size thresholds
๐ Multi-Filter Confluence System
The indicator employs up to 5 different filter types to validate momentum signals:
Level Concept Filter - Choose between:
- Support/Resistance Levels : Traditional pivot-based S/R zones with touch counting and break tracking
- Smart Money Concepts : Institutional order flow analysis including Order Blocks, Fair Value Gaps (FVGs), and market structure breaks
Trend Filter : EMA/SMA-based trend direction confirmation with alignment requirements
Breakout Filter : Detects price breakouts beyond recent highs/lows with percentage thresholds
Volatility Filter : ATR expansion confirmation to ensure signals occur during active market conditions
Market Session Filter : Filters signals to specific trading sessions (Tokyo, London, New York)
ADVANCED FEATURES
๐ฏ Smart Money Concepts Integration
Order Blocks : Identifies institutional supply/demand zones from major and minor structure breaks
Fair Value Gaps (FVGs) : Detects price imbalances and tracks their evolution through partial fills and inversions
Market Structure : Recognizes Break of Structure (BOS) and Change of Character (CHoCH) patterns
Retracement Patterns : Tracks HLH (Higher-Low-Higher) and LHL (Lower-High-Lower) institutional patterns
๐ Support/Resistance System
Multi-timeframe pivot detection (3, 5, 7-bar spans)
Volume-weighted strength calculation for level importance
Dynamic level merging and break tracking
Automatic level type classification (Support/Resistance/Flip zones)
โ๏ธ Intelligent Filtering Logic
ALL Mode : Requires all enabled filters to pass (high precision)
ANY Mode : Requires at least one filter to pass (higher frequency)
Real-time filter status tracking and visualization
Visual Features
Signal Markers : Clear triangular markers for qualified momentum bars
Unfiltered Signals : Optional display of raw momentum bars for comparison
Level Visualization : Dynamic S/R level boxes and lines with strength indicators
Structure Lines : BOS/CHoCH break visualization with major/minor classification
Fair Value Gaps : Color-coded boxes showing bullish/bearish FVGs with partial fill tracking and IFVG conversion
Order Blocks : Institutional supply/demand zones displayed as colored boxes with major/minor classification
Information Table : Real-time display of signal details and filter status
Session Boxes : Visual representation of active trading sessions
Practical Applications
โ
Swing Trading : Identify high-probability reversal and continuation setups
โ
Day Trading : Spot intraday momentum shifts with institutional backing
โ
Multi-Timeframe Analysis : Combine major and minor structure analysis
โ
Risk Management : Filter out low-quality setups using confluence requirements
โ
Educational : Understand market structure and institutional order flow
Customization Options
Adjustable momentum thresholds for different market conditions
Comprehensive filter settings with individual enable/disable controls
Visual customization for colors, sizes, and display preferences
Alert system with detailed signal information
Performance optimization settings for different chart timeframes
Who Should Use This Indicator
This indicator is suitable for traders who:
Want to combine multiple technical analysis approaches
Seek to understand institutional market behavior
Prefer confluence-based trading setups
Need customizable filtering for different market conditions
Value comprehensive signal validation over high-frequency alerts
The Momentum Confluence Detector transforms complex market analysis into clear, actionable signals by requiring multiple forms of confirmation before highlighting trading opportunities.
PnL Bubble [%] | Fractalyst1. What's the indicator purpose?
The PnL Bubble indicator transforms your strategy's trade PnL percentages into an interactive bubble chart with professional-grade statistics and performance analytics. It helps traders quickly assess system profitability, understand win/loss distribution patterns, identify outliers, and make data-driven strategy improvements.
How does it work?
Think of this indicator as a visual report card for your trading performance. Here's what it does:
What You See
Colorful Bubbles: Each bubble represents one of your trades
Blue/Cyan bubbles = Winning trades (you made money)
Red bubbles = Losing trades (you lost money)
Bigger bubbles = Bigger wins or losses
Smaller bubbles = Smaller wins or losses
How It Organizes Your Trades:
Like a Photo Album: Instead of showing all your trades at once (which would be messy), it shows them in "pages" of 500 trades each:
Page 1: Your first 500 trades
Page 2: Trades 501-1000
Page 3: Trades 1001-1500, etc.
What the Numbers Tell You:
Average Win: How much money you typically make on winning trades
Average Loss: How much money you typically lose on losing trades
Expected Value (EV): Whether your trading system makes money over time
Positive EV = Your system is profitable long-term
Negative EV = Your system loses money long-term
Payoff Ratio (R): How your average win compares to your average loss
R > 1 = Your wins are bigger than your losses
R < 1 = Your losses are bigger than your wins
Why This Matters:
At a Glance: You can instantly see if you're a profitable trader or not
Pattern Recognition: Spot if you have more big wins than big losses
Performance Tracking: Watch how your trading improves over time
Realistic Expectations: Understand what "average" performance looks like for your system
The Cool Visual Effects:
Animation: The bubbles glow and shimmer to make the chart more engaging
Highlighting: Your biggest wins and losses get extra attention with special effects
Tooltips: hover any bubble to see details about that specific trade.
What are the underlying calculations?
The indicator processes trade PnL data using a dual-matrix architecture for optimal performance:
Dual-Matrix System:
โข Display Matrix (display_matrix): Bounded to 500 trades for rendering performance
โข Statistics Matrix (stats_matrix): Unbounded storage for complete statistical accuracy
Trade Classification & Aggregation:
// Separate wins, losses, and break-even trades
if val > 0.0
pos_sum += val // Sum winning trades
pos_count += 1 // Count winning trades
else if val < 0.0
neg_sum += val // Sum losing trades
neg_count += 1 // Count losing trades
else
zero_count += 1 // Count break-even trades
Statistical Averages:
avg_win = pos_count > 0 ? pos_sum / pos_count : na
avg_loss = neg_count > 0 ? math.abs(neg_sum) / neg_count : na
Win/Loss Rates:
total_obs = pos_count + neg_count + zero_count
win_rate = pos_count / total_obs
loss_rate = neg_count / total_obs
Expected Value (EV):
ev_value = (avg_win ร win_rate) - (avg_loss ร loss_rate)
Payoff Ratio (R):
R = avg_win รท |avg_loss|
Contribution Analysis:
ev_pos_contrib = avg_win ร win_rate // Positive EV contribution
ev_neg_contrib = avg_loss ร loss_rate // Negative EV contribution
How to integrate with any trading strategy?
Equity Change Tracking Method:
//@version=6
strategy("Your Strategy with Equity Change Export", overlay=true)
float prev_trade_equity = na
float equity_change_pct = na
if barstate.isconfirmed and na(prev_trade_equity)
prev_trade_equity := strategy.equity
trade_just_closed = strategy.closedtrades != strategy.closedtrades
if trade_just_closed and not na(prev_trade_equity)
current_equity = strategy.equity
equity_change_pct := ((current_equity - prev_trade_equity) / prev_trade_equity) * 100
prev_trade_equity := current_equity
else
equity_change_pct := na
plot(equity_change_pct, "Equity Change %", display=display.data_window)
Integration Steps:
1. Add equity tracking code to your strategy
2. Load both strategy and PnL Bubble indicator on the same chart
3. In bubble indicator settings, select your strategy's equity tracking output as data source
4. Configure visualization preferences (colors, effects, page navigation)
How does the pagination system work?
The indicator uses an intelligent pagination system to handle large trade datasets efficiently:
Page Organization:
โข Page 1: Trades 1-500 (most recent)
โข Page 2: Trades 501-1000
โข Page 3: Trades 1001-1500
โข Page N: Trades to
Example: With 1,500 trades total (3 pages available):
โข User selects Page 1: Shows trades 1-500
โข User selects Page 4: Automatically falls back to Page 3 (trades 1001-1500)
5. Understanding the Visual Elements
Bubble Visualization:
โข Color Coding: Cyan/blue gradients for wins, red gradients for losses
โข Size Mapping: Bubble size proportional to trade magnitude (larger = bigger P&L)
โข Priority Rendering: Largest trades displayed first to ensure visibility
โข Gradient Effects: Color intensity increases with trade magnitude within each category
Interactive Tooltips:
Each bubble displays quantitative trade information:
tooltip_text = outcome + " | PnL: " + pnl_str +
"\nDate: " + date_str + " " + time_str +
"\nTrade #" + str.tostring(trade_number) + " (Page " + str.tostring(active_page) + ")" +
"\nRank: " + str.tostring(rank) + " of " + str.tostring(n_display_rows) +
"\nPercentile: " + str.tostring(percentile, "#.#") + "%" +
"\nMagnitude: " + str.tostring(magnitude_pct, "#.#") + "%"
Example Tooltip:
Win | PnL: +2.45%
Date: 2024.03.15 14:30
Trade #1,247 (Page 3)
Rank: 5 of 347
Percentile: 98.6%
Magnitude: 85.2%
Reference Lines & Statistics:
โข Average Win Line: Horizontal reference showing typical winning trade size
โข Average Loss Line: Horizontal reference showing typical losing trade size
โข Zero Line: Threshold separating wins from losses
โข Statistical Labels: EV, R-Ratio, and contribution analysis displayed on chart
What do the statistical metrics mean?
Expected Value (EV):
Represents the mathematical expectation per trade in percentage terms
EV = (Average Win ร Win Rate) - (Average Loss ร Loss Rate)
Interpretation:
โข EV > 0: Profitable system with positive mathematical expectation
โข EV = 0: Break-even system, profitability depends on execution
โข EV < 0: Unprofitable system with negative mathematical expectation
Example: EV = +0.34% means you expect +0.34% profit per trade on average
Payoff Ratio (R):
Quantifies the risk-reward relationship of your trading system
R = Average Win รท |Average Loss|
Interpretation:
โข R > 1.0: Wins are larger than losses on average (favorable risk-reward)
โข R = 1.0: Wins and losses are equal in magnitude
โข R < 1.0: Losses are larger than wins on average (unfavorable risk-reward)
Example: R = 1.5 means your average win is 50% larger than your average loss
Contribution Analysis (ฮฃ):
Breaks down the components of expected value
Positive Contribution (ฮฃ+) = Average Win ร Win Rate
Negative Contribution (ฮฃ-) = Average Loss ร Loss Rate
Purpose:
โข Shows how much wins contribute to overall expectancy
โข Shows how much losses detract from overall expectancy
โข Net EV = ฮฃ+ - ฮฃ- (Expected Value per trade)
Example: ฮฃ+: 1.23% means wins contribute +1.23% to expectancy
Example: ฮฃ-: -0.89% means losses drag expectancy by -0.89%
Win/Loss Rates:
Win Rate = Count(Wins) รท Total Trades
Loss Rate = Count(Losses) รท Total Trades
Shows the probability of winning vs losing trades
Higher win rates don't guarantee profitability if average losses exceed average wins
7. Demo Mode & Synthetic Data Generation
When using built-in sources (close, open, etc.), the indicator generates realistic demo trades for testing:
if isBuiltInSource(source_data)
// Generate random trade outcomes with realistic distribution
u_sign = prand(float(time), float(bar_index))
if u_sign < 0.5
v_push := -1.0 // Loss trade
else
// Skewed distribution favoring smaller wins (realistic)
u_mag = prand(float(time) + 9876.543, float(bar_index) + 321.0)
k = 8.0 // Skewness factor
t = math.pow(u_mag, k)
v_push := 2.5 + t * 8.0 // Win trade
Demo Characteristics:
โข Realistic win/loss distribution mimicking actual trading patterns
โข Skewed distribution favoring smaller wins over large wins
โข Deterministic randomness for consistent demo results
โข Includes jitter effects to prevent visual overlap
8. Performance Limitations & Optimizations
Display Constraints:
points_count = 500 // Maximum 500 dots per page for optimal performance
Pine Script v6 Limits:
โข Label Count: Maximum 500 labels per indicator
โข Line Count: Maximum 100 lines per indicator
โข Box Count: Maximum 50 boxes per indicator
โข Matrix Size: Efficient memory management with dual-matrix system
Optimization Strategies:
โข Pagination System: Handle unlimited trades through 500-trade pages
โข Priority Rendering: Largest trades displayed first for maximum visibility
โข Dual-Matrix Architecture: Separate display (bounded) from statistics (unbounded)
โข Smart Fallback: Automatic page clamping prevents empty displays
Impact & Workarounds:
โข Visual Limitation: Only 500 trades visible per page
โข Statistical Accuracy: Complete dataset used for all calculations
โข Navigation: Use page input to browse through entire trade history
โข Performance: Smooth operation even with thousands of trades
9. Statistical Accuracy Guarantees
Data Integrity:
โข Complete Dataset: Statistics matrix stores ALL trades without limit
โข Proper Aggregation: Separate tracking of wins, losses, and break-even trades
โข Mathematical Precision: Pine Script v6's enhanced floating-point calculations
โข Dual-Matrix System: Display limitations don't affect statistical accuracy
Calculation Validation:
// Verified formulas match standard trading mathematics
avg_win = pos_sum / pos_count // Standard average calculation
win_rate = pos_count / total_obs // Standard probability calculation
ev_value = (avg_win * win_rate) - (avg_loss * loss_rate) // Standard EV formula
Accuracy Features:
โข Mathematical Correctness: Formulas follow established trading statistics
โข Data Preservation: Complete dataset maintained for all calculations
โข Precision Handling: Proper rounding and boundary condition management
โข Real-Time Updates: Statistics recalculated on every new trade
10. Advanced Technical Features
Real-Time Animation Engine:
// Shimmer effects with sine wave modulation
offset = math.sin(shimmer_t + phase) * amp
// Dynamic transparency with organic flicker
new_transp = math.min(flicker_limit, math.max(-flicker_limit, cur_transp + dir * flicker_step))
โข Sine Wave Shimmer: Dynamic glowing effects on bubbles
โข Organic Flicker: Random transparency variations for natural feel
โข Extreme Value Highlighting: Special visual treatment for outliers
โข Smooth Animations: Tick-based updates for fluid motion
Magnitude-Based Priority Rendering:
// Sort trades by magnitude for optimal visual hierarchy
sort_indices_by_magnitude(values_mat)
โข Largest First: Most important trades always visible
โข Intelligent Sorting: Custom bubble sort algorithm for trade prioritization
โข Performance Optimized: Efficient sorting for real-time updates
โข Visual Hierarchy: Ensures critical trades never get hidden
Professional Tooltip System:
โข Quantitative Data: Pure numerical information without interpretative language
โข Contextual Ranking: Shows trade position within page dataset
โข Percentile Analysis: Performance ranking as percentage
โข Magnitude Scaling: Relative size compared to page maximum
โข Professional Format: Clean, data-focused presentation
11. Quick Start Guide
Step 1: Add Indicator
โข Search for "PnL Bubble | Fractalyst" in TradingView indicators
โข Add to your chart (works on any timeframe)
Step 2: Configure Data Source
โข Demo Mode: Leave source as "close" to see synthetic trading data
โข Strategy Mode: Select your strategy's PnL% output as data source
Step 3: Customize Visualization
โข Colors: Set positive (cyan), negative (red), and neutral colors
โข Page Navigation: Use "Trade Page" input to browse trade history
โข Visual Effects: Built-in shimmer and animation effects are enabled by default
Step 4: Analyze Performance
โข Study bubble patterns for win/loss distribution
โข Review statistical metrics: EV, R-Ratio, Win Rate
โข Use tooltips for detailed trade analysis
โข Navigate pages to explore full trade history
Step 5: Optimize Strategy
โข Identify outlier trades (largest bubbles)
โข Analyze risk-reward profile through R-Ratio
โข Monitor Expected Value for system profitability
โข Use contribution analysis to understand win/loss impact
12. Why Choose PnL Bubble Indicator?
Unique Advantages:
โข Advanced Pagination: Handle unlimited trades with smart fallback system
โข Dual-Matrix Architecture: Perfect balance of performance and accuracy
โข Professional Statistics: Institution-grade metrics with complete data integrity
โข Real-Time Animation: Dynamic visual effects for engaging analysis
โข Quantitative Tooltips: Pure numerical data without subjective interpretations
โข Priority Rendering: Intelligent magnitude-based display ensures critical trades are always visible
Technical Excellence:
โข Built with Pine Script v6 for maximum performance and modern features
โข Optimized algorithms for smooth operation with large datasets
โข Complete statistical accuracy despite display optimizations
โข Professional-grade calculations matching institutional trading analytics
Practical Benefits:
โข Instantly identify system profitability through visual patterns
โข Spot outlier trades and risk management issues
โข Understand true risk-reward profile of your strategies
โข Make data-driven decisions for strategy optimization
โข Professional presentation suitable for performance reporting
Disclaimer & Risk Considerations:
Important: Historical performance metrics, including positive Expected Value (EV), do not guarantee future trading success. Statistical measures are derived from finite sample data and subject to inherent limitations:
โข Sample Bias: Historical data may not represent future market conditions or regime changes
โข Ergodicity Assumption: Markets are non-stationary; past statistical relationships may break down
โข Survivorship Bias: Strategies showing positive historical EV may fail during different market cycles
โข Parameter Instability: Optimal parameters identified in backtesting often degrade in forward testing
โข Transaction Cost Evolution: Slippage, spreads, and commission structures change over time
โข Behavioral Factors: Live trading introduces psychological elements absent in backtesting
โข Black Swan Events: Extreme market events can invalidate statistical assumptions instantaneously
Premium/Discount with Candle Open stats [Herman]Premium/Discount with Stats
This indicator is designed to help traders identify and analyze premium/discount zones on any timeframe while automatically tracking statistics on price behavior relative to these zones. It is especially valuable for traders looking to structure entries, manage targets, and quantify market reactions to prior session ranges.
What it draws on the chart
โ
Range High and Low Lines
For each selected timeframe period (15min, 30min 1H, 4H, Daily), the indicator plots the high and low of the completed previous period.
These lines are color-coded dynamically based on sweep detection:
If the high was swept (price broke the previous high), the high line is marked as Premium.
If the low was swept, the low line is marked as Discount.
If both were swept or neither, it uses the default color settings.
โ
Midline
An optional midline at the 50% level of the previous periodโs high-low range.
Helpful for mean-reversion traders or anyone watching for retests of equilibrium.
โ
Quartile Lines (25%โ75%)
Optional additional lines at 25% and 75% of the previous range, helping traders visualize inner range subdivisions.
โ
Open Price Line
Marks the open price of the previous period as a horizontal reference.
โ
Background Fills
The region between low and midline is shaded with the Discount color.
The region between high and midline is shaded with the Premium color.
These optional fills help highlight the premium and discount zones visually.
โ
Current Incomplete Period Lines (optional)
You can choose to display provisional high, low, midline, quartiles, and open for the current forming period.
These update in real-time until the period closes.
Sweep Detection Logic
The indicator automatically tracks if the current period price sweeps above the previous periodโs high or below the low.
A "sweep" is simply defined as price exceeding the previous high/low while tracking is active.
The sweep status affects the colors of the premium/discount lines, helping traders see potential liquidity grabs or stop hunts.
What it counts and tracks (Statistics)
The script automatically compiles statistics over time:
โ
Total Touches
Counts how many times the price in a new period touches either the previous periodโs high or low.
A โtouchโ is registered once per side per period.
โ
Midline Returns
Counts how often, after touching the previous high/low, price returns to the previous periodโs midline.
Gives you a measure of mean-reversion success.
โ
Open Returns
Similarly, tracks how often price returns to the previous periodโs open after touching the previous high/low.
โ
Return Percentages
Displays the percentage of touches that result in a return to midline or open.
These percentages are calculated live on your chart and updated after each period closes.
โ
Stats Table
A customizable on-chart table summarizing all of these stats in real-time.
Helps traders evaluate the effectiveness of range-based trading setups over time.
How it Works (Technical details)
On each new bar, the script checks if a new period (as defined by your timeframe selection) has begun.
When a new period starts, the previous periodโs high, low, open, midline, quartiles are recorded and drawn on the chart.
The script then โwatchesโ the current period:
Updates provisional high and low.
Detects sweeps of previous highs/lows.
Tracks if price returns to the previous periodโs midline or open after those sweeps.
Increments statistical counters if conditions are met.
Background fills and lines update dynamically based on real-time data.
Intended Use Cases
This indicator is ideal for:
โ
Identifying premium/discount zones for swing or intraday trades.
โ
Spotting liquidity sweeps and possible manipulation zones.
โ
Structuring trades with logical, data-driven target zones (midline, open).
โ
Quantifying the probability of mean-reversion moves after liquidity events.
โ
Developing and backtesting range-based trading models with live stats.
Highly Customizable
Choose any timeframe for defining the premium/discount range.
Toggle visibility of midline, quartiles, open line, current period preview.
Full control over colors, line styles, line widths, and background shading.
Optional real-time statistical table with total counts and return percentages.
ALMA Shifting Band Oscillator | QuantMACALMA Shifting Band Oscillator | QuantMAC
๐ฏ Advanced Technical Analysis Tool Combining ALMA with Dynamic Oscillator Technology
The ALMA Shifting Band Oscillator represents a sophisticated fusion of the Arnaud Legoux Moving Average (ALMA) with an innovative oscillator-based signaling system. This indicator transforms traditional moving average analysis into a comprehensive trading solution with dynamic band visualization and precise entry/exit signals.
Core Technology ๐ง
Arnaud Legoux Moving Average Foundation
Built upon the mathematically superior ALMA calculation, this indicator leverages the unique properties of ALMA's phase shift and noise reduction capabilities. The ALMA component provides a responsive yet smooth baseline that adapts to market conditions with minimal lag.
Dynamic Band System
The indicator generates adaptive upper and lower bands around the ALMA centerline using statistical deviation analysis. These bands automatically adjust to market volatility, creating a dynamic envelope that captures price extremes and potential reversal zones.
Normalized Oscillator Engine
The heart of the system transforms price action relative to the dynamic bands into a normalized oscillator that oscillates around a zero line. This oscillator provides clear visual representation of momentum and position within the established bands.
Visual Features ๐จ
Multi-Pane Display Architecture
Primary oscillator plotted in separate pane for clarity
Dynamic band overlay on price chart with elegant fill visualization
ALMA centerline marked with distinctive styling
Customizable threshold lines for signal identification
Advanced Color Schemes
Choose from 9 professionally designed color palettes:
Classic series offering various aesthetic preferences
High contrast options for different chart backgrounds
State-based coloring that changes with market conditions
Candle coloring that reflects current oscillator state
Enhanced Visual Elements
Smooth gradient band fills for easy trend identification
Dynamic line thickness and styling options
Professional transparency settings for overlay clarity
Customizable threshold visualization
Signal Generation System ๐
Dual Threshold Architecture
The indicator employs two distinct threshold levels that create a sophisticated signal framework:
Long Threshold : Triggers bullish signal generation
Short Threshold : Activates bearish signal conditions
Intelligent State Management
Advanced state tracking ensures clean signal generation without false triggers:
Prevents redundant signals in same direction
Maintains position awareness for proper entries/exits
Implements crossover logic for precise timing
Flexible Trading Modes
Long/Short Mode : Full bidirectional trading capabilities
Long/Cash Mode : Conservative approach with cash positions during bearish conditions
Professional Analytics Suite ๐
Comprehensive Performance Metrics
Integrated real-time performance analysis including:
Maximum Drawdown percentage tracking
Sortino Ratio for downside risk assessment
Sharpe Ratio for risk-adjusted returns
Omega Ratio for comprehensive performance evaluation
Profit Factor calculation
Win rate percentage analysis
Half Kelly percentage for position sizing guidance
Total trade count and net profit tracking
Advanced Risk Management
Real-time equity curve tracking
Peak-to-trough drawdown monitoring
Downside deviation calculations
Risk-adjusted return measurements
Customization Options โ๏ธ
ALMA Parameter Control
ALMA Length (Default: 42) - Controls the lookback period for the moving average calculation. Lower values (20-30) create faster, more responsive signals but increase noise. Higher values (50-100) produce smoother signals with less false alerts but slower reaction to price changes.
ALMA Offset (Default: 0.68) - Determines the phase shift of the moving average. Values closer to 0 behave like a simple moving average. Values closer to 1 act more like an exponential moving average. 0.68 provides optimal balance between responsiveness and smoothness.
ALMA Sigma (Default: 1.8) - Controls the smoothness factor of the ALMA calculation. Lower values (1.0-2.0) create sharper, more reactive averages. Higher values (4.0-8.0) produce extremely smooth but slower-responding averages. Affects how quickly the ALMA adapts to price changes.
Source Selection - Choose between Close, Open, High, Low, or custom price combinations. Close price is standard for most analysis. HL2 or HLC3 can provide different market perspectives and reduce single-price volatility.
Oscillator Fine-tuning
Standard Deviation Length (Default: 27) - Determines the lookback period for volatility calculation. Shorter periods (10-20) make bands more reactive to recent volatility changes. Longer periods (40-60) create more stable bands that filter out short-term volatility spikes.
SD Multiplier (Default: 2.8) - Controls the width of the dynamic bands. Lower values (1.5-2.0) create tighter bands with more frequent signals but higher false signal rate. Higher values (3.0-4.0) produce wider bands with fewer but potentially more reliable signals.
Oscillator Multiplier (Default: 100) - Scales the oscillator for visual clarity. This is purely cosmetic and doesn't affect signal generation. Adjust based on your preferred oscillator range visualization.
Long Threshold (Default: 82) - Sets the level where bullish signals trigger. Lower values (70-80) generate more frequent long signals but may include weaker setups. Higher values (85-95) create fewer but potentially stronger bullish signals.
Short Threshold (Default: 50) - Determines where bearish signals activate. Higher values (55-65) produce more short signals. Lower values (35-45) wait for stronger bearish conditions before signaling.
Trading Mode Configuration
Long/Short Mode - Full bidirectional trading that takes both long and short positions. Suitable for trending markets and experienced traders comfortable with short selling.
Long/Cash Mode - Conservative approach that only takes long positions or moves to cash during bearish signals. Ideal for bull market conditions or traders who prefer not to short.
Display Customization
Color Schemes (9 Options) - Choose from Classic to Classic9 palettes. Each offers different visual contrast for various chart backgrounds and personal preferences.
Metrics Table Position - Place performance metrics in any of 6 chart locations: Top Left/Right, Middle Left/Right, Bottom Left/Right.
Show/Hide Metrics Table - Toggle the comprehensive performance analytics display on or off based on your analysis needs.
Date Range Limiter - Set specific start dates for backtesting and signal generation. Useful for testing strategies on specific market periods or excluding unusual market events.
Parameter Optimization Tips
Volatile Markets - Use shorter ALMA Length (25-35), lower SD Multiplier (2.0-2.5), and moderate thresholds
Trending Markets - Employ longer ALMA Length (45-60), higher SD Multiplier (3.0-4.0), and extreme thresholds
Sideways Markets - Try medium ALMA Length (35-45), standard SD Multiplier (2.5-3.0), and closer thresholds (75/55)
Higher Timeframes - Generally use longer periods and higher multipliers for smoother signals
Lower Timeframes - Opt for shorter periods and lower multipliers for more responsive signals
Practical Applications ๐ก
Trend Following
Identify and follow established trends using the dynamic band system and oscillator position relative to thresholds.
Momentum Analysis
Gauge market momentum through oscillator readings and their relationship to historical levels.
Reversal Detection
Spot potential reversal points when price reaches extreme oscillator levels combined with band interactions.
Risk Management
Utilize integrated metrics for position sizing and risk assessment decisions.
Technical Specifications ๐
Calculation Methodology
The indicator employs sophisticated mathematical formulations for ALMA calculation combined with statistical analysis for band generation. The oscillator normalization process ensures consistent readings across different market conditions and timeframes.
Performance Optimization
Designed for efficient processing with minimal computational overhead while maintaining calculation accuracy across all timeframes.
Multi-Timeframe Compatibility
Functions effectively across all trading timeframes from intraday scalping to long-term position trading.
Installation and Usage ๐
Simple Setup Process
Add indicator to chart
Configure ALMA parameters for your preferred responsiveness
Adjust threshold levels based on market volatility
Select desired color scheme and display options
Enable metrics table for performance tracking
Best Practices
Use multiple timeframe analysis for context
Monitor metrics table for strategy performance
Adjust parameters based on market conditions
This indicator represents a professional-grade tool designed for serious traders seeking advanced technical analysis capabilities with comprehensive performance tracking. The combination of ALMA's mathematical precision with dynamic oscillator technology creates a unique analytical framework suitable for various trading styles and market conditions.
๐ Transform your technical analysis with this advanced ALMA-based oscillator system!
โ ๏ธ IMPORTANT DISCLAIMER
Past Performance Warning: ๐โ ๏ธ
PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. Historical backtesting results, while useful for strategy development and parameter optimization, do not guarantee similar performance in live trading conditions. Market conditions change continuously, and what worked in the past may not work in the future.
Remember: Successful trading requires discipline, continuous learning, and adaptation to changing market conditions. No indicator or strategy guarantees profits, and all trading involves substantial risk of loss.
Kitty PMO [theUltimator5]Kitty PMO is a momentum analysis tool designed to visually track and interpret the Price Momentum Oscillator (PMO) โ with stylistic influence inspired by the charting approach made popular by โtheRoaringKitty.โ It aims to offer clear, actionable momentum signals directly overlaid on the chart without clutter or ambiguity, making it ideal for traders who prioritize simplicity and signal clarity.
At its core, the indicator calculates the PMO by applying a custom recursive smoothing function to the rate of change (ROC) of price. This smoothed momentum measure is then:
Amplified by a scaling factor (ร10),
Further smoothed using user-defined parameters,
Compared against a signal line (EMA of PMO),
And tracked with a secondary moving average (PMO MA) to capture medium-term trend inflections.
While the PMO and its associated signal lines can optionally be plotted, the indicator primarily emphasizes crossovers between the PMO MA and the other two components. When the PMO MA crosses above both the PMO and signal line, a green upward arrow (โ) is plotted below the price. When it crosses below both, a red downward arrow (โ) appears above the price โ making it easy to spot potential turning points in momentum.
Additionally, a floating info table can be toggled on to display all current user-defined parameters in a clean, resizable format. This makes the script ideal not just for technical execution but also for real-time strategy tuning and tracking across multiple timeframes.
The script includes optional alerts so you can be notified the moment a key crossover signal is triggered, without needing to keep your eyes glued to the screen.
Rev & Line - CoffeeKillerRev & Line - CoffeeKiller Indicator Guide
๐ Warning: This Indicator Repaints ๐ This indicator uses real-time calculations that may change based on future price action. As a result, signals (such as arrows, lines, or color changes) **can and will repaint** โ meaning they may appear, disappear, or shift after a candle closes.
**Do not rely on this tool alone for live trading decisions.** Use with caution and always confirm with non-repainting tools or additional analysis.(This indicator is designed to show me the full length of the trend and because of this there can be a smaller movement inside of the trend movement)
Welcome traders! This guide will walk you through the Rev & Line indicator, a sophisticated technical analysis tool developed by CoffeeKiller that combines multiple methodologies to identify market pivots, trends, and potential reversal points.
Core Components
1. ZigZag Analysis
- Dynamic pivot detection using ATR (Average True Range)
- Customizable sensitivity through ATR Reversal Factor
- Color-coded trend lines (green for upward, red for downward)
- Optional vertical lines at pivot points
- Real-time pivot point analysis
2. Donchian Channel Integration
- Traditional upper, lower, and middle bands
- Customizable length and displacement
- Channel-based entry signals
- Dynamic market structure visualization
3. Marker Lines System
- Dynamic support/resistance level tracking
- Pivot-based reset mechanism
- Optional fill zones between markers
- Percentage position tracking within range
4. Signal Generation System
- Confluence between ZigZag pivots and Donchian channels
- Up/down arrow visualization
- Alert system
Main Features
ZigZag Settings
- ATR Reversal Factor: Controls pivot sensitivity (default 3.2)
- Customizable line appearance:
Width control (default: 3)
Color selection (green for uptrend, red for downtrend)
Vertical line options at pivot points
Maximum vertical lines display limit
- Hide repainted option for more reliable signals
Donchian Channel Configuration
- Optional channel visibility toggle
- Length parameter for lookback period (default: 20)
- Displace option for time offset
- Bubble offset for visual placement
Marker Lines System
- High/low/middle marker lines with step-line visualization
- Dotted line projections for future reference
- Pivot-based reset mechanism
- Color-coded percentage position display
Signal Generation
- Triangle markers for signals
- Combined ZigZag and Donchian confluence
- Alert system for notifications
Visual Elements
1. Pivot Lines
- Green: Upward price movements
- Red: Downward price movements
- Customizable line width
- Optional vertical pivot markers with style options:
Solid lines for confirmed pivots
Dashed lines for older pivots
Dotted lines for most recent pivots
2. Donchian Channels
- Upper band (red): Resistance level
- Lower band (green): Support level
- Middle band (yellow): Median price line
- Customizable display options
3. Marker Lines
- High marker line (magenta): Tracks highest open price
- Low marker line (cyan): Tracks lowest open price
- Middle marker line (blue): 50% level between high/low
- Dotted line extensions for future price projections
4. Position Tracking
- Percentage position display within marker range
- Real-time calculations from 0% to 100%
- Label system for visual reference
Trading Applications
1. Trend Following
- Enter on confirmed ZigZag pivot points
- Use Donchian channel boundaries as targets
- Trail stops using marker lines
- Monitor for confluence between systems
2. Counter-Trend Trading
- Trade bounces from marker lines
- Use pivot confirmation for entry timing
- Set stops based on recent pivot points
- Target the opposite marker line
3. Range Trading
- Use high/low marker lines to define range
- Trade bounces between upper and lower markers
- Consider middle marker for range midpoint
- Monitor percentage position within range
4. Breakout Trading
- Enter on breaks above/below marker lines
- Confirm with Donchian channel breakouts
- Use ZigZag pivot confirmations
- Wait for arrow signals for additional confirmation
Optimization Guide
1. ZigZag Parameters
- Higher ATR Factor: Less sensitive, major moves only
- Lower ATR Factor: More sensitive, catches minor moves
- Adjust line width for chart visibility
- Balance vertical line count for clarity
2. Donchian Channel Settings
- Longer length: Smoother channels, fewer false signals
- Shorter length: More responsive, but potentially noisier
- Displacement: Offset for historical reference
- Consider timeframe when setting parameters
3. Marker Line Configuration
- Enable/disable based on trading style
- Toggle middle line for additional reference
- Adjust colors for visual clarity
- Enable/disable labels as needed
4. Signal Generation
- Use "Hide repainted" option for more reliable signals
- Combine ZigZag and Donchian signals for confirmation
- Set alerts based on confirmed pivot points
- Balance sensitivity with reliability
Best Practices
1. Signal Confirmation
- Wait for confirmed pivot points
- Check for Donchian channel interactions
- Confirm with price action
- Look for arrow signals at pivot points
2. Risk Management
- Use recent pivot points for stop placement
- Consider marker line boundaries for targets
- Don't trade against strong trends
- Wait for clear confluence between systems
3. Setup Optimization
- Start with default settings
- Adjust based on timeframe
- Fine-tune ATR sensitivity
- Match settings to trading style
Advanced Features
1. Alert System
- Customizable arrow alerts
- Pivot point notifications
- Text message alerts with ticker information
- Once-per-bar frequency option
2. Pivot Detection Logic
The indicator uses a sophisticated state-based approach to detect pivots:
- State transitions between "uptrend," "downtrend," and "undefined"
- ATR-based reversal detection
- Minimum movement threshold for pivot confirmation
- Historical pivot tracking and labeling
3. Marker Line Reset Mechanism
- Marker lines reset based on pivot detection
- Dynamic support/resistance level adjustment
- Percentage position calculation within range
- Automatic updates as market structure changes
Remember:
- Combine multiple confirmation signals
- Use appropriate timeframe settings
- Monitor both ZigZag and Marker signals
- Pay attention to Donchian channel interactions
- Consider market volatility when trading
This indicator works best when:
- Used with proper risk management
- Combined with other technical tools
- Applied to appropriate timeframes
- Signals are confirmed by price action
**DISCLAIMER**: This indicator and its signals are intended solely for educational and informational purposes. They do not constitute financial advice. Trading involves significant risk of loss. Always conduct your own analysis and consult with financial professionals before making trading decisions.
Key Levels by MoneyTribe21This custom script provides real-time tracking of key market price levels, helping traders identify critical support and resistance zones. It dynamically updates throughout the trading session, making it ideal for intraday trading, breakout strategies, and market structure analysis.
Features:
Real-Time Tracking of Key Price Levels:
ATH (All-Time High): Tracks the highest price ever reached for the asset.
PDH (Previous Day High): Marks the high of the last trading day,
PDL (Previous Day Low): Marks the low of the last trading day, serving as dynamic support.
Resistance Level: Based on the current dayโs high, signaling potential price rejection points.
Support Level: Based on the current dayโs low, indicating potential price bounces.
Daily Open Price: Tracks the exact market open price at the start of the trading session.
Works Across All Timeframes:
Designed for intraday, swing, and long-term trading.
Automatically adjusts levels for Forex, Stocks, Crypto, and Indices.
Fully Customizable Settings:
Modify line colors, thickness, and styles for better chart readability.
Enable/disable specific levels based on trading preference.
Works on all TradingView-compatible brokers and platforms.
How to Use This Indicator:
Breakout & Reversal Trading:
If price breaks above PDH, it may indicate bullish momentum.
If price breaks below PDL, it may signal a bearish continuation.
ATH levels can act as strong resistance zonesโwatch for breakouts or rejection.
Dynamic Support & Resistance:
Resistance Level (Current Day High): If price fails to break, it may signal a reversal.
Support Level (Current Day Low): If price bounces off, it may confirm a strong uptrend.
Daily Open for Trend Confirmation:
Above Daily Open: Market sentiment is bullish.
Below Daily Open: Market sentiment is bearish.
Customization Options:
Toggle individual price levels ON/OFF for a clutter-free chart.
Customize colors, line styles, and alerts for better visualization.
Set alerts for breakouts & retests of key levels.
Ideal for Traders Who:
Want high-probability support & resistance zones in real-time.
Trade breakouts, reversals, or trend continuations.
Use market structure analysis for informed decision-making.
Need automatic price tracking instead of drawing levels manually.
Compatible with all TradingView timeframes & assets (Forex, Stocks, Crypto, Indices).
Designed for both beginner and advanced traders.
Add this indicator to your chart and start tracking key levels instantly.
lib_mathLibrary "lib_math"
a collection of functions calculating without history operator to avoid max_bars_back errors
mean(value, reset)
โโParameters:
โโโโ value (float) : series to track
โโโโ reset (bool) : flag to reset tracking
@return returns average/mean of value since last reset
vwap(value, reset)
โโParameters:
โโโโ value (float) : series to track
โโโโ reset (bool) : flag to reset tracking
@return returns vwap of value and volume since last reset
variance(value, reset)
โโParameters:
โโโโ value (float) : series to track
โโโโ reset (bool) : flag to reset tracking
@return returns variance of value since last reset
trend(value, reset)
โโParameters:
โโโโ value (float) : series to track
โโโโ reset (bool) : flag to reset tracking
@return where slope is the trend direction, correlation is a measurement for how well the values fit to the trendline (positive means ), stddev is how far the values deviate from the trend, x1 would be the time where reset is true and x2 would be the current time
Bubu VWEMA & VWAPVolume Weighted Exponential Moving Average gives additional support / resistance lines to track as well as giving confidence to there respective EMAs.
A VWEMA will act much faster to volume spikes.
Intraday, Daily, and Weekly VWAPs are also shown to give more support / resistance lines to track.
The normal EMA lines (Thick ones) have colours based on a matrix calculated in the back. The Fast EMA is using 20 RSI indicators tracking how many cross above 50. The slow EMA is using 40 MACDs histograms when they cross mid point.
The VWEMA and the fill is graded on 20(fast) and 40(slow) VWEMAs crossovers.
Volume Intelligence Pro [Abusuhil]โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ VOLUME INTELLIGENCE TABLE - PROFESSIONAL VOLUME ANALYSIS INDICATOR
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ BILINGUAL SUPPORT: Full support for English and Arabic languages - switch instantly from settings!
๐ฏ COMPREHENSIVE VOLUME ANALYSIS DASHBOARD
This advanced indicator provides institutional-grade volume analysis through an elegant, customizable table that displays critical volume metrics in real-time. Designed for professional traders who need deep insights into market volume dynamics, order flow, and smart money movements.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โจ KEY FEATURES
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ท BILINGUAL INTERFACE
โข Seamless switching between English and Arabic
โข All metrics, labels, and signals fully translated
โข Perfect for international traders
๐ท VOLUME FUNDAMENTALS
โข Current Volume: Real-time volume tracking
โข Volume SMA: Moving average for volume comparison
โข Volume Ratio: Current volume vs average (identifies abnormal activity)
โข Volume % Change: Percentage change from previous bar
โข Volume Delta: Difference between buying and selling pressure
๐ท VOLUME SPIKE DETECTION (4 LEVELS)
โข Weak Spike: 1.5x average volume
โข Medium Spike: 2.0x average volume
โข Strong Spike: 2.5x average volume
โข Extreme Spike: 3.0x+ average volume
โข Visual alerts with color-coded indicators
๐ท ADVANCED BUY/SELL PRESSURE ANALYSIS
โข Buy Volume: Bullish candle volume accumulation
โข Sell Volume: Bearish candle volume accumulation
โข Buy Pressure %: Percentage of buying pressure
โข Sell Pressure %: Percentage of selling pressure
โข Pressure Dominance: Who controls the market (Buyers/Sellers/Neutral)
โข Candle Body Strength: Measures conviction in price movement
โข Imbalance Volume: Detects wick imbalances
โข Volume Delta (HLC3): Advanced delta calculation
โข Weighted Delta: Volume-weighted price movement
โข Pressure Lookback: Multi-candle pressure analysis (optimized for performance)
๐ท TECHNICAL INDICATORS INTEGRATION
โข VWMA (Volume Weighted Moving Average): Price vs VWMA positioning
โข OBV (On Balance Volume): Trend detection with EMA smoothing
โข OBV Divergence: Bullish/Bearish divergence detection
โข MFI (Money Flow Index): Overbought/oversold conditions
โข A/D Line (Accumulation/Distribution): Smart money tracking
๐ท AI-POWERED VOLUME INTELLIGENCE SCORING
โข Entry Power: Measures volume strength combined with price movement
โข Effort vs Result: Identifies climax situations (buying/selling exhaustion)
โข Reversal Volume Analysis: Tracks volume at reversal candles
โข Trend Integration: Combines trend direction with volume confirmation
โข Bullish/Bearish Points: 11-point scoring system
โข Volume Score: -100 to +100 scale (positive = bullish, negative = bearish)
โข Confidence Level: Reliability percentage of the signal
โข Final Signal: Clear BULLISH/BEARISH/NEUTRAL verdict
๐ท TRIPLE SIGNAL SYSTEM (Optional)
โข Signal 1: Volume Score Based (customizable thresholds)
โข Signal 2: Volume Spike + Candle Color (spike level selection)
โข Signal 3: OBV Divergence Detection
โข Independent on/off toggles for each signal
โข Visual signals plotted on chart with triangles
โข Combined signal alerts
๐ท COMPREHENSIVE ALERT SYSTEM
โข Volume spike alerts (configurable levels)
โข Signal 1, 2, 3 individual alerts
โข Combined buy/sell signal alerts
โข OBV trend change alerts
โข Strong buying/selling pressure alerts
โข Customizable alert frequency
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ TABLE STRUCTURE & DISPLAY
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
The indicator features a professional 3-column table with the following sections:
๐ COLUMN HEADERS:
โข INDICATOR: Metric name
โข VALUE: Current reading
โข STATUS: Visual status indicator (color-coded dots/icons)
๐ SECTION 1: VOLUME BASICS
Displays fundamental volume metrics with ratio indicators and percentage changes. Essential for understanding current market activity levels.
๐ SECTION 2: VOLUME SPIKE DETECTION
Real-time spike detection with 4 severity levels. Color-coded for instant recognition of abnormal volume.
๐ SECTION 3: BUY/SELL PRESSURE (ADVANCED)
Comprehensive order flow analysis with 10+ metrics. Includes advanced calculations like weighted delta, imbalance volume, and multi-candle pressure lookback.
๐ SECTION 4: VWMA ANALYSIS
Shows price position relative to volume-weighted moving average. Critical for identifying volume-supported moves.
๐ SECTION 5: OBV ANALYSIS
On Balance Volume trend and divergence detection. Helps identify smart money accumulation/distribution.
๐ SECTION 6: MFI ANALYSIS
Money Flow Index readings with overbought/oversold signals. Combines price and volume for comprehensive analysis.
๐ SECTION 7: A/D LINE
Accumulation/Distribution line trend analysis. Tracks institutional buying and selling.
๐ SECTION 8: VOLUME INTELLIGENCE
AI-powered scoring system with 11 evaluation points:
1. Volume strength assessment
2. Current buy/sell pressure
3. Multi-candle pressure lookback
4. Entry power calculation
5. Reversal volume tracking
6. VWMA position
7. OBV trend
8. OBV divergence
9. MFI signal
10. A/D trend
11. Trend-volume integration
Final output: Volume Score, Confidence Level, and highlighted FINAL SIGNAL.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ๏ธ CUSTOMIZATION OPTIONS
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐จ TABLE DISPLAY
โข Position: 9 locations (top-left, top-center, top-right, middle-left, etc.)
โข Size: 5 sizes (tiny, small, normal, large, huge)
โข Colors: Fully customizable background and text colors
โข Sections: Show/hide any section independently
๐ฏ VOLUME SETTINGS
โข Volume Average Length (default: 20)
โข Spike Thresholds: Adjustable multipliers for each level
โข Advanced Metrics: Lookback periods (optimized: 10 candles)
โข Reversal Analysis: Candle count (optimized: 5 candles)
๐ INDICATOR LENGTHS
โข OBV Smoothing: Default 14
โข MFI Period: Default 14
โข VWMA Length: Default 20
โข A/D Length: Default 14
๐ฏ SIGNAL SYSTEM
โข Enable/disable each signal independently
โข Customizable thresholds for Signal 1 (score & confidence)
โข Spike level selection for Signal 2
โข Show/hide signals on chart
โข Alert configuration for each signal type
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ PERFORMANCE & OPTIMIZATION
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
REPLAY MODE OPTIMIZED
โข Works flawlessly in TradingView Replay mode
โข Optimized calculations for fast historical analysis
โข No lag or freezing issues
โ
REAL-TIME EFFICIENCY
โข Lightweight code structure (50 labels/lines limit)
โข Smart caching of repeated calculations
โข Limited loop iterations for optimal performance
โข Updates only on last bar (table rendering)
โ
NON-REPAINTING
โข All signals are confirmed on bar close
โข No retrospective changes to historical signals
โข Reliable for backtesting and strategy development
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ก USAGE RECOMMENDATIONS
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ FOR DAY TRADING:
โข Use 15m-1H timeframes
โข Enable all sections for comprehensive analysis
โข Focus on Volume Spike and Buy/Sell Pressure sections
โข Set alerts for Strong and Extreme spikes
๐ FOR SWING TRADING:
โข Use 4H-1D timeframes
โข Focus on Volume Intelligence and OBV sections
โข Enable Signal 1 and Signal 3 for swing entries
โข Monitor divergences for trend reversals
๐ FOR SCALPING:
โข Use 1m-5m timeframes
โข Focus on Buy/Sell Pressure and Volume Basics
โข Enable Signal 2 for quick spike-based entries
โข Hide less relevant sections to reduce visual clutter
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ INDICATOR METHODOLOGY
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
This indicator combines classical volume analysis with modern algorithmic intelligence:
1. Volume Profiling: Identifies abnormal volume relative to historical averages
2. Order Flow Analysis: Separates buying and selling pressure using candle structure
3. Divergence Detection: Compares price action with volume indicators
4. Multi-Timeframe Approach: Uses smoothing and lookback for context
5. Scoring Algorithm: 11-point evaluation system for objective signal generation
6. Confluence Integration: Combines multiple indicators for higher probability setups
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๏ธ DISCLAIMER
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
This indicator is a tool for analysis and should not be used as the sole basis for trading decisions. Always combine with your own analysis, risk management, and trading plan. Past performance does not guarantee future results.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ SUPPORT & UPDATES
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โข Regular updates and improvements
โข Bug fixes and optimization
โข Feature requests considered
โข Community feedback welcomed
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ Happy Trading! May your volume analysis lead to profitable decisions! ๐
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ุฌุฏูู ู
ุนููู
ุงุช ุงููููููู
- ู
ุคุดุฑ ุงุญุชุฑุงูู ูุชุญููู ุญุฌู
ุงูุชุฏุงูู
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ุฏุนู
ุซูุงุฆู ุงููุบุฉ: ุฏุนู
ูุงู
ู ููุบุชูู ุงูุฅูุฌููุฒูุฉ ูุงูุนุฑุจูุฉ - ุงูุชุจุฏูู ุงูููุฑู ู
ู ุงูุฅุนุฏุงุฏุงุช!
๐ฏ ููุญุฉ ู
ุนููู
ุงุช ุดุงู
ูุฉ ูุชุญููู ุงููููููู
ู
ุคุดุฑ ู
ุชูุฏู
ูููุฑ ุชุญูููุงู ุงุญุชุฑุงููุงู ูููููููู
ู
ู ุฎูุงู ุฌุฏูู ุฃููู ููุงุจู ููุชุฎุตูุต ูุนุฑุถ ู
ูุงููุณ ุงููููููู
ุงูุญูููุฉ ูู ุงูููุช ุงููุนูู. ู
ุตู
ู
ููู
ุชุฏุงูููู ุงูู
ุญุชุฑููู ุงูุฐูู ูุญุชุงุฌูู ุฅูู ุฑุคู ุนู
ููุฉ ุญูู ุฏููุงู
ูููุงุช ุญุฌู
ุงูุณููุ ุชุฏูู ุงูุฃูุงู
ุฑุ ูุญุฑูุฉ ุงูุฃู
ูุงู ุงูุฐููุฉ.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โจ ุงูู
ูุฒุงุช ุงูุฑุฆูุณูุฉ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ท ูุงุฌูุฉ ุซูุงุฆูุฉ ุงููุบุฉ
โข ุงูุชุจุฏูู ุงูุณูุณ ุจูู ุงูุฅูุฌููุฒูุฉ ูุงูุนุฑุจูุฉ
โข ุฌู
ูุน ุงูู
ูุงููุณ ูุงูุชุณู
ูุงุช ูุงูุฅุดุงุฑุงุช ู
ุชุฑุฌู
ุฉ ุจุงููุงู
ู
โข ู
ุซุงูู ููู
ุชุฏุงูููู ุงูุนุฑุจ ูุงูุฏููููู
๐ท ุฃุณุงุณูุงุช ุงููููููู
โข ุงููููููู
ุงูุญุงูู: ุชุชุจุน ุญุฌู
ุงูุชุฏุงูู ูู ุงูููุช ุงููุนูู
โข ู
ุชูุณุท ุงููููููู
: ุงูู
ุชูุณุท ุงูู
ุชุญุฑู ููู
ูุงุฑูุฉ
โข ูุณุจุฉ ุงููููููู
: ุงูุญุฌู
ุงูุญุงูู ู
ูุงุจู ุงูู
ุชูุณุท (ูุญุฏุฏ ุงููุดุงุท ุบูุฑ ุงูุทุจูุนู)
โข ุชุบูุฑ ุงููููููู
%: ูุณุจุฉ ุงูุชุบููุฑ ู
ู ุงูุดู
ุนุฉ ุงูุณุงุจูุฉ
โข ุฏูุชุง ุงููููููู
: ุงููุฑู ุจูู ุถุบุท ุงูุดุฑุงุก ูุงูุจูุน
๐ท ุงูุชุดุงู ุงููุฌุงุฑุงุช ุงููููููู
(4 ู
ุณุชููุงุช)
โข ุงููุฌุงุฑ ุถุนูู: 1.5 ุถุนู ุงูู
ุชูุณุท
โข ุงููุฌุงุฑ ู
ุชูุณุท: 2.0 ุถุนู ุงูู
ุชูุณุท
โข ุงููุฌุงุฑ ููู: 2.5 ุถุนู ุงูู
ุชูุณุท
โข ุงููุฌุงุฑ ุดุฏูุฏ: 3.0+ ุถุนู ุงูู
ุชูุณุท
โข ุชูุจููุงุช ุจุตุฑูุฉ ู
ุน ู
ุคุดุฑุงุช ู
ูููุฉ
๐ท ุชุญููู ู
ุชูุฏู
ูุถุบุท ุงูุดุฑุงุก/ุงูุจูุน
โข ุญุฌู
ุงูุดุฑุงุก: ุชุฑุงูู
ุญุฌู
ุงูุดู
ูุน ุงูุตุงุนุฏุฉ
โข ุญุฌู
ุงูุจูุน: ุชุฑุงูู
ุญุฌู
ุงูุดู
ูุน ุงููุงุจุทุฉ
โข ุถุบุท ุงูุดุฑุงุก %: ูุณุจุฉ ุถุบุท ุงูุดุฑุงุก
โข ุถุบุท ุงูุจูุน %: ูุณุจุฉ ุถุบุท ุงูุจูุน
โข ุณูุทุฑุฉ ุงูุถุบุท: ู
ู ูุชุญูู
ูู ุงูุณูู (ุงูู
ุดุชุฑูู/ุงูุจุงุฆุนูู/ู
ุญุงูุฏ)
โข ููุฉ ุฌุณู
ุงูุดู
ุนุฉ: ูููุณ ููุฉ ุญุฑูุฉ ุงูุณุนุฑ
โข ุนุฏู
ุงูุชูุงุฒู: ููุชุดู ุงุฎุชูุงู ุชูุงุฒู ุงููุชุงุฆู
โข ุฏูุชุง ุงููููููู
(HLC3): ุญุณุงุจ ู
ุชูุฏู
ููุฏูุชุง
โข ุงูุฏูุชุง ุงูู
ุฑุฌุญ: ุญุฑูุฉ ุงูุณุนุฑ ุงูู
ุฑุฌุญุฉ ุจุงูุญุฌู
โข ุชุญููู ุงูุถุบุท ู
ุชุนุฏุฏ ุงูุดู
ูุน: ุชุญููู ุนุฏุฉ ุดู
ูุน (ู
ุญุณูู ููุฃุฏุงุก)
๐ท ุชูุงู
ู ุงูู
ุคุดุฑุงุช ุงููููุฉ
โข VWMA (ุงูู
ุชูุณุท ุงูู
ุฑุฌุญ ุจุงูุญุฌู
): ู
ููุน ุงูุณุนุฑ ู
ูุงุจู VWMA
โข OBV (ุงูุญุฌู
ุงูุชุฑุงูู
ู): ุงูุชุดุงู ุงูุงุชุฌุงู ู
ุน ุชู
ููุฏ EMA
โข ุชุจุงุนุฏ OBV: ูุดู ุงูุชุจุงุนุฏุงุช ุงูุตุนูุฏูุฉ/ุงููุจูุทูุฉ
โข MFI (ู
ุคุดุฑ ุชุฏูู ุงูุฃู
ูุงู): ุญุงูุงุช ุงูุชุดุจุน ุงูุดุฑุงุฆู/ุงูุจูุนู
โข ุฎุท A/D (ุงูุชุฑุงูู
/ุงูุชูุฒูุน): ุชุชุจุน ุงูุฃู
ูุงู ุงูุฐููุฉ
๐ท ูุธุงู
ุชูููู
ุฐูู ู
ุฏุนูู
ุจุงูุฐูุงุก ุงูุงุตุทูุงุนู
โข ููุฉ ุงูุฏุฎูู: ูููุณ ููุฉ ุงููููููู
ู
ุน ุญุฑูุฉ ุงูุณุนุฑ
โข ุงูุฌูุฏ ู
ูุงุจู ุงููุชูุฌุฉ: ูุญุฏุฏ ุญุงูุงุช ุงูุฐุฑูุฉ (ุงุณุชูุฒุงู ุงูุดุฑุงุก/ุงูุจูุน)
โข ุชุญููู ุญุฌู
ุงูุงูุนูุงุณ: ูุชุชุจุน ุงูุญุฌู
ุนูุฏ ุดู
ูุน ุงูุงูุนูุงุณ
โข ุชูุงู
ู ุงูุงุชุฌุงู: ูุฌู
ุน ุงุชุฌุงู ุงูุชุฑูุฏ ู
ุน ุชุฃููุฏ ุงููููููู
โข ุงูููุงุท ุงูุตุนูุฏูุฉ/ุงููุจูุทูุฉ: ูุธุงู
ุชูููู
ู
ู 11 ููุทุฉ
โข ุชูููู
ุงููููููู
: ู
ููุงุณ ู
ู -100 ุฅูู +100 (ู
ูุฌุจ = ุตุนูุฏูุ ุณุงูุจ = ูุจูุทู)
โข ู
ุณุชูู ุงูุซูุฉ: ูุณุจุฉ ู
ูุซูููุฉ ุงูุฅุดุงุฑุฉ
โข ุงูุฅุดุงุฑุฉ ุงูููุงุฆูุฉ: ุญูู
ูุงุถุญ (ุตุนูุฏู/ูุจูุทู/ู
ุญุงูุฏ)
๐ท ูุธุงู
ุงูุฅุดุงุฑุงุช ุงูุซูุงุซู (ุงุฎุชูุงุฑู)
โข ุงูุฅุดุงุฑุฉ 1: ุจูุงุกู ุนูู ุชูููู
ุงููููููู
(ุนุชุจุงุช ูุงุจูุฉ ููุชุฎุตูุต)
โข ุงูุฅุดุงุฑุฉ 2: ุงููุฌุงุฑ ุงููููููู
+ ููู ุงูุดู
ุนุฉ (ุงุฎุชูุงุฑ ู
ุณุชูู ุงูุงููุฌุงุฑ)
โข ุงูุฅุดุงุฑุฉ 3: ูุดู ุชุจุงุนุฏ OBV
โข ุชูุนูู/ุฅูุบุงุก ู
ุณุชูู ููู ุฅุดุงุฑุฉ
โข ุฅุดุงุฑุงุช ุจุตุฑูุฉ ุนูู ุงูุดุงุฑุช ุจู
ุซูุซุงุช
โข ุชูุจููุงุช ุฅุดุงุฑุงุช ู
ุฌู
ุนุฉ
๐ท ูุธุงู
ุชูุจููุงุช ุดุงู
ู
โข ุชูุจููุงุช ุงููุฌุงุฑ ุงููููููู
(ู
ุณุชููุงุช ูุงุจูุฉ ููุชููุฆุฉ)
โข ุชูุจููุงุช ูุฑุฏูุฉ ููุฅุดุงุฑุงุช 1ุ 2ุ 3
โข ุชูุจููุงุช ุฅุดุงุฑุงุช ุงูุดุฑุงุก/ุงูุจูุน ุงูู
ุฌู
ุนุฉ
โข ุชูุจููุงุช ุชุบููุฑ ุงุชุฌุงู OBV
โข ุชูุจููุงุช ุถุบุท ุงูุดุฑุงุก/ุงูุจูุน ุงูููู
โข ุชุฑุฏุฏ ุงูุชูุจููุงุช ูุงุจู ููุชุฎุตูุต
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ุจููุฉ ุงูุฌุฏูู ูุงูุนุฑุถ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
ูุชู
ูุฒ ุงูู
ุคุดุฑ ุจุฌุฏูู ุงุญุชุฑุงูู ู
ู 3 ุฃุนู
ุฏุฉ ู
ุน ุงูุฃูุณุงู
ุงูุชุงููุฉ:
๐ ุนูุงููู ุงูุฃุนู
ุฏุฉ:
โข ุงูู
ุคุดุฑ: ุงุณู
ุงูู
ููุงุณ
โข ุงูููู
ุฉ: ุงููุฑุงุกุฉ ุงูุญุงููุฉ
โข ุงูุญุงูุฉ: ู
ุคุดุฑ ุงูุญุงูุฉ ุงูุจุตุฑู (ููุงุท/ุฑู
ูุฒ ู
ูููุฉ)
๐ ุงููุณู
1: ุฃุณุงุณูุงุช ุงููููููู
ูุนุฑุถ ู
ูุงููุณ ุงููููููู
ุงูุฃุณุงุณูุฉ ู
ุน ู
ุคุดุฑุงุช ุงููุณุจ ูุงูุชุบูุฑุงุช ุงูู
ุฆููุฉ. ุถุฑูุฑู ูููู
ู
ุณุชููุงุช ูุดุงุท ุงูุณูู ุงูุญุงูู.
๐ ุงููุณู
2: ูุดู ุงููุฌุงุฑุงุช ุงููููููู
ูุดู ููุฑู ููุงููุฌุงุฑุงุช ู
ุน 4 ู
ุณุชููุงุช ู
ู ุงูุดุฏุฉ. ู
ููู ููุชุนุฑู ุงูููุฑู ุนูู ุงูุญุฌู
ุบูุฑ ุงูุทุจูุนู.
๐ ุงููุณู
3: ุถุบุท ุงูุดุฑุงุก/ุงูุจูุน (ู
ุชูุฏู
)
ุชุญููู ุดุงู
ู ูุชุฏูู ุงูุฃูุงู
ุฑ ู
ุน ุฃูุซุฑ ู
ู 10 ู
ูุงููุณ. ูุชุถู
ู ุญุณุงุจุงุช ู
ุชูุฏู
ุฉ ู
ุซู ุงูุฏูุชุง ุงูู
ุฑุฌุญุ ุญุฌู
ุนุฏู
ุงูุชูุงุฒูุ ูุชุญููู ุงูุถุบุท ู
ุชุนุฏุฏ ุงูุดู
ูุน.
๐ ุงููุณู
4: ุชุญููู VWMA
ูุนุฑุถ ู
ููุน ุงูุณุนุฑ ุจุงููุณุจุฉ ููู
ุชูุณุท ุงูู
ุฑุฌุญ ุจุงูุญุฌู
. ุญุงุณู
ูุชุญุฏูุฏ ุงูุญุฑูุงุช ุงูู
ุฏุนูู
ุฉ ุจุงูุญุฌู
.
๐ ุงููุณู
5: ุชุญููู OBV
ุงุชุฌุงู ุงูุญุฌู
ุงูุชุฑุงูู
ู ููุดู ุงูุชุจุงุนุฏุงุช. ูุณุงุนุฏ ูู ุชุญุฏูุฏ ุชุฑุงูู
/ุชูุฒูุน ุงูุฃู
ูุงู ุงูุฐููุฉ.
๐ ุงููุณู
6: ุชุญููู MFI
ูุฑุงุกุงุช ู
ุคุดุฑ ุชุฏูู ุงูุฃู
ูุงู ู
ุน ุฅุดุงุฑุงุช ุงูุชุดุจุน. ูุฌู
ุน ุจูู ุงูุณุนุฑ ูุงูุญุฌู
ููุชุญููู ุงูุดุงู
ู.
๐ ุงููุณู
7: ุฎุท A/D
ุชุญููู ุงุชุฌุงู ุฎุท ุงูุชุฑุงูู
/ุงูุชูุฒูุน. ูุชุชุจุน ุงูุดุฑุงุก ูุงูุจูุน ุงูู
ุคุณุณู.
๐ ุงููุณู
8: ุงูุฐูุงุก ุงูุงุตุทูุงุนู ูููููููู
ูุธุงู
ุชูููู
ุฐูู ู
ุน 11 ููุทุฉ ุชูููู
:
1. ุชูููู
ููุฉ ุงููููููู
2. ุถุบุท ุงูุดุฑุงุก/ุงูุจูุน ุงูุญุงูู
3. ุชุญููู ุงูุถุบุท ู
ุชุนุฏุฏ ุงูุดู
ูุน
4. ุญุณุงุจ ููุฉ ุงูุฏุฎูู
5. ุชุชุจุน ุญุฌู
ุงูุงูุนูุงุณ
6. ู
ููุน VWMA
7. ุงุชุฌุงู OBV
8. ุชุจุงุนุฏ OBV
9. ุฅุดุงุฑุฉ MFI
10. ุงุชุฌุงู A/D
11. ุชูุงู
ู ุงูุงุชุฌุงู ู
ุน ุงููููููู
ุงููุงุชุฌ ุงูููุงุฆู: ุชูููู
ุงููููููู
ุ ู
ุณุชูู ุงูุซูุฉุ ูุงูุฅุดุงุฑุฉ ุงูููุงุฆูุฉ ุงูู
ู
ูุฒุฉ.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ๏ธ ุฎูุงุฑุงุช ุงูุชุฎุตูุต
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐จ ุนุฑุถ ุงูุฌุฏูู
โข ุงูู
ููุน: 9 ู
ูุงูุน (ุฃุนูู-ูุณุงุฑุ ุฃุนูู-ูุณุทุ ุฃุนูู-ูู
ููุ ูุณุท-ูุณุงุฑุ ุฅูุฎ)
โข ุงูุญุฌู
: 5 ุฃุญุฌุงู
(ุตุบูุฑ ุฌุฏุงูุ ุตุบูุฑุ ุนุงุฏูุ ูุจูุฑุ ุถุฎู
)
โข ุงูุฃููุงู: ุฎูููุฉ ููุต ูุงุจู ููุชุฎุตูุต ุจุงููุงู
ู
โข ุงูุฃูุณุงู
: ุฅุธูุงุฑ/ุฅุฎูุงุก ุฃู ูุณู
ุจุดูู ู
ุณุชูู
๐ฏ ุฅุนุฏุงุฏุงุช ุงููููููู
โข ุทูู ู
ุชูุณุท ุงููููููู
(ุงูุชุฑุงุถู: 20)
โข ุนุชุจุงุช ุงูุงููุฌุงุฑ: ู
ุถุงุนูุงุช ูุงุจูุฉ ููุชุนุฏูู ููู ู
ุณุชูู
โข ู
ูุงููุณ ู
ุชูุฏู
ุฉ: ูุชุฑุงุช ุงูุชุญููู (ู
ุญุณูู: 10 ุดู
ูุน)
โข ุชุญููู ุงูุงูุนูุงุณ: ุนุฏุฏ ุงูุดู
ูุน (ู
ุญุณูู: 5 ุดู
ูุน)
๐ ุฃุทูุงู ุงูู
ุคุดุฑุงุช
โข ุชู
ููุฏ OBV: ุงูุชุฑุงุถู 14
โข ูุชุฑุฉ MFI: ุงูุชุฑุงุถู 14
โข ุทูู VWMA: ุงูุชุฑุงุถู 20
โข ุทูู A/D: ุงูุชุฑุงุถู 14
๐ฏ ูุธุงู
ุงูุฅุดุงุฑุงุช
โข ุชูุนูู/ุฅูุบุงุก ูู ุฅุดุงุฑุฉ ุจุดูู ู
ุณุชูู
โข ุนุชุจุงุช ูุงุจูุฉ ููุชุฎุตูุต ููุฅุดุงุฑุฉ 1 (ุงูุชูููู
ูุงูุซูุฉ)
โข ุงุฎุชูุงุฑ ู
ุณุชูู ุงูุงููุฌุงุฑ ููุฅุดุงุฑุฉ 2
โข ุฅุธูุงุฑ/ุฅุฎูุงุก ุงูุฅุดุงุฑุงุช ุนูู ุงูุดุงุฑุช
โข ุชููุฆุฉ ุงูุชูุจููุงุช ููู ููุน ุฅุดุงุฑุฉ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ุงูุฃุฏุงุก ูุงูุชุญุณูู
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
ู
ุญุณูู ููุถุน ุงูุฑูุจูุงู
โข ูุนู
ู ุจุณูุงุณุฉ ูู ูุถุน Replay ูู TradingView
โข ุญุณุงุจุงุช ู
ุญุณููุฉ ููุชุญููู ุงูุชุงุฑูุฎู ุงูุณุฑูุน
โข ูุง ุชูุฌุฏ ู
ุดุงูู ุชุฃุฎูุฑ ุฃู ุชุฌู
ูุฏ
โ
ููุงุกุฉ ุงูููุช ุงููุนูู
โข ุจููุฉ ููุฏ ุฎูููุฉ (ุญุฏ 50 ุนูุงู
ุฉ/ุฎุท)
โข ุชุฎุฒูู ุฐูู ููุญุณุงุจุงุช ุงูู
ุชูุฑุฑุฉ
โข ุชูุฑุงุฑุงุช ู
ุญุฏูุฏุฉ ููุญููุงุช ููุฃุฏุงุก ุงูุฃู
ุซู
โข ุชุญุฏูุซุงุช ููุท ุนูู ุขุฎุฑ ุดู
ุนุฉ (ุนุฑุถ ุงูุฌุฏูู)
โ
ุบูุฑ ูุงุจู ูุฅุนุงุฏุฉ ุงูุฑุณู
โข ุฌู
ูุน ุงูุฅุดุงุฑุงุช ู
ุคูุฏุฉ ุนูุฏ ุฅุบูุงู ุงูุดู
ุนุฉ
โข ูุง ุชูุฌุฏ ุชุบููุฑุงุช ุจุฃุซุฑ ุฑุฌุนู ุนูู ุงูุฅุดุงุฑุงุช ุงูุชุงุฑูุฎูุฉ
โข ู
ูุซูู ููุงุฎุชุจุงุฑ ุงูุฎููู ูุชุทููุฑ ุงูุงุณุชุฑุงุชูุฌูุงุช
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ก ุชูุตูุงุช ุงูุงุณุชุฎุฏุงู
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ููุชุฏุงูู ุงูููู
ู:
โข ุงุณุชุฎุฏู
ูุฑูู
ุงุช 15ุฏ-1ุณ
โข ูุนูู ุฌู
ูุน ุงูุฃูุณุงู
ููุชุญููู ุงูุดุงู
ู
โข ุฑูุฒ ุนูู ุฃูุณุงู
ุงููุฌุงุฑ ุงููููููู
ูุถุบุท ุงูุดุฑุงุก/ุงูุจูุน
โข ุถุน ุชูุจููุงุช ููุงููุฌุงุฑุงุช ุงููููุฉ ูุงูุดุฏูุฏุฉ
๐ ููุชุฏุงูู ุงูู
ุชุฃุฑุฌุญ:
โข ุงุณุชุฎุฏู
ูุฑูู
ุงุช 4ุณ-1ู
โข ุฑูุฒ ุนูู ุฃูุณุงู
ุงูุฐูุงุก ุงูุงุตุทูุงุนู ู OBV
โข ูุนูู ุงูุฅุดุงุฑุฉ 1 ูุงูุฅุดุงุฑุฉ 3 ูุฏุฎููุงุช ุงูุชุฃุฑุฌุญ
โข ุฑุงูุจ ุงูุชุจุงุนุฏุงุช ูุงูุนูุงุณุงุช ุงูุงุชุฌุงู
๐ ููู
ุถุงุฑุจุฉ:
โข ุงุณุชุฎุฏู
ูุฑูู
ุงุช 1ุฏ-5ุฏ
โข ุฑูุฒ ุนูู ุถุบุท ุงูุดุฑุงุก/ุงูุจูุน ูุฃุณุงุณูุงุช ุงููููููู
โข ูุนูู ุงูุฅุดุงุฑุฉ 2 ูุฏุฎููุงุช ุณุฑูุนุฉ ุจูุงุกู ุนูู ุงูุงููุฌุงุฑุงุช
โข ุฃุฎูู ุงูุฃูุณุงู
ุงูุฃูู ุตูุฉ ูุชูููู ุงูููุถู ุงูุจุตุฑูุฉ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ู
ููุฌูุฉ ุงูู
ุคุดุฑ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
ูุฌู
ุน ูุฐุง ุงูู
ุคุดุฑ ุจูู ุชุญููู ุงููููููู
ุงูููุงุณููู ูุงูุฐูุงุก ุงูุฎูุงุฑุฒู
ู ุงูุญุฏูุซ:
1. ุชุญุฏูุฏ ุงููููููู
: ูุญุฏุฏ ุงูุญุฌู
ุบูุฑ ุงูุทุจูุนู ูุณุจุฉ ููู
ุชูุณุทุงุช ุงูุชุงุฑูุฎูุฉ
2. ุชุญููู ุชุฏูู ุงูุฃูุงู
ุฑ: ููุตู ุถุบุท ุงูุดุฑุงุก ูุงูุจูุน ุจุงุณุชุฎุฏุงู
ุจููุฉ ุงูุดู
ุนุฉ
3. ูุดู ุงูุชุจุงุนุฏ: ููุงุฑู ุญุฑูุฉ ุงูุณุนุฑ ู
ุน ู
ุคุดุฑุงุช ุงููููููู
4. ููุฌ ู
ุชุนุฏุฏ ุงููุฑูู
ุงุช: ูุณุชุฎุฏู
ุงูุชู
ููุฏ ูุงูุชุญููู ุงูุฑุฌุนู ููุณูุงู
5. ุฎูุงุฑุฒู
ูุฉ ุงูุชูููู
: ูุธุงู
ุชูููู
ู
ู 11 ููุทุฉ ูุชูููุฏ ุฅุดุงุฑุงุช ู
ูุถูุนูุฉ
6. ุชูุงู
ู ุงูุชูุงุก: ูุฌู
ุน ุนุฏุฉ ู
ุคุดุฑุงุช ูุฅุนุฏุงุฏุงุช ุฐุงุช ุงุญุชู
ุงููุฉ ุฃุนูู
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๏ธ ุฅุฎูุงุก ุงูู
ุณุคูููุฉ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
ูุฐุง ุงูู
ุคุดุฑ ูู ุฃุฏุงุฉ ููุชุญููู ููุง ููุจุบู ุงุณุชุฎุฏุงู
ู ูุฃุณุงุณ ูุญูุฏ ููุฑุงุฑุงุช ุงูุชุฏุงูู. ุงุฌู
ุน ุฏุงุฆู
ุงู ู
ุน ุชุญูููู ุงูุฎุงุต ูุฅุฏุงุฑุฉ ุงูู
ุฎุงุทุฑ ูุฎุทุฉ ุงูุชุฏุงูู. ุงูุฃุฏุงุก ุงูุณุงุจู ูุง ูุถู
ู ุงููุชุงุฆุฌ ุงูู
ุณุชูุจููุฉ.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ุงูุฏุนู
ูุงูุชุญุฏูุซุงุช
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โข ุชุญุฏูุซุงุช ูุชุญุณููุงุช ู
ูุชุธู
ุฉ
โข ุฅุตูุงุญุงุช ุงูุฃุฎุทุงุก ูุงูุชุญุณูู
โข ุทูุจุงุช ุงูู
ูุฒุงุช ููุฏ ุงูุงุนุชุจุงุฑ
โข ู
ูุงุญุธุงุช ุงูู
ุฌุชู
ุน ู
ุฑุญุจ ุจูุง
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ุชุฏุงูู ุณุนูุฏ! ูุชู
ูู ุฃู ูููุฏู ุชุญููู ุงููููููู
ุฅูู ูุฑุงุฑุงุช ู
ุฑุจุญุฉ! ๐
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Quantum Flow ScannerOverview
The Quantum Flow Scanner is a comprehensive technical analysis indicator that combines trend detection, momentum analysis, and dynamic band systems to identify potential market opportunities. This indicator uses advanced filtering techniques and multi-factor detection strength calculations to help traders make informed decisions.
Key Features
Trend Detection System
Dual-period momentum analysis (Fast/Slow periods configurable)
Pattern recognition engine that analyzes recent price movements
Normalized momentum calculations adjusted for volatility
Bull and Bear detection generation based on trend changes
Dynamic Band System
Adaptive bands that adjust to market volatility using ATR (Average True Range)
Customizable band width and distance multipliers
Optional midline, upper band, and lower band displays
Visual channel fill options for enhanced clarity
Background color coding for trend direction
Detection Strength Rating
Multi-factor detection strength calculation (25-92% range)
Considers volatility, momentum, trend duration, and volume
Higher timeframe alignment analysis
Swing position evaluation
Real-time percentage display on detections
Performance Tracking
Live performance statistics table
Total detections counter
Successful detections vs unsuccessful detections tracking based on configurable stop loss and take profit levels
Success rate percentage calculation
Average detection strength monitoring
How It Works
The indicator employs a sophisticated filtering mechanism based on pole-zero placement algorithms to smooth price data and calculate dynamic bands. When price crosses these bands in conjunction with momentum shifts, the indicator generates Bull or Bear detections.
Detection strength is calculated using eight weighted factors:
Market volatility assessment
Momentum cluster analysis
Distance from dynamic midline
Trend consistency duration
Higher timeframe trend alignment
Volume profile analysis
Candle strength evaluation
Swing position context
Configuration Options
Period Settings:
Fast Period (1-200): Controls short-term momentum sensitivity
Slow Period (1-500): Defines longer-term trend context
Pattern Recognition Length (5-50): Sets momentum analysis window
Sensitivity Controls:
Distance Multiplier (1.0-10.0): Adjusts band width relative to volatility
Cluster Size (1-15): Number of bars analyzed for momentum clustering
Display Options:
Customizable detection colors
Optional detection markers and percentage labels
Dynamic band visibility toggles
Channel fill options
Background color coding
Performance Tracking:
Configurable stop loss and take profit levels (in points)
Optional performance statistics table
Success rate monitoring
Use Cases
This indicator is designed for:
Trend identification across multiple timeframes
Entry and exit timing optimization
Market volatility assessment
Detection quality evaluation through strength ratings
Strategy performance tracking
Important Notes
This indicator is for educational and informational purposes only
Past performance does not guarantee future results
Always use proper risk management and position sizing
Detections should be used as part of a comprehensive trading strategy
Test thoroughly on historical data before live trading
No indicator is 100% accurate; losses are part of trading
GOGO SCALPER# GOGO SCALPER - Advanced Multi-Timeframe Trading Indicator
## Overview
GOGO SCALPER is a comprehensive trading indicator that combines multiple proven trading concepts into one powerful tool. It provides automated bias detection, session analysis, market structure tracking, and high-probability entry signals for scalpers and day traders.
## Key Features
### ๐ฏ Auto Bias System
- **Dual Timeframe Analysis**: Automatically tracks both Lower Timeframe (LTF) and Higher Timeframe (HTF) bias using EMA-based momentum
- **Dynamic Confidence Scoring**: Real-time confidence percentage (0-100%) for BUY/SELL signals based on multiple market factors
- **Smart Signal Generation**: Only triggers entries when both timeframes align during active trading sessions
### ๐ Market Phase Detection
- **Expansion vs Consolidation**: Automatically identifies whether the market is in an expansion or consolidation phase
- **Multi-Metric Analysis**: Uses Bollinger Band Width, Average Daily Range (ADR), and ATR ratios to determine market conditions
- **Trend Strength Indicator**: Shows whether the current trend is STRONG or WEAK
### ๐ Killzone Session Management
- **Four Major Sessions**: Asia, London, NY AM, and NY PM killzones with customizable times
- **Visual Session Boxes**: Color-coded boxes highlighting active trading sessions
- **Session Range Tracking**: Displays the price range for each killzone session
- **Auto Time Remaining**: Shows countdown timer for active sessions
### ๐ Multi-Timeframe Structure Analysis
- **HTF Candle Visualization**: Displays H1, H4, and Daily candles as mini-charts on your current timeframe
- **Sweep Detection**: Automatically identifies bullish and bearish liquidity sweeps
- **Numbered Candle System**: Labels candles 1-5 leading up to sweeps for pattern recognition
- **Counter-Sweep Protection**: Filters out invalidated sweeps automatically
### ๐ Market Structure Tools
- **CISD (Close in Structure Detection)**: Identifies when price closes through pivot highs/lows
- **FVG Detection**: Automatically plots Fair Value Gaps (Bullish & Bearish) with mitigation tracking
- **H4 & Daily Open Lines**: Tracks key opening prices with dynamic extension
- **High/Low Levels**: Plots session highs and lows with breakout alerts
### ๐ Information Dashboard
- **Comprehensive Table Display**: Shows all critical information at a glance
- Current HTF and LTF bias
- Active session
- Trend strength
- Signal direction
- Confidence percentage
- Entry confirmation status
- Market phase (Expansion/Consolidation)
- Killzone ranges
### โก Entry Signal System
- **BUY Signal**: Triggers when price crosses above Bollinger Band upper level during bullish bias
- **SELL Signal**: Triggers when price crosses below Bollinger Band lower level during bearish bias
- **Session Filter**: Signals only activate during configured killzone sessions
- **Confirmation Labels**: Clear "Long Confirm!" or "Short Confirm!" messages with "Wait!" during invalid conditions
## How It Works
### Bias Calculation
The indicator compares current price against EMA on both lower and higher timeframes:
- **BULLISH**: Price above EMA
- **BEARISH**: Price below EMA
- **NEUTRAL**: Price at EMA
### Confidence Scoring
The confidence score (0-100%) is calculated using:
- HTF/LTF bias alignment (25%)
- Active session quality (20%)
- Volume analysis (15%)
- ATR momentum (15%)
- RSI position (15%)
- Trend strength (10%)
- BB position (10%)
### Market Phase Detection
Uses a voting system from three metrics:
- Bollinger Band Width relative to average
- Average Daily Range achievement percentage
- ATR ratio to moving average
When 2+ metrics vote for expansion, market is in "EKSPANSI" phase, otherwise "KONSOLIDASI".
## Best Use Cases
- **Scalping**: 1-5 minute charts with 15m/1H/4H higher timeframes
- **Day Trading**: 5-15 minute charts with 1H/4H/Daily higher timeframes
- **Session Trading**: Focus on London and NY AM sessions for highest probability setups
- **Confluence Trading**: Wait for HTF/LTF alignment + high confidence + active session
## Customization Options
- Adjustable EMA length and Bollinger Band settings
- Customizable killzone session times and colors
- Configurable HTF timeframes and candle count
- Toggle visibility for all components (FVGs, sweeps, lines, boxes)
- Flexible table position and display options
## Recommended Settings
- **1-3 minute charts**: Use 5m/15m/1H for HTF analysis
- **5-15 minute charts**: Use 1H/4H/Daily for HTF analysis
- **Focus on major sessions**: Enable London and NY AM for best results
- **Wait for 60%+ confidence**: Higher confidence = higher probability trades
## Notes
- Works best on liquid markets (Forex majors, indices, major crypto pairs)
- Designed for active trading sessions (avoid low-volume periods)
- Combines with price action for best results
- Not a standalone system - use proper risk management
---
**Disclaimer**: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose.
Smart Money Concepts [Riz]Smart Money Concepts is a comprehensive technical analysis tool for identifying institutional trading patterns and market structure. This indicator combines Smart Money Concepts (SMC), ICT methodology, and Wyckoff principles into one professional tool.
โจ KEY FEATURES
๐ VOLUMETRIC ORDER BLOCKS
โข Visual representation of supply/demand zones with volume distribution
โข Horizontal volume bars showing buy/sell composition inside each Order Block
โข Automatic mitigation tracking
โข Breaker Block detection (invalidated OBs acting as reversal zones)
โข Strength rating system: โ
Weak, โ
โ
Medium, โ
โ
โ
Strong
โข ATR-based size filtering to show only significant zones
๐ MARKET STRUCTURE DETECTION
โข Break of Structure (BOS) and Change of Character (CHoCH) identification
โข Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), Lower Lows (LL) labels
โข Internal structure pivots (iH/iL) for intraday analysis
โข Auto-adjusting swing length based on timeframe
โข Configurable confirmation methods (Close vs Wick-based)
๐ FAIR VALUE GAPS (FVG)
โข Automatic detection of bullish and bearish imbalances
โข Configurable mitigation percentage (default 50%)
โข Visual tracking until gaps are filled
โข Separate color schemes for clarity
๐ง LIQUIDITY ANALYSIS
โข Buy Side Liquidity (BSL) identification at swing highs
โข Sell Side Liquidity (SSL) identification at swing lows
โข Automatic sweep detection with visual confirmation
โข Real-time alerts when liquidity is taken
โ๏ธ PREMIUM & DISCOUNT ZONES
โข Dynamic range calculation based on configurable lookback period
โข Equilibrium (EQ) level identification
โข Previous Day High (PDH) and Previous Day Low (PDL) levels
โข Helps identify favorable entry zones
๐ REAL-TIME DASHBOARD
โข Live statistics on all detected patterns
โข Active Order Blocks and FVGs count
โข BOS/CHoCH occurrence tracking
โข Liquidity sweep counters
โข Recent market activity indicators
โข Current trend bias display
โข Fully customizable position and size
โ๏ธ CUSTOMIZATION OPTIONS
All aspects are fully customizable:
โข Swing Length (1-50 bars) with auto-adjust for timeframe
โข Max Active Order Blocks (10-100)
โข Volume bar position (Left/Right) with mirror option
โข Volume bar width percentage (10-50%)
โข ATR size filter for Order Blocks
โข Strength rating method (Touches/Age/Distance/Volume/Combined)
โข All colors and transparency levels
โข Dashboard position (9 locations available)
โข Comprehensive alert system for all events
๐ HOW IT WORKS
ORDER BLOCKS: Identified at the last candle before a Break of Structure. These represent institutional supply and demand zones. Volume is estimated based on candle characteristics and displayed as horizontal bars.
MARKET STRUCTURE: Tracks pivot highs and lows to determine if price is making Higher Highs/Higher Lows (bullish structure) or Lower Highs/Lower Lows (bearish structure). BOS indicates trend continuation, while CHoCH signals potential trend reversal.
LIQUIDITY: Swing highs represent Buy Side Liquidity where short positions have their stop losses. Swing lows represent Sell Side Liquidity where long positions have stop losses. The indicator tracks when these levels are "swept" by price.
FAIR VALUE GAPS: Three-candle patterns where the current candle's range doesn't overlap with the candle two bars ago, creating price imbalances that often get filled later.
๐ BEST PRACTICES
โข Use on all timeframes - Auto-adjust feature optimizes settings automatically
โข Look for confluence - Best setups occur when multiple concepts align (e.g., Order Block + liquidity sweep + discount zone)
โข Consider risk/reward - Use Premium/Discount zones to identify favorable entry areas
โข Respect market context - Order Blocks in the direction of overall trend tend to be more reliable
โข Volume matters - Higher volume percentages in the expected direction may indicate stronger zones
โ ๏ธ IMPORTANT NOTES
EDUCATIONAL TOOL: This indicator is designed for analysis and education, not as trading signals or investment advice.
VOLUME ESTIMATION: Buy/sell volume distribution is estimated based on candle characteristics since true buy/sell volume data is not available in Pine Script.
NO GUARANTEES: Past performance is not indicative of future results. All trading involves substantial risk.
RISK MANAGEMENT: Always use proper risk management and seek additional confirmation before making trading decisions.
OBJECT LIMITS: On very fast timeframes (1m, 5m) in highly volatile markets, the indicator may approach Pine Script's 500-object limit. Reduce max OBs/FVGs in settings if needed.
๐ง TECHNICAL SPECIFICATIONS
โข Pine Script Version: v6
โข Indicator Type: Overlay (displays on price chart)
โข Maximum Objects: Optimized to stay within Pine Script limits
โข Performance: Efficient rendering with configurable history management
โข Updates: Real-time on every bar close
๐ METHODOLOGY
This indicator combines concepts from:
โข Inner Circle Trader (ICT) methodology
โข Smart Money Concepts (SMC) framework
โข Wyckoff market analysis principles
โข Order flow and volume spread analysis
โ๏ธ DISCLAIMER
This indicator is for educational and informational purposes only. It is not financial advice. Trading financial instruments carries substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own research and consult with a licensed financial advisor before making any investment decisions. The author assumes no responsibility for any losses incurred from using this indicator.
FVG Snper PRO๐ฏ FVG Sniper โ Fair Value Gap Signal Engine
FVG Sniper is a professional imbalance-based entry tool built around the Nasdaq futures (NQ/MNQ) โ but the signal logic is general enough to apply to many liquid instruments (indices, FX, crypto, metals).
It automatically detects Fair Value Gaps (FVGs), tracks their lifecycle, and fires rule-based long/short signals only when price shows decisive intent away from those imbalances.
๐ What FVG Sniper Does
Detects FVGs automatically (no pivots)
Uses a strict 3-candle pattern to locate bullish and bearish imbalances directly from price action.
Tracks each FVG over time
For every FVG, FVG Sniper tracks:
When it was created
Whether it has ever been tapped
Whether it has been tapped since the last trade
Whether it has been invalidated (โinversion closeโ)
Session-gated execution
FVGs can be formed and tapped any time.
Only bars inside a defined signal session (e.g. 09:30โ12:00 New York time) are allowed to trigger entries.
FVGs are only eligible if they were created on the same trading day as the signal and after a specific time cutoff (e.g. 08:30 ET).
Tap-aware, breakout-based entries
The indicator looks for:
An FVG that has been tapped at least once since the last signal (if tap is required).
A decisive breakout of the previous barโs high or low coming off that FVG.
Multi-strategy overlay (for advanced use)
On top of the core engine, FVG Sniper offers several optional โSniper profilesโ (strategies) tuned around:
Session timing (e.g. morning / midday windows)
Volatility regimes
Lane cleanliness / opposite-side structure behavior
Range context (distance from session extremes)
You can toggle these profiles on/off to restrict signals to specific conditions โ but the exact internal filters and thresholds are not disclosed.
If at least one profile is enabled, a signal prints when any enabled profile likes the setup.
If no profiles are enabled, FVG Sniper shows the raw base FVG breakout signals from the core engine.
๐ง How to Use It
Primary use case: intraday futures (NQ/MNQ) on 1M timeframe.
FVG Sniper works best as:
A signal engine feeding your execution plans, or
A confirmation layer on top of your own context (HTF bias, news, higher-timeframe levels, etc.).
๐จ Visuals & Controls
Bullish and bearish FVG zones are drawn directly on the chart.
Optional mid-lines through each FVG.
Automatic delete or โfadeโ behavior when FVGs are invalidated.
Clear long/short markers at the signal bar.
Optional debug label to inspect which FVG produced the signal and key reference times.
โ ๏ธ Disclaimer
This script is for educational and research purposes only and is not financial advice.
Past performance does not guarantee future results. Always validate any signal logic in a simulator and adapt it to your own risk management, instrument, and timeframe.
VWAP Kalman FilterOverview
This indicator applies Kalman filtering techniques to Volume Weighted Average Price (VWAP) calculations, providing a statistically optimized approach to VWAP analysis. The Kalman filter reduces noise while maintaining responsiveness to genuine price movements, addressing common VWAP limitations in volatile or low-volume conditions.
Technical Implementation
Kalman Filter Mathematics
The indicator implements a state-space model for VWAP estimation:
- Prediction Step: xฬ(k|k-1) = xฬ(k-1|k-1) + v(k-1)
- Update Step: xฬ(k|k) = xฬ(k|k-1) + K(k)
- Kalman Gain: K(k) = P(k|k-1) / (P(k|k-1) + R)
Where:
- xฬ = estimated VWAP state
- K = Kalman gain (adaptive weighting factor)
- P = error covariance
- R = measurement noise
- Q = process noise
- v = optional velocity component
Core Components
Dual VWAP System
- Standard VWAP: Traditional volume-weighted calculation
- Kalman-filtered VWAP: Noise-reduced estimation with optional velocity tracking
- Real-time divergence measurement between filtered and unfiltered values
Adaptive Filtering
- Process Noise (Q): Controls adaptation to price changes (0.001-1.0)
- Measurement Noise (R): Determines smoothing intensity (0.01-5.0)
- Optional velocity tracking for momentum-based filtering
Multi-Timeframe Anchoring
- Session, Weekly, Monthly, Quarterly, and Yearly anchor periods
- Automatic Kalman state reset on anchor changes
- Maintains VWAP integrity across timeframes
Features
Visual Components
- Dual VWAP Lines: Compare filtered vs. unfiltered in real-time
- Dynamic Bands: Three-level deviation bands (1ฯ, 2ฯ, 3ฯ)
- Trend Coloring: Automatic color adaptation based on price position
- Cloud Visualization: Highlights divergence between standard and Kalman VWAP
- Signal Markers: Crossover and band-touch indicators
Trading Signals
- VWAP crossover detection with Kalman filtering
- Band touch alerts at multiple standard deviation levels
- Velocity-based momentum confirmation (optional)
- Divergence warnings when filtered/unfiltered values separate
Information Display
- Real-time VWAP values (both standard and filtered)
- Trend direction indicator
- Velocity/momentum reading (when enabled)
- Divergence percentage calculation
- Anchor period display
Input Parameters
VWAP Settings
- Anchor Period: Choose calculation reset period
- Band Multipliers: Customize deviation band distances
- Display Options: Toggle standard VWAP and bands
Kalman Parameters
- Length: Base period for calculations (5-200)
- Process Noise (Q: Higher values increase responsiveness
- Measurement Noise (R): Higher values increase smoothing
- Velocity Tracking: Enable momentum-based filtering
Visual Controls
- Toggle filtered/unfiltered VWAP display
- Band visibility options
- Signal markers on/off
- Cloud fill between VWAPs
- Bar coloring by trend
Use Cases
Noise Reduction
Particularly effective during:
- Low volume periods (pre-market, lunch hours)
- Volatile market conditions
- Fast-moving markets where standard VWAP whipsaws
Trend Identification
- Cleaner trend signals with reduced false crosses
- Earlier trend detection through velocity component
- Confirmation through divergence analysis
Support/Resistance
- Filtered VWAP provides more stable S/R levels
- Bands adapt to filtered values for better zone identification
- Reduced false breakout signals
Technical Advantages
1. Optimal Estimation: Mathematically optimal under Gaussian noise assumptions
2. Adaptive Response: Self-adjusting to market conditions
3. Predictive Element: Velocity component provides forward-looking insight
4. Noise Immunity: Superior noise rejection vs. simple moving average smoothing
Limitations
- Assumes linear price dynamics
- Requires parameter optimization for different instruments
- May lag during sudden volatility regime changes
- Not suitable as standalone trading system
Mathematical Background
Based on control systems theory, the Kalman filter provides recursive Bayesian estimation originally developed for aerospace applications. This implementation adapts the algorithm specifically for financial time series, maintaining VWAP's volume-weighted properties while adding statistical filtering.
Comparison with Standard VWAP
Standard VWAP Issues Addressed:
- Choppy behavior in low volume
- Whipsaws around VWAP line
- Lag in trend identification
- Noise in deviation bands
Kalman VWAP Benefits:
- Smooth yet responsive line
- Fewer false signals
- Optional momentum tracking
- Statistically optimized filtering
Alert Conditions
The indicator includes several pre-configured alert conditions:
- Bullish/Bearish VWAP crosses
- Upper/Lower band touches
- High divergence warnings
- Velocity shifts (if enabled)
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This open-source indicator is provided as-is for educational and trading purposes. No guarantees are made regarding trading performance. Users should conduct their own testing and validation before using in live trading.
Prophet Model [TakingProphets]The Prophet Model โ context pipeline (HTF PDA โ Sweep โ CISD โ EPE) with dynamic risk
Purpose
Informational overlay for organizing institutional context in real time. It does not issue buy/sell signals and is not financial advice. Use it to structure analysis and checklist-driven executionโnot to automate decisions.
What it does (modules at a glance)
Projects HTF PD Arrays (FVGs) onto your current chart and maintains only the nearest active array.
Validates directional bias using Candle Range Theory (CRT) on the same HTF.
Tracks Liquidity Sweeps (BSL/SSL) on HTF-aware pivots.
Confirms Change in State of Delivery (CISD) via displacement after a sweep.
Optionally refines entries with EPE when a local (internal) imbalance forms right after CISD.
Derives dynamic TP/BE/SL from measured displacement and recent extremes (not fixed distances).
Keeps a rules checklist (PDA tap โ CRT โ Sweep โ CISD) and a relationships table (common HTFโLTF pairings) to enforce process.
How it works (integration, not a mashup)
The modules are sequenced on one HTF time base so each step gates the next:
HTF PD Arrays (context zone). The model identifies valid HTF FVGs, filters tiny/weekend gaps, removes arrays that are invalidated by clean trades-through, and persists only the nearest PDA. This focuses attention on the institutional zone most likely to matter now.
CRT (directional gating). CRT on the same HTF establishes a provisional bias. No entries are implied; CRT simply permits or forbids the following steps. If CRT disagrees with the PDA context, the checklist remains incomplete.
Liquidity Sweep (event). The model tracks HTF-aware BSL/SSL pivots. A sweep only โcountsโ if it occurs in relation to the active PDA (tap/engagement). This prevents generic swing-high/low tags from triggering downstream logic.
CISD (confirmation). After a qualified sweep, the tool looks for displacement through the sequence open (the open of the impulsive leg beginning at or immediately after the sweep). Crossing that threshold confirms CISD, which marks a structural delivery shift consistent with the CRT bias.
EPE (refinement, optional). Immediately following CISD, the model scans for a fresh internal imbalance. If found quickly, it promotes that price area as the Easiest Point of Entry (EPE) and relabels the reference. If not, the CISD level remains primary.
Dynamic risk levels. TP/BE/SL are derived from the measured displacement around the CISD leg (e.g., BE โ 1ร leg, TP โ 2.25ร stretch; SL aligned to nearby structural extremes rather than a fixed pip offset). Levels update with structure and can display prices.
By chaining PDA โ CRT โ Sweep โ CISD โ (EPE) โ Risk on a single HTF backbone, the tool creates a coherent workflow where later signals simply do not appear without earlier context. Thatโs why this is not a bundle of independent features: each moduleโs output is another moduleโs input.
Concepts & operational rules (high level)
HTF PD Arrays (FVGs)
Uses a standard three-candle gap definition on the chosen HTF, with filters for weekend/tiny gaps.
Inverse mitigation: if price trades cleanly through an array, the box is removed and internal state resets.
Nearest-PDA persistence: when multiple arrays exist, only the closest remains visible to reduce clutter.
Optional right-extension draws lingering influence X bars forward.
Candle Range Theory (CRT)
Bullish CRT: candle 2 wicks below candle 1โs low but closes back inside candle 1โs range, without taking its high.
Bearish CRT: candle 2 wicks above candle 1โs high but closes back inside candle 1โs range, without taking its low.
Role: bias validation paired to CISD when alignments match the active PDA.
Liquidity Sweeps (BSL/SSL)
Tracks candidate HTF pivots as buy-/sell-side liquidity.
A sweep registers when price takes a tracked pivot in the vicinity of the active PDA.
CISD (Change in State of Delivery)
Finds the sequence open for the impulsive leg that begins at/after the sweep.
Bearish path (after BSL sweep): CISD when close < sequence-open.
Bullish path (after SSL sweep): CISD when close > sequence-open.
On confirmation, the model plots a CISD line, checks the box in the Strategy Checklist, and triggers risk calc.
EPE (Easiest Point of Entry)
Within a short window after CISD, scans for a local imbalance; if present, promotes that level as EPE.
If no imbalance forms, CISD remains the operative reference.
Dynamic TP / BE / SL
Built from the measured leg around CISD (not fixed pip steps).
Approximate geometry: BE โ 1ร leg, TP โ 2.25ร leg; SL respects nearby structural extremes.
Labels and price markers are optional.
Architecture notes
Maps the current chart to a higher timeframe (e.g., 15sโM5, M1โM15, M5โH1, M15โH4, H1โD, H4โW, DโM).
Retrieves HTF OHLC/time with no lookahead so structures update intrabar until the HTF bar closes.
Periodic cleanup clears obsolete lines/labels/boxes to keep charts responsive.
Inputs (summary)
FVGs/PD Arrays: show/hide, colors, borders, label size, right-extension, nearest-only toggle.
CRT: enable/disable, label style.
Sweeps/CISD/EPE: enable/disable, line/label styles, EPE window.
Risk Levels (TP/BE/SL): enable each, price labels on/off, colors.
Tables/Checklist: strategy checklist on/off; relationships table (common HTFโLTF pairings); text sizes and header colors.
Alerts (optional)
You may add alertconditions aligned with these events in your own workspace:
HTF PDA tap (bullish/bearish box)
CRT detected (bullish/bearish)
CISD confirmed (bullish/bearish)
EPE set/updated
Example messages:
โProphet: CISD confirmed on {{ticker}} / {{interval}}โ
โProphet: EPE refined at {{close}} ({{time}})โ
Notes & limitations
HTF values are provisional until the HTF bar closes; labels/levels can update while forming.
CISD/EPE are live conditions; they can form and later invalidate within the same HTF bar.
Liquidity relationships vary by market/regime; thin sessions and large gaps can affect clarity.
Educational tool only. No performance claims; no trade signals.
Originality & scope (for protected/invite-only publications)
A single HTF-synchronized engine sequences PDA โ CRT โ Sweep โ CISD โ (EPE) and withholds later steps unless prerequisites are met.
Nearest-PDA persistence and inverse-mitigation enforce focus on the most relevant institutional zone.
Displacement-based risk math ties TP/BE/SL to structure instead of static offsets.
Checklist + relationships table promote consistent, rules-first behavior and reduce discretionary drift.
Attribution: Concepts inspired by ICT (PD arrays/FVGs, CRT, sweeps, displacement, refined entries). Design, integration logic, and risk framework by TakingProphets.
Copeland Dynamic Dominance Matrix System | GForgeCopeland Dynamic Dominance Matrix System | GForge - v1
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๐ COMPREHENSIVE SYSTEM OVERVIEW
The GForge Dynamic BB% TrendSync System represents a revolutionary approach to algorithmic portfolio management, combining cutting-edge statistical analysis, momentum detection, and regime identification into a unified framework. This system processes up to 39 different cryptocurrency assets simultaneously, using advanced mathematical models to determine optimal capital allocation across dynamic market conditions.
Core Innovation: Multi-Dimensional Analysis
Unlike traditional single-asset indicators, this system operates on multiple analytical dimensions:
Momentum Analysis: Dual Bollinger Band Modified Deviation (DBBMD) calculations
Relative Strength: Comprehensive dominance matrix with head-to-head comparisons
Fundamental Screening: Alpha and Beta statistical filtering
Market Regime Detection: Five-component statistical testing framework
Portfolio Optimization: Dynamic weighting and allocation algorithms
Risk Management: Multi-layered protection and regime-based positioning
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๐ง DETAILED COMPONENT BREAKDOWN
1. Dynamic Bollinger Band % Modified Deviation Engine (DBBMD)
The foundation of this system is an advanced oscillator that combines two independent Bollinger Band systems with asymmetric parameters to create unique momentum readings.
Technical Implementation:
[
// BB System 1: Fast-reacting with extended standard deviation
primary_bb1_ma_len = 40 // Shorter MA for responsiveness
primary_bb1_sd_len = 65 // Longer SD for stability
primary_bb1_mult = 1.0 // Standard deviation multiplier
// BB System 2: Complementary asymmetric design
primary_bb2_ma_len = 8 // Longer MA for trend following
primary_bb2_sd_len = 66 // Shorter SD for volatility sensitivity
primary_bb2_mult = 1.7 // Wider bands for reduced noise
Key Features:
Asymmetric Design: The intentional mismatch between MA and Standard Deviation periods creates unique oscillation characteristics that traditional Bollinger Bands cannot achieve
Percentage Scale: All readings are normalized to 0-100% scale for consistent interpretation across assets
Multiple Combination Modes:
BB1 Only: Fast/reactive system
BB2 Only: Smooth/stable system
Average: Balanced blend (recommended)
Both Required: Conservative (both must agree)
Either One: Aggressive (either can trigger)
Mean Deviation Filter: Additional volatility-based layer that measures the standard deviation of the DBBMD% itself, creating dynamic trigger bands
Signal Generation Logic:
// Primary thresholds
primary_long_threshold = 71 // DBBMD% level for bullish signals
primary_short_threshold = 33 // DBBMD% level for bearish signals
// Mean Deviation creates dynamic bands around these thresholds
upper_md_band = combined_bb + (md_mult * bb_std)
lower_md_band = combined_bb - (md_mult * bb_std)
// Signal triggers when DBBMD crosses these dynamic bands
long_signal = lower_md_band > long_threshold
short_signal = upper_md_band < short_threshold
For more information on this BB% indicator, find it here:
2. Revolutionary Dominance Matrix System
This is the system's most sophisticated innovation - a comprehensive framework that compares every asset against every other asset to determine relative strength hierarchies.
Mathematical Foundation:
The system constructs a mathematical matrix where each cell represents whether asset i dominates asset j:
// Core dominance matrix (39x39 for maximum assets)
var matrix dominance_matrix = matrix.new(39, 39, 0)
// For each qualifying asset pair (i,j):
for i = 0 to active_count - 1
for j = 0 to active_count - 1
if i != j
// Calculate price ratio BB% TrendSync for asset_i/asset_j
ratio_array = calculate_price_ratios(asset_i, asset_j)
ratio_dbbmd = calculate_dbbmd(ratio_array)
// Asset i dominates j if ratio is in uptrend
if ratio_dbbmd_state == 1
matrix.set(dominance_matrix, i, j, 1)
Copeland Scoring Algorithm:
Each asset receives a dominance score calculated as:
Dominance Score = Total Wins - Total Losses
// Calculate net dominance for each asset
for i = 0 to active_count - 1
wins = 0
losses = 0
for j = 0 to active_count - 1
if i != j
if matrix.get(dominance_matrix, i, j) == 1
wins += 1
else
losses += 1
copeland_score = wins - losses
array.set(dominance_scores, i, copeland_score)
Head-to-Head Analysis Process:
Ratio Construction: For each asset pair, calculate price_asset_A / price_asset_B
DBBMD Application: Apply the same DBBMD analysis to these ratios
Trend Determination: If ratio DBBMD shows uptrend, Asset A dominates Asset B
Matrix Population: Store dominance relationships in mathematical matrix
Score Calculation: Sum wins minus losses for final ranking
This creates a tournament-style ranking where each asset's strength is measured against all others, not just against a benchmark.
3. Advanced Alpha & Beta Filtering System
The system incorporates fundamental analysis through Capital Asset Pricing Model (CAPM) calculations to filter assets based on risk-adjusted performance.
Alpha Calculation (Excess Return Analysis):
// CAPM Alpha calculation
f_calc_alpha(asset_prices, benchmark_prices, alpha_length, beta_length, risk_free_rate) =>
// Calculate asset and benchmark returns
asset_returns = calculate_returns(asset_prices, alpha_length)
benchmark_returns = calculate_returns(benchmark_prices, alpha_length)
// Get beta for expected return calculation
beta = f_calc_beta(asset_prices, benchmark_prices, beta_length)
// Average returns over period
avg_asset_return = array_average(asset_returns) * 100
avg_benchmark_return = array_average(benchmark_returns) * 100
// Expected return using CAPM: E(R) = Beta * Market_Return + Risk_Free_Rate
expected_return = beta * avg_benchmark_return + risk_free_rate
// Alpha = Actual Return - Expected Return
alpha = avg_asset_return - expected_return
Beta Calculation (Volatility Relationship):
// Beta measures how much an asset moves relative to benchmark
f_calc_beta(asset_prices, benchmark_prices, length) =>
// Calculate return series for both assets
asset_returns =
benchmark_returns =
// Populate return arrays
for i = 0 to length - 1
asset_return = (current_price - previous_price) / previous_price
benchmark_return = (current_bench - previous_bench) / previous_bench
// Calculate covariance and variance
covariance = calculate_covariance(asset_returns, benchmark_returns)
benchmark_variance = calculate_variance(benchmark_returns)
// Beta = Covariance(Asset, Market) / Variance(Market)
beta = covariance / benchmark_variance
Filtering Applications:
Alpha Filter: Only includes assets with alpha above specified threshold (e.g., >0.5% monthly excess return)
Beta Filter: Screens for desired volatility characteristics (e.g., beta >1.0 for aggressive assets)
Combined Screening: Both filters must pass for asset qualification
Dynamic Thresholds: User-configurable parameters for different market conditions
4. Intelligent Tie-Breaking Resolution System
When multiple assets have identical dominance scores, the system employs sophisticated methods to determine final rankings.
Standard Tie-Breaking Hierarchy:
// Primary tie-breaking logic
if score_i == score_j // Tied dominance scores
// Level 1: Compare Beta values (higher beta wins)
beta_i = array.get(beta_values, i)
beta_j = array.get(beta_values, j)
if beta_j > beta_i
swap_positions(i, j)
else if beta_j == beta_i
// Level 2: Compare Alpha values (higher alpha wins)
alpha_i = array.get(alpha_values, i)
alpha_j = array.get(alpha_values, j)
if alpha_j > alpha_i
swap_positions(i, j)
Advanced Tie-Breaking (Head-to-Head Analysis):
For the top 3 performers, an enhanced tie-breaking mechanism analyzes direct head-to-head price ratio performance:
// Advanced tie-breaker for top performers
f_advanced_tiebreaker(asset1_idx, asset2_idx, lookback_period) =>
// Calculate price ratio over lookback period
ratio_history =
for k = 0 to lookback_period - 1
price_ratio = price_asset1 / price_asset2
array.push(ratio_history, price_ratio)
// Apply simplified trend analysis to ratio
current_ratio = array.get(ratio_history, 0)
average_ratio = calculate_average(ratio_history)
// Asset 1 wins if current ratio > average (trending up)
if current_ratio > average_ratio
return 1 // Asset 1 dominates
else
return -1 // Asset 2 dominates
5. Five-Component Aggregate Market Regime Filter
This sophisticated framework combines multiple statistical tests to determine whether market conditions favor trending strategies or require defensive positioning.
Component 1: Augmented Dickey-Fuller (ADF) Test
Tests for unit root presence to distinguish between trending and mean-reverting price series.
// Simplified ADF implementation
calculate_adf_statistic(price_series, lookback) =>
// Calculate first differences
differences =
for i = 0 to lookback - 2
diff = price_series - price_series
array.push(differences, diff)
// Statistical analysis of differences
mean_diff = calculate_mean(differences)
std_diff = calculate_standard_deviation(differences)
// ADF statistic approximation
adf_stat = mean_diff / std_diff
// Compare against threshold for trend determination
is_trending = adf_stat <= adf_threshold
Component 2: Directional Movement Index (DMI)
Classic Wilder indicator measuring trend strength through directional movement analysis.
// DMI calculation for trend strength
calculate_dmi_signal(high_data, low_data, close_data, period) =>
// Calculate directional movements
plus_dm_sum = 0.0
minus_dm_sum = 0.0
true_range_sum = 0.0
for i = 1 to period
// Directional movements
up_move = high_data - high_data
down_move = low_data - low_data
// Accumulate positive/negative movements
if up_move > down_move and up_move > 0
plus_dm_sum += up_move
if down_move > up_move and down_move > 0
minus_dm_sum += down_move
// True range calculation
true_range_sum += calculate_true_range(i)
// Calculate directional indicators
di_plus = 100 * plus_dm_sum / true_range_sum
di_minus = 100 * minus_dm_sum / true_range_sum
// ADX calculation
dx = 100 * math.abs(di_plus - di_minus) / (di_plus + di_minus)
adx = dx // Simplified for demonstration
// Trending if ADX above threshold
is_trending = adx > dmi_threshold
Component 3: KPSS Stationarity Test
Complementary test to ADF that examines stationarity around trend components.
// KPSS test implementation
calculate_kpss_statistic(price_series, lookback, significance_level) =>
// Calculate mean and variance
series_mean = calculate_mean(price_series, lookback)
series_variance = calculate_variance(price_series, lookback)
// Cumulative sum of deviations
cumulative_sum = 0.0
cumsum_squared_sum = 0.0
for i = 0 to lookback - 1
deviation = price_series - series_mean
cumulative_sum += deviation
cumsum_squared_sum += math.pow(cumulative_sum, 2)
// KPSS statistic
kpss_stat = cumsum_squared_sum / (lookback * lookback * series_variance)
// Compare against critical values
critical_value = significance_level == 0.01 ? 0.739 :
significance_level == 0.05 ? 0.463 : 0.347
is_trending = kpss_stat >= critical_value
Component 4: Choppiness Index
Measures market directionality using fractal dimension analysis of price movement.
// Choppiness Index calculation
calculate_choppiness(price_data, period) =>
// Find highest and lowest over period
highest = price_data
lowest = price_data
true_range_sum = 0.0
for i = 0 to period - 1
if price_data > highest
highest := price_data
if price_data < lowest
lowest := price_data
// Accumulate true range
if i > 0
true_range = calculate_true_range(price_data, i)
true_range_sum += true_range
// Choppiness calculation
range_high_low = highest - lowest
choppiness = 100 * math.log10(true_range_sum / range_high_low) / math.log10(period)
// Trending if choppiness below threshold (typically 61.8)
is_trending = choppiness < 61.8
Component 5: Hilbert Transform Analysis
Phase-based cycle detection and trend identification using mathematical signal processing.
// Hilbert Transform trend detection
calculate_hilbert_signal(price_data, smoothing_period, filter_period) =>
// Smooth the price data
smoothed_price = calculate_moving_average(price_data, smoothing_period)
// Calculate instantaneous phase components
// Simplified implementation for demonstration
instant_phase = smoothed_price
delayed_phase = calculate_moving_average(price_data, filter_period)
// Compare instantaneous vs delayed signals
phase_difference = instant_phase - delayed_phase
// Trending if instantaneous leads delayed
is_trending = phase_difference > 0
Aggregate Regime Determination:
// Combine all five components
regime_calculation() =>
trending_count = 0
total_components = 0
// Test each enabled component
if enable_adf and adf_signal == 1
trending_count += 1
if enable_adf
total_components += 1
// Repeat for all five components...
// Calculate trending proportion
trending_proportion = trending_count / total_components
// Market is trending if proportion above threshold
regime_allows_trading = trending_proportion >= regime_threshold
The system only allows asset positions when the specified percentage of components indicate trending conditions. During choppy or mean-reverting periods, the system automatically positions in USD to preserve capital.
6. Dynamic Portfolio Weighting Framework
Six sophisticated allocation methodologies provide flexibility for different market conditions and risk preferences.
Weighting Method Implementations:
1. Equal Weight Distribution:
// Simple equal allocation
if weighting_mode == "Equal Weight"
weight_per_asset = 1.0 / selection_count
for i = 0 to selection_count - 1
array.push(weights, weight_per_asset)
2. Linear Dominance Scaling:
// Linear scaling based on dominance scores
if weighting_mode == "Linear Dominance"
// Normalize scores to 0-1 range
min_score = array.min(dominance_scores)
max_score = array.max(dominance_scores)
score_range = max_score - min_score
total_weight = 0.0
for i = 0 to selection_count - 1
score = array.get(dominance_scores, i)
normalized = (score - min_score) / score_range
weight = 1.0 + normalized * concentration_factor
array.push(weights, weight)
total_weight += weight
// Normalize to sum to 1.0
for i = 0 to selection_count - 1
current_weight = array.get(weights, i)
array.set(weights, i, current_weight / total_weight)
3. Conviction Score (Exponential):
// Exponential scaling for high conviction
if weighting_mode == "Conviction Score"
// Combine dominance score with DBBMD strength
conviction_scores =
for i = 0 to selection_count - 1
dominance = array.get(dominance_scores, i)
dbbmd_strength = array.get(dbbmd_values, i)
conviction = dominance + (dbbmd_strength - 50) / 25
array.push(conviction_scores, conviction)
// Exponential weighting
total_weight = 0.0
for i = 0 to selection_count - 1
conviction = array.get(conviction_scores, i)
normalized = normalize_score(conviction)
weight = math.pow(1 + normalized, concentration_factor)
array.push(weights, weight)
total_weight += weight
// Final normalization
normalize_weights(weights, total_weight)
Advanced Features:
Minimum Position Constraint: Prevents dust allocations below specified threshold
Concentration Factor: Adjustable parameter controlling weight distribution aggressiveness
Dominance Boost: Extra weight for assets exceeding specified dominance thresholds
Dynamic Rebalancing: Automatic weight recalculation on portfolio changes
7. Intelligent USD Management System
The system treats USD as a competing asset with its own dominance score, enabling sophisticated cash management.
USD Scoring Methodologies:
Smart Competition Mode (Recommended):
f_calculate_smart_usd_dominance() =>
usd_wins = 0
// USD beats assets in downtrends or weak uptrends
for i = 0 to active_count - 1
asset_state = get_asset_state(i)
asset_dbbmd = get_asset_dbbmd(i)
// USD dominates shorts and weak longs
if asset_state == -1 or (asset_state == 1 and asset_dbbmd < long_threshold)
usd_wins += 1
// Calculate Copeland-style score
base_score = usd_wins - (active_count - usd_wins)
// Boost during weak market conditions
qualified_assets = count_qualified_long_assets()
if qualified_assets <= active_count * 0.2
base_score := math.round(base_score * usd_boost_factor)
base_score
Auto Short Count Mode:
// USD dominance based on number of bearish assets
usd_dominance = count_assets_in_short_state()
// Apply boost during low activity
if qualified_long_count <= active_count * 0.2
usd_dominance := usd_dominance * usd_boost_factor
Regime-Based USD Positioning:
When the five-component regime filter indicates unfavorable conditions, the system automatically overrides all asset signals and positions 100% in USD, protecting capital during choppy markets.
8. Multi-Asset Infrastructure & Data Management
The system maintains comprehensive data structures for up to 39 assets simultaneously.
Data Collection Framework:
// Full OHLC data matrices (200 bars depth for performance)
var matrix open_data = matrix.new(39, 200, na)
var matrix high_data = matrix.new(39, 200, na)
var matrix low_data = matrix.new(39, 200, na)
var matrix close_data = matrix.new(39, 200, na)
// Real-time data collection
if barstate.isconfirmed
for i = 0 to active_count - 1
ticker = array.get(assets, i)
= request.security(ticker, timeframe.period,
[open , high , low , close ],
lookahead=barmerge.lookahead_off)
// Store in matrices with proper shifting
matrix.set(open_data, i, 0, nz(o, 0))
matrix.set(high_data, i, 0, nz(h, 0))
matrix.set(low_data, i, 0, nz(l, 0))
matrix.set(close_data, i, 0, nz(c, 0))
Asset Configuration:
The system comes pre-configured with 39 major cryptocurrency pairs across multiple exchanges:
Major Pairs: BTC, ETH, XRP, SOL, DOGE, ADA, etc.
Exchange Coverage: Binance, KuCoin, MEXC for optimal liquidity
Configurable Count: Users can activate 2-39 assets based on preferences
Custom Tickers: All asset selections are user-modifiable
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โ๏ธ COMPREHENSIVE CONFIGURATION GUIDE
Portfolio Management Settings
Maximum Portfolio Size (1-10):
Conservative (1-2): High concentration, captures strong trends
Balanced (3-5): Moderate diversification with trend focus
Diversified (6-10): Lower concentration, broader market exposure
Dominance Clarity Threshold (0.1-1.0):
Low (0.1-0.4): Prefers diversification, holds multiple assets frequently
Medium (0.5-0.7): Balanced approach, context-dependent allocation
High (0.8-1.0): Concentration-focused, single asset preference
Signal Generation Parameters
DBBMD Thresholds:
// Standard configuration
primary_long_threshold = 71 // Conservative: 75+, Aggressive: 65-70
primary_short_threshold = 33 // Conservative: 25-30, Aggressive: 35-40
// BB System parameters
bb1_ma_len = 40 // Fast system: 20-50
bb1_sd_len = 65 // Stability: 50-80
bb2_ma_len = 8 // Trend: 60-100
bb2_sd_len = 66 // Sensitivity: 10-20
Risk Management Configuration
Alpha/Beta Filters:
Alpha Threshold: 0.0-2.0% (higher = more selective)
Beta Threshold: 0.5-2.0 (1.0+ for aggressive assets)
Calculation Periods: 20-50 bars (longer = more stable)
Regime Filter Settings:
Trending Threshold: 0.3-0.8 (higher = stricter trend requirements)
Component Lookbacks: 30-100 bars (balance responsiveness vs stability)
Enable/Disable: Individual component control for customization
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๐ PERFORMANCE TRACKING & VISUALIZATION
Real-Time Dashboard Features
The compact dashboard provides essential information:
Current Holdings: Asset names and allocation percentages
Dominance Score: Current position's relative strength ranking
Active Assets: Qualified long signals vs total asset count
Returns: Total portfolio performance percentage
Maximum Drawdown: Peak-to-trough decline measurement
Trade Count: Total portfolio transitions executed
Regime Status: Current market condition assessment
Comprehensive Ranking Table
The left-side table displays detailed asset analysis:
Ranking Position: Numerical order by dominance score
Asset Symbol: Clean ticker identification with color coding
Dominance Score: Net wins minus losses in head-to-head comparisons
Win-Loss Record: Detailed breakdown of dominance relationships
DBBMD Reading: Current momentum percentage with threshold highlighting
Alpha/Beta Values: Fundamental analysis metrics when filters enabled
Portfolio Weight: Current allocation percentage in signal portfolio
Execution Status: Visual indicator of actual holdings vs signals
Visual Enhancement Features
Color-Coded Assets: 39 distinct colors for easy identification
Regime Background: Red tinting during unfavorable market conditions
Dynamic Equity Curve: Portfolio value plotted with position-based coloring
Status Indicators: Symbols showing execution vs signal states
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๐ ADVANCED TECHNICAL FEATURES
State Persistence System
The system maintains asset states across bars to prevent excessive switching:
// State tracking for each asset and ratio combination
var array asset_states = array.new(1560, 0) // 39 * 40 ratios
// State changes only occur on confirmed threshold breaks
if long_crossover and current_state != 1
current_state := 1
array.set(asset_states, asset_index, 1)
else if short_crossover and current_state != -1
current_state := -1
array.set(asset_states, asset_index, -1)
Transaction Cost Integration
Realistic modeling of trading expenses:
// Transaction cost calculation
transaction_fee = 0.4 // Default 0.4% (fees + slippage)
// Applied on portfolio transitions
if should_execute_transition
was_holding_assets = check_current_holdings()
will_hold_assets = check_new_signals()
// Charge fees for meaningful transitions
if transaction_fee > 0 and (was_holding_assets or will_hold_assets)
fee_amount = equity * (transaction_fee / 100)
equity -= fee_amount
total_fees += fee_amount
Dynamic Memory Management
Optimized data structures for performance:
200-Bar History: Sufficient for calculations while maintaining speed
Matrix Operations: Efficient storage and retrieval of multi-asset data
Array Recycling: Memory-conscious data handling for long-running backtests
Conditional Calculations: Skip unnecessary computations during initialization
12H 30 assets portfolio
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๐จ SYSTEM LIMITATIONS & TESTING STATUS
CURRENT DEVELOPMENT PHASE: ACTIVE TESTING & OPTIMIZATION
This system represents cutting-edge algorithmic trading technology but remains in continuous development. Key considerations:
Known Limitations:
Requires significant computational resources for 39-asset analysis
Performance varies significantly across different market conditions
Complex parameter interactions may require extensive optimization
Slippage and liquidity constraints not fully modeled for all assets
No consideration for market impact in large position sizes
Areas Under Active Development:
Enhanced regime detection algorithms
Improved transaction cost modeling
Additional portfolio weighting methodologies
Machine learning integration for parameter optimization
Cross-timeframe analysis capabilities
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๐ ANTI-REPAINTING ARCHITECTURE & LIVE TRADING READINESS
One of the most critical aspects of any trading system is ensuring that signals and calculations are based on confirmed, historical data rather than current bar information that can change throughout the trading session. This system implements comprehensive anti-repainting measures to ensure 100% reliability for live trading .
The Repainting Problem in Trading Systems
Repainting occurs when an indicator uses current, unconfirmed bar data in its calculations, causing:
False Historical Signals: Backtests appear better than reality because calculations change as bars develop
Live Trading Failures: Signals that looked profitable in testing fail when deployed in real markets
Inconsistent Results: Different results when running the same indicator at different times during a trading session
Misleading Performance: Inflated win rates and returns that cannot be replicated in practice
GForge Anti-Repainting Implementation
This system eliminates repainting through multiple technical safeguards:
1. Historical Data Usage for All Calculations
// CRITICAL: All calculations use PREVIOUS bar data (note the offset)
= request.security(ticker, timeframe.period,
[open , high , low , close , close],
lookahead=barmerge.lookahead_off)
// Store confirmed previous bar OHLC for calculations
matrix.set(open_data, i, 0, nz(o1, 0)) // Previous bar open
matrix.set(high_data, i, 0, nz(h1, 0)) // Previous bar high
matrix.set(low_data, i, 0, nz(l1, 0)) // Previous bar low
matrix.set(close_data, i, 0, nz(c1, 0)) // Previous bar close
// Current bar close only for visualization
matrix.set(current_prices, i, 0, nz(c0, 0)) // Live price display
2. Confirmed Bar State Processing
// Only process data when bars are confirmed and closed
if barstate.isconfirmed
// All signal generation and portfolio decisions occur here
// using only historical, unchanging data
// Shift historical data arrays
for i = 0 to active_count - 1
for bar = math.min(data_bars, 199) to 1
// Move confirmed data through historical matrices
old_data = matrix.get(close_data, i, bar - 1)
matrix.set(close_data, i, bar, old_data)
// Process new confirmed bar data
calculate_all_signals_and_dominance()
3. Lookahead Prevention
// Explicit lookahead prevention in all security calls
request.security(ticker, timeframe.period, expression,
lookahead=barmerge.lookahead_off)
// This ensures no future data can influence current calculations
// Essential for maintaining signal integrity across all timeframes
4. State Persistence with Historical Validation
// Asset states only change based on confirmed threshold breaks
// using historical data that cannot change
var array asset_states = array.new(1560, 0)
// State changes use only confirmed, previous bar calculations
if barstate.isconfirmed
=
f_calculate_enhanced_dbbmd(confirmed_price_array, ...)
// Only update states after bar confirmation
if long_crossover_confirmed and current_state != 1
current_state := 1
array.set(asset_states, asset_index, 1)
Live Trading vs. Backtesting Consistency
The system's architecture ensures identical behavior in both environments:
Backtesting Mode:
Uses historical offset data for all calculations
Processes confirmed bars with `barstate.isconfirmed`
Maintains identical signal generation logic
No access to future information
Live Trading Mode:
Uses same historical offset data structure
Waits for bar confirmation before signal updates
Identical mathematical calculations and thresholds
Real-time price display without affecting signals
Technical Implementation Details
Data Collection Timing
// Example of proper data collection timing
if barstate.isconfirmed // Wait for bar to close
// Collect PREVIOUS bar's confirmed OHLC data
for i = 0 to active_count - 1
ticker = array.get(assets, i)
// Get confirmed previous bar data (note offset)
=
request.security(ticker, timeframe.period,
[open , high , low , close , close],
lookahead=barmerge.lookahead_off)
// ALL calculations use prev_* values
// current_close only for real-time display
portfolio_calculations_use_previous_bar_data()
Signal Generation Process
// Signal generation workflow (simplified)
if barstate.isconfirmed and data_bars >= minimum_required_bars
// Step 1: Calculate DBBMD using historical price arrays
for i = 0 to active_count - 1
historical_prices = get_confirmed_price_history(i) // Uses offset data
= calculate_dbbmd(historical_prices)
update_asset_state(i, state)
// Step 2: Build dominance matrix using confirmed data
calculate_dominance_relationships() // All historical data
// Step 3: Generate portfolio signals
new_portfolio = generate_target_portfolio() // Based on confirmed calculations
// Step 4: Compare with previous signals for changes
if portfolio_signals_changed()
execute_portfolio_transition()
Verification Methods for Users
Users can verify the anti-repainting behavior through several methods:
1. Historical Replay Test
Run the indicator on historical data
Note signal timing and portfolio changes
Replay the same period - signals should be identical
No retroactive changes in historical signals
2. Intraday Consistency Check
Load indicator during active trading session
Observe that previous day's signals remain unchanged
Only current day's final bar should show potential signal changes
Refresh indicator - historical signals should be identical
Live Trading Deployment Considerations
Data Quality Assurance
Exchange Connectivity: Ensure reliable data feeds for all 39 assets
Missing Data Handling: System includes safeguards for data gaps
Price Validation: Automatic filtering of obvious price errors
Timeframe Synchronization: All assets synchronized to same bar timing
Performance Impact of Anti-Repainting Measures
The robust anti-repainting implementation requires additional computational resources:
Memory Usage: 200-bar historical data storage for 39 assets
Processing Delay: Signals update only after bar confirmation
Calculation Overhead: Multiple historical data validations
Alert Timing: Slight delay compared to current-bar indicators
However, these trade-offs are essential for reliable live trading performance and accurate backtesting results.
Critical: Equity Curve Anti-Repainting Architecture
The most sophisticated aspect of this system's anti-repainting design is the temporal separation between signal generation and performance calculation . This creates a realistic trading simulation that perfectly matches live trading execution.
The Timing Sequence
// STEP 1: Store what we HELD during the current bar (for performance calc)
if barstate.isconfirmed
// Record positions that were active during this bar
array.clear(held_portfolio)
array.clear(held_weights)
for i = 0 to array.size(execution_portfolio) - 1
array.push(held_portfolio, array.get(execution_portfolio, i))
array.push(held_weights, array.get(execution_weights, i))
// STEP 2: Calculate performance based on what we HELD
portfolio_return = 0.0
for i = 0 to array.size(held_portfolio) - 1
held_asset = array.get(held_portfolio, i)
held_weight = array.get(held_weights, i)
// Performance from current_price vs reference_price
// This is what we ACTUALLY earned during this bar
if held_asset != "USD"
current_price = get_current_price(held_asset) // End of bar
reference_price = get_reference_price(held_asset) // Start of bar
asset_return = (current_price - reference_price) / reference_price
portfolio_return += asset_return * held_weight
// STEP 3: Apply return to equity (realistic timing)
equity := equity * (1 + portfolio_return)
// STEP 4: Generate NEW signals for NEXT period (using confirmed data)
= f_generate_target_portfolio()
// STEP 5: Execute transitions if signals changed
if signal_changed
// Update execution_portfolio for NEXT bar
array.clear(execution_portfolio)
array.clear(execution_weights)
for i = 0 to array.size(new_signal_portfolio) - 1
array.push(execution_portfolio, array.get(new_signal_portfolio, i))
array.push(execution_weights, array.get(new_signal_weights, i))
Why This Prevents Equity Curve Repainting
Performance Attribution: Returns are calculated based on positions that were **actually held** during each bar, not future signals
Signal Timing: New signals are generated **after** performance calculation, affecting only **future** bars
Realistic Execution: Mimics real trading where you earn returns on current positions while planning future moves
No Retroactive Changes: Once a bar closes, its performance contribution to equity is permanent and unchangeable
The One-Bar Offset Mechanism
This system implements a critical one-bar timing offset:
// Bar N: Performance Calculation
// ================================
// 1. Calculate returns on positions held during Bar N
// 2. Update equity based on actual holdings during Bar N
// 3. Plot equity point for Bar N (based on what we HELD)
// Bar N: Signal Generation
// ========================
// 4. Generate signals for Bar N+1 (using confirmed Bar N data)
// 5. Send alerts for what will be held during Bar N+1
// 6. Update execution_portfolio for Bar N+1
// Bar N+1: The Cycle Continues
// =============================
// 1. Performance calculated on positions from Bar N signals
// 2. New signals generated for Bar N+2
Alert System Timing
The alert system reflects this sophisticated timing:
Transaction Cost Realism
Even transaction costs follow realistic timing:
// Fees applied when transitioning between different portfolios
if should_execute_transition
// Charge fees BEFORE taking new positions (realistic timing)
if transaction_fee > 0
fee_amount = equity * (transaction_fee / 100)
equity -= fee_amount // Immediate cost impact
total_fees += fee_amount
// THEN update to new portfolio
update_execution_portfolio(new_signals)
transitions += 1
// Fees reduce equity immediately, affecting all future calculations
// This matches real trading where fees are deducted upon execution
LIVE TRADING CERTIFICATION:
This system has been specifically designed and tested for live trading deployment. The comprehensive anti-repainting measures ensure that:
Backtesting results accurately represent real trading potential
Signals are generated using only confirmed, historical data
No retroactive changes can occur to previously generated signals
Portfolio transitions are based on reliable, unchanging calculations
Performance metrics reflect realistic trading outcomes including proper timing
Users can deploy this system with confidence that live trading results will closely match backtesting performance, subject to normal market execution factors such as slippage and liquidity.
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โก ALERT SYSTEM & AUTOMATION
The system provides comprehensive alerting for automation and monitoring:
Available Alert Conditions
Portfolio Signal Change: Triggered when new portfolio composition is generated
Regime Override Active: Alerts when market regime forces USD positioning
Individual Asset Signals: Can be configured for specific asset transitions
Performance Thresholds: Drawdown or return-based notifications
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๐ BACKTESTING & PERFORMANCE ANALYSIS
8 Comprehensive Metrics Tracking
The system maintains detailed performance statistics:
Equity Curve: Real-time portfolio value progression
Returns Calculation: Total and annualized performance metrics
Drawdown Analysis: Peak-to-trough decline measurements
Transaction Counting: Portfolio transition frequency
Fee Tracking: Cumulative transaction cost impact
Win Rate Analysis: Success rate of position changes
Backtesting Configuration
// Backtesting parameters
initial_capital = 10000.0 // Starting capital
use_custom_start = true // Enable specific start date
custom_start = timestamp("2023-09-01") // Backtest beginning
transaction_fee = 0.4 // Combined fees and slippage %
// Performance calculation
total_return = (equity - initial_capital) / initial_capital * 100
current_drawdown = (peak_equity - equity) / peak_equity * 100
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๐ง TROUBLESHOOTING & OPTIMIZATION
Common Configuration Issues
Insufficient Data: Ensure 100+ bars available before start date
[*} Not Compiling: Go on an asset's price chart with 2 or 3 years of data to
make the system compile or just simply reapply the indicator again
Too Many Assets: Reduce active count if experiencing timeouts
Regime Filter Too Strict: Lower trending threshold if always in USD
Excessive Switching: Increase MD multiplier or adjust thresholds
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๐ก USER FEEDBACK & ENHANCEMENT REQUESTS
The continuous evolution of this system depends heavily on user experience and community feedback. Your insights will help motivate me for new improvements and new feature developments.
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โ๏ธ FINAL COMPREHENSIVE RISK DISCLAIMER
TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This indicator is a sophisticated analytical tool designed for educational and research purposes. Important warnings and considerations:
System Limitations:
No algorithmic system can guarantee profitable outcomes
Complex systems may fail in unexpected ways during extreme market events
Historical backtesting does not account for all real-world trading challenges
Slippage, liquidity constraints, and market impact can significantly affect results
System parameters require careful optimization and ongoing monitoring
The creator and distributor of this indicator assume no liability for any financial losses, system failures, or adverse outcomes resulting from its use. This tool is provided "as is" without any warranties, express or implied.
By using this indicator, you acknowledge that you have read, understood, and agreed to assume all risks associated with algorithmic trading and cryptocurrency investments.
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (๐):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (๐ง):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (๐ฆ):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (โ๏ธ):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (๐ก๏ธ):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. Itโs designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
TIME-SPLT ACADEMY INDICATOR# TIME-SPLT ACADEMY CISD + FVG + TSM FRACTALS - Comprehensive Market Structure Analysis Tool
## Overview
This indicator combines three essential market structure analysis components into a unified trading tool: Change in State Direction (CISD), Fair Value Gaps (FVG), and TSM Fractals. This integration provides traders with a complete framework for identifying market structure breaks, price imbalances, and key pivot levels on any timeframe.
## Component 1: CISD (Change in State Direction)
**What it is:** CISD identifies significant breaks in market structure by tracking when price decisively breaks above previous swing highs (bullish CISD) or below previous swing lows (bearish CISD). This concept is fundamental to understanding trend changes and continuation patterns.
**How it works:**
- Monitors swing highs and lows using customizable pivot periods
- Tracks when price closes above a previous swing high (bullish structure break)
- Tracks when price closes below a previous swing low (bearish structure break)
- Draws horizontal lines from the pivot point to the break point with "CISD" labels
- Works on multiple timeframes simultaneously
**Trading Applications:**
- Identifies trend changes and continuation signals
- Provides entry signals on structure breaks
- Helps determine market bias and direction
## Component 2: FVG (Fair Value Gaps)
**What it is:** Fair Value Gaps are price imbalances that occur when there's a gap between the high of one candle and the low of another candle two periods later, with the middle candle not filling this gap. These represent areas where price moved inefficiently and often return to "fill" the gap.
**How it works:**
- Analyzes 3-candle patterns to identify gaps
- Bearish FVG: Gap between low and high where price dropped leaving unfilled space above
- Bullish FVG: Gap between high and low where price rose leaving unfilled space below
- Tracks 8 different candle body combinations for each direction (up, down, doji patterns)
- Monitors gap mitigation when price returns to fill the imbalance
- Changes color when gaps are partially or fully mitigated
**Gap Detection Logic:**
- Bearish FVG patterns: DDD, DDJ, JDD, UDJ, JDU, UDD, DDU, UDU
- Bullish FVG patterns: DUD, DUJ, JUD, UUJ, JUU, UUD, DUU, UUU
- (D=Down candle, U=Up candle, J=Doji candle)
**Trading Applications:**
- High-probability reversal zones when price returns to FVGs
- Support and resistance levels
- Target areas for limit orders
- Risk management reference points
## Component 3: TSM Fractals
**What it is:** TSM Fractals identify significant pivot highs and lows using Williams Fractal methodology. These mark potential reversal points and key support/resistance levels.
**How it works:**
- Identifies fractal highs: peaks where the center candle's high is higher than surrounding candles
- Identifies fractal lows: valleys where the center candle's low is lower than surrounding candles
- Uses customizable lookback periods (default 15) for fractal identification
- Displays horizontal lines with "$" symbols at fractal levels
- Maintains a configurable number of recent fractals on the chart
**Trading Applications:**
- Key support and resistance levels
- Potential reversal zones
- Confluence with other analysis tools
- Stop loss placement reference points
## Why This Combination Works
**Synergistic Analysis:** Each component provides different but complementary information:
1. **CISD** shows when market structure changes, indicating trend shifts or continuation
2. **FVGs** reveal where price has moved inefficiently and may return for rebalancing
3. **Fractals** highlight key pivot points that often act as support/resistance
**Trading Edge:** The combination allows for:
- **Entry Confirmation:** Wait for CISD breaks near unfilled FVGs at fractal levels
- **Risk Management:** Use FVG boundaries and fractal levels for stop placement
- **Target Selection:** Project moves to opposite FVGs or fractal levels
- **Market Context:** Understand whether you're trading with or against structure
## Key Features
**Multi-Timeframe CISD:**
- Customizable timeframe settings (Minute, Hour, Day, Week, Month)
- Adjustable swing length for pivot identification
- Customizable line styles, widths, and colors
- Optional alerts on structure breaks
**Advanced FVG Management:**
- Automatic gap size filtering
- Real-time mitigation tracking
- Color-coded active vs. mitigated gaps
- Optional pip value labels
- Large gap alerts for significant imbalances
**Intelligent Fractal Display:**
- Configurable fractal periods
- Maximum fractal count management
- Clean visual presentation
- Historical fractal preservation
## Settings & Customization
**CISD Settings:**
- Timeframe selection and multipliers
- Swing length adjustment (default 7)
- Line styling options
- Color customization for bullish/bearish breaks
- Alert toggle options
**FVG Settings:**
- Show/hide toggles for each direction
- Minimum gap size filtering
- Alert threshold for large gaps
- Color schemes for active and mitigated gaps
- Optional size labels in pips
**Fractal Settings:**
- Fractal period adjustment (default 15)
- Maximum display count (default 10)
- Show/hide toggle
## Educational Value
This indicator teaches traders to:
- Understand market structure concepts
- Recognize price inefficiencies
- Identify key pivot points
- Combine multiple analysis methods
- Develop systematic trading approaches
## Use Cases
**Swing Trading:** Identify major structure breaks with FVG confluence
**Day Trading:** Use lower timeframe CISDs with intraday FVGs
**Scalping:** Quick entries at FVG mitigation near fractal levels
**Position Trading:** Higher timeframe structure analysis with major FVGs
## Technical Implementation
- Utilizes Pine Script v6 for optimal performance
- Efficient array management for historical data
- Real-time calculations without repainting
- Memory-optimized box and line management
- Multi-timeframe data handling with proper security functions
This comprehensive tool eliminates the need for multiple separate indicators, providing everything needed for complete market structure analysis in one cohesive package. The educational component helps traders understand not just what the signals are, but why they work and how to use them effectively in different market conditions.
