Precision ORB FrameworkPrecision ORB Clean v17 is part of the Precision ORB Framework — a structured futures trading model developed around opening range behavior, VWAP positioning, liquidity sweeps, market acceptance, and clean breakout confirmation.
This framework was built specifically for NQ and index futures execution, combining:
• Opening Range Breakouts (ORB)
• VWAP trend alignment
• Liquidity sweep detection
• Acceptance candle confirmation
• Trend bias filtering
• REV? rejection warnings
• Dynamic TP1 / TP2 targeting
• Price discovery grid targets
The companion “Precision Entry Assistant” indicator was developed alongside this framework to assist with lower timeframe execution, market state filtering, and cleaner trade timing.
This is not intended to be a prediction tool or signal-selling script. The framework is designed to help traders structure decision-making around momentum, liquidity, volatility expansion, and disciplined execution.
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AetherEdge - Liquidity Sweep & Raid Detector🖊️ Overview
AetherEdge Liquidity Sweep & Raid Detector is a professional-grade smart money analysis tool that exposes institutional stop hunts (liquidity raids) and Equal Highs/Lows liquidity pools in real time. By integrating ATR-based significance filters, three-tier sweep confirmation modes, and projected stop hunt zones, it visualizes "who, where, and why" liquidity is being taken—enabling traders to follow institutional footprints with both reversal and continuation entries.
🔶 Key Features
ATR-Adaptive Pivot Detection: Extracts only meaningful swings
Equal Highs/Lows Detection: Auto-marks liquidity pools
3 Sweep Confirmation Modes: Wick Only / Close Back / Wick + Close Back
Stop Hunt Zone Projection: Boxes mark institutional revisit zones
3 Zone Management Modes: Keep / Fade on Touch / Remove on Touch
Reversal Candle Requirement: Filters false sweeps
Break of Structure (BOS): Auto-detects structural shifts
ATR Wick Size Filter: Eliminates shallow sweeps
Pending Sweep System: Configurable max-bars window for close-back confirmation
Statistics Dashboard: Bull/Bear sweep counts, EQH/EQL, active hot zones
🧠 Technical Architecture
This indicator implements core ICT/SMC concepts via pivot array management and a pending state machine to deliver a professional liquidity-tracking engine.
Significance Pivot Layer:
ta.pivothigh/low for confirmed swing detection
ATR × multiplier filters out "noise swings"
Arrays manage 50 historical pivots (price, bar, swept flag)
Equal Highs/Lows Detection:
Scans pivots within eqLookback
|currentPivot - prevPivot| ≤ ATR × tolerance for equality
Draws horizontal line at midpoint with optional right-extension
Sweep Detection Engine:
Wick Only: Wick breaks pivot + immediate inside close + reversal confirmation
Close Back: Wick breaks, then close returns inside within sweepMaxBars
Wick + Close Back: Both conditions met (highest precision)
Pending arrays handle multi-stage confirmation asynchronously
Stop Hunt Zones:
Box generated at sweep point with zoneHeightATR height
Extended zoneExtend bars right
Lifecycle managed via Fade / Remove / Keep on touch
BOS Detection: Unswept pivot's close-break marks structural shift
Memory Management: maxObjects FIFO-prunes all lines/labels/boxes
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (15M–1H): pivLen=10, atrMinMult=0.3, sweepMode=Wick + Close Back (standard)
ETH (5M–15M): pivLen=8, sweepMaxBars=4 for faster confirmation
SOL (high-vol): atrMinMult=0.5, sweepMinWick=0.4 for stricter filter
XRP (range): eqTolerance=0.15 to relax equality detection
Sweep Mode Selection:
Wick Only: High frequency, day trading / scalping
Close Back: Medium frequency, most balanced
Wick + Close Back: Highest precision, swing trading (recommended)
Zone Management:
Fade on Touch: Preserves history, tracks revisits (recommended)
Remove on Touch: Cleaner charts
Keep: Full retention, long-term analysis
Tuning:
Too many signals: atrMinMult=0.5, sweepMinWick=0.5
Too few signals: requireReverse=false, sweepMaxBars=5
Better EQ accuracy: eqMinDistance=5 to suppress clusters
💡 How to Use in Practice
Bullish Sweep (Low Grab) → Long: Buy the bounce after institutions raid downside stops
Bearish Sweep (High Grab) → Short: Sell after upside stop hunt
EQH/EQL → Liquidity Targets: Price likely magnetizes here
Stop Hunt Zone Revisit: Once-swept zones often retest
BOS + Sweep Combined: Strongest structural shift confirmation
AetherEdge Synergy:
WaveTrend Enhanced OS Extreme + Bullish Sweep = max-conviction bottom
Self-Evolving S/R Grid strong support + Low Grab = institutional-sync entry
SuperTrend Multi-Factor Bull flip + BOS Up = trend ignition capture
Multi-TF Strategy: 1H EQH for liquidity map → 15M sweep for entry → 5M for precision timing
⚠️ Important Notes
Pivot Lag: pivLen-bar delay inherent (no real-time detection)
ATR Filter Dependency: Sweep detection drops in low-volatility regimes
Close Back Pending: Auto-cancels after sweepMaxBars—watch for missed signals
EQ Over-detection: Large eqLookback may match outdated pivots
Zone Revisit: Probabilistic, not guaranteed
Sweeps in Strong Trends: Can be confused with breakouts—context confirmation required
🚨 Disclaimer
This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. Liquidity sweeps and stop hunt zones represent probabilistic market behavior patterns and do not guarantee profits. All trading decisions are made at your own risk and should be accompanied by proper risk management. Indicatore
AetherEdge - Volume Profile Session Analyzer🖊️ Overview
AetherEdge Volume Profile Session Analyzer is a professional-grade session analysis engine that constructs independent volume profiles for the three major global sessions—Asia / London / New York. It computes and visualizes POC (Point of Control), VAH (Value Area High), and VAL (Value Area Low) for each session in real time, clearly mapping the institutional trading concentration zones. This is a world-class market structure analysis tool that captures distinct liquidity layers across timezones.
🔶 Key Features
3-Session Independent Profiling: Constructs separate volume distributions for Asia / London / NY
Dynamic POC / VAH / VAL Calculation: Extracts institutional zones from price×volume heatmaps
Customizable Resolution: 10–150 bin precision grid for adjustable analytical granularity
Adjustable Value Area %: Define trading concentration zone with 50–95% custom setting
Session Background Shading: Instant visual identification of time periods
Previous Session Level Extension: Project past POC/VA levels forward as S/R
Timezone Support: GMT, Exchange, NY, London, Tokyo selectable
Integrated Dashboard: One-glance display of key levels across 3 sessions
AetherEdge Premium UI: Dark mode support, refined color palette
🧠 Technical Architecture
This indicator is an advanced market microstructure analysis system that decomposes Volume Profile theory along the time axis (sessions).
Session Detection Logic: Timezone-aware session judgment via time(timeframe.period, sessionStr, tzStr). Start/End edge detection captures precise session boundaries.
Bin Array Architecture: Each session maintains an independent array volume bin. Auto-resets at session start, archived as finalized values at end.
Volume Distribution Algorithm:
Splits each bar's price range (high-low) into corresponding bins within session range
binSize = sessionRange / bins calculates each bin's price width
perBin = barVolume / nCells evenly distributes bar volume across relevant bins
Results in true price-by-volume distribution
POC Calculation: Extracts maximum value index from bin array, identifies the price level with highest trading concentration
Value Area Calculation: Expands up/down from POC until cumulative volume reaches vaPct (default 70%). Defines the consensus zone of market participants.
Dynamic Range Update: hiRef = max(prevHi, currentHigh) continuously tracks session highs/lows
Persistence Management: var variables retain previous session values, drawing extension lines after session ends
Multi-Object Management: array, array, array integrate drawing objects per session
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults (24/7 markets):
BTC (1H–4H): bins=50, vaPct=70, high precision standard
ETH (15M–1H): bins=70 for high-resolution profile
SOL (high-vol): bins=40 for smoothing, noise reduction
XRP (mixed liquidity): bins=60, vaPct=68
Timezone Recommendations:
Crypto: GMT for global standard analysis
Stock CFDs: Exchange TZ
FX: America/New_York for major session sync
Tuning Guide:
Detailed Analysis: bins=100+ for precise institutional order detection
Macro View: bins=30 to extract major zones only
Wide VA: vaPct=80 for breakout point identification
Narrow VA: vaPct=60 for core zone clarity
Day Trading: extendPrev=true to leverage prior session boundaries
Swing: profileWidth=50 for enhanced visibility
💡 How to Use in Practice
POC Touch Reaction: Institutional position concentration zone—critical for pullback long / rebound short
VAH/VAL Break: Value Area breakthrough is a strong new trend initiation signal
Session Transition: Use POC position at London→NY handoff for day-trade direction bias
Asia POC Utility: Critical S/R during European/American sessions, range-trade baseline
VA Overlap Zones: Price areas where multiple session VAs coincide = ultra-strong S/R
Naked POC: Untested POCs act as future magnetic targets
AetherEdge Synergy:
Self-Evolving S/R Grid strong line + session POC alignment = ultra-high-conviction zone
Hull MA Ribbon Strong Bull + VAH break = perfect long entry
Order Block Detector OB + previous session VAL = institutional accumulation zone
Multi-TF Application: Confirm session VA on 4H chart → precision entry on 15M
⚠️ Important Notes
Session Time Configuration: Manual adjustment recommended at DST transitions
Initial Data Accumulation: Requires at least one completed session before display
Low-Volume Symbols: Profile precision degrades; reduce bin count
Bin Resolution Trade-off: Higher resolution = more precision but heavier rendering
Timezone Consistency: GMT/Exchange misconfiguration causes session boundary drift
max_boxes_count Limit: Old sessions auto-deleted on long-term display
🚨 Disclaimer
This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. Volume profile and session analysis is statistical visualization based on historical market data and does not guarantee future price behavior. All trading decisions are made at your own risk and should be accompanied by proper risk management.
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AetherEdge - Order Block + FVG Detector🖊️ Overview
AetherEdge Order Block + FVG Detector is a high-precision engine that integrates the two core elements of Smart Money Concepts (SMC)—Order Blocks (OB) and Fair Value Gaps (FVG)—to visualize institutional footprints on your chart. It features BOS (Break of Structure)-driven OB identification, automatic price gap mapping, mitigation tracking, and a beginner-friendly "Entry Zone" mode. The zone-based strategies practiced by professional traders are reproduced visually and systematically, all within a single indicator.
🔶 Key Features
BOS-Driven Order Block Detection: Auto-identifies the last opposite candle before structure break
Bullish/Bearish OB Support: Functions in both long and short scenarios
Auto Fair Value Gap Detection: Captures 3-bar price imbalances
Mitigation Tracking: Wick/Close toggle, Touch/50%/Full thresholds
Entry Zone Mode: 4 modes—OB only / FVG only / Either / Both overlap
Auto BOS Line Drawing: Visualizes structure break points
Active Zone Management: obMaxShow/fvgMaxShow retain only latest zones
Right-Side Auto Extension: Untouched zones extended forward
Integrated Dashboard: Active OB/FVG count, latest BOS direction
Premium AetherEdge UI: Dark-mode professional design
🧠 Technical Architecture
This indicator crystallizes SMC theory into implementable logic—a sophisticated zone detection engine.
Structure Detection (BOS Engine): ta.pivothigh/pivotlow detects swing points based on obSwingLen. lastSwingH/L updates dynamically; close > lastSwingH (with prior bar below) fires bosUp, and the inverse fires bosDown.
Order Block Identification Logic: On BOS trigger, scans up to 20 bars back for the "last opposite candle." On bullish BOS, the last bearish candle becomes Bull OB; on bearish BOS, the last bullish candle becomes Bear OB. This is the classical SMC logic capturing institutional "final accumulation/distribution points."
OB Data Structure: Custom type OB integrates box/top/bot/createdBar/bullish/mitigated/label. Array-based lifecycle management.
FVG Detection Logic: 3-bar pattern detects high < low (bullish FVG) or low > high (bearish FVG). fvgMinSize filters minimum gap size.
Mitigation Logic:
Wick mode: Consumed when wick reaches zone
Close mode: Consumed when close penetrates zone (stricter)
FVG uses Touch/50%/Full for graded judgment
Entry Zone Composition: 4 signal logics from OB+FVG overlap/standalone/selection modes. Both overlap = strongest confluence.
Dynamic Zone Management: Zones exceeding max array size auto-deleted, keeping chart clean.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H–Daily): obSwingLen=10, fvgMinSize=0.1, obMitigate=Wick (standard)
ETH (1H–4H): obSwingLen=8, fvgMinSize=0.05 for slightly higher sensitivity
SOL (High-Vol): obSwingLen=15, fvgMinSize=0.2 for large zones only
XRP (Range): obMitigate=Close, fvgFill=50% for strict judgment
Tuning Guide:
Day Trading: obSwingLen=8-10, obExtend=30-50, primary 1H
Swing: obSwingLen=20-30, obExtend=100 for macro zones
Scalping: obSwingLen=5, fvgMinSize=0.0 for short-term zones
Conservative Traders: ezRequire="Both overlap" for OB+FVG overlap only
Aggressive Traders: ezRequire="Either" to maximize signals
Excess Zones: obMaxShow=3, fvgMaxShow=5 to prioritize precision
Strong Trends: obSwingLen=15 for major structure only, avoiding fake BOS
💡 How to Use in Practice
Bull OB Retest: Price returning to Bull OB after BOS = golden long entry timing
FVG Fill Strategy: 50% gap fill confirms reversal, enter in trend direction
OB+FVG Overlap Zones: Both overlap mode pinpoints highest-conviction entries
BOS Line Break Confirmation: Retest after structure break confirms trend continuation
Multi-Timeframe Synergy: Higher-TF OB for direction + lower-TF FVG for precise entry
AetherEdge Synergy:
Self-Evolving S/R Grid strong line + OB overlap = triple confirmation
RSI Divergence Hunter signal + OB retest = max reversal conviction
Squeeze Breaker breakout direction + FVG fill = entry timing decision
Target Setting: Use opposite OB/FVG as profit-taking targets
⚠️ Important Notes
BOS Confirmation Lag: obSwingLen bars of confirmation delay; some real-time uncertainty
OB Search Range: No OB generated if no opposite candle exists within past 20 bars
Post-Mitigation Effect: Consumed OB/FVG tend to weaken; prioritize fresh zones
Consecutive BOS in Strong Trends: Mass OB generation possible in short periods; recommend limiting via obMaxShow
FVG Min Size: fvgMinSize=0 detects micro-gaps; tune based on market conditions
🚨 Disclaimer
This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. Order Block and FVG detection is a structural evaluation based on historical price data and does not guarantee future market behavior. All trading decisions are made at your own risk and should be accompanied by proper risk management.
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AetherEdge Self-Evolving S/R Grid🖊️ Overview
AetherEdge Self-Evolving S/R Grid is a revolutionary self-evolving support & resistance system that brings the philosophy of Q-learning into technical analysis. It learns from market reactions—rejections, breaks, and flips—as "rewards," automatically pruning low-value lines and constructing a Darwinian S/R grid where only functional levels survive. Moving beyond static line-drawing tools, it delivers a truly intelligent price-structure map that breathes and evolves alongside the market itself.
🔶 Key Features
Q-Learning-Style Score Updates: Reinforcement learning replicated through α (learning rate), γ (discount factor), and ε (exploration rate)
Self-Evolving Line Lifecycle: Fully automated birth → learning → reinforcement/pruning cycle
Multi-Source Grid Generation: Pivots / Williams Fractals / Round Numbers / Swing Extremes
Dynamic Reward System: Reject reward, break penalty, S/R flip bonus
ATR-Based Sensitivity: Market-adaptive touch tolerance and break confirmation
Volume-Weighted Reaction Detection: Filters out thin-volume false signals
Score-Based Visualization: Dynamic color, width, and transparency by line strength
Strong Zone Highlighting: Threshold-exceeding lines visually emphasized
Dying Line Display: Pre-pruning lines fade visibly
Comprehensive Stats Panel: Cumulative tracking of generations, deletions, rejects, breaks, flips
Safe Coordinate System: MAX_BARS_BACK clamping to prevent drawing errors on legacy bars
🧠 Technical Architecture
This indicator is an advanced system that reconstructs core reinforcement-learning concepts in Pine Script.
Q-Learning Update Equation: Applies newQ = Q + α × (R + γ × maxFutureQ − Q) to every active line each bar. Each line carries its own "value (Q-value)" and self-updates through market experience.
Reward Function: Price-line interactions are classified into three categories—rejection (+rejectReward), break (−breakPenalty), and S/R flip (+flipReward). Rewards are amplified when volume exceeds the moving average.
Exploration vs. Exploitation Dilemma: explorationRate governs new-line generation frequency. High ε aggressively probes uncharted price zones; low ε prioritizes exploitation of high-Q-value lines.
Grid Generation Strategy: Candidate prices extracted from four sources, deduplicated via existsNear(), validated against psychological levels via isRoundNumber(), and rendered safely using safeX1().
Line Lifecycle: bornBar → lastTouchBar → touches/rejects/breaks/flips accumulation → score update → if below minScore, active=false → automatic deletion.
Score Decay Mechanism: Unreacted lines decay by decayPerBar each bar, naturally pruning non-functional lines through a self-cleansing mechanism.
S/R Flip Detection: After a break, line type inverts; the pendingFlip flag monitors retests, and a flip reward is granted upon successful confirmation.
Visualization Layer: Dynamic line widths (>0.7=3px / >0.5=2px / else=1px) and transparency (80−score×60) by score.
Stats Panel: Five metrics cumulatively tracked via the stats array, with globalSuccessRate aggregating system-wide performance.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): learningRate=0.15, gridSpacingATR=1.5, maxLines=40, pivotStrength=10
ETH (1H): learningRate=0.20, gridSpacingATR=1.2, maxLines=50, genFrequency=15
SOL/XRP (15M): learningRate=0.25, gridSpacingATR=1.0, maxLines=60, decayPerBar=0.002
Long-Term (1D): learningRate=0.10, gridSpacingATR=2.5, maxLines=30, minScore=0.30
Tuning Guide:
Conservative Learning: α=0.05, γ=0.95 for slow, steady evolution—ideal for long-term trends
Aggressive Learning: α=0.30, ε=0.40 for rapid adaptation—ideal for high-volatility markets
Strict Pruning: minScore=0.35, decayPerBar=0.003 to keep only functional lines
Lenient Pruning: minScore=0.10, decayPerBar=0.0005 to retain more lines
High-Density Grid: gridSpacingATR=0.8, maxLines=80 for scalping
Sparse Grid: gridSpacingATR=3.0, maxLines=20 for long-term swing trading
Volume-Focused: Enable volumeBoost to reward only genuine reactions
Flip Hunting: flipReward=1.0 to emphasize structural reversals
💡 How to Use in Practice
Strong Lines (Yellow Highlight): colStrong lines represent high-Q-value proven levels—enter on confirmed rejection
S/R Flip Tracking: Post-break retest from the opposite side confirms a flip—powerful trend continuation signal
Breakout Strategy: Close beyond breakConfirm × ATR confirms the break—use as the origin of a new trend
Reverse Use of Dying Lines: colDying lines have lost relevance—useful as a filter to ignore stale levels
Multi-Timeframe Integration: Use higher-TF strong lines as anchor entries on lower TFs
AetherEdge Synergy: Combine with Pattern Recognition Trendline for pattern × S/R grid double confirmation
Round-Number Strategy: High-Q lines born from round numbers reflect psychological/institutional support
Grid Trading: High-density settings enable mechanical execution in ranging markets
⚠️ Important Notes
Initial Learning Period: At least 200–500 bars of data accumulation is required before signal quality stabilizes
Learning Reset: Settings changes or chart reloads reset all Q-values, requiring relearning
The ε Trap: Excessive exploration breeds low-quality lines; insufficient exploration delays adaptation
MAX_BARS_BACK Constraint: bornBar values older than 4500 bars are automatically clamped
maxLines Cap: When exceeded, oldest/lowest-score lines are auto-pruned
Regime Changes: During abrupt market shifts, legacy lines may temporarily lose validity until decay resolves them
🚨 Disclaimer
This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. Q-learning-style scores are evaluations based on historical price reactions and do not guarantee future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management.
Indicatore
AetherEdge Pattern Recognition Trendline🖊️ Overview
AetherEdge Pattern Recognition Trendline is a next-generation automated pattern recognition indicator that fuses classical chart pattern theory with an AI scoring engine. It detects major patterns such as Head & Shoulders, Double Top/Bottom, Triangle, Wedge, Flag/Pennant, and Rectangle in real time, while quantifying their reliability through a 5-dimensional AI score covering shape fit, symmetry, volume profile, time profile, and trend context. It is the ultimate platform for fully automating the artisanal art of chart analysis.
🔶 Key Features
8 Major Pattern Detections: H&S, IH&S, DT, DB, Triangle, Wedge, Flag/Pennant, Rectangle
5-Dimensional AI Scoring: Shape / Symmetry / Volume / Time / Trend Context
Dynamic Pivot Analysis: Up to 60-pivot historical buffer
Target Zone Projection: Measured-Move-based price targets
Automatic Neckline Drawing: Visualizes breakout reference levels
ATR-Based Tolerance: Allows natural fluctuation in peak/trough heights
Trend Context Evaluation: Incorporates EMA21/55 bias for pattern validity
Break Confirmation Logic: ATR-coefficient-based false-signal filter
Pattern Lifecycle Management: Tracks Detected / Completed / Failed states
Stats Panel: Cumulative count of detections, completions, and failures
🧠 Technical Architecture
This indicator is an advanced system that redefines classical technical theory through AI scoring.
Pivot Detection: Detects pivot highs/lows over pivotLR bars and stores price, bar index, and type (high=1, low=-1) in a structured buffer.
Pattern Library: Each pattern requires at least minPivots pivots and is geometrically validated against shape rules (e.g., H&S: center peak highest, shoulders at similar levels).
Shape Fit Score: Normalizes the deviation from the theoretical ideal using ATR × atrTol.
Symmetry Score: Quantifies time/price symmetry between left and right halves; misshaped patterns score low.
Volume Profile Score: Compares average volume in the early and late halves of the pattern; healthy patterns typically decay in volume during the late phase.
Time Profile Score: Patterns under 15 bars are penalized (0.3), peak around 30 bars (1.0), decay above 80 bars, and become stale beyond 150.
Trend Context Score: Bullish patterns (IH&S, DB, etc.) score higher in uptrends; bearish patterns score higher in downtrends; neutral patterns gain bonus when a clear directional bias exists.
AI Composite Score: Weighted average of all five components, evaluated against the minScore threshold.
Pattern UDT: A type-safe structure holding name, startBar, endBar, neckline, target, stop, score, col, bullish, completed, failed, and line references.
Lifecycle Tracking: Maintains Detected / Completed / Failed counters via the stats array.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): pivotLR=5, minScore=0.55, atrTol=0.5, zoneFactor=1.0
ETH (1H): pivotLR=4, minScore=0.50, atrTol=0.6, projBars=30
SOL/XRP (15M): pivotLR=3, minScore=0.45, atrTol=0.7, breakConfirm=0.3
Long-Term (1D): pivotLR=8, minScore=0.65, maxLookback=300, zoneFactor=1.5
Tuning Guide:
Strict Mode: minScore=0.70, wShape=0.40 to capture only premium patterns
Relaxed Mode: minScore=0.40, atrTol=0.8 to detect a wider range
Trend-Focused: wTrend=0.30, wShape=0.20 to prioritize trend alignment
Volume-Focused: wVol=0.35 to surface only volume-backed patterns
Symmetry-Focused: wSym=0.30 for clean structures (great for H&S, DT/DB)
Short-Term Scalping: projBars=20, breakConfirm=0.1 for early entries
Long-Term Investing: zoneFactor=1.5–2.0 for larger targets
💡 How to Use in Practice
H&S/DT detected + neckline break: Bearish entry; use target zone as profit objective
IH&S/DB detected + neckline break: Bullish entry; hold until Measured Move target
Triangle detected: Wait for breakout; trade in the direction once breakConfirm is exceeded
Wedge detected: Rising wedge = bearish reversal; falling wedge = bullish reversal
Flag/Pennant: Trend-continuation pattern, ideal for pullback/retest entries
Rectangle: Range trades at extremes; breakouts confirm directionality
Multi-Timeframe: Higher-TF pattern + lower-TF break = high-confidence setup
AetherEdge Synergy: Combine with Quantum-Inspired Breakout Scanner for probability × shape double confirmation
Failed Pattern Edge: A "Failed" verdict often acts as a strong reverse signal
⚠️ Important Notes
Pivot confirmation introduces a pivotLR-bar lag
Patterns are drawn upon completion; exercise caution for real-time entries
Excessively low minScore increases false detections
Patterns longer than maxLookback are excluded
Multiple overlapping patterns may be drawn simultaneously
Volume Score accuracy degrades on instruments with unreliable volume data
🚨 Disclaimer
This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. AI scores are evaluations based on historical shape rules and statistical properties and do not guarantee future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management. Indicatore
AetherEdge Quantum-Inspired Breakout Scanner🖊️ Overview
AetherEdge Quantum-Inspired Breakout Scanner is a next-generation breakout prediction engine fusing KNN (k-Nearest Neighbors) pattern recognition with neural-net-style weighted scoring, built on quantum-inspired probabilistic thinking. It instantly searches similar historical patterns within a 6-dimensional feature vector space and computes probability waveforms across three states: Up, Down, and Sideways. The most likely scenario is "observed" (collapsed) from the superposition — a truly quantum-inspired advanced approach.
🔶 Key Features
Dual AI Engine: Hybrid prediction via KNN + Neural Net
6-Dimensional Feature Space: Returns / Volatility / RSI / Momentum / Volume / Trend
3-State Probability Model: Up / Down / Sideways via softmax probability output
KNN Historical Learning: Dynamic accumulation of up to 2,000 past patterns
Tunable Neural Net Weights: Customizable across 6 features × 6 weights
Trendline Projection: Probabilistic projection fans from pivot anchors
Break Confirmation Logic: ATR-based false-positive suppression
Probability Histogram & Fan: Visualizes future scenarios
Stats Panel: Active predictions and dominant scenario in real time
🧠 Technical Architecture
This indicator integrates two pillars of machine learning into a quantum-inspired probability model.
Feature Engineering (6D): ①ret5 (5-bar return/ATR) ②volat (ATR volatility Z-score) ③rsiNorm (RSI deviation) ④mom (10-bar momentum/ATR) ⑤volNorm (volume Z-score) ⑥trendStr (DMI delta). All clipped to and normalized.
KNN Pattern Recognition: On barstate.isconfirmed, generates 3-state labels (Up/Side/Down) from forwardBars-ahead returns and stores them with 6 features in a ring buffer (up to knnLookback). Computes Euclidean distance to current vector, extracts top-K, votes for class probabilities.
Neural Net Scoring: Linear combination of 6 features × tunable weights (nnW1–nnW6) passed through softmax(temperature=nnTemp) to produce 3-class probabilities. Lower temperature sharpens conviction — a quantum-observation model.
Hybrid Mixing: hybridMix linearly interpolates KNN and NN probabilities (0=pure KNN, 1=pure NN, 0.5=balanced).
Sideways Classification: Range within sidewaysATR × ATR is labeled "Sideways," preventing noise-induced trend misclassification.
Trendline Projection: Anchors at pivotLR-detected pivots and projects projBars forward in the dominant direction. Drawn only when probability exceeds minProb.
Break Confirmation: Confirmed when price exceeds the line by breakATR × ATR, then highlighted.
Defensive Coding: Dynamic array-size alignment, explicit boundary checks, and local variables fully eliminate state leakage.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): knnK=15, forwardBars=10, hybridMix=0.5, minProb=0.45
ETH (1H): knnK=20, forwardBars=8, hybridMix=0.4, sidewaysATR=0.5
SOL/XRP (15M): knnK=10, forwardBars=6, hybridMix=0.6, minProb=0.50
Long-Term (1D): knnK=25, forwardBars=15, knnLookback=1000, sidewaysATR=0.8
Tuning Guide:
Pure KNN: hybridMix=0.0, knnK=20 to emphasize historical patterns
Pure NN: hybridMix=1.0, nnTemp=1.0 to emphasize weighted logic
Trending markets: sidewaysATR=0.4 to narrow Sideways and emphasize directionality
Ranging markets: sidewaysATR=1.0 to widen Sideways and suppress noise
High-conviction only: minProb=0.60, nnTemp=0.8 for sharp observation
Multi-scenario display: minProb=0.35, maxActive=5 for diversified prediction
Strict break filter: breakATR=0.3 to eliminate fakeouts
💡 How to Use in Practice
Up probability > 60% + confirmed break: Bullish entry, projection line as profit target
Down probability > 60% + downside break: Short on retest, bearish setup
Sideways dominant: Range trade or wait for breakout
Multi-Timeframe: Higher-TF Up probability + lower-TF break = high-confidence entry
Probability Fan Reading: Wide fan = uncertainty; narrow fan = high conviction
AetherEdge Synergy: Pair with Liquidity Void Detector for liquidity × probability synergy
Pivot Projection Use: Projection lines often act as dynamic S/R
Avoid: Stand aside when all three probabilities cluster around 33%
⚠️ Important Notes
Requires an initial accumulation period (at least forwardBars + 50 bars)
KNN accuracy improves progressively until knnLookback is filled
Neural net weights are not auto-trained; tuning per asset/timeframe is recommended
Long forwardBars reduces accuracy; too short increases noise
Optimal hybrid ratio varies with market regime
Probability model relies on past patterns; accuracy may drop in unprecedented regimes
🚨 Disclaimer
This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. KNN and neural-net-style probability models produce statistical forecasts based on historical data and do not guarantee future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management. Indicatore
AetherEdge Liquidity Void Detector🖊️ Overview
AetherEdge Liquidity Void Detector is a next-generation indicator that fuses liquidity sweep detection with a NeuraLib-style multivariate probability model to visualize the hidden "price vacuum zones" (Liquidity Voids) lurking in the market. It probabilizes the footprints left by smart money through an AI sigmoid function and forecasts the unfilled zones price is destined to revisit. The full institutional logic of Sweep → Void generation → Fill prediction is automated end-to-end.
🔶 Key Features
Automatic Liquidity Sweep Detection: Wick, rejection, and confirmation logic
NeuraLib-Style Probability Model: 4 features × weighted sigmoid for Void probability
Dynamic Void Zone Generation: ATR-based size filter + adjacent merging
Fill Direction Prediction: Arrows visualizing where price must go
Target Line Drawing: Clear Fill price markers
Volume Anomaly Confirmation: Dual-validation via Z-score + burst multiplier
Probability Labels: Numerical confidence per Void
Stats Panel: Active Void count and average probability in real time
🧠 Technical Architecture
This indicator is a sophisticated liquidity analysis engine merging Smart Money Concepts (SMC) with a machine-learning-style probability model.
Liquidity Sweep Detection: Detects temporary breaks of recent highs/lows over sweepLookback bars followed by wick rejection. Confirmed when upperWick/candleRng ≥ sweepWick, rejection ≥ sweepRejection × ATR, and a reversal candle prints.
4-Feature Probability Model: ①sweepStrength (rejection/ATR) ②volZ (volume Z-score) ③priceImpulse (3-bar move/ATR) ④gapMagnitude (open gap/ATR).
NeuraLib-style Sigmoid Scoring: p(void) = σ(1.8·sweep + 0.9·volZ + 0.6·impulse + 0.4·gap − 1.2). Logistic-regression-style scoring with pretrained-style fixed weights.
Volume Anomaly Confirmation: Triggered by volZ > volZThreshold OR volume > volMA × volBurstMul to suppress false positives.
Void Zone Generation: Boundaries determined by sweep direction. Bullish Void = vacuum below; Bearish Void = vacuum above.
Adjacent Merging: Auto-merges Voids within voidMergeATR × ATR proximity to reduce visual noise.
Lifecycle Management: Voids expire after voidMaxBars or transition to filled when price reaches them.
Fill Urgency Decay: fillUrgencyDecay exponentially decays urgency over time, prioritizing fresh Voids.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): sweepLookback=20, voidProbMin=0.55, volZThreshold=2.5
ETH (1H): sweepLookback=15, voidProbMin=0.60, sweepWick=0.65
SOL/XRP (15M): sweepLookback=12, voidProbMin=0.50, volBurstMul=2.5
Long-Term (1D): sweepLookback=30, voidProbMin=0.65, voidMaxBars=150
Tuning Guide:
High-frequency signals: voidProbMin=0.45, sweepWick=0.5 for looser detection
High-quality only: voidProbMin=0.70, sweepConfirm=2 for precision priority
Volatile markets: volZThreshold=3.0, volBurstMul=3.0 for stricter anomaly criteria
Quiet markets: sweepRejection=0.3, voidMinSize=0.3 for higher sensitivity
Scalping: voidExtendBars=20, fillUrgencyDecay=0.90 for short-term projection
Swing: voidExtendBars=80, voidMaxBars=150 for long-term Void tracking
💡 How to Use in Practice
Sweep → Reversal: Upward sweep + high-probability Bearish Void = short opportunity
Void Fill Trades: Enter along Fill direction arrow toward target line
Unfilled Zone Targets: Use high-probability Voids as profit targets
Multi-Timeframe: Sweep on lower TF inside higher-TF Void = sniper entries
Volume Anomaly Confirmation: Z-score + volBurst together = likely institutional flow
AetherEdge Synergy: Overlap with Regime-Aware Trend Channel boundaries → ultra-high-confidence zones
Avoid: Voids below 0.55 probability or sweeps without volume anomaly
⚠️ Important Notes
Liquidity Void is a hypothesis based on SMC; fills are not guaranteed
News-driven moves may behave outside model assumptions
Excessively low voidProbMin produces signal overload and noise
Weights are fixed and not optimized per specific instrument
Increasing confirmation bars boosts accuracy but adds latency
Merging may consolidate multiple Voids into one zone
🚨 Disclaimer
This indicator is provided for educational and research purposes only and does not constitute financial advice. Liquidity Void detection and probability modeling are based on historical data and fixed-weight coefficients, with no guarantee of future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management. Indicatore
AetherEdge Regime-Aware Trend Channel🖊️ Overview
AetherEdge Regime-Aware Trend Channel is a next-generation dynamic channel system in which K-means clustering automatically classifies the market into three regimes — Range, Trend, and Volatile — and generates regime-specific channels in real time. Unlike conventional one-size-fits-all channels, it identifies the market's underlying character through unsupervised machine learning and reshapes itself accordingly. Upon breakout, it projects a confidence-decaying forward cone, visualizing "where price is likely heading next."
🔶 Key Features
Automatic K-means Regime Classification: 4 features × 3 clusters
Regime-Specific Channels: Optimal length & width per Range/Trend/Volatile
Dynamic Retraining: Centroids recompute every recalcBars to track shifts
Breakout Detection: ATR threshold + confirmation bars filter false breaks
Forward Projection: Confidence-decaying cone for projBars after breakout
Regime Color Visualization: Blue=Range / Green=Trend / Orange=Volatile
Stats Panel: Real-time regime, centroid, and feature display
Multi-Timeframe Friendly: Self-adapting design across all timeframes
🧠 Technical Architecture
This indicator is an adaptive channel AI built around unsupervised K-means learning.
4-Dimensional Feature Vector: ①EMA21–50 divergence (f1: trend strength) ②Normalized stdev (f2: volatility) ③High–low range / ATR (f3: range-ness) ④EMA21 slope (f4: directionality) — multidimensional market character.
Min-Max Normalization: All features scaled to 0–1 over trainBars window for fair distance computation.
K-means Clustering: regimeK centroids initialized evenly across trainBars → maxIter iterations of nearest-centroid assignment → centroid update until convergence.
Centroid Classification: trendScore = c1 + c4, volaScore = c2, rangeScore = 1 − c3 − c1; the maximum determines regime type per centroid.
Real-time Prediction: Computes Euclidean distance from current vector to all centroids; nearest centroid's regimeType is adopted.
Adaptive Channel Engine: Different lengths (chRangeLen / chTrendLen / chVolaLen) per regime extract highs/lows, scaled by WidthMul × ATR.
Breakout Detection: Triggered when deviation exceeds breakATRMul × ATR for breakConfirmBars consecutive bars.
Forward Projection: Cone-shaped confidence band drawn projBars ahead with projDecay exponential decay.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): trainBars=500, regimeK=3, recalcBars=50, featureWin=20
ETH (1H): trainBars=400, regimeK=3, recalcBars=40, chTrendLen=50
SOL/XRP (15M): trainBars=300, regimeK=3, recalcBars=30, featureWin=15
Long-Term (1D): trainBars=800, regimeK=4, recalcBars=80, chTrendLen=100
Tuning Guide:
Granular classification: regimeK=4–5 (requires more data)
Fast response: recalcBars=20 for frequent retraining (heavier load)
Stability priority: recalcBars=100, maxIter=20 for robust centroids
Trend-following: trendWidthMul=2.5 for wider breakout tolerance
Mean-reversion: rangeWidthMul=0.8 for tight Range channels
Volatile markets: volaWidthMul=3.0 to suppress false signals
Conservative breakouts: breakConfirmBars=3, breakATRMul=0.7
💡 How to Use in Practice
Range Strategy: In blue regimes, fade extremes — mean-reversion plays
Trend Strategy: In green regimes, buy dips / sell rallies — trend-following
Volatile Strategy: In orange regimes, reduce size and await clean breaks
Breakout Follow-through: Enter in cone direction; consider TP at cone tail
Regime Shift Signal: Color changes mark regime transitions = strategy switch points
Multi-Timeframe: Higher-TF regime + lower-TF channel edges for entry precision
AetherEdge Synergy: When Adaptive Fibonacci AI ratios align with channel edges → ultra-high-confidence zones
⚠️ Important Notes
Regime classification is inactive until trainBars of data accumulates
K-means is initialization-sensitive; classification may fluctuate at retraining moments
Too-short recalcBars destabilizes centroids and adds noise
Sudden regime shifts may incur a few bars of lag
Excessive regimeK fragments data per cluster, reducing classification accuracy
The projection cone is a probabilistic forecast, not a price guarantee
🚨 Disclaimer
This indicator is provided for educational and research purposes only and does not constitute financial advice. K-means classification and projection forecasts are based on historical data and do not guarantee future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management. Indicatore
AetherEdge Adaptive Fibonacci AI🖊️ Overview
AetherEdge Adaptive Fibonacci AI is a machine-learning-driven adaptive Fibonacci engine that completely transcends traditional static ratios. A KNN (K-Nearest Neighbors) algorithm extracts the most similar historical regimes from up to 2,000 prior bars and learns which golden ratios actually worked in those analogous conditions. It then probabilistically computes a breakout probability for each level — a revolutionary tool that visualizes "which Fib will hold" as a forward-looking forecast.
🔶 Key Features
Dynamic Swing Detection: ATR-based filtering of meaningful swings only
KNN Similarity Engine: 6-dimensional feature × cosine similarity matching
Adaptive Fibonacci: Learns ratios that worked in similar regimes
3 Modes: Standard (fixed) / Adaptive (learned) / Hybrid (fused)
Breakout Probability Forecast: % probability displayed per level
3-Tier Color Coding: High / Mid / Low breakout probability
Predicted Extension Lines: 1.272 / 1.618 / 2.0 projection rendering
Dynamic Line Width: Higher similarity = thicker emphasis
Stats Panel: Visualizes similarity, ratio adoption, and processing state
🧠 Technical Architecture
This indicator is a KNN-driven Fibonacci AI that "learns from the past."
Swing Detection: pivothigh / pivotlow at swingLen, filtered by minSwingATR × ATR to eliminate noise. Swing ID tracking minimizes recomputation.
6-Dimensional Feature Vector: ①Return (f_ret) ②Volatility ratio (f_volaRatio) ③Volume ratio (f_volRatio) ④EMA slope (f_slope) ⑤Range position (f_rangePos) ⑥Normalized RSI (f_rsi) — vectorizes current market state.
Cosine Similarity: Computes similarity between the current vector and each historical state vector, quantifying regime resemblance on a -1 to 1 scale.
KNN Search: Scans historyBars range with sampleStep intervals, extracts top-K (knnK) similar states. similarityMin threshold filters out low-quality matches.
Level Harvesting: Reverse-engineers reactive price levels from analogous regimes, matches them against candidateRatios (18 ratios from 0.146 to 2.618), and accumulates them into adaptiveRatios.
Weighted Voting: Each ratio is quality-scored via similarity-weighted voting.
Breakout Probability: Calculated from price behavior (break vs. respect) around each level over probLookback bars.
Hybrid Mode: Combines standard Fibs with adaptive Fibs to harness both worlds.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): swingLen=10, knnK=8, historyBars=2000, similarityMin=0.55
ETH (1H): swingLen=8, knnK=10, historyBars=1500, similarityMin=0.50
SOL/XRP (15M): swingLen=6, knnK=12, historyBars=1200, similarityMin=0.45
Long-Term (1D): swingLen=15, knnK=6, historyBars=3000, similarityMin=0.60
Tuning Guide:
Precision: similarityMin=0.70, knnK=5 for ultra-selective matching
Maximum learning: historyBars=4000, sampleStep=2 to maximize data
Speed priority: sampleStep=5, featureLen=15 to reduce load
Trending markets: includeExt=true, showBeyond=true for projection emphasis
Range markets: adaptMode=Adaptive to prioritize learned ratios
Beginners: adaptMode=Hybrid offers the safest standard + AI dual view
💡 How to Use in Practice
High-Probability Bounce: colLowProb (green) levels are strong S/R — bounce entry candidates
Breakout Plays: colHighProb (red) levels signal high break expectation — follow-through trades
Adaptive Edge: Non-standard ratios (0.65, 0.707, etc.) appearing in Adaptive mode are regime-specific key levels
Extension Targets: Use 1.272 / 1.618 / 2.0 projections for take-profit
Multi-Timeframe: Higher TF adaptive Fibs + lower TF entry refinement
AetherEdge Synergy: When Neural OB Evolution elite OBs overlap an adaptive Fib → confluence powerhouse zone
⚠️ Important Notes
The first several hundred bars lack training data, so Adaptive mode may not function well early on
Increasing historyBars improves accuracy but raises computational load (trade-off)
Effectiveness drops during sudden regime shifts that have no historical analog
Levels are not drawn until swings confirm (swingLen bars later)
KNN processing executes on bar close — real-time reflection occurs on the next bar
Excessively large featureLen risks over-referencing outdated patterns
🚨 Disclaimer
This indicator is provided for educational and research purposes only and does not constitute financial advice. KNN similarity and probability forecasts are based on historical data and do not guarantee future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management.
Indicatore
AetherEdge Neural Order Block Evolution🖊️ Overview
AetherEdge Neural Order Block Evolution is a next-generation ICT/SMT tool that auto-detects institutional order blocks (OBs) and isolates only "elite OBs" through neural-network-style weighted scoring. Four core features — volume anomaly, liquidity sweeps, MTF consensus, and displacement — are aggregated via sigmoid activation, ruthlessly eliminating noise OBs and rendering only the true zones defended by smart money. This is the ultimate order block engine that visualizes institutional footprints with AI precision.
🔶 Key Features
Swing Pivot Detection: High-precision displacement-based OB identification
Neural Quality Score: 4-feature × weighted × sigmoid aggregation
Volume Anomaly Detection: Z-score-based abnormal volume scoring
Liquidity Score: Cluster detection + liquidity sweep recognition
MTF Consensus System: 3-timeframe trend bias integration
Displacement Score: ATR-based impulse strength evaluation
Elite OB Highlighting: Top-scored OBs marked with gold border
Lifecycle Management: Mitigated OBs preserved as history
Proximity Highlighting: Visual emphasis when price approaches OB
Stats Panel: Tracks created / broken / respected counts
🧠 Technical Architecture
This indicator is a neural evaluation engine designed to "decode institutional intent."
OB Detection: After swingLen pivot detection, scans within obLookback for the Last Up/Down Candle. Only candidates with displacement ≥ displaceMult × ATR qualify as OBs.
Volume Anomaly Score: Volume at OB formation Z-scored against 20-bar mean/std, then sigmoid-normalized to 0–1. Abnormal volume = institutional footprint.
Liquidity Score: Counts price clusters near OB level over 30 bars (liquidity pool detection) + identifies liquidity sweep patterns (e.g., sharp reversal after low sweep).
MTF Consensus: Retrieves close > EMA21 bias from mtf1/mtf2/mtf3. Aggregated with weights 1.0/1.2/1.5 (higher TF prioritized) and sigmoid-converted.
Displacement Score: Displacement size normalized by ATR, excess over displaceMult threshold sigmoid-mapped.
Neural Aggregation: Linear combination of 4 scores via wVolume / wLiquidity / wMTF / wDisplace yields final quality score. OBs below qualityMin are excluded.
Lifecycle: Break detected via mitigationMode (Wick / Close) and breakBuf × ATR. With keepBroken, OBs migrate to historyOBs and persist for historyBars.
Stats Tracking: totalCreated / Broken / Respects metrics for performance visualization.
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): swingLen=5, displaceMult=1.5, qualityMin=0.55, MTF=4H/D/W
ETH (1H): swingLen=4, displaceMult=1.3, qualityMin=0.50, MTF=1H/4H/D
SOL/XRP (15M): swingLen=3, displaceMult=1.2, qualityMin=0.45, MTF=15M/1H/4H
Long-Term (1D): swingLen=8, displaceMult=2.0, qualityMin=0.60, MTF=D/W/M
Weight Tuning:
Trending markets: Raise wMTF=0.40, wDisplace=0.30
Range markets: Raise wLiquidity=0.40, wVolume=0.35
Elite-only display: qualityMin=0.70 + maxBlocks=4
Maximum visibility: qualityMin=0.40 + maxBlocks=12
Early detection: obLookback=20 for wider search
Strict mode: displaceMult=2.0 for only powerful displacements
💡 How to Use in Practice
Elite OB Reversal: Gold-bordered OBs are top-priority entry zones
MTF Consensus Play: High sMTF OBs align with the macro-trend fortress
Post-Sweep OB: High sLiq OBs form after institutional stop hunts — extremely strong zones
Mitigation Strategy: Trade the first touch of an unmitigated OB
History OB Analysis: Broken historic OBs are future support/resistance flip candidates
AetherEdge Combinations: When KNN Breakout Fortress strong walls overlap an OB → ironclad zone
⚠️ Important Notes
Too-small swingLen creates excessive, noisy OBs
MTF settings must be higher than the current chart timeframe
Liquidity score depends on the prior 30 bars — accuracy drops on early bars
sVol becomes unreliable on assets without volume data
Neural weights should sum to approximately 1.0 for balanced scoring
🚨 Disclaimer
This indicator is provided for educational and research purposes only and does not constitute financial advice. Neural scores and OB quality evaluations are based on historical data and do not guarantee future performance. All trading decisions are made at your own risk and should be accompanied by proper risk management. Indicatore
Indicatore
Wavelet Energy Cascade [forexobroker]Wavelet Energy Cascade decomposes close into 4 dyadic Haar wavelet scales (lengths 1, 2, 4, 8) and tracks their energies. Coarse-scale dominance over fine-scale signals trending; fine-scale dominance signals noise. The coarse Haar smoothed line provides the entry trigger. Distinct from any moving average: wavelets are a multi-resolution decomposition with explicit energy bookkeeping per scale.
🔶 ALGORITHM
1. Haar coefficient at scale s = (avg of s most recent - avg of s prior) / sqrt(2).
2. Compute coefficients h1, h2, h4, h8.
3. Energy_s = math.sum(h_s^2, lookback) — squared coefficients summed over a rolling window.
4. fine = e1 + e2; coarse = e4 + e8.
5. Trending regime when coarse > k * fine.
6. Coarse Haar smoother (sma(close, 8)) provides the entry-trigger reference.
🔶 SIGNAL LOGIC
- Buy: trending AND close crosses coarse line up AND h8 > 0 AND not already long AND cooldown elapsed AND barstate.isconfirmed.
- Sell: trending AND close crosses coarse line down AND h8 < 0.
- Position-lock state machine.
🔶 INPUTS
- Energy Lookback (default 40)
- Coarse / Fine Dominance k (default 1.5)
- Cooldown Bars (default 4)
- Visual: dashboard, glow, coarse line, buy / sell colors
🔶 ALERTS
WEC Buy, WEC Sell, WEC Any Signal, WEC Trend Start, WEC Trend End, WEC Strong Trend, WEC Cross Up, WEC Cross Down, WEC Webhook JSON.
🔶 LIMITATIONS
- Haar is the simplest wavelet (orthogonal, but with sharp edges); for smoother decomposition, Daubechies-like wavelets would help — at higher Pine cost.
- Only 4 scales; missing very-low or very-high frequencies if they matter for the asset.
- Energy lookback dominates the smoothing trade-off; smaller = more responsive, noisier.
- The h8 sign gate ensures direction agrees with the coarse-scale wavelet; rare divergences are filtered.
Indicatore
Roll Spread Estimator [forexobroker]Roll Spread Estimator implements Roll (1984) — the bid-ask bounce induces negative serial covariance in observed returns. Spread = 2 * sqrt(-cov(r , r )) when cov is negative. Tight spread = liquid market = clean breakouts; wide spread = thin = breakouts to fade. Signals fire on EMA cross during a tight regime. Stacked-flag visual signature distinct from any other hero indicator.
🔶 ALGORITHM
1. r0 = close - close ; r1 = close - close .
2. cov(r0, r1) = sma(r0 * r1, N) - sma(r0, N) * sma(r1, N).
3. Spread = 2 * sqrt(-cov) when cov < 0; otherwise 0.
4. Quantile q (e.g., 70th percentile) over rolling window.
5. Tight regime when spread <= q.
6. EMA cross within tight fires entries.
🔶 SIGNAL LOGIC
- Buy: tight AND close crosses EMA up AND not already long AND cooldown elapsed AND barstate.isconfirmed.
- Sell: tight AND close crosses EMA down.
- Position-lock state machine.
🔶 INPUTS
- Cov Window (default 40)
- Tight Spread Quantile (default 0.7)
- Pullback EMA Length (default 8)
- Cooldown Bars (default 4)
- Visual: dashboard, glow (3 stacked flags), spread channel, buy / sell colors
🔶 ALERTS
RSE Buy, RSE Sell, RSE Any Signal, RSE Tight Start, RSE Tight End, RSE Bounce, RSE EMA Up, RSE EMA Down, RSE Webhook JSON.
🔶 LIMITATIONS
- Roll's estimator assumes the bid-ask bounce is the dominant source of negative covariance; trend-following autocorrelation can produce positive cov, in which case the estimator is undefined and returns 0.
- Forex and equities show clear bid-ask bounces; pure spot-market crypto is less consistent.
- The stacked-flag visual uses 3 overlapping flags for a thicker appearance — visually distinct but slightly heavier draw.
- Quantile-based threshold means tight is *relative* to recent history, not absolute.
Indicatore
PCA Projection Pulse [forexobroker]PCA Projection Pulse runs Principal Component Analysis on a 2-D feature space (z-scored close, z-scored volume) over a rolling window. The first principal component is the direction of maximum joint variance; projecting the current point onto PC1 measures how strongly current price-and-volume aligns with that direction. Signals fire on edge crossings of the projection threshold.
🔶 ALGORITHM
1. zC = (close - mean_close) / sd_close; zV = (volume - mean_volume) / sd_volume.
2. 2x2 covariance matrix entries: s11 = var(close), s22 = var(volume), s12 = cov(close, volume).
3. trace = s11 + s22; det = s11 * s22 - s12^2; eigenvalue lambda1 = (trace + sqrt(trace^2 - 4 * det)) / 2.
4. PC1 eigenvector ~ (lambda1 - s22, s12); normalise to unit length (ux, uy).
5. Projection = zC * ux + zV * uy.
6. Pulse regime when |projection| >= threshold.
🔶 SIGNAL LOGIC
- Buy: projection >= +threshold edge AND not already long AND cooldown elapsed AND barstate.isconfirmed.
- Sell: projection <= -threshold edge.
- Position-lock state machine.
🔶 INPUTS
- Covariance Window (default 40)
- Projection Threshold (default 0.50)
- Cooldown Bars (default 4)
- Visual: dashboard, glow, xcross with PC text, PC1 direction line, buy / sell colors
🔶 ALERTS
PPP Buy, PPP Sell, PPP Any Signal, PPP Pulse Up, PPP Pulse Down, PPP Strong Pulse, PPP Single Direc, PPP Same Sign, PPP Webhook JSON.
🔶 LIMITATIONS
- 2-D PCA captures only the dominant joint direction; higher-D variants (more features) would extract richer signals but require larger covariance matrices.
- Closed-form eigendecomposition is exact for 2x2 cases; for richer feature spaces an iterative solver is needed.
- Forex tick volume noise can dominate PC1; assets with reliable volume produce cleaner pulses.
- The "PC1 direction line" is anchored at EMA20 and scales projection by ATR — visual cue, not a level.
Indicatore
Ornstein Uhlenbeck Reverter [forexobroker]Ornstein-Uhlenbeck Reverter fits the continuous-time mean-reverting OU process dX = theta * (mu - X) * dt + sigma * dW to recent closes. Parameters theta (mean-reversion speed) and mu (long-run mean) are estimated by OLS. Half-life of reversion = ln(2) / theta. Signals fade overshoots beyond k * sigma — a statistically grounded mean-reversion entry.
🔶 ALGORITHM
1. Linear regression: r = a + b * close + e, where r = close - close (the negative one-step return).
2. theta_hat = -b (assuming dt = 1); mu_hat = a / theta_hat.
3. Half-life = ln(2) / theta. Sigma = stdev of OLS residuals.
4. Overshoot = (close - mu) / sigma.
5. Long fade: overshoot was below -k AND now turning back up. Short fade: above +k AND turning down.
🔶 SIGNAL LOGIC
- Buy: long fade trigger AND not already long AND cooldown elapsed AND barstate.isconfirmed.
- Sell: short fade trigger.
- Position-lock state machine.
🔶 INPUTS
- Fit Window (default 60)
- Overshoot k x sigma (default 1.5)
- Cooldown Bars (default 4)
- Visual: dashboard, glow, equilibrium hline, buy / sell colors
🔶 ALERTS
OUR Buy, OUR Sell, OUR Any Signal, OUR Underext, OUR Overext, OUR Extreme, OUR Fast Revert, OUR Drift Mode, OUR Webhook JSON.
🔶 LIMITATIONS
- OU assumes constant mu over the fit window; trending markets violate this and produce theta near zero (drift mode).
- OLS is sensitive to outliers; robust alternatives (MAD-based) may help on spike-heavy assets.
- The fit re-runs every bar; transitional periods produce unstable mu estimates.
- Mean-reversion strategies underperform during persistent trends; pair with a regime gate (e.g., Hurst < 0.5).
Indicatore
Indicatore
Indicatore
Indicatore
Best Times to Trade - FINPIPBest Times to Trade - FINPIP
by finpip.com | finpip.com
Stop guessing session times. Let your chart tell you when it actually moves.
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WHAT IS THIS?
Best Times to Trade - FINPIP is a free overlay indicator from finpip.com that analyzes your price history and ranks the exact minutes of the day — down to HH:MM — where reversals, volume spikes, and range expansions happen most often.
Every symbol behaves differently. EURUSD on the 5-minute chart is not NAS100 on the 1-minute. This tool builds a custom "heat schedule" for the market and timeframe you are viewing right now, so you can plan entries, watchlists, and alerts around real activity — not generic clock rules.
Think of it as a data-driven take on session timing and ICT-style "Silver Bullet" windows: the rankings come from your chart's volume and price action, not from a fixed template.
Pine Script® v6 | Overlay indicator
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KEY FEATURES
✓ Exact times (HH:MM) — not hour ranges. Example: 10:35, 14:12, 03:00
✓ 1,440 minute slots per day scored and ranked
✓ Three signal types: Reversals (R), Volume spikes (V), Range spikes (S)
✓ Weighted composite score — tune what matters most to you
✓ On-chart ranking table with finpip.com branding
✓ Background highlight when price enters a top-ranked minute
✓ Diamond markers when reversal + spike align during a top time
✓ Multiple timezones: New York, London, UTC, Chicago, Tokyo, Dubai
✓ Optional ICT reference tags for NY (03:00, 10:00, 14:00)
✓ 5–365 day lookback, up to 5,000 bars scanned
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HOW IT WORKS
On each bar in your lookback window, the indicator checks:
1. REVERSAL (R) — Strong rejection wick vs. candle body.
2. VOLUME SPIKE (V) — Volume above rolling average × multiplier.
3. RANGE SPIKE (S) — Bar range above ATR × multiplier.
Each bar is assigned to its open time (e.g. 10:35) in your timezone. Rates are calculated per minute, then combined into a weighted composite score. Only minutes with enough samples can rank.
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READING THE TABLE
Example: 10:35 ★ ICT · 0.142 · R12 V8 S15
10:35 = exact time | ★ ICT = optional NY ICT match | 0.142 = score | R/V/S = event counts
Row #1 is teal. Chart tints green on top-ranked minutes. Diamonds = reversal + spike at a top time.
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HOW TO USE IT
1. Add to the symbol and timeframe you trade.
2. Set your Timezone (NY, Dubai, London, etc.).
3. Use 1m or 5m charts for best minute accuracy.
4. Focus on your top 3–5 ranked times.
5. Combine with your own bias and structure — this shows WHEN, not direction.
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DISCLAIMER
Educational tool only. Not financial advice. Past behavior does not guarantee future results. Rankings are symbol- and timeframe-specific.
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ABOUT FINPIP
Created by finpip.com — finpip.com
© finpip.com | Mozilla Public License 2.0 Indicatore
Macro Market Health IndexThis comprehensive indicator, Macro Market Health and Dynamic Regime Scoring, is a multi-asset "Risk-ON / Risk-OFF" framework designed to quantify market health by analyzing six specialized macro pillars. Unlike static indicators, this tool features an adaptive weighting engine that calibrates the importance of each macro block based on its real-time relationship with the specific asset currently on your chart
.
The Six Pillars of Market Health
The indicator processes raw data through six "Blocks," each utilizing specific mathematical models to score health from 0 to 1
:
Risk Block (Sentiment): Monitors the VIX (normalized against a baseline of 22), Gold, and the USD/JPY "Carry Trade"
. It calculates a blend of absolute levels (distance from the 200-day EMA) and momentum (9-day ROC of the 21-day EMA) to detect capital flight into safe havens
.
Breadth Block (Internal Strength): Uses the S5FI (S&P 500 stocks above their 50-day EMA) and the SPX/RSP Spread
. If the standard S&P 500 is significantly outperforming the equal-weighted version, it signals poor "under the hood" participation
.
Equity Block (Momentum & Volume): Analyzes the S&P 500 using a Gaussian "Sweet Spot" model
. It scores the trend highest when the price is in a healthy range above the 200-day EMA, while also penalizing the score if relative volume trends (21-day vs 251-day EMA) show exhaustion
.
Crypto Block (Risk Appetite): Serves as a high-beta proxy by measuring Bitcoin's distance from its 50, 100, and 200-day EMAs
. These are weighted (45% for 50-day, 32% for 100-day, 23% for 200-day) to capture multi-timeframe risk sentiment
.
Growth Block (Economic Context): Tracks Crude Oil and its 50-day correlation to equities
. It identifies whether energy prices are acting as a growth driver or a risk-off inflationary headwind
.
Credit Block (Fixed Income): Monitors the HYG (High Yield Corporate Bonds) as a leading indicator of credit stress
. It evaluates both the trend level and the rate of change in corporate bond demand
.
Advanced Mathematical Components
To ensure all data is comparable, the script employs several core math engines:
Sigmoid Normalization: Squashes all outputs into a standard 0 to 1 range for easy comparison
.
Z-Score Sigmoids: Normalizes volatility and momentum by measuring how many standard deviations a value is from its mean
.
Gaussian Probability: Uses a bell-curve distribution to score the "Equity Block," rewarding trends that stay within a specific volatility-adjusted distance from the mean
.
Distance/ATR Scaling: All price distances from moving averages are divided by the ATR (Average True Range) to ensure the scores are relative to current market volatility
.
Data-Driven Weighting: Tailored to YOUR Asset
The standout feature is the Weight Engine, which rejects the "one-size-fits-all" approach. It calculates weights against the specific ticker (hlc3) on your chart
:
Asset Correlation: The indicator measures the real-time statistical correlation between each macro block and the specific stock you are trading
. If your stock starts moving in lockstep with Credit or Crypto, those blocks are automatically given more weight
.
Dynamic Volatility Scaling: It calculates the ratio of a block's volatility (CV) to your stock's volatility (CV) to dynamically adjust the lookback period (between 50 and 300 bars)
Adaptive Lookback Engine: The index uses the Coefficient of Variation (CV) (StandardDeviation/Mean) to compare macro environment volatility against your stock's volatility
. This ratio automatically scales the lookback window between 50 bars (for high-volatility responsiveness) and 300 bars (for stable trend smoothing)
.
Weighting & Analytics: Final weights blend asset-specific correlation with Inverse Volatility (1/SD) to prioritize correlated, stable, low-noise signals
.
Signals and Predictive Analytics
Trade Status Overlay: Provides a definitive "TRADE" (Score > 0.58) or "NO TRADE" (Score < 0.45) signal
.
Regime Classification: Categorizes the market into BULL MARKET, EARLY BULL, NEUTRAL, or RISK OFF
.
Swing Probability: Uses a Gaussian Survival Model to estimate the conditional probability that a "Risk-ON" or "Risk-Off" (current identified) regime will last for a user-defined "Swing Duration" (e.g., the next 21 bars)
.
Reliability Index: Based on the number of historical samples collected for that specific asset, it labels the probability data as Adequate, Marginal, or Low
. Indicatore
Tetris MARKET 🧩 Market Tetris Grid – Multi-Factor Market Structure Model
This indicator is a multi-layer market structure visualization system that combines volatility, momentum, volume flow, and trend direction into a unified “block-based” framework inspired by Tetris logic.
The purpose of this model is not to generate isolated signals, but to visualize the internal strength and alignment of market forces in real time.
🔬 CORE MATHEMATICAL STRUCTURE
The system is built on six independent market components:
1. ATR (Volatility Engine – Enhanced / Super ATR Concept)
The ATR component is not used in its standard form only.
Instead, it is normalized and interpreted as a relative volatility expansion engine.
It measures:
Market range expansion
Volatility pressure compared to historical average
Structural movement intensity
This is part of a broader “multi-source volatility interpretation” approach, where ATR is combined with range and true range behavior to represent real market energy rather than simple average true range.
2. Delta (Price Pressure)
Delta represents raw directional price movement:
Positive delta → buying pressure
Negative delta → selling pressure
It captures immediate micro-directional momentum of price.
3. OBV (Volume Flow Engine)
OBV is implemented in a stable cumulative form:
Volume is added on upward closes
Subtracted on downward closes
This reflects net capital flow direction rather than raw volume.
4. Momentum (Rate of Change)
Momentum is calculated using rate-of-change logic:
Measures speed of price movement
Identifies acceleration or deceleration phases
5. Volatility Ratio
Volatility is normalized using ATR relative to its moving average:
Detects expansion vs compression phases
Identifies structural volatility regime shifts
6. Trend Structure
Trend is defined as the difference between short and medium EMAs:
Represents directional bias
Filters noise from raw price movement
🧠 SCORING & PROBABILITY MODEL
All six components are normalized and aggregated into a unified score:
Positive score → bullish market bias
Negative score → bearish market bias
This score is then converted into:
Bullish Probability (%)
Bearish Probability (%)
The probability is not predictive in isolation, but represents multi-factor alignment strength across market dimensions.
🧱 VISUAL MODEL (TETRIS GRID LOGIC)
Each column represents one market force:
ATR / Delta / OBV / Momentum / Volatility / Trend
Block height reflects intensity, allowing visual detection of:
Market expansion zones
Compression phases
Directional imbalance
Structural alignment or divergence
⚠️ INTENDED USE
This indicator is designed for:
Market structure analysis
Trend strength visualization
Multi-factor confirmation
Institutional-style flow interpretation
It is not intended to be a standalone buy/sell system.
🇹🇷 TÜRKÇE AÇIKLAMA (RESMİ)
🧩 Market Tetris Grid – Çok Faktörlü Piyasa Yapı Modeli
Bu indikatör, volatilite, momentum, hacim akışı ve trend yönünü tek bir “blok tabanlı yapı” içinde birleştiren çok katmanlı bir piyasa görselleştirme sistemidir.
Amaç, tekil sinyal üretmek değil; piyasanın içsel güç dağılımını ve uyumunu gerçek zamanlı olarak görselleştirmektir.
🔬 TEMEL MATEMATİK YAPI
Sistem 6 ana piyasa bileşeni üzerine kuruludur:
1. ATR (Geliştirilmiş Volatilite Motoru – “Super ATR” Mantığı)
ATR sadece klasik haliyle kullanılmaz.
Bunun yerine:
normalize edilir
geçmiş ortalamalara göre göreceli gücü ölçülür
piyasa genişleme enerjisi olarak yorumlanır
Bu yapı, standart ATR’den farklı olarak:
👉 “çok kaynaklı volatilite yorumlama modeli” içerir.
Yani sadece ortalama gerçek aralık değil, piyasanın enerji artışı / daralması analiz edilir.
2. Delta (Fiyat Baskısı)
Delta, fiyatın anlık yönlü hareketini temsil eder:
Pozitif delta → alım baskısı
Negatif delta → satım baskısı
3. OBV (Hacim Akışı)
OBV şu mantıkla çalışır:
kapanış yukarıysa hacim eklenir
kapanış aşağıysa hacim çıkarılır
Bu yapı net para akış yönünü gösterir.
4. Momentum
Momentum, fiyat değişim hızını ölçer:
hızlanma / yavaşlama tespiti yapar
5. Volatilite Oranı
ATR’nin kendi ortalamasına oranı alınır:
piyasa sıkışma / genişleme tespiti
rejim değişimi analizi
6. Trend Yapısı
Kısa ve orta EMA farkı alınır:
yönlü bias üretir
gürültüyü filtreler
🧠 SKOR VE OLASILIK MODELİ
6 bileşen normalize edilerek tek bir skor oluşturur:
pozitif skor → bullish baskı
negatif skor → bearish baskı
Bu skor daha sonra:
Bullish %
Bearish %
olarak olasılığa çevrilir.
Bu değer tek başına tahmin değil, çoklu piyasa faktörlerinin hizalanma gücünü gösterir.
🧱 GÖRSEL MODEL (TETRIS MANTIĞI)
Her sütun bir piyasa gücünü temsil eder:
ATR / Delta / OBV / Momentum / Volatilite / Trend
Blok yüksekliği, ilgili faktörün gücünü gösterir.
Bu sayede:
genişleme bölgeleri
sıkışma alanları
yönsel dengesizlik
yapısal uyum / ayrışma
görsel olarak analiz edilebilir.
⚠️ KULLANIM AMACI
Bu indikatör:
piyasa yapısı analizi
trend gücü ölçümü
çok faktörlü doğrulama
kurumsal akış yorumlama
için tasarlanmıştır.
Tek başına al/sat sistemi değildir. Indicatore
