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.
Indicator
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. Indicator
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. Indicator
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. Indicator
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. Indicator
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.
Indicator
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. Indicator
Indicator
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.
Indicator
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.
Indicator
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.
Indicator
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).
Indicator
Indicator
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Indicator
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 Indicator
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
. Indicator
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. Indicator
Covenant Regime Register [JOAT]Covenant Regime Register
Introduction
Covenant Regime Register is an open-source market context indicator that classifies whether price is currently behaving like a directional auction or a rotational auction. Instead of treating trend detection as a single yes-or-no output, the script builds two competing probability streams and continuously updates which state has stronger evidence.
The problem this indicator solves is context drift. Many tools are applied the same way in every environment even though trending conditions and ranging conditions reward very different decisions. Covenant Regime Register separates those environments first, then exposes confidence, directional efficiency, and bias so the trader can decide whether to lean into continuation logic or step back into rotation logic.
Core Concepts
1. Multi-factor regime observations
The regime engine does not rely on one input. It blends normalized returns, normalized volatility, directional efficiency, and slope persistence into a two-state regime model:
logReturn = math.log(close / nz(close , close))
realizedVol = ta.stdev(logReturn, volatilityLength)
efficiencyRatio = math.abs(close - close ) / math.sum(math.abs(ta.change(close)), efficiencyLength)
This keeps the classification grounded in both movement quality and volatility behavior.
2. Probabilistic state competition
Directional and rotational states each receive an emission score. Those scores are then smoothed through a persistence-heavy probability engine so the output does not flip on every small fluctuation:
posteriorTrend = emissionTrend * priorTrend
posteriorRange = emissionRange * priorRange
trendProb := trendProb + learningInput * (targetTrend - trendProb)
The result is a stable state register rather than a noisy binary switch.
3. Confidence-aware classification
The script only considers a regime confirmed when the dominant state exceeds the user-defined confidence threshold on a confirmed bar. This helps reduce false transitions during temporary turbulence.
4. Probability spread visualization
Trend probability and range probability are plotted together, while the spread between them is shaded as a separate area. This lets the user see whether the market is decisively one-sided or only marginally biased.
5. Institutional dashboard
The top-right dashboard reports current state, confirmation status, trend probability, range probability, efficiency, and directional bias using a restrained dark palette designed to stay readable on a clean chart.
Features
Two-state regime model: Directional auction versus rotational auction
Multi-factor classification: Uses returns, volatility, efficiency, and slope instead of a single oscillator threshold
Probability outputs: Trend and range are shown as separate probability streams
Confidence gate: Regimes are only considered confirmed above the user-defined threshold
Spread visualization: Shows the separation between the two competing states
Dark institutional dashboard: Compact top-right panel with current state and supporting metrics
Confirmed-bar regime alerts: Alerts only fire when a new regime is confirmed on bar close
Non-repainting design: Uses only current-timeframe information and confirmed-bar state transitions
Input Parameters
Regime Engine:
Return Lookback: Smoothing window for the return series
Volatility Lookback: Window used to normalize realized volatility
Efficiency Length: Measures directional travel versus rotational travel
Probability Learning: Controls how quickly the posterior probabilities adapt
Trend Confirmation Threshold: Minimum dominant probability required before a regime is treated as confirmed
Visual System:
Show Regime Backdrop
Show Probability Spread
Show State Ribbon
Show Dashboard
How to Use This Indicator
Step 1: Read the dominant state
If Trend Probability is above Range Probability and the confidence threshold is met, the market is behaving more directionally. If Range Probability dominates, the market is behaving more rotationally.
Step 2: Check confirmation
Use the confirmation state before treating the output as actionable. Developing readings can still change as the current bar closes.
Step 3: Use efficiency and bias together
High efficiency with strong directional bias supports continuation logic. Low efficiency with range dominance supports mean-reversion or lower-aggression decision making.
Step 4: Apply it as a filter
This indicator is best used as a context layer for other tools. It is not intended to predict the next bar by itself.
Indicator Limitations
Regime models classify the present environment; they do not forecast future direction
Extremely fast reversals can temporarily lower confidence before the new state stabilizes
Range and trend can overlap during transition periods, so marginal readings should be treated cautiously
Originality Statement
Covenant Regime Register is original in how it combines normalized return behavior, normalized volatility, directional efficiency, and slope persistence into a compact two-state probability register with an explicit confidence gate. It is published because:
The script produces competing regime probabilities rather than a single trend flag
The classification emphasizes state persistence and bar-close confirmation instead of hyper-reactive regime flipping
The dashboard surfaces regime context in a compact format suitable for use as a decision filter alongside other indicators
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice and does not guarantee future market behavior. All regime classifications are derived from historical and current price behavior and can produce false or delayed readings. Always use independent judgment and proper risk management.
Indicator
MATC + Ichimoku Projection 📌 MATC ICHIMOKU PROJECTION — SYSTEM DESCRIPTION (ENGLISH)
MATC Ichimoku Projection is a hybrid market structure and momentum system that combines a custom mathematical trend engine (MATC) with a projected Ichimoku Cloud model to detect high-probability breakout zones.
The system does not rely on a single indicator. Instead, it builds a multi-layer confirmation model using trend structure, volatility expansion, and projected equilibrium zones.
🔷 1. MATC STRUCTURE (Market Adaptive Trend Calculation)
MATC is a dynamic band-based trend engine built on:
Percentage-based upper/lower deviation from price
Smoothed mirror expansion of volatility
EMA-based stabilization of structural levels
This creates:
Dynamic resistance
Dynamic support
Midline equilibrium
MATC interprets market compression and expansion cycles mathematically, rather than using static indicators.
🔷 2. ICHIMOKU PROJECTION CLOUD
Instead of traditional Ichimoku interpretation, this system uses a projected cloud state:
Span A = (Tenkan + Kijun) / 2
Span B = highest/lowest range equilibrium
The cloud is used not as lagging support/resistance, but as a:
🔹 Future regime filter (bull or bear probability zone)
🔷 3. STRONG BUY LOGIC (PROBABILITY EVENT)
A Strong Buy signal is generated only when two independent systems align:
✔ MATC Breakout Condition
Price must break above MATC resistance:
This confirms structural expansion
✔ Ichimoku Bull Regime
Span A > Span B (green cloud state)
Indicates bullish future market structure
✔ Combined Confirmation
A Strong Buy occurs only when:
MATC breakout AND Ichimoku green regime align
This creates a dual-projection confirmation system, reducing false signals significantly.
🔷 4. SINGLE EVENT PER CLOUD CYCLE
To avoid overtrading and duplicate signals:
Each Ichimoku green cloud phase generates only one Strong Buy
Signal reset occurs only when a new cloud regime begins
This ensures:
One institutional-style confirmation per market expansion phase
🔷 5. SYSTEM CHARACTERISTICS
This model is:
✔ Non-repainting in logic structure
✔ Regime-based (trend + future projection)
✔ Volatility adaptive
✔ Designed for breakout confirmation rather than prediction
✔ Built to reduce fake breakouts via dual confirmation
⚠️ LIMITATIONS / DISCLAIMER
Like all technical systems, MATC Ichimoku Projection:
Does not guarantee accuracy
Can generate false signals in low-volume or choppy markets
Works best in trending or expanding volatility environments
Should be used with risk management filters
📌 CONCLUSION
MATC Ichimoku Projection is a dual-layer market intelligence system combining:
Structural trend mathematics (MATC)
Future probability cloud modeling (Ichimoku Projection)
The goal is not to predict the market, but to identify:
“Where probability of expansion becomes statistically dominant”
📌 MATC ICHIMOKU PROJEKSİYON — SİSTEM AÇIKLAMASI (TÜRKÇE)
MATC Ichimoku Projection, özel bir matematiksel trend yapısı (MATC) ile projeksiyon Ichimoku Bulutu’nun birleştirildiği hibrit bir piyasa analiz sistemidir.
Sistem tek bir indikatöre dayanmaz. Bunun yerine çok katmanlı doğrulama modeli kullanır:
Trend yapısı
Volatilite genişlemesi
Gelecek rejim tahmini
🔷 1. MATC YAPISI (Piyasa Adaptif Trend Hesaplama)
MATC sistemi şu matematiksel yapıya dayanır:
Fiyatın yüzde bazlı üst/alt bandı
Volatiliteye göre genişleyen mirror yapı
EMA ile yumuşatılmış trend seviyeleri
Sonuç olarak:
Dinamik destek
Dinamik direnç
Orta denge (midline)
MATC, piyasayı sabit çizgilerle değil:
genişleme ve sıkışma döngüleriyle matematiksel olarak okur
🔷 2. ICHIMOKU PROJEKSİYON BULUTU
Klasik Ichimoku yerine burada:
Span A = Tenkan + Kijun ortalaması
Span B = yüksek/düşük denge bölgesi
Bulut artık gecikmeli değil:
🔹 “gelecek piyasa rejimi filtresi” olarak kullanılır
🔷 3. STRONG BUY MANTIĞI (PROBABILİTY EVENT)
Strong Buy sinyali sadece iki sistem aynı anda doğrulama verdiğinde oluşur:
✔ MATC kırılımı
Fiyat MATC direncini yukarı kırar
✔ Ichimoku yeşil rejim
Span A > Span B
✔ birleşik doğrulama
İki sistem aynı anda bullish rejim üretir
Bu yapı:
çift projeksiyonlu doğrulama sistemi oluşturur
🔷 4. HER BULUTTA TEK SİNYAL
Aynı bölgede tekrar sinyal üretmemek için:
Her yeşil bulutta sadece 1 STRONG BUY
Yeni bulut başladığında reset
Bu sayede:
“kurumsal tek onay sistemi” davranışı oluşur
🔷 5. SİSTEM ÖZELLİKLERİ
✔ Repaint mantığına karşı filtrelenmiş yapı
✔ Trend + gelecek rejim sistemi
✔ Volatilite adaptif
✔ Breakout odaklı
✔ Fake sinyalleri azaltmak için çift doğrulama
⚠️ SINIRLAR
Her sistem gibi %100 doğruluk garanti etmez
Yatay piyasalarda false signal üretebilir
Trend ve hacimle birlikte kullanılması önerilir Indicator
Lorentzian Dreamer Retrieval Architecture (DAFE)Lorentzian Dreamer Retrieval Architecture
A Unified Framework for Probabilistic Market Analysis, Integrating Memory Retrieval and Generative World Modeling
█ CHAPTER 1: THE PHILOSOPHY - MEMORY REMEMBERS. DREAM IMAGINES.
Traditional technical analysis operates on a core assumption: that the future will resemble the past. It employs indicators to search for historical patterns, assuming that a pattern that resolved bullishly before will do so again. This is a one-dimensional view, often blind to the infinite nuance of context. What if the market is in a different volatility regime? What if the underlying order flow is different? What if the macro-structure has changed?
The Lorentzian Dreamer Retrieval Architecture (LDRA) was created to address this fundamental problem. It is not a predictive "signal" generator; it is a sophisticated, dual-core engine designed to model the market's probable future path by synergizing two powerful, opposing concepts:
The Memory (The Retrieval Engine): This is the system's "past." It is a powerful k-Nearest Neighbors (k-NN) engine that uses a Lorentzian distance kernel to search through hundreds of past market states. It finds the historical moments that are most geometrically similar to the current moment and retrieves what happened next. It asks: " When the market looked and felt exactly like this before, what was the outcome? "
The Dream (The Imagination Engine): This is the system's "future." It is a simplified "World Model," inspired by concepts from advanced computational theory. It has learned a mathematical representation of the market's "rules of motion." It takes the current market state as a seed and runs thousands of forward-looking simulations—"dreams"—to explore a vast tree of possible future trajectories. It asks: " Given the current state and the learned rules of physics, what are the most probable future paths? "
The LDRA is the unification of these two forces. It is a system that grounds its imagination of the future in its memory of the past. The final output is not a simple buy or sell arrow, but a rich, multi-dimensional analysis of the market's state, its probable direction, and the system's confidence in its own analysis. It is a tool for thinking in probabilities, not certainties.
█ THE LDRA CORE ARCHITECTURE - A DEEP DIVE INTO THE ENGINES
This is not a simple "mashup." It is a hierarchical, multi-stage processing pipeline where each component performs a specific, critical function.
THE ENCODER - From Chaos to Coherence
The market is a sea of noisy data—RSI, MACD, Volume, Volatility, and dozens of other metrics. The first step is to transform this high-dimensional chaos into a low-dimensional, coherent representation. The LDRA does this with an "Encoder," a fixed neural network-style layer that takes in 13+ raw market features and compresses them into a 6-dimensional "latent vector." This vector is a unique mathematical fingerprint, a "state," that encapsulates the essential character of the current market condition. This is the language the rest of the system speaks.
THE RETRIEVAL ENGINE - The Power of Lorentzian Memory
This is the system's memory bank. It stores hundreds of past latent vectors and their known outcomes. When a new state is encoded, this engine's job is to find the most similar memories.
Why Lorentzian?: Standard k-NN uses Euclidean distance (a straight line), which is brittle. A single outlier feature can make two otherwise similar states appear distant. The Lorentzian Kernel (log(1 + d^2)) is a more robust distance metric used in advanced statistics. It is far less sensitive to outliers, allowing it to find more meaningful and robustly similar historical states. It is a superior memory retrieval mechanism.
The Output: The engine retrieves the k (e.g., 8) most similar past states and performs a similarity-weighted aggregation of their outcomes. The result is the Lorentzian Prediction (lor_pred)—a powerful forecast based purely on historical precedent.
THE IMAGINATION ENGINE - The "Dreamer" World Model
This is the forward-looking, generative component. It is a simplified World Model that has learned the "transition dynamics" of the market—how one latent state is likely to evolve into the next.
How it "Dreams": It takes a seed state (either the current state or a retrieved memory state) and projects it one step into the future using its learned transition model. It then takes that future state and projects it again, and again, for a set "depth." This creates a single "dream"—a plausible future trajectory.
The Rollouts: The engine doesn't just have one dream. It runs multiple "rollouts," each time injecting a tiny amount of structured noise to explore different branches of the probability tree. It then aggregates the outcomes of all these imagined futures.
The Output: The result is the Dream Residual (WM_RESID)—the model's expectation of the future, generated from its internal understanding of market physics, completely independent of the historical retrieval. It also calculates its Uncertainty in this prediction.
THE FUSION & GATING MECHANISM
This is where memory and imagination are unified. The system calculates a Trust Gate score. This score is high if the Memory and Dream engines agree, if the Dream engine is certain of its own prediction, and if the system's historical prediction error is low. This gate then determines how much of the "Dream" is blended with the "Memory."
The Result: A Fused Score that is primarily driven by historical data (the Lorentzian prediction) but is intelligently nudged and refined by the forward-looking imagination of the Dreamer engine. The final Fused Confidence is a blend of the retrieval confidence, the imagination certainty, and the Trust Gate itself.
█ THE LIBRARY ECOSYSTEM - A SYMPHONY OF CONFLUENCE
The LDRA is the central hub, but it is made exponentially more powerful by its seamless integration with other DAFE libraries.
MarketStructureLib (MSL): This library provides the structural map of the battlefield. It performs a deep, quantitative analysis of market structure, identifying swing points, liquidity corridors, volume profiles, and "siege" levels under repeated attack. The LDRA ingests this data, using proximity to a significant structural level (like a siege resistance or a volume profile POC) as a powerful confluence factor. This grounds the LDRA's abstract analysis in the concrete reality of support and resistance.
DafeRCMLib (RCM): This is the system's final conviction layer. The LDRA's final prediction, along with intelligence from the MSL and its own internal footprint delta, is injected as "External Evidence" into the RCM engine. The RCM's Integrity score—its confidence in the current market's "truthfulness"—is then used as a master filter and confidence booster for the LDRA's output. A high-confidence LDRA prediction during a low-integrity RCM state is a warning of a potential trap.
DafeVisualsLib (Viz): This is the canvas upon which this complex data is painted. The LDRA leverages the VizLib's advanced rendering capabilities, including its theme engine and drawing helpers, to create the stunning and information-rich 3D manifold visualization. It is the engine that translates the abstract mathematics into an intuitive, perceptual experience.
The Footprint Data Source: The indicator's "order flow" sense is powered by PulseWire's request.footprint() data when available on the selected asset. This provides real, tick-level buy vs. sell volume data. In a demonstration of robustness, if native footprint data is not available, the system automatically and gracefully falls back to a sophisticated Synthetic Footprint Engine that estimates the delta from OHLCV data, ensuring universal functionality.
█ THE VISUALIZATION ENGINE - THE 3D MANIFOLD
The LDRA's output is a high-information-density, three-dimensional, animated data visualization called the "Manifold." This is a rendering engine built using the DafeVisualsLib, designed to give you an intuitive, "at-a-glance" feel for the flow of market possibilities.
The 3D Engine: The entire manifold is rendered in a simulated 3D space with a perspective camera. You can control the Camera Tilt Angle and Perspective Strength to change your viewpoint. An optional Auto-Rotate feature provides a cinematic view for presentations.
The Wireframe Manifold Mesh: The core visual is a flowing, ribbon-like structure. This is the historical trajectory of the market's latent state, projected into 3D space.
Longitudinal Curves: The main lines that flow from past to present, representing the path of the market's "fingerprint" through time.
Latitudinal Curves: The cross-ribs that give the manifold its shape, colored by the market regime at that point in time (e.g., Green for Trend, Red for Mean-Reversion).
The Memory Nebula: This is a 3D scatter plot of historical memory points that are relevant to the current state. They appear as glowing particles within the manifold, showing you the "constellation" of past events that are influencing the current prediction.
The k-NN Constellation Lines: When the retrieval engine is active, faint, dotted lines are drawn from the current point on the manifold back to the specific memory points in the Nebula that it has identified as the "k-Nearest Neighbors." This gives you a direct, visual confirmation of which past events are informing the present analysis.
The Dream Projection: From the very tip of the manifold (the current bar), the system projects several faint, glowing "ghost" trajectories into the future. These are the "dreams"—the possible future paths imagined by the World Model. Their convergence or divergence gives you an immediate sense of the model's certainty.
The Iso-Reference Planes: To ground the 3D visualization, the engine draws a horizontal grid plane (the "floor") and a vertical "Scanner Slice"—a translucent plane that helps you perceive depth and position within the manifold.
█ THE COMPLETE USER MANUAL - INPUT CONFIGURATION
Every parameter of the engine is exposed for professional-level fine-tuning. Understanding these controls is key to mastering the LDRA.
LDRA Core Engine
Memory Lookback: The number of past bars the k-NN engine searches through. A larger value provides a richer memory bank but may increase script loading time.
KNN Neighbors: The number (k) of most similar historical patterns to retrieve. A smaller k (3-5) makes the prediction sensitive to very specific matches. A larger k (10-15) creates a smoother, more generalized prediction.
Outcome Horizon: The number of bars into the future the system looks at to determine the "outcome" of a past event. A shorter horizon trains the system for scalping; a longer horizon trains it for swing trading.
Base Gamma: Controls the "width" of the Lorentzian distance kernel. A lower gamma makes the search highly sensitive to small differences. A higher gamma allows for more "fuzzy" matches, focusing on the overall geometric shape of the state.
Latent Dim: The number of dimensions in the encoded state vector. Higher dimensions can capture more nuance but require more data to be effective. 6 is the recommended balance.
WM Base LR: The base "Learning Rate" for the Dreamer (World Model). This controls how quickly the model updates its internal understanding of the market's transition dynamics.
Imagination Depth & Paths: Depth is how many steps into the future each "dream" runs. Paths is the number of separate "dreams" (rollouts) it simulates on each bar.
Discount: A factor that determines how much the Dreamer values immediate rewards versus distant ones in its simulations.
Warmup Bars: The number of historical bars the system must process to build its initial memory bank before it begins making predictions.
Min Similarity: The minimum similarity score required for a historical pattern to even be considered by the k-NN engine. Acts as a quality filter.
Manifold Visualization
Theme: Select from a suite of professionally designed color palettes to customize the entire visual experience.
Ribbon Span & Stride: Span controls the historical length of the manifold ribbon. Stride is a performance setting that controls how frequently the latitudinal ribs are drawn.
Ribbon Height & Depth: These control the vertical and Z-axis scaling of the 3D manifold in the indicator pane.
Camera Controls (Tilt, Auto-Rotate, Perspective): Direct control over the 3D camera, allowing you to change your viewpoint and add cinematic effects.
Visual Toggles (Constellation, Nodes, Grid, Nebula, Isosurf): Enable or disable specific layers of the visualization to create a cleaner or more data-rich view.
Dashboards & Other Libraries
The inputs for RCM, MSL, and the Dashboards are extensive. They allow you to fine-tune every aspect of the confluence libraries, from pivot lengths in the Market Structure Engine to the display size and position of the diagnostic panels. Please refer to the dedicated publications for those libraries for a full breakdown.
█ INTERPRETING THE DASHBOARDS
State Dashboard: This is your high-level summary.
Confluence: The primary output. It shows the Score (the strength of agreement between all engines) and the Components (how many engines are in agreement).
Lor / Flow / RCM / Structure: A breakdown of the individual signals from the Lorentzian engine, the Footprint/Delta engine, the RCM, and the MSL.
Regime: The current market personality (e.g., Trend, MeanRev).
Diagnostics Dashboard: A look under the hood for the advanced analyst.
Lor Pred & Assurance: The raw prediction from the memory retrieval and its statistical confidence.
Dream Resid & Unc: The raw prediction from the imagination engine and its uncertainty.
WM Accuracy & Pred Err: A real-time measure of how well the Dreamer engine is predicting the market's next move.
Trust Gate: The final score that determines how much the system trusts its own imagination vs. its memory.
█ DEVELOPMENT PHILOSOPHY
The Lorentzian Dreamer Retrieval Architecture is a culmination of years of research into the intersection of physics, computer science, and financial markets. It was born from the belief that the future of technical analysis is not about finding a better lagging indicator, but about building better models of the market itself. This tool is a step in that direction. It is for the serious trader, the quantitative analyst, and the systems thinker who is not just looking for signals, but for a deeper, more profound understanding of the probabilistic nature of the market.
█ DISCLAIMER AND BEST PRACTICES
THIS IS AN ADVANCED ANALYTICAL TOOL: This indicator provides a sophisticated probabilistic analysis, not direct financial advice. It is a decision-support tool designed to be the centerpiece of a comprehensive trading framework.
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The probabilities and scores are based on historical data and statistical models, not guarantees of future outcomes.
USE THE FULL CONTEXT: Do not trade based on a single number. The highest-quality insights come from confluence across all engines. A high "Confluence Score" on the main dashboard is the most robust output. Check the RCM "Integrity" and the "Market State" before acting.
GIVE IT TIME TO BUILD MEMORY: The indicator requires a "Warmup" period to build its initial memory bank. Its analysis will become progressively more robust as it processes more data.
**This script is heavy.
"The art of prophecy is very difficult, especially with respect to the future."
— Mark Twain
— Dskyz, Trade with insight. Trade with anticipation. (Don't follow the trend, be the trend) Indicator
Futures Sessions US+ UK+ JapanThis indicator displays key market session open and close times directly on the chart using clean vertical lines.
It includes session markers for:
US Futures session
UK session
Asia / Japan session
Each session can be individually switched on or off, and users can customise the open and close times, colours, line style, line width, and previous session visibility.
The US Futures lines are anchored to America/New_York time, so they automatically adjust when UK and US daylight saving time changes occur. This helps UK-based traders keep the correct futures open and close reference points without needing to manually update the script.
Main Features
Toggle US Futures, UK, and Japan session lines on/off
Separate open and close lines for each session
Custom colours for each session
Optional previous session lines
Adjustable previous session transparency
Session names displayed next to each line
Session names can be positioned at the top, middle, or bottom of the line
Adjustable label text colour, background colour, and font size
Built-in alerts for session open and close times
Designed for intraday traders who want clean session timing references
Default Session Times
US Futures Close: 16:45 New York time
US Futures Open: 18:00 New York time
UK Open: 08:00 London time
UK Close: 16:30 London time
Japan Open: 09:00 Tokyo time
Japan Close: 15:00 Tokyo time
All session times are editable in the indicator settings. Indicator
Elaris FVG Inversion ProElaris FVG Inversion Pro
Advanced Fair Value Gap & Inversion Mapping System
Elaris FVG Inversion Pro is a professional-grade market structure and imbalance visualization tool designed to help traders identify fair value gaps (FVGs), inversion fair value gaps (IFVGs), and potential reaction zones directly on the chart.
The indicator focuses on price inefficiencies created by aggressive directional movement and highlights areas where price may revisit, react, continue, or reverse.
Instead of displaying excessive noise, the system uses smart filtering logic, mitigation tracking, and optional trend confirmation to provide a cleaner and more structured view of market imbalance behavior.
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Core Features
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• Bullish & Bearish Fair Value Gap Detection
Automatically identifies three-candle imbalance structures in real time.
• Inversion Fair Value Gaps (IFVG)
Detects when previously established imbalance zones transition into potential reversal zones after invalidation.
• Non-Repainting Confirmed Detection
Signals and zones can be confirmed only after candle close to maintain stable historical plotting.
• Smart Gap Filtering
Optional ATR, volume, and EMA trend filters help reduce low-quality or insignificant zones.
• Dynamic Zone Management
Zones automatically extend forward and can be configured to expire or hide after mitigation.
• Mitigation Tracking
Supports multiple mitigation models including:
* Proximal
* Midpoint (50%)
* Distal
• Quality Scoring System
Each zone is evaluated using volatility and structure-based conditions to help prioritize stronger imbalances.
• Dark & Light Mode Compatible
Designed for clean visibility across different PulseWire chart themes.
• Professional Dashboard
Displays active bullish, bearish, and inversion zones along with trend-state information.
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How It Works
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Bullish FVG:
Forms when price leaves an upside imbalance between candles, potentially creating a future demand area.
Bearish FVG:
Forms when price leaves a downside imbalance, potentially acting as future supply.
IFVG:
Occurs when price invalidates an existing FVG and the zone transitions into a potential reversal area.
The indicator can be used for:
* Trend continuation setups
* Pullback entries
* Liquidity-based reactions
* Market structure analysis
* Confluence with support/resistance or liquidity concepts
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Best Practices
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Higher timeframe FVGs generally produce stronger reactions than lower timeframe gaps.
Using the optional EMA and volume filters may help improve signal quality during volatile or choppy market conditions.
For confirmation-based trading approaches, combine FVG reactions with structure breaks, momentum shifts, or liquidity sweeps.
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Notes
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This indicator is intended for technical analysis and educational purposes.
No indicator guarantees future performance, and traders should always apply proper risk management and independent confirmation before making trading decisions.
Indicator
