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Bitcoin vs Inverted DXY Regime | Astral Vision Bitcoin vs Inverted DXY Regime | Astral Vision 🌠💠 Inspired by: @JackGreenCrypto This indicator plots the Z-Score of Bitcoin's log price and the inverted Z-Score of the DXY Dollar Index on the same scale, shifted forward in time by a configurable lag, to visualize the historical inverse relationship between dollar strength and Bitcoin price and to identify divergences between the two series before they resolve in price. Calculation ⚙️ Both Bitcoin and DXY are first transformed by taking their natural logarithm. Working in log space is essential for two reasons: Bitcoin has moved across several orders of magnitude, so raw price differences between 2015 and 2024 are not comparable; and the inverse relationship between DXY and Bitcoin is proportional rather than linear, meaning a 10% move in DXY has a proportionally similar impact regardless of absolute price level. The Z-Score is then computed independently for each series using the same formula: subtract the simple moving average of the log series over the lookback window from the current log value, then divide by the standard deviation of the same window. The result for each series is: Z = (log(price) - SMA(log(price), N)) / StDev(log(price), N), where N is the configurable Z-Score lookback. This expresses each series in units of standard deviations above or below its own rolling mean, making the two series directly comparable on the same scale regardless of their absolute price differences. The DXY Z-Score is then multiplied by -1 to invert it. Since DXY and Bitcoin historically move in opposite directions, the uninverted DXY Z-Score would mirror Bitcoin rather than track it. After inversion, the two series should theoretically overlap or move together during periods of normal inverse correlation. Divergences, where Bitcoin's Z-Score moves in one direction while the inverted DXY Z-Score moves in the other, signal a breakdown in the typical relationship that has historically tended to resolve by one series catching up to the other. The inverted DXY line is plotted with a configurable forward offset in bars. This operationalizes the observed lag between dollar index movements and their full transmission into Bitcoin price, allowing the DXY signal to be read as a leading indicator rather than a coincident one. Plots 📊 Bitcoin log Z-Score over the configurable lookback window Inverted DXY log Z-Score shifted forward by the configurable lag Inputs 🎛️ Z-Score Lookback: rolling window for mean and standard deviation applied to both series DXY Lag: bars to shift the inverted DXY signal forward in time Colors 🎨 5 Astral Vision presets + custom override. Default: Paradiso. Purpose 🎯 Standard correlation tools between Bitcoin and DXY operate on raw prices or percentage returns, which are distorted by the enormous scale difference between the two assets and by Bitcoin's exponential growth trend. Computing both in log space and normalizing via Z-Score removes these distortions entirely, placing both series on equal statistical footing. The forward shift on the DXY line turns a coincident correlation chart into a directional leading signal: when the inverted DXY is rising ahead of Bitcoin's Z-Score, it suggests dollar weakness is building that has not yet fully priced into Bitcoin, and vice versa. Disclaimer ⭕️ This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions.
Indicator
by AstralVision
AetherEdge Hybrid Quantum-Inspired Predictor🖊️ Overview AetherEdge Hybrid Quantum-Inspired Predictor is a next-generation 3-class (UP/DOWN/SIDE) probability prediction engine that fuses three heterogeneous models: a quantum-mechanical wavefunction approach, a K-Nearest Neighbors historical analog search, and a self-learning neural network. Born-rule probabilities derived from complex amplitudes ψ, distance-weighted K-NN voting, and Softmax-based self-optimizing neurons all converge into a single ensemble distribution. With a complete visualization system featuring a radar chart, pie chart, and historical analogs, it decodes the market's "superposition state." 🔶 Key Features 3-Layer Hybrid Architecture: Quantum + KNN + Neural Quantum Layer: Complex wavefunction ψ, Gaussian amplitudes, decoherence, phase KNN Layer: K-nearest analog search in 3D feature space Neural Layer: Online gradient descent + Softmax + Weight decay 3-Class Classification: UP/DOWN/SIDE probability distribution 3 Core Features: Price Structure / Liquidity / Sentiment Proxy On-Chart Radar Chart: 3-axis visualization On-Chart Pie Chart: Instant state distribution view Historical Analogs Display: Top-3 similar past patterns Quantum Internal State Monitor: Re(ψ), Im(ψ), |ψ|², entropy State Flip Markers: Auto-detection of directional transitions Strategy Suggestion Engine: LONG BIAS / SHORT BIAS / RANGE FADE, etc. 🧠 Technical Architecture This indicator is designed as an ensemble predictor of three heterogeneous models. Feature Engineering: Price Structure: EMA20/50/100 + RSI + ATR-deviation composite (tanh normalized) Liquidity: Close position + wick asymmetry + VWAP deviation + sweep detection Sentiment Proxy: Vol-Z + price-volume divergence + A/D + volume spike Quantum Layer: Wavefunction Construction: ψ_state = Σ A·e^(iθ) per feature Gaussian Amplitude: A(f, target) = exp(-(f-target)²/(2σ²)) Phase Intensity: θ = f × qPhase × π/2 + offset Born Rule: P_state = |ψ|² / Σ|ψ|² Decoherence: P' = (1-γ)·P + γ/3 for quantum→classical transition Quantum Coherence: 1 - H/log(3) state clarity KNN Layer: Euclidean distance search in 3D feature space Distance-weighted voting w = 1/(1+d) Aggregates labels (UP/DOWN/SIDE) at past kHorizon-bar future Records Top-3 analogs (rank, distance, realized return) Neural Layer: 3-class Softmax classifier (3 inputs → 3 outputs, 12 parameters) Cross-Entropy Gradient: g = p - target Update Rule: w_new = w·decay - lr·g·feature Weight Decay: Prevents overfitting + forgets old patterns Ensemble Integration: Weighted average: P = (q·P_q + k·P_k + n·P_n) / Σw After normalization, max-probability class becomes dominant state Strategy Engine: domProb ≥ 0.55 + UP → LONG BIAS domProb ≥ 0.55 + DOWN → SHORT BIAS domProb ≥ 0.55 + SIDE → RANGE FADE confidence < 0.15 → HIGH UNCERTAINTY ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (4H): qWeight=0.35, kWeight=0.35, nWeight=0.30, kK=20 ETH (1H): qWeight=0.30, kWeight=0.40, nWeight=0.30, kHorizon=5 SOL (high-vol): qSigma=1.5, qDecoher=0.15, kThresh=0.5 XRP (short-term): kHorizon=3, kThresh=0.2, nLR=0.02 Quantum Layer: qWeight=0.20: Conservative (suppress quantum contribution) qWeight=0.35: Standard qWeight=0.50: Experimental (quantum-dominant) qSigma=0.8: Sharp (clear states) qSigma=1.2: Standard qSigma=2.0: Smooth (high uncertainty) qDecoher=0.05: Pure quantum qDecoher=0.10: Standard qDecoher=0.30: Strong classical approximation KNN Layer: kK=10: Curated matches (sharp) kK=20: Standard kK=40: Smooth (conservative) kLookback=300: Lightweight kLookback=500: Standard kLookback=1000: Long-term patterns kThresh=0.2%: Sensitive (short TF) kThresh=0.3%: Standard kThresh=0.5%: Conservative (long TF) Neural Layer: nLR=0.005: Cautious learning (stable) nLR=0.015: Standard nLR=0.05: Fast adaptation (unstable) nDecay=0.999: Long-term memory nDecay=0.997: Standard nDecay=0.99: Quick forgetting 💡 How to Use in Practice LONG BIAS + High Q-Coherence: Strongest buy signal, consider entry SHORT BIAS + High Confidence: Strongest sell signal, build short RANGE FADE: Range strategies, sell premium, fade both extremes HIGH UNCERTAINTY: Reduce positions, observe mode State Flip → UP: Early trend transition, early entry All Top-3 Analogs Same Direction: Strong historical evidence, raise confidence 3-Model Consensus: Quantum/KNN/Neural all UP → highest confidence 3-Model Divergence: Split opinion, exercise caution Dir Bias > 0.3: Strong upward bias Vol Anomaly Detected: Suspend forecasts during normal-time logic AetherEdge Synergy: Self-Evolving S/R Grid: LONG BIAS + support reaction = high-win-rate entry Volatility Regime GAN: SHORT BIAS + EXPAND forecast = powerful drop setup SMC AI Confidence: 3-model consensus + high-conf zone = conviction entry Neural Divergence Hunter: State Flip + divergence = reversal confirmation ⚠️ Important Notes Initial Learning Period: Neural & KNN immature until bar_index > 110 Model Divergence: Split opinions signal weak signal strength Quantum is Approximation: Mathematical analogy, not actual quantum computing History Dependent: Cannot handle unprecedented market events Computation Load: Radar & pie rendering slightly heavy Repaint: Runs at barstate.islast, displayed only at last bar Neural Weights: Reset to defaults on chart reload Confidence < 0.15: Near-uniform distribution, recommend avoiding trades 🚨 Disclaimer This indicator is an advanced hybrid prediction tool for educational and research purposes only and does not constitute financial advice. "Quantum-inspired" is a mathematical analogy, not actual quantum computing. The 3-model ensemble prediction is a probabilistic method and does not guarantee future price movements. Use with thorough validation and proper risk management.
Indicator
by AetherEdge
AetherEdge Volatility Regime GAN Simulator🖊️ Overview AetherEdge Volatility Regime GAN Simulator is a next-generation volatility forecasting engine that encodes the current market state as a 6-dimensional feature vector, performs K-NN search against a historical database of up to 500 bars, and generates 100 Monte Carlo paths of likely future trajectories. By integrating Haar wavelet decomposition, Softmax-weighted sampling, and K-means regime clustering, it visualizes the future σ distribution as a complete fan chart and histogram. Inspired by Generative Adversarial Networks, it reconstructs future scenarios from historical market patterns — an innovative simulator for the modern trader. 🔶 Key Features 3-Scale Volatility: Short/Medium/Long σ (annualized) Haar Wavelet Decomposition: high/mid/low frequency triple-scale 6D Feature Vector: σ-z (×3) + VoV-z + Wavelet-z (×2) K-NN Analog Search: Top-K nearest historical states Softmax-Weighted Sampling: Temperature τ controls similarity allocation Monte Carlo Path Generation: 100 paths build future distribution K-Means Regime Clustering: CALM/NORMAL/ELEVATED/STRESSED/EXTREME/CRISIS Fan Chart Visualization: P5-P95, P25-P75, median line Terminal Distribution Histogram: σ_T probability density Complete Statistical Dashboard: E , median, CI90, skew, regime distribution Match Quality Indicator: ⟨d⟩ assesses analog availability 🧠 Technical Architecture This indicator is designed as a history-based generative model. Feature Engineering: Log-return ret = ln(close/close ) 3-scale stdev × √(annFactor) for annualized σ Haar wavelet: |W_s| = √(s/2) × |mean_recent - mean_older| 200-bar z-score normalization (outlier robust) 6D Feature Vector: z1=σS, z2=σM, z3=σL, z4=VoV, z5=W_hi, z6=W_mid K-NN Search: Euclidean distance d = √Σ(z_now - z_hist)² Top kNeighbors selected Softmax Sampling: w_i = exp(-d_i / τ) / Σexp(-d_j / τ) Low τ → focus on closest, High τ → diversify Monte Carlo Path Generation: Per simulation: cumulative probability samples a neighbor σ-ratio projection pathVol = volS × (histFutVol / nVol) Innovation noise + noiseAmp × volS × U(-0.5, 0.5) Quantile Computation: P5/P25/P50/P75/P95 extracted at each time step Fan chart + smooth polyline rendering K-Means Clustering: 2D space (σ-z, VoV-z) classified into 4-6 regimes Lloyd's algorithm for kmIters iterations σ-z ascending sort (regime 0 = calmest) Match Quality Metric: ⟨d⟩ < 1.0: TIGHT (high reliability) ⟨d⟩ < 2.5: LOOSE (moderate) ⟨d⟩ ≥ 2.5: POOR (no analog → warning) ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (Daily): volLen=20, annFactor=365, historyLen=500 ETH (4H): volLen=14, volMed=42, kNeighbors=15 SOL (high-vol): volLen=10, nSims=200, noiseAmp=0.12 XRP (short-term): volLen=14, forecastLen=10, kNeighbors=15 History Depth: historyLen=200: lightweight, recent only historyLen=500: standard (recommended) historyLen=1000+: long-term pattern reference K-NN Settings: kNeighbors=10: curated, sharp forecast kNeighbors=20: standard (balanced) kNeighbors=50: smooth, conservative Temperature Parameter: tempSample=0.3: elite concentration (focus on nearest) tempSample=1.0: standard (recommended) tempSample=3.0: diversification (high uncertainty) Monte Carlo: nSims=50: lightweight, low-resolution nSims=100: standard nSims=200+: high-resolution histogram Noise Amplitude: noiseAmp=0.05: conservative (history-faithful) noiseAmp=0.08: standard noiseAmp=0.15: exploratory (unknown scenarios) Regime Count: nRegimes=3: coarse (simple) nRegimes=4: standard nRegimes=6: granular (CALM→CRISIS) 💡 How to Use in Practice EXPAND forecast + STRESSED: Buy options / strengthen hedges CONTRACT forecast + CALM: Range strategies / sell premium Narrow CI90 + TIGHT match: High-confidence forecast, execute strategy Wide CI90 + POOR match: Unknown territory, exercise caution P95 > 2× volS: Tail-risk alert, reduce position Regime Transition Detection: CALM → ELEVATED: Caution mode STRESSED → EXTREME: Crisis approaching EXTREME → STRESSED: Storm passing Skew Interpretation: right-tail: Upside risk dominant (vol spike possible) symmetric: Standard scenario left-tail: Downside risk dominant (vol crash possible) AetherEdge Synergy: SMC AI Confidence Engine: CONTRACT forecast + high-conf zone = compression breakout setup Self-Evolving S/R Grid: EXPAND forecast + line cluster = breakout preparation Neural Divergence Hunter: STRESSED + divergence = elevated reversal probability All-in-One Dashboard: Regime + direction = comprehensive judgment ⚠️ Important Notes History Dependency: Waits for bar_index > 210 to accumulate history (no early rendering) POOR Match Warning: ⟨d⟩ > 3.0 indicates low forecast reliability History-Based Prediction: Cannot handle unprecedented market events Fan Chart Width: Wide CI90 indicates high uncertainty Repaint Behavior: Runs only on barstate.islast, displayed at last bar Computation Load: Large nSims × forecastLen may slow rendering Cluster Initialization Sensitivity: K-means convergence depends on initial values (increase kmIters for stability) 🚨 Disclaimer This indicator is an advanced volatility-simulation tool for educational and research purposes only and does not constitute financial advice. GAN-style Monte Carlo prediction is a stochastic simulation method and does not guarantee future volatility. Historical patterns may not repeat — particularly during unprecedented events, the forecast may fail. Use with thorough validation and proper risk management.
Indicator
by AetherEdge
AetherEdge Multi-Feature Neural Divergence Hunter🖊️ Overview AetherEdge Multi-Feature Neural Divergence Hunter is a revolutionary divergence engine that simultaneously detects divergences across 6 independent oscillators and self-evolves neural weights based on each feature's empirical accuracy. Every feature (RSI/MACD/Stoch/CCI/MFI/MOM) detects both Regular and Hidden divergences in parallel, and Softmax-normalized dynamic weights evaluate confluence. With its 3D visual encoding (color × size × shape), the type and strength of every signal is identifiable at a glance — a next-generation divergence hunter. 🔶 Key Features 6 Parallel Oscillator Detection: RSI / MACD Hist / Stochastic / CCI / MFI / Momentum Up to 12 Divergence Variants (2 types × 6 features) tracked simultaneously Softmax Neural Weight Learning: Auto-redistribution based on empirical accuracy Reward-Based Weight Updates: Bonus for correct, penalty for wrong Confluence Counting: Separate Regular/Hidden tracking, combined for trigger 3D Visual Encoding: Color: REG Bull/Bear, HID Bull/Bear, MIXED — 5-class system Size: tiny→huge based on confluence count (5 levels) Shape: Regular=triangle, Hidden=arrow, Mixed=label Composite Neural Oscillator: Weighted average centered display Confluence Glow: Background opacity reflects strength Per-Feature Accuracy Tracking: Individual hit-rate records Dynamic Divergence Lines: Width and style identify type 🧠 Technical Architecture This indicator is a neural-weight-learning system processing 6 oscillators in parallel. Feature Normalization: RSI/Stoch/MFI: Native 0-100 MACD Hist/CCI/Momentum: Normalized to 0-100 over 100-bar range Pivot Storage Method (Pine v5 compatible): Eliminates dynamic history access by storing previous-pivot oscillator values 12 slots managed: prevPL_rsi, prevPL_macd, ... Divergence Detection (per feature): Regular Bull: Price LL + Oscillator HL Hidden Bull: Price HL + Oscillator LL Regular Bear: Price HH + Oscillator LH Hidden Bear: Price LH + Oscillator HH Distance filter: minBarsBet ≤ pivot gap ≤ maxBarsBet Neural Weight Learning: Evaluates return after rewardLook bars per signal Correct (ret > 0): w += learnRate × |ret| × 100 Wrong (ret < 0): w -= learnRate × |ret| × 50 (asymmetric penalty) Weight constraint: Softmax Normalization (temperature τ): w_i = exp((rawW_i - max) / τ) / Σexp(...) Low τ → concentrate on best; High τ → uniform Composite Neural Oscillator: nnOsc = Σ(value_i × w_i) / Σw_i - 50 3-bar EMA smoothing, 5-zone state detection 3D Visual Encoding: Color = type, Size = strength, Shape = category — 3-dimensional information transmission ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (4H): pivotLen=5, minConfluence=2, learnRate=0.08 (standard) ETH (1H): pivotLen=4, minConfluence=2, rewardLook=8 SOL (high-vol): pivotLen=6, minConfluence=3, rewardLook=12 XRP (short-term): pivotLen=3, minConfluence=2, rewardLook=5 Pivot Sensitivity: pivotLen=3: high-sensitivity, noisy pivotLen=5: standard pivotLen=8–10: HTF, certainty-focused Confluence Threshold: minConfluence=1: many signals, false positives minConfluence=2: standard (recommended) minConfluence=3: curated, fewer minConfluence=4+: ultra-curated, rare Learning Parameters: learnRate=0.05: conservative, stable learnRate=0.08: standard learnRate=0.15: fast, unstable risk softmaxTemp=0.5: elite concentration (focus on best 1-2) softmaxTemp=1.5: standard (balanced) softmaxTemp=3.0: uniform (diversity) Feature Selection Strategy: Oscillator-focus: RSI + Stoch + MFI (momentum group) Trend-focus: MACD + CCI + Momentum Full parallel: All 6 ON (recommended, NN auto-curates) 💡 How to Use in Practice 5+ Confluence + REG MIXED: Strongest reversal signal (huge size emphasis) 3 REG Bull + NN 80%: High-probability reversal-buy candidate 3 HID Bull: Trend-continuation pullback buy MIXED (REG+HID): Transition zone / unstable market Neural Oscillator Usage: EXTREME OB (>35): Overheated, watch for reversal EXTREME OS (<-35): Bottom zone, watch for bounce OS zero-cross + divergence: Direction confirmation Learning Maturity Check: Per-feature Acc% > 60%: feature is reliable Acc% < 45%: feature is failing, ignore Many Pending: still learning, wait AetherEdge Synergy: SMC AI Confidence Engine: A+ zone + 5× confluence = ultimate alignment NeuraNet Predictor: Direction match + REG MIXED = double confirmation RL Signal Optimizer: CONFIDENT + divergence = strong entry All-in-One Dashboard: HIGH-CONVICTION + huge label = supreme reversal signal ⚠️ Important Notes Pivot Lag: Divergence confirms pivotLen bars later (no real-time detection) Weight Reset: Chart reload resets weight learning Learning Maturity: Per-feature Acc% reliable only after Signals ≥ 20 Overfitting Risk: High learnRate + long runtime may over-skew weights Hidden Divergence Nature: Continuation signal — avoid counter-trend misuse MIXED Interpretation: Simultaneous REG+HID = transitional confusion, be cautious Feature Disabling: Turning off unused features improves weight allocation 🚨 Disclaimer This indicator is an advanced divergence-analysis tool for educational and research purposes only and does not constitute financial advice. Neural weight learning is a stochastic optimization method and does not guarantee future profits. Divergences suggest reversal or continuation possibilities but do not guarantee outcomes. Use with thorough validation and proper risk management.
Indicator
by AetherEdge
GLI Trend Analysis | Astral Vision GLI Trend Analysis | Astral Vision 🌠💠 This indicator plots the Global Liquidity Index and its exponential moving average, using the EMA crossover as a directional trend signal for the global monetary environment. The GLI is constructed from the same comprehensive 21-source aggregation used across the Astral Vision liquidity suite: 17 major central bank balance sheets converted to USD via live FX rates, minus the Fed's non-stimulative liabilities (Reverse Repo Facility and Treasury General Account), plus M2 money supply for the US, EU, China, and Japan. Calculation ⚙️ The GLI is computed as: Fed balance sheet, minus RRP (Reverse Repo: overnight cash parked at the Fed by money market funds, which drains liquidity from the financial system), minus TGA (Treasury General Account: the US government's cash balance at the Fed, which also drains liquidity when it grows), plus the balance sheets of the Bank of Japan, People's Bank of China, Bank of England, ECB, Reserve Bank of India, Bank of Canada, Reserve Bank of Australia, Swiss National Bank, Central Bank of Russia, Central Bank of Brazil, Bank of Korea, Reserve Bank of New Zealand, Sveriges Riksbank, and Bank Negara Malaysia, each converted to USD by multiplying by the corresponding live FX rate, plus M2 money supply for the US, EU (converted via EURUSD), China (converted via CNYUSD), and Japan (converted via JPYUSD). The subtraction of RRP and TGA from the Fed balance sheet is a critical correction absent from simpler GLI implementations. The Fed's balance sheet includes liabilities that do not actually inject money into the financial system: when RRP balances are high, money market funds are lending cash back to the Fed overnight, effectively withdrawing it from circulation. Similarly, a growing TGA means the government is holding more cash at the Fed rather than spending it into the economy. Subtracting both produces a more accurate measure of net liquidity actually available to financial markets. An EMA of configurable length is applied to the resulting GLI series. When GLI is above its EMA, global liquidity is in an uptrend relative to its own smoothed baseline, historically associated with expanding risk appetite and upward pressure on Bitcoin and other risk assets. When GLI is below its EMA, the trend is contractionary. Both the GLI line and the background color on the price chart are shifted forward in time by a configurable offset in bars, operationalizing the empirically documented lead-lag relationship between global liquidity inflections and Bitcoin price response. Plots 📊 GLI line colored by its position relative to the EMA, shifted forward by the configurable offset EMA line as a neutral reference Background color on the price chart reflecting GLI trend direction, shifted forward by the same offset Inputs 🎛️ EMA Length: smoothing period for the GLI trend baseline Lead Offset: bars to shift both the GLI signal and the background color forward in time Colors 🎨 5 Astral Vision presets + custom override. Default: Infinito. Purpose 🎯 A standard EMA crossover applied to Bitcoin price measures Bitcoin's own momentum without any external reference. This indicator applies the same crossover logic to global central bank liquidity, producing a regime signal that is causally upstream of Bitcoin price rather than derived from it. The forward shift separates this tool from coincident liquidity indicators by explicitly positioning the signal as a leading reference, reflecting the time lag between liquidity creation and its transmission into asset prices. The RRP and TGA correction further distinguishes it from simpler GLI charts available elsewhere, which overstate liquidity by including Fed liabilities that do not reach financial markets. Disclaimer ⭕️ This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions.
Indicator
by AstralVision
AetherEdge SMC AI Confidence Engine🖊️ Overview AetherEdge SMC AI Confidence Engine is a next-generation SMC engine fusing Smart Money Concepts—the language of institutional footprints—with 8-factor AI confidence scoring and kNN pattern matching. It auto-detects the three pillars of smart-money zones (Order Blocks, Fair Value Gaps, Liquidity Sweeps) and instantly delivers an A+ to D conviction grade by integrating eight weighted factors: structure integrity, displacement, volume, MTF consensus, kNN similarity, imbalance, freshness, and confluence. A data-driven SMC intelligence that answers the eternal question: "Which zone is real?" 🔶 Key Features 3 Core SMC Zone Detection: Order Block / Fair Value Gap / Liquidity Sweep 8-Factor AI Scoring: Structure / Displacement / Volume / MTF / kNN / Imbalance / Freshness / Confluence kNN Pattern Matching: 5-D feature space, K-nearest historical analogs A+/A/B/C/D Grades: Intuitive five-tier conviction ranking 3-Layer MTF Consensus: 1H / 4H / D triple confirmation BOS/CHoCH Detection: Auto-identifies structural breaks Mitigation Tracking: Records zone-touch events; Fresh/Mitigated state Confluence Detection: Auto-bonuses overlapping zones Dynamic Visualization: Transparency, border & size scale with score Top-Zone Detail Panel: Full 8-factor breakdown with bar visualization Ranking Table: Up to 10 zones sorted by composite score Custom Weights: Tune all 8 factors to fit your trading style 🧠 Technical Architecture This indicator elevates SMC theory into a statistical / machine-learning framework, delivering industry-leading smart-money intelligence. Zone Detection Logic: Order Block: Strong displacement (ATR×1.5+) + volume Z>0.5 + BOS triggers backward scan to last opposite-colored candle FVG: 3-bar gap exceeding ATR × fvgMinSize Liquidity Sweep: Recent swing pierced by sweepTol × ATR then reversal close 8-Factor Scoring (each normalized 0–100): Structure Integrity — trend alignment + BOS bonus Displacement Strength — birth-time displacement magnitude Volume Profile — birth-time volume Z-score MTF Consensus — sum of 3 HTF EMA20/50 slopes (–3 to +3) kNN Pattern Match — 5-D feature K-NN average forward magnitude Imbalance Ratio — zone size ÷ ATR Freshness/Age — exp(-age/150) decay, –30 if mitigated Confluence Score — bonus per overlapping zone kNN Engine: Features: displacement / volZ / bodyRatio / atrRatio / rangeRatio 300-bar history buffer, K=5 nearest by Euclidean distance Average 5-bar-forward absolute move scored probabilistically Weighted Composite: composite = Σ(factor_i × weight_i) / Σweights Dynamic Update: All active zones rescored every confirmed bar Grade Mapping: ≥85=A+, ≥75=A, ≥65=B, ≥50=C, <50=D ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (4H–D): Pivot=5, OBLookback=50, MTF=4H/D/W ETH (1H–4H): Pivot=5, fvgMinSize=0.3, MTF=1H/4H/D (standard) SOL (high-vol): Pivot=7, sweepTol=0.15, fvgMinSize=0.4 XRP (short-swing): Pivot=3, OBLookback=30, MTF=15M/1H/4H Pivot Length (structure sensitivity): 3: agile, noisy 5: standard 7–10: major-structure only, HTF use FVG Min Size: 0.2: catch fine gaps 0.3: standard 0.5: strong FVGs only Weight Tuning by Style: Trend Follower: Structure↑, MTF↑, Displacement↑ Counter-Trader: kNN↑, Freshness↑, Imbalance↑ Scalper: Volume↑, Displacement↑, Freshness↑ Swing Trader: MTF↑, Confluence↑, Structure↑ Min Score Display: 40: standard (hide D) 65: B+ only, clean view 75: A+ only, ultra-curated kNN Config: K=3: sharp similarity, unstable K=5: standard K=10–15: smooth, averaged 💡 How to Use in Practice A+ Zones (≥85%): Gold-bordered ultimate-conviction zones, primary thesis A Zones (75–85%): High quality, multi-factor agreement, entry candidates B Zones (65–75%): Reference level, combine with other evidence C/D (<65%): Information only, generally ignore Fresh + A+: Untouched apex zone, optimal ambush Mitigated + High Score: Past battlefield, high re-test reaction probability Confluence Areas: OB + FVG + Sweep overlap = institutional decision zones Trade Scenarios: Bull OB A+ + MTF Strong Bull: Golden pullback long Bear FVG A + Liquidity Sweep up-pierce: Short opportunity BOS Up + Recent A+ Zone: Trend-confirmation entry AetherEdge Synergy: Self-Evolving S/R Grid: A+ zone + strong S/R line overlap = ultimate alignment LSTM Forecaster: Bullish prediction + Bull OB A+ = dual AI conviction NeuraNet Predictor: Direction match + zone reach = high-probability entry All-in-One Dashboard: HIGH-CONVICTION + A+ zone = supreme alignment Multi-TF Workflow: Identify A+ zones on D/4H → precise entry on 1H/15M ⚠️ Important Notes kNN Warm-up: <300 bars yields unstable similarity, fixed 50-score Zone Cap: Oldest zones auto-pruned beyond maxZones Weight Normalization: Auto-normalized regardless of total—relative influence MTF Lag: HTF references confirmed bars; first few bars unfinalized Detection Delay: Pivots confirm pivotlen bars later; real-time slight delay Zone Overlap: Same OB may be detected multiple times—use as confluence signal 🚨 Disclaimer This indicator is an SMC analysis support tool provided for educational and research purposes only and does not constitute financial advice. AI confidence scores are probabilistic indicators based on mathematical approximation and do not guarantee future profits. Scores are one input among many—combine with independent analysis and proper risk management.
Indicator
by AetherEdge
AetherEdge Adaptive LSTM-inspired Forecaster🖊️ Overview AetherEdge Adaptive LSTM-inspired Forecaster is a next-generation price forecasting engine that recreates the core machinery of the LSTM (Long Short-Term Memory) neural network—the cornerstone of deep learning—directly in Pine Script. Featuring a multi-layered memory structure of forget, input, output gates and cell state, augmented by an Attention mechanism that dynamically references the most relevant past moments, plus residual connections that bypass training instability, it fuses multi-timeframe features to render 1–30 bar price forecasts and uncertainty bands with exceptional fidelity. 🔶 Key Features LSTM-style Cell: Full implementation of forget/input/output gates + cell state Parallel Hidden Units: 3–16 units with diverse time constants τ for layered memory Attention Mechanism: Similarity + recency-decayed weighted context Residual Connection: Skip connection bypassing vanishing-gradient pitfalls Multi-Timeframe Features: HTF (1H) + HHTF (4H) hierarchical inputs 8-D Input Vector: retZ / volZ / RSI / Slope / Mom / HTF Ret / HTF Bias / HHTF Bias Multi-Step Forecast: 1–30 bar projection lines Uncertainty Bands: √t-decay confidence intervals (inner 1σ / outer 2σ) 3 Band Methods: Residual Std / ATR / Hybrid Hidden Unit Visualization: τ, Cell, Hidden & activity bars per unit Attention Heatmap: 30-bar attention weight distribution Hit Score: Real-time evaluation of last forecast accuracy Bullish/Bearish Flip & Band-Breach Alerts 🧠 Technical Architecture This indicator is an industry-leading neural forecasting engine fully reconstructing the mathematical essence of LSTM, the apex of RNN-family models, in Pine Script. LSTM Cell Equations (per unit u): Forget Gate f_t = σ(W_f · x_t + b_f) — past memory retention Input Gate i_t = σ(W_i · x_t + b_i) — new information intake Candidate g_t = tanh(W_g · x_t + b_g) — new memory candidate Output Gate o_t = σ(W_o · x_t + b_o) — exposure to hidden state Cell Update c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t Hidden h_t = o_t ⊙ tanh(c_t) τ Diversity: Each unit assigned distinct τ, processing short-to-long memory in parallel Pseudo-Weight Generation: Deterministic sin/cos phase for stable initialization MTF Influence Boost: Input gate amplified by HHTF Bias magnitude Attention: Score = h_curr × h_past − |h_curr − h_past|×0.5 + recency decay Softmax normalization (temperature-adjustable) → context = weighted mean Residual Path: Upper-half units add raw retZ × residualMix × (1-τ) Output Projection: h_aggregated × σ_returns × 0.5 + Attention + Residual contributions Uncertainty Model: σ_h = σ_base × fanOut^h × √h (band widens with horizon) Decay Forecast: Multi-step uses stepRet × 0.82^(h-1) (extrapolation decay suppresses overshoot) ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (4H–D): Hidden=8, Seq=30, Forecast=10 (standard) ETH (1H–4H): Hidden=10, Seq=40, MTF Weight=0.4 SOL (high-vol): Hidden=12, Forget=0.88, fanOut=1.20 XRP (short-bias): Hidden=6, Seq=20, Forecast=5 Hidden Units (Memory Capacity): 3–5: Lightweight, short-pattern specialized 8: Standard (recommended) 12–16: Rich memory, heavier compute Sequence Length (Attention Range): 20: Recency-focused 30: Standard 50–100: Long context, lower TFs Learning Rate α (lrBase): 0.05–0.10: Conservative, smooth 0.15: Standard 0.25–0.50: Agile, noisier Forget Gate Base: 0.85–0.90: Agile forgetting, regime-shift responsive 0.92: Standard 0.95–0.98: Long memory, range-favoring Attention Temperature: 0.5–1.0: Sharp focus, specific-pattern referencing 1.5: Standard 2.5–5.0: Even attention, stable but dull Band Method: Residual Std: Empirical, most precise ATR: Simple, valid early Hybrid: Geometric mean, robust 💡 How to Use in Practice Strong Bullish Forecast: σ-breach prediction + positive attention = long entry Strong Bearish Forecast: Opposite, short consideration Band Breach: Price exceeded ±2σ band, overheating warning Forecast Flip: expectedRet zero-cross, early regime-shift signal Hit Score > 70%: Last forecast accurate, current trustworthy Hit Score < 40%: Sudden volatility regime, low confidence—wait Hidden Unit Activity: Multiple units same direction = strong consensus; mixed = regime transition Attention Heatmap purple-dominant: Strong reference to past similar pattern, repeating-scenario likely Attention dispersed: No precedent, novel territory—tread cautiously AetherEdge Synergy: KNN Regime Classifier: Trending▲ + LSTM bullish forecast = dual AI conviction NeuraNet Predictor: Direction agreement amplifies signal Self-Evolving S/R Grid: LSTM band edges ≈ S/R lines = strong reaction candidates All-in-One Dashboard: HIGH-CONVICTION + Hit Score > 70% = ultimate alignment Multi-TF: 4H LSTM direction → 1H strategy selection → 15M precision entry ⚠️ Important Notes Pseudo-Weights: No pre-trained weights; deterministic phase-based approximation Learning Reset: Cell states wipe on reload; rebuild required First 50 Bars: States unfinalized, forecasts invalid Compute Load: Hidden=16 + Seq=100 is heavy; use 8/30 on low-spec MTF lookahead_off: No future-peeking, real-time integrity guaranteed Multi-Step Drift: Error accumulates with horizon; Forecast=5–10 recommended Bands are Probabilistic: σ-range exceedance can occur; not absolute ceilings/floors 🚨 Disclaimer This indicator is a deep-learning-inspired forecasting model provided for educational and research purposes only and does not constitute financial advice. LSTM-style predictions are mathematical approximations and do not guarantee future profits. Forecast accuracy depends on market conditions, asset, and timeframe. Combine with independent analysis and proper risk management.
Indicator
by AetherEdge
AetherEdge NeuraNet Trend Predictor🖊️ Overview AetherEdge NeuraNet Trend Predictor is a next-generation AI-driven trend forecasting indicator that fully implements a true Multi-Layer Perceptron (MLP) neural network in Pine Script. Nine-dimensional features flow through input → hidden → output layers, with weights self-updating every bar via backpropagation + Nesterov momentum + L2 regularization. This is the pinnacle of machine learning engines on PulseWire—a system that perpetually evolves with market structure changes. 🔶 Key Features True MLP Implementation: 9-H-1 architecture (hidden 3–12 neurons) Online Learning: Deterministic gradient descent updating weights every bar Nesterov-Style Momentum: Accelerated convergence via gradient inertia L2 Regularization: Overfitting suppression Xavier Initialization: Theoretically optimal weight init Binary Cross-Entropy Loss: Optimal for probabilistic prediction 9-D Feature Engine: Price/EMA/RSI/ATR/Volume/MACD/BB/DMI/Structure Robust Normalization: z-score based statistical standardization Live Accuracy Tracking: Cumulative prediction accuracy % Loss EMA Monitoring: Visualized learning progress Network Visualization: Real-time input → hidden activation → output Probability Bar (right edge): 10-tier prediction visualization Confidence Computation: Auto-calculated via |prob - 0.5| × 2 AI VERDICT: 5-tier decision support messaging 🧠 Technical Architecture This indicator is an advanced neural network implementation grounded in genuine deep learning theory. Network Structure: Input layer: 9 neurons (feature vector) Hidden layer: 3–12 neurons (tanh activation) Output layer: 1 neuron (sigmoid, probability) Total params: 9×H + H + H + 1 Feature Engineering (9-D): f1: Price/EMA Fast deviation f2: EMA Spread (Fast vs Slow) f3: RSI normalized f4: ATR% z-score f5: Volume z-score (log-transformed) f6: MACD Histogram normalized f7: Bollinger Band Position f8: DMI differential f9: Structural position (high/low range) Forward Pass: h_j = tanh(Σ x_i × W1_ij + b1_j) prob = sigmoid(Σ h_j × W2_j + b2) Backpropagation: Output error: err = ŷ - y (BCE gradient) Hidden error: δ = err × W2 × (1 - h²) (tanh derivative) L2-regularized gradient: g = δ × x + λ × W Nesterov update: v = β×v - η×g; W += v Target Signal: Next bar direction (close > close ) Warmup: Signals activate after 200 bars of training Learning Statistics: Train Steps / Accuracy / Loss EMA / 100-bar Loss ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (1H–4H): Standard (Hidden=6, η=0.05, β=0.9) ETH (15M–1H): Hidden=8, η=0.03 for precision SOL (high-vol): λ=0.005 to enhance overfitting suppression XRP (range): Hidden=4, η=0.07 for rapid adaptation Learning Rate (η): 0.01–0.03: Stability-focused, long-term trends 0.05: Balanced (recommended) 0.1–0.2: Fast adaptation for regime shifts Hidden Neurons: 3–4: Simple, low overfit risk 6: Balanced (recommended) 8–12: High capacity, requires sufficient data Warmup Period: 200 bars: Standard 500 bars: High-precision focus 100 bars: Quick activation Thresholds: Conservative: Long=0.75, Short=0.25, MinConf=0.7 Standard: Long=0.65, Short=0.35, MinConf=0.6 Aggressive: Long=0.6, Short=0.4, MinConf=0.5 💡 How to Use in Practice VERDICT = STRONG LONG CONVICTION: Neural net at >80% conviction—strongest entry Accuracy > 55%: Network in hot streak, high signal trust Accuracy < 50%: Regime shift in progress, stand aside Loss EMA declining: Learning healthy, precision improving Confidence > 70%: High-quality signal, full position consideration Feature panel watch: All features unidirectional = multi-faceted consensus Hidden activations: ●●● (strong) aligned = internal network consensus Probability bar extreme (>90%): Watch for reversal risk AetherEdge Synergy: All-in-One Dashboard HIGH-CONVICTION + NeuraNet STRONG = dual AI confirmation Self-Evolving S/R Grid strong line + NeuraNet aligned = ML × RL fusion Liquidity Sweep Bull Sweep + NeuraNet > 0.8 = institutional + AI sync Multi-TF: 4H for direction → 1H for NeuraNet wait → 15M for precision entry ⚠️ Important Notes Learning Reset: Weights initialize on every chart reload—warmup mandatory Initial Warmup: Minimum 200 bars required before predictions Accuracy Metric: Cumulative; treat <100 samples as preliminary Overfit Risk: Excess Hidden + low λ = overfitting—balance carefully Diverging η: η > 0.2 risks weight explosion—use cautiously Regime Changes: Sudden market shifts temporarily reduce Accuracy, auto-recovers Compute Load: Hidden=12 is heavy—use 6 on low-spec setups 🚨 Disclaimer This indicator is a machine learning demonstration provided for educational and research purposes only and does not constitute financial advice. Neural network predictions are statistical estimates based on historical patterns and do not guarantee future profits. Do not blindly trust AI model outputs—combine with independent analysis and proper risk management.
Indicator
by AetherEdge
Market Euphoria Index - MEI - Predict Market Tops & BottomsA composite macro indicator designed to peak BEFORE major equity market tops — not during the crash. Most "fear & greed" gauges are coincident: they spike with the panic, not before it. The MEI flips that. It measures how much euphoria and late-cycle stress have built up over months of bull market, so it tends to peak in the run-up to a top rather than at the bottom. What it captured historically (visual backtest on monthly SPX): — Climbed into the red zone ahead of the August 1987 top — Peaked ahead of the March 2000 dot-com top — Peaked ahead of the October 2007 GFC top — Peaked ahead of the January 2022 top — Dropped to the extreme-fear zone near every major bear-market bottom that followed The six components (default weights): — Price extension vs 5-year SMA (30%) — the primary leading signal. SPX has been 25-55% above its 5-year average at every major top since 1980. — Yield curve un-inversion clock (25%) — tracks months since the 10Y-2Y spread last went negative. Peak warning is 0-12 months after the curve un-inverts (this is the actual recession trigger, historically). — VIX 12-month average (15%) — captures sustained complacency, not single panic spikes. Low for a year = top buildup. — Inflation re-acceleration (10%) — 6-month change in CPI YoY. Late-cycle inflation shocks (2000, 2007, 2022) are the classic top catalyst. — Real rate stress (10%) — 10Y nominal yield minus CPI YoY. High and rising = tightening financial conditions. — Fed cycle position (10%) — high and plateauing = peak late cycle; aggressive cuts = bottom signal. How to read the line: — Above 80 (red) = EXTREME EUPHORIA, historical top zone, reduce risk — 65 to 80 (orange) = Euphoria warning, late cycle, tighten stops — 35 to 65 (gray) = Neutral — 20 to 35 (green) = Fear, opportunity zone — Below 20 (lime) = EXTREME FEAR, historical bottom zone Best timeframe: monthly (M) or weekly (W) on SPX, NDX, QQQ, or SPY. All economic data is fetched at monthly resolution regardless of your chart's timeframe, so the indicator reads the same whether you're on D, W, or M. Customizable: every component weight and threshold is exposed in the settings. Bump up "Price extension" if you want more sensitivity to bubbles, or "Yield curve" if you trust macro recession signals more than price action. Built-in alerts: Euphoria warning, Extreme euphoria, Fear, Extreme fear — all four crossovers are configurable from the alert menu. To overlay on SPX: right-click the indicator name, then "Move pane to" then "Above", then "Pin to scale" to give it its own axis on the price chart. Data sources (all free, built into PulseWire): SP:SPX, FRED:T10Y2Y, CBOE:VIX, ECONOMICS:USIRYY, FRED:FEDFUNDS, TVC:US10Y Honest limitations: — This is a macro/cyclical tool, useless for intraday or short-term timing. — Designed to call major bull/bear turning points, not 10-20% corrections. — The 2020 COVID crash was an exogenous shock no macro model could predict; the MEI would not have warned you. — Past patterns are not guarantees. Current dynamics (AI capex, geopolitics, structural inflation) may break historical relationships. — Not financial advice. Use alongside your own analysis.
Indicator
by brian7683
Marcador de Sesiones (Operativa PY, CME y bolsas globales)Para agregar los inicios de las principales bolsas mundiales (Nueva York, Londres, Tokio y Sídney), debemos tener en cuenta que estos mercados cambian de horario a lo largo del año debido al horario de verano (DST) en sus respectivos países. Para que el indicador sea 100% exacto y automático sin importar la época del año, la mejor estrategia en Pine Script v6 es calcular el momento exacto utilizando la zona horaria nativa de cada mercado. Al pasarle a la función is_time_in_bar la zona horaria específica de cada país (por ejemplo, America/New_York para NY o Europe/London para Londres), PulseWire calcula de forma automática cuándo abren esos mercados en base a sus propias reglas de cambio de hora. Esto significa que cuando Paraguay cambie su hora o EE. UU. cambie la suya, las líneas verticales se moverán solas a la vela correcta de tu gráfico sin que tengas que actualizar manualmente los parámetros del script. Detalles de los horarios incluidos: - Londres: Abre a las 08:00 y cierra a las 16:30 (Hora del Reino Unido). - Nueva York (NYSE/Acciones): Abre a las 09:30 y cierra a las 16:00 (Hora de la Costa Este de EE. UU.). - Tokio: Abre a las 09:00 y cierra a las 15:00 (Hora de Japón). - Sídney: Abre a las 08:00 y cierra a las 16:00 (Hora del Este de Australia).
Indicator
by elenocaballero
Institutional Retail Trap PRODescription: Institutional Retail Trap PRO is an advanced order flow indicator designed to identify the behavioral difference between institutional ("smart money") participants and retail traders — and more importantly, to detect when retail traders are caught on the wrong side of the market. Unlike standard momentum indicators, this tool weights price movement against relative volume and candle displacement to estimate who is actually driving price at any given moment. How it works: The indicator calculates buy and sell pressure based on where price closes within each candle's range, then scales that pressure by relative volume (current volume vs. average volume). This creates a raw flow signal that is split into two streams — one representing institutional activity and one representing retail activity — both normalized using RSI to produce clear, readable oscillator lines. The two lines: Aqua line (Commercials / Smart Money) — rises when volume-weighted buying pressure dominates, suggesting institutional accumulation or aggressive buying Red line (Retail Traders) — the inverse, rising when retail-style selling or chasing behavior dominates Key levels: Above 60 — dominant zone, one side is in control 50 — equilibrium, no clear dominance Below 20 — exhaustion zone, the dominant flow is fading Signals: Lime circle (Smart Money Entry) — smart money line crosses above retail line while still in low territory, suggesting institutional accumulation beginning Orange circle (Smart Money Exit) — smart money line crosses below retail line from high territory, suggesting distribution Red triangle down (Retail Long Trap) — retail is overextended long while smart money is weak and price closes bearish with displacement — classic bull trap setup Aqua triangle up (Retail Short Trap) — retail is overextended short while smart money is strong and price closes bullish with displacement — classic bear trap setup Background shading: Green background — smart money in dominant zone Red background — retail flow dominant, potential exhaustion or reversal ahead Displacement filter: A built-in displacement filter ensures signals only trigger when the candle body exceeds the average range by a configurable multiplier. This filters out low-conviction signals during choppy, low-momentum periods. Best used with: Price action context, key support and resistance levels, volume profile, and multi-timeframe analysis. Works on all liquid markets — indices (ES, NQ, DAX), forex majors, gold (XAUUSD), and high-volume crypto pairs. Most effective on the 1m, 3m, 5m, and 15m timeframes for intraday trading. Settings: Flow Length — RSI normalization period (default 14) Flow Smoothing — EMA smoothing on the raw flow signal (default 5) Volume Average — baseline volume period for relative volume calculation (default 20) Displacement Strength — multiplier for the candle body displacement filter (default 1.5) Show Trap Signals — toggle all signals on/off Show Zones — toggle zone fills on/off Background Coloring — toggle background shading on/off Note: This indicator does not use real-time order book or Level 2 data. Institutional flow is estimated using price-volume analysis relative to recent averages. Always use in confluence with other tools and proper risk management. Past signals do not guarantee future results.
Indicator
by M_nissen
Smart Money vs Retail Flow This indicator estimates the balance of power between institutional ("smart money") participants and retail traders by analyzing the relationship between price movement and volume relative to its average. How it works: Smart money tends to move markets with purpose — large volume accompanied by decisive price movement. Retail traders often chase price, buying tops and selling bottoms. This indicator attempts to separate these two behaviors by weighting price momentum against normalized volume activity, then applying RSI-based normalization to create two oscillating lines that reflect each group's relative dominance. The two lines: Aqua line (Commercials / Smart Money) — rises when significant price movement is backed by above-average volume, suggesting institutional participation Red line (Retail Traders) — the inverse signal, rising when retail-style flow dominates Key levels: Above 70 — dominant zone. When the smart money line crosses above 70, institutions are aggressively entering Below 30 — exhaustion zone. A crossover back above 30 on the smart money line triggers a buy signal 50 — midline equilibrium between buyers and sellers Signals: Green triangle (Smart Money Buy) — smart money line crosses back above 30, suggesting institutional accumulation after a pullback Red triangle (Retail Trap) — retail line crosses above 70, suggesting retail traders are overextended and a reversal may follow Background shading: Green background — smart money in dominant zone Red background — retail flow in dominant zone, potential exhaustion or trap Best used with: Price action context, support/resistance levels, and volume profile. Works on all assets and timeframes. Most effective on liquid markets such as indices (ES, NQ, DAX), major forex pairs, and high-volume crypto pairs. Settings: Momentum Length — RSI normalization period (default 14) Volume Length — baseline volume average period (default 20) Smoothing — EMA smoothing applied to raw flow signal (default 5) Note: This indicator does not use actual order book or Level 2 data. Smart money flow is estimated using price-volume weighting relative to average volume. Use in confluence with other analysis for best results.
Indicator
by M_nissen
ULTIMATE SMC BREAKOUT AIThis is a Pine Script v5 trading indicator named "ULTIMATE SMC BREAKOUT AI". Summary of what it does and how it works: Purpose Detects breakout entries aligned with trend, plots suggested entry, stop-loss and two take-profit levels, highlights order blocks, an equilibrium line, and potential reversal zones. Provides alert conditions for buy/sell breakouts and reversal zones. User inputs (configurable) Fast/slow EMA lengths (default 50, 200) ATR length and SL multiplier (default ATR14, SL = 1.2 * ATR) TP1 and TP2 risk-reward multipliers (default 1.5 and 3.0) Confirmation bars (default 2) Toggles to show EMAs, Order Blocks, Equilibrium, Reversal Zones Core logic Trend filter: computes fast and slow EMAs. upTrend if EMA_fast > EMA_slow and price > EMA_fast; downTrend if EMA_fast < EMA_slow and price < EMA_fast. EMAs plotted if enabled. Market structure breakout: defines a 20-bar swing high/low. A bullish breakout is price > prior swingHigh; bearish breakout is price < prior swingLow. A breakout must persist for confirmBars to be considered confirmed. Entry signals: buySignal requires confirmed bullish breakout + upTrend; sellSignal requires confirmed bearish breakout + downTrend. buyFinal/sellFinal are first-bar triggers (only on the breakout bar). ATR-based SL/TP: uses ATR to set SL distance (SL = entry ± ATR * slATR). Risk = distance entry↔SL. TP1 and TP2 set at entry ± (risk * tpRR). Labels: when buyFinal/sellFinal occur the script places an on-chart label with ENTRY, TP1, TP2 prices. Additional plotted/annotated elements Order Blocks: highlights single-bar bullish order blocks (prior bar bearish, current close > prior high) and bearish order blocks (prior bar bullish, current close < prior low) as semi-transparent green/red boxes extending ~15 bars forward. Equilibrium zone: plots mid-price of 50-bar highest high and lowest low (a yellow line). Predictive Reversal Zones: marks potential reversal boxes when: Liquidity sweep detected (current high > highest high of 10 bars prior OR current low < lowest low of 10 bars prior), RSI conditions (RSI > 75 for bearish reversal, RSI < 25 for bullish reversal), Candle direction confirming (bearish candle for bearish reversal, bullish for bullish reversal). These draw colored boxes and labels for "REVERSAL BUY/SELL ZONE". Background color: faint green for upTrend, faint red for downTrend. Alerts Built-in alertconditions for BUY SIGNAL, SELL SIGNAL, Bullish Reversal, Bearish Reversal (triggerable in PulseWire alerts). Practical notes / behavior Breakouts use a 20-bar swing lookback and require confirmationBars (default 2) — shorter/longer confirmation changes sensitivity. SL/TP are purely mechanical from ATR—no partial fills, order execution logic, or trade management beyond TP1/TP2 drawn. Order blocks detection is simplistic (single-bar pattern) and not a complex multi-bar block identification. Reversal zones use a combination of liquidity sweep + extreme RSI + candle direction; they are predictive markers, not guarantees. Designed for overlay on price; many plotted boxes/labels may clutter smaller timeframes or active instruments.
Indicator
by shary890
AetherEdge - All-in-One Dashboard🖊️ Overview AetherEdge All-in-One Dashboard is the ultimate professional command center that unifies four core analytical pillars—Trend, Momentum, Volatility, and Volume—with a sophisticated Signal Probability Engine. Each module independently computes scores, then weighted aggregation combined with an "Alignment Score" and pseudo live Win-Rate tracking delivers final verdicts—revealing multi-dimensional market truths that no single indicator can capture. A 25-row professional dashboard plus AI-powered VERDICT instantly supports every trading decision. 🔶 Key Features 4-Module Integrated Analysis: Trend / Momentum / Volatility / Volume Signal Probability Engine: Quantifies Bull/Bear probability in % Alignment Score: Auto-adjusts confidence via inter-module dispersion Weighted Scoring System: Fully customizable module weights Pseudo Live Win-Rate Tracking: Auto-tallies 5-bar prediction accuracy AI VERDICT: 4-tier recommendations (HIGH-CONVICTION / Consider / Await / No Edge) Squeeze Detection & Background Highlight: BBW percentile pre-explosion alerts Volatility Regime Classification: 5 tiers (EXTREME HIGH → SQUEEZE) Money Flow Analysis: OBV + CMF + MFI composite scoring Multi-Indicator Coverage: EMA/MACD/RSI/Stoch/ADX/ATR/BB all unified 5-Tier Signal Strength: ULTRA / STRONG / MODERATE / WEAK / NONE 3 Sizes × 6 Positions: Fully flexible dashboard layout 7 Alert Types: Signal / Squeeze / Volume / Trend cross coverage 🧠 Technical Architecture This indicator is a high-end decision-support system built on a multi-axis score integration engine with alignment-based confidence correction. Trend Module: EMA Stack (21/50/200) alignment score (±100) Price vs EMAs positional score ADX × DMI directional score Weighted blend (EMA 0.4 / Price 0.3 / ADX 0.3) Momentum Module: RSI normalized (-100 to +100) MACD histogram % scaling Stochastic %K/%D cross detection Weighted blend (RSI 0.35 / MACD 0.4 / Stoch 0.25) Volatility Module: ATR% percentile rank within volRegLen window BB Width Percentile for Squeeze detection Squeeze grants +30 bonus score (expansion imminent) Volume Module: OBV vs EMA slope analysis CMF (Chaikin Money Flow) ×200 scaled MFI normalized score Weighted blend (OBV 0.35 / CMF 0.3 / MFI 0.35) Signal Probability Engine: Weighted composite = Σ(module × weight) / total weight Bull Prob = 50 + composite × 0.5 Alignment Score = 100 - σ(modules) Final Prob = (max × Alignment/100 × 0.7) + (max × 0.3) Win-Rate Tracking: Pending array logs signal → 5-bar outcome auto-aggregation, displaying cumulative Win-Rate and trade count AI VERDICT: Integrates probability, direction, and Squeeze state to generate 4-tier action recommendations ⚙️ Recommended Settings & Tuning Guide Crypto Defaults: BTC (1H–4H): Standard defaults (EMA 21/50/200, sigMinProb=70) ETH (15M–1H): emaFast=13, sigMinProb=72 for precision SOL (high-vol): volRegLen=150, wVol=1.0 for explosion detection XRP (range): wMom=1.5, wTrend=1.0 for momentum focus Weight Strategies: Trend Follower: wTrend=2.0, wMom=1.0 Reversal Trader: wMom=1.5, wVol=1.2 (leverage Squeeze bonus) Volume-Centric: wVolume=1.5, wTrend=1.0 Balanced: Default (wTrend=1.5, wMom=1.2, wVol=0.8, wVolume=1.0) Signal Quality: Ultra-Selective: sigMinProb=80, ULTRA/STRONG only (max Win-Rate) Standard: sigMinProb=70 (recommended) Frequency-Focused: sigMinProb=60, MODERATE+ Dashboard Sizes: Compact: Multi-chart layouts Normal: Standard use Large: Deep analysis / streaming (recommended) 💡 How to Use in Practice VERDICT = HIGH-CONVICTION: Strongest signal, consider full position Squeeze ACTIVE: Confirm direction, then enter on breakout (Await Breakout displayed) Alignment > 70%: High confidence, technicals unified—win-rate boost Alignment < 40%: Caution, market chaos—stand aside Win-Rate > 60% + Bull Prob > 75%: System on hot streak, trade aggressively Volume INFLOW + Trend STRONG BULL: Maximum continuation confidence ATR EXTREME HIGH: Reduce position size, tighten risk management MACD Bull Cross + RSI 30–60 + ADX > 25: Ideal long setup AetherEdge Synergy: Liquidity Sweep Detector Bullish Sweep + AIO HIGH-CONVICTION LONG = institutional sync Self-Evolving S/R Grid strong support + ULTRA Long = perfect dip-buy WaveTrend Enhanced Bull Cross + all modules aligned = textbook bottom Multi-TF Strategy: 4H for trend → 1H for signal wait → 15M for precision entry ⚠️ Important Notes Win-Rate Metric: Cumulative since chart load; treat <20 samples as preliminary Low Alignment: Auto-discounts signal confidence Squeeze Bonus: Volatility precursor, not directional—confirm separately Weak ADX (< 25): Trend score reliability degraded Avoid Over-Optimization: Tune 2-3 params at a time Signal Trigger: Requires both Composite=0 cross AND probability threshold 🚨 Disclaimer This indicator is a technical analysis tool provided for educational and research purposes only and does not constitute financial advice. Signal probabilities, win-rates, and VERDICTs are statistical estimates based on historical data and do not guarantee future profits. All trading decisions are made at your own risk and should be accompanied by proper risk management.
Indicator
by AetherEdge
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
by AetherEdge
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
by AetherEdge
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
by AetherEdge
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
by AetherEdge
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
by AetherEdge
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