OPEN-SOURCE SCRIPT
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.
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.
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ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน
สคริปต์โอเพนซอร์ซ
ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน