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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 [-3, +3] 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.
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 [-3, +3] 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.
开源脚本
秉承TradingView的精神，该脚本的作者将其开源，以便交易者可以查看和验证其功能。向作者致敬！您可以免费使用该脚本，但请记住，重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供，也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。
开源脚本
秉承TradingView的精神，该脚本的作者将其开源，以便交易者可以查看和验证其功能。向作者致敬！您可以免费使用该脚本，但请记住，重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供，也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。