OPEN-SOURCE SCRIPT
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: [0.2, 10.0]
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.
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: [0.2, 10.0]
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.
Script open-source
Nello spirito di TradingView, l'autore di questo script lo ha reso open source, in modo che i trader possano esaminarne e verificarne la funzionalità. Complimenti all'autore! Sebbene sia possibile utilizzarlo gratuitamente, ricordiamo che la ripubblicazione del codice è soggetta al nostro Regolamento.
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.
Script open-source
Nello spirito di TradingView, l'autore di questo script lo ha reso open source, in modo che i trader possano esaminarne e verificarne la funzionalità. Complimenti all'autore! Sebbene sia possibile utilizzarlo gratuitamente, ricordiamo che la ripubblicazione del codice è soggetta al nostro Regolamento.
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.