OPEN-SOURCE SCRIPT
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[σ_T], 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[1])
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.
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[σ_T], 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[1])
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.
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Skrypt open-source
W zgodzie z duchem TradingView twórca tego skryptu udostępnił go jako open-source, aby użytkownicy mogli przejrzeć i zweryfikować jego działanie. Ukłony dla autora. Korzystanie jest bezpłatne, jednak ponowna publikacja kodu podlega naszym Zasadom serwisu.
Wyłączenie odpowiedzialności
Informacje i publikacje nie stanowią i nie powinny być traktowane jako porady finansowe, inwestycyjne, tradingowe ani jakiekolwiek inne rekomendacje dostarczane lub zatwierdzone przez TradingView. Więcej informacji znajduje się w Warunkach użytkowania.