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.
Script de código abierto
Fiel al espíritu de TradingView, el creador de este script lo ha convertido en código abierto, para que los traders puedan revisar y verificar su funcionalidad. ¡Enhorabuena al autor! Aunque puede utilizarlo de forma gratuita, recuerde que cualquier republicación del código está sujeta a nuestras Normas internas.
Exención de responsabilidad
La información y las publicaciones no constituyen, ni deben considerarse como, asesoramiento o recomendaciones financieras, de inversión, de trading u otro tipo, proporcionadas o respaldadas por TradingView. Obtenga más información en Condiciones de uso.
Script de código abierto
Fiel al espíritu de TradingView, el creador de este script lo ha convertido en código abierto, para que los traders puedan revisar y verificar su funcionalidad. ¡Enhorabuena al autor! Aunque puede utilizarlo de forma gratuita, recuerde que cualquier republicación del código está sujeta a nuestras Normas internas.
Exención de responsabilidad
La información y las publicaciones no constituyen, ni deben considerarse como, asesoramiento o recomendaciones financieras, de inversión, de trading u otro tipo, proporcionadas o respaldadas por TradingView. Obtenga más información en Condiciones de uso.