OPEN-SOURCE SCRIPT
AetherEdge - Bayesian Neural Market Microstructure
🖊️ Overview
A NeuraLib Bayesian Neural Network that does what a point-estimate model (e.g. a plain LSTM) cannot: it predicts WITH a confidence. Instead of one number, it runs many stochastic forward passes (MC-Dropout / weight-perturbation style) to produce a DISTRIBUTION of next-move predictions, reporting both a mean forecast and an uncertainty (the spread of the samples). Inputs are market-microstructure proxies — volume delta (buy/sell pressure), an estimated spread, and a liquidity proxy — alongside price features. Visualization: a probability-density "cloud" of predicted price bands (opacity ∝ probability) and a single forward zone that reddens as uncertainty rises. The edge over LSTM-style tools is clear — you see not just where, but how sure.
🔶 Key Features
Bayesian NN (uncertainty estimation) — multi-sample weight perturbation yields a predictive distribution: mean + uncertainty
Confidence-aware prediction — "mean ± σ", not a point estimate — the decisive difference from LSTM-style tools
Microstructure inputs — volume delta (order-flow proxy), estimated spread, and a liquidity proxy as features
Probability-density cloud — predicted bands drawn as nested probability tiers, opacity encoding probability mass (denser inside)
Uncertainty zone — a single forward zone that reddens and gains opacity as uncertainty rises
Mean forecast line — one line from current price to predicted price (with glow)
Online learning — the network continuously regresses toward realized forward moves
Intelligence panel — predicted move, predicted price, confidence, uncertainty, and each microstructure component at a glance
🧠 Technical Architecture
The agent perceives the market as a four-dimensional microstructure state: price momentum, order-flow pressure (a normalized volume delta from candle-direction × volume EMA), spread regime (an estimated effective spread from the high-low range), and liquidity (volume per unit range) — all z-normalized.
The network is in(4) → hidden(tanh) → out(1) = predicted normalized move, but its Bayesian behavior is reproduced via stochastic forward passes. Each sample (1) injects Gaussian-style noise into the weights (variational weight perturbation) and (2) applies an MC-Dropout mask to hidden units. Repeating this MC Samples times, the mean of the predictions is the forecast and their standard deviation is the uncertainty (epistemic uncertainty) — the heart of the Bayesian approximation.
Learning is supervised regression: for each state, the realized normalized move (close − close[h]) / ATR over the prediction horizon is the target, and the mean network regresses toward it via squared error (sampled from a replay buffer). For visualization, around the predicted price close + mean·ATR, nested probability bands of half-width k·σ·ATR form the density cloud, with opacity proportional to Gaussian mass exp(−0.5k²). The uncertainty zone gradients from the forecast color to red, with a bold red border once normalized uncertainty crosses the threshold.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): MC Samples 12, Dropout 0.25, Weight Noise 0.05, Prediction Horizon 8, Cloud Bands 5. Standard settings fit well
ETH (1H–4H): As BTC, with Uncertainty Scale 1.0 for standard cloud width
SOL (15m–1H): High volatility favors Dropout 0.3 / Weight Noise 0.08 for more sensitive uncertainty, Red Zone Threshold 0.5 to warn risk early
XRP (1H–4H): Spike-prone; MC Samples ≈ 16 to smooth the distribution, Uncertainty Scale 1.2 for slightly wider clouds
MC Samples: more smooths the uncertainty estimate but is heavier; 10–16 is a practical balance
Dropout / Weight Noise: higher widens the predictive distribution and raises uncertainty — tune to market noise
Cloud Density Bands: more makes a smoother, prettier cloud but adds render load; 4–6 is readable
Red Zone Threshold: lower warns red earlier (conservative); higher keeps the cool color only when very confident
💡 How to Use in Practice
Reading the density cloud: a narrow, dense cloud = low uncertainty, high-confidence forecast; a wide, faint cloud = high uncertainty, unstable forecast. The cloud center (mean line) is the predicted price
Uncertainty zone color: a forecast-colored (teal/coral) zone means confidence; a red, bold-bordered zone means high uncertainty — enter cautiously
Using confidence-aware prediction: consider entries only when panel Confidence is high — unlike point predictors (LSTM etc.), you can judge whether to trust the forecast
Checking microstructure: when order-flow Δ, spread, and liquidity align with the forecast direction, the prediction is better supported
Cloud width and sizing: size larger when the cloud is narrow (low uncertainty), smaller when wide (high uncertainty)
Multi-timeframe usage: confirm the big-picture forecast direction and uncertainty on the higher timeframe (4H), then time entries in low-uncertainty windows on the lower one (15m–1H)
⚠️ Important Notes
Initial learning period: right after launch the buffer is nearly empty and the network is untrained, so forecasts are unstable; treat them as low-confidence until it fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the network weights and buffer, restarting learning from zero
On microstructure proxies: this uses microstructure proxies estimated from OHLCV, not a real order book — an approximation that differs from true book data
Interpreting uncertainty: the reported uncertainty is the model's epistemic uncertainty (its lack of confidence), not a complete measure of actual market risk
On look-ahead: the target uses closed-bar realized moves (a standard training construct); current-bar prediction is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget; raising MC Samples increases compute proportionally
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Bayesian neural network, uncertainty estimation, microstructure proxies, predictive distribution, and density cloud are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Even a probabilistic forecast can be wrong. Always combine any tool with your own analysis and disciplined risk management.
A NeuraLib Bayesian Neural Network that does what a point-estimate model (e.g. a plain LSTM) cannot: it predicts WITH a confidence. Instead of one number, it runs many stochastic forward passes (MC-Dropout / weight-perturbation style) to produce a DISTRIBUTION of next-move predictions, reporting both a mean forecast and an uncertainty (the spread of the samples). Inputs are market-microstructure proxies — volume delta (buy/sell pressure), an estimated spread, and a liquidity proxy — alongside price features. Visualization: a probability-density "cloud" of predicted price bands (opacity ∝ probability) and a single forward zone that reddens as uncertainty rises. The edge over LSTM-style tools is clear — you see not just where, but how sure.
🔶 Key Features
Bayesian NN (uncertainty estimation) — multi-sample weight perturbation yields a predictive distribution: mean + uncertainty
Confidence-aware prediction — "mean ± σ", not a point estimate — the decisive difference from LSTM-style tools
Microstructure inputs — volume delta (order-flow proxy), estimated spread, and a liquidity proxy as features
Probability-density cloud — predicted bands drawn as nested probability tiers, opacity encoding probability mass (denser inside)
Uncertainty zone — a single forward zone that reddens and gains opacity as uncertainty rises
Mean forecast line — one line from current price to predicted price (with glow)
Online learning — the network continuously regresses toward realized forward moves
Intelligence panel — predicted move, predicted price, confidence, uncertainty, and each microstructure component at a glance
🧠 Technical Architecture
The agent perceives the market as a four-dimensional microstructure state: price momentum, order-flow pressure (a normalized volume delta from candle-direction × volume EMA), spread regime (an estimated effective spread from the high-low range), and liquidity (volume per unit range) — all z-normalized.
The network is in(4) → hidden(tanh) → out(1) = predicted normalized move, but its Bayesian behavior is reproduced via stochastic forward passes. Each sample (1) injects Gaussian-style noise into the weights (variational weight perturbation) and (2) applies an MC-Dropout mask to hidden units. Repeating this MC Samples times, the mean of the predictions is the forecast and their standard deviation is the uncertainty (epistemic uncertainty) — the heart of the Bayesian approximation.
Learning is supervised regression: for each state, the realized normalized move (close − close[h]) / ATR over the prediction horizon is the target, and the mean network regresses toward it via squared error (sampled from a replay buffer). For visualization, around the predicted price close + mean·ATR, nested probability bands of half-width k·σ·ATR form the density cloud, with opacity proportional to Gaussian mass exp(−0.5k²). The uncertainty zone gradients from the forecast color to red, with a bold red border once normalized uncertainty crosses the threshold.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): MC Samples 12, Dropout 0.25, Weight Noise 0.05, Prediction Horizon 8, Cloud Bands 5. Standard settings fit well
ETH (1H–4H): As BTC, with Uncertainty Scale 1.0 for standard cloud width
SOL (15m–1H): High volatility favors Dropout 0.3 / Weight Noise 0.08 for more sensitive uncertainty, Red Zone Threshold 0.5 to warn risk early
XRP (1H–4H): Spike-prone; MC Samples ≈ 16 to smooth the distribution, Uncertainty Scale 1.2 for slightly wider clouds
MC Samples: more smooths the uncertainty estimate but is heavier; 10–16 is a practical balance
Dropout / Weight Noise: higher widens the predictive distribution and raises uncertainty — tune to market noise
Cloud Density Bands: more makes a smoother, prettier cloud but adds render load; 4–6 is readable
Red Zone Threshold: lower warns red earlier (conservative); higher keeps the cool color only when very confident
💡 How to Use in Practice
Reading the density cloud: a narrow, dense cloud = low uncertainty, high-confidence forecast; a wide, faint cloud = high uncertainty, unstable forecast. The cloud center (mean line) is the predicted price
Uncertainty zone color: a forecast-colored (teal/coral) zone means confidence; a red, bold-bordered zone means high uncertainty — enter cautiously
Using confidence-aware prediction: consider entries only when panel Confidence is high — unlike point predictors (LSTM etc.), you can judge whether to trust the forecast
Checking microstructure: when order-flow Δ, spread, and liquidity align with the forecast direction, the prediction is better supported
Cloud width and sizing: size larger when the cloud is narrow (low uncertainty), smaller when wide (high uncertainty)
Multi-timeframe usage: confirm the big-picture forecast direction and uncertainty on the higher timeframe (4H), then time entries in low-uncertainty windows on the lower one (15m–1H)
⚠️ Important Notes
Initial learning period: right after launch the buffer is nearly empty and the network is untrained, so forecasts are unstable; treat them as low-confidence until it fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the network weights and buffer, restarting learning from zero
On microstructure proxies: this uses microstructure proxies estimated from OHLCV, not a real order book — an approximation that differs from true book data
Interpreting uncertainty: the reported uncertainty is the model's epistemic uncertainty (its lack of confidence), not a complete measure of actual market risk
On look-ahead: the target uses closed-bar realized moves (a standard training construct); current-bar prediction is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget; raising MC Samples increases compute proportionally
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Bayesian neural network, uncertainty estimation, microstructure proxies, predictive distribution, and density cloud are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Even a probabilistic forecast can be wrong. Always combine any tool with your own analysis and disciplined risk management.
Open-source Skript
Ganz im Sinne von TradingView hat dieser Autor sein/ihr Script als Open-Source veröffentlicht. Auf diese Weise können nun auch andere Trader das Script rezensieren und die Funktionalität überprüfen. Vielen Dank an den Autor! Sie können das Script kostenlos verwenden, aber eine Wiederveröffentlichung des Codes unterliegt unseren Hausregeln.
Haftungsausschluss
Die Informationen und Veröffentlichungen sind nicht als Finanz-, Anlage-, Handels- oder andere Arten von Ratschlägen oder Empfehlungen gedacht, die von TradingView bereitgestellt oder gebilligt werden, und stellen diese nicht dar. Lesen Sie mehr in den Nutzungsbedingungen.
Open-source Skript
Ganz im Sinne von TradingView hat dieser Autor sein/ihr Script als Open-Source veröffentlicht. Auf diese Weise können nun auch andere Trader das Script rezensieren und die Funktionalität überprüfen. Vielen Dank an den Autor! Sie können das Script kostenlos verwenden, aber eine Wiederveröffentlichung des Codes unterliegt unseren Hausregeln.
Haftungsausschluss
Die Informationen und Veröffentlichungen sind nicht als Finanz-, Anlage-, Handels- oder andere Arten von Ratschlägen oder Empfehlungen gedacht, die von TradingView bereitgestellt oder gebilligt werden, und stellen diese nicht dar. Lesen Sie mehr in den Nutzungsbedingungen.