OPEN-SOURCE SCRIPT
AetherEdge - Adaptive Volume Profile
🖊️ Overview
AE-AVP pairs an auction-theory volume profile (POC and value area) with an engine that learns whether a value-area-edge break gets accepted (a real breakout) or rejected (faded back into value). It shows where volume concentrated and puts a probability on the most tradeable moment — the fight at the value-area edge.
🔶 Key Features
A real volume profile — POC, value area (VAH/VAL), and node strength drawn as a right-side histogram
ML: acceptance/rejection — online-learns whether an edge break continues or reverts; low probability flags a fade to the POC
RL self-tuning threshold — a UCB bandit tunes the acceptance threshold per volatility regime
Signals — distinguishes accepted breakouts (follow) from edge rejections (fade to POC)
POC/VAH/VAL are usable horizontal levels
Learning/signals on bar close — no repaint
🧠 Technical Architecture
Volume profile: the recent lookback bars' price range is split into rows bins; each bar's volume is distributed across the bins its [low, high] spans. POC = highest-volume bin; value area = expand from the POC toward the heavier neighbour until vaPct% (default 70%) of volume is enclosed → VAH/VAL; node strength = bin volume / max.
ML (online logistic regression): when price breaks above VAH or below VAL, seven features (edge node strength, approach momentum, volume surge, stretch from POC, trend, volatility regime, VA width — oriented to the break direction) feed a model of P(acceptance). Events are held in an internal array and labeled by a triple barrier (accept = beyond the edge by k·ATR; reject = back inside; time-out discarded) — no future data.
RL (UCB contextual bandit): tunes the acceptance threshold per volatility regime (ATR-percentile terciles); the reward is tied to the same edge resolution, so ML and RL share one judgment loop.
Signals: acceptance (P ≥ threshold) → follow the break direction; rejection (P ≤ 1 − threshold) → fade toward the POC.
Honest scope: a linear classifier + a UCB bandit over standard profile/price features. Not deep learning, not a guarantee.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: profile lookback, bins (rows), value-area %, acceptance/rejection distance (ATR), horizon M, threshold search range.
Larger lookback → big-picture POC (stable, slower); smaller → sensitive to recent volume
More bins → higher resolution (finer, heavier to draw)
Higher value-area % → a wider VA and stricter break conditions
Guide: BTC/ETH (15m–1H) defaults (lookback 150, rows 24, VA 70%); 4H/daily a longer lookback; high-vol alts slightly wider acceptance/rejection distances
P(acceptance), threshold, and accuracy are coarse until warmup plus enough edge events accumulate
💡 How to Use in Practice
The POC is a magnet price; VAH/VAL are the battle lines — range inside value, direction on an edge break
Acceptance signals (BRK) mean follow the break; rejection signals (REJ) mean fade the edge toward the POC
Use P(acceptance) and node strength to judge whether a break is real (breaking a weak node with high acceptance)
Leave the auto threshold to the learner by default
Combine with a higher-timeframe value area or a trend tool for added precision
⚠️ Important Notes
Needs a learning period (warmup); weights and the Q-table re-learn on input/symbol/timeframe change
The profile is computed from closed bars; the histogram shows the current window
An edge break does not guarantee acceptance (which is exactly why it's shown as a probability)
Probability, not a guarantee — always use stops and position sizing
🚨 Disclaimer
This indicator is for educational and informational purposes only and is not financial advice or a recommendation to buy or sell. No method guarantees future profits; past performance does not indicate future results, and trading carries the risk of loss. All trading decisions are your own — use proper backtesting and disciplined risk management.
AE-AVP pairs an auction-theory volume profile (POC and value area) with an engine that learns whether a value-area-edge break gets accepted (a real breakout) or rejected (faded back into value). It shows where volume concentrated and puts a probability on the most tradeable moment — the fight at the value-area edge.
🔶 Key Features
A real volume profile — POC, value area (VAH/VAL), and node strength drawn as a right-side histogram
ML: acceptance/rejection — online-learns whether an edge break continues or reverts; low probability flags a fade to the POC
RL self-tuning threshold — a UCB bandit tunes the acceptance threshold per volatility regime
Signals — distinguishes accepted breakouts (follow) from edge rejections (fade to POC)
POC/VAH/VAL are usable horizontal levels
Learning/signals on bar close — no repaint
🧠 Technical Architecture
Volume profile: the recent lookback bars' price range is split into rows bins; each bar's volume is distributed across the bins its [low, high] spans. POC = highest-volume bin; value area = expand from the POC toward the heavier neighbour until vaPct% (default 70%) of volume is enclosed → VAH/VAL; node strength = bin volume / max.
ML (online logistic regression): when price breaks above VAH or below VAL, seven features (edge node strength, approach momentum, volume surge, stretch from POC, trend, volatility regime, VA width — oriented to the break direction) feed a model of P(acceptance). Events are held in an internal array and labeled by a triple barrier (accept = beyond the edge by k·ATR; reject = back inside; time-out discarded) — no future data.
RL (UCB contextual bandit): tunes the acceptance threshold per volatility regime (ATR-percentile terciles); the reward is tied to the same edge resolution, so ML and RL share one judgment loop.
Signals: acceptance (P ≥ threshold) → follow the break direction; rejection (P ≤ 1 − threshold) → fade toward the POC.
Honest scope: a linear classifier + a UCB bandit over standard profile/price features. Not deep learning, not a guarantee.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: profile lookback, bins (rows), value-area %, acceptance/rejection distance (ATR), horizon M, threshold search range.
Larger lookback → big-picture POC (stable, slower); smaller → sensitive to recent volume
More bins → higher resolution (finer, heavier to draw)
Higher value-area % → a wider VA and stricter break conditions
Guide: BTC/ETH (15m–1H) defaults (lookback 150, rows 24, VA 70%); 4H/daily a longer lookback; high-vol alts slightly wider acceptance/rejection distances
P(acceptance), threshold, and accuracy are coarse until warmup plus enough edge events accumulate
💡 How to Use in Practice
The POC is a magnet price; VAH/VAL are the battle lines — range inside value, direction on an edge break
Acceptance signals (BRK) mean follow the break; rejection signals (REJ) mean fade the edge toward the POC
Use P(acceptance) and node strength to judge whether a break is real (breaking a weak node with high acceptance)
Leave the auto threshold to the learner by default
Combine with a higher-timeframe value area or a trend tool for added precision
⚠️ Important Notes
Needs a learning period (warmup); weights and the Q-table re-learn on input/symbol/timeframe change
The profile is computed from closed bars; the histogram shows the current window
An edge break does not guarantee acceptance (which is exactly why it's shown as a probability)
Probability, not a guarantee — always use stops and position sizing
🚨 Disclaimer
This indicator is for educational and informational purposes only and is not financial advice or a recommendation to buy or sell. No method guarantees future profits; past performance does not indicate future results, and trading carries the risk of loss. All trading decisions are your own — use proper backtesting and disciplined risk management.
Skrypt open-source
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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.