OPEN-SOURCE SCRIPT
AetherEdge - Pattern Edge Learner
🖊️ Overview
Most pattern tools hard-code the textbook ("engulfing = bullish"). AE-PAT throws that out and measures each shape's forward edge from this market's own history. It encodes recent price action as a normalized signature and aggregates what past analogues did next via kernel regression — telling you which shapes actually work now, as an up-probability, expected move, and confidence (effective neighbors). It also shows learned win-rates for five classic candlestick patterns.
🔶 Key Features
Learned shape signatures — encodes the last L bars as an ATR-normalized shape and learns the forward edge from analogues
Non-parametric (kernel regression) — estimates "this shape did X" from data, no hard-coded lore
Up-probability + expected move + confidence — including how many analogues exist (effective neighbors)
Measured win-rates of classic patterns — bull/bear engulfing, hammer, shooting star, doji (the record, not the textbook)
Pattern signals — fire only when the learned edge is strong with enough analogues
Bar coloring for the edge; bar-close training and signals — no repaint
🧠 Technical Architecture
Shape signature: the last L bars encoded as four features each (body / upper wick / lower wick / return, normalized by each bar's ATR = scale-invariant), F = L×4 dims.
ML (Nadaraya-Watson kernel regression): a memory bank stores, each bar, the signature from N bars ago plus its realized outcome (up/down + forward return; no lookahead). The current signature is weighted against the whole memory by a Gaussian kernel exp(−dist²/2h²), yielding P(up), expected move, and effective neighbors (confidence). The estimate adapts as shapes change.
Stats (Beta-Bernoulli): online win-rates for five classic patterns, updated from the outcome N bars after each occurrence.
Honest scope: kernel regression + Bernoulli counting (no RL). Not deep learning, not a guarantee.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: signature window L, outcome horizon N, up threshold (ATR), memory size, kernel bandwidth, bull/bear edge thresholds, min effective neighbors.
Window L ≈ 3–5 (larger captures complex shapes but yields fewer analogues)
N is the bars to measure the outcome; 8–15 for 15m–1H
Bandwidth controls match tolerance (smaller = stricter/fewer neighbors, larger = smoother)
Min effective neighbors ensures firing only with enough analogues
⚠️ It's a memory × dimension scan; if you hit a "script takes too long" error on long timeframes, lower the memory size
💡 How to Use in Practice
Use the pattern triangles for shape-backed entries
Read P(up) and expected move for direction/size, and stand aside when effective neighbors is low
Use the classic-pattern table to check "is the hammer working now / what's the engulfing win-rate" and drop assumptions
Combine with AE-MRB (mean reversion) or AE-CORR (drivers) to layer shape × context
⚠️ Important Notes
Needs a learning period (warmup) and memory to build; memory resets on timeframe/parameter change
Past analogues are not guaranteed to resolve the same now (regime shifts break it)
Rare shapes have few neighbors and lower confidence
Probability, not a guarantee — always do your own due diligence and use risk management
🚨 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 decisions are your own — use proper validation and disciplined risk management.
Most pattern tools hard-code the textbook ("engulfing = bullish"). AE-PAT throws that out and measures each shape's forward edge from this market's own history. It encodes recent price action as a normalized signature and aggregates what past analogues did next via kernel regression — telling you which shapes actually work now, as an up-probability, expected move, and confidence (effective neighbors). It also shows learned win-rates for five classic candlestick patterns.
🔶 Key Features
Learned shape signatures — encodes the last L bars as an ATR-normalized shape and learns the forward edge from analogues
Non-parametric (kernel regression) — estimates "this shape did X" from data, no hard-coded lore
Up-probability + expected move + confidence — including how many analogues exist (effective neighbors)
Measured win-rates of classic patterns — bull/bear engulfing, hammer, shooting star, doji (the record, not the textbook)
Pattern signals — fire only when the learned edge is strong with enough analogues
Bar coloring for the edge; bar-close training and signals — no repaint
🧠 Technical Architecture
Shape signature: the last L bars encoded as four features each (body / upper wick / lower wick / return, normalized by each bar's ATR = scale-invariant), F = L×4 dims.
ML (Nadaraya-Watson kernel regression): a memory bank stores, each bar, the signature from N bars ago plus its realized outcome (up/down + forward return; no lookahead). The current signature is weighted against the whole memory by a Gaussian kernel exp(−dist²/2h²), yielding P(up), expected move, and effective neighbors (confidence). The estimate adapts as shapes change.
Stats (Beta-Bernoulli): online win-rates for five classic patterns, updated from the outcome N bars after each occurrence.
Honest scope: kernel regression + Bernoulli counting (no RL). Not deep learning, not a guarantee.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: signature window L, outcome horizon N, up threshold (ATR), memory size, kernel bandwidth, bull/bear edge thresholds, min effective neighbors.
Window L ≈ 3–5 (larger captures complex shapes but yields fewer analogues)
N is the bars to measure the outcome; 8–15 for 15m–1H
Bandwidth controls match tolerance (smaller = stricter/fewer neighbors, larger = smoother)
Min effective neighbors ensures firing only with enough analogues
⚠️ It's a memory × dimension scan; if you hit a "script takes too long" error on long timeframes, lower the memory size
💡 How to Use in Practice
Use the pattern triangles for shape-backed entries
Read P(up) and expected move for direction/size, and stand aside when effective neighbors is low
Use the classic-pattern table to check "is the hammer working now / what's the engulfing win-rate" and drop assumptions
Combine with AE-MRB (mean reversion) or AE-CORR (drivers) to layer shape × context
⚠️ Important Notes
Needs a learning period (warmup) and memory to build; memory resets on timeframe/parameter change
Past analogues are not guaranteed to resolve the same now (regime shifts break it)
Rare shapes have few neighbors and lower confidence
Probability, not a guarantee — always do your own due diligence and use risk management
🚨 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 decisions are your own — use proper validation and disciplined risk management.
오픈 소스 스크립트
트레이딩뷰의 진정한 정신에 따라, 이 스크립트의 작성자는 이를 오픈소스로 공개하여 트레이더들이 기능을 검토하고 검증할 수 있도록 했습니다. 작성자에게 찬사를 보냅니다! 이 코드는 무료로 사용할 수 있지만, 코드를 재게시하는 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니다. 자세한 내용은 이용 약관을 참조하세요.
오픈 소스 스크립트
트레이딩뷰의 진정한 정신에 따라, 이 스크립트의 작성자는 이를 오픈소스로 공개하여 트레이더들이 기능을 검토하고 검증할 수 있도록 했습니다. 작성자에게 찬사를 보냅니다! 이 코드는 무료로 사용할 수 있지만, 코드를 재게시하는 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니다. 자세한 내용은 이용 약관을 참조하세요.