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AetherEdge - Flow Anomaly Markov
🖊️ Overview
AE-FAM detects abnormal large-participant activity statistically and forecasts what follows (continuation or reversal) with a learned Markov chain. Abnormal large activity can't be captured by a fixed volume threshold — what is abnormal depends on the joint distribution of volume and price movement, and that drifts over time. AE-FAM detects it adaptively with a 2D Mahalanobis distance and answers, via a conditional Markov chain, "after this kind of event, what has the market actually done?"
🔶 Key Features
2D Mahalanobis-distance anomaly detection — judges joint volume/movement anomalies against the recent covariance (not a fixed threshold)
Conditional Markov chain — learns 5-state transition probabilities online, modeled separately for anomaly vs normal bars
Post-event continuation / reversal probabilities and an expected-direction score
The same event reads as continuation or trap depending on the learned transitions
Anomaly-bar background highlight, bull/bear anomaly markers, and BUY/SELL signals from the expected direction
A gold HUD with Mahalanobis D², event type, current state, continuation/reversal %, expected score, and predicted next state
Stats and transitions update on closed bars — no repaint
🧠 Technical Architecture
Anomaly (Mahalanobis): the mean vector and 2×2 covariance of features [log(volume), |range|/ATR] are estimated online by EWMA, and each bar's Mahalanobis distance D² is computed with a closed-form 2×2 inverse (no matrix library). When D² exceeds a threshold and volume is above average, it is an "anomaly event"; direction is the return sign (bull/bear).
States (5 buckets): the return z-score is discretized into strong-down / down / flat / up / strong-up.
Markov chain (ML): a first-order Markov chain's transition counts (2×5×5 = [anomaly?][from][to]) are tallied online with a Laplace prior and aged by a decay factor for adaptation. The current state's transition row (conditioned on the current anomaly status) is normalized into a next-state distribution; the expected-direction score = Σ (state value) × probability, with continuation/reversal probabilities from the same row.
Honest scope: Mahalanobis anomaly detection plus a count-based first-order Markov chain. Not deep learning, and not a guarantee of the future.
⚙️ Recommended Settings & Tuning Guide
Key parameters: anomaly threshold (D²), covariance EWMA, transition memory (decay), expected-score threshold (thrScore), warmup.
Raise the D² threshold → only rarer, more extreme events (~9 is a strong 2-DoF outlier)
Raise the covariance EWMA → faster-adapting distribution estimate; lower → steadier
Lower decay → adapts faster to recent transition behavior; 1.0 → never forgets
Crypto starting points (tune on your chart):
BTC / ETH (15m–1H): defaults are the baseline (D² 9, decay 0.999)
SOL / XRP and high-vol alts: raise the D² threshold to ~11 to exclude noisy spikes
Scalping (1–5m): raise covariance EWMA (0.05) for fast adaptation, decay 0.995
Swing (4H–daily): longer warmup to fill the transition statistics, decay 1.0 to learn long-run tendencies
Raising thrScore narrows signals to events where the Markov expected direction is clear
💡 How to Use in Practice
Read intent: bull anomaly + high continuation = accumulation (follow), bull anomaly + high reversal = possible trap / liquidity grab (consider fading)
Expected-direction score: sign is the bias, magnitude the confidence; signals fire when this score and an anomaly event agree
Continuation/reversal %: quantifies the post-event tone — trend-follow when continuation dominates, stay cautious when reversal does
Mode display: post-event (just after an anomaly) vs normal switches which transition model is being read
Combinations: pair with AE-AMF's big-picture momentum or AE-IRM's mean-reversion probability, and use FAM's anomaly + transition forecast to judge entry quality
⚠️ Important Notes
Learning period: no signals until warmup bars; the covariance and transition matrix need time to spin up
Learning reset: changing inputs, symbol, or timeframe re-learns the internal state (covariance and transition counts)
Probability, not a guarantee: the Markov model is first-order (depends only on the latest state) and simple — it cannot capture complex dependencies
Depends on rare events: anomalies are rare, so a given state×anomaly transition statistic can be coarse until it accumulates
🚨 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-FAM detects abnormal large-participant activity statistically and forecasts what follows (continuation or reversal) with a learned Markov chain. Abnormal large activity can't be captured by a fixed volume threshold — what is abnormal depends on the joint distribution of volume and price movement, and that drifts over time. AE-FAM detects it adaptively with a 2D Mahalanobis distance and answers, via a conditional Markov chain, "after this kind of event, what has the market actually done?"
🔶 Key Features
2D Mahalanobis-distance anomaly detection — judges joint volume/movement anomalies against the recent covariance (not a fixed threshold)
Conditional Markov chain — learns 5-state transition probabilities online, modeled separately for anomaly vs normal bars
Post-event continuation / reversal probabilities and an expected-direction score
The same event reads as continuation or trap depending on the learned transitions
Anomaly-bar background highlight, bull/bear anomaly markers, and BUY/SELL signals from the expected direction
A gold HUD with Mahalanobis D², event type, current state, continuation/reversal %, expected score, and predicted next state
Stats and transitions update on closed bars — no repaint
🧠 Technical Architecture
Anomaly (Mahalanobis): the mean vector and 2×2 covariance of features [log(volume), |range|/ATR] are estimated online by EWMA, and each bar's Mahalanobis distance D² is computed with a closed-form 2×2 inverse (no matrix library). When D² exceeds a threshold and volume is above average, it is an "anomaly event"; direction is the return sign (bull/bear).
States (5 buckets): the return z-score is discretized into strong-down / down / flat / up / strong-up.
Markov chain (ML): a first-order Markov chain's transition counts (2×5×5 = [anomaly?][from][to]) are tallied online with a Laplace prior and aged by a decay factor for adaptation. The current state's transition row (conditioned on the current anomaly status) is normalized into a next-state distribution; the expected-direction score = Σ (state value) × probability, with continuation/reversal probabilities from the same row.
Honest scope: Mahalanobis anomaly detection plus a count-based first-order Markov chain. Not deep learning, and not a guarantee of the future.
⚙️ Recommended Settings & Tuning Guide
Key parameters: anomaly threshold (D²), covariance EWMA, transition memory (decay), expected-score threshold (thrScore), warmup.
Raise the D² threshold → only rarer, more extreme events (~9 is a strong 2-DoF outlier)
Raise the covariance EWMA → faster-adapting distribution estimate; lower → steadier
Lower decay → adapts faster to recent transition behavior; 1.0 → never forgets
Crypto starting points (tune on your chart):
BTC / ETH (15m–1H): defaults are the baseline (D² 9, decay 0.999)
SOL / XRP and high-vol alts: raise the D² threshold to ~11 to exclude noisy spikes
Scalping (1–5m): raise covariance EWMA (0.05) for fast adaptation, decay 0.995
Swing (4H–daily): longer warmup to fill the transition statistics, decay 1.0 to learn long-run tendencies
Raising thrScore narrows signals to events where the Markov expected direction is clear
💡 How to Use in Practice
Read intent: bull anomaly + high continuation = accumulation (follow), bull anomaly + high reversal = possible trap / liquidity grab (consider fading)
Expected-direction score: sign is the bias, magnitude the confidence; signals fire when this score and an anomaly event agree
Continuation/reversal %: quantifies the post-event tone — trend-follow when continuation dominates, stay cautious when reversal does
Mode display: post-event (just after an anomaly) vs normal switches which transition model is being read
Combinations: pair with AE-AMF's big-picture momentum or AE-IRM's mean-reversion probability, and use FAM's anomaly + transition forecast to judge entry quality
⚠️ Important Notes
Learning period: no signals until warmup bars; the covariance and transition matrix need time to spin up
Learning reset: changing inputs, symbol, or timeframe re-learns the internal state (covariance and transition counts)
Probability, not a guarantee: the Markov model is first-order (depends only on the latest state) and simple — it cannot capture complex dependencies
Depends on rare events: anomalies are rare, so a given state×anomaly transition statistic can be coarse until it accumulates
🚨 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.
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ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน
สคริปต์โอเพนซอร์ซ
ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน