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AetherEdge - TIDE | Flow + RL Oscillator
🖊️ Overview
AE-TIDE is a lower-panel oscillator measuring participation-weighted money flow and its excess — the "Overflow" of one-sided activity. Where most flow tools simply display that excess, AE-TIDE adds a reinforcement-learning agent that learns, from realized outcomes, whether fading an Overflow exhaustion actually leads to a reversal in this market. It also scores every divergence with a Bayesian posterior, automatically down-weighting the divergences that do not work. It is a flow oscillator that gets smarter per market rather than relying on fixed thresholds.
🔶 Key Features
Participation-weighted Money Flow ([-50, 50]) plus an Overflow band for one-sided extremes
A fast Hyperwave momentum line for early shifts in drive
An RL (tabular Q-learning) agent that learns when fading exhaustion pays, firing reversal signals only then
Beta-Bernoulli online reliability scoring for divergences — each one shows a learned reliability %
Divergences below relGate are hidden automatically
Gold-framed HUD; no repaint — every signal gates on bar close
🧠 Technical Architecture
Flow: impulse = (ATR-normalized return) × (relative volume), EMA-smoothed and z-scored into a [-50, 50] Money Flow; the part beyond ± threshold is the Overflow. Hyperwave is the same normalization on a short EMA.
RL (Q-learning, TD(0)): state = flow regime (overflow side ×3 × momentum slope ×2 = 6 states), actions = {wait, fade}. The fade reward is the realized return conditioned on the overflow side at the moment the fade was taken (fade an overbought flow → rewarded if price falls). Update Q(s,a) ← Q + α[r + γ·maxQ(s′) − Q]; the fade action is optimistically initialized to guarantee exploration. A signal fires only when fade-value > 0 (learned to pay) and an Overflow exhaustion triggers.
Bayesian divergences: regular divergences are detected on oscillator pivots; each (side × extreme) bucket holds a Beta(a, b) posterior, updated by the realized outcome divK bars later. The displayed reliability is the posterior mean.
Honest scope: classic tabular Q-learning plus Beta-Bernoulli estimation — not deep RL, not a crystal ball.
⚙️ Recommended Settings & Tuning Guide
Key parameters: ofThr (Overflow threshold), rlAlpha / rlGamma / warmup, divK (outcome horizon), relGate (minimum reliability to show).
Raise ofThr → stricter overflow (suits alts); lower → more sensitive
Crypto starting points (tune on your chart):
BTC / ETH (1H–4H): defaults are the baseline (ofThr = 30)
SOL / XRP and high-vol alts: wicky and spiky — ofThr = 35–40, relGate = 0.6 for proven divergences only
Scalping (5–15m): shorter hwLen, divK = 5–8 for quick verdicts
Swing (daily): divK = 15–20, longer warmup so the agent learns enough
The RL needs time to learn a market's character; in trending markets fade signals thin out automatically (correct behavior)
💡 How to Use in Practice
FADE signals: appear only when Overflow recedes from an extreme AND the agent has learned that fading pays in this regime — use as a reversal trigger
Reading Overflow: a bright color is excessive one-sided participation; when it stalls or recedes, that exhaustion often precedes a reversal
Reliability-scored divergences: readings above ~70% mark contexts that historically reversed; ~50% means stand aside
Multi-timeframe: combine higher-timeframe flow direction with lower-timeframe FADE entries
Combinations: pair AE-TIDE's exhaustion fades with directional signals from AE-QUORUM or AE-KALMAN to separate trend pullbacks from range reversals
⚠️ Important Notes
Learning period: no FADE signals appear until the Q-learning agent spins up (warmup bars) — this is by design
Reliability starts at 50%: each divergence bucket sits near neutral until its posterior accumulates enough samples
Learning reset: changing inputs, symbol, or timeframe re-learns the internal state
Fading is countertrend: fading a strong trend too early is dangerous; even when the agent suppresses it, the final call is yours
🚨 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-TIDE is a lower-panel oscillator measuring participation-weighted money flow and its excess — the "Overflow" of one-sided activity. Where most flow tools simply display that excess, AE-TIDE adds a reinforcement-learning agent that learns, from realized outcomes, whether fading an Overflow exhaustion actually leads to a reversal in this market. It also scores every divergence with a Bayesian posterior, automatically down-weighting the divergences that do not work. It is a flow oscillator that gets smarter per market rather than relying on fixed thresholds.
🔶 Key Features
Participation-weighted Money Flow ([-50, 50]) plus an Overflow band for one-sided extremes
A fast Hyperwave momentum line for early shifts in drive
An RL (tabular Q-learning) agent that learns when fading exhaustion pays, firing reversal signals only then
Beta-Bernoulli online reliability scoring for divergences — each one shows a learned reliability %
Divergences below relGate are hidden automatically
Gold-framed HUD; no repaint — every signal gates on bar close
🧠 Technical Architecture
Flow: impulse = (ATR-normalized return) × (relative volume), EMA-smoothed and z-scored into a [-50, 50] Money Flow; the part beyond ± threshold is the Overflow. Hyperwave is the same normalization on a short EMA.
RL (Q-learning, TD(0)): state = flow regime (overflow side ×3 × momentum slope ×2 = 6 states), actions = {wait, fade}. The fade reward is the realized return conditioned on the overflow side at the moment the fade was taken (fade an overbought flow → rewarded if price falls). Update Q(s,a) ← Q + α[r + γ·maxQ(s′) − Q]; the fade action is optimistically initialized to guarantee exploration. A signal fires only when fade-value > 0 (learned to pay) and an Overflow exhaustion triggers.
Bayesian divergences: regular divergences are detected on oscillator pivots; each (side × extreme) bucket holds a Beta(a, b) posterior, updated by the realized outcome divK bars later. The displayed reliability is the posterior mean.
Honest scope: classic tabular Q-learning plus Beta-Bernoulli estimation — not deep RL, not a crystal ball.
⚙️ Recommended Settings & Tuning Guide
Key parameters: ofThr (Overflow threshold), rlAlpha / rlGamma / warmup, divK (outcome horizon), relGate (minimum reliability to show).
Raise ofThr → stricter overflow (suits alts); lower → more sensitive
Crypto starting points (tune on your chart):
BTC / ETH (1H–4H): defaults are the baseline (ofThr = 30)
SOL / XRP and high-vol alts: wicky and spiky — ofThr = 35–40, relGate = 0.6 for proven divergences only
Scalping (5–15m): shorter hwLen, divK = 5–8 for quick verdicts
Swing (daily): divK = 15–20, longer warmup so the agent learns enough
The RL needs time to learn a market's character; in trending markets fade signals thin out automatically (correct behavior)
💡 How to Use in Practice
FADE signals: appear only when Overflow recedes from an extreme AND the agent has learned that fading pays in this regime — use as a reversal trigger
Reading Overflow: a bright color is excessive one-sided participation; when it stalls or recedes, that exhaustion often precedes a reversal
Reliability-scored divergences: readings above ~70% mark contexts that historically reversed; ~50% means stand aside
Multi-timeframe: combine higher-timeframe flow direction with lower-timeframe FADE entries
Combinations: pair AE-TIDE's exhaustion fades with directional signals from AE-QUORUM or AE-KALMAN to separate trend pullbacks from range reversals
⚠️ Important Notes
Learning period: no FADE signals appear until the Q-learning agent spins up (warmup bars) — this is by design
Reliability starts at 50%: each divergence bucket sits near neutral until its posterior accumulates enough samples
Learning reset: changing inputs, symbol, or timeframe re-learns the internal state
Fading is countertrend: fading a strong trend too early is dangerous; even when the agent suppresses it, the final call is yours
🚨 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.
סקריפט קוד פתוח
ברוח האמיתית של TradingView, יוצר הסקריפט הזה הפך אותו לקוד פתוח, כך שסוחרים יוכלו לעיין בו ולאמת את פעולתו. כל הכבוד למחבר! אמנם ניתן להשתמש בו בחינם, אך זכור כי פרסום חוזר של הקוד כפוף ל־כללי הבית שלנו.
כתב ויתור
המידע והפרסומים אינם מיועדים להיות, ואינם מהווים, ייעוץ או המלצה פיננסית, השקעתית, מסחרית או מכל סוג אחר המסופקת או מאושרת על ידי TradingView. קרא עוד ב־תנאי השימוש.
סקריפט קוד פתוח
ברוח האמיתית של TradingView, יוצר הסקריפט הזה הפך אותו לקוד פתוח, כך שסוחרים יוכלו לעיין בו ולאמת את פעולתו. כל הכבוד למחבר! אמנם ניתן להשתמש בו בחינם, אך זכור כי פרסום חוזר של הקוד כפוף ל־כללי הבית שלנו.
כתב ויתור
המידע והפרסומים אינם מיועדים להיות, ואינם מהווים, ייעוץ או המלצה פיננסית, השקעתית, מסחרית או מכל סוג אחר המסופקת או מאושרת על ידי TradingView. קרא עוד ב־תנאי השימוש.