OPEN-SOURCE SCRIPT
AetherEdge - Intermarket Correlation
🖊️ Overview
Correlation tables are static; AE-CORR learns which markets actually drive this symbol. Alongside rolling correlation and beta of the chart symbol versus six reference markets (BTC / ETH / crypto cap / DXY / Gold / S&P 500, etc.), an online regression learns each market's predictive weight, producing a next-bar return forecast (with a conformal-calibrated band) and an intermarket directional bias.
🔶 Key Features
Learned driver analysis — learns which markets drive the next move (identifies the dominant driver)
Rolling correlation + beta — live coupling and sensitivity per reference
1-bar lead-lag — how much a reference's prior move predicts the next bar (signed)
Predicted next-bar return — with a ± conformal band for honest uncertainty
Intermarket bias — long/short/flat from the structure
Coupling — overall co-movement strength (a risk-on/off gauge)
Chart triangles + alerts on bias flips; bar-close updates — no repaint
🧠 Technical Architecture
Stats: rolling correlation (ta.correlation) and beta = correlation × (chart σ / ref σ) (contemporaneous coupling and sensitivity).
ML (online linear regression): features are six standardized reference returns plus the chart's own momentum, trained on lag-1 (weights that predict the next bar, updated without lookahead). The learned weights expose each market's drive and sign; the largest is shown as the dominant driver. The live prediction estimates the next-bar return (%) from current returns.
Calibration (split-conformal): the (1-α) quantile of realized residuals forms the band, so predicted ± band matches the target coverage empirically.
Honest scope: linear regression over standard features + nonparametric conformal calibration (no RL). Not deep learning, not a guarantee.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: reference timeframe, correlation length, standardization length, learning rate, band significance (1-α), calibration window, bias threshold (× vol), the six references.
Correlation length ≈ 50 is standard; shorter tracks recent coupling, longer is steadier
References are fully swappable (for crypto, BTC/ETH/TOTAL/DXY/GOLD/SPX are classics); blank = skipped
Bias threshold = how many multiples of normal vol the prediction must exceed (≈ 0.5)
Pin references to a higher timeframe for a macro-structure bias
⚠️ The number of referenced markets (security calls) is capped
💡 How to Use in Practice
Check the dominant driver and watch that market as a lead
Use the sign of correlation to design hedges/diversification (negative-corr markets as insurance)
Use predicted next ± band for short-term expectation and risk
Use the bias triangles as an intermarket filter on single-symbol signals
Combine with AE-VOL (volatility) or AE-BPE (breakout probability) to layer driver × level
⚠️ Important Notes
Needs a learning period (warmup); weights re-learn on reference/timeframe change
Correlation is not causation (coupling can break down)
The next-bar forecast is short-horizon and probabilistic — large shocks will miss
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.
Correlation tables are static; AE-CORR learns which markets actually drive this symbol. Alongside rolling correlation and beta of the chart symbol versus six reference markets (BTC / ETH / crypto cap / DXY / Gold / S&P 500, etc.), an online regression learns each market's predictive weight, producing a next-bar return forecast (with a conformal-calibrated band) and an intermarket directional bias.
🔶 Key Features
Learned driver analysis — learns which markets drive the next move (identifies the dominant driver)
Rolling correlation + beta — live coupling and sensitivity per reference
1-bar lead-lag — how much a reference's prior move predicts the next bar (signed)
Predicted next-bar return — with a ± conformal band for honest uncertainty
Intermarket bias — long/short/flat from the structure
Coupling — overall co-movement strength (a risk-on/off gauge)
Chart triangles + alerts on bias flips; bar-close updates — no repaint
🧠 Technical Architecture
Stats: rolling correlation (ta.correlation) and beta = correlation × (chart σ / ref σ) (contemporaneous coupling and sensitivity).
ML (online linear regression): features are six standardized reference returns plus the chart's own momentum, trained on lag-1 (weights that predict the next bar, updated without lookahead). The learned weights expose each market's drive and sign; the largest is shown as the dominant driver. The live prediction estimates the next-bar return (%) from current returns.
Calibration (split-conformal): the (1-α) quantile of realized residuals forms the band, so predicted ± band matches the target coverage empirically.
Honest scope: linear regression over standard features + nonparametric conformal calibration (no RL). Not deep learning, not a guarantee.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: reference timeframe, correlation length, standardization length, learning rate, band significance (1-α), calibration window, bias threshold (× vol), the six references.
Correlation length ≈ 50 is standard; shorter tracks recent coupling, longer is steadier
References are fully swappable (for crypto, BTC/ETH/TOTAL/DXY/GOLD/SPX are classics); blank = skipped
Bias threshold = how many multiples of normal vol the prediction must exceed (≈ 0.5)
Pin references to a higher timeframe for a macro-structure bias
⚠️ The number of referenced markets (security calls) is capped
💡 How to Use in Practice
Check the dominant driver and watch that market as a lead
Use the sign of correlation to design hedges/diversification (negative-corr markets as insurance)
Use predicted next ± band for short-term expectation and risk
Use the bias triangles as an intermarket filter on single-symbol signals
Combine with AE-VOL (volatility) or AE-BPE (breakout probability) to layer driver × level
⚠️ Important Notes
Needs a learning period (warmup); weights re-learn on reference/timeframe change
Correlation is not causation (coupling can break down)
The next-bar forecast is short-horizon and probabilistic — large shocks will miss
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.
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.