OPEN-SOURCE SCRIPT
AetherEdge - Wavelet Multi-Resolution
🖊️ Overview
AE-WAVE decomposes price into multiple frequency scales using the à trous ("with holes") wavelet transform and reconstructs a clean "true price" by removing high-frequency noise. The scales are drawn as stacked translucent ribbons — a "frequency strata" of the market — revealing its hierarchical structure from raw chatter (top layer) to macro trend (bottom). Thanks to perfect reconstruction, denoise strength 0 means zero lag; higher values mean smoother. An adaptive layer learns each band's predictive power online.
🔶 Key Features
à trous wavelet decomposition: causal decomposition into 5 frequency scales
Soft-threshold denoising: removes high-frequency noise while preserving structure
Perfect reconstruction: denoise 0 = raw price (zero lag), strength tunes smoothness
3 scaling filters: B3 Spline (smoothest) / Linear / Haar (sharpest)
Adaptive scale reliability: learns online which bands predict
Frequency strata rendering: high freq thin/light, low freq thick/deep stacked ribbons
High-frequency bias: extra denoising on noise-heavy high-freq bands
BUY/SELL on denoised slope flip + macro trend filter
HUD showing band energy, reliability, dominant band
Per-event alerts
🧠 Technical Architecture
This implements the à trous (starlet) wavelet transform. From c0 = price, each scale c_{j+1} smooths c_j with a scaling filter (B3 spline [1,4,6,4,1]/16, etc.), spacing the filter taps by 2^j bars (causal, past only). Detail w_j = c_{j-1} − c_j is the band component. The telescoping identity c0 = c_J + Σ w_j means reconstruction equals raw price exactly with no thresholding (zero lag). Each detail is soft-thresholded w' = sign(w)·max(|w|−λ,0) (λ = estimated noise σ × strength × high-freq bias), reconstructed as denoised = c_J + Σ(thresholded w_j). The adaptive layer scores each scale's detail sign against forward returns, learning reliability via EWMA. Confidence blends reliability, trend agreement, and low noise. Signals fire on denoised slope flip + macro trend direction, or on a macro trend cross.
⚙️ Recommended Settings & Tuning Guide
Wavelet Levels (J) 5 is a starting point — higher for a smoother macro trend. B3 Spline filter is smoothest and common; Haar for faster response in ranges; Linear in between. Denoise Strength 1.0 baseline; 0 gives raw price (no denoise), higher is smoother but adds lag. Noise Estimation Window 50 estimates noise σ. High-Freq Denoise Bias 1.5 adds denoising to high-freq bands; raise for noisy instruments. Signal Mode "slope + trend filter" is robust; "macro trend cross" gives clearer signals. Adaptive Scale Reliability ON is recommended.
💡 How to Use in Practice
Apply to chart and 5 frequency-strata ribbons appear with the denoised main line in directional color. The ribbon "thickness" shows each band's activity; the HUD's FREQUENCY BANDS shows each band's energy and learned reliability. A dominant "S5 macro" band means macro-trend-driven; "S1 chatter" means noise-dominant ranging — read the market's character. When high-reliability bands align with the trend, signal conviction rises. Divergence/convergence of denoised price from the macro trend line gauges trend strength. Higher denoise strength gives smooth swing signals; lower gives sensitive ones.
⚠️ Important Notes
This is a causal, undecimated wavelet (B3 spline / Linear / Haar), not a full Daubechies decimated DWT. The adaptive scale reliability is online EWMA, not deep learning. The transform math, however, is genuine à trous. Due to the scaling filter's group delay, the macro scale (c_J) represents a lagged macro trend. The denoised price has minimal lag thanks to perfect reconstruction, but higher denoise strength introduces smoothing lag. It uses past data only, so it does not repaint. All signals are probabilistic decisions based on historical data and do not guarantee future profits.
🚨 Disclaimer
This indicator is provided for educational and informational purposes only and does not constitute financial or investment advice. No signal guarantees future profit, and past performance does not indicate future results. All trading decisions are made at your own risk.
AE-WAVE decomposes price into multiple frequency scales using the à trous ("with holes") wavelet transform and reconstructs a clean "true price" by removing high-frequency noise. The scales are drawn as stacked translucent ribbons — a "frequency strata" of the market — revealing its hierarchical structure from raw chatter (top layer) to macro trend (bottom). Thanks to perfect reconstruction, denoise strength 0 means zero lag; higher values mean smoother. An adaptive layer learns each band's predictive power online.
🔶 Key Features
à trous wavelet decomposition: causal decomposition into 5 frequency scales
Soft-threshold denoising: removes high-frequency noise while preserving structure
Perfect reconstruction: denoise 0 = raw price (zero lag), strength tunes smoothness
3 scaling filters: B3 Spline (smoothest) / Linear / Haar (sharpest)
Adaptive scale reliability: learns online which bands predict
Frequency strata rendering: high freq thin/light, low freq thick/deep stacked ribbons
High-frequency bias: extra denoising on noise-heavy high-freq bands
BUY/SELL on denoised slope flip + macro trend filter
HUD showing band energy, reliability, dominant band
Per-event alerts
🧠 Technical Architecture
This implements the à trous (starlet) wavelet transform. From c0 = price, each scale c_{j+1} smooths c_j with a scaling filter (B3 spline [1,4,6,4,1]/16, etc.), spacing the filter taps by 2^j bars (causal, past only). Detail w_j = c_{j-1} − c_j is the band component. The telescoping identity c0 = c_J + Σ w_j means reconstruction equals raw price exactly with no thresholding (zero lag). Each detail is soft-thresholded w' = sign(w)·max(|w|−λ,0) (λ = estimated noise σ × strength × high-freq bias), reconstructed as denoised = c_J + Σ(thresholded w_j). The adaptive layer scores each scale's detail sign against forward returns, learning reliability via EWMA. Confidence blends reliability, trend agreement, and low noise. Signals fire on denoised slope flip + macro trend direction, or on a macro trend cross.
⚙️ Recommended Settings & Tuning Guide
Wavelet Levels (J) 5 is a starting point — higher for a smoother macro trend. B3 Spline filter is smoothest and common; Haar for faster response in ranges; Linear in between. Denoise Strength 1.0 baseline; 0 gives raw price (no denoise), higher is smoother but adds lag. Noise Estimation Window 50 estimates noise σ. High-Freq Denoise Bias 1.5 adds denoising to high-freq bands; raise for noisy instruments. Signal Mode "slope + trend filter" is robust; "macro trend cross" gives clearer signals. Adaptive Scale Reliability ON is recommended.
💡 How to Use in Practice
Apply to chart and 5 frequency-strata ribbons appear with the denoised main line in directional color. The ribbon "thickness" shows each band's activity; the HUD's FREQUENCY BANDS shows each band's energy and learned reliability. A dominant "S5 macro" band means macro-trend-driven; "S1 chatter" means noise-dominant ranging — read the market's character. When high-reliability bands align with the trend, signal conviction rises. Divergence/convergence of denoised price from the macro trend line gauges trend strength. Higher denoise strength gives smooth swing signals; lower gives sensitive ones.
⚠️ Important Notes
This is a causal, undecimated wavelet (B3 spline / Linear / Haar), not a full Daubechies decimated DWT. The adaptive scale reliability is online EWMA, not deep learning. The transform math, however, is genuine à trous. Due to the scaling filter's group delay, the macro scale (c_J) represents a lagged macro trend. The denoised price has minimal lag thanks to perfect reconstruction, but higher denoise strength introduces smoothing lag. It uses past data only, so it does not repaint. All signals are probabilistic decisions based on historical data and do not guarantee future profits.
🚨 Disclaimer
This indicator is provided for educational and informational purposes only and does not constitute financial or investment advice. No signal guarantees future profit, and past performance does not indicate future results. All trading decisions are made at your own risk.
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Отказ от ответственности
Информация и публикации не предназначены для предоставления и не являются финансовыми, инвестиционными, торговыми или другими видами советов или рекомендаций, предоставленных или одобренных TradingView. Подробнее читайте в Условиях использования.
Скрипт с открытым кодом
В истинном духе TradingView, создатель этого скрипта сделал его открытым исходным кодом, чтобы трейдеры могли проверить и убедиться в его функциональности. Браво автору! Вы можете использовать его бесплатно, но помните, что перепубликация кода подчиняется нашим Правилам поведения.
Отказ от ответственности
Информация и публикации не предназначены для предоставления и не являются финансовыми, инвестиционными, торговыми или другими видами советов или рекомендаций, предоставленных или одобренных TradingView. Подробнее читайте в Условиях использования.