OPEN-SOURCE SCRIPT
AetherEdge Adaptive LSTM-inspired Forecaster
🖊️ Overview
AetherEdge Adaptive LSTM-inspired Forecaster is a next-generation price forecasting engine that recreates the core machinery of the LSTM (Long Short-Term Memory) neural network—the cornerstone of deep learning—directly in Pine Script. Featuring a multi-layered memory structure of forget, input, output gates and cell state, augmented by an Attention mechanism that dynamically references the most relevant past moments, plus residual connections that bypass training instability, it fuses multi-timeframe features to render 1–30 bar price forecasts and uncertainty bands with exceptional fidelity.
🔶 Key Features
LSTM-style Cell: Full implementation of forget/input/output gates + cell state
Parallel Hidden Units: 3–16 units with diverse time constants τ for layered memory
Attention Mechanism: Similarity + recency-decayed weighted context
Residual Connection: Skip connection bypassing vanishing-gradient pitfalls
Multi-Timeframe Features: HTF (1H) + HHTF (4H) hierarchical inputs
8-D Input Vector: retZ / volZ / RSI / Slope / Mom / HTF Ret / HTF Bias / HHTF Bias
Multi-Step Forecast: 1–30 bar projection lines
Uncertainty Bands: √t-decay confidence intervals (inner 1σ / outer 2σ)
3 Band Methods: Residual Std / ATR / Hybrid
Hidden Unit Visualization: τ, Cell, Hidden & activity bars per unit
Attention Heatmap: 30-bar attention weight distribution
Hit Score: Real-time evaluation of last forecast accuracy
Bullish/Bearish Flip & Band-Breach Alerts
🧠 Technical Architecture
This indicator is an industry-leading neural forecasting engine fully reconstructing the mathematical essence of LSTM, the apex of RNN-family models, in Pine Script.
LSTM Cell Equations (per unit u):
Forget Gate f_t = σ(W_f · x_t + b_f) — past memory retention
Input Gate i_t = σ(W_i · x_t + b_i) — new information intake
Candidate g_t = tanh(W_g · x_t + b_g) — new memory candidate
Output Gate o_t = σ(W_o · x_t + b_o) — exposure to hidden state
Cell Update c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t
Hidden h_t = o_t ⊙ tanh(c_t)
τ Diversity: Each unit assigned distinct τ, processing short-to-long memory in parallel
Pseudo-Weight Generation: Deterministic sin/cos phase for stable initialization
MTF Influence Boost: Input gate amplified by HHTF Bias magnitude
Attention:
Score = h_curr × h_past − |h_curr − h_past|×0.5 + recency decay
Softmax normalization (temperature-adjustable) → context = weighted mean
Residual Path: Upper-half units add raw retZ × residualMix × (1-τ)
Output Projection: h_aggregated × σ_returns × 0.5 + Attention + Residual contributions
Uncertainty Model: σ_h = σ_base × fanOut^h × √h (band widens with horizon)
Decay Forecast: Multi-step uses stepRet × 0.82^(h-1) (extrapolation decay suppresses overshoot)
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H–D): Hidden=8, Seq=30, Forecast=10 (standard)
ETH (1H–4H): Hidden=10, Seq=40, MTF Weight=0.4
SOL (high-vol): Hidden=12, Forget=0.88, fanOut=1.20
XRP (short-bias): Hidden=6, Seq=20, Forecast=5
Hidden Units (Memory Capacity):
3–5: Lightweight, short-pattern specialized
8: Standard (recommended)
12–16: Rich memory, heavier compute
Sequence Length (Attention Range):
20: Recency-focused
30: Standard
50–100: Long context, lower TFs
Learning Rate α (lrBase):
0.05–0.10: Conservative, smooth
0.15: Standard
0.25–0.50: Agile, noisier
Forget Gate Base:
0.85–0.90: Agile forgetting, regime-shift responsive
0.92: Standard
0.95–0.98: Long memory, range-favoring
Attention Temperature:
0.5–1.0: Sharp focus, specific-pattern referencing
1.5: Standard
2.5–5.0: Even attention, stable but dull
Band Method:
Residual Std: Empirical, most precise
ATR: Simple, valid early
Hybrid: Geometric mean, robust
💡 How to Use in Practice
Strong Bullish Forecast: σ-breach prediction + positive attention = long entry
Strong Bearish Forecast: Opposite, short consideration
Band Breach: Price exceeded ±2σ band, overheating warning
Forecast Flip: expectedRet zero-cross, early regime-shift signal
Hit Score > 70%: Last forecast accurate, current trustworthy
Hit Score < 40%: Sudden volatility regime, low confidence—wait
Hidden Unit Activity: Multiple units same direction = strong consensus; mixed = regime transition
Attention Heatmap purple-dominant: Strong reference to past similar pattern, repeating-scenario likely
Attention dispersed: No precedent, novel territory—tread cautiously
AetherEdge Synergy:
KNN Regime Classifier: Trending▲ + LSTM bullish forecast = dual AI conviction
NeuraNet Predictor: Direction agreement amplifies signal
Self-Evolving S/R Grid: LSTM band edges ≈ S/R lines = strong reaction candidates
All-in-One Dashboard: HIGH-CONVICTION + Hit Score > 70% = ultimate alignment
Multi-TF: 4H LSTM direction → 1H strategy selection → 15M precision entry
⚠️ Important Notes
Pseudo-Weights: No pre-trained weights; deterministic phase-based approximation
Learning Reset: Cell states wipe on reload; rebuild required
First 50 Bars: States unfinalized, forecasts invalid
Compute Load: Hidden=16 + Seq=100 is heavy; use 8/30 on low-spec
MTF lookahead_off: No future-peeking, real-time integrity guaranteed
Multi-Step Drift: Error accumulates with horizon; Forecast=5–10 recommended
Bands are Probabilistic: σ-range exceedance can occur; not absolute ceilings/floors
🚨 Disclaimer
This indicator is a deep-learning-inspired forecasting model provided for educational and research purposes only and does not constitute financial advice. LSTM-style predictions are mathematical approximations and do not guarantee future profits. Forecast accuracy depends on market conditions, asset, and timeframe. Combine with independent analysis and proper risk management.
AetherEdge Adaptive LSTM-inspired Forecaster is a next-generation price forecasting engine that recreates the core machinery of the LSTM (Long Short-Term Memory) neural network—the cornerstone of deep learning—directly in Pine Script. Featuring a multi-layered memory structure of forget, input, output gates and cell state, augmented by an Attention mechanism that dynamically references the most relevant past moments, plus residual connections that bypass training instability, it fuses multi-timeframe features to render 1–30 bar price forecasts and uncertainty bands with exceptional fidelity.
🔶 Key Features
LSTM-style Cell: Full implementation of forget/input/output gates + cell state
Parallel Hidden Units: 3–16 units with diverse time constants τ for layered memory
Attention Mechanism: Similarity + recency-decayed weighted context
Residual Connection: Skip connection bypassing vanishing-gradient pitfalls
Multi-Timeframe Features: HTF (1H) + HHTF (4H) hierarchical inputs
8-D Input Vector: retZ / volZ / RSI / Slope / Mom / HTF Ret / HTF Bias / HHTF Bias
Multi-Step Forecast: 1–30 bar projection lines
Uncertainty Bands: √t-decay confidence intervals (inner 1σ / outer 2σ)
3 Band Methods: Residual Std / ATR / Hybrid
Hidden Unit Visualization: τ, Cell, Hidden & activity bars per unit
Attention Heatmap: 30-bar attention weight distribution
Hit Score: Real-time evaluation of last forecast accuracy
Bullish/Bearish Flip & Band-Breach Alerts
🧠 Technical Architecture
This indicator is an industry-leading neural forecasting engine fully reconstructing the mathematical essence of LSTM, the apex of RNN-family models, in Pine Script.
LSTM Cell Equations (per unit u):
Forget Gate f_t = σ(W_f · x_t + b_f) — past memory retention
Input Gate i_t = σ(W_i · x_t + b_i) — new information intake
Candidate g_t = tanh(W_g · x_t + b_g) — new memory candidate
Output Gate o_t = σ(W_o · x_t + b_o) — exposure to hidden state
Cell Update c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t
Hidden h_t = o_t ⊙ tanh(c_t)
τ Diversity: Each unit assigned distinct τ, processing short-to-long memory in parallel
Pseudo-Weight Generation: Deterministic sin/cos phase for stable initialization
MTF Influence Boost: Input gate amplified by HHTF Bias magnitude
Attention:
Score = h_curr × h_past − |h_curr − h_past|×0.5 + recency decay
Softmax normalization (temperature-adjustable) → context = weighted mean
Residual Path: Upper-half units add raw retZ × residualMix × (1-τ)
Output Projection: h_aggregated × σ_returns × 0.5 + Attention + Residual contributions
Uncertainty Model: σ_h = σ_base × fanOut^h × √h (band widens with horizon)
Decay Forecast: Multi-step uses stepRet × 0.82^(h-1) (extrapolation decay suppresses overshoot)
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H–D): Hidden=8, Seq=30, Forecast=10 (standard)
ETH (1H–4H): Hidden=10, Seq=40, MTF Weight=0.4
SOL (high-vol): Hidden=12, Forget=0.88, fanOut=1.20
XRP (short-bias): Hidden=6, Seq=20, Forecast=5
Hidden Units (Memory Capacity):
3–5: Lightweight, short-pattern specialized
8: Standard (recommended)
12–16: Rich memory, heavier compute
Sequence Length (Attention Range):
20: Recency-focused
30: Standard
50–100: Long context, lower TFs
Learning Rate α (lrBase):
0.05–0.10: Conservative, smooth
0.15: Standard
0.25–0.50: Agile, noisier
Forget Gate Base:
0.85–0.90: Agile forgetting, regime-shift responsive
0.92: Standard
0.95–0.98: Long memory, range-favoring
Attention Temperature:
0.5–1.0: Sharp focus, specific-pattern referencing
1.5: Standard
2.5–5.0: Even attention, stable but dull
Band Method:
Residual Std: Empirical, most precise
ATR: Simple, valid early
Hybrid: Geometric mean, robust
💡 How to Use in Practice
Strong Bullish Forecast: σ-breach prediction + positive attention = long entry
Strong Bearish Forecast: Opposite, short consideration
Band Breach: Price exceeded ±2σ band, overheating warning
Forecast Flip: expectedRet zero-cross, early regime-shift signal
Hit Score > 70%: Last forecast accurate, current trustworthy
Hit Score < 40%: Sudden volatility regime, low confidence—wait
Hidden Unit Activity: Multiple units same direction = strong consensus; mixed = regime transition
Attention Heatmap purple-dominant: Strong reference to past similar pattern, repeating-scenario likely
Attention dispersed: No precedent, novel territory—tread cautiously
AetherEdge Synergy:
KNN Regime Classifier: Trending▲ + LSTM bullish forecast = dual AI conviction
NeuraNet Predictor: Direction agreement amplifies signal
Self-Evolving S/R Grid: LSTM band edges ≈ S/R lines = strong reaction candidates
All-in-One Dashboard: HIGH-CONVICTION + Hit Score > 70% = ultimate alignment
Multi-TF: 4H LSTM direction → 1H strategy selection → 15M precision entry
⚠️ Important Notes
Pseudo-Weights: No pre-trained weights; deterministic phase-based approximation
Learning Reset: Cell states wipe on reload; rebuild required
First 50 Bars: States unfinalized, forecasts invalid
Compute Load: Hidden=16 + Seq=100 is heavy; use 8/30 on low-spec
MTF lookahead_off: No future-peeking, real-time integrity guaranteed
Multi-Step Drift: Error accumulates with horizon; Forecast=5–10 recommended
Bands are Probabilistic: σ-range exceedance can occur; not absolute ceilings/floors
🚨 Disclaimer
This indicator is a deep-learning-inspired forecasting model provided for educational and research purposes only and does not constitute financial advice. LSTM-style predictions are mathematical approximations and do not guarantee future profits. Forecast accuracy depends on market conditions, asset, and timeframe. Combine with independent analysis and proper risk management.
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ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
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สคริปต์โอเพนซอร์ซ
ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน